Agricultural biomarkers for array technology .
Date: 2010-07-01
Institutions: European cooperation in science and technology
Subjects: agronomic research, biology, electronic component, plant breeding

 
 
 
 
COST- the acronym  for  European COoperation  in the field of Scientific  and Technical Research- is the 
oldest  and  widest  European  intergovernmental  network  for  cooperation  in  research.  Established  by  the 
Ministerial  Conference  in  November  1971,  COST  is  presently  used  by  the  scientific  communities  of  35 
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The funds provided by COST - less than 1% of the total value of the projects - support the COST coopera-
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cess” (participation is open also to the scientific communities of countries not belonging to the European 
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the main characteristics of COST.  

As precursor of advanced multidisciplinary research COST has a very important role for the realisation of 
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Web: www.cost.esf.org 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
COST Action 853 

Agricultural Biomarkers  

for Array Technology 

2001-2007 

Edited by 

Jürg E. Frey 

and  

Frédérique Pasquer 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Distributor: 
AGROSCOPE CHANGINS-WÄDENSWIL ACW 
Dr. Jürg E. Frey and Dr. Frédérique Pasquer 
Research Plant Protection, Molecular Diagnostics and Epidemiology 
Swiss Federal Research Station for Horticulture 
CH-8820 Wädenswil, Switzerland 

Impressum 
AGRICULTURAL BIOMARKERS FOR ARRAY TECHNOLOGY 
Edited by  Jürg E. Frey and Frédérique Pasquer 
Includes 14 chapters with 53 authors and list of cost 853 participants,  p 94. 

ISBN  978-3-033-01770-2 

Cover photographs: Agroscope Changins-Wädenswil ACW databank 

Further information on the use of agricultural biomarkers and array technology can be found on the web site of the  
COST Action 853 on www.cost853.ch 

  ESF provides the COST Office through an EC contract 

  COST is supported by the EU RTD Framework programme 

© COST Office / Agroscope Changins-Wädenswil ACW 

Printed in Switzerland by Berti Druck AG, Rapperswil, www.bertidruck.ch  
and bound by Eibert AG, Eschenbach, www.eibert.ch 

Legal notice 
All rights reserved. Reproduction is authorized provided the source (contributors) is acknowledged or with prior permission of the 
copyright owners. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table of contents 

Table of Contents..........................................................................................................III 

Contributors................................................................................................................ VII 

Foreword ....................................................................................................................... IX 

Five years of COST Action 853 – a short overview..................................................... 1 
Introduction .................................................................................................................. 1 
Promises and achievements of the Action.................................................................... 2 
Highlights and successes of the Action ........................................................................ 3  
Highly successful meetings....................................................................................... 3 
New STSM workshop format .................................................................................... 3 
Evaluation and validation of various microarray systems....................................... 3 
Protein detection  ..................................................................................................... 4 
Gene expression studies  .......................................................................................... 5 
Progress in general knowledge of microarray probe design ................................... 5 
Perception of COST Action 853................................................................................... 5 
Conclusion.................................................................................................................... 6 
References .................................................................................................................... 6 

Microarray hybridization .............................................................................................. 7 
Abstract......................................................................................................................... 7 
Introduction .................................................................................................................. 7 
General concepts of nucleic acids hybridization and probe design.............................. 7 
Melting Temperature ................................................................................................ 8 
Overall probe similarity ........................................................................................... 8 
Longest identical sequence stretch........................................................................... 8 
The steric hindrance ................................................................................................. 8 
Particular problems of surface-array hybridization ...................................................... 8 
Conclusion.................................................................................................................. 10 
Acknowledgments ...................................................................................................... 10 
References .................................................................................................................. 10 

ARB and SILVA: A software environment and databases for ribosomal RNA  
sequence data ................................................................................................................ 12 
Abstract....................................................................................................................... 12 
Introduction ................................................................................................................ 12 
Materials and Methods ............................................................................................... 13 
Results and Discussion ............................................................................................... 13 
Sequence editor and alignment .............................................................................. 13 
Phylogenetic tree reconstruction............................................................................ 14 
Probe design and evaluation .................................................................................. 14 
Conclusions ................................................................................................................ 15 
References .................................................................................................................. 15 

Development of genus specific primers for identification of Tobamoviruses ......... 17 
Abstract....................................................................................................................... 17 
Introduction ................................................................................................................ 17 
Material and Methods................................................................................................. 18 

 
 
 
 
 
 
Results and Discussion ............................................................................................... 19 
References .................................................................................................................. 19 

Applications in virus detection .................................................................................... 21 
Abstract....................................................................................................................... 21 
Introduction ................................................................................................................ 21 
Materials and methods................................................................................................ 22 
Design and printing of arrays ................................................................................ 22 
Nucleic acid extraction........................................................................................... 22 
Labelling of cDNA.................................................................................................. 22 
Hybridisation and washing..................................................................................... 22 
Microarray scanning .............................................................................................. 22 
Results ........................................................................................................................ 22 
Discussion................................................................................................................... 23 
Acknowledgements .................................................................................................... 23 
References .................................................................................................................. 24 

Electrochemical methods for DNA and protein detection........................................ 25 
Abstract....................................................................................................................... 25 
Introduction ................................................................................................................ 25 
Discussion................................................................................................................... 26 
Chemical and Electrochemical polymerization of polypyrrole.............................. 26 
Formation of biological recognition part of DNA and protein affinity sensors..... 28 
Electrochemical detection methods used for DNA and Protein detection ............. 28 
Recent developments and conclusions ....................................................................... 30 
Acknowledgement ...................................................................................................... 31 
References .................................................................................................................. 31 

Protein chip applications ............................................................................................. 35 
Abstract....................................................................................................................... 35 
Introduction ................................................................................................................ 35 
Material and Methods................................................................................................. 37 
Kunitz-type inhibitor expression purification and inhibition assays...................... 37 
Chip production...................................................................................................... 38 
Protein immobilisation and hybridisation.............................................................. 38 
Control of the protein chip ..................................................................................... 38 
Detection of mite/insect allergenic proteases by capture on the protease inhibitor 
 chip ........................................................................................................................ 38 
Results ........................................................................................................................ 39 
Discussion................................................................................................................... 42 
Acknowledgements .................................................................................................... 42 
References .................................................................................................................. 42 

Luminex technology: Multiplex detection using xMAP technology. ....................... 44 
Introduction ................................................................................................................ 44 
Material and methods ................................................................................................. 45 
Results ........................................................................................................................ 46 
Conclusions ................................................................................................................ 46 
References .................................................................................................................. 46 

Plant, animal and food pathogens analyzed using genome-wide microarrays:  
towards improved diagnostics and control of bacterial pathogens.......................... 47 
Abstract....................................................................................................................... 47 
Introduction ................................................................................................................ 47 
Materials and methods................................................................................................ 48 

 
 
DNA isolation and labelling................................................................................... 48 
Potato pathogens .................................................................................................... 48 
EHEC bacteria ....................................................................................................... 48 
C. botulinum ........................................................................................................... 48 
Data analysis .......................................................................................................... 48 
Results and discussion................................................................................................ 48 
Comparison of bacterial pathogens of potato ........................................................ 48 
Genomic comparison of Clostridium botulinum with DNA microarrays............... 49 
Microarray-based studies on gene content of Escherichia coli O157 ................... 50 
Conclusions ................................................................................................................ 50 
Acknowledgements .................................................................................................... 51 
References .................................................................................................................. 51 

Analysis of methanotroph community structure by a functional oligonucleotide  
MDM : potential and shortcomings............................................................................ 52 
Introduction ................................................................................................................ 52 
Material and Method .................................................................................................. 52 
Oligonucleotide probe design................................................................................. 52 
Microarray preparation ......................................................................................... 53 
Environmental DNA preparation ........................................................................... 53 
Target preparation ................................................................................................. 53 
Hybridisation.......................................................................................................... 54 
Scanning and data analysis .................................................................................... 55 
Results and discussion................................................................................................ 55 
An example – the pmoA microarray for methanotrophs ........................................ 55 
Application examples – potential and shortcomings.............................................. 56 
Conclusions ................................................................................................................ 59 
Acknowledgements .................................................................................................... 60 
References .................................................................................................................. 60 

Diagnostic Application of Padlock Probes – Multiplex Detection of Plant Pathogens 
 using Universal Microarrays ...................................................................................... 63 
Abstract....................................................................................................................... 63 
Introduction ................................................................................................................ 63 
Materials and methods................................................................................................ 64 
Nucleic acids used in the study............................................................................... 64 
Padlock probe design ............................................................................................. 64 
Ligation, capturing and exonuclease treatment ..................................................... 64 
Real-time PCR ........................................................................................................ 64 
LATE-PCR.............................................................................................................. 64 
Microarray preparation and hybridisation ............................................................ 65 
Results ........................................................................................................................ 65 
Design and testing of diagnostic padlock probes specificity.................................. 65 
Validation – PLP-based multiplex detection of plant pathogenic organisms ........ 66 
Validation: PLP-based multiplex detection of plant pathogenic organisms  
spiked to water and soil.......................................................................................... 67 
Discussion................................................................................................................... 68 
Acknowledgements .................................................................................................... 69 
References .................................................................................................................. 69 

Quantitative multiplex detection of plant pathogens using a novel ligation probe- 
based system coupled with universal, high-throughput real-time PCR on  
OpenArraysTM............................................................................................................... 71 
Abstract....................................................................................................................... 71 
Introduction ................................................................................................................ 71 

 
 
Methods ...................................................................................................................... 73 
Nucleic acids used in the study............................................................................... 73 
PRI-lock probe design ............................................................................................ 73 
Ligation, capturing and exonuclease treatment ..................................................... 73 
Real-time PCR (ABI) and Biotrove OpenArrayTM real-time PCR.......................... 73 
Results ........................................................................................................................ 75 
PRI-lock probe design and evaluation of assay performance................................ 75 
Application of the PRI-lock based multiplex quantitative detection on the  
Biotrove OpenArrayTM platform ............................................................................. 75 
Quantification and validation................................................................................. 75 
Discussion................................................................................................................... 76 
Conclusions ................................................................................................................ 77 
Acknowledgements .................................................................................................... 78 
References .................................................................................................................. 78 

A 16S rDNA-based microarray for high-throughput diversity analysis of  
complex bacterial communities ................................................................................... 80 
Abstract....................................................................................................................... 80 
Introduction ................................................................................................................ 80 
Design and resulting characteristics of the 16S microarray ....................................... 81 
Probe characteristics.............................................................................................. 81 
Taxonomic range allowed by 16S probes............................................................... 81 
Microarray probe set.............................................................................................. 81 
DNA targets and hybridization conditions ................................................................. 82 
General and population specific DNA targets ....................................................... 82 
Hybridization conditions ........................................................................................ 82 
Mismatch level and probe specificity ..................................................................... 82 
Data processing .......................................................................................................... 83 
Filtration and normalisation .................................................................................. 83 
Quantification......................................................................................................... 83 
Detection limit ........................................................................................................ 84 
Microarray analysis of bacterial communities ........................................................... 84 
Bacterial communities of a maize field .................................................................. 85 
Microarray analysis of communities of soils safe, conducive or suppressive to  
take-all.................................................................................................................... 86 
Conclusion.................................................................................................................. 87 
Acknowledgements .................................................................................................... 87 
References .................................................................................................................. 87 

Analysis of data from expression microarrays for studying plant-pathogen  
interactions.................................................................................................................... 89 
Abstract....................................................................................................................... 89 
Introduction ................................................................................................................ 89 
Materials and Methods ............................................................................................... 90 
Results and Discussion ............................................................................................... 90 
Conclusions ................................................................................................................ 91 
References .................................................................................................................. 92 

Participants involved in COST 853 meetings and workshops.................................. 94 

 
 
CONTRIBUTORS 

Adam  Günter,  University  of  Hamburg,  Department  of  Biology,  BioCentre  Klein  Flottbek,  Hamburg, 
Germany 

Aittamaa Marja, Department of Applied Biology, University of Helsinki, Finland 

Auvinen Petri, Institute of Biotechnology, University of Helsinki, Finland 

van Beckhoven Jose, Plant Research International B.V., Wageningen, The Netherlands 

Bergervoet Jan, Plant Research International B.V., Wageningen, The Netherlands 

De  Blasi  Marialuce  D, CNR-ISPA, Institute of Sciences of Food Productions, National Research Council, 
Lecce, Italy 

Bodrossy  Levente,  Department  of  Bioresources/Microbiology,  ARC  Seibersdorf  research  GmbH, 
Seibersdorf, Austria 

Bonants Peter, Plant Research International B.V., Wageningen, The Netherlands 

Boonham Neil, Central Science Laboratory, York, United Kingdom 

Cimaglia  Fabio, CNR-ISPA, Institute of Sciences of Food Productions, National Research Council, Lecce, 
Italy. 

van Doorn Ronald, Plant Research International B.V., Wageningen, The Netherlands 
Erban Tomas, VURV, Institute of Crop Productions, Prague, Czech Republic 

Frey  Jürg  E.,  Agroscope  Changins-Wädenswil  Research  Station  ACW,  Molecular  Diagnostics  and 
Epidemiology, Wädenswil, Switzerland 

Glöckner  Frank  Oliver,  Microbial  Genomics  Group,  Max  Planck  Institute  for  Marine  Microbiology, 
Bremen, Germany; Jacobs University Bremen GmbH, Bremen, Germany 

Heinze  Cornelia,  University  of  Hamburg,  Department  of  Biology,  BioCentre  Klein  Flottbek,  Hamburg, 
Germany 

Hinderink Katja, Department of Food and Environmental Hygiene, University of Helsinki, Finland 

Hubert Jan, VURV, Institute of Crop Productions, Prague, Czech Republic  

Jacobson James W., Luminex Corporation, Austin, USA 

Kiviniemi Katri, Department of Food and Environmental Hygiene, University of Helsinki, Finland 

Kogovšek  Polona  National  Institute  of  Biology,  Department  of  Biotechnology  and  Systemic  Biology, 
Ljubljana, Slovenia 

Korkeala Hannu, Department of Food and Environmental Hygiene, University of Helsinki, Finland 
Kowalchuk George A., NIOO-Centre for Terrestrial Ecology, Heteren, the Netherlands 
Krizkova-Kudlikova Iva, VURV, Institute of Crop Productions, Prague, Czech Republic  

Leppäranta Outi, Finnish Food Safety Authority Evira, Helsinki, Finland 

Lindström Miia, Department of Food and Environmental HygieneUniversity of Helsinki, Finland 

 
 
 
 
 
 
Mattinen Laura, Department of Applied Biology, University of Helsinki, Finland 

Myllys Vesa, Finnish Food Safety Authority Evira, Helsinki, Finland 
Nesme  Xavier,  Université  de  Lyon,  CNRS  INRA,  Ecologie  Microbienne  UMR  5557-USC  1193, 
Villeurbanne , France. 
Oger-Desfeux Christine,  Université de Lyon,  IFR 41, DTAMB, PRABI, Villeurbanne , France 

Nevas Mari, Department of Applied Biology, University of Helsinki, Finland 

Nykäsenoja Suvi, Finnish Food Safety Authority Evira, Helsinki, Finland 
Ortenberg Elen, BioTrove, Inc. 12 Gill Street, Woburn, MA 01801-1728, USA 

Pasquer  Frédérique,  Agroscope Changins-Wädenswil Research Station ACW, Molecular Diagnostics and 
Epidemiology, Wädenswil, Switzerland 
Peplies Jörg, Ribocon GmbH, D-28359 Bremen 

Peters Jeroen, Plant Research International B.V., Wageningen, The Netherlands 

Pfunder Monika, Ecogenics GmbH, Schlieren, Switzerland 

Pirhonen Minna, Department of Applied Biology, University of Helsinki, Finland 

Poltronieri  Palmiro,  CNR-ISPA,  Institute  of  Sciences  of  Food  Productions,  National  Research  Council, 
Lecce, Italy. 

Pompe-Novak Maruša, National Institute of Biology, Department of Biotechnology and Systemic Biology, 
Ljubljana, Slovenia 

Ramanavicius  Arunas,  Centre  of  Nanotechnology  and  Material  Science,  Department  of  Analytical  and 
Environmental Chemistry, Faculty of Chemistry, Vilnius University, Vilnius, Lithuania 

Ramanaviciene  Almyra,
Vilnius University, Vilnius, Lithuania 

Laboratory of Immunoanalysis and Nanotechnology, Institute of Immunology of 

Rantala Leila, Finnish Food Safety Authority Evira, Helsinki, Finland 
Salles Joana F., Plant Research International B.V., Wageningen, The Netherlands  
Santino  Angelo, CNR-ISPA, Institute of Sciences of Food Productions, National Research Council, Lecce, 
Italy. 

Schoen Cor D., Plant Research International B.V., Wageningen, The Netherlands 

Sessitsch  Angela,  Department  of  Bioresources/Microbiology,  ARC  Seibersdorf  research  GmbH, 
Seibersdorf, Austria  

Sip Miroslav, University of South Bohemia, Ceske Budejovice, Czech Republic 

Szemes Marianna, Plant Research International B.V., Wageningen, The Netherlands 

Somervuo Panu, Department of Applied Biology, University of Helsinki, Finland 

Tomlinson Jenny, Central Science Laboratory, York, United Kingdom 

Valkonen Jari P.T., Department of Applied Biology, University of Helsinki, Finland 

Willingmann  Peter, University of Hamburg, Department of Biology, BioCentre Klein Flottbek, Hamburg, 
Germany 

van der Wolf Jan M, Plant Research International B.V., Wageningen, The Netherlands 

 
 
 
 
 
 
Foreword 

I had the pleasure of managing this COST Action at two distinct phases. First, at its inaugural meeting and 
later, having re-joined the new COST office, when I became involved in organizing its final conference held 
in 2007. I left the COST office shortly after this, and am therefore in the fortunate position of writing this 
foreword with the benefit of both hindsight and a certain detachment. 
Possibly the most important attribute of COST 853 was its openness. COST Actions are always intended to 
be open frameworks for cooperation, but Jürg Frey and his colleagues pursued this idea relentlessly. At the 
outset, the idea for this COST Action arose from a group of scientists whose backgrounds were largely but 
not exclusively in plant agriculture. At the zenith of the Action, the spectre had widened considerably, as the 
content of this book demonstrates. This is a very real example of multidisciplinarity, the coming together of 
scientists  from  varied  backgrounds  and  interests  working  together  around  a  common  theme  -  microarray 
technology.  
This  openness  was  also  evident  by  the  constant  involvement  of  partners  from  industry  which  enabled  a 
fruitful  exchange  between  those  developing  the  newest  techniques  and  those  who  could  potentially  apply 
them in a variety of different fields and circumstances. Without this mix of industry and public sector, the 
Action  would  not  have  progressed  as  much  as  it  did  since  microarray  technology  is  a  very  rapidly 
developing field. The rapidity and accuracy of the analyses we can perform in 2007 is very different from 
when  the  Action  started  in  2001,  but  COST  853  managed  to  successfully  accompany  this  exciting 
development. 
This foreword would not be complete without a word for the chair of the Action, Dr. Jürg Frey and the quite 
remarkable core team of scientists who worked closely with him at every stage. To say it was a pleasure to 
work  with  them  is  banal,  it  was  in  fact  an  immense  pleasure!  Jürg  was  also  helped  by  an  exceptional 
Management  Committee  and  also  by  his  trusted  co-workers,  first  Monica  Pfunder  and  then  Frédérique 
Pasquer taking care of the detailed tasks that made every meeting so enjoyable, productive and scientifically 
successful.  

Professor John Williams 
I.N.R.A., Nouzilly, France 

December 2007 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Five years of COST Action 853 – a short overview 

Jürg E. Frey1*, Frédérique Pasquer1 and Monika Pfunder2 

1  Agroscope  Changins  Wädenswil  Research  Station 
ACW,  Department  of  Plant  Protection  Molecular 
Diagnostics and Epidemiology, Schloss, Lab.4 
8820 Wädenswil, Switzerland 

2 Ecogenics GmbH, Wagistr 23, CH-8952 Schlieren, 
Switzerland 

*Corresponding author e-mail: juerg.frey@acw.admin.ch 

Introduction 
The main objective of the COST Action 853 was to establish and support microarray technology as a new 
tool for breeding, diagnosis, and high throughput screening in the field of agriculture.  
A major motivation behind tackling this task was an unsatisfactory situation in diagnostics of agronomically 
relevant organisms. At the onset of this action, many different gene fragments of diverse genes were used by 
different laboratories for taxon identification. Thus, each diagnostic laboratory had to master a broad range 
of  different  analytical  details,  making  the  task  of  molecular  diagnostics  cumbersome  and  laborious. 
Therefore,  simple  and  economic  methods  were  sought  to  allow  robust  identification  at  the  required 
taxonomic level for a wide range of organisms. 
Microarray technology seemed to provide the potential to solve this problem by its virtually unlimited power 
for parallel analysis. One simple strategy was to use several genes with an optimal discrimination capability 
at the required taxonomic level, i.e., species, strain, serogroup, etc., and to design several diagnostic probes 
per  taxon  to  maximise  the  accuracy  and  robustness  of  the  assay.  Basically,  this  technology  enables  the 
production  of  microarray  chips  that  can  be  used  for  large  taxonomic  groups  such  as  all  agronomically 
relevant  bacteria,  nematodes,  insects,  etc.  As  the  result of  such  an  assay  is  a  simple  pattern  of  spots,  data 
analysis  and  interpretation  should  not  present  any  difficulties.  Because  such  an  assay  would  be  simple  to 
perform it could easily be adapted in diagnostic laboratories.  
COST action 853 was organised in five  strongly networking  working groups  (Figure  1):  WG1  –  Nucleic-
Acid Based Microarrays, led by Dr. P. Bonants (HRI, NL), with the main objective to collect all available 
information on suitable DNA and RNA markers for phytodiagnostic purposes. Furthermore, members of this 
group should explore different microarray methods. WG2 – Protein Based Microarrays, led by Drs. I. Barker 
and  N.  Boonham  (CSL,  UK),  had  the  same  objective  as  WG1  but  with  a  focus  on  proteins.  WG3  – 
Bioinformatics and Information Dissemination, led by Drs. P. von Rohr and U. Wagner (FGCZ, UNI/ETH 
Zurich,  CH),  provided  the  necessary  expertise  in  bioinformatics  and  a  platform  for  coordination  and 
information dissemination. The latter two topics were covered mainly by a subgroup “Internet Office” (J.E. 
Frey, Agroscope ACW, CH). WG4 – Chip Production and Analysis, led by Prof. D. Blohm (University of 
Bremen, DE), provided sophisticated equipment for spotting, microarray hybridization and reading, together 
with  the  required  expertise  that  was  very  beneficial  to  all  members  of  this  action.  WG5  –  Microarray 
Technology for Environmental Monitoring, led by Dr. X. Nesme (University of Lyon, F), had the objective 
to collect information to develop microarray technology for environmental monitoring in agro-ecosystems. 
The action was chaired by Dr. J.E. Frey and Prof. G. Adam. Within this consortium we could offer the full 
set of necessary equipment, such as spotters, hybridisation tools and readers, as well as the required skills to 
all action members.  
The concept of this action was attractive to many researchers working in the field of agronomy and so the 
action grew rapidly to 21 signatory countries.  
The  individual  research  teams  involved  in  this  COST  Action  work  in  many  different  fields  in  the  area  of 
agricultural  research.  Identification  of  plant  pathogens  and  characterization  of  genes  relevant  for  crop 
breeding  are  among  the  main  topics  common  to  all  members  of  this  Action.  DNA-based  identification  of 
pests and pathogens was already well established before this Action was initiated. However, as mentioned  
1  

  
 
 
 
 
 
above, major problems existed because of the great diversity of pests and pathogens that had to be dealt with 
and the lack of harmonization with respect to the gene(s) and gene fragments best suited for diagnosis of this 
broad range of target organisms. 
In the course of our action we spent much effort for the development of specific techniques, probe design 
and strategies for target labelling. Many interesting achievements were first communicated at some of our 
Working Group Meetings. Furthermore, many new contacts were established, eventually resulting in a dense 
network that included companies and other research organisations, for example via several FP6 projects of 
the European Union.  

Figure 1: Organisation of the COST Action 853 in five interconnected working groups. 

In  the  following,  we  will  give  a  short  overview  on  some  of  the  highlights,  successes  and  problems 
encountered in the course of COST Action 853. 

Promises and achievements of the Action 
In the Memorandum of Unterstanding of our Action, the following main benefits were expected. First and 
most  importantly,  the  proposed  Action  should  provide  a  platform  to  introduce  and  support  this  important 
new  technology  to  many  European  countries.  Secondly,  the  broad  availability  of  this  widely  applicable 
technology thus provided should promote and protect development of agricultural products in Europe. The 
expected benefits were summarized as follows: 

1. Assessment of the suitability of different microarray techniques to the field of phytodiagnostics and 
animal/plant breeding 
2. Establishment  of  a  database  containing  all  relevant  information  on  nucleic  acid  sequences  and 
proteins suitable for microarray - based phytodiagnosis 
3. Coordination/standardisation  of  microarray  chip  composition  and  production,  profiting  from  the 
combined knowledge of all participating countries 
4. Implementation of specific microarray technologies in participating countries, thereby establishing 
this important new technology in European phytodiagnosis 
5. Significant improvement in harmonization of European phytodiagnosis 
6. Support of this new phytodiagnostic technique on a broad European level 

The contributions in this book provide convincing evidence that these major goals were clearly achieved.  
Within  the  course  of  our  Action,  all  collaborators  and  their  laboratories  were  fully  or  partially  financially 
supported by their COST member countries providing salaries and equipment. The collaborators used these 
resources for top-level research and development of methods which resulted in a wealth of new discoveries. 
Most of the efforts undertaken for our Action had an agricultural basis/aim and thus, the results are directly 
in  line  with  the  expected  benefits  for  our  Action.  Thus,  we  assessed  a  wide  range  of  different  microarray 

2  

  
 
 
techniques,  and  successfully  coordinated  microarray  chip  production  and  implementation  of  specific 
technologies.  Thereby  we  provided  a  platform  for  this  technology  and  significantly  contributed  to  the 
harmonization  of  European  phytodiagnosis.  One  aim  was,  however,  only  partially  achieved.  Initially,  we 
intended to assemble a database containing all relevant information for diagnosis (point 2 above). However, 
one  of  the  important  lessons  learned  in  this  Action  was  that  probe  design  is  intimately  dependent  on  the 
specific technical parameters used and that no algorithms exist that satisfyingly predict probe behaviour (see 
below). Thus, probes with acceptable hybridization characteristics when used in one specific assay proved to 
be virtually non-functioning in another. Furthermore, as some of the research was performed together with 
private companies, it was impossible to obtain all relevant information for creating this cumulative database. 
Despite  these  problems,  we  were  able  to  collect  all important  progresses  and  achievements  of  this  Action 
and we made the relevant information available on our homepage (www.cost853.ch). 

Highlights and successes of the Action 

Highly successful meetings 
In  the  course  of  this  action  we  convened  seven  management  committee  meetings  and  five  meetings  of 
chairpersons  and  WG  leaders.  These  administrative  meetings  are  important  to  the  overall  success  of  any 
Action as it is the place where strategies are decided and tasks and duties are distributed. The management 
committee  members  of  this  Action  always  participated  with  great  enthusiasm  and  thus  built  a  robust 
collaborative  and  scientific  basis  for  our  Action.  In  addition  all  nine  working  group  meetings  were  very 
interesting  and  stimulating.  These  working  group  meetings  were  always  very  much  up-to-date  as 
internationally  renowned  experts  in  the  respective  fields  were  invited  in  all  of  them.  The  meetings  were 
organized in a way allowing profound discussions and thereby a thorough comprehension of the presented 
materials and techniques.  

New STSM workshop format 
At the onset of this Action, only few of the collaborating scientists had hands-on knowledge on microarray 
technology or direct access to it. There was thus an urgent need for this kind of training. However, because 
of the high costs of the equipment and the fact that it was a very young technology, only few members of 
our  consortium  could  offer  the  required  equipment  and  space.  This  represented  a  major  problem,  as  the 
organisational tool provided by COST for such training was the short term scientific mission (STSM). These 
missions  were  originally  designed  to  allow  individual  scientists  visiting  a  specialist  laboratory  to  obtain 
training in a specific technique. Thus far, none of the potential hosts had the capacity to provide training for 
more than 40 individual scientists which was the initial number of applications we obtained in response to 
our first query. To enable accommodation of the observed need in hands-on microarray and bioinformatics 
training, we initiated the new COST format of “STSM workshops”. Such workshops may accommodate up 
to  20  trainees  and  were  thus  an  ideal  format  for  our  needs.  Between  2003  and  2006,  we  conducted  three 
highly successful STSM workshops totalising 43 participants. This training together with the many fruitful 
contacts  within  our  Action  may  have  contributed  to  the  significant  increase  in  progresses  that  could  be 
observed in this field as our Action went on. 

Evaluation and validation of various microarray systems 
Many different microarray systems have been proposed within our Action frame and much effort went into 
testing their respective pros and cons. To list all individual technologies covered within this Action is not 
possible in this short overview and therefore, we just highlight few and refer to the remainder of this book 
and our homepage for the others.  
Among  the  simplest  systems  is  one  that  uses  fluorescently  labelled  PCR  products  for  hybridization  on 
oligonucleotide microarrays. In this method, gene fragments containing the diagnostic information (e.g., the 
mitochondrial cytochrome oxidase I gene in many insects) for taxon identification are PCR amplified using 
fluorescently  labelled  primers.  The  amplicons  are  then  hybridized  to  a  microarray  slide  containing 
discriminatory oligonucleotide probes. This simple concept was shown in our laboratory as well as in others 
to  be  very  robust  and  simple  to  use,  and  it  was  shown  that  it  is  applicable  to  diagnostic  problems  in  all 
organisms including viruses, bacteria, insects, mammals and plants (e.g. chapters Boonham and Tomlinson, 
and Aittamaa et al.; Deyong et al., 2004; Tobler et al., 2006; Pfunder et al., 2004; Frey and Pfunder, 2006;  

3  

  
2006, and Pasquer et al., 2008).  
The  same  approach  was  initially  used  by  another  laboratory  in  our  consortium  from  Vienna  (Austria)  for 
detection and identification of microorganisms in environmental samples using 16S probes (Bodrossy and 
Sessitsch, 2004). However, it was shown that in mixed samples, the detection limit of individual species was 
only  around  5%  which  was  considered  too  high.  This  group  therefore  developed  an  alternative  labelling 
strategy, sequence-specific end-labelling of oligonucleotides (SSELO; Kostic et al., 2007). Here, labelling 
occurs  on  the  chip  after  hybridization.  The  probe  has  a  free  3’  end  and  upon  binding  a  matching  target 
sequence, a single base extension reaction is performed using fluorescently labelled nucleotides. With this 
strategy  it  was  possible  to  lower/increase  the  detection  limit  of  individual  microorganisms  in  mixtures  to 
around 0.1% (chapter Bodrossy and Sessitch).  
All above mentioned approaches use an initial PCR amplification step to produce enough labelled DNA for 
hybridization.  For  the  analysis  of  mixed  samples  or  heterogeneous  templates,  this  step  comes  with  the 
disadvantage  that  unequal  amplification  of  different  target  sequences  may  distort  the  quantitative 
representation  of  the  original  occurrence  in  the  PCR  products.  The  use  of  Padlock  probes  provides  an 
elegant solution to this problem (chapter Bonants et al.). These probes contain two probe sites oriented head 
to  head  together  with  primer  sites  and  a  zip-code  sequence.  The  two  probe  sites  hybridize  to  a  matching 
target  site  and,  with  a  full  match,  the  gap  between  the  two  probe  sites  is  closed  with  a  ligation  reaction. 
Then,  the  circularized  Padlock  probe  is  amplified  using  the  in-built  primer  sites.  Finally,  the  amplified 
probes are de-multiplexed on a microarray carrying the inverse recognition sequences for the zip-codes. This 
system is highly sensitive and specific, and because all amplicons have identical primer sites the quantitative 
representation is maintained. 
To  characterize  microbial  soil  communities  our  colleagues  from  Lyon  (France)  followed  another  strategy. 
They use high-density microarrays covering the 16S-rDNA. This strategy allows to perform high-resolution 
differentiation  among  different  microbial  soil  communities  (chapter  Nesme  and  Oger-Desfeux).  One 
drawback of this solution is that data analysis is relatively complex and requires good statistical knowledge.   
Quantification is inherently difficult with microarrays and if needed for a specific application represents an 
additional  level  of  complexity  for  the  development  of  diagnostic  microarrays.  Our  colleagues  from 
Wageningen  (The  Netherlands)  followed  a  unique  way  to  circumvent  these  problems  by  using  the 
microarray format for highly parallel quantitative real-time PCR (chapter Van Doorn et al.). In collaboration 
with BioTrove Inc., they developed a microarray allowing >3000 real-time PCR reactions in parallel starting 
with the ligated Padlock/PRI-lock probes as template. This system combines the advantage of highly precise 
quantification  of  real-time  assays  with  the  high  degree  of  multiplex  analysis  provided  by  the  microarray 
format. The drawback of this approach is the need for high precision optics on a special analytical system 
and thus it is currently not widely applicable.  
Alternatives to optical detection were also developed in our consortium. Our colleagues from Lithuania are 
studying  systems  to  allow  highly  sensitive  capture  of  target  molecules  by  biosensors  and  amperometric 
detection  of  electrochemical  signals  (chapter  Ramanavicius  and  Ramanaviciene.).  This  work  is  of  high 
relevance as it lends itself to miniaturization and avoids expensive optical detection. However, in contrast to 
the above mentioned technologies this approach is still in its infancy. 

Protein detection  
Most of the efforts of our COST Action 853 went into the field of DNA arrays and only limited resources 
were spent for Working Group 2, protein arrays. This had several reasons. First, it still is very much simpler 
to work with DNA than with proteins because DNA is a very robust molecule that can easily be stored and 
handled  and  will  not  rapidly  deteriorate  at  room  temperature.  Second,  while  DNA  or  RNA  are  relatively 
easily accessible for analysis, protein diagnostics requires antibodies or alternative capture probes that first 
have to be developed for most organisms. Finally, nucleic acid and protein assays cannot easily be mixed. 
Furthermore, if antibodies for only one individual in a set of organisms to be diagnosed are lacking, there is 
a  need  for  both  tests  (protein  and  nucleic  acid  assay).  Thus,  only  few  of  our  collaborators  were  studying 
protein  arrays.  Poltronieri  and  collaborators  managed  to  produce  an  interesting  chip  allowing  detection  of 
important  allergens  due  to  mites  in  food  product  and  dust.  This  original  approach  allows  the  detection  of 
health-threatening  proteases  at  an  extremely  low  level  compared  to  usual  detection  techniques  (chapter 
Cimaglia et al.).  
Nevertheless,  protein  detection  today  is  a  very  frequently  used  tool  for  virus  detection.  For  example,  the 

4  

  
group  of  Jan  Bergervoet  is  greatly  improving  the  detection  sensitivity  of  potato  viruses  by  immunoassays 
using the Luminex xMAP technology (chapter Bergervoet et al.). 

Gene expression studies  
Finally,  some  members  of  our  consortium  were  involved  in  the  “original”  approach  of  microarray 
technology,  i.e.,  gene  expression  analysis.  The  group  of  Marusa  Pompe-Novak  (chapter  Kogovšek  and 
Pompe-Novak) was studying plant-pathogen interactions and the lessons learned in this project were of great 
value  to  many  colleagues  in  the  action  who,  as  an  extension  of  their  skills  in  diagnostic  microarrays, 
continue  with  projects  on  gene  expression  or  start  to  use  quantitative  information  in  the  analysis  of  their 
diagnostic microarrays.  

Progress in general knowledge of microarray probe design 
The major challenge in all microarray based diagnostic systems is probe design. The goal is to find genetic 
regions  that  show  no  or  low  variation  between  individuals  of  a  taxon  but  with  very  clear  differentiation 
between different taxa (e.g., chapter Adam). The search for such regions is usually based on DNA sequence 
alignments  of  candidate  genes.  For  microorganisms,  the  “gold-standard”  for  such  a  gene  is  the  16S 
ribosomal RNA. Over a decade ago, (Ludwig et al., 2004)started to build a database, ARB, containing all 
known 16S rDNA sequences of microorganisms, and which includes sophisticated algorithms for alignment 
and  primer  and  probe  design  (chapter  Glöckner  and  Peplies,  chapter  Nesme  and  Oger-Desfeux).  The 
program may also be used with other genes (chapter Bodrossy and Sessitch). Within our consortium, we had 
several training courses for this important tool. 
One  of  the  problems  with  probe  design  is  the  huge  variation  in  hybridization  performance  of  different 
probes. We soon found that there is a serious gap between the performance of probes on the chip surface as 
observed in our assays compared to the expected performance based on our calculations of expected melting 
behaviour (Pfunder et al., 2004), a serious problem that is still not solved (e.g. chapter Sip and Pozhitkov et 
al., 2006). The most frustrating consequence of this problem is that obtaining optimized probe sets requires 
on-chip selection. This implies the design of several probes per target sequence hoping that at least one will 
perform well for the test under development. In cases with limited sequence divergence between taxa, there 
are narrow limits to the range of possibilities for probe design. It is not uncommon to find that, although the 
target  sequence  initially  looked  promising,  none  of  the  possible  probes  performs  well  enough  to  be  of 
practical use and that, therefore, it is necessary to search for alternative target sequences. It is an unfortunate 
fact that the availability of such alternatives is to date still very restricted in many important pest organisms. 
The process of microarray optimization is therefore sometimes both very time-consuming and expensive. 
Unfortunately, as mentioned above, our work also revealed that probes cannot easily be transferred between 
systems.  As  a  matter  of  fact,  the  hybridization  behaviour  of  probes  that  were  selected  in  an  optimization 
scheme for one probe set is often less than acceptable if used in other hybridisation conditions. This is the 
main  reason  why  we  have  abstained  from  establishing  a  probe  database.  All  relevant  information  may  be 
obtained from the respective publications that are listed on our website.  

Perception of COST Action 853 
Our  Action  has  raised  a  lot  of  interest  in  many  other  COST  Actions  and  generally  in  the  diagnostic  field 
across Europe, including the private sector. Many of our meetings were visited by interested scientists not 
directly  involved  in  COST  themselves,  and  by  representatives  of  companies  working  in  this  field.  The 
positive  impression  made  by  the  presentations  of  COST  members  was  a  major  incentive  for  many 
companies to start collaborations with individual laboratories of our Action. Importantly, it initiated science 
networks  and  set  the  grounds  for  many  parallel  research  projects,  such  as  FP6  projects  (e.g.,  Fish&Chips 
Project  of  Prof.  Blohm;  http://www.fish-and-chips.uni-bremen.de/PostNuke/html/)  and  nationally  funded 
projects. 
To accommodate the general interest in our Action, we were invited by the COST office to organise a Trans-
COST meeting covering all important aspects of our Action and including all relevant development of the 
other  interested  COST  Actions.  Members  of  COST  Actions  853,  858,  861,  863  and  926,  and  of  several 
private  companies  were  participating  at  this  meeting  held  on  May,  22-24,  2007,  in  St.  Feliu  de  Guixols 
(Spain) together with the last meeting of our Action. This very interesting and stimulating joint meeting was 
attended  by  many  top  experts  in  the  field  of  diagnostics  and  was  a  formidable  demonstration  of  the  great 

5  

  
overall success of our COST Action 853, both with respect to the scientific and technological achievements 
and to the positive impression we made in the scientific community and in the public appearance. 

Conclusion 
COST  Action  853  was  a  very  timely  action  that  enabled  the  collaborating  scientists  to  participate  in  the 
development  and  establishment  of  a  new  and  highly  relevant  technology.  Many  different  microarray 
technologies  were  evaluated  and  the  experience  gained  represents  a  highly  valuable  reference  for  further 
work.  All  members  of  the  Action  were  very  active  and  acted  as  ambassadors  for  the  Action  within  their 
countries. This is the main reason for its wide public appearance. This Action has significantly contributed 
to  improve  European  collaboration  and  an  increased  level  of  harmonisation  in  agricultural  diagnosis. 
Overall,  COST  Action  853  was  highly  successful  and  the  established  knowledge  will  be  an  important 
cornerstone for continuing efforts in the field of microarray diagnostics for many years to come.   

References 
Bodrossy L, and Sessitsch A. 2004. Oligonucleotide microarrays in microbial diagnostics. Current Opinion 

in Microbiology 7: 245-254. 

Deyong Z, Willingmann P, Heinze C, Adam G, Pfunder M, Frey B, and Frey JE. 2004. Differentiation of 
cucumber  mosaic  virus  isolates  by  hybridization  to  oligonucleotides  in  a  microarray  format.  Journal  of 
Virological Methods 116: 139-146. 

Frey JE, and Pfunder M. 2006. Molecular techniques for identification of quarantine insects and mites: The 
potential of microarrays. In: Molecular Diagnostics: Current Technology and Applications. J. R. Rao, C. 
C. Fleming, and J. E. Moore, eds, pp 141-162 . Norfolk: Horizon Scientific Press. 

Kostic  T,  Weilharter  A,  Rubino  S,  Delogu  G,  Uzzau  S,  Rudi  K,  Sessitsch  A,  and  Bodrossy  L.  2007.  A 
microbial  diagnostic  microarray  technique  for  the  sensitive  detection  and  identification  of  pathogenic 
bacteria in a background of nonpathogens. Anal Biochem 360: 244-54. 

Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, Buchner A, Lai T, Steppi S, Jobb G, 
Forster W, Brettske I, Gerber S, Ginhart AW, Gross O, Grumann S, Hermann S, Jost R, Konig A, Liss T, 
Lussmann R, May M, Nonhoff B, Reichel B, Strehlow R, Stamatakis A, Stuckmann N, Vilbig A, Lenke 
M, Ludwig T, Bode A, and Schleifer KH. 2004. ARB: a software environment for sequence data. Nucleic 
Acids Res 32: 1363-71. 

Pasquer F, Frey B, and Frey JE. 2008. Identification of cherry incompatibility alleles by microarray. Plant 

Breeding 127:413-417. 

Pfunder  M,  Holzgang  O,  and  Frey  JE.  2004.  Development  of  microarray-based  diagnostics  of  voles  and 
shrews for use in biodiversity monitoring studies, and evaluation of mitochondrial cytochrome oxidase I 
vs. cytochrome b as genetic markers. Mol Ecol 13: 1277-86. 

Pozhitkov  A,  Noble  PA,  Domazet-Loso  T,  Nolte  AW,  Sonnenberg  R,  Staehler  P,  Beier  M,  and  Tautz  D. 
2006.  Tests  of  rRNA  hybridization  to  microarrays  suggest  that  hybridization  characteristics  of 
oligonucleotide probes for species discrimination cannot be predicted. Nucleic Acids Res 34: e66. 

Tobler NE, Pfunder M, Herzog K, Frey JE, and Altwegg M. 2006. Rapid detection and species identification 

of Mycobacterium spp. using real-time PCR and DNA-microarray. J Microbiol Methods 66: 116-24. 

6  

  
 
 
Microarray hybridization 

Miroslav Sip 

University of South Bohemia 
 J. Boreckeho 27 
370 11 Ceske Budejovice  
Czech Republic 

 e-mail: sip@zsf.jcu.cz 

Abstract 
Design  of  microarrays  for  diagnostic  purposes  is  based  on  rules  derived  mainly  from  general  concepts  of 
hybridization and probe design in bulk solutions. The real conditions on a microarray surface significantly 
differ from bulk solution conditions as a result of the immobilization of probes and interactions of probes 
and  targets  with  the  surface.  Multiple  interactions  of  probe  fragments  with  the  surface,  dynamics  of  the 
system and statistics of fragment binding modes should be taken into account. In this article we present a 
brief  summary  of  requirements  concerning  the  melting  temperature,  the  probe  similarity,  the  longest 
identical sequence stretch and the steric hindrance of the probes. The influence of the microarray surface and 
the various types of microarray surface chemistry that can compromise theoretical predictions of quality of 
probes based on state-of -the-art algorithms is discussed.  

Introduction 
The main attraction of using nucleic acids in detection and diagnostics is i) the specificity of interaction that 
makes  it  possible  to  distinguish  two  molecules  differing  in  only  one  nucleotide  and  ii)  the  apparent 
simplicity  to  predict  and  to  design  such  a  specific  interaction.  To  find  a  particular  nucleic  acid  sequence 
among many others, just take its complementary sequence, label it, mix and follow the hybridization pattern. 
Every living organism, from viroids that are nothing but tiny pieces of nucleic acids up to human beings, is 
characterized by its genetic information written in form of a nucleotide sequence. The microarray detection 
method  is  based  on  fragments  of  nucleic  acids  (probes)  that  selectively  hybridize  to  the  nucleic  acid 
sequences present in the sample (targets).  
The idea of a DNA-microarray is to have a large number of such probes (virtually unlimited, practically up 
to  hundreds  of  thousands)  which  can  simultaneously  interact  with  the  labeled  targets  of  the  sample.  To 
distinguish among individual probes, they are spotted or synthesized on a surface so that they together form 
a two-dimensional array, where each probe is characterized by its coordinates.  
The  microarray  thus  enables  simultaneous  detection  of  a  great  number  of  sequences  in  one  reaction.  The 
probe design must take into account that all spots/sequences on the microarray have to work under the same 
conditions  (temperatures,  solution  composition,  etc.)  except  for  sequences  of  individual  probes.  Probe 
design for a microarray requires optimization of the whole set of probes and of the hybridization conditions 
to get the best performance. For a real microarray it is often difficult to attain optimum stringency for all 
spots, because it is impossible to individually tune all hybridization parameters for every spot. 

General concepts of nucleic acids hybridization and probe design 
Molecular  hybridization  processes  on  a  microarray  can  be  described  and  quantified  in  the  first 
approximation by classical concepts of nucleic acids hybridization in solutions. The main factors influencing 
melting temperature and kinetics are the base sequence (Breslauer et al., 1986) and the composition of the 
solution  which  determines  its  ionic  strength.  Several  points  have  to  be  considered  in  microarray  probe 
design:  the  melting  temperature,  the  probe  similarity,  the  longest  identical  sequence  stretch  and  the  steric 
hindrance of the probes. 

7  

  
 
 
 
 
 
 
Melting Temperature   
The  melting  temperature  (Tm)  should  be  approximately  the  same  for  all  probes  on  the  microarray.  Tm  is 
defined as the temperature at which half of the DNA strands are in the double-helical state and the other half 
are single-stranded. Software tools for probe design perform the melting temperature calculation using one 
of the empiric formulas. These calculations give a more or less precise starting value of Tm. Probably the 
best agreement with experimental values yields the so-called nearest neighbour thermodynamic theory. This 
concept recognizes the influence of sequence on Tm. It assumes that the binding between strands in a duplex 
depends  not  only  on  its  base  composition  but  also  on  its  sequence  (e.g.  AT/TA  binding  parameters  are 
different from those for TA/AT). The Tm in Kelvin is then calculated using the formula 

dH
dH

Tm
Tm

=
=

dS
dS

+
+

R
R

ln
ln

c
c
4
4

+
+

log6.16
log6.16

K
[
K
[
10
10

+
+

]
]

where  dH  is  the  enthalpy  for  helix  formation,  dS  is  the  entropy  for  helix  formation,  R  is  the  molar  gas 
constant and c is the total oligonucleotide strand concentration. The formula takes into account salt molar 
concentration [K+]. For details and examples see the original article by Breslauer et al. (1986). The melting 
temperature  reflects  the  free  energy  of  duplex  formation.  The  dS  and  dH  parameters  are  determined 
experimentally  and  may  considerably  differ  from  the  Breslauer’s  values.  The  thermodynamic  parameters 
and  nearest  neighbours  calculation  method  presented  by  Santa  Lucia  (1998)  are  widely  used  for  Tm 
estimation in oligonucleotide probe design.  
The strategy for optimum array performance especially when targeting a higher number of closely related 
genes  is  to  design  and  maximize  the  number  of  oligonucleotide  probes  originating  from  a  set  of  highly 
similar sequences. The use of multiple probes to cover a given target increases the assay reliability.  

Overall probe similarity 
The  hybridization  specificity  may  be  affected  by  a  variety  of  probe  design  factors,  including  the  overall 
sequence similarity, the distribution and positions of mismatching bases and the size of the longest identical 
stretch as shown by Liebich (2006).  
Hybridization specificities increase with overall similarities between probe and target sequences, as shown 
by  Liebich  et  al.,  2006.  Under  the  described  experimental  conditions,  they  could  establish  a  logarithmic 
relationship between probe-target similarity and the percentage of hybridized probes. 

Longest identical sequence stretch 
When  the  same  data  were  analyzed  in  relation  to  the  length  of  a  common  identical  sequence  stretch,  a 
similar  relationship  to  that  for  percent  overall  similarity  was  found.  The  percentage  of  probes  that  gave 
positive  hybridization  signals  was  considerably  lower  for  probes  with  up  to  20-bp  identical  sequence 
stretches  to  the  target  sequences  (about  10%)  than  those  with  at  least  22-bp  identical  sequence  stretches 
(about  25%),  suggesting  that  a  20-bp  identical  sequence  stretch  could  be  an  appropriate  cutoff  value  for 
probe design (Liebich et al., 2006). Another confirmation of these empirical rules was given by Bystricka et 
al.(2005) and He (2005). 

The steric hindrance 
At last but not at least, the design of probes and determination of experimental conditions should minimize 
any form of steric hindrance affecting the hybridization process. The most obvious form of steric hindrance 
concerns  formation  of  higher  order  structures  like  hairpins  in  both  the  probe  and  target  molecules  that 
prevent  the  formation  of  the  duplex.  By  choosing  proper  probe  sequences  and  hybridizing  non-modified 
nucleic acids in ‘bulk’ solution this problem can be minimized.   

Particular problems of surface-array hybridization 
The  surface  plays  an  important  role.  There  are  three  major  differences  with  respect  to  the  bulk  solution 
conditions: 
1) The capture probes are immobilized to a certain extent by surface binding and therefore their molecular 
dynamics  is  different  from  that  describing  a  system  of  two  free  strands  in  a  solution.  Kinetics  of 
hybridization is much slower and the true equilibrium point is rarely reached in real microarray applications. 

8  

  
 
Kinetics were measured on photolithographic microarrays (Glazer et al., 2006), spotted surface microarrays 
and  gel-based  microchips  (Sorokin  et  al.,  2006).  The  hybridization  kinetics  is  slower  in  gel-based 
microchips  than  in  spotted  surface  microarrays  but  the  fluorescence  signals  and  mutation  discrimination 
efficiency  is  better  for  the  gel-based  microarrays.  The  authors  attribute  this  fact  to  better  immobilization 
efficiency and to the higher thermodynamic association constants for duplex formation within gel pads. The 
hybridization  kinetics  can  be  accelerated  by  recirculation  in  microfluidic  devices.  Lee  et  al.  (2006)  report 
that  recirculation  shortened  the  time  of  perfect  match  target-probe  hybridization  from  6  hours  to  2  hours 
compared to static hybridization. Fast kinetics are attained also in lateral flow microarrays (Carter and Cary, 
2007)  which  allow  analyte  detection  times  of  <120  s  and  sub-femtomole  sensitivity  with  ten-microliter 
sample volume (excluding sample preparation and amplification). 
2)  The  presence  of  the  surface  affecting  all  processes  in  its  vicinity  and  especially  the  interactions  of  the 
capture probe with the surface represent an important steric hindrance making single strand – double strand 
transitions more difficult. A microarray surface effect on binding affinities was confirmed recently by Zhang 
et al. (2007) but so far no practical tool has been developed considering this effect for the probe design. 
3)  The  surface,  charged  or  uncharged,  represents  an  electrostatics  generating  artefact  influencing  the 
concentration profiles of ions in the vicinity of bound molecules. Therefore, the real values of concentrations 
influencing the duplex formation may be different from those expected.  
Most problems with a considerable percentage of flawed capture probes that are theoretically fulfilling all 
the  requirements  but  practically  do  not  meet  the  expectations  arise  from  a  total  neglect  of  the  above-
mentioned surface phenomena on an array during the standard probe design.  
The surface of a microarray is even in the simplest cases very complex. While the target binds to the probe 
always by weak hydrogen bonds to form a duplex, the probes bind to the surface by weak non-covalent and/
or strong covalent bonds, depending on the used surface chemistry.  
Although  DNA  can  be  printed  directly  onto  a  clean  glass  surface  (Call  et  al.,  2001),  better  results  are 
generally  obtained  with  modified  surfaces  such  as  poly-L-lysine,  3-aminopropyltrimethoxysilane,  3-
glycidoxypropyltrimethoxysilane  and  aldehyde  or  carboxylic  acid.  Epoxide,  isothiocyanate  and  aldehyde 
activated  glass  surfaces  form  covalent  links  with  amine-terminated  cDNA  and  amine-terminated 
oligonucleotides. Non-modified DNA binds by weak interactions onto amine-functionalized surfaces such as 
poly-L-lysine and 3-aminopropyltrimethoxysilane. Surfaces modified with polyamidoamine dendrimer bind 
non-modified  DNA  electrostatically.  Impact  of  surface  chemistry  and  blocking  strategies  on  DNA 
microarrays was studied by Taylor et al. (2003).  
The picture of a microarray containing probes as fragments that are attached to the surface just by one end 
and  otherwise  extend  free  into  the  solution  is  obviously  not  true.  A  more  realistic  image  must  consider 
additional aspects like multiple interactions of probe fragments with the surface, dynamics of the system and 
the fact that the intensity of a particular spot is given by a statistical set of many fragments hybridizing to 
that spot. 
Multiple  interactions  of  the  fragments  are  caused  mainly  by  negative  charges  of  nucleic  acids  located  on 
phosphate  groups.  With  positively  charged  surfaces  like  poly-L-lysine,  the  attractive  interaction  is  direct. 
However,  DNA  binds  by  multiple  interactions  onto  negatively  charged  surfaces  if  positively  charged  ions 
are present in a sufficient concentration in the solution. Indeed, divalent cations can be used to immobilize 
nucleic  acids  for  AFM  imaging  (Atomic  Force  Microscope  imaging;  Pastushenko,  2002).  AFM  imaging 
clearly shows the DNA molecules collapsed onto the planar surface.  
The system of non-covalently bound probes is highly dynamic. At room temperature the probes are subject 
to thermal fluctuations resulting in partial detachment from the surface and a transient exposure of binding 
sites that enable formation of the duplex with the target fragment. 
The  signal  of  a  spot  is  the  sum  of  signals  emitted  by  labelled  targets  situated  within  the  spot.  The 
conformational space of duplex formation is explored by a large set of molecules, exceeding in most cases 
106 molecules (probes) per spot. For example, this number ranges from several millions of capture probes 
per spot for Affymetrix and NimbleGen arrays to around 108 probes per spot for cDNA arrays and extends 
to over 1010 for oligonucleotide-based arrays. The targets that found favourable conditions for binding in the 
context  of  both  capture  probes  and  local  surface  remain  attached  to  the  probe  and  can  emit  a  signal.  The 
large number of involved molecules statistically averages out the influence of local surface inhomogeneities 
and enables hybridization with a subset of randomly bound probes.  

9  

  
Conclusion 
All  the  above-mentioned  factors  should  be  taken  into  account  as  parameters  during  the  process  of 
microarray probe design. Due to the complexity of such a task, no state-of -the-art algorithms are capable of 
predicting  exactly  the  quality  of  probes  for  microarray  hybridization.  A  detailed  study  aimed  at  short 
oligonucleotide probes clearly shows the limits of standard algorithms (Pfunder and Frey, 2005) .  
Even commercial microarrays suffer from a high percentage of poorly performing probes. One way how to 
minimize  this  drawback  is  to  adopt  special  methods  of  data  analysis  taking  into  account  the  probe 
performance (Expression Analysis).  
As long as theoretical predictions of probe performance remain only the first approximation, the importance 
of experimental validation of probes cannot be overestimated. During the design of microarrays for practical 
routine  applications,  excessive  testing  should  be  performed  to  show  the  performance  of  the  chip  under 
various  conditions.  This  should  be  stressed  especially  for  applications  involving  complex  samples, 
containing  many  target  sequences,  where  mutual  interactions  and  cross-hybridizations  are  possible.  The 
following chapters present convincing evidence of successful microarray design for agricultural practice.  

Acknowledgments 
This work was supported by the grant COST 853.002 of the Ministry of Education (Czech Republic). 

References 
Breslauer  KJ,  Frank  R,  Blocker  H  Marky  LA.  1986.  Predicting  DNA  duplex  stability  from  the  base 

sequence. Proc Natl Acad Sci U S A 83, 3746-3750. 

Bystricka D, Lenz O,Mraz I, Pilterova L, Kmoch S, Sip M. 2005. Oligonucleotide-based microarray: a new 

improvement in microarray detection of plant viruses. Journal of Virological Methods 128, 182. 

Call  DR,  Chandler  DP,  Brockman  F.  2001.  Fabrication  of  DNA  microarrays  using  unmodified 

oligonucleotide probes. Biotechniques 30, 368-72, 374, 376 . 

Carter DJ and Cary RB. 2007. Lateral flow microarrays: a novel platform for rapid nucleic acid detection 

based on miniaturized lateral flow chromatography.. Nucleic Acids Res 35, e74. 

Expression Analysis. 2005. REDI REDuction of Invariant Probes analysis. Expression Analysis Technical 

Note http://www.expressionanalysis.com/pdf/REDI_TechNote_2005.pdf 

Glazer  M,  Fidanza  JA,  McGall  GH, Trulson  MO,  Forman  JE,  Suseno  A,  Frank  CW  .  2006.  Kinetics  of 
oligonucleotide  hybridization  to  photolithographically  patterned  DNA  arrays.  Anal  Biochem  358,  225-
238. 

He  Z,  Wu  L,  Li  X,  Fields  MW,  Zhou  J.  2005.  Empirical  establishment  of  oligonucleotide  probe  design 

criteria. Appl Envron Microbiol 71, 3753-3760. 

Lee HH, Smoot J, McMurray Z, Stahl DA, Yager P. 2006. Recirculating flow accelerates DNA microarray 

hybridization in a microfluidic device.. Lab Chip 6, 1163-1170. 

Liebich  J,  Schadt  CW,  Chong  SC,  He  Z,  Rhee  SK,  Zhou  J.  2006.  Improvement  of  oligonucleotide  probe 
design criteria for functional gene microarrays in environmental applications. Appl Environ Microbiol 72, 
1688-1691. 

Pastushenko  VP,  Kaderabek  R,  Sip  M,  Borken  C,  Kienberger  F,  Hinterdorfer  P.  2002.  Reconstruction  of 

DNA shape from AFM data. Single Molecules 3, 117. 

Pfunder M, and Frey JE. 2005. Dissociation analysis in polymerase chain reaction and 1x SSC buffer as a 
prerequisite  for  selection  of  13mer  microarray  probe  sets  with  uniform  hybridization  behavior..  Mol 
Biotechnol 29, 1-10. 

SantaLucia  JJ.  1998.  A  unified  view  of  polymer,  dumbbell,  and  oligonucleotide  DNA  nearest-neighbor 

thermodynamics. Proc Natl Acad Sci U S A 95, 1460-1465. 

10  

  
 
Sorokin NV, Chechetkin VR, Pan'kov SV, Somova OG, Livshits MA, Donnikov MY, Turygin AY, Barsky 
VE,  Zasedatelev  AS.  2006.  Kinetics  of  hybridization  on  surface  oligonucleotide  microchips:  theory, 
experiment, and comparison with hybridization on gel-based microchips.. J Biomol Struct Dyn 24, 57-66. 

Taylor S, Smith S, Windle B, Guiseppi-Elie A. 2003. Impact of surface chemistry and blocking strategies on 

DNA microarrays.. Nucleic Acids Res 31, e87. 

Zhang  L,  Wu  C,  Carta  R,  Zhao  H.  2007.  Free  energy  of  DNA  duplex  formation  on  short  oligonucleotide 

microarrays. Nucleic Acids Res 35, e18. 

11  

  
 
ARB and SILVA: A software environment  
and databases for ribosomal RNA sequence data 

Frank Oliver Glöckner1, 2* and Jörg Peplies3 

1Microbial  Genomics  Group,  Max  Planck  Institute 
for  Marine  Microbiology,  D-28359  Bremen,  Ger-
many 
2Jacobs  University  Bremen  GmbH,  D-28759  Bre-
men, Germany 

* Corresponding author email: fog@mpi-bremen.de 

3Ribocon GmbH, D-28359 Bremen 

Abstract 
Sequencing  the  ribosomal  RNA  (rRNA)  genes  is  currently  the  method  of  choice  for  nucleic  acid-based 
detection of microbes, assessment of microbial diversity, and phylogenetic reconstruction. The ARB (latin, 
‘arbor’ = tree) software suite has been accepted by researchers worldwide as a standard tool for large scale 
ribosomal  RNA  analysis.  More  than  12  years  of  development  have  already  been  invested  in  the  software 
package  and  the  corresponding  high  quality  rRNA  alignments.  The  interactive  software  tool  ARB  was 
recently supplemented by SILVA (Latin, ‘silva’ = forest), a set of up to date, quality controlled, and aligned 
rRNA  datasets  comprising  Bacteria,  Archaea,  and  Eukarya.  SILVA  is  designed  as  an  automatic  software 
pipeline for sequence retrieval, quality assignment, and the alignment of nucleic acid sequences based on the 
latest  ARB  alignments.  Two  precompiled  sequence  datasets  for  ARB  are  offered  for  download  on  the 
SILVA website: (1) the reference (Ref) datasets, comprising only high quality, nearly full length sequences 
suitable for in-depth phylogenetic analysis and probe design and (2) the comprehensive Parc datasets with 
all publicly available rRNA sequences longer than 300 nucleotides suitable for biodiversity analyses. ARB 
and SILVA are freely available at www.arb-home.de and www.arb-silva.de, respectively. 
Keywords: ribosomal RNA, ARB, database, SILVA, alignments, phylogeny, FISH, diagnostics 

Introduction 
Large scale sequencing of the three domains of life has become the standard approach to access the genetic 
potential  of  organisms.  The  increasing  amount  of  sequence  data  is  of  central  importance  for  molecular 
taxonomy, diversity analysis and the identification of microorganisms. Initiated by the pioneering studies of 
Pace, Olsen, Giovannoni and Ward (Pace et al. 1985; Olsen et al. 1986; Giovannoni et al. 1988; Ward et al. 
1990) over 15 years ago, the ribosomal RNA (rRNA) molecule has been established as the “gold-standard” 
for  the  investigation  of  the  phylogeny  and  ecology  of  microorganisms  (Amann  et  al.  1995;  Pace  1997). 
Today more than 500,000 publicly available small and large subunit (SSU and LSU) rRNA sequences ask 
for appropriate software tools and specialized quality controlled databases. In anticipation of this impending 
deluge of rRNA data, the development of the ARB software suite and the curation of its associated databases 
began more than 12 years ago (Ludwig et al. 2004). ARB offers a graphical user interface and a wide variety 
of  interacting  software  tools  built  around  a  common  database.  The  ARB  project  has  been  recently 
supplemented  by  SILVA  which  provides  structured,  integrative  knowledge  databases  for  small  and  large 
subunit  rRNAs  (Prüsse  et  al.  2007).  Although  several  other  databases  exist,  the  ARB/SILVA  project  is 
currently  the  only  one  which  incorporates  homologous  small  subunit  sequences  (SSU)  from  Eukaryotes 
(18S) and additionally offers large subunit (LSU) databases (28S/28S) comprising sequences from all three 
domains.  Based  on  regularly  offered  international  workshops  and  the  ARB  mailing  list,  it  is  currently 
estimated that the ARB software suite and its databases are employed worldwide by several thousand users 
from academia and industry. 

12  

  
 
 
 
 
 
 
 
 
 
Materials and Methods 
The  ARB  software  was  developed  for  UNIX  systems  and  their  derivatives.  The  major  part  of  the  source 
code was written in C++ and C; some parts were written in Perl and Java and other script languages. The 
graphical  environment  is  based  upon  the  Open  Motif  library.  Besides  proprietary  developments  like  the 
ARB parsimony tool and the interactive sequence and secondary structure editor as well as probe design and 
evaluation  functionalities,  some  programs  of  the  PHYLIP  package  for  phylogeny  inference  (http://
evolution.genetics.washington.edu/phylip.html)  were  incorporated  as  components  directly  interacting  with 
the central database. Recently, PhyML (Guindon and Gascuel 2003) and RAxML (Stamatakis et al. 2005) 
were included for fast maximum-likelihood-based phylogenetic analyses of nucleic and amino acid sequence 
data.  
SILVA  provides  two  types  of  precompiled  databases  for  both  small  and  large  subunit  ribosomal  RNA 
sequences in ARB format: the high-quality Ref databases and the comprehensive Parc databases. The Ref 
databases are subsets of Parc, which are exclusively comprised of nearly full length 16S/18S and 23S/28S 
rRNA sequences. A sequence is accepted if it is at least 1200 bases long. Additionally, sequences as short as 
900 bases are included if they belong to the domain Archaea. Sequences in the LSU Ref database have to be 
at least 1900 bases long. For quality control, all sequences that could not be unambiguously aligned or are of 
low quality were removed from the Ref databases. Both Ref databases are supplemented with a guide tree. 
The rRNA Parc databases are a collection of all quality checked and automatically aligned rRNA sequences 
longer than 300 bases. All sequences in the ARB/SILVA databases are associated with a rich set of sequence 
and process parameters. The databases are regularly updated and follow the release cycle and numbering of 
the EMBL database. 

Results and Discussion 
ARB  is  a  comprehensive  software  package  originally  developed  for  ribosomal  RNA  (rRNA)  data. 
Nowadays,  it  can  be  used  for  any  nucleic-  or  amino-acid  sequence  data.  The  central  idea  is  to  arrange  a 
database of sequences and associated descriptive information according to the phylogenetic relationships of 
the corresponding organisms. This phylogenetic tree is visualised and can be used for walking through the 
database via simple mouse clicks (Figure 1). Furthermore, to facilitate in-depth analysis of molecular data, a 
comprehensive  selection  of  software  tools  is  integrated  into  ARB.  These  are  controlled  via  a  common 
graphical user interface and they interact directly with one another as well as with the database. 
For  rRNA  data,  the  SILVA-project  provides  databases  with  quality  checked  and  aligned  sequences.  Any 
additional data related to the individual sequences can be stored in structured database fields or linked via 
local or worldwide networks. With the “search and query” system for database management, users are able 
to perform complex queries to e.g. create sequence subsets according to the rich amount of contextual and 
processing information provided for each rRNA sequence by SILVA. Detailed numerical information about 
the sequence and alignment quality, as a well as the potential for sequence anomaly (chimeras) facilitate the 
selection of sequence subsets for high quality tree reconstructions.  
All information stored in the database, along with the sequence data such as bibliography, user-made entries 
or information calculated on-line from the database entries, can be shown at the terminal nodes of the tree.  

Sequence editor and alignment 
A powerful sequence editor which can be used for nucleic acid and amino acid sequences allows sequence 
editing,  string  search,  visualisation  of  base  pairing  and  positional  variability.  All  colours  and  symbols  are 
user-defined,  and  separate  “align”  and  “edit”  modes  prevent  erroneous  changes.  Furthermore,  the  editor 
hosts an automatic aligner that is able to align new sequences according to a reference alignment. For DNA 
and  RNA  sequences  it  is  supported  by  a  powerful  suffix  tree  server  which  automatically  picks  closely 
related  sequences  from  the  reference  alignment  as  templates.  The  editor  is  also  capable  to  handle  protein 
sequence data and synchronisation of nucleic acid and amino acid sequence alignments is possible. Pattern 
search functionalities and the simultaneous secondary structure editor help the scientist to evaluate probe or 
primer targets. 

13  

  
 
 
 
 
Figure  1:  The  ARB  workbench.  All  functionalities  can  be  accessed  from  a  central  graphical  user  interface  grouped 
around a tree. 

Phylogenetic tree reconstruction 
For  phylogenetic  tree  reconstruction,  several  methods  are  available  as  part  of  the  package.  This  involves 
distance  matrix,  parsimony  and  maximum  likelihood  approaches.  Furthermore,  a  special  maximum 
parsimony  approach  has  been  implemented  in  ARB  that  allows  the  reconstruction  and  evaluation  of  large 
trees. One prominent feature is the possibility to add sequences of different length to an existing tree without 
changing the overall topology. Optimisation of trees can be applied to complete and user-selected sub-trees 
and intermediate stages can be stored.  

Probe design and evaluation 
In just three steps high-quality molecular probes can be designed and visualized including selection of target 
organisms, probe calculation and probe match. For fluorescence in situ hybridisation (FISH), the additional 
ARB multi-probe software and the visualization of in situ accessibility is especially useful. The probe design 
function of ARB offers various settings to define e.g. target group coverage and the number of allowed out-
group hits in a flexible way, a clear must for probe design based on the extremely diverse and large rRNA 
databases. All potential target binding sites for the selected group of sequences are then calculated by ARB 
and displayed in a concise table which allows easy assessment of the candidates by the user. Afterwards the 
preferred candidates can be manually evaluated by various search and visualization tools. The probe match 
tool  allows  a  fast  search  of  all  probe  binding  sites  within  the  complete  dataset,  including  a  user-defined 
number  of  mismatches.  Quality  and  position  of  the  mismatches  are  indicated  and  target  and/or  non-target 
sequences are automatically shown in the tree of the ARB main window for an intuitive overview of probe 
coverage  and  specificity.  Moreover,  probe  binding  sites  including  mismatches  can  be  visualized  on  the 
alignment level as well as in the secondary structure viewer of ARB (Figure 2). In the ARB editor and the 
probe match tool, the pre-calculated probe sequences can also be altered by hand to easily access the impact 
of these modifications. 

14  

  
 
 
 
 
 
 
 
Figure 2: The ARB primary and secondary structure editor. Probes or primers can be easily visualized (orange). 

Conclusions 
The combination of ARB and SILVA provides a flexible software workbench for sequence analysis as well 
as comprehensive, quality controlled, richly annotated and aligned reference rRNA databases to support the 
molecular assessment of biodiversity, as well as investigations of the evolution of organisms. Applications 
of  the  databases  range  from  basic  research  in  microbiology  and  molecular  ecology  to  the  detection  of 
contaminants  and  pathogens  in  biotechnology  and  medicine.  Molecular  taxonomy  and  diagnostics  have 
already revolutionized our view on microbial diversity on Earth (Hong et al. 2006; Pedros-Alio 2006; Sogin 
et al. 2006), and the added value of molecular techniques for the determination of eukaryotic diversity has 
recently  been  documented  by  Tautz  et  al.  (2002).  The  ARB/SILVA  duet  is  designed  to  assist  in  the  daily 
effort to keep pace with the increasing amount of data flooding our general-purpose primary databases. 

References 
Amann RI, Ludwig W, Schleifer KH. 1995. Phylogenetic identification and in situ detection of individual 

microbial cells without cultivation. Microbiol. Rev. 59:143-169. 

Giovannoni  SJ,  DeLong  EF,  Olsen  GJ,  Pace  NR.  1988.  Phylogenetic  groupspecific  oligodeoxynucleotide 

probes for identification of single microbial cells. J. Bacteriol. 170:720-726. 

Guindon  S,  Gascuel  O.  2003.  A  simple,  fast,  and  accurate  algorithm  to  estimate  large  phylogenies  by 

maximum likelihood. Syst. Biol. 52:696-704. 

Hong SH, Bunge J, Jeon SO, Epstein SS. 2006. Predicting microbial species richness. Proc. Natl. Acad. Sci. 

USA 103:117-122. 

Ludwig W , Strunk O, Westram R, Richter L, Meier H, Yadhukumar, Buchner A, Lai T, Steppi S, Jobb G, 
Förster W, Brettske I, Gerber S, Ginhart AW, Gross O, Grumann S, Hermann S, Jost R, König A, Liss T, 
Lüssmann R, May M, Nonhoff B, Reichel B, Strehlow R, Stamatakis A, Stuckmann N, Vilbig A, Lenke 
M, Ludwig T, Bode A, Schleifer KH. 2004. ARB: a software environment for sequence data. Nucleic Acid 
Res. 32:1363-1371. 

Olsen GJ, Lane DJ, Giovannoni SJ, Pace NR, Stahl DA. 1986. Microbial ecology and evolution: a ribosomal 

RNA approach. Annu. Rev. Microbiol. 40:337-365. 

Pace NR 1997. A molecular view of microbial diversity and the biosphere. Science 276:734-740. 

Pace NR, Stahl DA, Olsen GJ, Lane DJ. 1985. Analyzing natural microbial populations by rRNA sequences. 

15  

  
 
 
ASM News 51:4-12. 

Pedros-Alio C. 2006. Marine microbial diversity: can it be determined? Trends Microbiol. 14:257-263 

Prüsse E, Quast C, Knittel K, Fuchs B, Ludwig W, Peplies J, Glöckner FO (2007) SILVA: a comprehensive 
online  resource  for  quality  checked  and  aligned  ribosomal  RNA  sequence  data  compatible  with  ARB. 
Nucleic Acid Res 35(21):7188-96. 

Sogin  ML,  Morrison  HG,  Huber  JA,  Welch  DM,  Huse  SM,  Neal  PR,  Arrieta  JM,  Herndl  GJ.  2006. 
Microbial  diversity  in  the  deep  sea  and  the  underexplored  "rare  biosphere".  Proc.  Natl.  Acad.  Sci.  USA 
103:12115-12120. 

Stamatakis  A,  Ludwig  T,  Meier  H.  2005.  RAxML-III:  a  fast  program  for  maximum  likelihood-based 

inference of large phylogenetic trees. Bioinformatics 21:456-463. 

Tautz D, Arctander P, Minelli A, Thomas RH, Vogler AP. 2002. DNA points the way ahead of taxonomy - 
In  assessing  new  approaches,  it's  time  for  DNA's  unique  contribution  to  take  a  central  role.  Nature 
418:479-479. 

Ward  DM,  Weller  R,  Bateson  MM.  1990.  16S  rRNA  sequences  reveal  numerous  uncultured 

microorganisms in a natural community. Nature 345: 63 -65. 

16  

  
 
 
 
Development of genus specific primers for identification of Tobamoviruses 

Günter Adam 

University of Hamburg 
Department of Biology  
BioCentre Klein Flottbek 
Ohnhorststr. 18  
22609 Hamburg, Germany  

e-mail: guenter.adam@iangbot.uni-hamburg.de 

Abstract 
The detection and differential diagnosis of plant pathogens via polymerase chain reaction (PCR) is already 
routine  and  important  especially  in  cases  for  which  no  suitable  antibodies  are  available  or  the  highest 
sensitivity is of importance. As in most of cases the detection of a virus in the sample is enough to exclude it 
from further processing, a genus or even family specific test would suffice. This reduces costs and time for 
testing. However, a second test using the amplicons allows differential diagnosis down to the species or even 
beyond. In this paper we describe the development of genus specific primers suitable to detect species of the 
genus Tobamovirus. 

Introduction 
With  the  increase  of  sequence  information  we  have  now  the  ability  to  develop  not  only  species-specific 
primers,  but  also  primer  pairs  that  are  suitable  to  amplify  whole  genera  or  even  families.  The  facts  that 
especially support this for plant viruses are briefly:  

1.  the tendency of plant viral coat proteins (CP) to be highly conserved at the species level due to the 

lack of CP-directed antibodies in their hosts 

2.  the  additional  function  of  the  CP  for  vector  transmitted  viruses  to  support  transmission  and  its 

function for long distance transport in the plant hosts  

3.  the over centuries proven suitability  of  the  CP  as  taxonomically  most important protein for species 

demarcation by serology. 

The latter point indicates in addition that enough diversity exists inside the CP to allow for differentiation 
using the amplified CP ORF as shown by Letschert et al. (2002). 
The  first  paper  on  this  topic  was  published  in  1999  by  van  der  Vlugt  et  al.  who  demonstrated  that  many 
members of the family Potyviridae could be detected by one primer pair flanking part of the CP. Since then 
many more, at least genus-specific, primers have been described: for Tobamoviruses (Letschert et al., 2002), 
Potex-viruses  (van  der  Vlugt  &  Berendsen,  2002),  Viti-  and  Foevaviruses  (Dovas  &  Katis,  2003)  and 
Nepoviruses (Digiaro et al., 2007).  
The identification of genome areas suitable for general primers with the ability to react with more than one 
species  of  a  taxon  can  be  done  with  many  different  approaches.  Alignment  of  known  sequences  would 
appear to be the simplest and led to success in case of the Potyvirus primers. Choosing other targets with 
even more conservation to be expected could be a solution as shown by the work published by van der Vlugt 
and  Berendsen  (2002)  or  Dovas  and  Katis  (2003)  for  Closteroviruses  where  the  RNA  dependent  RNA 
polymerase was selected. In these cases, highly degenerated primers were necessary for success. 
For the genus Tobamovirus, we have chosen another approach to ascertain that the primers to be developed 
were  safe  against  genetic  diversity.  For  Tobamoviruses,  two  regions  flanking  the  coat  protein  are  highly 
conserved  in  their  sequence  resulting  in  specific  structures  of  the  unpacked  RNA.  These  structures  are 
necessary for viral replication and particle formation. At the very 3’-end, the RNA can fold into a tRNA-like 
structure that becomes aminoacylated with histidin (Figure 1a). Upstream of this, mostly in the movement 

17  

  
 
 
 
 
 
 
protein ORF, is a second highly conserved loop structure which is necessary to initiate particle assembly, 
called “origin of assembly” (Figure 1a). For these two sequence stretches forward and reverse primers were 
developed (Figure 1c) which at least for the subgroup (sg) 1 cluster allowed the amplification for all member 
species (Figure 1b). 

Figure 1: Development of general primers for subgroup 
1  of  Tobamoviruses.  a:  Genome  structure  of  TMV  and 
position  of  the  primers;  b:  Amplification  results  with 
different  virus  species  of  sg  1  (the  double  band  with 
ORSV is due to a duplication of the reverse primer  se-
quence, see also Figure 3); c: Sequence of the primers 

Figure 2: Clustering of the genus Tobamovirus into 
subgroups (modified from Lartey et al., 1996). 

According  to  the  nucleic  acid  sequence  data  of  their  CP,  members  of  the  genus  Tobamovirus  form  three 
clusters that correlate with host range differences and serological crossreactions (Lartey et al., 1996; Heinze 
et  al.,  2006)  (Figure  2).  Members  of  sg  2  and  sg  3  could  not  be  amplified  by  the  primer  pair  for  sg  1. 
However, available sequence data of the sg 2 and sg 3 species indicated that the reverse primer Tob 1 should 
fit and only the forward primers had to be adapted for each subgroup (Heinze et al., 2006). 

Material and Methods 
The  virus  isolates  that  we  have  used  are  summarized  in  detail  in  two  publications  (Letschert  et  al.,  2002; 
Heinze et al., 2006). We have used at least seven different isolates per virus species. Each isolate was cloned 
by three successive local lesion transfers before propagation on systemic hosts.  
The  amplified  CP  of  each  isolate  was  inserted  into  a  plasmid  and  transformed  into  Escherichia  coli 
(Letschert et  al.,  2002).  Five  independent  transformed  colonies  were  picked,  their  plasmid  re-isolated  and 
the  insert  sequenced.  Only  if  all  five  sequences  revealed  no  significant  differences  and  their  serological 
reactions in ELISA as well as immuno-electron-microscopy were unequivocal, the viral isolate was declared 
clean. 
From  the  determined  sequences,  we  have  developed  species-specific  forward  primers  inside  the  CP  ORF 
(Table  1)  allowing  either  species  identification  by  semi-nested  PCR  starting  from  the  PCR-products 
obtained with genus-specific primers, or a species-specific RT-PCR using Tob1 as reverse primer. 
The  species-specific  primer  oligonucleotides  were  also  tested  in  silico  with  the  program  “Amplify”  for 
Macinthosh  computers.  To  accomplish  this,  a  test-DNA  sequence  was  generated  from  known  amplicon 
sequences  to  be  tested  (see  Figure  3  for  accession  numbers).  If  the  specific  forward  primer  binds  on  any 
other position in this generated test sequence, the used software would generate a virtual amplicon. 

18  

  
 
 
 
 
 
 
 
Table 1: Species-specific Primers used 

Virus species 

specific forward primer 

TMV 

ToMV 

5’ CGG TCA GTG CCG AAC AAG AA 3’ 

5’ CGG AAG GCC TAA GGA AGG GAA GC 3’ 

TMGMV 

5’ AAR TAA ATA AYA GTG GTA AGA AGG G 3’ 

PMMV 

ORSV 

5’ GGG TTT GAA TAA GGA AGG GAA GC 3’ 

5’ AGG TGA TAG TGA TGT TGG TAT T 3’ 

Results and Discussion 
We  could  show  that  one  reverse  and  three  forward  primers  with  only  few  degenerated  nucleotides  could 
successfully  be  used  to  amplify  the  complete  or  almost  complete  CP  of  the  accepted  and  also  tentative 
species  of  the  genus  Tobamovirus.  We  could  also  show  that,  for  each  species,  a  specific  forward  primer 
could  be  designed  that  allowed  a  differential  diagnosis  via a  semi-nested  PCR  using  the  amplicons  of  the 
general RT-PCR (Figure 4).  

Figure 3: Virtual PCR with ORSV-specific oligos. Tobacco mosaic 
virus  (TMV),  Tomato  mosaic  virus  (ToMV),  Paprika  mild  mosaic 
virus (PaMMV), Pepper mild mosaic virus (PMMV), Tobacco mild 
green  mosaic  virus  (TMGMV),  Odontoglossum  ring  spot  virus 
(ORSV).  

Figure  4:  Semi-nested  PCR  with  species-
specific  forward  primers.  After  a  general 
RT-PCR,  part  of  the  amplified  products 
was  used  for  a  semi-nested  PCR  and  the 
obtained products were separated on a 1% 
agarose  gel.  Acronyms  are  as  in  Figure  3. 
M = λ DNA digested with PstI (number of 
bp are indicated at right). 

The results from the in silico testing (Figure 3) suggested that the unique oligonucleotides should only prime 
with their specific sequences. This was confirmed by the results shown in Figure 4 where semi-nested PCRs 
amplified only the respective specific target. 
These results show that specific oligonucleotides are suitable to serve as forward primer together with the 
general tobamovirus reverse primer for species specific RT-PCR even when mixed infections are present. It 
may  be  also  possible  to  use  these  22  to  25-mer  oligonucleotides  as  capture  probes  in  an  array  approach 
similar to the work described by Zhang et al. (2005). 

References 
Digiaro M, Elbeaino T, Martelli GP. 2007. Development of degenerate and species-specific primers for the 
differential  and  simultaneous  RT-PCR  detection  of  grapevine-infecting  Nepoviruses  of  subgroups  A,  B 
and C. Journal of Virological Methods 141: 34-40. 

Dovas CI, Katis NI. 2003. A spot nested RT-PCR method for the simultaneous detection of members of the 
19  

  
 
 
 
 
 
 
 
 
Vitivirus and Foveavirus genera in grapevine. Journal of Virological Methods 107: 99-106. 

Heinze C, Lesemann DE, Ilmberger N, Willingmann P, Adam G. 2006. The phylogenetic structure of the 
cluster of Tobamovirus species serologically related to Ribgrass Mosaic Virus (RMV) and the sequence of 
Streptocarpus Flower Break Virus (SFBV). Archives of Virology 151: 763-74. 

Lartey  RT,  Voss  TC,  Melcher  U.  1996.  Tobamovirus  evolution:  Gene  overlaps,  recombination  and 

taxonomic implications. Mol. Biol. Evol. 13: 1327–1338. 

Letschert  B,  Adam  G,  Lesemann  DE,  Willingmann  P,  Heinze  C.  2002.  Detection  and  differentiation  of 
serologically  cross-reacting  Tobamoviruses  of  economical  importance  by  RT-PCR  and  RT-PCR-RFLP. 
Journal of Virological Methods 107: 1-10. 

van  der  Vlugt  RAA,  Steffens  P,  Cuperus  C,  Barg  E,  Lesemann  DE,  Bos  L,  Vetter  HJ.  1999.  Further 
evidence  that  Shallot  Yellow  Stripe  Virus  (SYSV)  is  a  distinct  Potyvirus  and  reidentification  of  welsh 
onion yellow stripe virus as SYSV strain. Phytopathology 89: 148-55. 

van der Vlugt RAA, Berendsen M. 2002. Development of a general Potexvirus detection method. European 

Journal of Plant Pathology 108: 367-71. 

Zhang  D,  Willingmann  P,  Heinze  C,  Adam  G,  Pfunder  M,  Frey  B,  Frey  JE.  2005.  Differentiation  of 
Cucumber  Mosaic  Virus  isolates  by  hybridization  to  oligonucleotides  in  a  microarray  format.  Journal  of 
Virological Methods 23: 101-108. 

20  

  
 
Applications in virus detection 

Neil Boonham* and Jenny Tomlinson 

Central Science Laboratory 
 Sand Hutton 
York, YO41 1LZ 
United Kingdom 

*Corresponding author: e-mail: n.boonham@csl.gov.uk 

Abstract 
Currently, a wide variety of methods is available to diagnostic testing services and is in use on a day-to-day 
basis.  The  methods  used  are  diverse  including  morphological  identification  using  microscopy  or  electron 
microscopy,  methods  detecting  proteins  from  the  organisms  such  as  ELISA  or  electrophoresis,  molecular 
methods identifying the nucleic acid of the organisms such as PCR, RT-PCR, real-time PCR, reverse PAGE, 
nucleic  acid  hybridisation  and  finally  traditional  bioassays  such  as  inoculation  of  test  plants  or  growth  on 
selective media. For true diagnostic purposes i.e. identification of the causal agent of disease, often the more 
traditional investigational techniques are most useful. For viruses, this often means test plant inoculation and 
electron microscopy. In the context of diagnostics, microarray methods could enable the detection of large 
numbers of target pathogens using a generic technology.  

Introduction 
Accurate diagnosis of disease causing agents is an essential prerequisite for effective control. To this end a 
wide spectrum of methods have been developed and are currently in use in diagnostic laboratories on a day-
to-day basis. Most of the methods used for viral disease diagnosis are geared towards specific detection of a 
single  target.  However,  there  are  two  scenarios  when  this  is  less  than  ideal,  firstly  when  presented  with  a 
symptomatic plant with unknown aetiology, and secondly, when screening for the health of a given plant, 
e.g.  during  the  vegetative  propagation  chain  for  ornamentals.  In  each  of  these  cases,  it  is  common  for  a 
number of different methods to be executed in parallel to reach a final result. 
Some ‘multi target’ generic assays have been used for plant virus diagnosis, although most of these methods 
continue to be useful as investigational tools they can be limited to a small range of targets with a specificity 
often limited to the genus level. Electron microscopy has been used for many years as a ‘multi target’ assay 
and  although  very  useful  for  detecting  and  discriminating  rod  shaped  particles,  the  presence  of  spherical 
viruses is very difficult to detect. In each case it is common to only identify an unknown to the genus level. 
Assays  based  on  infectivity,  can  also  be  described  as  multi  target,  however,  completely  ‘universal’ 
indicators do not exist for all viruses and many viruses are not mechanically transmissible. Detection of viral 
coat  protein  using  mass  spectrometry  such  as  MALDI  TOF  (Matrix  Assisted  Laser  DIssorption  Time  Of 
Flight mass spectrometry) has been reported for viruses (Thomas et al., 1998) and can also be described as a 
generic. However, these methods are only of very niche application to viruses which reach unusually high 
titres in some hosts and are of no use for viruses that do not have a protein target. All of these methods have 
a  basic  drawback  in  common,  since  each  is  based  on  a  property  that  is  common  at  the  genus  level  (e.g. 
particle  morphology,  coat  protein  size  or  local  lesion  host).  Diagnosis  to  species  level  will  often  require 
further testing with another method. Microarray technology could allow the detection of a large number of 
different viruses in a single generic assay (Boonham et al., 2007). This paper describes a method developed 
for the detection of plant viruses using a microarray based on oligonucleotides spotted onto glass slides and 
a dual colour reporting system. 

21  

  
 
 
 
 
 
 
Materials and methods 

Design and printing of arrays 
The  arrays  comprised  of  oligonucleotides,  50nt  in  length,  amino-linked  at  the  5’end,  spotted  onto  epoxy 
coated glass slides. The approach used for designing the oligonucleotides was to download all the sequences 
available  for  the  viruses  of  interest  from  the  EMBL  database  and  compile  multiple  sequence  alignments 
using the Clustal V method from the MegAlign multiple alignment package (DNA Star). Conserved regions 
were identified within the sequence of a given species, which would give discrimination between the species 
of  interest.  In  addition  the  Basic  Local  Alignment  Search  Tool  (BLAST  at  http://www.ncbi.nlm.nih.gov/
BLAST/) was used to identify any homology with the chosen probe designs and other genes (in particular 
plant genes) present on the database. 
The  microarrays  were  fabricated  by  MWG  Biotech  (Ebersberg,  Germany),  and  were  based  on  5′amino-
modifed oligonucleotides spotted in duplicate to epoxy coated glass microscope slides (Nexterion E, Schott 
NA Inc.). The slides were stored in a cool dark place ready for hybridisation. 

Nucleic acid extraction 
RNA  extractions  were  carried  out  using  a  CTAB  method  adapted  from  Chang  et  al.,  (1993)  followed  by 
further clean up using an RNeasy column (Qiagen Ltd., Crawley, UK). RNA was eluted from the column 
using 200μl of nuclease free water. An aliquot of the resulting RNA was diluted in nuclease free water and 
the concentration estimated following spectrophotometric estimation at 260 and 280 nm. RNA was extracted 
from  plants  infected  singly  with Turnip  mosaic  virus  (TuMV),  Tomato  bushy  stunt  virus  (TBSV),  Pepino 
mosaic virus (PepMV) and from uninfected plants. 

Labelling of cDNA 
Synthesis,  labelling  and  purification  of  cDNA  were  carried  out  from  50μg  of  RNA  using  the  CyScribe™ 
Post-Labeling  Kit  (Amersham  Pharmacia)  following  manufacturer’s  protocols.  The  procedure  involves 
synthesis  of  cDNA  incorporating  amino  allyl  (AA)  dNTP’s  followed  by  post-labelling  of  cDNA  with 
reactive Cy Dye. The strategy of labelling was to extract total RNA from both infected and healthy plants. 
The RNA from the infected plants was converted to cDNA and labelled with Cy5 whilst the RNA from the 
infected  plants  was  converted  to  cDNA  and  labelled  with  Cy3.  The  two  labelled  cDNA  targets  were  then 
mixed together and hybridised to an array.  

Hybridisation and washing 
Array  hybridisation  was  carried  out  using  array  buffers  supplied  by  MWG  Biotech  and  Gene  Frames 
(ABgene) following the manufacturer’s protocols. Briefly, labelled cDNA was mixed together and with 30μl 
of array hybridisation buffer and heat denatured for 3 min at 95°C, before being incubated on ice for 3 min. 
The  labelled  cDNA  was  then  pipetted  onto  the  array  surface  and  gene-frame  cover  slip  applied. 
Hybridisation  was  carried  out  in  an  array  cassette  to  maintain  humidity  (HybChamber  -  Gene  Machines, 
California,  USA)  for  16  h  at  42°C.  Following  incubation  the  slides  were  washed  for  5  min  in  2x  SSC 
containing  0.1%  SDS,  5  min  in  1x  SSC  and  5  min  in  0.5x  SSC.  All  wash  steps  were  carried  out  in  pre-
warmed buffer at 30°C. The slides were dried and stored in opaque slide boxes at room temperature. 

Microarray scanning 
The hybridised arrays were scanned using an Axon Instruments GenePix 4000B array scanner (A1-Biotech, 
Bedford,  UK),  using  GenePix  Pro  3.0  software.  Scanning  was  carried  out  at  532nm  and  635nm  with 
photomultiplier  tube  (PMT)  settings  in  the  range  (600-950)  in  order  to  minimise  the  number  of  saturated 
pixels.  

Results 
Since a dual label approach was taken, cDNA for the control genes should be present in both the Cy3 and 
the Cy5 labelled reactions. Thus when this hybridisation is performed on the array, the control spots should 
give signals at both 532nm and 635nm and should appear yellow in a ratio/overlapping image. Any virus 
RNA from the test plant should be labelled only in the Cy5 reaction and the cDNA will not be present in the 

22  

  
healthy  plant  (labelled  with  Cy3)  thus  the  virus  spots  should  appear  red  in  the  ratio  image.  The  results 
(figure 1) show that detection mostly occurred only with the homologous capture probes. 

A
A
A

BB
B

CC
C

DD
D

Figure  1:  Examples  of  ratio  array  images  from  a  healthy  plant  (A) 
and plants infected with (B) Pepino mosaic virus, (C) Tomato bushy 
stunt virus and (D) Turnip mosaic virus. 

Discussion 
Many  methods,  either  molecular  or  serological,  have  been  developed  and  published  for  virus  diagnostics. 
Most  of  these  methods  are  directed  at  very  specific  detection  of  an  individual  virus  target.  As  such  these 
methods are very suited to survey work, and also often used for diagnosis or screening. In this function, they 
are  usually  used  as  one  assay  in  a  panel  of  assays  for  the  detection  of  possible  disease  causing  agents 
(parallel testing) or to confirm the identity of a disease causing agent (identified or suspected) following the 
testing  by  another  method  (confirmatory  testing).  In  an  ideal  world,  diagnostic  (as  opposed  to  detection) 
methods tend to be much more ‘investigational’ in as much as they can be used for “finding the causal virus 
and recognising it” (Bos, 1999) with no prior knowledge of the agent being investigated. In the diagnostic 
arena,  microarray  methodology  is  currently  being  used  to  streamline  parallel  screening  as  it  is  currently 
carried  out.  These  panel  type  screens  are  often  based  around  hosts  or  groups  of  hosts,  and  array  methods 
have  been  published  for  a  number  of  plant  infecting  viruses  (Boonham  et  al.,  2003;  Lee  et  al.,  2003; 
Bystricka et al., 2003; Bystricka et al., 2005; Deyong et al., 2005; Agindotan and Perry, 2007).  
For  array  techniques  to  really  succeed  in  making  an  impact  in  the  virus  detection  arena,  a  number  of 
technical  drawbacks,  highlighted  by  initial  work,  need  to  be  overcome.  Firstly,  the  sensitivity  of  the 
approach is low when compared to PCR methods, and although useful level of sensitivity is achieved ideally 
a generic screen would be more sensitive than ELISA. Secondly, design of oligonucleotides is an issue in 
which a significant number of oligonucleotide probes designed do not perform as expected.  
In  addition  to  these  technical  problems,  current  approaches  are  based  on  the  detection  of  a  list  of  known 
targets, as such they do not answer one of the more fundamental problems, that of detection of unknowns. In 
areas of plant pathology where pests or disease causing organisms are isolated (either as individuals in the 
case  of  insects  or  in  culture  in  the  case  of  bacteria  and  fungal  pathogens),  the  approach  of  sequencing 
conserved  genes  is  becoming  a  tool  of  trade.  The  method  has  been  used  for  many  years,  but  is  now 
generically  referred  to  as  ‘DNA  barcoding’  and  enables  not  only  the  identification  of  known  (and 
sequenced)  organisms  but  also  the  identification  of  unknowns.  The  method  is  based  upon  the  generic 
amplification of conserved genes and as such enables the generation of sequence data from previously un-
described organisms. Viruses do not have conserved genes that enable generic amplification, so a method 
based on array hybridisation to more broadly specific regions of sequence (perhaps at the genus level) may 
enable  the  detection  of  unknowns  within  known  genera.  This  approach  may  however  ultimately  be 
superseded by modern de novo direct sequencing techniques. Methods based on pyrosequencing enable the 
generation of very large amounts of sequence from a library of the ‘unknown’. If the host genome sequence 
is  known,  the  pathogen  sequence  (including  any  new  or  unknown  targets)  can  be  extracted  from  it  in  an 
approach  that  could  be  completely  generic  regardless  of  the  nature  of  the  target  pathogen  or  host  (Cox-
Foster et al., 2007).  

Acknowledgements 
The  work  was  funded  by  the  European  Union  project  DIAGCHIP  (QLK5-2001-02270)  and  the  European 
Union  COST  853  Action  (http://www.cost853.ch/).  The  following  people  participated  in  the  COST  853 

23  

  
 
 
 
 
 
 
 
 
 
 
 
 
 
workshop:  M.  Duffraisse  (France),  I.  Franke-Whittle  (Austria),  P.  Kogovsek  (Slovenia),  H.  Krecic 
(Slovenia),  B.  Martinez-Bono  (Spain),  S.  Mayrhofer  (Austria),  F.  Monéger  (France),  K.  Petrzik  (Czech 
Republic),  A.  Ramanaviciene  (Lithuania),  A.  Ramanavicius  (Lithuania),  W.  Sledz  (Poland),  N.  Stralis-
Pavese (Austria), D. Zhang (Germany), S. Gonzalez (Denmark), J. Frey (Switzerland), A. Loy (Austria) and 
M. Pompe-Novak (Slovenia). 

References 
Agindotan B and Perry KL. 2007. Macroarray Detection of Plant RNA Viruses Using Randomly Primed and 

Amplified Complementary DNAs from Infected Plants. Phytopathology 97:119-127. 

Boonham N, Walsh K, Smith P, Madagan K, Graham I, Barker IJ. 2003. Detection of potato viruses using 
microarray  technology:  towards  a  generic  method  for  plant  viral  disease  diagnosis.  J  Virol  Methods. 
1082:181-7. 

Boonham  N,  Tomlinson  J,  and  Mumford  R.  2007.  Microarrays  for  rapid  identification  of  plant  viruses. 

Annual review of Phytopathology 45: 14.1-14.22. 

Bos,  L.  1999.  Disease  diagnosis  and  routine  virus  detection.  In  Plant  viruses,  unique  and  intriguing 

pathogens, ed. L. Bos, pp. 193-216. Leiden: Backhuys Publishers. 

Bystricka D, Lenz O, Mraz I, Piherova L, Kmoch S and Sip M. 2005. Oligonucleotide-based microarray: A 

new improvement in microarray detection of plant viruses. J Virol Methods 128 1-2: 176-182. 

Bystricka D, Lenz O, Mraz I, Dedic P, Sip M. 2003. DNA microarray: parallel detection of potato viruses. 

Acta Virol. 471:41-4. 

Chang S, Puryear J. and Cairney J. 1993. A simple and efficient method for isolating RNA from pine trees. 

Plant Molecular Biology Reporter, 11: 113-6. 

Cox-Foster  DL,  Conlan  S,  Holmes  EC,  Palacios  G,  Evans  JD,  Moran  NA,  Quan  P,  Briese  T,  Hornig  M, 
Geiser DM, Martinson V, vanEngelsdorp D, Kalkstein AL, Drysdale A, Hui J, Zhai J, Cui L, Hutchison 
SK, Simons JF, Egholm M, Pettis JS, Lipkin, WI.  2007.  A  Metagenomic  Survey  of  Microbes  in  Honey 
Bee Colony Collapse Disorder. Science 318 (5848): 283-7. 

Deyong  Z,  Willingmann  P,  Heinze  C,  Adam  G,  Pfunder  M,  Frey  B,  Frey  JE.  2005.  Differentiation  of 
cucumber  mosaic  virus  isolates  by  hybridization  to  oligonucleotides  in  a  microarray  format.  J.  Virol. 
Methods 123:101-8. 

Lee  GP,  Min  BE,  Kim  CS,  Choi  SH,  Harn  CH,  Ryu  KH.  2003.  Plant  virus  cDNA  chip  hybridization  for 

detection and differentiation of four cucurbit-infecting tobamoviruses. J. Virol.Methods 110: 19-24. 

Thomas J J, Falk B, Fenselau C, Jackman J and Ezzell J. 1998. Viral characterization by analysis of capsid 

proteins. Analytical chemistry, 70, 3863-3867. 

24  

  
 
 
 
Electrochemical methods for DNA and protein detection 

Arunas Ramanavicius1 * and Almyra Ramanaviciene1,2  

1  Centre  of  Nanotechnology  and  Material  Science, 
Department  of  Analytical  and  Environmental 
Chemistry,  Faculty  of  Chemistry,  Vilnius  Univer-
sity, Naugarduko 24, 03225 Vilnius 6, Lithuania; 

2 Laboratory  of  Immunoanalysis  and  Nanotechnol-
ogy, Institute of Immunology of Vilnius University, 
Moletu pl.29, LT-08409 Vilnius 21, Lithuania;  

* Corresponding author e-mail: arunas@imi.lt 

Abstract 
Recently, electrochemical methods have become an important tool for detection of high molecular weight 
analytes  such  as  DNA  and  some  proteins.  The  DNA  and  the  majority  of  proteins  are  redox  inactive  at 
potentials in the range of -600 mV to +600 mV vs. Ag/AgCl that are used for amperometric detection. For 
this reason there are several general strategies for electrochemical detection of mentioned analytes: (i) based 
on  application  of  redox  labels  and  followed  by  amperometric  detection;  (ii)  direct  detection  of  formation 
affinity  based  complexes  by  registration  of  impedance  changes.  Direct  detection  methods  are  the  most 
attractive as they facilitate and simplify the detection procedure. Some direct electrochemical methods for 
detection  of  DNA  and  proteins  are  introduced  in  this  chapter.  To  perform  impedimetric  detection,  special 
modification  methods  that  form  layers  sensitive  to  target  DNA  or  protein  on  the  surface  of  electrodes  are 
desired. In addition significant changes in capacitance and/or electrochemical resistance after formation of 
DNA  or  protein  complex  are  requested.  Some  conducting  polymers  are  applied  for  such  modification 
purpose. Polypyrrole (Ppy) is one of the most extensively used conducting polymers in design of DNA and 
protein sensors. Here, we review recent advances in application of polypyrrole in immunosensors and DNA 
sensors.  Significant  attention  is  paid  to  immobilization  of  biologically  active  Ppy  molecules  during 
electrochemical deposition of this polymer. Some unique properties of this polymer, including formation of 
molecular imprints and prevention of some undesirable electrochemical interactions, are discussed. 
Keywords:  Bioelectrochemistry;  Conducting  polymers;  Polypyrrole;  Biosensor;  DNA 
sensor;   
Immunosensor;  Molecularly  imprinted  polymers;  Nanotechnology;  Electrochemistry;  Fluorescence;  QCM; 
SPR. 

Introduction 
Bioanalytical methods are rapidly evolving towards detection of target protein and DNA because both types 
of  analytes  are  very  important  for  biomedicine,  forensic  investigations,  biochemistry  and  biotechnology. 
Various  immunoassays  and  nucleic  acid-based  tests  have  been  well  established  for  many  years  as  the 
cornerstone of detection technologies. These assays are sensitive, selective and, in general, highly resistant 
to  interference  from  complex  sample  matrices.  However,  both  antibody-  and  nucleic  acid-based  detection 
systems require a priori knowledge of the target and development of specific reagents; multiplexed assays 
can become increasingly problematic when attempting to detect a plethora of different targets, the identities 
of which are unknown (Ngundi et al., 2006). In an effort to circumvent many of the limitations inherent in 
these  conventional  assays,  other  biological  recognition  systems  –  biosensors  –  were  developed  as  more 
competitive  alternatives.  Up  to  now  the  majority  of  DNA  and  protein  detection  strategies  in  biosensors, 
DNA-chips  and  protein  chips  were  based  on  optical  detection  methods  with  clear  domination  of 
fluorescence detection. However, optical detection is not always optimal, especially if the samples are turbid 
and/or  coloured  and/or  contain  fluorescent  materials  or  fluorescence  quenchers.  All  these  mentioned 
problems  may  be  avoided  if  electrochemical  methods  are  applied.  Electrochemical  methods  are  evolving 
rapidly towards solving of challenging bioanalytical problems, including specificity, stability and sensitivity. 

25  

  
 
 
 
 
 
Recently,  electrochemical  methods  became  an  important  tool  for  detection  of  high  molecular  weight 
analytes like DNA and some proteins. The DNA and the majority of proteins are redox inactive at potentials 
in  the  range  of  -600  mV  to  +600  mV  vs.  Ag/AgCl  which  potential  is  the  most  used  for  amperometric 
detection.  For  this  reason,  there  are  several  general  strategies  for  electrochemical  detection  of  mentioned 
analytes:  (i)  based  on  application  of  redox  labels  and  followed  by  amperometric  detection;  (ii)  direct 
detection of formation of affinity based complexes by registration of impedance changes. Direct detection 
methods  are  the  most  attractive  since  they  facilitate  and  simplify  the  detection  procedure.  Some  direct 
electrochemical  methods  for  detection  of  DNA  and  proteins  are  introduced  in  this  chapter.  To  perform 
impedimetric detection, special modification methods that form layers sensitive to target DNA or protein on 
the surface of electrode are desired. In addition, significant changes in capacitance and/or electrochemical 
resistance  after  formation  of  DNA  or  protein  complex  are  requested.  Some  conducting  polymers  can  be 
exploited  as  an  excellent  tool  for  the  preparation  of  biologically  sensitive  layers  with  sensitivity  to  target 
DNA  and  target  proteins.  Some  conducting  polymers  doped  and/or  covalently  modified  by  biomaterials 
exhibit  unique  affinity  properties  (Ramanaviciene  and  Ramanavicius,  2004a)  that  can  be  easily  applied  in 
the design of electrochemical sensors.  
Polypyrrole  is  one  of  the  most  extensively  used  conducting  polymers  in  design  of  bioanalytical  sensors 
(Adeloju and Wallance, 1996). Versatility of this polymer is determined by a number of properties: redox 
activity (Han et al., 2005), ability to form nanowires at room temperature with conductivity ranging from 10-
4 S cm-1 to 10-2 S cm-1 (Khomenko et al., 2005), ion-exchange and ion discrimination capacities (Johanson et 
al.,  2005,  Weidlich  et  al.,  2005),  electro-chromic  effect  depending  on  electrochemical  polymerization 
conditions and charge/discharge processes (Krivan et al., 2005), strong absorptive properties towards gases 
(Chehimi et al., 2004), proteins (Azioune et al., 2005), DNA (Saoudi et al., 2000), catalytic activity (Raoof 
et al., 2004, Khomenko et al., 2005, Ramanavicius et al., 2004), corrosion protection properties (Hi et al., 
2005),  etc.  Most  of  these  properties  are  depending  on  the  synthesis  procedure  as  well  as  on  the  dopant’s 
nature.  Polypyrrole  (Ppy)  is  one  of  the  most  extensively  used  conducting  polymers  in  the  design  of  DNA 
and  protein  sensors.  Polypyrrole  might  be  electrochemically  generated  and  deposited  as  semiconducting 
layer  on  the  electrodes  made  of  different  conducting  materials  including  platinum,  gold  and  carbon. 
Versatility  of  Ppy  is  determined  by  the  following:  its  biocompatibility,  its  capability  to  transduce  energy 
arising  from  interaction  of  analyte  and  analyte-recognizing-site  into  electrical  signals  that  are  easily 
monitored, its capability to protect electrodes from interfering materials, its easy electrochemical deposition 
on the surface of any type of electrodes. Nowadays, Ppy becomes an important tool for nanotechnological 
applications  (Malinauskas  et  al.,  2005).  Electrochemical  Ppy  deposition  technique  is  under  development 
with various types of electrochemical sensors and biosensors. Dependent on biomolecules entrapped within 
Ppy during electrochemical deposition of this polymer, the electrochemical affinity sensors are divided into: 
(i)  immunosensors  based  on  immobilized  proteins  exhibiting  affinity  (Ramanaviciene  and  Ramanavicius, 
2002), (ii) DNA sensors based on covalently immobilized and/or entrapped ssDNA (Wang, 1999, Wang and 
Jiang, 2000, Ramanaviciene and Ramanavicius, 2004c), (iii) affinity sensors based on molecularly imprinted 
polymers (Ramanaviciene and Ramanavicius, 2004b).  
The aim of this study is to review major advances and applications of electrochemical methods in the design 
of  affinity  sensors  including  DNA  sensors,  immunosensors,  and  molecularly  imprinted  polymer  based 
sensors.  As  conducting  polymer,  polypyrrole,  among  other  polymers,  is  used  most  in  design  of 
electrochemical biosensors. Therefore, the main attention will be focused on applications of this polymer. 

Discussion  

Chemical and Electrochemical polymerization of polypyrrole  
According  to  our  knowledge,  the  conducting  polymer  polypyrrole  was  for  the  first  time  chemically 
synthesized  by  Angeli  in  1912  (Angeli,  1916).  But  it  did  not  receive  any  considerable  attention  until  the 
conducting state of conjugated polymers was detected by AJ. Heeger, AG. MacDiarmid and H. Shirakawa 
(Chiang  et  al.,  1977).  Nowadays,  polypyrrole  is  synthesized  by  conventional  chemical  methods  and  more 
innovative and versatile electrochemical methods. Several major ways are applied for polypyrrole synthesis 
which  are  based  on  induction  of  polymerization  by  different  factors:  (i)  chemical  initiation  by  oxidative 
agents  (Angeli,  1916,  Henry  et  al.,  2001),  (ii)  photo  induced  synthesis  (Fang  et  al.,  2002),  (iii) 
electrochemical  polymerization  by  anodic  current  (Schuhmann  et  al.,  1990).  With  all  mentioned 
26  

  
technologies, synthesized Ppy is insoluble in all common solvents because of strong inter-chain interactions 
(Oh  et  al.,  2001).  All  polymerization  initiation  methods  mentioned  have  particular  applications,  e.g. 
chemical  initiation  by  oxidative  agents  might  be  successfully  applied  if  a  large  amount  of  polypyrrole  is 
needed  for  application  in  the  design  of  chromatography  columns  (Deore  et  al.,  1999)  or  for  some  other 
purposes. By using chemical (Henry et al., 2001) or even biochemical methods (Ramanavicius et al., 2005), 
it is easy to prepare Ppy particles of different and/or controlled size, ranging from several nanometers up to 
several micrometers and/or containing various inclusions. Moreover, by chemical methods it is possible to 
uniformly perform over-oxidation of this polymer, which is of special interest for affinity chromatography 
since  molecularly  imprinted  Ppy  might  be  produced,  which  may  exhibit  selectivity  to  molecules  ranging 
from  the  small  organics  (Liang  et  al.,  2005,  Ebarvia,  2005,  Trojanowicz  and  Wcislo,  2005)  to  high 
molecular weight biomolecules (Ramanaviciene and Ramanavicius, 2004b). Photo-induced Ppy synthesis is 
attractive  in  photolithographic  application  of  this  polymer,  since  it  allows  alterations  in  synthesized  Ppy 
morphology by change of excitation light wavelength and it might theoretically be applied for the design of 
electronic chips. However, because of a slow light induced polymerization rate, this polymerization type is 
still not very frequently applied compared to chemical or electrochemical polymerization.  
By  using  chemically  induced  polymerization,  the  Ppy  is  mainly  produced  in  bulk  solution  and  just  some 
amount of synthesized polypyrrole is covering the surface of introduced materials. It means that chemically 
induced polymerization is not very efficient with respect to deposition of Ppy on some surfaces. Moreover, 
Ppy is almost insoluble in common solvents, except when it is doped with proper agents that increase the 
solubility  of  this  polymer  (Pokrop  et  al.,  2004),  but  it  also  means  that  deposition  (e.g.  by  solvent 
evaporation) of this polymer from the solution is possible at the stage where the polymer is still in the form 
of colloid particles, i.e. before its precipitation (Ramanavicius et al., 2005). However, the major obstacle for 
use of this deposition method for designing of Ppy based sensors is a poor adherence of Ppy synthesized by 
this  technique  to  the  electrode  surfaces.  Such  disadvantages  might  be  reduced  if  electrochemical 
polymerization on specially pre-treated conducting surfaces is applied (Schuhmann et al., 1997). It allows 
deposition  of  Ppy  over  the  electrodes  placed  in  any  type  of  electrochemical  cell.  The  electrochemical 
polymerization has found an application as a general method for deposition of thin Ppy layers. By varying of 
the current passing through the electrochemical cell, potential thickness and morphology of deposited Ppy 
layer might be controlled (Schmidt et al., 1993). Different solvents including acetonitrile and water might be 
applied for electrochemical deposition of Ppy. As the Ppy synthesis might be performed from water solution 
at neutral pH, it is useful for entrapment of various biomaterials within polypyrrole. Such biomaterials might 
be  small  organic  molecules,  proteins,  DNA,  viruses  and  even  living  cells.  Buffers  with  high  buffering 
capacitance are requested for such electrochemical polymerization/entrapment. Otherwise, local production 
of a large amount of protons in the course of the polymerization may negatively affect the properties of the 
biomolecules to be entrapped within Ppy. Electrochemically synthesized Ppy has some attractive features, 
such as good conductivity and very high adherence of these films to the mostly used substrates for biosensor 
design leading towards sufficient stability of biosensors, even in a neutral pH region. On the other hand, the 
electrochemical  properties  of  Ppy  strongly  depend  on  the  redox  state  of  this  polymer.  Moreover, 
electrochemical  polymerization  is  applied  for  deposition  of  polypyrrole  layers  inside  geometrically 
complicated  electrochemical  cells  (Habermuller  and  Schuhmann,  1998)  and  there  is  almost  no  doubt  that 
this  polymerization  method  might  be  extremely  useful  for  deposition  of  Ppy  layers  inside  microfluidic 
devices including lab-on-a-chip devices and other bio-chips. 
In  particular  cases,  over-oxidized  Ppy  might  be  synthesized electrochemically. Then, entrapped  molecules 
and/or dopants might be extracted from the Ppy structure by application of special solvents. In such cases, 
the so called molecularly imprinted polymers (MIP’s) might be designed.  
At positive potentials, an over-oxidation of Ppy is occurring, leading  to lowering of Ppy conductivity and 
originating  in  an  easier  leakage  of  anionic  molecules  if  they  were  included  into  the  polymeric  backbone. 
Over-oxidation of Ppy appears at lower positive potentials in a water and/or oxygen containing environment 
and  in  this  case  it  is  leading  to  partial  destruction  of  the  polymeric  backbone  and  generation  of  oxygen-
containing  (carboxyl,  carbonyl  and  hydroxyl)  groups.  Over-oxidized  Ppy  has  been  used  in  many 
electroanalytical applications that utilize its permselectivity and is often used as discrimination membrane, 
which  significantly  increases  selectivity  of  electrochemical  biosensors  (Ramanavicius,  2000,  Geise  et  al., 
1991). This technology enables to prepare large variation of nanostructured polymeric layers with different 
analytical  characteristics  even  if  the  same  bulk  solution  is  used  for  polymerisation.  In  terms  of  possible 

27  

  
variations  and  control  of  polymerization  conditions,  electrochemical  polymerization  is  generally  more 
versatile  than  the  chemical  one.  It  was  demonstrated  that  the  surface  of  electrochemically  deposited  Ppy 
after  some  additional  procedures  of  electrochemical/chemical  functionalization  might  be  covalently 
modified by enzymes (Ramanavicius et al., 2000). Those structures were applied in a biocatalytic biosensor 
design, and it was demonstrated that Ppy layers modified by the same enzyme exhibit significantly different 
selectivity  towards  various  substrates  if  different  Ppy  modification  approaches  are  applied.  Moreover, 
combination of electrochemical techniques with some chemical surface modification techniques is useful for 
development  of  new  nanostructured  polymers.  In  some  particular  cases,  chemical  methods  are  more 
convenient for technological applications. Chemically synthesized polypyrrole might be applied for affinity 
chromatography  purposes  (Trojanowicz  and  Wcislo,  2005).  Chemical  and  electrochemical  Ppy  synthesis 
methods are finding application in different technological areas including affinity sensors. The capability of 
electrochemical  polypyrrole  synthesis  is  significantly  extended,  since  some  different  electrochemical 
techniques might be applied for deposition of Ppy over the electrodes: constant potential electrodeposition, 
galvanostatic deposition, cyclic voltammetry, and potential pulse techniques (Schuhmann et al., 1997).  
According to our experience based on application of conducting polymers in biosensor design, the pulsed 
potential  technique  is  the  most  suitable  for  nanostructuring  of  Ppy  by  entrapment  of  biologically  active 
materials within backbone of this polymer (Schuhmann et al., 1997). 

Formation of biological recognition part of DNA and protein affinity sensors 
In the majority of affinity sensors, immobilization of biomolecules including single-stranded DNA is a key 
factor during the creation of any type of biosensors and/or bio-chips. To reach a high analytical information 
density, miniaturization of separated bi- recognition parts is required (Livache et al., 1998). Several different 
strategies  have  been  used  in  common  DNA-  and  Protein-arrays:  a  mechanical  spotting  or  synthesis  of 
oligonucleotides  (ODN)  on  an  activated  support  (Yershov  et  al.,  1996,  Blanchard  et  al.,  1996),  a 
photochemical  process  (Pease  et  al.,  1994)  and  an  electrochemical  process  (Livache  et  al.,  1994).  Among 
mentioned methods only electrochemical polymerization involves the application of conducting conjugated 
polymers  (CP)  such  as  polypyrrole.  Some  steps  of  this  technology  were  described  in  previous  articles 
(Livache et al., 1994, Ramanavicius, 2006). The electrochemical oxidization of pyrrole forms, in one step, a 
solid  polypyrrole  film,  which  covers  the  surface  of  the  electrode.  The  geometrical  shape  and  size  of  the 
formed Ppy layer is limited to the electrochemically active surface of the electrode. Electropolymerization 
allows significant miniaturization of spots used for electrochemical detection. Efficiency of electrochemical 
Ppy  deposition  has  been  recently  demonstrated  in  DNA-sensors  devoted  for  genotyping  (Livache  et  al., 
1998).  In  addition,  the  versatility  of  this  immobilization  procedure  allows  the  immobilization  of 
polypeptides  (Heiduschka  et  al.,  1996)  and  proteins  including  enzymes  (Ramanavicius  et  al.,  1999, 
Schuhmann  et  al.,  1997,  Shin  and  Kim,  1996,  Schuhmann  and  Kittsteiner-Eberle,  1991).  In  addition, 
molecular  imprints  for  detection  of  proteins  with  rigid  3D  structure  might  be  formed  if  such  proteins  are 
added  into  polymerization  bulk  solution  and  are  removed  from  the  polymeric  structure  after  formation  of 
polymeric layer (Ramanaviciene and Ramanavicius, 2004b). 

Electrochemical detection methods used for DNA and Protein detection  
Concerning the detection of analytical signal, there are two major types of DNA and protein detections: i) 
one  requires  some  additional  electrochemically  active  labels,  ii)  the  second  is  based  on  detection  of 
electrochemically  detectable  changes  without  application  of  additional  electrochemically  active  labels. 
Application  of  electrochemically  active  labels  allows  significant  increase  in  sensitivity  of  bioanalytical 
systems  for  DNA  and  protein  detection,  as  these  analytes  in  most  cases  are  almost  electrochemically 
inactive.  Application  of  additional  redox  labels  allows  to  use  more  basic  electrochemical  detection 
techniques including amperometric detection at constant potential and/or application of cyclic amperometric 
detection.  Additional  electrochemically  labelled  compounds  including  ssDNA  fragments  or  secondary 
antibodies  that  are  labelled  with  redox  active  organic  compounds  and  redox  enzymes  are  applied  in 
mentioned  amperometric  affinity  sensors.  Sometimes  in  DNA  sensors,  redox-active  DNA  intercalating 
compounds are applied that can generate specific amperometric signals. 
However,  working  in  “label-free  mode”  is  more  attractive  compared  to  labelled  detection  methods,  as  it 
allows:  i)  to  reduce  number  of  chemicals  involved  in  detection,  ii)  to  reduce  number  of  manipulations 
requested for analysis, iii) to measure analytical signal in “real-time” (Warsinke, 2000). Usually, in label-

28  

  
free detection the amperometric signals are low because any auxiliary redox processes are registered. In this 
case, just small changes in charge densities and/or conductivity of electrochemical system are exploited for 
transduction  of  analytical  signal.  It  seems  that  the  mechanism  of  the  Ag-Ag  interaction  at  CP  based 
electrodes  involves  the  variation  in  the  capacitive  properties  of  the  polymer  modified  with  materials 
exhibiting  biological  recognition  (Shin  and  Kim,  1996).  Reduction  of  the  number  of  additional  reagents 
including very reactive redox mediators is a key issue for the development of bioanalytical systems that can 
operate in vivo. Such systems are mainly devoted for detection of polypeptides, biologically active proteins 
and  antibodies  that  exhibit  selectivity  towards  compounds  that  are  applied  in  this  case.  However,  direct 
detection based on electrochemical affinity sensors is less sensitive compared to methods based on indirect 
analyte detection (Skladal, 1997). 
Regarding electrochemical techniques employed for detection of analytical signals, affinity sensors might be 
traditionally divided into conductometric, amperometric, capacitive and potentiometric sensors.  
Conductivity  measurements  are  most  simple  from  the  technical  point  of  view  and  have  been  adapted  in 
immunoassays  where  secondary  antibodies  labelled  by  enzymes  were  applied.  The  major  requirement  for 
such  enzymes  is  the  ability  to  catalyse  reactions  generating  a  high  number  of  ions.  Enzyme  -urease-  is 
sometimes applied in this kind of conductometric sensors. 
Traditional constant potential amperometric techniques have been used mainly for the indirect detection of 
DNA and proteins. Redox mediators as well as redox active enzymes are usually applied in this case for the 
generation  of  amperometric  signal.  To  monitor  binding  events  without  using  any  labelled  compounds 
usually  alternating  potential  amperometric  techniques  are  applied.  Sinusoidal  alteration  of  potential  at 
different  frequencies  is  applied  where  so-called  electrochemical  impedance  spectroscopy  is  used  for 
detection of interaction between immobilized biologically selective compound and analyte of interest (e.g. 
target  DNA  or  proteins).  The  potential  steps  are  applied  in  the  case  where  so-called pulsed  amperometric 
detection  is  used  for  the  detection.  In  pulsed  amperometric  detection  as  well  as  in  electrochemical 
impedance  spectroscopy  based  methods,  potentials  are  applied  between  working  electrode  modified  with 
biological  recognition  layer  and  the  reference  electrode.  Registered  current  peaks,  if  potential  pulses  are 
applied, and changes in electrochemical impedance spectra might be directly related to the concentration of 
the analyte in solution (Bender and Sadik, 1998). A conducting polymer modified electrode has been used in 
affinity  sensors  for  DNA  and  protein  detection.  Such  sensors  are  mainly  based  on  electrochemical 
impedance  measurements  or  based  on  pulsed  amperometric  detection  (Ramanaviciene  and  Ramanavicius, 
2004b,  Ramanaviciene  and  Ramanavicius,  2004c).  Besides  the  amperometric  detection  methods,  methods 
based  on  electrochemical  capacitance  measurements  can  be  used  for  the  real-time  and  label-free 
measurement of the interaction between affinity reagents. The capacitance of such system depends on the 
thickness and dielectric properties of biological recognition layer before and after interaction with analyte 
(Moreno-Hagelsieb et al., 2007). 
Potentiometric  detection  usually  exploits  relationship  described  by  the  Nernst  equation:  E=E0+RT/zFlnam 
where E is the electromotive force, E0 is the electrode standard potential, R the gas constant, T the absolute 
temperature,  z  the  ion  charge,  F  the  Faraday  constant  and  am  the  activity  of  the  measured  ion  mZ+.  The 
altered surface potential, generated at an ion-selective electrode, provides a route to detect the ionic product 
formed  by  an  enzymatic  reaction.  Regarding  to  Nernst  equation,  the  change  of  electrode  potential  is 
proportional to the logarithm of ion  activity, if the other cell potentials of the overall electrochemical cell 
remain constant.  
Typically, potentiometric detection is less sensitive than amperometric detection. The detection limit usually 
is  in  the  order  of  micromoles  in  potentiometric  detection.  Moreover,  this  method  lacks  selectivity,  and  a 
complex  relation  between  ion  activity  and  ion  concentration  results  in  some  uncertainties  in  applying  the 
Nernst  equation  precisely  to  detect  analytes  in  real  samples.  In  addition,  potentiometric  electrodes  are 
dramatically  influenced  by  pH  and  ionic  strength  (Kuann  and  Guilbault,  1987).  To  reduce  the  here 
mentioned disadvantages, specialized ion-selective electrodes are applied for more precise measurement of 
analyte concentrations (Oesch et al., 1986). Such electrodes might be directly integrated into Ion-Selective 
Field  Effect  Transistors  (ISFET)  (Janata,  1994).  Here,  the  ISFET  gate  is  modified  by  a  special  polymeric 
layer, which is sensitive to certain ions. In such a structure, surface potential is generated in the gate region 
by  surface  ions  from  a  solution.  This  locally  generated  potential  then  modulates  current  flow  across  the 
silicon semiconductor present in the ISFET structure. 
Theoretically,  conductometric  measurements  might  be  applied  for  indirect  electrochemical  detection  of 

29  

  
DNA and proteins. However, conductivity measurements are difficult due to the variable ionic background 
of  clinical  samples  and  the  relatively  small  conductivity  changes  that  are  observed  in  such  high  ionic 
strength  solutions.  A  solution  in  this  case  is  application  of  a  second  comparative  ‘blank’  electrochemical 
system  (Ghindilis  et  al.,  1998).  Future  potentiometric  immunosensor  may  employ  the  technique  of  ion-
channel  switches.  The  basis  of  this  innovative  technique  is  that  recognition  events,  e.g.  antibody-antigen 
binding, change the number of properly aligned and properly functioning ion channels across a lipid bilayer 
membrane  and  thus  ionic  conductance  change  is  achieved.  This  technique  is  applicable  to  receptor-, 
antibody- and nucleotide-based applications to detect picomolar analyte concentrations  in  complex  media, 
not lower because of the amplification due to the ionic flows (Cornell et al., 1997). 
In  indirect  electrochemical  sensors  for  detection  of  DNA  and  proteins,  the  binding  reaction  is  visualized 
indirectly via an auxiliary reaction by an electrochemically active compound. Amperometric transducers in 
indirect electrochemical immunoassays are used much more frequently than others (Livache et al., 1998). As 
antibodies  and  antigens  are  usually  not  electrochemically  active  within  the  desired  potential  range,  redox-
active compounds can be applied as labels for indication (Schuhmann et al., 1997). For the amperometric 
detection at constant potential, electrochemical labels should have the following properties: they should be 
reversibly  electroactive,  should  not  cause  electrode  fouling,  and  chemical  groups  for  covalent-attachment 
should be available (Treloar et al, 1994). Electrochemically detectable species, such as nitrophenol, H2O2, 
and  NH3  that  are  substrates  or  products  of  enzymatic  reactions  catalysed  by  alkaline  phosphatase, 
horseradish  peroxidase,  and  urease,  are  important  for  electrochemical  detection.  Nitrophenol  and  H2O2 
mostly are detected amperometrically at constant potential, while ammonia is electrochemically inactive at 
low potentials and can only be detected potentiometrically by an ammonia gas-sensing electrode (Liu et al., 
2000). 
Indirect  electrochemical  detection  of  DNA  and  proteins  can  be  divided  into  two  major  subtypes:  non-
amplified and amplified electrochemical detection. In non-amplified electrochemical detection, the presence 
of one molecule of analyte will generate one electrochemical equivalent of registered signal, such detection 
is usually based on application of basic redox molecules like ferocene derivatives. Since the sensitivities of 
amperometric sensors for the redox compounds usually are in the micromolar range, non-amplified assays 
make only sense if the concentrations of analytes are in such a range (Yershov et al., 1996). 
For more sensitive detection of DNA and proteins, the amplification of electroactive equivalents is required. 
A relatively basic way to amplify any kind of analytical signal is a pre-concentration step. However, the so-
called  “redox  recycling”  allows  amplification  of  analytical  signal  without  any  pre-concentration  (Deore  et 
al., 1999). In this case, the redox compound is oxidized and reduced in a cyclic manner so that the detection 
of one labelled antigen or antibody molecule will generate multiple signal equivalents. Redox recycling can 
be performed in different ways by using electrode-electrode, electrode-enzyme or enzyme-enzyme couples 
(Schuhmann et al., 1997). 

Recent developments and conclusions 
During the last four years, we have developed an electrochemical system for detection of target DNA, which 
was  based  on  pulsed  amperometric  detection  (Ramanaviciene  and  Ramanavicius,  2004c).  Pulsed 
amperometric detection allowed to simplify electrochemical equipment required for detection of target DNA 
binding.  The  same  system  was  applied  for  detection  of  protein  binding  towards  molecularly  imprinted 
polypyrrole (Ramanaviciene and Ramanavicius, 2004b). In all studies related to DNA and protein detection, 
we  have  applied  differently  modified  layers  of  conducting  polymer,  i.e.  polypyrrole.  According  to  our 
experience  such  conducting  polymer  layers  are  very  useful  for  development  of  such  electrochemical 
systems,  as  signal  changes  registered  by  pulsed  amperometric  detection  method  are  several  orders  of 
magnitude higher than when such semiconducting layers are not applied. Nowadays, in detection of DNA 
and  proteins,  electrochemistry  is  often  combined  with  surface  plasmon  resonance  (Zhang  et  al.,  2007, 
Kausaite  et  al.,  2007),  quartz  crystal  microgravimetric  measurements  (Wei  et  al.,  2006,  Ramanaviciene  et 
al.,  2004)  and  fluorescence  measurements  (Willner  et  al.,  2007,  Ramanavicius  et  al.,  2007)  to  enhance 
sensitivity and selectivity of combined methods. 

30  

  
 
 
Acknowledgements  
This  work  was  financially  supported  by  Agency  for  International  Science  and  Technology  Development 
Programmes in Lithuania COST action 853. 

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Protein chip applications 

Fabio Cimaglia1, Marialuce Daniela De Blasi1, Angelo Santino1, Tomas Erban2, Iva Krizkova-Kudlikova2, 
Jan Hubert 2and Palmiro Poltronieri 1* 

1CNR-ISPA,  Institute  of  Sciences  of  Food  Produc-
tions, National Research Council, Lecce, Italy. 

2VURV,  Research  Institute  of  Crop  Productions, 
Prague, Czech Republic 

* Corresponding author e-mail: palmiro.poltronieri@ispa.cnr.it  

Abstract  
The aim of this work was the testing of a diagnostic tool for the detection of allergenic proteins from stored 
product mites/cockroaches and house dust mites. Species-specific antibodies were tested for specificity and 
cross-reactivity, and detection limit by ELISA method. In this work, protein chips were manufactured either 
manually, using a MicroCaster, or robotically, using a SpotArray. The proteins tested were six recombinant 
Kunitz-type protease inhibitors (PI), two of group A (with anti-cathepsin D and anti-trypsin activity), two of 
group  B  (with  anti-trypsin  and  anti-chymotrypsin  activity)  and  two  of  group  C  (with  anti-cathepsin  B 
activity),  and  the  soybean  Bowman-Birk  inhibitor.  These  proteins  were  immobilized  onto  epoxy  activated 
glass slides. Protein chips were hybridised with serial dilutions of extracts from adult mites/insects and from 
spent growth media (SGM) in order to capture the allergenic proteases. Protein A was labelled with Alexa-
555 and used for detection of polyclonal antibodies directed against the proteases. The detection limit was 
0.5µg  protein  extract  for  Aleuroglyphus  ovatus  and  0.75µg  for  Tyrophagus  putrescentiae  specimens  and 
protein extracts from the faeces fraction (SGM). By comparison, the detection limit of ELISA method was 
2.5µg. The protease inhibitor-chips can allow rapid screening of proteases and their specificity by analysing 
the binding to each class of inhibitors. Results of protease binding to each KPI and recognition of allergenic 
proteases showed the practicability of protein microarrays as a diagnostic tool for detection of  mite/insect 
contaminants in foods and work environment. 

Introduction 
In  the  past  few  years,  protein  chips  have  become  a  powerful  tool  for  diagnostics  and  analysis  of  protein 
functions  and  protein-protein  interactions.  This  technology  allows  fast,  easy  and  parallel  detection  of 
multiple addressable elements from a minute amount of sample in a single experiment. It has been applied to 
analyse protein and protein-protein interaction as well as antibody-antigen, enzyme-substrate and enzyme-
inhibitor interaction (Mc Beath and Schreiber, 2000; Templin et al., 2003; Cretich et al., 2006). In the recent 
years, the European Community and ESF supported the research on protein chips and the collaboration of 
institutes with main focus on these technologies (Schellenberg and Stribel, 2004). To this aim, the COST853 
network  activity  was  established  to  develop  tools  for  Array  technology  dedicated  to  the  detection  of 
agricultural biomarkers. 
Assay  systems  based  on  chip  technology  are  currently  applied  for  the  identification,  quantification  and 
functional analysis of proteins. Protein chip technology is of major interest for proteomic research as well as 
for  diagnostic  applications  since  this  miniaturized  and  parallelized  assay  systems  can  be  used  for  the 
identification  of  biomarkers  and  the  validation  of  potential  target  molecules.  However  such  technology 
shows  some  problems  in  terms  of  protein  stability.  These  problems  are  due  to  the  unstable  structure  and 
functionality  of  protein.  Protein-protein  interaction  takes  place  by  different  means  such  as  electrostatic 
forces,  hydrogen  bonds  and/or  weak  hydrophobic  van  der  Waals  interactions  (Templin  et  al.,  2003). 
Moreover  protein  interaction  does  not  only  depend  on  their  primary  structure,  but  also  on  their  tertiary 
structure. For these reasons, an improvement in the efficiency of protein immobilization on the slide surface, 

35  

  
 
 
 
 
 
in  terms  of  functional  conservation  and  of  the  reproducibility  of  the  amount  of  immobilized  protein  is 
needed.  
Proteins  can  be  immobilized  onto  substrates  in  a  non-covalent  interaction  by  adsorption  on  glass  slides 
coated with poly-L-lysine (Haab et al., 2001), agarose (Afanassiev et al., 2000), polyacrylamide, hydrogels 
(Arenkov  et  al.,  2000),  on  membranes  made  with  polystyrene,  poly-vinylidene  fluoride  (PVDF),  on  thin 
nitrocellulose  film  (FAST  slides,  Schleicher-and-Schuell),  either  on  hydrophobic  or  hydrophilic  surfaces 
(Angenendt  et  al.,  2003);  or  in  a  covalent  manner  on  glass  surface  functionalized  by  aldehyde  residues, 
epoxy  groups  (Tam  et  al.,  2002),  or  even  by  using  cross-linking  agents.  A  drawback  of  random  covalent 
immobilization is a partial block of the active sites of the proteins that become then less accessible to the 
interacting molecules. The covalent binding enhances the stability of immobilized proteins but could cause 
changes  in  the  protein  conformation.  Structural  modification  of  the  protein  can  result  from  the 
immobilization  through  interaction  between  the  support  and  the  protein,  or  from  changes  in  the  protein 
microenvironment. The non-covalent adhesion of antibodies to nitrocellulose has been extensively used in 
ELISA  method.  However,  an  oriented  immobilization  could  provide  better  spatial  accessibilities  of  active 
binding  sites  than  non-oriented  immobilization.  Therefore  the  greatest  difficulty  in  the  realization  of  a 
protein-chip is the preservation of functionality of the immobilized proteins. 
An  application  of  protein  chips  is  the  activity-based  detection  of  enzymes  on  a  microarray,  such  as  the 
screening  of  anti-protease  specificity  using  fluorescently-labelled  inhibitors.  In  this  approach,  commercial 
enzymes are immobilized onto epoxy-activated slides and incubated with labelled inhibitors: the formation 
of  inhibitor-bound  enzyme  complexes  is  detected  by  a  fluorescence-based  microarray  scanner.  The 
screening of both enzyme activity and specificity in a chip format has been also successfully performed. A 
series  of  peptide  substrates  conjugated  with  fluorogenic  coumarine  bound  to  aldehyde-activated  surfaces 
(Salisbury et al., 2002) were used to screen enzymatic cleavage and release of coumarin and to find substrate 
specificity  of  proteases.  In  a  similar  study,  different  proteases  and  phosphatases  were  incubated  with 
fluorogenic substrates spotted onto poly-L-lysine or amino activated glass slides, to screen for their activities 
(Uttamchandani et al., 2005).  
Using protein chip technology, we report in this paper the use of a protein microarray system for detection 
of  insect  proteases,  known  as  major  allergens  in  food  and  environment,  using  recombinant  Kunitz-type 
protease inhibitors (KPI) immobilized onto a glass slide. Previously, we have validated the KPI chip applied 
to the characterisation of protease specificity towards potato KPI inhibitors induced by fungal infection, and 
in  characterisation  of  new  KPIs  cloned  in  wild  varieties  of  the  Solanum  genus  (Speransky  et  al.,  2007). 
Kunitz-type  inhibitors  (22-24  kDa)  are  a  polymorphic  class  of  plant  protease  inhibitors  found  in  seeds, 
storage organs and vegetative tissues, and play a role in plant defence against pathogens and insects (Ryan, 
1990). The Kunitz-type inhibitors found in potato are represented by three major homology groups A, B and 
C. KPI-A group includes inhibitors of aspartic proteinases such as cathepsin D. The KPI group B contains 
inhibitors  of  serine  proteinases  grouped  in  the  chymotrypsin  clan,  as  trypsin,  chymotrypsin,  and  elastase. 
The KPI group C includes proteins that inhibit bacterial subtilisins and plant cysteine proteinases as well as 
some  non-proteinase  enzymes  such  as  invertase  and  α-amylase.  Considering  the  role  of  Kunitz-type 
inhibitors  as  components  of  plant  defence  mechanisms  against  insects  and  pathogens  (Linthorst,  1991; 
Boulter,  1993;  Schuler  et  al.,  1998;  Turrà,  2006),  their  polymorphism  may  be  the  result  of  adaptive 
evolution  of  the  plant  in  response  to  insect  and  microbial  or  fungal  pests.  It  is  known  that  insects  and 
microbial  pathogens  are  able  to  adapt  to  these  inhibitors  and  to  render  them  ineffective  (Marchetti  et  al., 
2000).  This  can  be  counteracted  by  a  large  repertoire  of  functional  inhibitor  variants  and  the  evolution  of 
new  variants  (Lison  et  al.,  2006).  Therefore,  the  development  of  a  microarray  strategy  to  perform  high-
throughput  screenings  of  protease  inhibitors  could  be  a  valid  tool  either  for  evolutionary  studies  or  for 
discovering new inhibitors for applicative use. The Kunitz-type inhibitors were chosen as model to study the 
interaction between protease and protease inhibitors on a glass slide and to set up an on-chip inhibitory assay 
protocol since the inhibitory activity against specific proteases are usually assayed by biochemical tests. The 
proteases were chosen from the results of protease inhibition specificity using colorimetric assays, and from 
protease binding assays using the protein chip format.  
Stored  product  and  house  dust  arthropods  include  many  species  affecting  human  health.  Mites  as  Acarus 
siro,  Dermatophagoides  farinae,  Tyrophagus  putrescentiae,  and  beetles  as  Tribolium  castaneum  are  often 
found  in  contaminated  stored  products  and  farming  environments.  The  bodies  and  metabolites  of  these 
arthropods can contaminate stored foods and grain, thus introducing health-threats through the food chain. 

36  

  
At least twenty classes of major allergens are known to be present in house dust and stored product mites. 
Six of these protein classes are proteases/hydrolases (Table 1). The proteases are serine-type (chymotrypsin, 
trypsin-like)  and  cysteine-type  (Stewart  and  Robinson,  2003).  In  addition,  other  enzymes  with  hydrolase 
activity  can  also  be  targeted  by  the  Kunitz-type  inhibitors  with  unknown  function  (as  the  anti-invertase 
activity present in some KPI group C protein). A further level of selectivity was introduced in the detection 
step, performed using species-specific polyclonal antibodies. The method described in this paper, based on 
binding and purification of mite or beetle enzymes, and detection with species-specific antibodies, can allow 
high-throughput,  rapid  screening  and  content  of  proteases,  addressing  also  the  enzyme  specificity  by  its 
binding to each KPI.  

Table 1: The major allergen classes in house dust mites, storage product mites and storage products insect. 

Material and Methods 

Kunitz-type inhibitor expression purification and inhibition assays 
Trypsin, chymotrypsin, cathepsin B, papain, subtilisin, pancreatic elastase, protease inhibitors as the soybean 
Bowman-Birk  inhibitor,  and  BSA  were  provided  commercially  (Sigma,  St.  Louis,  US).  The  sequences 
corresponding to the mature inhibitor polypeptides from Solanum stoloniferum (KPI-A), S. palustre (KPI-B) 
and S. tuberosum (KPI-C) were cloned in the pQE30 expression vector, to transform Escherichia coli BL21 
DE3 strain. The KPI sequences were expressed in E. coli cultures (100 ml) grown at 37°C, transferred to 25°
C and induced with 1 mM IPTG for 24 h. Cells were harvested by centrifugation at 5000 g and resuspended 
with  5  mL  of  Bugbuster  (Novagen,  Merck,  Darmstadt,  DE)  supplied  in  50  mM  Tris-HCl  buffer,  pH  8.0 
containing benzonase. After incubation for 30 min at room temperature, the homogenates were centrifuged 
at 16000 g for 20 min at 4 °C and the clear supernatants applied to a His select cartridge column (1.25 ml, 
Sigma). Elution was carried out according to the manufacturer’s instructions. The KPI proteins, purified by 
Ni+ affinity column, were not further HPLC processed, due to the good purity of recovered proteins. Protein 
identity  was  checked  by  western  blot  using  a  polyclonal  antibody  raised  against  potato  PKPI  B10  protein 
(Speransky et al. 2007).  
Recombinant  Kunitz-type  inhibitor  proteins  were  tested  for  their  ability  to  inhibit  the  serine  proteases 
trypsin,  α-chymotrypsin, elastase, subtilisin and the cysteine proteases papain and cathepsin B at different 
molar  ratios  of  inhibitor  and  protease  by  using  N-benzoyl-arginine-nitroanilide  as  substrate  according  to 
previous  reports  (Santino  et  al.  1998).  Group  B  KPI  proteins  were  biochemically  characterised  and 

37  

  
 
 
differentiated (determination of Ki using bovine trypsin). In further experiments KPI-B1 and KPI-B4 were 
used among the group B cluster, as their Ki values (345 nM and 84 nM, respectively) indicated high bovine 
trypsin inhibition even at low substrate concentrations. The KPI-B proteins were also found to inhibit trypsin 
activity in mite extracts.  

Chip production 
We  used  epoxy-group  surface  activation  of  glass  slides  for  the  covalent  binding  of  proteins.  Glass  slides 
were cleaned in piranha solution (H2SO4 : H2O2, 7 : 3) for 2 hours. The slides were washed thoroughly three 
times  with  deionised  water  and  ethanol  (95%  w/v),  and  dried  with  flushing  nitrogen.  For  the 
functionalization  with  epoxy-groups,  cleaned  slides  were  then  incubated  for  2  hours  with  a  solution 
containing  ethanol,  acetic  acid  (0,1  %  w/v)  and  3-glycidoxypropyldimethoxymethylsilane  (1%  w/v)  in 
deionised  water.  After  incubation  the  slides  were  cleaned  with  ethanol  3  times  and  incubated  at  150  °C 
overnight. The slides were then washed with ethanol three times and dried under flushing nitrogen. 

Protein immobilisation and hybridisation  
Six recombinant His-tagged Kunitz-type protease inhibitors (KPI-A1 and, KPI-A2 for KPI group A, KPI-B1 
and  KPI-B4  for  group  B,  KPI-C3  and  KPI-C4  for  group  C)  from  Solanaceae  species  were  chosen  for 
immobilization  onto  the  epoxy-activated  slides.  All  proteins  were  desalted  with  Zeba  desalt  spin  columns 
(Pierce, Rockford, US). The recombinant Kunitz-type protease inhibitors, bovine serum albumin (BSA), as 
negative  control,  and  the  commercial  soybean  Bowman-Birk  inhibitor  were  diluted  at  different 
concentration (0.1, 0.2, and 0.4 mg/ml) in NaHCO3 (0.1M, pH 9) / glycerol (40% w/v) buffer and arrayed on 
epoxy- slides. Other spotting buffers were also tested: trehalose, saccharose and polyethylene glycol were 
used  as  hydrating  agents  and  compared  to  glycerol  for  homogeneity  and  reproducibility  of  spot  sizes. 
Printing  was  performed  in  triplicate  for  each  protease  at  4°C  and  about  60%  humidity.  A  MicroCaster 
(Schleicher-and-Schuell, Whatman, Brentford, UK) was used to print manually the slides, delivering 60-80 
nl drops. The robotic printing using the SpotArray 24 (Perkin Elmer, Waltham, USA) allowed deposition of 
1  nl  drops  with  miniaturisation  of  the  sub-arrays,  good  reduction  of  spot  variability  and  higher 
reproducibility  of  results  compared  to  the  manually  printed  spots.  The  slides  were  incubated  at  room 
temperature  (RT)  for  2  hours  in  a  humidity  chamber  (70%  humidity).  The  resulting  protein  chips  were 
sealed in a box and kept in dark for storage at 4 ºC. Experiments  with  slides  stored for periods of two to 
three months performed as well as the freshly printed slides. Before use, the slides were incubated for 1 h on 
a shaker at RT in a solution containing 0.5M glycine and 1% w/v BSA in phosphate buffer saline (PBS) to 
block the free epoxy groups, then washed with PBS + BSA (0.1% w/v) and dried by centrifugation at 500 g 
x 2 min. 

Control of the protein chip 
Hybridisation control was performed to evaluate the result of KPI printing: 1 mg of protease was labelled 
with  Alexa-555  protein  labelling  kit  (Molecular  Probes,  Invitrogen,  Carlsbad,  US) and  purified  to remove 
free dyes with gel-filtration biogel P-6 (Bio-Rad, Richmond, US) according to the manufacturer’s protocols. 
50 ul of protease in Tris buffer (0.1M pH 7.5 for trypsin and chymotrypsin and 0.1M pH 6.5 for cathepsin B) 
containing  the  labelled  proteases  (5  μl  of  stock  solutions,  serially  diluted  to  0.01,  0.1  and  1mg/ml  final 
concentrations) was applied to the printed slide using the coverslip method and incubated for 1 h in the dark 
at RT in a sealed chamber (70% humidity). After incubation the slides were subsequently washed with PBS 
containing 0.2% Tween-20 and 0.1% BSA, with shaking at 4°C during 15 min, then washed twice in PBS + 
BSA (0.05% w/v) with shaking at 4°C during 5 min and dried by centrifugation at 500 g during 2 min. The 
slide was scanned with Array Scanner 428 (Affymetrix, S. Clara, US). 

Detection of mite/insect allergenic proteases by capture on the protease inhibitor chip 
Mite  cultures  (Acarus  siro,  Dermatophagoides  farinae,  Aleuroglyphus  ovatus,  Lepidoglyphus  destructor, 
Tyrophagus putrescentiae) and rabbit immunization to produce polyclonal antibodies were done at VURV 
in Prague. Protein extracts of frozen adults or from spent growth media (SGM) were weighted, washed with 
water and homogenised in water or PBS. Protein concentration was determined using the Bradford method 
(Bio-Rad). Further experiments were made using 5-10 µl of total protein extract (1 to 1.5 µg/µl) diluted 1:5 

38  

  
or 1:10 in PBS, of which 20 µl aliquot was used in hybridization assays (final concentration of total proteins 
between 0.5 and 1 µg). The Protease inhibitor chips were hybridised with this protein fraction, washed with 
Tween-20/Tris  buffer  saline  (TBS-T),  as  in  Western  blot  protocols,  then  incubated  with  the  polyclonal 
antibody, diluted 1:20 (0.5 µg/ml). After removal of aspecifically bound antibodies by washing in TBS-T; 
the  slides  were  incubated  with  the  protein  A  fluorescently  labelled  for  the  detection  of  signals  (Figure  1). 
Slides were scanned with an Affymetrix 428 dual-laser scanner, and images saved as TIFF and Bitmap files 
using ScanAnalyse. The sensibility of antigen detection on protein chips were compared to results obtained 
using dot blot and ELISA assays, detected with the same polyclonal antibodies (Kudlikova et al 2004).  

Figure 1: Scheme of the experimental protocol for mite proteins hybridisation and detection with polyclonal antibody/
protein  A  complex.  Total  mite  extracts  were  incubated  with  the  protease  inhibitor  chips  (left).  Then,  the  protease-
protease inhibitor interaction was visualised by the binding of polyclonal antibodies specific to that mite species.  

Results  
A series of His-tagged potato Kunitz-type protease inhibitors (KPI), two of group B (KPI-B1 and KPI-B4); 
two  of  group  A  (KPI-A1  and  KPI-A2);  and  two  of  group  C  (KPI-C3  and  KPI-C4)  were  purified  from 
recombinant  E.  coli  cell  cultures  and  used  for  biochemical  characterization  of  proteases.  In  a  colorimetric 
assay  using  protease  substrates  (Santino  et  al.  1998),  papain,  elastase,  cathepsin  B,  cathepsin  D, 
chymotrypsin and trypsin were tested for inhibition of their activity. These assays showed that trypsin was 
inhibited by recombinant KPIs of groups A and B but not by group C; chymotrypsin was inhibited only by 
group B while cathepsin B was inhibited to some extent by all groups (data not shown).  
Then, we tested the anti-mite antibodies produced in-house for the detection of mite proteins using dot blot 
hybridisation as an established method for detecting small amount of contaminating proteins in a complex 
protein mixture (Figure 2). 

  Concentrations:    1        2.5     5       10      25       50     75     100 µg/ml 

Figure 2: Example of detection of mite proteins using dot blot method. This biochemical assay gave results similar to 
those  obtained  by  sandwich-ELISA,  using  anti-mite  polyclonal  antibodies  and  secondary  antibodies  labelled  with 
horse-radish peroxidase. Sensibility of the assay was good, starting from 1 µg/µl.  

39  

  
 
 
 
 
 
 
 
 
 
 
Then,  we  shifted  to  KPI  protein  chip  combined  with  the  protein  A-polyclonal  antibody  for  mite  protease 
allergen detection. The first challenge of the protein chip was in chip manufacturing. In this work the partial 
denaturation of proteins after binding to the hydrophobic surface in silanized glass was studied. The proteins 
used  for  spotting  required  the  addition  of  protective  substances  in  the  spotting  buffer,  and  we  tested  the 
addition  of  glycerol  (MacBeath  and  Schreiber,  2000),  trehalose  (Kusnezow  et  al.,  2003),  saccharose 
(Avseenko et al., 2001) and polyethylene glycol (PEG) of low molecular weight (Lee and Kim, 2002).The 
recombinant Kunitz-type inhibitors and the positive controls were reconstituted at increasing concentration 
(0.1, 0.2, and 0.4 mg/ml) in NaHCO3 buffer with 40% protective agent and spotted in triplicate. The array 
was  designed  to  contain  the  soybean  Bowman-Birk  inhibitor  (SBBI)  as  trypsin  /chymotrypsin  binding 
control.  Glycerol  in  these  tests  was  found  to  outperform  all  the  other  additives.  Glycerol  enhanced  the 
binding and stability of KPIs resulting in higher fluorescence signals, which could be related to prolonged 
hydration of proteins (Olle et al. 2005). In this case, the proteins remained hydrated for longer time during 
the  incubation  step  after  the  printing  process,  probably  because  water  evaporated  more  slowly  in  the 
glycerol-containing  spots.  This  allowed  the  completion  of  reaction  and  covalent  binding  between  epoxy 
functional  groups  on  the  slide  surface  and  the  functional  groups  (amine,  hydroxyl  and  thiol  group)  in  the 
proteins (McBeath and Schreiber 2000). 
Kunitz-type protease inhibitors were further characterised towards their affinity for protease after binding to 
the  glass  surface  to  compare  the  proportion  of  native  form  and  evaluate  the  level  of  denaturation  using 
lowest  concentrations  of  trypsin.  We  compared  the  immobilization  of  trypsin  and  chymotrypsin  inhibitors 
printed  in  replicate  onto  epoxy  slides  in  presence  of  glycerol  (40%  w/v),  and  visualised  the  hybridization 
with  trypsin  labelled  with  Alexa-555.  Serial  dilutions  of  the  protease  were  tested.  Protein  activity  on  the 
array surface was studied for each protease-protease inhibitor interaction to evaluate binding independently 
of  the  amount  and  density  of  protein  bound  in  each  spot  (Gordus  and  Mc  Beath,  2006).  The  binding  and 
interaction between trypsin and SBBI, KPI-B and KPI-A inhibitors at decreasing trypsin concentrations (10-
100 ng/μl) was analyzed. High sensitivity of detection was found at the lowest trypsin dilutions for KPI-A 
inhibitors,  and  was  put  in  relation  to  highest  stability  of  KPI-A  bound  to  the  glass.  Conversely,  group  B 
KPIs  showed  to  have  lost  50%  of  trypsin  binding  activity  compared  to  their  protease-binding  activity  in 
solution.  This  could  have  been  caused  by  the  steric  hindrance,  lower  rotation  freedom  after  covalent 
attachment, or protein denaturation caused by the hydrophobic glass surface. However, due to high trypsin 
affinity, the decrease of native KPI-B in respect to total KPI-B spotted did not affect the ability to capture 
trypsin and chymotrypsin at 100 ng/μl or higher concentration levels. Therefore, the protein chip provided a 
simple,  qualitative  confirmation  of  protease  inhibitor-protease  binding  and  specificity  for  qualitative 
analysis.  In  quantitative  analysis  of  inhibitory  activity,  to  compare  two  or  more  inhibitors  on  the  chip, 
various concentration points of the probe/substrate/protease partner were tested. Overall, the results showed 
that the immobilized inhibitors interacting with the proteases confirm the protease specificity results found 
using biochemical tests.  
Biochemical assays showed that Kunitz-type PIs possessed anti-trypsin activity in mite and insect extracts. 
Our data allowed us to use our KPI chip as a new diagnostic tool for detection of mite allergens and as a 
protease capture system using the binding properties of the KPI toward such enzymes. 

  KPI-A2     KPI-B4    KPI-C4    SBBI             KPI-A2   KPI-B4   KPI-C4    SBBI   
  KPI-A1     KPI-B1    KPI-C3    BSA              KPI-A1   KPI-B1   KPI-C3    BSA   
  A. ovatus (adults)    0.35 µg/µl                           A. ovatus  (SGM extracts)  0.1 µg/µl  

Figure  3:  Comparison  of  detection  of  Aleuroglyphus  ovatus  proteases  in  adults  and 
SGM extracts using protein chips.  

40  

  
 
 
 
 
 
 
 
 
 
 
 
 
  KPI-A2  KPI-B4  KPI-C4  SBBI                      KPI-A2   KPI-B4  KPI-C4    SBBI 
  KPI-A1  KPI-B1  KPI-C3   BSA                      KPI-A1   KPI-B1  KPI-C3     BSA             
  T. castaneum (adults) 0.2 µg/µl                    T.  castaneum (SGM) 0.1 µg/µl 

Figure  4:  Comparison  of  detection  of  Tribolium  castaneum  proteases  in  adults  and 
SGM extracts using protein chips. 

Detection  of  allergen  proteases  and  other  hydrolytic  enzymes  was  observed  using  Alexa-555  labelled 
protein-A and polyclonal Abs specific to mite species A. siro, L. s destructor, T. putrescentiae, A. ovatus, 
and to the beetle species T. castaneum, respectively. Positive results were obtained with extract dilutions as 
low as 1:20 (a high sensitivity limit, corresponding to 500 ng of total proteins), and good resolution at 1:5 
dilution (corresponding to 7.5 µg of total proteins) (Figures 3 and 4, showing the detection of A. ovatus and 
T. castaneum, respectively). For most of the species, the total protein extract (1.5 mg/ml) did not produce 
good  signals,  whereas  clear  and  better  signals  were  observed  starting  from  the  1:2  and  1:4  dilutions.  It  is 
known that these extracts contain lipids, chitin and polysaccharides that may block the protein interactions 
on  the  chip  and  could  bind  to  the  glass  surface  and  to  KPI  probes,  thus  inhibiting  the  detection.  We 
performed all the protein chip studies using serial dilutions of the total extracts starting from 1:4, 1:10 and 
1:20.  A  good  differentiation  between  SGM,  rich  in  digestive  proteases  (Krizkova-Kudlikova  et  al.,  2006), 
and arthropod adults, containing a different pool of proteases, was observed (Table 2). This differentiation 
was also made possible by the availability of different polyclonal antibodies specifically produced against 
different  antigen  sources  (SGM,  larvae,  and  adults).  A  possible  contribution  of  the  growth  stage-specific 
antibody in the selective recognition of a particular class of protease cannot be excluded. 

Table 2: Differential detection of proteases from various storage product pests. 

Trypsin/
chymotrypsin  

Serine proteases  

Aspartic proteases  

Cysteine proteases 
 or hydrolases  

Aleuroglyphus ovatus 
 adults  
Aleuroglyphus ovatus 
 SGM  
Tribolium castaneum 
 adults  
Tribolium castaneum 
  SGM  
Lepidoglyphus 
 adults  
Thyrophagus putresc. 
 adults  
Acarus siro 
 adults  

intense  

intense  

none  

none  

good  

intense  

weak  

intense  

strong  

intense  

weak  

good  

good  

intense  

strong  

strong  

strong  

good  

good  

intense  

none  

none  

intense  

good  

good  

weak  

none  

none  

An  other  advantage  of  the  protein  chip  method,  compared  to  the  dot  blot  method,  is  the  reduction  of 
hybridisation volumes with the concentration of the probe on a very small surface, so that the total protein 
amount is reduced between 10 to 20 times. Furthermore, the sensibility of the protein chip method is higher: 
concentrations  useful  for  detection  are  only  0.2  µg/µl  or  higher,  corresponding  to  a  5  times  increase  in 
sensibility compared to the dot blot method.  

41  

  
 
 
 
 
   
 
 
 
 
  
Discussion 
The KPI protein chip experiments were made possible by the availability of a wide set of species-specific 
polyclonal antibodies, raised in rabbit against each mite or beetle species. This set of polyclonal antibodies, 
possessing low cross-reactivity and good discrimination at species or family level, were characterized in a 
previous work (Kudlikova et al. 2004). Specificity of antibodies allowed the differentiation of these food/
house contaminating pests using both the ELISA method and the protein chip experiments. In comparison to 
the performance of ELISA method, the resolution was good even with lower amounts of sample used, and 
sensibility  was  increased  of  1  order  of  magnitude  in  respect  to  dot  blot  hybridization  (500  ng  in  protein 
chips, compared to 5 µg of proteins in dot blot, Figure 2). This can be explained by the higher background 
caused by antibody binding to membranes, whereas the glass slide showed low signal-to-noise ratios. 
Detection and differentiation of protease allergens was made possible using the protease inhibitor chips. In 
this  work,  the  results  of  protein  chip  and  dot  blot  techniques  were  compared  to  find  the  unique  protease 
profiles  for  each  of  the  species  studied.  Proteases  are  considered  a  threat  because  they  can  trigger  allergy 
through their interaction with the immune system cells (Shakib and Gough, 2000). A diagnostic system able 
to recognise and detect low levels of allergen proteases could be very useful in food safety applications as 
well  as  in  analysis  of  agricultural  biomarkers.  This  protein  chip  tool  could  allow  the  detection  and 
differentiation  of  insects  and  their  excrements  in  contaminated  food,  as  well  as  the  possibility  to  detect 
specific classes of protease allergens. Further work is needed to assess the feasibility of protein extraction 
from  environmental  samples,  but  the  results  obtained  using  the  spent  growth  media  are  encouraging  and 
show  the  feasibility  of  detection  from  media  where  mite  fed  on,  and  which  represents  a  prototype  of  the 
stored grain environment.  

Acknowledgements 
We thank the COST office for Short Term Mission STSM-853-02607 to T. Erban. This work was supported 
by a grant of the Czech Ministry of Education No. 1P04OC853.003.  

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for protein and antibody microarray applications. J Chromatogr A. 1009, 97-104. 

Afanassiev V, Hanemann V, Wolfl S. 2000. Preparation of DNA and protein micro  arrays on glass slides 

coated with an agarose film. Nucleic Acids Res 28, 1–5. 

Arenkov P, Kukhtin A, Gemmell A, Voloshchuk S, Chupeeva V, Mirzabekov A. 2000. Protein microchips: 

use for immunoassay and enzymatic reactions Anal Biochem 278, 123–131. 

Avseenko  NV,  Morozova  T,  Ataullakhanov  FI,  Morozov  VN.  2001.  Immobilization  of  proteins  in 

immunochemical microarrays fabricated by electrospray deposition. Anal Chem 73, 6047–6052. 

Boulter D. 1993. Insect pest control by copying nature using genetically engineered crops. Phytochemistry 

34, 1453:1466. 

Cretich M, Damin F, Pirri G, Chiari M. 2006. Protein and peptide arrays: recent trends and new directions. 

Biomol Eng 23, 77-88. 

Gordus  A,  Mc  Beath  G.  2006.  Circumventing  the  Problems  Caused  by  Protein  Diversity  in  Microarrays: 

Implications for Protein Interaction Networks. J. Am. Chem. Soc. 128, 13668-13669. 

Haab BB, Dunham MJ, Brown PO. 2001. Protein microarrays for highly parallel detection and quantitation 

of specific proteins and antibodies in complex solutions. Genome Biol 2(2):Research0004.1-0004.13. 

Krizkova-Kudlikova  I,  Hubert  J,  Erban  T,  Klaudyova  A,  AulickyR,  Stejskal  V.  2006.  Degradation  of 
cockroach allergen Bla g 2: Does the amount of the allergen increase after partial microbial degradation of 
faeces? 9th Int. Working Conference on stored product Protection, PS9-5-6144, 1108-1113. 

Kudlikova I, Stejskal V, Hyblova J, Chalupnikova J, Hubert J. 2004. Polyclonal antibodies for detection of 

flour mite Acarus siro (Acari:Acaridida). Phytophaga, 14, 711-720.  

42  

  
 
 
Kusnezow  W,  Jacob  A,  Walijew  A,  Diehl  F,  Hoheisel  JD,  2003.  Antibody  microarrays:  An  evaluation  of 

production parameters. Proteomics 3, 254–264. 

Lee CS, Kim BG. 2002. Improvement of protein stability in protein microarray. Biotechnol. Lett 24, 839–

844. 

Linthorst HJM. 1991. Pathogenesis-related proteins in plants. Crit Rev Plant Sci 10, 123-150. 

Lison  P,  Rodrigo  I,  Conejero  V.  2006.  A  novel  function  for  the  cathepsin  D  inhibitor  in  tomato.  Plant 

Physiol 142, 1329-1339. 

Marchetti  S,  Delledonne  M,  Fogher  C,  Chiaba´  C,  Chiesa  A,  Savazzini  F,  Giordano  A.2000.  Soybean 
Kunitz,  C-II  and  PI-IV  inhibitor  genes  confer  different  levels  of  insect  resistance  to  tobacco  and  potato 
transgenic plants. Theor Appl Genet 101, 519-526. 

Mc  Beath  G,  Schreiber  PL.  2000.  Printing  proteins  as  microarrays  for  high-throughput  function 

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Olle EW, Messamore J, Deogracias MP, McClintock SD, Anderson TD, Johnson KJ. 2005. Comparison of 
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43  

  
Luminex technology:  
Multiplex detection using xMAP technology. 

Jan H.W. Bergervoet 1*, Jeroen Peters 1, Jan M. van der Wolf 1, Jose van Beckhoven 1, JamesW. Jacobson 2 

1 Plant Research International B.V. 
Bornsesteeg 65 (Building 122) 
6708PD Wageningen 
The Netherlands 

2 Luminex Corporation 
 12212 Technology Blvd  
Austin, TX  78727 
USA 

*Corresponding author email: jan.bergervoet@wur.nl 

Introduction 
Detection of pathogens in the food and feed production chain needs to be fast, reliable and preferably at low 
cost.  Annually,  millions  of  tests  are  run  by  inspection  services  and  companies  using  a  double  antibody 
sandwich  ELISA  (DAS-ELISA).  In  each  ELISA  test,  generally  only  one  virus  or  bacteria  is  detected, 
requiring  running  a  number  of  assays  in  parallel  and  this  results  in  additional  costs  in  terms  of  labour, 
disposables, chemicals and waste as compared to an assay in which all viruses or bacteria can be targeted 
simultaneously in one sample, namely a multiplex assay. 
Several multiplex assays for plant viruses have been developed in the last few years, based on the detection 
of nucleic acids, but these methods have never been adopted by the inspection services for high throughput 
(HTP)  screening.  These  technologies  require  laborious  and  expensive  RNA  purification  procedures. 
Moreover,  nucleic  acid  based  amplification  procedures  are  prone  to  cross-contamination  and  inhibition, 
resulting in false-positive and false-negative reactions, respectively. 
For  multiplex  detection  of  plant  pathogens,  the  Luminex  xMAP  technology  may  be  a  true  cost-effective 
alternative for ELISA. This technology has proven its value already for high throughput multiplex screening 
in  a  number  of  fields  such  as  human  diagnostics  (Kellar  and  Douglass,  2003)  and  food  microbiology 
(Dunbar et al., 2003) by using protein and nucleic acid based techniques.  
In  the  xMAP  microsphere  immunoassays  (MIA),  antibody-coated  beads  are  added  to  the  samples  after 
transfer  to  a  microtiter  plate  and  this  mixture  is  incubated.  Subsequently  the  secondary  antibodies 
conjugated with a reporter fluorochrome are added and measured on a Luminex 100 ST. Up to 100 different 
bead sets are available which can be analyzed simultaneously.  
The workflow of this technology is comparable to ELISA; after sample preparation (Figure 1.1) the samples 
are  transferred  to  a  microtiter  plate  (Figure  1.2)  and  the  pre-mixed  beads  coated  with  the  antibodies  are 
added  (Figure  1.3)  and  incubated.  The  specific  bead-antibody  combination  will  recognize  the  antigens(s) 
(Figure 1.4). After washing the secondary antibodies are added and these will bind also to the antigens. After 
incubation the samples are measured (Figure 1.5) and the obtained results are graphically displayed (Figure 
1.6). The signals of each bead set are shown in a separate histogram. In general the whole procedure can be 
performed in less than 2 hours. 

44  

  
 
 
 
 
 
 
 
 
 
 
 
 
 
1 

5 

2 

3 

4 

6 

Figure 1: Schematic overview of the MIA workflow: 1. Sample preparation, 2. Sample 
transfer,  3.  Addition  of  premixed  bead-antibody  combination  to  the  samples,  4. 
Antibodies binding the antigens, 5. Performing of the measurement, 6. Display of the 
results.  

Material and methods 
Bead coupling was performed as described by (Peters et al., 2006). Sample extraction and wash, no-wash 
assays were performed as described by (Bergervoet et al., 2007). The major difference between a wash and 
no-wash procedure is the bead type used; for the no wash assay the standard beads are used, whereas for the 
wash assay paramagnetic beads are used. These paramagnetic beads allow the use of wash steps at different 

Figure  2:  Detection  of  Potato  virus  X  (PVX)  in  a 
simplex  microsphere  immunoassay.  Naturally  in-
fected leaf extracts of PVY, PVX, PLRV, PVM: Po-
tato  Virus  M;  PVV:  Potato  Virus  V;  PVA:  Potato 
Virus  A  and  PBRSV  (Potato  Black  RingSpot  Virus 
were  tested).  Results  obtained  with  a  standard  bead 
set are shown in the left panel; results obtained with 
paramagnetic beads are shown in the right panel. De-
tection  signals  exceeding  twice  the  background  val-
ues (shaded areas) are considered as a positive signal. 

45  

Figure  3:  DAS–ELISA  detection  of  Potato  Virus  Y 
(PVY (cid:132)), Potato Virus X (PVX (cid:132)) and Ppotato Lea-
fRoll  Virus  (PLRV  (cid:132)).  Naturally  infected  leaf  ex-
tracts and positive controls containing partially puri-
fied  virus  material  were  tested.  Detection  signals 
exceeding three times the background value (shaded 
areas) are considered as a positive signal (n=3). 

  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
points in the assay and in this way material which could obstruct the sample probe, or could interfere with 
the assay otherwise, can be removed. 

Results  
Some  types  of  plant  material  are  prone  to  cause  problems  using  the  Luminex  analyzer  because  of  probe 
clogging.  When  this  happens,  the  use  of  paramagnetic  beads  could  avoid  this  problem as  washing  steps 
could be included. Comparison of the standard beads versus the magnetic beads revealed that the signal to 
noise ratio improved. The average non specific signal strength which was of approx. 200 MFI using standard 
beads (Figure 2, left panel) decreased to approx. 50 MFI using paramagnetic beads (Figure 2, right panel).  
When  compared  to  the  standard  DAS  –  ELISA  as  performed  by  the  Dutch  inspection  services,  the  MIA 
assay performed comparably (Figure 3). Results of the ELISA (Figure 3, panel PVY, PVX and PLRV) show 
that all viruses were detected correctly and that the negative control is below the background level. For the 
MIA, the results were comparable, all viruses were correctly detected and the negative control was below 
the background level (Figure 3, MIA panel). Furthermore, no false positive or negative were observed.  

Conclusions 
MIA offers a number of advantages when compared to ELISA. In MIA, a large number of beads (replicates) 
are measured per sample (typically 100–200), whereas for ELISA, only a few replicates per sample are used 
at maximum. This higher number results in an improved precision compared to ELISA. Another advantage 
of MIA method is speed; a complete MIA can be finished in approximately 2 hours, including the time used 
by the Luminex analyzer (approx. 30 min per 96-well plate), while the standard procedure for detection of 
potato  viruses  by  DAS-ELISA  is  completed  in  16  hours.  Furthermore,  the  viruses  can  be  detected  in  a 
multiplex  assay,  which  reduces  sample  preparation  time  and  requires  less  sample  material,  reagents  and 
disposables. The multiplex MIA can also be extended to other potato viruses, thus increasing the benefit of 
MIA compared to ELISA.  
For  the  comparison  of  standard  beads  versus  paramagnetic  beads,  it  can  be  concluded  that  in  general  the 
paramagnetic  beads  are  superior  to  standard  beads  (used  in  a  no  wash  assay),  in  respect  to  an  improved 
signal to noise ratio and the avoidance of probe clogging. However, if probe clogging is not a problem, the 
no-wash assay has still an advantage above the paramagnetic beads because fewer steps are involved. 

References 
Bergervoet  JHW,  Peters  J,  van  Beckhoven  JRCM,  van  den  Bovenkamp  GW,  Jacobson,  JW  and  van  der 

Wolf JM. 2007. Multiplex Microsphere Immuno-detection of Potato Virus Y, X and PLRV. submitted 

Dunbar  SA,  Vander  Zee  CA,  Oliver  KG,  Karem  KL  and  Jacobson  JW.  2003.  Quantitative,  multiplexed 
detection  of  bacterial  pathogens:  DNA  and  protein  applications  of  the  Luminex  LabMAP™  system. 
Journal of Microbiological Methods 53, 245-252. 

Kellar  KL  and  Douglass  JP,  2003.  Multiplexed  microsphere-based  flow  cytometric  immunoassays  for 

human cytokines. Journal of Immunological Methods 279, 277-285. 

Peters J, Sledz W, Bergervoet JHW and Van der Wolf JM. 2006. An enrichment microsphere immunoassay 
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European Journal of Plant Pathology. 

46  

  
 
 
 
Plant, animal and food pathogens analyzed using genome-wide microarrays: 
towards improved diagnostics and control of bacterial pathogens 

Marja Aittamaa1, Petri Auvinen2, Katja Hinderink3, Katri Kiviniemi3, Hannu Korkeala3, Outi Leppäranta4, 
Miia Lindström3, Laura Mattinen1, Vesa Myllys4, Mari Nevas3, Suvi Nykäsenoja4, Minna Pirhonen1, Leila 
Rantala4, Panu Somervuo1,2,3,4 and Jari P.T. Valkonen1* 

1  Department  of  Applied  Biology,  PO  Box  27,  FIN-
00014 University of Helsinki, Finland 
2 Institute of Biotechnology, PO Box 56, FIN-00014 
University of Helsinki, Finland 

3 Department  of  Food  and  Environmental  Hygiene, 
PO  Box  66,  FIN-00014  University  of  Helsinki, 
Finland 
4  Finnish  Food  Safety  Authority  Evira,  Mustialan-

*Corresponding author: e-mail: jari.valkonen@helsinki.fi 

Abstract 
The  aim  of  the  study  was  to  utilize  whole-genome  sequence data of the important bacterial pathogens for 
designing microarrays and use them to compare genomic differences of the biologically different strains of 
bacterial pathogens of potato, E. coli infecting cattle and humans, and Clostridium botulinum causing food 
poisoning. All assays resulted in data that are useful in bacterial diagnostics.  

Introduction 
There are many bacterial pathogens that cause severe yield losses in cultivated crops and the harvested yield, 
are transmitted from animals to humans, or cause food poisoning and endanger human and animal health. 
Potatoes  are  affected  by  many  bacterial  pathogens  that  are  widely  distributed  in  potato  production  areas 
(Lehtonen  et  al.,  2004;  Van  der  Wolf  and  De  Boer,  2007).  Clostridium  botulinum  is  a  dangerous  food 
pathogen  that  produces  a  potentially  lethal,  paralytic  neurotoxin  during  its  growth.  Based  on  their 
physiology, C. botulinum strains are divided into four distinct groups I-IV, with groups I (proteolytic) and II 
(nonproteolytic)  being  human  pathogens  and  producing  neurotoxin  types  A,  B,  E,  or  F  (Lindström  and 
Korkeala,  2006;  Sebaihia  et  al.,  2007).  Enterohaemorrhagic  Escherichia  coli  (EHEC)  is  a  food  and 
waterborne pathogen with a very low infective dose. EHEC infection poses a significant risk of two serious 
complications, hemorrhagic colitis and haemolytic uremic syndrome. Production of Shiga toxins (stx) and 
adherence-mediating factor intimin (eaeA) are considered important in the pathogenesis (Karch et al., 1999; 
O’Connel, 2007). Most EHEC outbreaks have been caused by E. coli O157 strains, and their main reservoir 
is considered to be cattle (Lahti et al., 2002). 
The  whole  genomes  of  many  bacterial  pathogens  have  been  recently  resolved.  Owing  to  the  continuously 
improving efficiency of the DNA sequencing technologies, genomes of bacterial species and strains will be 
sequenced  in  increasing  numbers  and  the  accumulation  of  whole-genome  data  will  accelerate.  Hence,  the 
whole-genome  sequences  of  bacteria  will  become  the  most  important  basis  for  development  of  molecular 
diagnostic tools. The aim of this study was to utilize the currently available whole-genome sequence data of 
important bacterial pathogens in plants, animals and food for comparison of the biologically differing strains 
to obtain data that would be useful in bacterial diagnostics.  

47  

  
 
 
 
 
 
 
 
 
 
 
 
Materials and methods 

DNA isolation and labelling 
Total DNA was extracted with the appropriate methods optimised for the different bacterial species studied. 
DNA was quantified and fluorescently labelled with Cy3 or Cy5 (GE Healthcare).  

Potato pathogens 
Whole-genome sequences of Erwinia carotovora subsp. atroseptica (Eca) (recently reclassified as Pectobacterium atrosepticum), 
Streptomyces scabies (Ss) and Clavibacter michiganensis ssp. sepedonicus (Cms) were used. Probes were designed for all genes 
of  Eca,  ca. 50  % of  the genes  of Ss,  and  ca.  30 % of  the  genes of  Cms.  Probes were also designed for  the  16S-23S  intergenic 
region  sequences,  the  nip  genes  of  Erwinia  species  encoding  a  necrosis-inducing  protein  (Mattinen  et  al.,  2004),  and  the 
pathogenicity island of Streptomyces turgidiscabies (St) (Kers et al., 2005). All sequence data except those for the nip genes and 
those  for  Ss  and  Cms  whole-genome  sequences  were  obtained  from  Genbank.  Probes  were  designed  using  OligoArray  2.0 
software (Rouillard et al. 2003). Melting temperature (Tm) range was calculated for 40-nucleotide (nt) probes whose GC content 
was 45-55 %. The resulting Tm range (82-90°C) was used as a design criterion for all probes on the array. Subsequently, a poly(T) 
linker was added to each probe to reach the final length of 60 nt. The resultant 60-mer probes were synthesized in situ on Agilent 
8*15K custom arrays (Agilent). Each array contained 9676 unique probes. Several strains of each bacterial species were tested. 

EHEC bacteria 
The OciChip E. coli O157 Arrays (Ocimum Biosolutions) were used for the comparison of gene content of 
pathogenic and non-pathogenic E. coli strains. Twenty-one E. coli strains were selected for the studies, of 
which 19 belonged to serogroup O157.  

C. botulinum 
A total of 60 strains of C. botulinum from various sources were compared on the DNA microarray (Institute 
of Food Research, IFR; Norwich, UK) based on the genome of the group I, type A strain of C. botulinum 
(ATCC 3502) (Sebaihia et al., 2007). The array contains 3433 probes for chromosomal DNA, 18 probes to 
plasmid genes and also probes for genes associated with neurotoxicity.  

Data analysis 
Hybridizations  were  carried  out  using  the  microarray  manufacturers’  protocols  and  repeated  two  or  more 
times.  Microarray  slides  were  scanned  with  the  GenePix  4200  AL  scanner  (Axon  Instruments).  Image 
analysis  and  spot  segmentation  were  done  with  GenePix  Pro  6.0  software.  Spot  intensity  values  were 
investigated  using  the  kernel  density  method  for  the  data  on  potato  pathogens  (see  Results)  since  no 
reference  samples  were  used.  In  the  experiments  on  the  EHEC  bacteria  and  C.  botulinum,  normalized 
logarithmic ratios were computed between the test and reference sample for each probe on the array.  

Results and discussion 

Comparison of bacterial pathogens of potato  
The whole-genome information was utilized in probe design to minimize the possibility that a probe would 
hybridize to more than a single locus in the bacterial species. When investigating the shape of the histogram 
based on logarithmic signal intensities from an array, usually three peaks could be clearly distinguished. It 
allowed classifying the probes into three categories: I) no signal, II) non-specific probe (weak signal), and 
III)  specific  probe  (strong  signal).  Analysis  and  classification  of  the  unknown  sample  was  based  on  the 
probes belonging to the third category. An array-specific threshold to identify probes belonging to this group 
was determined for each array/hybridization separately (Figure 1). The closely related species, e.g., Eca, E. 
carotovora  subsp.  carotovora  (Ecc)  (currently  Pectobacterium  carotovorum)  and  E.  chrysanthemi  (Ech) 
(recently  reclassified  to  Dickey  spp.),  and  on  the  other  hand  Ss  and  St,  were  detected  with  many  same 
probes.  However,  for  each  species  and  subspecies  tested,  a  characteristic  pattern  of  signals  was  observed 
which  identified  the  bacterium.  Furthermore,  the  probes  for  ITS  and  nip  gene  provided  species  and 
subspecies-specific signals, which further confirmed identification (Figure 1).  

48  

  
 
 
Figure 1: Logarithmic (log2) signal intensities obtained following hybridization with the genomic 
DNA of Erwinia carotovora subsp. atroseptica (Eca). The signals of the 9676 probes on the array 
are grouped according to the species and specific genomic regions or genes used for probe design. 
The array-specific threshold signal intensity to identify probes belonging to category III (‘specific’ 
signals)  which  were  used  for  comparison  with  data  obtained  with  other  species  is  shown  by  the 
dotted line. The highest and most specific signals for Eca were obtained with probes targeting the 
Nip genes (four different lengths of probes tested) and probes targeting various other genes of Eca 
(circled signals). 

0
0
6
D
O

2

1,6

1,2

0,8

0,4

0

0 mM 0.05 mM 0.1 mM

0 mM 0.05 mM 0.1 mM

BI

BII

Figure  2:  Optical  density  measurement  after  overnight  growth  of  two  genetically 
different  strains  (BI  and  BII)  of  C.  botulinum  type  B  (Group  I)  in  liquid  medium 
with different concentrations of sodium arsenite. 

Genomic comparison of Clostridium botulinum with DNA microarrays 
A  total  of  60  C.  botulinum  strains  from  various  sources  were  compared.  The  preliminary  analysis  of  data 
showed  significant  differences  between  groups  I  and  II  of  C.  botulinum,  confirming  the  previous 
phylogenetic studies suggesting that the two groups have distinct genetic backgrounds. The data enabled the 
development of a straightforward PCR assay to distinguish between the two groups (Dahlsten et al. 2006), 
which  was  a  major  improvement  in  the  diagnostics  of  C.  botulinum.  Within  C.  botulinum  group  I,  strains 

49  

  
 
producing  the  same  toxin  type  were  mainly  grouped  together.  Moreover,  two  large  clusters  of  nearly 
identical  group  I  type  B  strains  of  Nordic  origin  were  observed.  These  two  clusters  differed  by  their 
resistance  to  arsenic  (Figure  2)  and  cadmium.  Corresponding  differences  were  observed  in  physiological 
tests in nutrient broth (Lindström et al., 2007). A more detailed analysis of these results is ongoing.  

Microarray-based studies on gene content of Escherichia coli O157 
Prior  to  microarray  experiments,  the  tested  strains  were  divided  into  four  groups:  I)  six  strains  associated 
with human disease, II) six strains with eae and stx genes, III) five strains with eae but no stx genes, and IV) 
four non-EHEC strains. The microarray profile-based similarities between strains were compared with the 
existing  classification  of  strains  described  above.  Group  IV  differed  from  all  other  groups,  which  was 
explained by the absence of ca. 100 genes. Differences between groups I, II, and III were smaller and there 
was variation within each group. A set of genes, which best explained the differences between pathogenic 
(group I) and possibly pathogenic (group III) strains, was selected using statistical analysis. Based on this set 
of genes, the strains belonging to group II were computed against the strains of group I and III, in order to 
categorize the group II strains as pathogenic or possibly pathogenic (Figure 3).  

Figure 3: Dendrogram showing relations between the studied  E. coli strains based on a selected 
set  of  genes  (692  genes;  Wilcoxon  Mann-Whitney  Test,  p  <  0.05).  The  first  number  of  each 
sample  refers  to  the  original  classification  of  strains  in  four  groups  (1-4  =  I-IV,  respectively). 
Slight  differences  of  the  results  from  repeated  experiments  are  reflected  by  the  same  strain 
appearing  multiple  times  in  the  dendrogram;  however,  the  placement  of  the  strains  within  the 
main groups was stable over repeated experiments. On the right side, strains of group IV clustered 
together. The strains of groups I and III mainly formed their own clusters, but the strains of group 
II were distributed within these two main clusters. In the middle of the tree, three strains from the 
three  groups  (strains  E658,  E23,  and  E169)  presented  similar  hybridisation  patterns  for  the 
selected set of genes and therefore clustered together.  

Conclusions 
The  genome-wide  microarray  analysis  was  found  to  be  a  powerful  tool  for  detecting  genetic  differences 
between  the  biologically  different  strains  of  bacterial  pathogens.  A  large  amount  of  scientifically  novel 
information was obtained. The data from all pathogen groups had also diagnostic value. The array and data 
obtained on potato pathogens were of direct diagnostic use. The results obtained on C. botulinum were used 
for making a novel PCR-based test. The microarray analysis of EHEC bacteria revealed 100 candidate genes 
associated  with  pathogenicity.  In  future  studies,  the  results  and  experience  will  be  used  to  design  new 
microarrays with higher resolution on genetic differences. The methods for data analysis will also be further 
developed taking into consideration the particular challenges of efficient and cost-effective diagnostic use of 
microarrays.  

50  

  
Acknowledgements 
Financial  support  from  the  Ministry  of  Agriculture  and  Forestry  (grant  3326/501/2002)  and  the  Finnish 
Funding Agency for Technology and Innovation (Tekes) (grant 2431/31/04) is gratefully acknowledged. 

References 
Dahlsten  E,  Lindström  M,  Somervuo  P,  and  Korkeala  H.  2006.  PCR  assay  for  differentiating  between 
group I (proteolytic) and group II (nonproteolytic) strains of Clostridium botulinum. Interagency Botulism 
Research Coordinating Committee Meeting, Silver Spring, Maryland, USA, p. 77. 

Karch H, Schubert S, Zhang D, Zhang W, Schmidt H, Ölschläger T and Hacker J. 1999. A genomic island, 
termed high-pathogenicity island, is present in certain non-O157 Shiga toxin-producing Escherichia coli 
clonal lineages. Infection and Immunity 67, 5994-6001. 

Kers JA, Cameron CD, Joshi MV, Burkhalid RA, Morello JE, Wach MJ, Gibson DM and Loria R. 2005 A 
large,  mobile  pathogenicity  island  confers  plant  pathogenicity  on  Streptomyces  species.  Molecular 
Microbiology 55, 1025-1033.  

Lahti  E,  Eklund  M,  Ruutu  P,  Siitonen  A,  Rantala  L,  Nuorti  P,  and  Honkanen-Buzalski  T.  2002.  Use  of 
phenotyping  and  genotyping  to  verify  transmission  of  Escherichia  coli  O157:  H7  from  dairy  farms. 
European Journal of Clinical Microbiology 21, 189-195 

Lindström  M,  Kiviniemi  K,  Hinderink  K,  Somervuo  P,  Nevas  M,  Auvinen  P,  Carter  A,  Mason  D,  Peck 
MW,  and  Korkeala  H.  2007.  Genomic  DNA  microarray  analysis  of  Nordic  group  I  (proteolytic) 
Clostridium botulinum type B strains reveals two clusters with distinct resistance to arsenic and cadmium. 
(Submitted). 

Lindström M, and Korkeala H. 2006. Laboratory diagnostics of botulism. Clinical Microbiology Reviews 

19, 298-314. 

Lehtonen MJ, Rantala H, Kreuze JF, Bång H, Kuisma L, Koski P, Virtanen E, Vihlman K. and Valkonen 
JPT.  2004.  Occurrence  and  survival  of  potato  scab  pathogens  (Streptomyces  species)  on  tuber  lesions: 
quick diagnosis based on a PCR-based assay. Plant Pathology 53, 280-287. 

Mattinen  L,  Tshuikina  M,  Mäe  A,  and  Pirhonen  M.  2004.  Identification  and  characterization  of  Nip, 
necrosis-inducing  virulence  protein  of  Erwinia  carotovora subsp.  carotovora.  Molecular  Plant–Microbe 
Interaction 17, 1366–1375. 

O'Connell D. 2007. Bacterial physiology: Stuck on you... Nature Reviews Microbiology 5, 571. 

Rouillard J-M, Zuker M, and Gulari, E. 2003. OligoArray 2.0: design of oligonucleotide probes for DNA 

microarrays using a thermodynamic approach. Nucleic Acids Research 31, 3057-3062. 

Sebaihia  M,  Peck  MW,  Minton  NP,  Thomson  NR,  Holden  MT,  Mitchell  WJ,  Carter  AT,  Bentley  SD, 
Mason DR, Crossman L, Paul CJ, Ivens A, Wells-Bennik MH, Davis IJ, Cerdeno-Tarraga AM, Churcher 
C, Quail MA, Chillingworth T, Feltwell T, Fraser A, Goodhead I, Hance Z, Jagels K, Larke N, Maddison 
M, Moule S, Mungall K, Norbertczak H, Rabbinowitsch E, Sanders M, Simmonds M, White B, Whithead 
S, and Parkhill J. 2007. Genome sequence of a proteolytic (Group I) Clostridium botulinum strain Hall A 
and comparative analysis of the clostridial genomes. Genome Research 17, 1082-1092. 

Van  der  Wolf,  JM  and  De  BoerSH.  2007.  Bacterial  pathogens  of  potato.  In:  Potato  Biology  and 
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51  

  
Analysis of methanotroph community structure by a functional 
oligonucleotide MDM : potential and shortcomings 

Levente Bodrossy* and Angela Sessitsch 

Department of Bioresources/Microbiology  
ARC Seibersdorf research GmbH 
A-2444 Seibersdorf 
Austria 

*Corresponding author e-mail: Levente.Bodrossy@arcs.ac.at 

Introduction 
Microbial  diagnostic  microarrays  (MDMs)  based  on  functional  genes  (also  termed  functional  gene  arrays, 
FGAs)  consist  of  nucleic  acid  probes  targeted  against  functional  genes  –  i.e.,  genes  conferring  a  specific 
function to the microorganisms carrying them, such as nitrogen metabolism or methane oxidation (Wu et al., 
2001; Taroncher-Oldenburget al., 2003; Bodrossy et al., 2003). The probes applied may be short (typically 
15-30mer) (Bodrossy et al., 2003; Stralis-Pavese et al., 2004) and long (typically 40-70mer) (Denef et al., 
2003; Taroncher-Oldenburg et al., 2003; Tiquia et al., 2004) oligonucleotides as well as PCR amplified gene 
fragments  (Wu  et  al.,  2001;  Cho  and  Tiedje,  2001;  Dennis  et  al.,  2003).  Microarrays  based  on  short 
oligonucleotides are bound to be applied in combination with a consensus PCR amplifying the target gene 
from a wide range of (optimally all) microbes (Loy et al., 2002; Wilson et al., 2002; Bodrossy et al., 2003; 
Bonch-Osmolovskaya et al., 2003). The PCR amplification is required to narrow down the complexity of the 
labelled target allowing detectable signal.  
Functional  oligonucleotide  MDMs  employing  short  oligos  enable  a  phylogenetic  investigation  of  a 
functionally  defined  microbial  community.  The  phylogeny  in  this  case  is  that  of  the  functional  gene,  the 
validity  of  which  needs  to  be  investigated  in  advance  (i.e.  compared  to  that  based  on  16S  or  other 
established phylogenetic markers). The advantage of this type of MDM is that it focuses the labelling onto 
this defined group of bacteria, enabling the detection of otherwise minor microbial groups. Furthermore, the 
presence and existence of so far unknown members of a functional group may also be indicated (as opposed 
to rRNA based microarrays). 
Long oligonucleotide and gene fragment MDMs enable a more limited resolution, thus signals arising from 
bacteria carrying closely related proteins are not readily differentiated (Li, et al., 2005). On the other hand, a 
range of genes, encoding proteins with related functions, can easily be targeted on a single array which in 
turn can deliver information on the metabolic potential of the entire microbial community. 

Material and Method 

Oligonucleotide probe design 
Database  and  phylogenetic  trees  were  constructed  and  oligonucleotide  probes  were  designed  using  the 
phylogenetic  software  package  ARB  (Strunk  et  al.,  2000).  A  comprehensive  database  containing  all 
published  pmoA/amoA  and  related  sequences,  as  well  as  many  unpublished  ones  was  established. 
Alignments were made using Old Aligner function in ARB_EDIT. Neighbor joining DNA and protein trees 
were constructed and used to guide the probe design process. Probes were designed using the Probe Design 
and Probe Match functions, accessing a PT-server database created from the ARB database. Outputs of the 
Probe  Match  function  were  imported  into  CalcOligo  2.03  (www.calcoligo.org).  CalcOligo  was  used  to 
create  an  Excel  table  indicating  predicted  melting  temperatures  (based  on  the  nearest  neighbor  model  and 
SantaLucia  parameters),  length  and  GC  content  of  the  probes  and  the  number  of  weighted  mismatches 

52  

  
 
 
 
 
 
 
between each probe-target pair. Nearest neighbor Tm values were calculated with concentration settings of 
250  nM  for  oligonucleotide  and  50  mM  for  Na+.  Factors  for  weighing  mismatches  in  CalcOligo  were  as 
follows: position 5' 1st: 0.3; 5' 2nd: 0.6; 5' 3rd: 1.0; 3' 1st: 0.3; 3' 2nd: 0.8; 3' 3rd: 1.1; all other positions: 1.2; 
basepairs:  dArC:  1.2;  dTrC:  1.2;  dGrU:  0.7;  dTrG:  0.4;  all  other  mismatched  basepairs  1.0.  Probe-target 
pairs  with  weighted  mismatch  values  of  up  to  1.5  were  expected  to  yield  positive hybridisation  under  the 
conditions applied. 

Microarray preparation 
Oligonucleotides for immobilization were custom synthesized (VBC Genomics, Vienna, Austria) with a 5' 
NH2 group, followed by a C12 spacer and five thymidines residues preceding the probe sequence. A 384 well 
flat bottom plate was prepared with 30 μl of 50 μM oligonucleotide solutions in 50% (v/v) DMSO. Samples 
were  spotted  with  an  OmniGrid  spotter  (1  TeleChem  SMP3  pin)  at  50%  relative  humidity  (using  the 
humidity controller of the spotter) and 22°C, onto silylated slides (with aldehyde chemistry, Cel Associates, 
Houston). Arrays were always spotted in triplicate to enable a statistical correction for errors. Spotted slides 
were incubated overnight at room temperature (22 °C) at <30% relative humidity, rinsed twice in 0.2% (w/
v)  SDS  for  2  min  at  room  temperature  with  vigorous  agitation  to remove  the  unbound  DNA.  Slides  were 
then  rinsed  twice  in  distilled  water  (dH2O)  for  2  min  at  room  temperature  with  vigorous  agitation, 
transferred into dH2O, preheated to 95-100°C for 2 min, and allowed to cool at room temperature (~5 min). 
Slides were treated in a freshly (immediately before use) prepared sodium borohydride solution for 5 min at 
room temperature to reduce free aldehydes. Preparation of the sodium borohydride solution: 0.5 g NaBH4 
was  dissolved  in  150  ml  phosphate  buffered  saline  (PBS;  8g  NaCl,  0.2g  KCl,  1.44g  Na2HPO4,  0.24g 
KH2PO4, in 1000 ml H2O, pH 7.4, autoclaved) then 44 ml of 100% ethanol was added to reduce bubbling. 
Slides were rinsed three times in 0.2% (w/v) SDS and once in dH2O for 1 min each at room temperature. 
Finally,  slides  were  dried  individually  using  an  airgun  fitted  with  a  cotton  wool  filter  inside  (to  keep  oil 
microdroplets away from the slide surface). Dried slides were stored at room temperature in the dark prior to 
use. 

Environmental DNA preparation 
DNA was prepared from soil samples using a method based on the FastDNA spin kit for soil (QBiogene). 
0.3  g  soil  and  780  μl  lysis  buffer  (100  mM  NaPO4  pH  7.0;  1%  CTAB;  1.5  M  NaCl;  5  mg/ml  lysozyme 
(added right before use)) was added into a Multimix FastPrep tube and incubated at 37°C for 30 minutes. 
MT buffer (122 μl) was added and tubes were shaken in the FastPrep instrument for 30 seconds at 5.5 m/s. 
Samples were centrifuged for 15 min at 12,000 x g and 700 μl supernatant were collected. 500 μl lysis buffer 
and  50  μl  MT  buffer  were  added  to  the  FastPrep  tubes,  extraction  was  repeated  and  a  second  700  μl  of 
supernatant was transferred into separate Eppendorf tubes. At this step, 2 x 700 μl supernatant was obtained 
from  each  sample.  5  μl  of  10  mg/ml  freshly  made  proteinase  K  was  added  to  each  tube.  Tubes  were 
incubated at 65 °C for 30 min. Samples were extracted with phenol-chloroform-isoamyl alcohol (25:24:1), 
followed by a chloroform-isoamyl alcohol (24:1) extraction. 125 μl of 7.5 M potassium acetate was added, 
samples  were  incubated  on  ice  for  5  min  and  then  centrifuged  at  12,000  x  g  for  10  min.  Supernatants 
(2x600 μl per soil sample) were transferred to new tubes, 700 μl Binding Matrix was added and tubes were 
mixed  for  5  min  in  an  Eppendorf  shaker.  Binding  Matrix  with  bound  DNA  was  pelleted  by  1  min 
centrifugation at 12,000 x g, supernatant was discarded and pellet was resuspended in 500 μl Wash Buffer. 
The resulting suspension was added into a Spinfilter, and centrifuged for 1 min at 10000 rpm. Eluate was 
discarded  and  pellet  was  washed  again  in  500  μl  Wash  Buffer.  After  discarding  the  second  eluate,  the 
Spinfilter was centrifuged for another 10 seconds to dry the pellet. The filter was taken into a new tube, 200 
μl of TE pH 8.0 was added, and after 1 min incubation at room temperature centrifuged for 1 min at 12,000 
x g. The eluate collected in the catch tube contained the purified DNA which was stored at -20 °C until use. 

Target preparation 
(5'-
The  pmoA/amoA  genes  were 
(5'-
GGBGACTGGGACTTCTGG-3') 
and 
T A A T A C G A C T C A C T A T A G C C G G M G C A A C G T C Y T T A C C - 3 ' )  
( 5 ' -
TAATACGACTCACTATAGGAASGCNGAGAAGAASGC-3') where B=(C/G/T), M=(A/C), Y=(C/T), S=
(C/G)  and  N=(A/C/G/T).  Primers  T7-mb661  and  T7-A682  were  specific  for  methanotrophs  and  for 

forward  primer  pmoA189 
reverse  primers  T7-mb661 

amplified  using 
either  one  of 

o r   T 7 - A 6 8 2  

the 
the 

53  

  
5 '  

3 '  

a n d  

h e a d  

r e g i o n s  

n o n - r e l a t e d  

( T 3 c - p m o A 1 8 9  
and  T7c-mb661 

methanotrophs / AOBs / homologous genes from environmental libraries, respectively. The reverse primers 
contained the T7 promoter site (5'-TAATACGACTCACTATAG-3') at their 5' end, which enabled T7 RNA 
polymerase mediated in vitro transcription using the PCR products as templates. For each target, three PCR 
reactions of 50 μl volume each, consisting of 1x PCR buffer, 1.5mM MgCl2, 50nM for each four dNTPs, 15 
pmol of both primers, 1 ng genomic/environmental DNA or 0.1 ng cloned PCR product as template, and 1U 
of Taq polymerase (Invitrogen), were performed in a Hybaid Combi Thermal Reactor TR2 using Taq DNA 
polymerase in accordance with the manufacturer’s instructions. Amplification conditions were: 95°C for 5 
min before template was added; then 32 cycles of: 1 min at 95°C; 1 min at 58 °C for mb661 or at 56 °C for 
pmoA682; 1 min at 72°C ; followed by a final elongation step of 10 min at 72°C. PCR products were pooled 
and  purified  using  the  HighPure  PCR  purification  kit  (Roche  Diagnostics  GmbH,  Mannheim,  Germany), 
according  to  manufacturer's  instructions.  Purified  DNA  was  dissolved  in  ultrapure  water  to  a  DNA 
concentration of 50 ng/μl and stored at -20°C. 
Alternatively, a new, two-step PCR was applied to amplify the pmoA and related genes from environmental 
DNA  samples.  In  the  first  step,  15  cycles  were  carried  out  with  composite  primers  composed  of  pmoA-
- 
s p e c i f i c  
r e g i o n s  
CAGAGATGCAAATTAACCCTCACTAAAGGGNGACTGGGACTTCTGG, 
- 
CCAAGCCTTCTAATACGACTCACTATAGCCGGMGCAACGTCYTTACC). The second step consisted 
of  an  extra  25  cycles  carried  out  with  primers  consisting  of  only  the  head  regions  (T3c  - 
CAGAGATGCAAATTAACCCTCACTAAAG  and  T7c  -  CCAAGCCTTCTAATACGACTCACTATAG). 
Exact  PCR  conditions  were  as  above  with  the  following  modifications.  Only  1.5  pmoles  of  primers  T3c-
pmoA189 and T7c-mb661 were added per 50 μl reaction initially. After the completion of the first 15 cycles, 
15 pmoles of each primer T3c and T7c were added with an extra 1 U of Taq DNA polymerase and a further 
25 cycles were carried out. Annealing temperature was 58 °C throughout the entire PCR protocol. This two-
step PCR was found to yield community composition results comparable to those obtained with traditional 
PCR using the pmoA specific primers only. Further, it was possible to amplify pmoA and related genes from 
samples which did not give rise to PCR products with the traditional method.  
Working under RNAse-free conditions, in vitro transcription was carried out as follows: 8 μl purified PCR 
product  (50  ng/μl),  (50  ng/μl),  4  μl  5x  T7  RNA  polymerase  buffer,  2  μl  DTT  (100  mM),  0.5  μl  RNAsin 
(40 U/μl) (Promega), 1 μl each of ATP, CTP, GTP (10 mM), 0.5 μl UTP (10 mM), 1 μl T7 RNA polymerase 
(40 U/μl) (Gibco BRL) and 1 μl Cy3 or Cy5-UTP (5 mM) were added into a 1.5 ml microcentrifuge tube 
and incubated at 37 °C for 4 hours. RNA was purified immediately using the Qiagen RNeasy kit according 
to manufacturer's instructions. Purified RNA was eluted into 50 μl dH2O. RNA yields and dye incorporation 
rates were measured by spectrophotometry. 
Purified RNA was fragmented by incubating with 10 mM ZnCl2 and 20 mM Tris.Cl (pH 7.4) at 60 °C for 30 
min. Fragmentation was stopped by the addition of 10 mM EDTA pH 8.0 to the reaction and putting it on 
ice.  RNAsin  (1  μl  40  U/μl)  was  added  to  the  fragmented  target.  Fragmented,  labelled  RNA  targets  were 
stored  at  -20  °C.  Length  of  the  fragmented  RNA  target  was  measured  by  running  the  sample  on  an  ABI 
capillary sequencer as well as running on a thin, 2% agarose gel applied onto a standard microscope slide 
and subsequent scanning in a GenePix 4000A scanner. 
Reference targets and artificial target mixtures for testing the quantification potential were synthesized by 
mixing  known  amounts  of  purified  PCR  products  and  carrying  out  in  vitro  transcription  and  target 
fragmentation as described above. 

Hybridisation 
No preHybridisation was done. Hybridisation was carried out in a custom tailored aluminum block used as 
an insert for a temperature controlled Belly Dancer (Stovall Life Sciences Inc., Greensboro, NC, USA) set at 
maximum bending (about 10°). The Hybridisation block was preheated to 55 °C for at least 30 min to allow 
the  temperature  to  stabilize.  An  Eppendorf  incubator  was  also  preheated  to  65  °C.  HybriWell  (Grace 
BioLabs)  stick-on  Hybridisation  chambers  (200 μl  in  volume)  were  applied  onto  the  slides  containing  the 
arrays. Assembled slides were preheated on top of the Hybridisation block. For each Hybridisation, 124 μl 
DEPC-treated water, 2 μl 10% SDS, 4 μl of 50x Denhardt’s reagent (Sigma), 60 μl 20x SSC (3 M sodium 
chloride, 0.3 M sodium citrate, pH 7.0) and 10 μl target RNA (corresponding to about 400 ng RNA) were 
added into a 1.5 ml Eppendorf tube and incubated at 65 °C for 1-15 min. Preheated Hybridisation mixtures 
were applied onto assembled slides via the port in the lower positions (to minimize risk of air bubbles being 

54  

  
trapped  within  the  chamber).  Chambers  were  sealed  with  seal  spots  (Grace  BioLabs)  and  incubated 
overnight at 55 °C at 30-40 rpm circulation and maximum bending. 
Following  Hybridisation,  HybriWell  chambers  were  removed  individually  and  slides  were  immersed 
immediately into 2xSSC, 0.1% (w/v) SDS at room temperature (22 °C). Slides were washed by shaking at 
room temperature for 5 min in 2xSSC, 0.1% (w/v) SDS; twice for 5 min in 0.2x SSC and finally for 5 min in 
0.1x SSC. Slides were dried individually using an airgun with a cotton wool filter inside. Slides were stored 
at room temperature in the dark and scanned the same day. 

Scanning and data analysis 
Hybridized  slides  were  scanned  at  3  lines  to  average,  at  10  μm  resolution  with  a  GenePix  4000A  laser 
scanner  (Axon,  Foster  City,  Calif.,  USA)  at  wavelengths  of  532  nm  and  635  nm  for  Cy3  and  Cy5, 
respectively.  Fluorescent  images  were  captured  as  multi-layer  tiff  images  and  analyzed  with  the  GenePix 
Pro 3.0 software (Axon). Microsoft Excel was used for statistical analysis and presentation of results. 
Results  were  normalized  to  a  positive  control.  Hybridisation  signal  for  each  probe  was  expressed  as 
percentage of the signal (median of signal minus background) of the positive control probe mtrof173 on the 
same array. As each slide contained triplicate arrays, normalized signal intensities of the triplicate spots on a 
slide were used to determine average results and standard deviations. Several probes produced non-specific 
background signal up to 3% of their maximum signal (obtained with perfect match targets). Hybridisation 
between  a  probe  and  a  target  was  thus  considered  positive  if  the  signal  was  at  least  5%  of  the  strongest 
signal  obtained  for  that  probe  with  the  validation  set  of  reference  strains  /  clones.  For  probes,  where  no 
perfect  match  reference  target  was  available  or  the  strongest  signal  was  less,  than  60  (%  of  the  signal 
obtained  for  mtrof173),  this  reference  value  was  arbitrarily  set  to  60.  This  was  found  to  minimize  false 
positive calls while not creating any false negative call. 
Even though no dedicated negative controls were applied, for each individual hybridisation over 70% of all 
probes present on the array were negative controls. 

Results and discussion 

An example – the pmoA microarray for methanotrophs 
The  gene  encoding  the  particulate  methane  monooxygenase  gene  (pmoA)  –  the  key  enzyme  in  methane 
oxidation  –  was  chosen  for  the  development  of  a  microarray  (Bodrossy  et  al.,  2003)  to  identify 
methanotrophs,  bacteria  which  are  capable  of  utilizing  methane  as  their  sole  source  of  carbon  and  energy 
(Murrell et al. 1998; Hanson and Hanson 1996). These bacteria therefore play an essential role in mitigating 
the greenhouse effect by metabolizing most of the methane produced in diverse soil, sediment and aqueous 
environments,  from  landfill  site  cover  soils  to  upland  forest  soils,  from  freshwater  lake  to  deep  sea 
sediments,  from  arctic  seawater  to  hot  springs.  Understanding  the  factors  influencing  their  diversity  and 
activity is of high importance in order to adapt human activities to maintain and protect optimal microbial 
methane oxidation. 
The  particulate  membrane  bound  MMO  (pMMO)  is  present  in  all  known  methanotrophs  (Hanson  and 
Hanson, 1996) except for Methylocella (Dedysh et al. 2000). The sequence of pmoA encoding the 27 kDa 
subunit  of  pMMO  reflects  evolutionary  relationships  amongst  pmoA  containing  bacteria  (Holmes  et  al., 
1995).  The  ammonia  monooxygenase  (AMO)  of  autotrophic  ammonia  oxidizing  bacteria  (AOB)  is 
evolutionarily related to pMMO and pmoA has a high degree of identity with amoA, the gene encoding for 
the corresponding subunit of the AMO (McDonald and Murrell 1997). Both pmoA and amoA genes can be 
present in one to three, generally highly similar copies in the genomes of methane and ammonia oxidizers 
(McTavish et al. 1993; Semrau et al. 1995; Stolyar, et al. 2001; Purkhold et al. 2000;Bourne et al. 2001). 
There are genes cloned from environmental samples, distantly related to pmoA/amoA, where it is not clear 
whether the encoded enzyme is oxidizing methane and/or ammonia, or neither of them (Bourne et al., 2001; 
Henckel et al., 2000; Holmes et al., 1999). Our current database, consisting of published sequences, as well 
as unpublished sequences from collaborators and ourselves, consists of over 2300 nearly full-length (470 bp 
or longer) sequences and a further 1000 partial sequences (320-470 bp in length) (Figure 1).  
The  pmoA  microarray  has  been  updated  and  extended  several  times  (i.e.  (Stralis-Pavese  et  al.,  2004))  to 
include the novel sequence information accumulated in public databases.  
The  current  (unpublished)  version,  pmoA  array  #5,  contains  138  (17-27  oligomer)  probes  targeting  all 

55  

  
methanotrophs (including uncultivated members) as well as the related ammonium monooxygenase (amoA) 
genes  of  ammonium  oxidizing  bacteria,  and  the  phylogenetic  resolution  achieved  was  below  the  species 
level – as known in January 2006. The array contains multiple, hierarchically nested probes wherever this 
was  possible,  to  increase  the  confidence  of  the  results  and  to  enable  the  detection  of  novel  groups.  The 
method  relies  on  previous  PCR  amplification  as  reasoned  above.  PCR  amplicons  are  converted  into  Cy3-
labelled,  fragmented,  single-stranded  RNA  targets  via  in  vitro  transcription  and  subsequent  chemical 
fragmentation.  Each  array  contains  three  subarrays,  the  signals  from  which  are  always  averaged.  Average 
probe  signals  are  then  normalised  against  a  positive  control  (targeting  the  forward  PCR  primer  applied  to 
amplify  the  pmoA  gene).  As  the  hybridisation  capacity  of  the  probes  may  vary  by  up  to  two  orders  of 
magnitude,  the  (normalised)  signal  of  each  probe  has  still  to  be  compared  to  a  reference  value.  These 
reference values are obtained during validation, where the array is hybridised with targets from pure strains 
or environmental clones. The set of these validation (reference) targets provides at least one perfect match 
hybridisation  to  almost  all  probes  on  the  array.  The  reference  value  for  each  probe  is  then  the  maximal 
hybridisation  signal  obtained  with  a  perfect  match  target  during  the  validation  process.  Raw  hybridisation 
data are extracted via a spot segmentation software (GenePix 6.0 in our case), followed by analysis using 
custom-recorded  macros  in  Excel.  The  best  visualisation  of  complex  community  data  is  achieved  via  a 
gradual  colour  coded  representation,  as  generated  by  the  software  Genespring  (and  also  offered  by  the 
conditional formatting option in Excel 2007) (Stralis-Pavese et al., 2004). (Figure 2) 

Outline of the experimental approach
Outline of the experimental approach

Sampling
Sampling
lyophilisation
lyophilisation

DNA purification
DNA purification
bead beating
bead beating

Preparation of labelled targets
Preparation of labelled targets
PCR with T7 modified primers
PCR with T7 modified primers
In vitro transcription with 
In vitro transcription with 
direct Cy3-UTP incorporation
direct Cy3-UTP incorporation

Fragmentation
Fragmentation
ZnII, alkalic pH
ZnII, alkalic pH

T
T
a
a
r
r
g
g
e
e
t
t

p
p
r
r
e
e
p
p
a
a
r
r
a
a
t
t
i
i

o
o
n
n

M
M

i
i
c
c
r
r
o
o
a
a
r
r
r
r
a
a
y
y

p
p
r
r
e
e
p
p
a
a
r
r
a
a
t
t
i
i

o
o
n
n

Sequence database: establishment
Sequence database: establishment
ARB software package
ARB software package

Design of oligo probe set, 
Design of oligo probe set, 
in silico predictions
in silico predictions
ARB and CalcOligo
ARB and CalcOligo

Array printing 
Array printing 
aldehyde slides, 
aldehyde slides, 
5’-NH2-C12-TTTTT-17-26mer-3’,
5’-NH2-C12-TTTTT-17-26mer-3’,
50% DMSO, contact printing
50% DMSO, contact printing

Hybridisation
Hybridisation
5xSSC, O/N, 55°C
5xSSC, O/N, 55°C

Refinement of
Refinement of
probe design / in silico predictions 
probe design / in silico predictions 

Scanning
Scanning
immediately, close-to-saturation
immediately, close-to-saturation

Data mining
Data mining
normalisation to positive controls
normalisation to positive controls
comparing results to:
comparing results to:
1. in silico predictions
1. in silico predictions
2. those obtained with reference sequences
2. those obtained with reference sequences

Detection / identification 
Detection / identification 
of microorganisms
of microorganisms

Quantitative assessment
Quantitative assessment
of the microbial community composition 
of the microbial community composition 

Figure 2: Experimental scheme of microbial diagnostic 
microarray development. 

Figure  1:    pmoA/amoA  based  radial  phylogenetic  tree 
of  methanotrophs,  nitrifyers  and  functionally  related 
bacteria.  Major  clades  are  indicated.  “?”  refers  to  a 
group  of  sequences  known  exclusively  from  environ-
mental clones, where the function of the encoded pro-
tein  is  uncertain.  “Novel  MOB?”  refers  to  a  clade 
where  increasing  indirect  evidence  as  well  as  a  few 
cultivated  members  strongly  suggest  that  the  encoded 
protein  is  responsible  for  methane  oxidation.  AOB  – 
Ammonia oxidisers. Type Ia, Type Ib, and Type II re-
fers to characterised groups of aerobic methanotrophs, 
with many cultivated members. RA14 and USC-γ refer 
to  two  groups  of  (uncultivated)  atmospheric  methane 
oxidisers. 

Application examples – potential and shortcomings 
The  pmoA  microarray  has  been  applied  in  a  number  of  projects  and  environments.  We  will  use  examples 
from these projects to illustrate the potential and limitations of, primarily, short functional oligonucleotide 
microarrays. 

56  

  
 
 
 
 
 
 
 
 
 
 
 
The  currently  applied  methodology  enables  one  user  to  analyse  about  80  samples  during  an  ordinary 
working week day, including all steps from DNA purification to analysis. Consumables prices are between 
22 and 27 € per sample (not including the price for spotting the microarray itself, which may vary widely 
depending on the service provider). The target created thus is sufficient for five hybridisations and can be 
stored  at  -20°C  for  at  least  two  years  without  noticeable  decrease  in  quality  (as  judged  by  a  repeated 
hybridisation following such storage). Sensitivity of the assay is determined by the relative abundance of the 
targeted  sequence  (targeted  species,  genus,  subspecies  or  any  other  phylogenetic  level)  within  the  entire 
community that is amplified via the PCR applied. In this sense, the sensitivity is 5% - that is, a population 
that is at least 5% of the total community is detected with very high certainty, while those below this limit 
may or may not be detected, depending on the probes targeting them (Stralis-Pavese et al., 2004).  

Ammonia
Ammonia
Ammonia
Ammonia
oxidisers
oxidisers
oxidisers
oxidisers

0.9
0.9

0.8
0.8

0.7
0.7

0.6
0.6

0.5
0.5

0.4
0.4

0.3
0.3

20 cm
20 cm
20 cm
20 cm

10 cm
10 cm
10 cm
10 cm

60 cm
60 cm
60 cm
60 cm

50 cm
50 cm
50 cm
50 cm

30 cm
30 cm
30 cm
30 cm

20 cm
20 cm
20 cm
20 cm

10 cm
10 cm
10 cm
10 cm

D
D

A
A

B
B

GA+
GA+
GA+

Type II
Type II
Type II
Type II

Type Ib
Type Ib
Type Ib
Type Ib

Type Ia
Type Ia
Type Ia
Type Ia

Universal
Universal
Universal
Universal
M.capsa
M.capsa
M.capsa
M.capsa
RA14
RA14
RA14
RA14
LP21
LP21
LP21
LP21

Figure  3:  Analysis  of  methano-
troph  communities  in  soils  of  a 
lysimeter  experiment  simulating 
landfill  conditions.  A.  Lysimeter 
experiment  testing  the  effect  of 
different  vegetation  regimes  on 
methane  oxidation.  B.  Sampling 
soils  at  different  depths  of  lysime-
ter pots. C. Microarray analysis by 
using  a  pmoA  oligonucleotide  mi-
croarray  (Stralis-Pavese,  Sessitsch, 
Weilharter,  Reichenauer,  Riesing, 
Csontos,  Murrell,  and  Bodrossy 
2004).  D.  Summarised  results  of 
the microarray analysis. Results of 
individual  microarray  experiments 
were  first  normalized  to  positive 
control probe mtrof173, then to the 
reference  values  determined  indi-
vidually  for  each  probe,  averaged 
between  parallels  and  displayed 
using  the  GeneSpring  software.  In 
essence,  a  value  of  1.0  indicates 
maximum  achievable  signal  for  an 
individual  probe,  while  a  value  of 
0.1 indicates that about 10% of the total PCR product hybridized to that probe. Colour coding is indicated on the side 
bar. GA+ = Grass-alfalfa mixture, G+ = Grass, P+ = Poplar, M+ = Miscanthus, BS+ = Bulk soil. For each vegetation 
cover and each depth layer, results from three parallel lysimeters are shown. 

BS+
BS+
BS+

M+
M+
M+
M+

G+
G+
G+
G+

C
C

P+
P+
P+
P+

10 cm
10 cm
10 cm
10 cm

20 cm
20 cm
20 cm
20 cm

30 cm
30 cm
30 cm
30 cm

50 cm
50 cm
50 cm
50 cm

60 cm
60 cm
60 cm
60 cm

10 cm
10 cm
10 cm
10 cm

20 cm
20 cm
20 cm
20 cm

30 cm
30 cm
30 cm
30 cm

50 cm
50 cm
50 cm
50 cm

60 cm
60 cm
60 cm
60 cm

50 cm
50 cm
50 cm
50 cm

60 cm
60 cm
60 cm
60 cm

30 cm
30 cm
30 cm
30 cm

10 cm
10 cm
10 cm
10 cm

20 cm
20 cm
20 cm
20 cm

30 cm
30 cm
30 cm
30 cm

50 cm
50 cm
50 cm
50 cm

60 cm
60 cm
60 cm
60 cm

0.2
0.2

0.1
0.1

0.0
0.0

The  high  throughput,  moderate  costs  and  high  resolution  of  the  method  make  it  especially  suited  for  the 
analysis and visualisation of spatial heterogeneity (Stralis-Pavese et al., 2004) (Figure 3). 
Hierarchically  nested  probe  sets  enable  the  detection  of  hitherto  unknown  taxa.  This  was  shown  by  the 
analysis of methanotroph communities from upland forest soil samples (Knief et al., 2005) (microarray data 
unpublished) (Figure 4). Similarly, the array enables a  rapid screening for environments or environmental 
niches harbouring particular groups. 
By purifying environmental RNA, followed by RT-PCR, the microarray can be applied to analyse the active 
community  as  opposed  to  the  total  (active,  less  active,  dormant,  etc.)  community  (Bodrossy  et  al.,  2006) 
(Figure 5). 
The  potential  of  the  microarray  technology  to  diagnose  environmental  changes  was  demonstrated  by  an 
accidental  result.  A  lysimeter  (GA+  20)  within  a  large-scale  experiment  (Stralis-Pavese  et  al.,  2004) 
displayed  a  most  unusual  pattern  of  methanotroph  diversity.  Besides  the  diversity  found  in  other,  parallel 
(GA+) lysimeters, significant diversity of type Ia and type Ib methanotrophs was detected. Checking the gas 
profile  revealed  that  there  had  been  a  major  fault  in  the  lysimeter,  most  likely  a  gas  leakage  near  to  the 
bottom  (70  cm),  where  biogas  was  added.  Type  Ia  methanotroph  populations  were  previously  shown  to 

57  

  
 
 
Type Ia
Type Ia
Type Ia
Type Ia

Type Ib
Type Ib
Type Ib
Type Ib

Type II
Type II
Type II
Type II

M.capsa
M.capsa
M.capsa
M.capsa
RA14
RA14
RA14
RA14

Universal
Universal
Universal
Universal
LP21
LP21
LP21
LP21

Ammonia
Ammonia
Ammonia
Ammonia
oxidisers
oxidisers
oxidisers
oxidisers

l
l
l

m
m
m
u
u
u
d
d
d
a
a
a
c
c
c
o
o
o
y
y
y
t
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M
M
M

l
l
l

s
s
s
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t
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s
s
y
y
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c
c
c
o
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M
M
M

l
l
l

A
A
A
s
s
s
i
i
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t
t
t
s
s
s
y
y
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c
c
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h
h
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M
M
M

l
l
l

B
B
B
s
s
s
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s
s
y
y
y
c
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c
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M
M
M

l
l
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p
p
p
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y
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T
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s
s
s
o
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M
M
M

l
l
l

4
4
4
1
1
1
A
A
A
R
R
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d
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(
(
(

Figure  4:  Analysis  of  metha-
notroph  communities  inhabit-
ing  different  upland  soils  by 
using  the  pmoA  microarray. 
Samples  #7  and  #9  indicate 
the  presence  of  novel  metha-
notrophs  as  hybridisation  sig-
nals  were  only  obtained  with 
generic  probes  for  methano-
trophs.  Confirmatory  results 
were  obtained  by  analysis  of 
pmoA clone libraries (Knief et 
al., 2005).  

1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
13
13
14
14
15
15

1
1
1
R
R
R
M
M
M

Type Ia
Type Ia
Type Ia
Type Ia

Type Ib
Type Ib
Type Ib
Type Ib

Type II
Type II
Type II
Type II

M.capsa
M.capsa
M.capsa
M.capsa
RA14
RA14
RA14
RA14

LP21
LP21
LP21
LP21

Universal
Universal
Universal
Universal

Ammonia
Ammonia
Ammonia
Ammonia
oxidisers
oxidisers
oxidisers
oxidisers

1.0
1.0
1.0
0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

0
0
0

0.1
0.1
0.1
0
0
0

0.0
0.00
0.0
0
0

10 cm
10 cm
20 cm
20 cm
30 cm
30 cm
40 cm
40 cm
50 cm
50 cm
10 cm
10 cm
20 cm
20 cm
30 cm
30 cm
40 cm
40 cm
50 cm
50 cm

DNA
DNA

RNA
RNA

s
s
s
a
a
a
n
n
n
o
o
o
m
m
m
M
M
M

.
.
.

r
r
r
e
e
e
t
t
t
c
c
c
a
a
a
b
b
b
.
.
.
M
M
M

i
i
i

m
m
m
u
u
u
b
b
b
o
o
o
r
r
r
c
c
c
m
m
m
M
M
M

.
.
.

i
i
i

a
a
a
I
I
I

e
e
e
p
p
p
y
y
y
T
T
T

b
b
b
I
I
I

e
e
e
p
p
p
y
y
y
T
T
T

l
l
l

m
m
m
u
u
u
d
d
d
a
a
a
c
c
c
.
.
.
M
M
M

s
s
s
i
i
i
t
t
t
s
s
s
y
y
y
c
c
c
.
.
.
M
M
M

I
I
I
I
I
I

e
e
e
p
p
p
y
y
y
T
T
T

s
s
s
e
e
e
b
b
b
o
o
o
r
r
r
p
p
p
c
c
c
i
i
i
r
r
r
e
e
e
n
n
n
e
e
e
G
G
G

s
s
s
h
h
h
p
p
p
o
o
o
r
r
r
t
t
t
o
o
o
n
n
n
a
a
a
h
h
h
t
t
t
e
e
e
m
m
m

r
r
r
o
o
o

f
f
f

Figure 5: Methanotroph community analy-
ses  with  the  pmoA  oligonucleotide  mi-
croarray  using  environmental  DNA  and 
mRNA as target. 

compete more efficiently with type II methanotrophs under relatively high oxygen and low methane partial 
pressures (Amaral and Knowles 1995; Graham et al., 1993; Henckel et al., 2000; Macalady et al., 2002).  

58  

  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
GA+ 20 lysimeter with anomalous results
GA+ 20 lysimeter with anomalous results

Gas profiles:  • CH4 , ■ O2
Gas profiles:  • CH4 , ■ O2

Type Ia
Type Ia
Type Ia
Type Ia

Type Ib
Type Ib
Type Ib
Type Ib

Type II
Type II
Type II
Type II

M.capsa
M.capsa
M.capsa
M.capsa
RA14
RA14
RA14
RA14

LP21
LP21
LP21
LP21

M.trophs
M.trophs
M.trophs
M.trophs

Ammonia oxidisers
Ammonia oxidisers
Ammonia oxidisers
Ammonia oxidisers

Rest of GA+ lysimeters: average of results
Rest of GA+ lysimeters: average of results

Type Ia
Type Ia
Type Ia
Type Ia
Type Ia

Type Ib
Type Ib
Type Ib
Type Ib
Type Ib

Type II
Type II
Type II
Type II
Type II

M.capsa
M.capsa
M.capsa
M.capsa
M.capsa
RA14
RA14
RA14
RA14
RA14

LP21
LP21
LP21
LP21
LP21

M.trophs
M.trophs
M.trophs
M.trophs

Ammonia oxidisers
Ammonia oxidisers
Ammonia oxidisers
Ammonia oxidisers
Ammonia oxidisers

5.0
5.0
5.0

10.0
10.0
10.0

15.0
15.0
15.0

5.0
5.0
5.0

10.0
10.0
10.0

15.0
15.0
15.0

GA+ 20 depth
GA+ 20 depth
GA+ 20 depth
GA+ 20 depth

0.9
0.9

0.8
0.8

0.7
0.7

0.6
0.6

0.5
0.5

0.4
0.4

0.3
0.3

0.2
0.2

0.1
0.1

GA+
GA+
GA+
GA+
GA+

depth
depth
depth
depth
depth

0.0
0.0

Trust
Trust

0.0
0.0
0.0

0
0
0
10
10
10
20
20
20
30
30
30
40
40
40
50
50
50
60
60
60

0.0
0.0
0.0

0
0
0

10
10
10
20
20
20

30
30
30
40
40
40
50
50
50
60
60
60

Figure  6:  Methano-
community 
troph 
profile  of  lysimeter 
GA+ 20 compared to 
the  average  profile 
of  three  other  GA+ 
lysimeters (data from 
Figure  3).  Gas  depth 
profiles 
also 
shown. 

are 

These conditions were fulfilled by the bottom layers of this particular lysimeter, whereby gas concentration 
ratios were shifted towards low methane - high oxygen (Figure 6). 
Microarrays also lend themselves as tools to follow, visualise and evaluate various enrichment and isolation 
strategies and their various biases. Figure 7 shows the results of such an experiment. Without going into any 
details,  it  is  striking,  how  heavily  even  small  differences  change  the  community  composition.  Not  less 
striking  is  the  temporal  change  of  the  community  composition  over  time  –  populations  become  dominant 
and then disappear again. When looking at the results, the 5% detection limit of the array should be kept in 
mind – that is, absent signals don’t necessarily indicate the absence of a group, but rather that its relative 
abundance within methanotrophs is below this threshold. 

Conclusions 
As  the  fine  technical  details  and  correspondingly  the  exact  potential  and  limitation  of  MDMs  vary  from 
method to method, we attempted to illustrate the general applicability of these tools by focusing on the one 
we know best, our own.  
Key characteristics of the presented method are: 

•  requirement for previous PCR amplification of the target gene 
•  detection threshold at 5% relative abundance 
•  phylogenetic resolution at or below the species level 
•  hierarchically nested probe sets (genus, family level) 
•  80 samples per week per user, from sample to results 
•  22-27€/sample consumables costs (excl. microarray spotting) 
•  focuses onto a functional group of bacteria 
•  enables the identification and detection of uncultivated members of this functional group 

By  combining  high  resolution,  high  throughput  and  affordable  overall  costs,  functional  oligonucleotide 
MDMs  are  a  powerful  tool  for  mapping  the  spatial  and  temporal  variability  and  dynamics  of  microbial 
community structure in the environment. 

59  

  
 
 
 
 
 
 
 
 
 
 
Type Ia
Type Ia

Type Ib
Type Ib

?
?

Type Ic -
Type Ic -

Type II
Type II

Type II2ndpmoA
Type II2ndpmoA
M.capsa
M.capsa
Wsh1 &
Wsh1 &
RA14
RA14

2
2

Universal
Universal

Ammonia
Ammonia
oxidisers
oxidisers

Novel MOB?
Novel MOB?

???
???

Inoculum
Inoculum
Inoculum

Standard
Standard
Standard

+ Vitamin
+ Vitamin
+ Vitamin

low O2
low O2
low O2

low CH4
low CH4
low CH4

low CH4 + Vit
low CH4 + Vit
low CH4 + Vit

0.1x medium
0.1x medium
0.1x medium

low Cu
low Cu
low Cu

Figure 7: Visualisation of enrichment bias. An environmental sample containing a broad diversity of methanotrophs 
was  used  to  inoculate  liquid  enrichments  under  “standard”  and  slightly  varying  conditions.  Low  O2  /  CH4  :  ~1% 
concentration  of  the  corresponding  gases.  0.1x  medium:  standard  medium  in  10x  dilution.  Low  Cu:  same  diluted 
medium, without the addition of copper (an essential cofactor for one of the forms of the methane monooxygenase). 
Each block is organised into a time series, from the top towards the bottom. 

Acknowledgements 
Related  work  at  Seibersdorf  research  was  funded  by  the  Fonds  zur  Förderung  der  wissenschaftlichen 
Forschung, Austria and the European Science Foundation (P15044 and 05 EDIV FP018 METHECO). LB is 
indebted to Fodor Szilvia for her support and fresh, independent views and ideas.  

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62  

  
 
 
 
Diagnostic Application of Padlock Probes – Multiplex Detection of Plant 
Pathogens using Universal Microarrays  

Peter Bonants*, Marianna Szemes, Ronald van Doorn and Cor D. Schoen 

Plant Research International B.V.  
Droevendaalsesteeg 1  
Wageningen 6708PB  
The Netherlands 

Corresponding author e-mail: peter.bonants@wur.nl  

Abstract  
Padlock  probes  (PLP)  are  long  oligonucleotides,  whose  ends  are  complementary  to  adjacent  target 
sequences.  Upon  hybridisation  to  the  target,  the  two  ends  are  brought  into  contact,  allowing  PLP 
circularisation by ligation. PLPs provide extremely specific target recognition that is followed by universal 
amplification and microarray detection. Since target recognition is separated from downstream processing, 
PLPs enable the development of flexible and extendable diagnostic systems, targeting diverse organisms. To 
adapt  padlock  technology  for  diagnostic  purposes,  we  optimised  PLP  design  to  ensure  high  specificity, 
eliminating  ligation  on  non-target  sequences  under  real-world  assay  conditions.  We  designed  and  tested 
different PLPs to target various plant pathogens at the genus, species and sub-species levels, and developed 
a  prototype  PLP-based  plant  health  chip.  Excellent  specificity  was  demonstrated  towards  the  target 
organisms. The developed multiplex diagnostic system was validated using genomic DNAs of characterized 
isolates and artificial mixtures thereof, but also of practical samples. 

Introduction 
The  accurate  identification  and  detection  of  pathogenic  microorganisms  or  other  targets  of  interest  has 
become  increasingly important  in  clinical  diagnostics  and  other  pest  management  strategies.  Traditionally, 
the  predominant  techniques  aimed  at  this  goal  target  only  a  single  pathogen  per  assay,  making 
comprehensive screening of samples laborious and time-consuming. 
To  increase  efficiency,  it  is  desirable  to  develop  multiplex  assays  which  can  detect  several  pathogens 
simultaneously.  Microarrays  may  enable  highly  parallel  detection  of  diverse  organisms  (Bodrossy  and 
Sessitsch, 2004). Padlock probes (PLP) offer a means of combining pathogen-specific molecular recognition 
and universal amplification, thereby increasing sensitivity and multiplexing capabilities without limiting the 
range of potential target organisms. PLPs are long oligonucleotides of approximately 100 bases containing 
target  complementary  regions  at  both  their  5’  and  3’  ends  (Figure  1).  These  regions  recognise  adjacent 
sequences  on  the  target  DNA  (Nilsson  et  al.,  1994)  and  universal  primer  sites  and  a  unique  sequence 
identifier, the so-called ZipCode, lie between these segments. Upon hybridisation the ends of the probes get 
into  adjacent  position  and  can  be  joined  by  enzymatic  ligation.  This  ligation  and  the  resulting  circular 
molecule  can  only  take  place  when  both  end  segments  recognise  their  target  sequences  correctly.  Non-
circularised  probes  are  removed  by  exonuclease  treatment,  while  the  circularised  ones  may  be  amplified 
with  universal  primers.  Subsequently,  the  target-specific  products  are  detected  by  a  universal  cZipCode 
microarray  (Shoemaker  et  al.,  1996).  PLPs  have  been  shown  to  have  good  specificity  and  very  high 
multiplexing  capabilities  in  genotyping  assays  (Hardenbol  et  al.,  2003).  They  are  promising  for  multiplex 
diagnostic analyses, since one can expect limited bias in the universal amplification step because of the use 
of  non-degenerate,  universal  primers  and  the  uniform  size  of  the  amplicons.  Furthermore,  universal 
microarrays  may  provide  less  non-specific  hybridisation  allowing  a  higher  dynamic  range  of  pathogen 
detection.  

63  

  
 
 
 
 
 
 
Figure  1:  Scheme  of  the  padlock  probe  (PLP)  composition,  ligation,  PCR  amplification  and  hybridization  on  a 
microarray.  

Materials and methods 

Nucleic acids used in the study 
The  pathogenic  organisms  were  derived  from  the  culture  collection  of  Plant  Research  International  B.V. 
Genomic  DNAs  were  extracted  as  previously  described  (Bonants  et  al.,  1997).  PLPs  and  target 
oligonucleotides, listed in Tables 1 and 2, were synthesised by Eurogentec S.A. (Seraing, Belgium).  

Padlock probe design 
Relevant  nucleic  acid  sequences  derived  from  Genbank  and  from  independent  sequencing  studies  were 
aligned by using ClustalW. Diagnostic sequences were identified for each target group. Potential PLP target 
complementary regions were selected in a way that the discriminatory nucleotides would bind to the 3’arm 
region. Further design rules are described in (Szemes et al., 2005). The PLP arm sequences were combined 
(P1:  5’  CTCGACCGTTAGCAGCATGA  3’;  P2:  5’ 
with 
CCGAGATGTACCGCTATCGT  3’)  and  a  ZipCode  sequence.  The  unique  identifier  was  chosen  from 
GeneFlexTM TagArray set (Affymetrix) in a way to minimize PLP secondary structures.  

the  universal  primer 

sites 

Ligation, capturing and exonuclease treatment 
Genomic DNA was fragmented by digestion using EcoRI, HindIII and BamHI (New England Biolabs) for 
30 min.Cycled ligation, capturing of ligated probes and exonuclease treatment were performed as previously 
described (Szemes et al., 2005; Van Doorn et al., 2007). 

Real-time PCR 
Amplification  of  ligated  PLPs  was  followed  in  real-time  using  an  ABI  Prism  7700  Sequence  Detector 
System (Applied Biosystems) and the qPCR kit (Eurogentec) (Szemes et al., 2005).  

LATE-PCR 
For  microarray  hybridisation,  circularised  PLP  probes  were  amplified  in  LATE-PCR  (linear-after-the-
exponential  PCR)  (Sanchez  et  al.,  2004)  to  produce  a  large  amount  of  ssDNA  amplicons  (Szemes  et  al., 
2005).  

64  

  
 
 
Table  1:  Target-complementary  regions  and  ZipCode  sequences  of  the  developed  diagnostic  PLPs.  Nucleotides  or 
gaps  due  to  deletions  used  to  discriminate  targets  from  most  similar  non-target  sequences  are  underlined.  Bold 
characters indicate polymorphism within the target group.  

Targeted species/group 

5' target complementary sequence (5’- 3’) 

Phytophthora spp. 
Phytophthora cactorum 
Verticillium dahliae 
Phytophthora infestans 
Fusarium oxysporum 
Myrothecium roridum 
Phytophthora nicotianae 
Rhizoctonia solani AG 4-2 

Rhizoctonia solani AG 4-1 
Pythium ultimum 
Meloidogyne hapla 

TATCTAGTTAAAAGCAGAGACTTTCGTC 
GACTTTCGTCCCCACAGTATAATCAGTATTAAGGAAT 
TTTATACCAACGATACTTCTGAGTGTT 
TCGATTCGTGGTATGGTTGGCTTCGGCT 
GCGAGTCCCAACACCAAGCTGTGCTTG 
CGGTGGTGGCCATGCCGTAAAACACC 
TAGTAGTCTTTTTTTCTTTTAAACCCATTCCTTAAT 
GACTTCTGTCTACTTAATTCATATAAACTCAATT 

GGTCCAATAAAGTTCCTTCCCCCCTAGAAAA 
CGAAAAAACGAACGCAACCATGTGAGACACTT 
GTTTATCGTTGTGAATGGCTGTCGCTGGTG 

3' target complementary 
sequence (5’- 3’) 

CTGCTGAAAGTTGC 
TATCTAGTTAAAAGCAAG 
CATCAGTCTCTCTG 
CGTTAATGGAGAAATGC 
GGAACGCGAATTAAC_ 
ACTCGCATTGGAGCT 
GCTTCGGCCTGATT 
CTT_CTACTCCCCCTT_ 

AGTCCAA_GGAGAGTA___ 
CGACAGATTCTCGAT 
ATTCGAATAGTCTCAAC 

ZipCode sequence (5'-3') 

GTCACGTATGGTTCGCTGCT 
ACTCCAGTGCCAAGTACGAT 
CGTTCCTAAAGCTGAGTCTG 
GCACTAACTGGTCTGGGTCA 
ATGCAGCGTAGGTATCGACT 
AATGCTCACATCGCAGGTAC 
TCCCGAATGACAAGGCACGA 
TGTGATAATTTCGACGAGGC 

ATTAACTCGACTGCCGCGTG 
TCGCCGTTGGTCTGTATGCA 
CTTCGTGGCTAGTCTGTGAC 

Table  2:  Design  and  experimental  characteristics  of  the  PLP  set.  Probes  were  named  after  the  targeted  species/
subgroup. Lengths and melting temperatures (Tm) of PLP target-complementary regions are indicated. The number of 
nucleotides discriminating the targeted sequence from that of the known most similar non-target organisms is shown 
for each PLP. Sensitivity was defined as the lowest concentration of perfectly matching oligonucleotide that could be 
detected under standard assay conditions. 

Name 

PLP P-spp 
PLP P-cac 
PLP V-dahl 
PLP P-inf 
PLP F-oxy 
PLP Myr-ror 
PLP P-nic 
PLP Rhiz-4-2 
PLP Rhiz-4-1 
PLP Pyt-u 
PLP Mel-h 

5' arm 

3' arm 

Length (nt) 

Tm (°C) 

Length (nt) 

Tm (°C) 

Closest non-target relative 

nbr of discrimi-
nating 
nucleotides 

Sensitivity (fM) 

29 
37 
28 
28 
27 
27 
36 
36 
32 
32 
30 

60.0 
65.8 
58.3 
68.9 
68.9 
69.0 
61.0 
59.2 
66.2 
66.2 
65.7 

14 
18 
14 
17 
15 
15 
14 
15 
15 
15 
17 

39.5 
39.6 
35.4 
44.0 
41.5 
46.5 
43.6 
41.0 
39.5 
40.6 
41.1 

Pythium splendens 
Phytophthora nicotianae 
Verticillium alboatrum 
Phytophthora sojae 
Fusarium equiseti 
Myrothecium verrucaria 
Phytophthora cactorum 
Rhizoctonia solani AG 4-1 
Rhizoctonia solani AG 4-2 
Pythium splendens 
Meloidogyne incognita 

1 
2 
2 
3 
3 
5 
7 
7 
8 
10 
15 

2 
20 
2 
2 
2 
0.2 
2 
2 
2 
0.2 
2 

Microarray preparation and hybridisation 
Complementary  ZipCode  (cZipCode)  oligonucleotides  carrying  a  C12  linker  and  a  5’  NH2  group  were 
synthesised  and  spotted  on  Nexterion  MPX-E16  epoxy-coated  slides  by  Isogen  B.V.  (Utrecht,  The 
Netherlands)  according  to  manufacturer’s  instructions  (Schott  Nexterion).  Hybridisation,  washing  and 
analysis of microarray data were performed as described before (Szemes et al., 2005). 

65  

  
 
 
 
 
 
 
Results 

Design and testing of diagnostic padlock probes specificity 
For diagnostic applications, the high discriminatory power of the ligation is of prime importance, since very 
similar non-target DNA molecules can be present potentially in much higher concentration than the target 
DNA. Therefore, we aimed to optimise the reaction conditions and PLP design for maximum discrimination 
of single mismatches, which subsequently could be extrapolated to diagnostic assay design. 
Based  on  the  principles  described  before  (Szemes  et  al.,  2005),  we  designed  PLPs  targeting  ten 
economically important plant pathogens (Table 1). In each case we selected discriminatory areas within the 
ITS  regions  of  rRNA  operons  because  of  their  high  copy  number  (Atkins  and  Clark,  2004),  which  could 
significantly increase the sensitivity of the assay. Furthermore, ITS regions have been extensively used in 
phylogenetic studies (Cooke et al., 2000) and a large number of sequences are available for plant pathogenic 
organisms,  which  may  ensure  reliable  assay  design.  Sequences  available  in  Genbank  and  those  obtained 
from independent sequencing studies (Bonants, unpublished results) were aligned, and diagnostic regions for 
each target organism were selected. Preferably, we chose regions containing more than one discriminatory 
nucleotides  and  very  few  polymorphic  positions  within  the  targeted  species/subgroups.  The  3’  arm 
sequences were selected to be 14-18 nucleotide-long and had a Tm around 40°C (Table 2). In general, the 3’ 
arm  sequence  hybridised  to  the  discriminatory  region  and  contained  a  highly  destabilizing  mismatch  or  a 
gap at the 3’ end when bound to the non-target sequence. The 5’ arm sequences were 27-37 nucleotide-long. 
In  an  attempt  of  hierarchical  diagnostic  analysis,  we  also  designed  a  genus-specific  PLP  to  target  all 
Phytophthora  species  and  discriminate  them  from  related  oomycetes.  After  selecting  the  target-
complementary regions, they were combined with the universal primer site sequences and a unique ZipCode 
sequence was selected for each probe.  
The developed probes were tested for sensitivity and discriminatory power using synthetic oligonucleotides 
representing  target  nucleic  acids  and  the  most  similar  non-target  sequences  (Szemes  et  al.,  2005).  As  the 
final  analysis  was  to  be  performed  on  array,  we  chose  the  LATE-PCR  protocol  (Sanchez  et  al.,  2004)  to 
achieve  efficient  amplification  and  produce  large  amount  of  ssDNA  in  one  step,  which  is  ideal  for 
microarray hybridisation. In all the subsequent experiments, this method was used to amplify ligated PLPs. 
Fixed  amounts  of  PLPs  were  ligated  on  their  respective  target  and  on  the  related,  but  non-target 
oligonucleotides, present in a wide concentration range.  

Validation – PLP-based multiplex detection of plant pathogenic organisms 
A  mix  of  the  developed  11  PLPs  was  ligated  on  various  genomic  DNAs,  treated  with  exonucleases  and 
subjected to LATE-PCR using Cy3-labeled forward primer. The labelled PLP amplicons were analysed on 
multi-chamber, low-density universal microarray, which enabled the simultaneous assay of 16 samples on a 
single  slide.  The  tag  array  used  in  our  experiments  contained  30  probes  in  9  replicates,  together  with  90 
hybridisation control probes distributed over the deposition area. This layout allows for the future extension 
of the PLP set to target other pathogens and enables high-throughput screening.  
Using the developed PLP set, we tested genomic DNAs from a panel of well-characterized isolates of plant 
pathogenic organisms (Figures 2 a-g). In each case, 1 ng genomic DNA could be specifically and reliably 
detected without any cross-reaction. All the Phytophthora species were correctly recognized by PLP Phyt-
spp, including P. cactorum, which contained two adjacent mismatches with the 5’ arm sequence of the probe 
(Table 1). This polymorphism was apparently well tolerated, resulting in a positive signal. For four probes 
(PLPs  P-cac,  P-nic,  P-inf  and  V-dahl)  analysis  was  also  performed  with  DNA  of  a  very  closely  related 
organism (Table 2), but no cross-reaction was observed, indicating excellent specificity.  
Next,  we  evaluated  the  ability  of  the  developed  diagnostic  system  to  detect  several  pathogens  in  parallel. 
Mixtures of equal amounts of genomic DNAs representing three targeted organisms were tested (Figures 2 
h-j).  In  two  out  of  three  cases,  the  pathogens  were  correctly  and  unambiguously  identified  by  all  four 
cognate probes, resulting in detection at the genus and species/subgroup level. The components of the third 
mixture, P. cactorum, R. solani AG 4-1 and V. dahliae were also correctly identified by using the species/
subgroup-specific probes. The PLP Phyt-spp signal, however, was below the threshold, most probably due 
to the two adjacent mismatches with P. cactorum DNA. 
To  explore  the  sensitivity  of  the  system  in  a  multiplexed  setting,  we  tested  the  detection  threshold  for  F. 
oxysporum and M. roridum in the presence of a large excess of the other target DNA (Figure 2 k-l). As little 

66  

  
as  0.5  pg  of  F.  oxysporum  DNA  could  be  detected  in  the  presence  of  500  pg  of  M.  roridum  DNA, 
corresponding to a dynamic range of 1000. In a reverse situation, the detection threshold was 5 pg for M. 
roridum  in  the  presence  of  500  pg  Fusarium  DNA,  indicating  that  a  reciprocal  dynamic  range  of  100  is 
achievable using this system.  

Figure  2:  Detection  of  genomic  DNAs  corresponding  to  individual  (a  -  g)  and 
complex  pathogen  samples  (h  -  l)  on  a  universal  microarray.  The  analyzed  targets 
were: (a) P. cactorum; 1 ng (b) P. nicotianae, 1 ng; (c) P. sojae, 1 ng; (d) R. solani 
AG 4-2, 1 ng; (e) M. hapla, 1 ng; (f) F. oxysporum, 1 ng; (g) M. roridum, 1 ng; (h) 
Pyt.  ultimum,  500  pg;  M.  hapla,  500  pg  and  P.  nicotianae,  500  pg;  (i)  P.  infestans, 
500 pg; R. solani AG 4-2, 500 pg and M. roridum, 500 pg; (j) P. cactorum, 500 pg; R. 
solani  AG  4-1,  500  pg  and  V  .  dahliae,  500  pg.  (k)  F.  oxysporum,  0.5  pg  and  M. 
roridum, 500 pg; (l) F. oxysporum, 500 pg and M. roridum 5 pg. 

A 

B 

Figure 3: (A) Detection of DNA isolated from filters. Water was spiked with different pathogens and 
filtrated  through  a  Millipore  filter  (0.22  µ).  DNA  was  isolated  and  tested  with  padlock probesmix-
tures.  The  analysed  pathogens  were  Phytophthora  nicotianae,  Verticillium  dahliae  and  Agrobacte-
rium tumefaciens. (B) Detection of DNA isolated from soil samples. Soil was spiked with different 
pathogens and DNA was isolated and tested with padlock probes mixtures. The analysed pathogens 
were Meloïdogyne hapla, Verticillium dahliae and Agrobacterium tumefaciens 

67  

  
 
 
Validation: PLP-based multiplex detection of plant pathogenic organisms spiked to water and soil 
To analyse practical samples in a multiplexed setting, we spiked water and soil respectively with different 
pathogens.  Water  was  spiked  with  propagules  of  Phytophthora  nicotianae,  Verticillium  dahliae  and 
Agrobacterium  tumefaciens.  Soil  samples  were  spiked  with  Meloïdogyne  hapla,  Verticillium  dahliae  and 
Agrobacterium tumefaciens. DNA was extracted as described and isolated. DNA samples were tested with a 
mixture of PLPs. The spiked organisms could easily be detected in water (Figure 3A) and soil (Figure 3B). 

Discussion 

In this study we investigated the diagnostic application of padlock probes in a multiplex setting for the first 
time. Based upon our findings, we developed a PLP- and universal microarray-based assay for the detection 
of several important plant pathogens.  
The  possibility  to  detect  a  mutant  allele  in  500-times  excess  wild  type  sequence  was  previously 
demonstrated (Thomas  et  al.,  1999).  No  multiplex  PLP-based  diagnostic system was, however, developed 
and  characterized  to-date.  For  genotyping  versatile  and  highly-multiplexed  PLP-based  assays  were 
developed (Hardenbol et al., 2003; Baner et al., 2003; Alsmadi et al., 2003). In these assays, however, the 
discrimination  power  of  PLPs  was  found  to  be  much  lower.  Hardenbol  reported  an  average  ratio  of 
maximum  allele  signal  vs.  maximum  non-allele  signal  of  only  17  (Hardenbol  et  al.,  2003).  The  striking 
discrepancy probably arises from the fact that in a highly-multiplexed reaction positive signals are decreased 
due to competition, while background signals increase cumulatively. Although the reported discrimination is 
sufficient  for  reliable  allele  calls  in  a  highly  multiplexed  setting,  diagnostic  assays  require  a  much  higher 
discriminatory range.  
Therefore,  to  adapt  PLP  technology  for  multiplexed  pathogen  detection,  we  aimed  to  increase  PLP 
discriminatory  power.  To  these  ends,  we  tested  different  PLP  design  strategies  and  determined  the 
discriminatory  power  by  real-time  PCR.  Since  the  circularised  ligation  products  were  in  the  linear 
quantification  range  of  the  applied  method,  the  obtained  numbers  reflected  the  discriminatory  power  of 
ligation without amplification bias. We found that asymmetric PLP design, in which a long 5’ arm serves as 
an  anchor  sequence  and  the  binding  of  a  short  3’  arm is  an  equilibrium  process,  could  increase  mismatch 
discrimination by almost one order of magnitude. Faruqi and colleagues also demonstrated the superiority of 
asymmetric PLP design (Baner et al., 2003). Their assay conditions and evaluation method, however, were 
very  different  from  those  used  in  this  study.  We  set  our  conditions  according  to  the  planned  multiplex 
detection system, so that the results could be directly extrapolated. A further advantage of the asymmetric 
design is that while the 3’ arm may ensure excellent specificity, the binding of the long 5’ arm is quite stable 
and might tolerate potential mismatches caused by polymorphisms within the target group.  
We targeted the ITS region of rRNA operons for pathogen detection because of their high copy number and 
the large database of sequences for this region to aid assay design. Based on alignments of several hundred 
sequences, eleven PLPs were designed to detect economically important plant pathogens at genus, species 
and subspecies level. Since many of the isolates whose sequences were used in the design process were not 
available,  we  carried  out  the  initial  PLP  testing  by  using  complementary  oligonucleotides.  These  tests 
showed that the PLPs had comparable sensitivity and a discriminatory range of 105-107 and above. PLP P-
cac, one of the probes with the lowest discriminatory range, was further tested for specificity using genomic 
DNAs.  The  probe  proved  to  be  perfectly  specific:  no  cross-reaction  was  observed  with  105  times  higher 
amount  of  non-cognate  DNA  than  the  demonstrated  sensitivity  towards  its  target.  This  amount  was  much 
higher than what one may expect in real-world samples, suggesting that no false ligation is likely to occur in 
a real assay.  
The PLP design and the assay procedures were streamlined to provide reliable performance. New probes are 
incorporated into the multiplex set only after they were proven to have sufficient sensitivity and specificity 
in the oligonucleotide test. The ligated PLPs were amplified in LATE-PCR, a one-tube method to produce 
large  amount  ssDNA  amplicons,  which  are  ideal  for  microarray  hybridization.  Furthermore,  since  there  is 
linear  amplification  after  the  exponential  phase  in  LATE-PCR,  it  better  reflects  the  initial  template  ratios 
even at end point detection (Szemes et al., 2005). We believe this property could be exploited in the future 
for  semi-quantitative  pathogen  detection.  The  labelled  PLP  amplicons  were  analyzed  on  16-well 
microarrays, enabling sufficient through-put. In the near future, 48- and later 96-well formats are expected to 
68  

  
 
become  available,  thereby  fulfilling  the  need  for  high-throughput  sample  analysis  on  low-density  arrays 
(Schott Nexterion). 
The  developed  multiplex  detection  system  was  validated  with  characterized  isolates  representing  target 
organisms as well as close non-target relatives. In addition, artificial mixtures of pathogen genomic DNAs 
were  tested.  In  all  cases,  the  targeted  pathogens  were  correctly  identified  and  discriminated  from  related 
non-target organisms.  
The  sensitivity  and  dynamic  range  of  detection  was  determined  using  F.  oxysporum  and  M.  roridum 
genomic DNAs. Target DNAs of 0.5-5 pg could be detected in the presence of a large excess of the other 
target DNA, with a dynamic range of 100 to 1000. It is important to note that in all the assays an even larger 
amount  of  non-target  DNA  (20  ng)  was  always  present,  which  did  not  interfere  with  detection.  The 
sensitivity and dynamic range of the developed method compare favourably with those of other diagnostic 
microarrays (Alsmadi et al., 2003; Faruqi et al., 2001; Bodrossy et al., 2003; Castiglioni et al., 2004).  
In this study we described a PLP-based multiplex diagnostic system. The presented method offers numerous 
advantages  over  other  approaches  ((Alsmadi  et  al.,  2003;  Faruqi  et  al.,  2001;  Bodrossy  et  al.,  2003; 
Castiglioni  et  al.,  2004;  Denef  et  al.,  2003;  Lievens  et  al.,  2003).  There  is  no  practical  restrain  on  the 
selection of the targeted pathogens. One may enlarge or reduce the PLP set at any time to increase the range 
of detected organisms or to focus on a particular group. Using PLPs and universal arrays, target recognition 
becomes independent of the downstream processes, including amplification and array analysis, reducing cost 
and allowing multipurpose applications.  
By  changing  the  design  of  the  padlock  probes  quantitative  analyses  can  also  be  achieved  by  using  a  new 
platform  of  real  time  PCR,  the  OpenArrayTM  system  of  BioTrove  (Van  Doorn  et  al.,  2007).  Other 
amplification  strategies  of  the  circularised  padlock  probes  such  as  rolling  circle  amplification  (RCA)  are 
being explored as well as other detection platforms (e.g. nanoparticles and Luminex). 

Acknowledgements 
The authors wish to acknowledge the contribution of Isogen Inc. (Utrecht, The Netherlands) to production of 
microarrays  used  in  this  study.  Marianna  Szemes  was  supported  by  Marie  Curie  Fellowship  (QLK4-CT-
2001-51872) during the course of this project. The authors thank the Dutch Ministry of Agriculture, Nature 
and Food quality (DWK program 397III) and The Product Board for HorticuIture (Productschap Tuinbouw) 
for financial support.  

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Quantitative multiplex detection of plant pathogens using a novel ligation 
probe-based system coupled with universal, high-throughput real-time PCR 
on OpenArraysTM 

Ronald van Doorn1,2,§, Marianna Szemes1,3,§, Peter Bonants1, George A. Kowalchuk2,4, Joana F. Salles1,5, 
Elen Ortenberg6, and Cor D. Schoen1,* 

1Plant Research International B.V., Droevendaalses-
teeg 1, 6708 PB, Wageningen, the Netherlands;  
Tel: +31-317-476-026; Fax: +31-317-418-094  
2NIOO-Centre for Terrestrial Ecology, P.O. Box 40, 
6666 ZG, Heteren, the Netherlands;  
3University  of  Bristol,  Department  of  Cellular  and 
Molecular  Medicine,  University  Walk,  Bristol,  BS8 
1TD, United Kingdom;  

4Free University of Amsterdam, Institute of Ecologi-
cal  Science,  De  Boelelaan  1085,  1081  HV,  Amster-
dam, the Netherlands;  
5UMR  CNRS  5557–  Université  Lyon  1,  USC  INRA 
1193,  Microbial  Ecology  Centre,  F-69622  Villeur-
banne, France;  
6BioTrove,  Inc.  12  Gill  Street,  Woburn,  MA  01801-
1728, USA 

*Corresponding author e-mail: cor.schoen@wur.nl 
§The first two authors contributed equally to this work.  

Abstract 
Diagnostics  and  disease-management  strategies  require  technologies  enabling  the  simultaneous  detection 
and  quantification  of  a  wide  range  of  pathogenic  microorganisms.  Most  multiplex  quantitative  detection 
methods available suffer from compromises between the level of multiplexing, throughput and accuracy of 
quantification.  Here,  we  demonstrate  the  efficacy  of  a  novel  high-throughput  ligation-based  assay  for 
simultaneous  quantitative  detection  of  multiple  plant  pathogens.  The  ligation  probes,  designated  PRI-lock 
probes, are long oligonucleotides with target complementary regions at their 5’ and 3’ ends. Upon perfect 
target  hybridization,  the  PRI-lock  probes  are  circularized  via  enzymatic  ligation,  subsequently  serving  as 
template  for  individual,  standardized  amplification  via  unique  probe-specific  primers.  Adaptation  to 
OpenArraysTM, which can accommodate up to 3072 PCR amplifications, performed in 33 nl, allowed high-
throughput real-time quantification. The assay combines the multiplex capabilities and specificity of ligation 
reactions  with  high-throughput  real-time  PCR  in  the  OpenArrayTM,  resulting  in  a  flexible,  quantitative 
multiplex diagnostic system. The performance of the PRI-lock detection system was demonstrated using 13 
probes  targeting  several  significant  plant  pathogens  at  different  taxonomic  levels.  All  probes  specifically 
detected their corresponding targets and provided perfect discrimination against non-target organisms with 
very  similar  ligation  target  sites.  The  nucleic  acid  targets  could  be  reliably  quantified  over  5-6  orders  of 
magnitude with a dynamic detection range of more than 104. Pathogen quantification was equally robust in 
single  target  versus  mixed  target  assays.  This  novel  assay  enables  very  specific,  high-throughput, 
quantitative detection of multiple pathogens over a wide range of target concentrations and should be easily 
adaptable for versatile diagnostic purposes.  

Introduction 
Current technologies for multiplex, quantitative analyses frequently suffer from compromises between the 
level  of  multiplexing,  throughput  and  accuracy  of  quantification.  In  general,  for  the  detection  of  nucleic 
acids,  microarrays  and  macroarrays  can  provide  very  high  levels  of  multiplexing  (Sholberg  et  al.,  2005; 
Tambong et al. 2006), but yield a limited range of accurate quantitative information (Lievens et al., 2005a) 
and  relatively  low  sample  throughput  compared  to  real-time,  quantitative  PCR  (qPCR)  (Call,  2005;  Ding 

71  

  
 
 
 
 
 
and  Cantor,  2004).  At  present,  qPCR  provides  the  most  reliable  means  of  target  quantification,  and  it  is 
suitable for the analysis of relatively large sample numbers (Klein, 2002; Schena et al., 2004). Nowadays, 
successful  multiplex  qPCR-based  pathogen  detection  methodologies  have  been  realized  (Lievens  et  al., 
2006;  Schena  et  al.,  2006;  Tooley  et  al.,  2006),  however,  due  to  a  limited  number  of  potential  dyes,  the 
attainable level of multiplexing is low (Ding and Cantor, 2004; Mackay, 2004).  
Recently, a conceptually new, high-throughput platform has become available for real-time PCR, capable of 
accommodating  more  than  3000  reactions  per  array  (Morrison  et  al.,  2006).  The  OpenArrayTM  has  48 
subarrays, allowing parallel testing of up to 48 samples, and each subarray contains 64 microscopic through-
holes  of  33  nl  volumes  (Figure  1A).  Primers  are  pre-loaded  and  dried  down  in  user  specified  holes.  The 
holes function as capillaries, accurately self metering sample and master mix added by an automated loader. 
Hydrophilic  holes  and  hydrophobic  array-surface  coatings  ensure  the  sample  remains  isolated  through 
surface tension.  
The assay system described in this report incorporates the most attractive features of several approaches to 
provide a unique detection platform that combines multiplex technology with stringent diagnostic standards: 
(1)  high  specificity  is  achieved  via  the  use  of  ligation-based  circularization  probes,  (2)  real-time  PCR 
methodology  provides  excellent  sensitivity  and  accurate  quantification,  and  (3)  adaptation  to  the  newly 
developed OpenArrayTM technology facilitates high-throughput sample screening.  

Figure  1:  Schematic  overview  of  the 
proposed assay. (A) OpenArrayTM archi-
tecture. The OpenArrayTM has 48 subar-
rays  and  each  subarray  contains  64  mi-
croscopic  through-holes  of  33  nl  vol-
ume.  The  primers  are  pre-loaded  into 
the  holes.  The  sample  combined  with 
the  reaction  mix  is  auto-loaded  due  to 
the surface tension, provided by the hy-
drophilic  coating  of  the  holes  and  the 
hydrophobic  surface  of  the  array.    (B) 
PRI-lock  probe  design.  T1a  and  T1b 
indicate  target  complementary  regions. 
Unique primer sites ensure specific am-
plification (forward: F1 and reverse: R1) 
and  each  PRI-lock  contains  a  universal 
sequence  and  a  desthiobiotin  moiety 
(dBio). (C) Multiple target specific PRI-
lock  probes  are  ligated  on  fragmented 
DNA samples. T1a and T1b bind to ad-
jacent  sequences  of  the  target  and  in 
case of a perfect match, the probe is circularized by a ligase. The probes are captured via the desthiobiotin moiety 
using magnetic streptavidin-coated beads. The PRI-lock probes are washed and quantitatively eluted from the beads. 
Unreacted probes are removed by exonuclease treatment. (D) Circularized probes are loaded and independently am-
plified on the Biotrove OpenArrayTM platform using PRI-lock probe specific primers. The amplification is monitored 
using SYBR-Green and the ligated PRI-lock probes are quantified based on the threshold cycle number (CT) of the 

Here, we propose that a new, ligation based probe assay can bridge the gap between highly specific target 
recognition and high-throughput, multiplex pathogen quantification. Circularizable ligation probes (Padlock 
probes)  (Nilsson  et  al.,  1994)  have  previously  been  applied  successfully  for  multiplex  pathogen  detection 
(Szemes  et  al.,  2005),  but  do  not  allow  quantification.  In  the  currently  developed  assay,  the  circularized 
probes  are  amplified  by  using  probe-unique  primer  pairs  via  real-time  PCR,  enabling  accurate  target 
quantification in a highly multiplex format. The utilized probes have been termed PRI-lock probes and they 
consist  of  two  target  complementary  regions,  one  at  each  terminus  of  the  probe  (Figure  1B).  The  target 
complementary arms are connected via a compound linker sequence, containing unique primer binding sites, 
a generic sequence and a desthiobiotin moiety for specific capture and release (Hirsch et al., 2002) (Figure 

72  

  
 
1B). The primer binding sites were selected to ensure optimal amplification under universal conditions and 
lack of interaction during the ligation and PCR steps. 
In the implemented strategy, mixtures of multiple PRI-lock probes are ligated on fragmented DNA (Figure 
1C). Target recognition is achieved by specific hybridization of both arm sequences, and efficient ligation 
occurs  only  if  the  end  nucleotides  perfectly  match  the  target  (Nilsson  et  al.,  2006).  Therefore,  the  probes 
confer  superior  specificity  (Szemes  et  al.,  2005;  Nilsson  et  al.,  2006).  After  ligation,  the  probes  are 
reversibly  captured  via  the  desthiobiotin  moiety,  treated  with  exonuclease  (Figure  1C)  and  finally 
individually assayed via real-time PCR on OpenArrayTM plates (Figure 1D).  
In this study, we characterize the quantification power of circularizable ligation probes over a range of target 
concentrations  and  multiple  target  ratios  and  report  the  development  of  a  high-throughput,  quantitative 
multiplex  diagnostic  assay.  The  specificity,  sensitivity,  linear  quantification  range,  and  the  dynamic 
detection  range  of  the  developed  assay  were  demonstrated  using  13  pathogen  specific  PRI-lock  probes, 
ligated on individual and mixed target DNAs, followed by real-time PCR on OpenArraysTM.  

Methods 

Nucleic acids used in the study 
Microorganisms  were  derived  from  the  culture  collection  of  Plant  Research  International  BV  (Table  1). 
Genomic  DNAs  from  all  microorganisms  were  isolated  using  the  Puregene  Genomic  DNA  isolation  kit 
(Gentra/Biozym, Landgraaf, the Netherlands) according to the manufacturer’s instructions. Ligation targets 
for  assay  optimization  were  generated  using  500  pg  extracted  genomic  DNA  as  PCR  template.  Generic 
primers were used to amplify selected genes as described previously (Van Doorn et al., 2007). The PRI-lock 
probes listed in Table 1 and all the other oligonucleotides used in this study were synthesized by Eurogentec 
SA (Seraing, Belgium). 

PRI-lock probe design 
The PRI-lock probe target complementary regions were engineered according to previously described design 
criteria (Szemes et al., 2005). Relevant nucleic acid sequences derived from Genbank and from independent 
sequencing  studies  were  aligned  by  using  ClustalW.  Diagnostic  sequences  were  identified  for  each  target 
group. Potential PRI-lock probe target complementary regions were selected in a way that the discriminatory 
nucleotides  would  bind  to  the  3’arm  region.  Further  design  rules  are  described  elsewhere  (Szemes  et  al., 
2005; Van Doorn et al., 2007). 

Ligation, capturing and exonuclease treatment  
Genomic DNA was fragmented by digestion using EcoRI, HindIII and BamHI (New England Biolabs) for 
30 min, and used as template. Cycled ligation, capturing of ligated probes and exonucleae treatment were 
performed  as  previously  described  (Szemes  et  al.,  2005;  Van  Doorn  et  al.,  2007).  PCR  fragments  and 
genomic  DNAs  were  used  as  templates  for  ligation.  To  monitor  the  ligation  efficiency  and  provide  a 
reference for normalization, an Internal Ligation Control (ILC) PRI-lock probe was constructed (Van Doorn 
et al., 2007).  

Real-time PCR (ABI) and Biotrove OpenArrayTM real-time PCR  
Amplification of ligated PRI-lock probes was monitored in real-time using the 7500 Real-Time PCR system 
(Applied  Biosystems,  Foster  City,  USA).  Amplification  of  ligated  PRI-lock  probes  was  followed  in  real-
time using an OpenArrayTM NT Cycler (BioTrove Inc., Woburn, USA). OpenArrayTM subarrays were pre-
loaded by Biotrove with the selected primer pairs. Each primer pair was spotted in duplicate. Samples were 
loaded  into  OpenArrayTM  plates  using  the  OpenArrayTM  NT  Autoloader  according  to  the  manufacturer’s 
protocols.  The  PCR  OpenArrayTM  thermal  cycling  protocol  consisted  of  90°C  for  10  min,  followed  by 
cycles of 28 s at 95°C, 1 min at 55°C and 70 s at 72°C (imaging step).  

73  

  
 
 
 
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Results 

PRI-lock probe design and evaluation of assay performance 
The  newly  designed  probing  system  was  experimentally  tested  using  13  PRI-lock  probes  engineered  to 
detect  several  economically  important  plant  pathogens  at  different  taxonomic  levels  (Table  1).  The  target 
complementary regions were selected as previously described, and the specificity of the probes was verified 
(Szemes et al., 2005). Unlike padlock probes, each of the PRI-lock probes was designed with unique primer 
binding sites allowing quantitative detection. The PRI-lock probes were also engineered with a desthiobiotin 
moiety  between  the  primers  sites  for  reversible  PRI-lock  probe  capture,  washing  and  release  using 
streptavidin-coated  magnetic  beads.  The  introduction  of  this  additional  purification  step  removes  excess 
non-target  DNAs  and  possible  enzyme-inhibiting  compounds,  resulting  in  increased  efficiency  of 
exonuclease treatment and reduced assay background (data not shown). 
The  developed  PRI-lock  probe  system  was  validated  in  several  steps.  To  evaluate  the  specificity  of  the 
designed PRI-lock probes, a mixture of 14 probes (13 target probes plus one internal ligation control) was 
ligated  on  various  individual  DNA  targets.  The  samples  were  screened  for  amplification  with  all  the 
individual  PRI-lock  probe  specific  primer  pairs  in  conventional  real-time  PCR.  All  the  target  sequences 
were specifically detected without exception (Table 2) and no amplification of ligated PRI-lock probes with 
other,  non-cognate  primer  pairs  occurred  (data  not  shown).  In  this  and  all  subsequent  experiments,  the 
amplification  efficiency  of  the  PCR  control  was  found  to  be  uniform  (data  not  shown)  and  the  CT  values 
were normalized using the ILC. To further ensure specificity towards the targeted pathogens, 3 non-target 
organisms with very similar ligation sites (1, 3 and 7 mismatches compared to the perfect ligation sequence) 
for the PRI-lock mixture were tested. The rest of the probes discriminated their targets from the most similar 
non-target  sequence  based  on  more  than  7  mismatches  and  were  not  tested.  In  agreement  with  previous 
results (Szemes et al., 2005), no signal was observed in the presence of the non-target organisms with very 
similar ligation sites (Van Doorn et al., 2007), demonstrating the specific target recognition by the designed 
PRI-lock probes.  
To  evaluate  the  performance  of  the  PRI-lock  system  in  a  multiplex  setting,  the  probe  mix  was  ligated  on 
several target DNA mixtures and analyzed in conventional real-time PCR. Multiple target DNA sequences 
were detected with no statistically significant change in the observed CT values compared with the CT values 
observed for single-target detection (Table 2).  
In  diagnostic  applications,  we  expect  to  quantify  several  target  organisms  over  large  concentration 
differences.  To  investigate  the  sensitivity  and  linear  quantification  range  of  the  PRI-lock  probe  system  in 
conventional real-time PCR, a 10-fold dilution series of all selected targets were detected and quantified. A 
linear quantification range of 6 orders of magnitude was achieved with a sensitivity of 103 -105 target copies/
μl initial ligation mixture for all targets and corresponding PRI-lock probes.  

Application of the PRI-lock based multiplex quantitative detection on the Biotrove OpenArrayTM 
platform 
On a conventional real-time PCR machine, analysis of a single sample however, would typically require 14 
separate  reactions,  one  for  each  probe,  making  large  scale  screenings  time  consuming,  laborious  and 
expensive. A solution was offered by the Biotrove OpenArrayTM system, which provides a platform for the 
parallel analysis of 48 samples against the 14 PRI-lock probes. To compare the performance of the PRI-lock 
system  in  the  Biotrove  OpenArrayTM  with  the  performance  in  conventional  real-time  PCR,  samples 
containing single or multiple targets were ligated and analyzed on the Biotrove OpenArrayTM platform. The 
obtained CT values were found to be similar to the CT values observed in the conventional real-time PCR 
(Table  2),  although  the  number  of  target  copies  per  PCR  was  lower  in  the  OpenArrayTM  due  to  smaller 
volume. 

Quantification and validation 
To  enable  accurate  pathogen  quantification  of  unknown  samples,  calibration  curves  were  constructed  for 
each of the 13 PRI-lock probe targets. Because the ligation of different probes is independent, the calibration 
curves could be constructed based on CT values measured in a multiplex setting. The PRI-lock probes were 
ligated on a 10-fold dilution series of all target DNA sequences and amplified in the Biotrove OpenArrayTM 

75  

  
platform. The obtained calibration curves showed that all the target pathogens were quantified over at least 5 
orders of magnitude (Van Doorn et al., 2007). The correlation between the logarithmic target concentration 
and the observed CT value was very high for all the detected pathogens (lowest R2 = 0.984, average R2 = 
0.993).    
The  developed  multiplex  system  was  tested  on  individual  practical  samples  from  recirculation  water 
systems. Pathogens, detected with traditional methods, were also perfectly detected in multiplex setting. 

Table 2: Specificity and multiplexing of the PRI-lock probe system in conventional real-time PCR and in the Biotrove 
OpenArrayTM for single and multiple targets. 

Single target 

Multiplex mix 1 

Multiplex mix  2 

Multiplex mix 3 

PRI-lock Probe 

CT(AB) 

CT(BT) 

CT(AB) 

CT(BT) 

CT(AB) 

CT(BT) 

CT(AB) 

CT(BT) 

Phytophthora spp. 

15.5 (0.14)  16.4 (0.17) 

15.7 (0.10)  16.7 (0.25) 

16.1 (0.04)  16.8 (0.12) 

16.3 (0.16) 

15.9 (0.09) 

P. infestans 

A. tumefaciens 

14.8 (0.12)  15.4 (0.18) 

16.4 (0.24)  16.2 (0.04) 

G. Proteo bacterial spp 

17.0 (0.11)  17.7 (0.08) 

-- 

-- 

-- 

-- 

-- 

-- 

M. roridum 

F. oxysporum 

16.2 (0.07) 

17.4 (0.11) 

E. carotovora carotovora 

15.1 (0.15) 

V. dahliae 

16.9 (0.01) 

V. alboatrum /  V. tricorpus 

19.6 (0.11) 

M. hapla 

R. solani AG 2-2 

R. solani AG 4-1 

R. solani AG 4-2 

17.0 (0.30) 

16.3 (0.03) 

14.0 (0.02) 

19.3 (0.10) 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

16.4 (0.02)  17.2 (0.05) 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

15.2 (0.08)  15.7 (0.12) 

15.1 (0.02) 

15.0 (0.08) 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

15.8 (0.28) 

15.9 (0.06) 

17.7 (1.03) 

17.1 (0.04) 

16.7 (0.12) 

17.0 (0.16) 

17.2 (0.08) 

17.3 (0.04) 

15.2 (0.24) 

15.4 (0.05) 

16.1 (0.04) 

15.8 (0.03) 

19.9 (0.29) 

18.0 (0.10) 

18.2 (0.23) 

16.9 (0.11) 

15.8 (0.10)  17.1 (0.12) 

16.2 (0.03) 

16.2 (0.04) 

14.6 (0.13)  15.0 (0.11) 

14.5 (0.13) 

14.4 (0.09) 

19.5 (0.14)  20.0 (0.18) 

19.3 (0.07) 

18.8 (0.03) 

The PRI-lock probes were ligated on 106 targets/μl ligation mixture. CT values were normalized using the ILC control 
PRI-lock probe. Data represent average CT values of three PCR replicates in the conventional real-time platform (n=3) 
and of four PCR replicates in the Biotrove OpenArrayTM (n=4). Standard deviations are indicated between brackets. 
AB:  samples  run  on  the  conventional  real-time  PCR  platform.  BT:  Samples  tested  on  the  Biotrove  OpenArrayTM 
platform. Multiplex mix 1: P. soj. and M. ror. DNA, Multiplex mix 2: P. inf., R. sol. AG 2-2, R. sol. AG 4-1 and R. 
sol. AG 4-2 DNA, Multiplex mix 3: ligation mixture containing all the DNA targets indicated in table 1, --: not tested.  

Discussion 
In this study, we demonstrated a specific, multiplex, PRI-lock probe-based, high-throughput detection assay 
using  the  Biotrove  OpenArrayTM  platform  for  the  detection  and  quantification  of  plant  pathogens.  The 
described application serves as a model for the development of rapid, molecular detection systems that offer 
an unprecedented combination of specificity, high throughput capabilities and robust target quantification. 
To evaluate the designed PRI-lock probes and corresponding primer pairs, the probes were ligated on single 
target  sequences  and  tested  using  conventional  real-time  PCR.  In  all  cases,  the  targets  were  correctly 
detected and no false positives were observed, indicating highly specific ligation of the PRI-lock probes on 
their  respective  targets  and  no  cross  reaction  with  closely  related  targets.  In  addition,  the  influence  of 
multiple targets on PRI-lock probe based detection was tested, and it was demonstrated that the presence of 
multiple  targets  had  no  statistically  significant  influence.  Our  multiplex  detection  system  provided  truly 
independent  detection  of  the  different  pathogens,  with  no  evidence  of  inhibition  due  to  possible  ligation 
competition. 

76  

  
 
 
  
Quantitative  diagnostic  assays  require  a  linear  range  of  quantification  of  several  orders  of  magnitude.  We 
showed  that  ligation  of  PRI-lock  probes  can  reflect  well  the  target  quantity  over  at  least  5  orders  of 
magnitude.  
The sensitivity of detection was determined by testing a 10-fold dilution series of ligation targets for all the 
PRI-lock  probes.  Sensitivities  of  103  and  104  target  copies/μl  initial  ligation  mixture  were  achieved, 
depending on the PRI-lock probe.  
Both, the inter-array and assay-to-assay variation of the PRI-lock system were very low, demonstrating the 
reproducibility  and  quantitative  reliability  of  the  PRI-lock  detection  assay.  The  developed  quantitative 
multiplex detection assay was validated by testing various artificial mixtures of target DNAs. The observed 
dynamic  range  is  an  improvement  over  most  previously  developed  multiplex  pathogen  detection  assays, 
where  the  dynamic  range  was  often  limited  to  100-1000  (Szemes  et  al.,  2005;  Denef  et  al.,  2003).  In  the 
Biotrove OpenArrayTM platform, target PCR amplifications are completely independent of each other, and, 
consequently,  no  PCR  competition  among  the  different  ligated  PRI-lock  occurs.  The  dynamic  range  of 
detection  in  the  PRI-lock  detection  assay  is  therefore,  as  long  as  the  PRI-lock  probes  ligate  on  their 
respective  targets,  practically  unlimited.  Finally,  artificial  mixtures  of  genomic  DNAs  in  different 
concentration ratios were tested. The components of the pathogen mixture were identified, and the original 
target  input  was  calculated  using  the  calibration  formulas.  Moreover,  the  ratios  of  the  targets  among  the 
different ligation samples were correctly identified as well.  
Circularization  probes  have  previously  been  applied  successfully  for  the  detection  of  multiple  plant 
pathogens in diagnostic samples (Szemes et al., 2005), but without the ability to quantify target numbers. To 
our knowledge, this report presents the first time that numerous plant pathogens could be simultaneously and 
accurately  quantified  using  specific  circularization  probes  in  a  single  assay.  For  future  applications, 
however, higher multiplexing is intended and therefore, the number of PRI-lock probes will be increased. 
Currently, assay background is considered as the biggest obstacle for increasing the level of multiplexing in 
traditional circularization probe-based diagnostic assays. Traditional circularization probes contain generic 
primer sites for PCR amplification (Szemes et al., 2005). Multiplex PCR via general primer sites carries the 
potential  for  competition  during  amplification,  with  a  cumulative  increase  in  background,  which  reduces 
assay sensitivity. In contrast, each PRI-lock probe carries a unique pair of primer binding sites, unrelated to 
the sequences of all the other probes. Circularized PRI-lock probes can therefore be amplified individually. 
Increasing  the  level  of  multiplexing  would  not  be  expected  to  increase  the  background  signal.  To  further 
guarantee  low  background,  even  in  highly  multiplex  settings,  we  are  currently  developing  a  universal 
TaqMan®  probe  which  hybridizes  to  the  generic  sequence  incorporated  in  all  the  PRI-lock  probes.  In 
addition,  including  a  universal  TaqMan®  probe  should  speed  up  data  analysis,  and  therefore  sample 
throughput, since there is no need to conduct amplicon dissociation curve analysis. Given the independent 
PCR  amplification  of  the  ligated  PRI-lock  probes  and  the  three  slide  capacity  of  the  OpenArrayTM  NT 
Cycler,  it  should  be  feasible  to  engineer  ultra-high  throughput  arrays  for  the  quantitative  detection  of 
hundreds of targets simultaneously.  

Conclusions 
To date, most multiplex pathogen detection has been performed using traditional hybridization microarrays 
(Call, 2005; Lievens et al., 2005b). Although such platforms typically allow highly multiplex detection, they 
generally  offer  relatively  low  sample  throughput  and  yield  limited  quantitative  information  compared  to 
qPCR (Abruzzo et al., 2005). In this study, we described a high-throughput diagnostic system that combines 
very specific multiplex pathogen detection with accurate quantification over a range of target concentrations. 
The PRI-lock probes, combined with the OpenArrayTM system, offer a flexible and adaptable design of high-
throughput,  quantitative  multiplex  detection  assays,  since  the  target  recognition  is  separated  from  further 
downstream  processing.  It  should  be  noted,  although  we  have  demonstrated  that  large  quantities  of  non-
target DNA do not influence the accuracy of PRI-lock probe-based detection, PRI-lock performance remains 
to  be  examined  within  field  applications.  The  PRI-lock  system  described  is  readily  modifiable  and 
expandable to include an almost unlimited range of potential targets, providing an easily accessible platform 
for versatile diagnostic applications. 

77  

  
 
 
Acknowledgements 
The  authors  would  like  to  thank  Tom  Morrisson  for  helpful  suggestions  concerning  the  manuscript.  This 
work  was  funded  by  the  Dutch  Ecogenomics  consortium,  workpackage  3.4.  Financial  support  was  also 
obtained from the ministry of Agriculture, Nature & Food Quality and  the Product Board for Horticulture 
Productschap Tuinbouw, the Netherlands. A patent application for desthiobiotin labeled PRI-lock probes is 
currently pending (PCT/NL2005/000807).  

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79  

  
 
 
 
 
 
 
 
 
A 16S rDNA-based microarray for high-throughput diversity analysis  
of complex bacterial communities 

Xavier Nesme1* and Christine Oger-Desfeux2 

1 Université de Lyon; Université Lyon 1;  
CNRS;  INRA;  Ecologie  Microbienne  UMR  5557, 
USC 1193, 16 rue R. Dubois, Domaine Scientifique 
de La Doua, 69622 Villeurbanne cedex, France. 

2  Université  de  Lyon;  Université  Lyon  1;  IFR  41, 
DTAMB, PRABI, 16 rue R. Dubois, Domaine Scien-
tifique  de  La  Doua,  69622  Villeurbanne  cedex, 
France. 

* Corresponding author e-mail: nesme@biomserv.univ-lyon1.fr 

Abstract 
The improved understanding of the global functioning of microbial communities requires tools to rapidly 
describe spatiotemporal variations of their various taxon components. For this purpose, our group devel-
oped  a  16S  biochip  for  high  throughput  and  simultaneous  characterizations  of  bacterial  taxa  in  complex 
environments, and especially agricultural soils and rhizospheres. The chip was tested with pure cultures as 
well  as  metagenomes  of  soil  and  rhizosphere  communities.  Complex  community  compositions  obtained 
from statistical treatment of biochip data were verified by alternative methods such as cloning/sequencing 
and  quantitative  PCR  performed  with  the  same  metagenomes.  The  project  provided  relevant  information 
about the required probe characteristics and hybridization conditions, the level of probe specificity accord-
ing to mismatches, the number of probes yielding false positive or negative results, and the biochip sensi-
tivity, measured by the smallest fraction of a community that could be detected with enough confidence. 
The project in fine demonstrated the great potential of the high throughput microarray approach for aspects 
of environmental microbiology that need the simultaneous identification of huge numbers of taxa. 

Introduction 
Determining the taxonomic composition of bacterial communities is a constant request in microbial ecol-
ogy. This is needed for a better knowledge of the taxon distribution in ecosystems or for more applied pur-
poses such as the detection of pathogens, the monitoring of the fate of inoculated biocontrol agents or of 
the whole community in polluted soils. Moreover, because taxa are often present in huge number and be-
cause individual taxonomic identifications require a lot of expertise, taxonomic inventory of complex com-
munities is yet poorly documented. As a result, the respective role of taxa in the global functioning of mi-
crobial communities is badly known. Our group is thus developing a general taxonomic microarray to ana-
lyse and monitor the diversity of complex bacterial community by targeting the 16S gene.  
Microarrays have begun to be used for the detection and identification of microbial strains, species, genera 
or higher taxa in a single assay. The most obvious targets are rrs and rrl (i.e. 16S and 23S rRNA genes) 
and  the  feasibility  of  a  microarray  approach  for  bacterial  identification  has  been  tested  by  Bodrossy  and 
Sessitsch (2004). Functional genes, e.g. pmoA, nifH have also been used to assess functional communities 
(Jenkins et al., 2004; Bodrossy et al., 2003). In contrast to functional genes, the high level of sequence con-
servation of the 16S rRNA gene and its ubiquity in the prokaryotic domain (Woese and Fox, 1977) allow 
access by PCR to almost all bacteria (Bruce et al., 1992). Consequently, one of the great advantages of the 
16S rRNA gene is the availability of the largest databases for a single gene (Cole et al., 2005). These data-
bases were used to construct the ARB 16S rRNA database, which contains correctly aligned sequences of 
high quality (Ludwig et al., 2004). The 16S rRNA gene can be used to design probes targeting bacteria at 
various taxonomic levels, from phyla and classes to species.  
The present report reviewed results obtained during the project that described the development and the vali-
dation of a multi-purpose 16S microarray either with pure cultures (Sanguin et al., 2006a) or with natural 
bacterial communities from bulk or rhizosphere soils (Sanguin et al., 2006b), and its practical application 

80  

  
 
 
 
 
for analyzing bacterial communities differences between soils which are either safe, conducive or suppres-
sive to take-all (Sanguin et al., 2006c).  

Design and resulting characteristics of the 16S microarray 

Probe characteristics 
Probes complementary to rrs sequences (16S rDNA gene) were designed to allow hybridization with am-
plified ribosomal DNA or ribosomal RNA (Figure 1). Preliminary tests showed that 18–21 nt long probes 
exhibited  the  best  compromise  between  sensitivity,  specificity,  and  the  necessity  to  homogenize  probes 
with respect to their dissociation temperature. The chosen probe length was thus generally 20 nt with some 
modifications when length could not be adjusted to 20 nt as it affected specificity. Most probes were cho-
sen  with  predicted  melting  temperatures  of  65  ±  5  °C  and  a  minimum  GC  content  of  50%.  In  addition, 
probes  having  selective  nucleotides  (corresponding  to  mismatches)  located  in  a  central  position  were  fa-
voured  to  enhance  specificity.  Probes  with  strong  hairpin  structures  (∆G  ≤  -  2  kcal  mol-  1,  Tm  ≥  50  °C) 
were removed from the probe set.  

Figure 1: Nested phylogenetic strategy design and 
localization  of  rrs  probes  allowing  identification 
of  Agrobacterium  members  at  various  taxonomic 
levels from cluster of species (16S OTU) to phyla 
and  domains.  The  probe  quality  was  systemati-
cally tested with Agrobacterium pure cultures. The 
same  design  strategy  was  used  for  other  taxa  in-
cluded  in  the  biochip  but  the  specificity  of  probe 
responses  to  hybridizations  could  be  tested  only 
when  pure  cultures  were  available.  The  unavail-
ability  of  numerous  bacteria  in  pure  culture  is  a 
known  limitation  for  the  exhaustive  testing  of  all 
16S probes. 

Taxonomic range allowed by 16S probes 
The  finest  taxonomic  level  allowed  by  the  rrs  diversity  is  higher  than  the  species  because  in  most  taxa, 
such as Agrobacterium (Mougel et al., 2002), readily different bona fide (i.e. genomic) species may actu-
ally have identical 16S rDNA. In the present report, the term 16S operational taxonomic unit (OTU) was 
thus  used  to  designate  bacteria  with  the  same  16S  rRNA  gene  sequence.  In  addition,  a  single  nucleotide 
polymorphism (SNP) is often not enough to discriminate clusters of closely related species. For instance, in 
the species complex Agrobacterium tumefaciens (i.e. biovar 1), several probes are required to unambigu-
ously distinguish one OTU from one another.  
Conversely,  rrs  is  known  to  allow  the  design  of  probes  targeting  bacteria  at  various  taxonomic  levels 
higher than 16S OTUs (Figure 1). The phylogenetic software package ARB (Ludwig et al., 2004) (http://
www.arb-home.de) with the ARB 16S rRNA database (ssu02jun.arb) was used to design probes and pre-
dict their specificity. The selected probes target a wide range of taxonomic levels from superior taxa phyla, 
class, families, genera and 16S OTUs. As a result of the nested phylogenetic strategy, OTUs hybridized to 
several  probes  corresponding  to  various  taxonomic  levels.  Thus,  even  if  there  is  no  16S  OTU  specific 
probe for some bacteria in the present probe set, there will always be at least one probe designed for higher 
taxonomic levels that would hybridize to any new strain.  

Microarray probe set 
The present set of validated probes (ca. 170) is available in supplemental data of Sanguin's papers (2006 a, 
b, c). The number of probes currently tested reach however ca. 800 and is still increasing, and will be made 
available  soon.  The  most  common  taxa  in  soil  and  rhizospheres,  Alpha-,  Beta-,  Delta-,  Epsilon-
proteobacteria, Actinobacteria, Acidobacteria, Planctomycetes, Firmicutes, Bacteroidetes, Actinobacteria, 
Verrucomicrobia, Chloroflexi, Cyanobacteria, were the more intensively investigated. With some families, 
such  as  Bradyrhizobiaceae,  only  few  probes  for  identification  below  the  family  level  could  be  obtained. 

81  

  
 
This failure illustrates the limit of the 16S rRNA approach to discriminate between closely related taxa in 
certain cases. 

DNA targets and hybridization conditions 

General and population specific DNA targets 
The target DNA hybridized on the biochip was obtained by PCR amplification of rrs. For standard analy-
ses with pure cultures or for analyzing whole communities, PCR were performed with "universal" primers 
PA and PH' (Bruce et al., 1992). We verified that it was also possible to have access to taxonomic subsets 
of the whole community, for instance agrobacteria or pseudomonads, by using more selective primers. This 
is a very important potential of our 16S biochip approach. The specificity of the PCR reaction is so high 
that  subsets  of  the  whole  community,  which  are  hardly  accessible  with universal  primers,  could  be  thor-
oughly analyzed using more selective primers (Sanguin et al., 2006 b, c). 

Hybridization conditions 
In preliminary studies, PCR products were directly labelled by using a Cy3 labelled PA primer. Although, 
this 5'-end-labeling procedure was found efficient with probes designed in the 5' end of rrs, for unknown 
reasons it failed to provide significant hybridization signals with probes designed in the 3' end of rrs. Tar-
get products were later labelled with Cy3-dCTP (Amersham Biosciences Europe GmbH, Saclay, France) 
during  PCR  in  order  to  generate  internally  labelled  double-stranded  DNA  which  hybridized  all  probes 
whatever their localization in rrs. Alternatively, the 16S amplicons were labelled by in vitro transcription 
as described by Stralis-Pavese et al. (2004), using in this case  Cy3-UTP (Amersham Biosciences Europe 
GmbH,  Saclay) 
(5’-
TAATACGACTCACTATAG-3’) terminus in 5’ to allow processing of the T7 RNA polymerase. 

obtained  with 

and  PCR 

containing 

products 

primers 

a  T7 

Mismatch level and probe specificity 
The expected specificity of probes was estimated by determining potentially matching 16S OTUs with the 
function Probe Match of ARB against the totality of the ARB 16S rRNA database. Then, intensities of sig-
nal hybridization were plotted against weighted mismatch (WMM) values. With the chosen labelling and 
hybridization conditions (overnight hybridization at 50°C, washing at room temperature by shaking for 5 
min in 2X SSC containing 0.03% SDS and then twice for 5 min in 0.5X SSC), an optimal WMM cut-off of 
2 was found to be the best compromise between unpredicted positive hybridizations and unpredicted nega-
tive hybridizations: 2.8% and 0.8% for false positive and false negative hybridizations, respectively. How-
ever,  the  latest  results  obtained  with  RNA  instead  of  DNA  for  hybridizations,  the  cut-off  is  1.5  WMM 
(Grundmann, personal communication). This has important implications for designing new probes as well 
as for the interpretation of microarray results.  
Significant  hybridization  signals  were  never  found  with  16S  OTU  displaying  more  than  3.5  WMM.  For 
subsequent data analyses, bacteria targeted by each probe with up to 3.5 WMM were listed with the Probe 
Match function. The Probe Match output files were then transferred to CalcOligo version 1.07 (Bodrossy et 
al., 2003). The CalcOligo output file was used to fill a spreadsheet with the WMM values of microarray 
probes with each 16S rRNA gene sequence of the ARB database. This spreadsheet was used hereafter to 
analyze hybridization data (Figure 2). As a follow up of CalcOligo, ARB outputs are now transferred to a 
web-interfaced database (Prestat et al., 2005) designed to store and query a higher number of probe/target 
relationships. This was a necessary development as we now have more than one thousand probes available. 
This database is also designed to store all other ecological data and experimental conditions as required by 
MIAME standards as well as microarray results and statistical analyses to facilitate integration of data. Col-
lection of feedback information on the behaviour and quality of probe responses is an important objective 
of this project. 

Data processing  
An important milestone of microarray studies is the way by which raw data are processed to provide reli-
able and comparable results between slides. 
The basic probe pattern on the microarrays consisted of two spots for each of the control (i.e. universal)  

82  

  
Figure  2:  Flow  charts  of  manual  and  bioinfor-
matics  methods  used  to  select  probes  and  to 
keep  their  characteristics  in  a  spreadsheet  for 
integration in subsequent analyses of microarray 
hybridization data. The most relevant character-
istic of a probe is its expected level of specific-
ity - expressed as the number of weighted  mis-
match  (WMM  between  0  and  3.5)  -  with  16S 
OTUs of the ARB database. 

probes EUB342 and EUB338, four spots for the probe AntiPA (used as landmarks for image analysis) and 
one spot for each feature probe. The basic pattern was replicated six times on a microarray, leading thus to 
six spots per feature probe per microarray.  

Filtration and normalisation 
Pre-processing and subsequent statistical analyses use scripts developed from the R statistical software en-
vironment (R Development Core Team 2007, http://www.R-project.org).  
Individual spots were considered positive if 80% of the spot pixels had an intensity higher than the median 
local background pixel intensity plus twice the standard deviation of the local background. This procedure 
decreases the probability of false positive signals to 2.5%. Moreover, a given feature probe was considered 
as truly hybridized when at least three of six replicate spots provided a significantly positive hybridization.  
Using square root of intensity signals, statistics showed that best method of data normalization for reliable 
comparisons of different slides was given by expressing the intensity of each spot as the percentage of the 
total intensity signal of its local basic pattern.  

Quantification 
When microarrays are hybridized with DNA from pure cultures, all probes are thought to be in contact with 
an equal amount of targets. Intuitionally, similar hybridization signal intensities were expected. However, 
in spite of the great care taken for probe design and even in case of perfect match in stoichiometric condi-
tions,  hybridization  intensity  values  span  over  a  large  range.  Most  probes  with  perfect  mach  displayed  a 
relative  hybridization  intensity  varying  from  20%  to  150  %  of  the  intensity  obtained  with  the  universal 
probe EUB used as reference (with a median value at 96%). The median value was still 64% of the EUB 
intensity with probes having 0.1 to 1.9 WMM, but hybridization intensities dropped dramatically for 2 or 
more WMM. The underlying cause of intensity variations is not clear, but they are reproducible with the 
same relative ranking. The use of the 16S microarray for quantification needs thus the knowledge of the 
relative  intensity  obtained  for  each  probe  in  case  of  perfect  match.  This  was  only  possible  for  a  limited 
number of probes, but, as indicated above, not reasonably feasible as the number of probes increased dra-
matically and because targeted OTUs are not available in pure culture. This is a major limitation of the pre-
sent 16S microarray especially when it is used with environmental samples consisting in a mixture of 16S 
OTUs in variable amount. 
A semi-quantitative scale was established for metagenomic DNAs based on the quartiles of the normalized 
hybridization intensities with four classes of relative fluorescence units (RFU) : > 3%, 1.5-3%, 0.9-1.5%, 
and < 0.9%. However, comparison of microarray results for environmental samples was carried out on the 
normalized  intensity  signals  by  principal  component  analysis  (PCA)  using  the  package  ade4  (http://
pbil.univ-lyon1.fr/ADE-4/)  for  R  or  by  hierarchical  cluster  analysis  and  the  K-Means  test  using  R.  The 
PCA  was  performed  by  using  the  covariance  matrix,  and  the  hierarchical  cluster  analysis  was  based  on 
Ward’s method. 

83  

  
 
 
 
 
 
 
 
 
 
Detection limit 
Spiking experiments were done with 16S rRNA PCR products from bacteria not present in the tested soil. 
Microarray analyses showed that the detection limit was 0.03% of a DNA type in a complex metagenome. 
This means 0.45 ng of DNA or at least 108 amplicon copies. This detection limit should be extrapolated 
cautiously  to  estimate  the  detection  threshold  of  community  members,  since  DNA  extraction  and  PCR 
from complex samples were not taken into account in the experiment. Nevertheless, the probes for Agro-
bacterium biovar 1 hybridized in the case of bulk soil, in which this taxon amounts to 103 CFU g/1 (Vogel 
et al., 2002), and this level is among the lowest detection levels published so far in microarray studies. 

Microarray analysis of bacterial communities  
The 16S microarray was used to analyze the taxonomic content of complex soil and rhizosphere communi-
ties. In all instances, results were satisfyingly validated by comparison with data obtained by the accurate 
but long and expensive cloning sequencing approach performed with the same metagenomic PCR products 
(Figure 3).  

Figure 3: Comparison of the diversity and number of taxa inferred from the cloning/sequencing analy-
sis to quantitative microarray data both obtained with the same PCR products obtained from a maize 
rhizosphere community (adapted from Sanguin et al., 2006a). Present results focused on the Agrobacte-
rium sub-community obtained with Agrobacterium selective primers. More than 83% of the sequenced 
clones  were  identified  as  Agrobacterium  members,  among  which  a  large  majority  had  the  same  16S 
sequences than TT111, B6or C58, and the reference strains of Agrobacterium sp. G1, G4 and G8, re-
spectively. In parallel, probes TT111.128, B6.128 and C58.128 delivered the  most intense hybridiza-
tion signal of the microarray. Interestingly, the selectivity of the Agrobacterium primers was low and 
16S  from  related  taxa  Sinorhizobium,  Rhizobium  as  well  as  the  more  distant  Hyphomicrobium  were 
detected by both methodologies. 

The 16S microarray was found to be efficient in rapidly providing a "snapshot" of the taxon composition of 
a given community for instance a maize rhizosphere (Sanguin et al., 2006a). It was also found to be par-
ticularly relevant to contrast taxon composition differences between related communities such as bulk soil 
and rhizosphere from the same location (Sanguin et al., 2006b) or soils indemn, conducive or suppressive 
to take all (Sanguin et al., 2006c). 

84  

  
 
 
 
 
 
Bacterial communities of a maize field 
At the whole communities level, the studied maize rhizosphere revealed a high dominance of Proteobacte-
ria (97%), mainly Betaproteobacteria (64%) and Alphaproteobacteria (28%), whereas only 3% were affili-
ated  with  Acidobacteria.  Conversely,  Proteobacteria  (57%)  and  Acidobacteria  (17%)  dominated  the  re-
lated bulk soil, but several major phyla were also observed, i.e., Planctomycetes, Bacteroidetes, Gemmati-
monadetes, Chloroflexi/ Thermomicrobia, Verrucomicrobia, Actinobacteria, and Firmicutes, as well as the 
TM7 division. 
For taxonomic levels below that of the phylum, strong hybridization signals were observed for several Al-
phaproteobacteria probes, such as those targeting the three families Rhizobiaceae, Bradyrhizobiaceae, and 
Brucellaceae and Sphingomonas spp. For the Agrobacterium spp. taxon, probes yielded positive signals of 
various intensity levels, among which the three specific probes targeting the genomic species of Agrobacte-
rium biovar 1 yielded strong hybridization signals (Figure 3).  
Differential taxonomic content of related communities can be obtained by plotting directly the normalized 
hybridization intensities of probes. By this way, one can have a direct access to major taxa as well as to 
taxa specific of one community (Figure 4). As a result, by contrast to what was revealed by Agrobacterium 
probes  of  the  specific  and  relative  abundance  of  Agrobacterium  members  in  the  maize  rhizopshere, 
Rhizo157  indicates  the  occurrence  of  a  much  large  amount  of  Rhizobium  sp.  in  the  bulk  soil  than  in  the 
maize rhizosphere. This rapidly showed that related genera Agrobacterium and Rhizobium, both present in 
the studied soil, should have differential rhizospheric affinities for maize. 
This is in agreement with our knowledge of the prevalence of Agrobacterium in this biotope. In addition, 
the detection of other taxa, e.g. Sinorhizobium and Rhizobium is in agreement with previous data from the 
same experimental site (Teyssier-Cuvelle et al., 1999).  

  Figure 4: Comparison of microarray hybridization patterns obtained with bulk and maize  

rhizosphere soils from the same location (adapted from Sanguin et al., 2006b). 

Microarray analysis of communities of soils safe, conducive or suppressive to take-all  
Take-all  is  an  important  wheat  disease  caused  by  the  soil-borne  fungus  Gaeumannomyces  graminis  var. 
tritici. Disease severity can be high, but a decline of take-all disease may take place in the following years 
in case of wheat monocropping. Microbial populations known to be associated to take-all decline (disease 
suppressiveness)  include  cultivable  antagonistic  fluorescent  Pseudomonas  spp.  producing  the  antifungal 
compound 2,4-diacetylphloroglucinol. Changes in the diversity of rhizosphere pseudomonads linked with 

85  

  
 
 
 
 
 
 
 
 
 
take-all  decline  of  wheat  was  monitored  based  on  the  use  of  a  taxonomic  microarray  which  contains  ca. 
700 probes targeting bacteria at various taxonomic levels. Some probes (ca. 70) target pseudomonads, in-
cluding groups of biocontrol Pseudomonas strains (ca. 20 probes).  
Rhizosphere samples were collected at the INRA station of La Gruche (Brittany, France) from plots grown 
with wheat for one year (treatment PI; low level of take-all disease), five years (treatment PV; high level of 
disease) or ten years (treatment PX; low level of disease, suppressiveness reached). This experimental set-
up enabled comparison of treatments under same conditions of soil composition, microclimate, wheat culti-
var and farming techniques.  

Figure  5:  Cluster  analysis  of  microarray  data  obtained  after  PCR  of  Pseudomonas  populations  in  the  wheat 
rhizosphere. Marked and reproducible differences were found between: PI (safe soil), PV (conducive soil) and PX 
(supressive soil for take-all) (adapted from Sanguin et al., 2006c). Results are in accordance with quantitative PCR 
data obtained for the total pseudomonads. Overall, treatment PX appears to be associated with a particular composi-
tion in biocontrol pseudomonads (which comprise biocontrol strains producing 2,4- diacetylphloroglucinol).  

PCA analyses revealed a clustering of both samples and probe intensity according to treatments (Figure 5). 
The main differences were found between conducive (PV) and suppressive (PX) soils.  
taxa  of  conducive  soil  were:  Gammaproteobacteria  (Pseudomonas),  Alphaproteobacteria 
Major 
(Sphingomonadaceae,  Rhizobiaceae,  Phyllobacteriaceae),  Betaproteobacteria  (Burkholderia  cepacia,  Ni-
trosospira,  Variovorax,  Acidovorax,  Thiomonas,  Polaromonas),  Bacteroidetes,  Flavobacteria 
(Flavobacterium), Verrucomicrobia, Actinobacteria.  
Major taxa of suppressive soil were: Alphaproteobacteria (Azospirillum), Planctomycetes, Nitrospira, Aci-
dobacteria, Chloroflexi, Firmicutes (Thermoanaerobacter). 
At the whole community level, the conducive soil showed the most marked differences. However, while 
whole taxonomic patterns of PI and PX are similar, suggesting a certain degree of resilience after the dis-
ease peak, microarray analysis of the Pseudomonas sub-community revealed that pseudomonads from the 
suppressive (PX) soil clearly differ from those of the safe initial soil (PI). This is in agreement with previ-
ous  knowledge  about  Pseudomonas  taxa  involved  in  take-all  (McSpadden-Gardener  and  Weller,  2001). 
However,  this  work  provided  these  information  in  a  quite  instant  manner  compared  to  former  time-
consuming methodologies. In addition, it also delivered a lot of yet unknown information about simultane-
ous and unforeseen variations of a lot of other taxa. The role or the sensitivity of those taxa in the evolution 
of soils during the take-all disease sequence is presently completely unknown. However, whole taxa varia-
tion certainly disserves to be studied in order to better managed take-all supressiveness. 

86  

  
 
 
 
Conclusion 
The full scale application of 16S rRNA-based taxonomic microarray relies on a highly efficient probe set. 
The exploration of 16S rRNA databases carried out in this project led to the conclusion that the potential 
targets can be predicted based on a level of WMM defined by the hybridization conditions. The compari-
son of microarray and cloning-sequencing results demonstrated the efficacy of the microarray on complex 
samples.  Microarray  analyzes  demonstrated  changes  in  community  structures  due  for  instance  to 
rhizosphere effect or temporal evolution in response to take-all. Probe set validation, detection limit, and 
the discrimination between related environmental samples confirms the potential of the 16S rRNA microar-
ray for a systematic exploration of bacterial diversity. The 16S rRNA-based taxonomic microarray is thus a 
promising  tool  to  pursue  bacterial  diversity  studies  aiming  at  detecting,  identifying  and  comparing  the 
members of bacterial communities at various taxonomic levels in complex environments. 

Acknowledgements 
This  work  was  supported  by  programme  Genome  from  CNRS,  with  a  postdoc  position  for  C.O.  from 
Rhône-Alpes  Genopole  and  a  PhD  grant  for  HS  from  Ministère  de  la  Recherche;  programme  ACI  from 
Ministère de la Recherche; programme Bio-ingéniérie pour la Santé, Génomique et Innovations Médicales, 
from Ministère de la Recherche and Ministère des Finances; programme Thématique Prioritaire from Ré-
gion  Rhônes-Alpes,  and  COST  Action  853  Agricultural  Biomarkers  for  Array  Technology.  We  thank  L. 
Bodrossy (ARC Seibersdorf Research, Austria) and J. Bernillon (DTAMB) for help and discussions. The 
project made use of the DTAMB/Genopole Rhône-Alpes gene array platform located at University Lyon 1. 

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88  

  
 
Analysis of data from expression microarrays  
for studying plant-pathogen interactions 

Polona Kogovšek and Maruša Pompe-Novak 

National  Institute  of  Biology,  Department  of  Bio-
technology  and  Systemic  Biology,  Večna  pot  111, 
1000 Ljubljana, Slovenia 

*Corresponding author e-mail: Marusa.Pompe.Novak@nib.si 

Abstract 
In recent years microarrays have been employed to monitor gene expression in plant – pathogen interaction 
studies on a scale much larger than previously possible. Nevertheless, data analysis still remains a challenge. 
Several  software  packages  are  available  for  data  analysis.  Using  gene  ontologies,  differentially  expressed 
genes can be functionally classified. Recently, tools have been developed that integrate microarray data with 
biological  processes  associated  with  the  genes,  which  enables  visualization  of  plant-specific  metabolic 
pathways. One of the biggest tasks in data analysis is the final interpretation of differentially expressed gene 
functions  in  plant  metabolism.  However,  newly  developed  software  and  visualization  tools,  developed  to 
connect  genes  with  their  roles  and  involvement  in  certain  metabolic  pathways,  offer  an  insight  into 
alterations in metabolic processes and have contributed to more ready explanations of biological processes. 

Introduction 
The surfaces of plant organs, both above and below ground, are continuously and permanently exposed to a 
diverse  range  of  organisms,  including  microbial  pathogens,  nematodes  and  insects.  Microorganisms  are 
capable  of  causing  disease  in  various  plant  hosts  and  are  responsible  for  important  economic  damage  to 
crops. Plants, in fact, are equipped with a variety of defence mechanisms, which include preformed defences 
such as waxes, cell wall components, and secondary metabolites. When detecting a pathogen, plants activate 
a number of defences, such as the hypersensitive response, increased expression of defence-related genes, 
production  of  antimicrobial  compounds,  lignin  formation  and  oxidative  burst.  Thus,  resistance  is  due  to  a 
combination  of  physical  and  chemical  barriers,  which  are  either  preformed  or  induced  after  the  infection. 
Disease  results  from  either  failure  of  the  recognition  event  or  the  ability  of  the  pathogen  to  avoid  or 
overcome  the  resistance  response  (Gómez-Gómez,  2004).  To  understand  the  development  of  the  disease, 
changes  in  plant  metabolic  pathways  that  include  alterations  in  the  levels  and/or  partitioning  of  different 
metabolites  need  to  be  investigated.  Many  different  approaches  have  been  used  in  studying  metabolic 
pathway  alterations,  including  studies  of  transcriptomes,  proteomes,  metabolomes  and  fluxomes,  which 
provide systematic data. 
Several  analytical  approaches  are  available  for  studying  changes  in  gene  expression,  among  them 
hybridization  based  methods  (Northern  blot  hybridization,  Dot  blot  hybridization,  in  situ  hybridization), 
polymerase  reaction  based  methods  (primer  extension,  reverse  transcription  PCR  (RT-PCR),  competitive 
RT-PCR,  RT-qPCR,  serial  analysis  of  gene  expression  (SAGE),  AFLP  (amplified  fragment  length 
polymorphism),  subtractive/suppressive  cDNA  libraries  (SSH  libraries)),  in  silico  methods  (expressed 
sequence 
tags  (EST)  analysis  (digital  northern)),  and  microarray-based  approaches  (cDNAs  or 
oligonucleotides).  Microarray  technology  is  one  of  the  most  powerful  methods  of  monitoring  gene 
expression, as it enables simultaneous analysis of the expression of a whole population of genes, in some 
cases even of the whole transcriptome (Barrett and Kawasaki, 2003).  
Besides its successful application to detecting and identifying plant viruses on several agricultural important 
crops  (Boonham  et  al.,  2003;  Bystricka  et  al.,  2005),  microarray  technology  has  received  the  most 
widespread interest of all transcript profiling approaches (reviewed in Fiehn et al., 2001).  

89  

  
 
 
 
 
 
The  first  microarray  study  of  gene  expression  in  plant-pathogen  interaction  was  on  maize  infected  with 
pathogenic  fungi  Cochliobolus  carbonum  (Baldwin  et  al.,  1999).  Later  studies  reported  changes  in  gene 
expression  in  Arabidopsis  following  infection  with  different  fungi  and  bacteria  or  treatment  with  elicitors 
(reviewed in Wan et al., 2002). Schenk and coworkers used cDNA microarrays for following the expression 
of  2375  genes  in  Arabidopsis  thaliana  infected  with  Alternaria  brassicicola  and  after  treatment  with 
salicylic  acid,  methyl  jasmonate  or  ethylene  (Schenk  et  al.,  2000).  Genes  involved  in  systemic  acquired 
resistance  in  A.  thaliana  have  also  been  investigated  (Maleck  et  al.,  2000).  The  first  microarray  study  on 
plant  –  virus  interactions  was  published  in  2003,  reporting  alteration  in  the  gene  expression  profile  of 
Arabidopsis  thaliana  following  infection  with  Tobacco  mosaic  virus  (Golem  et  al.,  2003).  In  more  recent 
research, responses to other viruses in Arabidopsis (Huang et al., 2005; Marathe et al., 2004; Whitham et al., 
2003), as well as in poplar (Smith et al., 2004), maize (Shi et al., 2005), Nicotiana benthamiana (Senthil et 
al., 2005) and potato (Pompe-Novak et al., 2006), were monitored.  

Materials and Methods  
In  contrast  to  diagnostic  microarrays,  expression  microarrays  provide  huge  amount  of  data  which  need 
correct  analysis  to  provide  biologically  meaningful  interpretation  (Fiehn  et  al.,  2001).  Microarray  data 
analysis  consists  of  a  few  steps  from  the  analysis  of  the  scanned  microarray  image  and  normalization,  to 
searching  for  significantly  differentially  expressed  genes  and  data  visualization.  With  image  analysis 
software, spots on the array are annotated and their signal and background intensities calculated. Intensity of 
the  background  can  be  determined  either  as  the  average  of  the  spot  intensities  where  no  hybridisation  is 
expected, or as the average of the spot’s surroundings. A spot where no hybridisation is expected could arise 
from genes from non-related species; alternatively, ‘empty spots’ correspond to spots without any probes. If 
the spot’s surroundings are used for background estimation, global (over the whole array) or local (around 
each spot) background can be used in calculations. Image analysis software is also used for quality control 
of the spots, where spots are included or excluded from further analysis on the basis of spot shape, intensity 
of  the  signal,  signal  to  noise  ratio  or  regression  correlation  (Baebler  et  al.,  2005).  Because  of  unequal 
incorporation  ability  and  unequal  brightness  of  dyes,  and  the  possibility  of  differences  in  the  amounts  of 
control and treated cDNA samples, normalization of spot signals is needed (Huang et al., 2005). The signals 
can be normalized to a group of ‘normalization genes’ (spike-in controls, e.g. luciferase), internal controls 
(housekeeping genes or 18S rRNA) or to the overall hybridization signal. Most often, the microarray data 
are normalized with the loess (locally weighted linear regression), the signal-dependent cubic method or vsn 
(variance stabilization normalization) (Huang et al., 2005). 

for  microarray  analyses 

Results and Discussion 
In  the  process  of  searching  for  significantly  differentially  expressed  genes,  statistical  analysis  is  used, 
following the statistical model that accounts for most of the variability of the experiment. Statistical analysis 
is  followed  by  exploratory  analysis  and  data  visualization.  Data  mining,  extraction  of  useful  and 
understandable patterns from potentially large volumes of heterogeneous data, can also be very informative 
and is often used as a complement to statistical analyses (Kralj et al, 2006). Very informative presentation of 
the  data  can  be  obtained  by  exploratory  methods  such  as  principal  component  analysis  (PCA)  or  self-
organizing maps (SOM; Aharoni et al., 2002). Several software packages are available for data analysis, like 
packages  designed 
(limma,  affy,  marray,  arraymagic  etc., 
www.bioconductor.org)  and  the  TIGR  microarray  analysis  suite  TM4  (www.tm4.org).  Differentially 
expressed  genes  can  be  functionally  classified  using  several  gene  ontologies.  The  most  popular  Gene 
Ontology (www.geneontology.org) categorizes each gene with respect to the molecular function, biological 
process,  and  cellular  component  of  the  gene  product  (MIPS  -  Munich  Information  Center  for  Protein 
Sequences;  Arabidopsis  Functional  genomics  consortium  classifications).  Until  recently,  many  microarray 
results  of  plant-pathogen  interactions  have  been  presented  as  tables  of  differentially  expressed  genes 
classified into functional categories (review in Wan et al, 2002; Dardick 2007) that lack the connection to 
actual metabolic pathways.  
Recently, tools have been developed that integrate microarray data with biological processes associated with 
the genes, which enables visualization of plant-specific metabolic pathways (AraCyc, MapMan and KaPPA-
view).  The  use  of  MapMan  offers  the  possibility  to  paint  out  microarray  profiling  experiments  onto 
diagrams  of  metabolic  pathways  or  processes,  and  to  visualize  the  responses  of  gene  expression  in  a 

in  R 

90  

  
biological context. The principle of the MapMan ontology is a hierarchical BIN-based structure. Each BIN 
comprises items of similar biological function, and can be further split into sub-BINs corresponding to sub-
modes of the biological function (Thimm et al. 2004; Usadel et al. 2005).  
In Figure 1, a metabolism overview scheme exported from MapMan is presented. Each field on the scheme 
presents  a  metabolic  pathway  and  blocks  correspond  to  genes  involved  in  that  metabolic  pathway.  The 
intensity of the colour is relative to the ratio of gene expression in treated and control sample. However, in 
the metabolic pathways both gene inhibitors and enhancers are included and in the same field the expression 
of genes in the different directions are expected.  
The  high  quality  statistic  analysis  is  crucial  step  before  data  importation  to  the  MapMan  or  in  any  other 
visualisation software as visualisation of raw or statistically not significant data can be misleading. 
The advantages of MapMan include specificity for plant metabolic pathways and processes, and flexibility, 
since new metabolic schemes can be added. In addition, the system can be adapted for different microarray 
platforms, such as potato, tomato and others (Rotter et al., 2007). 

Figure  1:  Metabolism  overview  scheme  exported  from  MapMan.  Each  block  presents  a  gene/clone 
within  a  given  pathway  and  the  intensity  of  the  colour  corresponds  to  the  ratio  between  the  gene 
expressions in treated and control sample.  

Conclusions 
Microarray experiments have been used in different plant-pathogen studies. Up to now it has been shown 
that  all  steps  in  the  experiment,  from  the  homogeneity  of  plant  material,  environmental  conditions  and 
technical  performance,  to  the  data  analysis,  have  to  be  planned  and  done  precisely  in  order  to  obtain 
meaningful results. Microarrays enable analysis of a huge number of genes simultaneously but the analysis 
and interpretation of the large data sets obtained still presents a challenge. In the data analysis step there are 
many  possible  ways  to  normalize  the  data  and  to  determine  differentially  expressed  genes.  The  final 
interpretation  of  differentially  expressed  gene  functions  in  plant  metabolism  is  one  of  the  biggest  tasks  in 

91  

  
 
 
data analysis. However, newly developed software and visualization tools, developed to connect genes with 
their  roles  and  involvement  in  certain  metabolic  pathways,  offer  an  insight  into  alterations  in  metabolic 
processes and have contributed to more ready explanations of biological processes.  

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93  

  
Participants involved in COST 853 meetings and workshops 

AUSTRIA  
ABELL  Guy,  ARCS  Seibersdorf  research  GmbH,  2444  Seibersdorf  Tel  +43  50  550  3623  Email 

Guy.Abell@arcs.ac.at 

BODROSSY  Levente,  ARCS  Seibersdorf  research  GmbH,  2444  Seibersdorf  Tel+43  50  550  3632  Email 

levente.bodrossy@arcs.ac.at 

HEBERLE-BORS  Erwin,  Institut  für  Mikrobiologie  und  Genetik,  Universität  Wien,  Bohrgasse  9,  1030 

Wien, Tel+ 43 1 4277 54603 Email erwin@gem.univie.ac.at 

FRANKE-WHITTLE  Ingrid,  Institute  for  Microbiology,  Technikerstrasse  25,  6020  Innsbruck  Tel+43 

5125076394 Email ingrid.whittle@uibk.ac.at  

INSAM Heribert, Microbial Ecology Working Group, Institute of Microbiology, University of Innsbruck, 

Technikerstr. 25, 6020 Innsbruck Tel+43 512 507 6009 Email Heribert.Insam@uibk.ac.at  

KLAMMER Susanne, Microbial Ecology Working Group, Institute of Microbiology, University of Inns-

bruck, Technikerstr. 25, 6020 Innsbruck Tel+43 512 507 6009 

KOSTIC Tanja, ARCS Seibersdorf research GmbH, 2444 Seibersdorf Email tanja.kostic@arcs.ac.at 
LOY Alexander, Abteilung Mikrobielle Ökologie, Institut für Ökologie und Naturschutz, Universität Wien, 

Althanstrasse 14, A-1090 Wien, Tel +43 1 4277 54207 Email loy@microbial-ecology.net  

MAGHULY  Fatemeh, 

Institut  of  applied  Microbiology  Tel  +43  1  360066562  Email 

fate-

meh.maghuly@boku.ac.at 

MITTERMAYR Christian, Lambda GmbH, Industriestr. 6, 4240 Freistadt Tel+43 7942 75055 900 Email 

office@lambda.at 

PREININGER Claudia, ARC Seibersdorf research GMBH, Environmental and Life Sciences, Biotechnol-
ogy Working Group, 2444 Seibersdorf Tel+43 (0)50 550 3527 Email claudia.preininger@arcs.ac.at 
SANDJONG  Bertrand,  Department  of  Bioresources  /  Microbiology,  ARC  Seibersdorf  research  GmbH, 

2440 Seibersdorf Tel+44 50550 3536 Email bertrand.sandjong@arcs.ac.at 

STRALIS-PAVESE  Nancy,  ARC  Seibersdorf  research  GmbH,  Division  of  Environmental  and  Life  Sci-

ences A-2244 Seibersdorf Email Nancy.Stralis-Pavese@arcs.ac.at  

TRAJANOSKI Zlatko, Institut für Elektro- und Biomedizinische, Technik TU Graz, Inffeldgasse 18, 8010 

Graz Tel+43 316 873 5332 Email trajanoski@ibmt.tu-graz.ac.at 

BELGIUM 
 BAEYEN  Steve,  Agricultural  Research  Centre,  Department  of  Crop  Protection,  Phytopathology,  B.Van 

Gansberghelaan 96, 9820 Merelbeke Tel+32-9-2722470 Email s.baeyen@clo.fgov.be  

CALLEBAUT  Alfons,  Veterinary  and  Agrochemical  Research  Centre  VAR,  Leuvensesteenweg  17,  B-

3080 Tervuren Tel+ 32 2 769 22 62 Email alcal@var.fgov.be  

CLAES  Loes,  Scientia  Terrae  Research  Institute,  Fortsesteenweg  30A,  PO  BOX  2860,  Sint-Katelijne-

Waver Tel +32 15 30 55 90 Email lcl@scientiaterrae.org 

JUSTÉ Annelies, Scientia Terrae Research Institute, Fortsesteenweg 30A, PO BOX 2860, Sint-Katelijne-

Waver Tel 0032 15 30 55 90 Email aju@scientiaterrae.org 

LIEVENS  Bart,  Scientia  Terrae  Research  Institute,  Fortsesteenweg  30A,  PO  BOX  2860,  Sint-Katelijne-

Waver Tel +32 15 30 55 90 Email bli@scientiaterrae.org 
MAES  Martine,  Centrum  Landbouwkundig  Onderzoek 

,  Afdeling  Plantenziekten,  Burg  Van 

Gansberghelaan 96, 9820 Merelbeke Tel+ 32 9 272 24 74 Email m.maes@clo.fgov.be 

MESSENS  Eric,  Department  of  Plant  Systems  Biology,  Ghent  University,  Vlaams  Interuniversitair 
Instituut  ,  voor  Biotechnologie  VIB,  Technology  Park  927,  9052  Gent  Tel+32  (0)9  3313809  Email 
ermes@psb.ugent.be 

STOL Bouktje, Science Officer Agriculture and Biotechnology, Food Science, COST Office, 149, Avenue 

Louise - P.O. BOX 12 – 1050 BRUSSELS Tel: +32 2 533 38 25 Email bstol@cost.esf.org 

94  

  
 
 
 
 
 
 
BULGARIA 
TODOROVSKA Elena, AgroBioInstitute, "Dragan Tzankov" 8, 1164 Sofia, Tel+359 721 2552 Email ele-

natod@mail.bg 

CYPRUS 
FASOULA   Dionysia  A.,  Molecular  Genetics  and  Breeding,  Agricultural  Research  Institute,  P.O.  Box 

22016, 1516 Nicosia Tel+357 22 403 125 Email dfasoula@arinet.ari.gov.cy  

IOANNIS Ioannides, Molecular Genetics and Breeding, Agricultural Research Institute, P.O. Box 22016, 

1516 Nicosia Tel+357 22 403 152 Email ioannide@arinet.ari.gov. 

CZECH REPUBLIK 
BUNCEK  Martin,  Generi  Biotech  s.r.o.,  Machkova  587,  500  11  Hradec  Kralove  11  Tel+420495056151 

Email martin.buncek@generi-biotech.com  

BYSTŘICKÁ Dagmar ZSF JU, Dělnická 19, 370 06 České Budějovice Tel: +42 387873032 Email: dag-

mar.bystricka@seznam.cz 

ČEŘOVSKÁ Noemi, Institute of Experimental Botany CAS, Na Karlovce 1, 160 00 Prague 6, Czech Re-

public Email: cerovska@ueb.cas.cz  

KASALOVA  Tereza  University  of  South  Bohemia,  Branisovska  31,  Ceske  BudejoviceTel:  +42  737  731 

863 Email: tereza.kasalova@seznam.cz 

KINKOROVÁ Judita Department of Zoology, Charles Univerzity, Viničná 7, CZ-128 44 Praha 2, Czach 

Republic Email: e- Email: kinkor@natur.cuni.cz  

LENZ  Ondrej,  Biology  centre  ASCR,  37001  Ceske  Budejovice  Tel+420  385  310  357  Email 

lenz@umbr.cas.cz 

MIKOLAJSKA Sarka , Generi Biotech s.r.o., Machkova 587, 500 11 Hradec Kralove 11 Tel (00420) 49 

5056394 Email sarka.mikolajska@generi-biotech.com  

PETRZIK  Karel,  Institute  of  Plant  Molecular  Biology,  Academy  of  Sciences,  Branisovska  31,  37005 

Ceske Budejovice Tel +420 38 53 00 357 Email petrzik@umbr.cas.cz  

SPAK  Josef,  Institute  of  Plant  Molecular  Biology,  Academy  of  Sciences,  Branisovska  31,  37005  Ceske 

Budejovice Tel+420 38 777 55 46 Email spak@umbr.cas.cz 

SIP Miroslav, University of South Bohemia, Faculty of Health and Social Studies, Departement of Health 
Physics and Biophysics, Dukelska 3, 37001 Ceske Budejovice Tel+601 273 498 Email sip@zsf.jcu.cz 

DENMARK 
BENDIXEN  Christian,  University  of  Aarhus,  Tjele  Tel  +45  89991356/  +45  22111303  Email  chris-

tian.bendixen@agrsci.dk 

NICOLAISEN  Mogens,  Danish  Institute  of  Agricultural  Sciences,  Forsøgsvej  1,  Research  Center  Flak-

kebjerg, 4200 Slagelse, Tel+45 58 11 34 70 Email mogens.nicolaisen@agrsci.dk 

JAKOBSEN Jørgen, Danish Institute of agricultural Sciences, research Centre Flakkebjerg, 4200 Slagelse 

Tel +45 58113300 Email jorgen.jakobsen@agrsci.dk  

RAMSING  Niels,  EXIQON  ,  Bygstubben  9,  2950  Vedbaek  Tel  +45  45  66  08  88  Email  ram-

sing@exiqon.com 

SUPRONIENE  Skaidre,  Danish  Institute  of  Agricultural  Sciences,  Forsøgsvej  1,  Research  Center  Flak-

kebjerg, 4200 Slagelse, Tel+45 89993670 Email skaidre.suproniene@agrsci.dk 

FINLAND  
AUVINEN Petri, Institute of Biotechnology, Viikinkaari 9 , P.O. Box 56, 00014 University of Helsinki Tel 

+358 9 1911 Email Pauvinen@operoni.helsinki.fi  

DEGEFU  Yeshitila,  MTT,  Agrifood  Research  Finland,  Tutkimusasemantie  15,  92400  Ruukki,  Finland 

Email: yeshitila.degefu@mtt.fi  

KORKEALA  Hannu,  University  of  Helsinki,  Department  of  Food  and  Environmental  Hygiene,  Helsinki 

Tel +358 9 19157100, +358 400 701535 Email hannu.korkeala@helsinki.fi 

LEPPÄRANTA  Outi,  Finnish  Food  Safety  Authority  Evira,  Helsinki  Tel  +358  20  77  24490  Email 

outi.lepparanta@evira.fi 

LINDSTRÖM Miia, Department of Food and Environmental Hygiene, University of Helsinki Tel +358-9-

95  

  
 
 
 
 
19157107 Email miia.lindstrom@helsinki.fi 

NYKÄSENOJA Suvi, National Veterinary and Food Research Institute, P.O. Box 45 Hämeentie 57, FIN-

00581 Helsinki, Finland Email: suvi.nukasenoja@eela.fi  

ROMANTSCHUK Martin, Dept. Ecological and Environmental Sci. , University of Helsinki , Niemenkatu 

73, FIN-15140 Lahti Tel+358-3-892 20334 Email martin.romantschuk@helsinki.fi  

SIVONEN Kaarina, Department of Applied Chemistry and Microbiology, P.O.Box 56, Biocenter Viikki, 

00014 Helsinki University Tel+358-9-19159270 Email Kaarina.Sivonen@Helsinki.Fi  

SOMERVUO Panu, University of Helsinki, Department of Applied Biology Tel +358-9-19158964 Email 

panu.somervuo@helsinki.fi 

VALKONEN Jari, University of Helsinki, Department of Applied Biology, P. O. Box 27, 00014 Helsinki 

Tel+358 9 191 58 387 Email jari.valkonen@helsinki.fi 

VUORINEN Anssi, Department of Applied Biology, P.O. Box 27, 00014 University of Helsinki Tel +358-

9-19158595 Email anssi.vuorinen@helsinki.fi 

FRANCE 
CAMPS  Céline,  ISVV  /  INRA  Bordeaux,  Villenave  d’Ornon  Tel  +33  5  57  12  23  75  Email 

ccamps@bordeaux.inra.fr 

CANDRESSE   Thierry,  INRA  Bordeaux-Aquitaine,  Virologie  Végétale,  IBVM,  Domaine  de  la  Grande 

Rerrade, BP 81, 33883 Villenave-d'Ornon Tel+33 5 57 12 23 89 Email tc@bordeaux.inra.fr 

CHARLES  Hubert,  UMR  INRA/INSA  de  Lyon  203,  Biologie  Fonctionnelle,  Insectes  et  Interactions 
(BF2I),  INSA  LYON  Bat.  Louis  Pasteur,  69621  Villeurbanne  Cedex  Tel  +33  (0)472  438  085  Email 
hcharles@insa.insa-lyon.fr 

DAVID Maude, Université Claude Bernard, Lyon, France Email maude.david@yahoo.fr 
DUFFRAISSE Marilyne, UCBL-CGMC UMR5534, Equipe "ADN fossile et évolution moléculaire" Email 

duffraisse@cgmc.univ-lyon1.fr  

GAUTIER Christian, CNRS Lyon Email cgautier@biomserv.univ-lyon1.fr 
GRUNDMANN  Geneviève,  Ecologie  Microbienne,  UMR  CNRS  5557  USC  INRA  1193,  Université 
Claude Bernard - Lyon 1, Bâtiment Gregor Mendel, 16, rue Dubois, 69622 Villeurbanne Cedex Tel+ 
33 4 72 44 82 89 Email grundman@biomserv.univ-lyon1.fr  

HATEY  François,  INRA,  Laboratoire  de  Génétique  Cellulaire  Castanet-Tolosan  Tel  +33 561 285  113 

Email francois.hatey@toulouse.inra.fr 

HERRERA Aude, Ecologie Microbienne, UMR CNRS 5557 USC INRA 1193, Université Claude Bernard 
- Lyon 1, Bâtiment Gregor Mendel, 16, rue Dubois, 69622 Villeurbanne Cedex Tel + 33 4 72 44 82 89 
Email herrera@biomserv.univ-lyon1.fr 

KYSELKOVA Martina, Université Claude Bernard, Lyon, France Email mkyselko@univ-lyon1.fr 
MARAIS-COLOMBEL Armelle, INRA, UMR GDPP, Virology group Villenave d’Ornon Tel +33 5 57 12 

23 79 Email amarais@bordeaux.inra.fr 

MOENNE LOCCOZ Yvan, UMR CNRS 5557 Ecologie Microbienne, Université Lyon 1Tel 00 33 4 72 43 

13 49 Email moenne@biomserv.univ-lyon1.fr 

MONEGER Françoise, Centre de Génétique Moléculaire et Cellulaire, Université Claude Bernard, Lyon I, 
  69622  Villeurbanne  Cedex  Email  Fran-

16,  Rue  Raphaël  Dubois,  Bâtiment  G.  Mendel 
coise.moneger@ens-lyon.fr 

NESME  Xavier, INRA-CNRS-Université Lyon 1, Ecologie Microbienne, Campus de La Doua, 16 rue R. 

Dubois, 69622 Villeurbanne Cedex Tel+ 33 4 72 44 82 89 Email nesme@biomserv.univ-lyon1.fr 

OGER-DESFEUX  Christine,  Université  Claude  Bernard  Lyon  1,  Laboratoire  d'Ecologie  Microbienne, 
UMR CNRS 5557, 43, boulevard du 11 novembre 1918, 69622 Villeurbanne Cedex Tel +32 4 72 44 82 
89 Email oger@biomserv.univ-lyon1.fr  

PERRIERE Guy, Laboratoire de Biométrie et Biologie Évolutive Université Claude Bernard – Lyon 1 Tel 

+33 472 44 62 96 Email perriere@biomserv.univ-lyon1.fr 

PEYRET Pierre University of Clermont Ferrand, Email pierre.peyret@univ-bpclermont.fr  
REMENANT Benoit, UMR 5557 Ecologie microbienne, Bat G. Mendel, Université Claude Bernard Lyon 
I,  43  Boulevard  du  11  Novembre  1918,  69622  Villeurbanne  cedex  Email  benoit.remenant@univ-
lyon1.fr 

WILLIAMS John, INRA Tours, Email john.williams@tours.inra.fr 

96  

  
 
GERMANY  
ADAM Günter, Institut für Angewandte Botanik, Universität Hamburg, Ohnhorststr. 18, 22609 Hamburg 

Tel+ 49 40 42816 562 Email guenter.adam@iangbot.uni-hamburg.de 

BLANK Livia, Forschungsanstalt Geisenheim, Tel +49-6722-502-646 Email l.blank@fa-gm.de 
BLOHM Dietmar, Universität Bremen, FB2-UFT, Centre of Applied Gensensorik CAG, Leobener Strasse, 

28359 Bremen Tel +49-421-218-63420 Email dhb@biotec.uni-bremen.de 

FELK Angelika, Biocenter Klein Flottbek, AMPIII, Ohnhorststr. 18, 22609 Hamburg Tel +0049 40 42816 

331 Email angie.felk@bzf.botanik.uni-hamburg.de 

FÖRSTER  Frank,  University  Hamburg,  Biocenter  Klein  Flottbek,  ++49  177  899  1702 

foersterfrank@gmx.de 

HOHEISEL Jörg, Division of Functional Genome Analysis, Deutsches Krebsforschungszentrum, Heidel-

berg Email j.hoheisel@dkfz.de 

KOCHZIUS Mark, Universität Bremen Tel+49 (0) 421-218-7225 Email kochzius@uni-bremen.de 
LUDWIG Wolfgang, Department of Microbiology , Technische Universität München, Am Hochanger 4 , 
85350 Freising Tel +49 8161 71 5451(Microbiology), +49 89 289 22370 (Bioinformatics, ARB) Email 
ludwig@mikro.biologie.tu-muenchen.de 

MEIER Harald, Technical University München, Email meierh@informatik.tu-muenchen.de  
MEYER  Stephanie,  Meyer  Stephanie,  Universität  Hamburg,  Biozentrum  Klein  Flottbek,  Ohnhorststraße 

18, 22609 Hamburg Tel +4940 42816 309 Email ste.meyer@botanik.uni-hamburg.de 

SALOMON Siegfried , University of Hamburg, , Biozentrum Klein Flottbek, Dept. Molecular Phytopatho-
logy  and  Genetics,  Ohnhorststr.  18  ,  D-22609  Hamburg  Tel  +49  40  428  16297  Email  ssalo-
mon@botanik.uni-hamburg.de  

SCHÄFER  Wilhelm,  University  Hamburg  Tel  +49  40  428  12  266  Email  schaefer@botanik.uni-

hamburg.de 

SCHAUZU Marianna, Federal Institute for Risk Assessment, Berlin, Tel +49 30 8412 3758 Email maria-

anna.schauzu@bfr.bund.de 

SCHLOTER Michael, GSF Nat Res Center for Environment and Health, Ingolstädter Landstr. 1, 85758 

Oberschleissheim, Tel: +49 893 187 2304, Email: schloter@gsf.de 

SCHOLTEN  Stefan,  Universität  Hamburg,  Biozentrum  Klein  Flottbek,  Ohnhorststraße  18,  22609  Ham-

burg Tel +49 40 428 16309 Email s.scholten@botanik.uni-hamburg.de 

TEMPLIN Markus F., Head Assay Development, NMI – Naturwissenschaftliches und Medizinisches Insti-
tut an der Universität Tübingen, Markwiesenstraße 55, 72770 Reutlingen Email: templin@nmi.de  
WINTER  Stephan,  DSMZ  Plant  Virus  Department,  Braunschweig  Tel  +49  531  299  3780  Email 

S.Winter@bba.de 

ZHANG Deyong, Abteilung Pflanzenschutz, Biozentrum Klein Flottbek, Universität Hamburg, Ohnhorst-

Str. 18, D-22609 Hamburg Email Zhang.Deyong@iangbot.uni-hamburg.de 

 GREECE 
KATIS Nicolaos I., Aristotle University of Thessaloniki, Faculty of Agriculture, Plant Pathology , 54124 

Thessaloniki Tel+ 30310 998857 Email katis@agro.auth.gr 

HUNGARY 
KRISBAI László, Central Service for plant Protection and soil Conservation, Budaõrsi út 141-145, 1118 

Budapest Tel+36 1 309 1000 Email krizbai.laszlo@ontsz.hu  

VARGA  Gabriella,  Research  Institute  for  Viticulture  and  Enology,  Kecskemét-Miklóstelep,  Tel  +36 

76/494888 Email varga.g@szbkik.hu 

 VARGA Kristina, Central Service for plant Protection and Soil Conservation, Budaõrsi út 141-145, 1118 

Budapest Tel+36 1 309 1000 EmailVarga.Krisztina@ntksz.ontsz.hu 

ISRAEL 
AHARONI  Asaph,  Weizmann  Institute  of  Science,  Rehovot  Tel  +972  8  9343643  Email  as-

aph.aharoni@weizmann.ac.il 

97  

  
 
 
 
  
 
ITALY 
BUDRONI  Marilena,  Sezione  di  Microbiologia  Dip.  Scienze  Ambientali  Agrarie  Biotecnologie  Agroali-

mentari, Sassari Tel +39 079229314 Email mbudroni@uniss.it 

CARBONE Fabrizio, ENEA, Rotondella Tel +39 0835 974746 Email fabrizio.carbone@trisaia.enea.it 
DE  MARTA  Patrick,  Biodiversity  s.r.l.,  Via  Corfù,  71,  25100  Brescia  Tel  +39  030  3099035  Email  pa-

trick.demarta@libero.it 

DEMONTIS Maria Antonietta, Dipartimento di Protezione delle Piante, Università degli Studi di Sassari, 

Via Enrico De Nicola 9, I07100 Sassari Tel + 39 079 229318 Email mademo@uniss.it  

DURSO  Oscar  Fernando,  Università  Degli  Studi  Leche,  Dipartmento  Biologia,  Via  Prov.le  Lecce-

Monteroni, 73100 Lecce Email oscarfernando.durso@poste.it  

FERRARINI Alberto, Universita di Verona, Tel +39 045 802 7058 Email alberto.ferrarini@gmail.com 
FIRRAO Giuseppe, Dipartimento di Biologia Applicata alla Difesa , delle Piante, Università di Udine, via 

Scienze, 208, 33100 Udine Tel +39 0432 558501 Email firrao@pldef.uniud.it  

GLISSANT David, Universita di Verona, Tel +39 045 802 7058 Email dglissant@yahoo.fr 
GRECO Simona, Biotecgen srl, c/o Lab. Fisiologia Generale, DiSTeBA-Università di Lecce, Ecotekne-Via 

Prov.le Monteroni, 73100 Lecce Tel +390832 298617 Email s.greco@physiology.unile.it  

MIGHELI  Quirico, Department of Plant Protection, University of Sassari, Via E. de Nicola 9, 07100 Sas-

sari Tel+39 079 22 92 95 Email qmigheli@uniss.it 

MINAFRA Angelantonio, Centro di Studio del CNR sui Virus, e le Virosi delle Colture Mediterranee, Via 

Amendola 165/A, 70126 Bari Tel +39 80 544 2935 Email csvvam05@area.ba.cnr.it  

PERROTTA Gaetano, ENEA, Rotondella Tel+39 0835 974746 Email gaetano.perrotta@trisaia.enea.it 
POLTRONIERI  Palmiro,  ISPA-CNR,  via  Monteroni  km.  7,  73100  Lecce  Tel+39  08  32  42  26  09  Email 

palmiro.poltronieri@ispa.cnr.it 

PRIMICERI Elisabetta, ISPA-CNR,  via  Monteroni  km.  7,  73100  Lecce  Tel+39  832-298338  Email  elisa-

betta.primiceri@libero.it 

QUARTA  Angela,  Institute  of  Sciences  of  Food  Production  –  CNR,  Via  Monteroni,  Lecce,  Italy  Email: 

angela.quarta@libero.it  

TRAINOTTI  Livio,  Department  of  Biology,  University  of  Padua  Tel  +39  049  827  6292  Email 

livio.trainotti@unipd.it 

SCHERM Barbara, Dipartimento di Protezione delle Piante, Università degli Studi di Sassari, Via Enrico 

De Nicola 9, 07100 Sassari Tel +39 079 229295 Email b_scherm@yahoo.de 

LITHUANIA 
 RAMANAVICIUS  Arunas,  Vilnius  University,  Faculty  of  Chemistry,  Naugaduko  24,  03225  Vilnius 

Tel+370 60032332 Email arunas@imi.lt 

 RAMANAVICIENE Almira, Vilnius University, Institute of Immunology, Moletu pl. 29, 08409 Vilnius 

Tel+370 67203653 Email almyra@imi.lt 

THE NETHERLANDS  
VAN AMERONGEN Aart, Agrotechnology & Food Sciences Group, P.O. Box 17, 6700 AA, Wageningen 

Tel: +31-317-475214 Email: aart.vanamerongen@wur.nl 

BERGERVOET  Jan,  Plant  Research  International,  Droevendaalsesteeg  1,  6708  PB  Wageningen  Tel+31 

317 4 7 69 78 Email jan.bergervoet@wur.nl  

BONANTS Peter, Plant Research International, Droevendaalsesteeg 1, 6708 PB Wageningen Tel+31 317 

476213 Email peter.bonants@wur.nl 

VAN  DEN  BOVENKAMP  Gé,  Nederlandse  Algemene  Keuringsdienst  NAK  Postbus  1115,  8300  BC 

Emmeloord Tel: +31 527-635400 Email: gbovenkamp@nak.nl 

BUIMER  Mark,  Naktuinbouw  Sotaweg  22,  2370  AA,  Roelofarendsveen  Tel:  +31  71  3326158  Email: 

m.buimer@naktuinbouw.nl 

CHEROUTRE Goedele, Sanquin Research Plesmanlaan 125, 1066 CX, Amsterdam, Tel +31 20 5123467 

Email: g.cheroutre@sanquin.nl 

VAN DOORN Ronald, Plant Research International, Business Unit Biointeractions and Plant Health, Dro-

evendaalsesteeg 1, 6708 PB Wageningen Tel +31 317 476 274 Email: ronald.vandoorn@wur.nl  

GLAS Annuska, Agendia BV Slotervaart Medical Center, floor 9D, Louwes weg 6, 1066 EC Amsterdam, 

98  

  
 
  
The Netherlands Tel: +31 20 512 6249 Email: annuska.glas@agendia.com 

HEILIG Hans, Laboratory of Microbiology, Hesselink van Suchtelenweg 4, 6703 CT Wageningen Tel +31 

317 484250 Email hans.heilig@algemeen.micr.wag-ur.nl 

VAN HOEK Angela, RIKILT – Institute of Food Safety Bornsesteeg 45, 6708 PD Wageningen Tel: +31 

317 475556 Email: angela.vanhoek@wur.nl 

VAN  HOOF  Richard,  Plant  Research  International  BV  Droevendaalsesteeg  1,  PO  BOX  16,  6708  PB 

Wageningen Tel: +31 317 476085 Email: richard.vanhoof@wur.nl 

JONGEDIJK Gerard, Naktuinbouw, Caro van Eyckstraat 17, 6708 NH Wageningen, Tel: +31 317 416255, 

Email: g.jongedijk@naktuinbouw.nl 

KOENRAADT  Harrie,  Naktuinbouw,  Sotaweg  22,  2370  AA,  Roelofarendsveen,  Tel:  +31  71  3326158, 

Email: h.koenraadt@naktuinbouw  

KONSTANTINOV Sergey, Laboratory of Microbiology, Hesselink van Suchtelenweg 4, Wageningen Tel 

+31 317 484250 Email sergey.konstantinov@algemeen.micr.wag-ur.nl  
PRINS Theo, RIKILT, Wageningen Tel+31.317.47560 Email theo.prins@wur.nl 
RAJILIC Mirjana, Laboratory of Microbiology, Hesselink van Suchtelenweg 4, 6703 CT Wageningen Tel 

+31 317 484250 Email mirjana.rajilic@algemeen.micr.wau.nl  

ROEST Kees, Laboratory of Microbiology, Hesselink van Suchtelenweg 4, 6703 CT Wageningen Tel+31 

317 483118 Email kees.roest@algemeen.micr.wag-ur.nl  

SALLES Joana, Plant Research International, Bornsesteeg 65, 6708 PD, Wageningen Tel +31 317 475856 

Email joana.salles@wur.nl  

SCHOEN Cor D., Plant Research International, Business Unit Biointeractions and Plant Health, Droeven-

daalsesteeg 1, 6708 PB Wageningen Tel +31 317 476026 Email cor.schoen@wur.nl  

SENECHKIN Ilya, Plant Research International, Droevendaalsesteeg 1, PO BOX 16, 6700 AA Wagenin-

gen, Tel: + 31 317 477113, Email: ilya.senechkin@wur.nl  

SZEMES Marianna, Plant Research International, Wageningen University and Research Center, Droeven-

daalsesteg 1., Wageningen 6708PB Tel +31-317-476-026 Email marianna.szemes@wur.nl 

WAALWIJK  Cees,  Plant  Research  International,  Droevendaalsesteeg  1,  6708  PB  Wageningen,  Tel  +31 

317 476286 Email: cees.waalwijk@wur.nl 

VAN  DER  WOLF  Jan,  Plant  Research  International,  PO  Box  16,  6700  AA  Wageningen,  Email 

Jan.vanderWolf@wur.nl 

ZIJLSTRA  Carolien,  Plant  Research  International  BV,  Droevendaalsesteeg  1,  PO  BOX  16,    6708  PB 

Wageningen, Tel +31 317 476155, E-mail carolien.zijlstra@wur.nl 

NORWAY 
HEIR Even, Matforsk AS, Oslovn. 1, Ås Tel +47 6497 0100 Email even.heir@matforsk.no  
RUDI Knut, Matforsk AS, Osloveien 1, Ås Tel +47 6497 0100 Email knut.rudi@matforsk.no 
SLETNER KLEMSDAL  Sonja, The Norwegian Crop Research Institute, Plant Protection Centre, Høgsko-

leveien 7, Ås Tel+47 6494 9257 Email sonja.klemsdal@planteforsk.no 

POLAND 
BATURO  Anna,  University  of  Technology  and  Agriculture,  Department  of  Phytopathology,  Ks.  Kor-

deckiego 20, 85-225 Bydgoszcz, Email: baturo-a@atr.bydgoszcz.pl  

BISKUP Edyta, Intercollegiate Faculty of Biotechnology, University of Gdańsk & Medical University of 

Gdańsk, Kładki 24, Email: plantago@poczta.onet.pl  

FIGLEROWICZ Marek, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Z. Noskowskiego 

12/14, 61-704 Poznań, Email: marekf@ibch.poznan.pl  

FORMANOWICZ Piotr, Institute of Computing Science, Poznań University of Technology, Piotrowo 3a, 
60-965 Poznań, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 
Poznań, Email: Piotr.Formanowicz@cs.put.poznan.pl  

GRINHOLC Mariusz, Intercollegiate Faculty of Biotechnology University of Gdansk Tel+4858 301 28 07 

Email grinholc@biotech.ug.gda.pl 

HANDSCHUCH Luiza, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Z. Noskowskiego 

12/14, 61-704 Poznań, Email: luizahan@ibch.poznan.pl  

JAFRA Sylwia, University of Gdansk, ul. Kladki 24, Gdansk, Tel: +48 58 301 22 41 ext. 315, Fax: +48 58 

99  

  
 
 
301 28 07, Email: jafra@biotech.univ.gda.pl  

KISIEL  Agnieszka,,  Institute  of  Bioorganic  Chemistry,  Polish  Academy  of  Sciences,  Z.  Noskowskiego 

12/14, 61-704 Poznań, Email: akisiel@ibch.poznan.pl  

LEWOSZ  Jerzy,  Plant  Breeding  and  Acclimatization  Institute,  76-009  Bonin  3,  Koszalin,  Email:  jle-

wosz@wp.pl  

LOJKOWSKA Ewa, University of Gdansk, Kladki 24, 80 – 822 Gdansk Tel +48 58 320 22 48 Email lo-

jkowsk@biotech.univ.gda.pl 

LUKANOWSKI  Aleksander,  University  of  Technology  and  Agriculture,  Department  of  Phytopathology, 

Ks. Kordeckiego 20, 85-225 Bydgoszcz, Email: luk-al@atr.bydgoszcz.pl  

MALINOWSKI  Tadeusz,  Research  Institute  of  Pomology  and  Floriculture,  Pomologiczna  18,  96-100 

Skierniewice Tel +48 46 833 20 21 Email tmalin@insad.pl   

OBRĘPALSKA-STĘPLOWSKA  Aleksandra,  Plant  Protection  Institute,  Miczurina  20,  Pozanń,  Email: 

ao.steplowska@ior.poznan.pl  

PIECZUL Katarzyna, Plant Protection Institute, Miczurina 20, Poznań, Email: kapie1@wp.pl  
PILECKI  Tomasz  Plant  Breeding  and  Acclimatization  Institute,  76-009  Bonin  3,  Koszalin,  Email: 

tomasz_pilecki@o2.pl 

PODKOWIŃSKI Jan, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Z. Noskowskiego 

12/14, 61-704 Poznań, Email: jantsp@ibch.poznan.pl 

PRZYSOWA Joanna Intercollegiate Faculty of Biotechnology, University of Gdańsk & Medical Univer-

sity of Gdańsk, Kładki 24, Gdańsk, Email: jomal@o2.pl  

PULAWSKA  Joanna,  Research  Institute  of  Pomology  and  Floriculture,  ul.  Pomologiczna  18,  96-100 

Skierniewice Tel+48 46 8345366 Email jpulaw@insad.pl 

ROMANOWSKI Tomasz, Intercollegiate Faculty of Biotechnology University of Gdansk Tel 0048 58 301 

28 07 Email kwinto@poczta.onet.pl 

SLAWIAK Monika, Intecollegiate Faculty of Biotechnology, University of Gdansk, Medical University of 

Gdansk, 24 KLADKI str., 80 - 822 Gdansk Tel +48 58 320 22 48 Email monikaslawiak@wp.pl 

SLEDZ  Wojciech,  Biotechnology  UG  &  AMG,  Kladki  24,  80-822  Gdansk  Tel  +4858  301  22  41  Email 

sledz@biotech.univ.gda.pl 

WALERON Małgorzata, Intercollegiate Faculty of Biotechnology, University of Gdańsk & Medical Uni-

versity of Gdańsk, Kładki 24, Gdańsk, Email: waleron@biotech.univ.gda.pl 

WALERON Krzysztof, Intercollegiate Faculty of Biotechnology, University of Gdańsk & Medical Univer-

sity of Gdańsk, Kładki 24, Gdańsk, Email: waleron@biotech.univ.gda.pl  

WIEJACHA  Katarzyna,  Research  Institute  of  Pomology  and  Floriculture,  Horticulture  Department, 

Warynskiego 14 Str., 96-100 Skierniewice Tel+48 46 833 20 21 Email kwieja@insad.pl 

PORTUGAL 
FONSECA Filomena, Faculdade de Ciências do Mar e do Ambiente, Universidade do Algarve, Campus de 

Gambelas, 8000-117 Faro Tel+351289 800 995 Email ffonseca@ualg.pt  

GONÇALO Silva, University of Algarve CDCTPV, Email gsilva@ualg.pt 
MENDES  Marta,  Institute  for  Molecular  and  Cell  Biology,  Porto  Tel  +351  226  074  900  Email 

mvm@ibmc.up.pt 

NOLASCO  Gustavo,  Universidade  do  Algarve  –  FERN,  Campus  de  Gambelas,  8005-139  Faro  Tel+351 

289 800 960 Email gnolasco@ualg.pt 

SLOVENIA  
KOGOVŠEK  Polona,  National  Institute  of  Biology,  Vecna  pot  111,  1000  Ljubljana  Tel+386  1  4233388 

Email polona.kogovsek@nib.si 

KLJUN  Sasa,  National  Institute  of  Biology,  Vecna  pot  111,  1000  Ljubljana  Tel+386  1  4233388  Email 

sasa.kljun@nib.si 

KRECIC  Hana,  Department  of  Plant  Physiology  and  Biotechnology  National  Institute  of  Biology  Email 

hana.krecic@nib.si 

MILAVEC Mojca, National institute of biology, Vecna pot 111, PO BOX 1000, Ljubljana, Tel: +38 6-1-

42-33-388 Email: Mojca.milavec@nib.si  

POMPE-NOVAK  Marusa,  National  Institute  of  Biology,  Vecna  pot  111,  1000  Ljubljana  Tel+386  1 

100  

  
 
 
4233388 Email Marusa.Pompe.Novak@nib.si 

RAVNIKAR  Maja  National  Institute  of  Biology,  Večna  pot  111,  1000  Ljubljana,  Slovenia  Email: 

maja.ravnikar@nib.si  

SPAIN 
AGHA Saif, IRTA Lleida Tel + 34 973 00 3434 Email safsoof23@hotmail.com 
CAMBRA Mariano, Instituto Valenciano de Investigacions Agrarias IVIA, Dpt. Plant Protection and Bio-
technology,  Apdo.  Oficial,  46113  Moncada  Valencia  Tel+34  96  139  10  00  Email  mcam-
bra@master.ivia.es 

CANOVAS Angela, IRTA Lleida Tel +34 973003432 Email angela.canovas@irta.es 
MARTINEZ-BONO Barbara, University of Valencia, Biochem. and Molec. Biol. Department, Dr Moliner 

50, PO BOX 46100, Burjassot, Valencia Tel +34 96 3543795 Email Barbara.M.Bono@uv.es  

MARTINEZ-GINER Maria, IRTA Lleida Tel +34 973003432 Email maria.martinez@irta.es 
OLMOS CASTELLO Antonio, Instituto Valenciano de Investigaciones Agrarias (IVIA), Departamento de 
Protección Vegetal y Biotechnología, Laboratorio de Virología e Immunología, Apartado Oficial, 461-
33 Moncada, Valencia Tel+34 96 3424 000 Email aolmos@ivia.es 

OSTE Christian, Nimblegen Inc., Barcelona Tel +34 669-57-16-54, Email coste@nimblegen.com 
PENA Romi, IRTA Lleida Tel + 34 973 00 34 34 Email romi.pena@irta.es  
PÉREZ Pablo Llop, IVIA (Instituto Valenciano de Investigaciones Agrarias), Dpto. Protección Vegetal y 
Biotecnología, Laboratorio de Bacteriologia, Cta. Moncada-Náquera km 4,5, 46113 Moncada, Valencia 
Tel +34 963424000 Email pllop@ivia.es 

PEREZ-ORTIN  José E., Universitat de Valencia, DNA – Chip Service,  Molines 50, 46100 Burjanot Tel 

+34 96 386 44 46, Email jose.e.perez@uv.es 

PORTILLO-BOCANEGRA Mary Esperanza, Universitad de Castilla Toledo, Tel+34.925. 26.57.52 Email 

mary.portillo@uclm.es 

RUIZ Ana, Departamento de Bioquimica y Biologia Molecular, Dr. Moliner 50, 46100 Burjassot, Valencia 

Tel +34 96 3544 446 Email ana.belen.ruiz@uv.es  

SANZ-ALFÉREZ  Soledad,  Universidad  autonoma  de  Madrid  Tel  +34-91  4978197  Email  sole-

dad.sanz@uam.es 

SWEDEN 
STENLID Jan, Dept of Forest Mycology and Pathology, Swedish University of Agricultural Sciences, PO 

Box 7026, 750 07 Uppsala Tel+46 18 67 18 07 Email Jan.Stenlid@mykopat.slu.se 

SYVANEN  Anna-Christine,  Uppsala  University,  Entr  70,  Res  Dept  2;  Academic  Hospital,  Uppsala  Tel 

+46-18-6112959 Email Ann-Christine.Syvanen@medsci.uu.se 

SWITZERLAND 
BARKOW Simon FGCZ, UniETH Zürich, Email simon.barkow@fgcz.ethz.ch 
BECKMANN Christiane, Kantonales Laboratorium, Kontrolstelle für Chemie- und Biosicherheit (KCB), 
Kannenfeldstrasse , PF, 4012 Basel Tel +41 (0) 61 385 25 00 Email christiane.beckmann@kl.bs.ch  
BELBAHRI Lassaad, Lab. Of Applied Genetics, University of Applied Sciences of Western Switzerland, 
School  of  Engineering  of  Lullier,  150  Route  de  Presinge,  1254  Jussy,  Switzerland  Email:  Las-
saad.belbahri@etat.ge.ch  

BITTERLIN Walter, BIOREBA AG, Chr. Merian-Ring 7, 4153 Reinach Tel+41 61 712 11 15 Email bit-

terlin@bioreba.ch  

DUFFY  Brion,  Agroscope  FAW  Wädenswil,  Swiss  Federal  Research  Station  for  Horticulture,  8820 

Wädenswil Tel+41 1 783 6416 Email duffy@acw.admin.ch 

FREY Jürg  Ernst,  Agroscope  FAW  Wädenswil,  Swiss  Federal  Research  Station  for  Horticulture,  8820 

Wädenswil Tel+41 1 783 63 32 Email juerg.frey@acw.admin.ch 

FREY  Beatrice,  Agroscope  FAW  Wädenswil,  Swiss  Federal  Research  Station  for  Horticulture,  8820 

Wädenswil Tel+41 1 783 63 32 Email beatrice.frey@faw.admin.ch  

JÄGER Jochen, Hamilton AG, Bonaduz, Email jjaeger@hamilton.ch 
KÖLLIKER Roland, Eidg. Forschungsanstalt für Agrarökologie und Landbau, FAL Reckenholz, Moleku-
ro-
lare  Ökologie,  Reckenholzstr.  191,  8046  Zürich  Tel+41  1  377  7345  Email 

101  

  
 
 
 
land.koelliker@fal.admin.ch  

LEFORT Francois, Laboratory of Applied Genetics, Agronomy Department, School of Engineering of Lul-
lier,  University  of  Applied  Sciences  of  Western  Switzerland,  150  route  de  Presinge,  1254  Jussy 
Tel+041 22 759 95 27 Email francois.lefort@etat.ge.ch 

LÜSCHER  Marcel,  Bioreba  AG,  Chr.  Merian-Ring  7,  4153  Reinach  Tel  +41  61  712  1115  Email 

luescher@bioreba.ch  

OBERHÄNSLI Thomas, Bioreba AG, Chr. Merian-Ring 7, 4153 Reinach Tel+41 61 712 1115 Email ober-

haensli@bioreba.ch  

PASQUER Frédérique,  Agroscope  FAW  Wädenswil,  Swiss  Federal  Research  Station  for  Horticulture, 

8820 Wädenswil Tel+41 1 783 6424 Email frederique.pasquer@acw.admin.ch 

PFUNDER  Monika Ecogenics GmbH, Wagistr 23, CH-8952 Schlieren 
REHRAUER Hubert, FGCZ, UniETH Zürich, Email hubert.rehrauer@fgcz.ethz.ch 
SCHLAPBACH Ralph, Coordinator, Functional Genomics Center Zurich, Winterthurerstrasse 190, Zurich 

Tel +41 1 635 3920 Email ralph.schlapbach@fgcz.unizh.ch  

SCHMIDHEINI Tobias, Microsynth GmbH, Schützenstrasse 15, 9436 Balgach Tel +41 (0) 71 722 8333 

Email t.schmidheini@microsynth.ch  

VON  ROHR  Peter,  Institute  of  Scientific  Computing  ,  ETH-Zentrum,  IFW  D27.1,  CH  -  8092  Zürich 

Tel+41 1 632 7473 Email vonrohr@inf.ethz.ch  

WAGNER  Ulrich, Transcriptome Bioinformatics, Functional Genomics Center Zurich, Uni ETH Zürich, 

Winterthurerstrasse 190, 8057 Zürich Tel+41 0 44 635 3924 Email ulrich.wagner@fgcz.ethz.ch 

WIDMER Franco, Molecular Ecology, Swiss Federal Research Station for , Agroecology and Agriculture 
(FAL-Reckenholz),  Reckenholzstrasse  191,  8046  Zürich  Tel  +41  1  377  7376  Email 
franco.widmer@fal.admin.ch 

UNITED KINGDOM 
ADAMS  Ian,  Central  Science  Laboratory,  Sand  Hutton,  York  YO41  1LZ  Tel+441904  462330  Email 

i.adams@CSL.gov.uk 

BOONHAM  Neil, Head Immunological & Molecular Methods Team, Central Science Laboratory, Sand 

Hutton, York YO41 1LZ Tel+44 1904 462332 Email n.boonham@csl.gov.uk 

CLOUGH  James,  Oxford  Gene  Technology,  Yarnton  Oxford  Tel  44  1865  856359  Email 

James.clough@ogt.co.uk 

COOKE David,  Host-Parasite  co-evolution,  Scottish  Crop  Research  Institute,  Invergowrie,  Dundee  DD2 

5DA Tel+44 1382 568 506 Email dcooke@scri.sari.ac.uk 

DANIELL  Tim,  PSI,  SCRI,  Invergowrie,  Dundee  DD2  5DA  Tel  +44  1382  568530  Email 

t.daniell@scri.sari.ac.uk  

DANKS Chris, Central Science Laboratory, Sand Hutton, York YO41 1LZ Email c.danks@csl.gov.uk 
DONOVAN  Gina,  Central  Science  Laboratory,  Sand  Hutton,  York  YO41  1LZ  Email 

g.donovan@csl.gov.uk 

GLOVER  Rachel,  Central  Science  Laboratory,  Sand  Hutton,  York  YO41  1LZ  Tel+44  1904  462000  (x 

3215) Email r.glover@csl.gov.uk  

HUBER Elisabeth, ARK-Genomics Roslin Tel +44 13152 74306 Email Elisabeth.Huber@bbsrc.ac.uk  
TOMLINSON Jenny, Central Science Laboratory, Sand Hutton, York YO41 1LZ Tel +44 1904 462000 (x 

3215) Email j.tomlinson@csl.gov.uk  

WALSH Kathy, Central Science Laboratory Sand Hutton, York YO41 1LZ Email k.walsh@csl.gov.uk 

The completeness and correctness of the addresses cannot be guaranteed. 

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