Proteomics

######## PANEL

Christophe Plomion <plomion@pierroton.inra.fr> INRA - Bordeaux

Fredrik Sterky
[fredrik.sterky@biotech.kth.se](mailto:redrik.sterky@biotech.kth.se)
Royal Institute of Technology

Cetin Yuceer [mcy1@ra.msstate.edu](mailto:mcyl@ra.msstate.edu)
Mississippi State University

**A. Current State of Proteomics**

Integrated approaches that combine the systematic sequencing of
expressed genes and the analysis of mRNA expression for a large number
of genes are now considered as strategies for tracking the genes of
interest. However, while the *Populus* genome project provides us with
an overview of the genes in poplar trees, it is becoming increasingly
clear that this is only a very fragmentary beginning of understanding
their role and function. The old paradigm of one gene for one protein is
not anymore valid, and in eukaryotic cells the situation is more like
6-8 proteins per gene. Thus, while there may be only 25,000 to 100,000
genes in a plant species, there are many more resultant proteins,
including splice variants and essential post-transcriptional
modifications. Genome information is not sufficient to define all of
these protein components. For example, studying mRNA quantities provides
only a partial view of gene product expression due to: (i) large
differences between mRNA and protein turnovers, i.e., a protein can
still be abundant while the mRNA is no longer detectable because its
synthesis has stopped, (ii) post-translational modifications of proteins
such as removal of signal peptides, phosphorylation and glycosylation,
which play important roles in their activity and sub-cellular
localization, and (iii) complex interactions with other proteins. These
processes cannot be deduced from microarray or nucleic acid-based
methodologies. The problems become even more complicated since complex
network of proteins can be very divergent in different organs or
developmental phases of the same organism, despite the same genomic
information. Given that the genetic information is only indicative of
the cell's potential and does not reflect the actual state in a given
cell at a given time, the concept of "proteome," (PROTEin complement
expressed by a genOME), has emerged to provide complementary and
critical information by revealing the regulation, activities, quantities
and interaction of every protein in the cell and how these quantities
respond to developmental and environmental signals. Consequently,
**proteomics is now considered as a priority by many universities and
research institutes** and is starting to be widely applied to the model
plant Arabidopsis and other important crop species as well. However,
forest tree proteomics in general and more specifically poplar
proteomics still remains largely embryonic, in spite of the fact that a
large collection of expressed sequence tags (ESTs) is available and
genome sequencing for poplar will be completed very soon. The *Populus*
proteomics effort will result in discoveries about the cell's protein
machinery that could yield important applications in forestry. Such
knowledge could lead to an understanding of the molecular basis of soft
and hardwood formation, reproductive development, juvenility and
maturity, and biotic and abiotic stress responses.

Proteomics has now branched into four specific disciplines : protein
profiling, delination of protein-protein interactions,
post-translational modifications, and structural analysis.

**1. Protein Profiling:**

The standard approach has been the combination of high-resolution **Two
Dimensional-Poly Acrylamide Gel Electrophoresis and Mass Spectrometry
(2D-PAGE-MS)**. Proteins are resolved on a 2D-PAGE and identified using
MS. This technique enables differential display proteomics for
comparison of protein abundance in diverse contexts. Protein solubility
and detection issues can still be improved. Recently, alternative
technologies (**MudPIT, ICAT, Protein Chips**) have been described to
resolve, identify and sometimes quantify most of the expressed proteins
in complex protein mixtures. The followings are the potential
technologies that can be established and made available to the
researchers working on *Populus* genomics.

**1.1. 2D-PAGE-MS:** is the most widely used method for protein
separation, quantification and identification. Recent improvements in
2D-PAGE like new IPG strips and staining techniques allow a considerable
progress in resolution and reproducibility. In large number of samples,
thousands of protein spots can be individually displayed, quantified,
compared, and identified by using dedicated software. Mass spectrometry
has proven to be effective in (i) **characterizing** proteins at the
femtomoles to picomoles levels, (ii) evaluating post-translational
modifications, and studying macromolecular complexes. In addition,
dramatic gains in analytical sensitivity already allow proteomic
analyses to be carried out on small number of cells, which largely
obviate the need for molecular amplification strategies. The
accessibility to mass spectrometers is therefore considered as a
critical point in post-genomic projects.

Further improvements and refinements should be sought in 2D-PAGE-MS.
2D-PAGE detects the most abundant and soluble proteins, resulting in
poor separation and visualization of total protein content of a cell.
Low abundance proteins like transcription factors and protein kinases
are not detected on standard 2D-gel protein maps. To partially overcome
this problem, proteins can be separated into sub-fractionations
according to cell type or sub-cellular compartments, such as plasma
membrane, chloroplast, or mitochondria. The nuclear and hydrophobic
membrane associated proteins are not properly dissolved. New detergents
in combination with thiourea seem promising for the recovery of such
proteins on 2D-PAGE.

**1.2. Multidimensional Protein Identification Technology (MudPIT):**
Multidimensional liquid chromatography has recently been developed to
resolve low abundance proteins, proteins with extreme p*I* and *M*~r~,
and integral membrane proteins. However, it is not suitable for the
quantitative detection of differences in protein abundance of cells in
different states.

**1.3. Isotope-Coded Affinity Tags (ICAT):** This recent technology
enables a high throughput and direct comparison analysis of the
**relative abundance** measurements of the same peptide ion from **two
sample populations** after affinity-based purification. In a mixture,
the two populations of peptides are individually labelled with two
different ICAT reagents, one being eight mass units heavier than the
other. Fractions are analyzed by MS following the mixture being passed
through affinity media. The difference in protein abundance is then
estimated directly by the difference in peak height of the two
corresponding peptide ions. ICAT is only used for proteins containing
cysteine and not suitable for the complex analysis of many samples or
the study of the effect of several factors.

**1.4. Reporter Ligand Proteomics:** This technology aims to produce
reporter ligands (antibodies or other specific affinity reagents) that
are unique for each protein and that can be used for protein expression
profiling, tissue and cellular localisation and analysis of protein size
variations. A nucleic acid sequence corresponding to a small part of the
protein (100-150 aa) are designed, amplified by RT-PCR, and cloned into
an expression vector. A fusion protein is produced containing an albumin
binding part and a His-tag for affinity purification. After
immunisations, the polyclonal antibodies obtained are purified using the
same protein used for immunisation to increase the specificity of the
antibodies. The technology is based on a combination of established
protocols that has been optimised for higher throughput. The fragments
used are big enough to generate a good immune response but small enough
to enable high success rate in *E.coli* production. Also, by using only
parts of proteins, the uniqueness of the fragments can be ensured and
trans-membrane regions (and other problematic regions of the proteins)
can be avoided. The technology is under development but has been
successfully applied on human genes in combination with tissue
microarrays. The main bottleneck is the cost of immunisations.

**2. Deliniation of Protein-Protein Interactions**:

Since most cellular processes are carried out by multiprotein complexes,
the identification and analysis of protein interaction networks are the
keys to understand how the ensemble of expressed proteins (proteome) is
organized into functional units. Several approaches to study protein
interactions include co-purification or direct purification of complexes
by bi-dimensional chromatography. The classical yeast two-hybrid system
results in high rate of false positive and is associated with the
inability to define protein complexes that involve many disparate P-P
interactions. New techniques such as TAP-TAG (Cellzome), FLAG-TAG (MDS
proteomics), CHH-TAG (CSHL), and GFP-TAG-TAG (Bordeaux University)
coupled with mass spectrometry allow the characterization of
multiprotein complexes in a large scale approach. The study of the
complexome is one of the main objectives of far more advanced functional
genomics projects such as Human, Yeast and Arabidopsis. We therefore
consider highly redundant to address this level during the first phase
of the poplar genome project. Rather, one should use knowledge
(protein-protein interaction map) and reagents (e.g., antibodies) from
these advanced projects to address biological questions that are either
unique or important to trees, by focusing our investigations at the
individual protein level.

**3. Post-translational Modifications:**

These modifications like phosphorylation or glycosylation affect both
the properties of proteins and the interactions with other factors. They
are not apparent from genomic sequence or mRNA expression data and can
only be addressed at the protein level.

4\. Structural Analysis:

### B. Goals

1\. Short-term goals (1-2 years) 

-   Define specific biological questions where proteomics will generate
    valuable pieces of information, rather than accumulate extensive
    data

-   Develop/adopt protocols to improve sensitivity, resolution,
    reliability, throughput, and information of 2D-PAGE-MS for diverse
    *Populus* tissues.

-   Optimise protocols for immunostaining of multiple *Populus* tissues
    and apply reporter ligand proteomics for a limited number of key
    genes.

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-   Develop a common web-based database to store, query, track *Populus*
    proteome data:

    -   Define a minimum community standard for 2D PAGE --MS
        representation to facilitate data comparison, exchange and
        verification.

    -   Develop a public repository center for mass spectrometry data
        where the many levels of MS data are stored and are available
        for data mining (raw data, peak lists, peptide identification,
        protein identification). Such repository should also include the
        description of the experiment to allow users to query across
        experiments to observe concomitant changes across studies. This
        repository center should of course have access to all Populus
        ESTs and to the genomic sequence.

    -   Provide researchers with direct links from each annotated gene
        to all available data from the proteome studies, including
        selected external databases.

2\. Mid-term goals (2-5 years)

# Profile *Populus* proteomes at various developmental stages and in response to agriculturally important biotic and abiotic signals. 

3\. Long-term goals (5+ years)

-   Identify biochemical function of unique proteins

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-   Delineate protein-protein interactions and develop an understanding
    of post-translational modifications under various
    biological/developmental and environmental conditions for targeted
    proteins.

### C. Strategies to achieve the goals

##### Overall there is a profound lack in our community of appropriately trained scientists in proteomics. Developing a network (linked to the IPGC) will help to capitalize on proteomic expertise and instrumentation existing in few forest tree biology laboratories. This network will (i) review the existing facilities by surveying the proteomics capacity in the community, (ii) organize the cross-laboratory facilities, and (iii) try to stimulate research in poplar proteomics.

##### Establish a publically available virtual proteomics laboratory, from which current and new technologies will be available to achieve the proposed goals.

-   Develop training programs in proteomics.

-   Develop a state-of-the-art information system for storing and mining
    proteomic data in an harmonized manner in connection with the
    [Informatics, Annotation & Database Development]{.underline} group.
    In its simplest form, proteomic analysis should play a role in
    genome annotation by providing evidence that a particular gene is
    expressed in vivo and under which circumstances and by providing the
    number of protein isoforms encoded by a particular gene, and the
    molecular nature of the isoforms (e.g. phosphorylation)

-   Define the genetic features of proteins (such as polymorphisms, mode
    of transmission) in connection with the [Physical Mapping & Marker
    Development]{.underline} group

-   Compare proteome, transcriptome and metabolome data in connection
    with the [Gene Expression/Microarrays]{.underline} and [Metabolic
    Characterization & Metabolomics]{.underline} groups.

## 

## D. Potential future applications

**1. Study the genetic variability of qualitative and quantitative
protein variants**

-   Map the expressed genome (using conventional genetic mapping
    strategy applied for proteins)

-   Evaluate the modifications of protein expression in respect to
    genetic factors (PQL mapping)

-   Determine the extent to which quantitative phenotypic variance is
    mirrored by quantitative variation of proteins (QTL/PQL coincidence)

# 2. Study the variability of proteome expression in physiological studies

-   'Differential display proteomics' between organs, developmental or
    physiological stages and contrasted abiotic or biotic conditions
    will be used to identify clusters of proteins for which the
    expression is idiotypic for a specific state. Technologies such as
    2D-PAGE, ICAT and **Reporter Ligand Proteomics** could be used.

3\. Establish predictive mathematical descriptions of biological systems

-   by combining DNA sequence, mRNA profiles, protein expression and
    metabolite concentration as well as information about dynamic
    spatio-temporal changes in these molecules.