# Patent

## Title
Use of gene activity classifiers for the in vitro classification of gene expression profiles of patients with infectious/non-infectious multiple organ failure

## Abstract

The present invention relates to the use of gene activity markers for classification of patients suffering from infectious and non-infectious multiple organ failure, respectively.
The present invention in particular relates to gene activity markers for classification of patients as “not infected without multiple organ failure” or as “not suffering from infectious multiple organ failure” or as “suffering from infectious multiple organ failure”, the gene activity markers being polynucleotides selected from the group consisting of: SEQ ID I.1, SEQ ID I.2, SEQ ID I.3, SEQ ID I.4, SEQ ID I.5, SEQ ID I.6, SEQ ID I.7, SEQ ID I.8 and SEQ ID I.9 or partial sequences thereof.

## Background
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to the use of gene activity markers for the classification of patients suffering from infectious and non-infectious multiple organ failure, respectively.
The invention further relates to the use of said gene activity classificators as stored value parameters in devices used for in vitro diagnosis for patients with infectious and non-infectious multiple organ failure. Furthermore, the invention relates to a device for in vitro diagnosis of patients suffering from infectious multiple organ failure and non-infectious multiple organ failure, respectively.
Further, the invention relates to the use of gene activity marker/classificators for the classification of gene expression profiles of patients for assessing the therapeutic effects of active substances for the treatment of infectious multiple organ failure and non-infectious multiple organ failure, respectively.
2. Description of the Related Art
Despite advances in pathophysiological understanding and the supportive treatment, the multiple organ failure syndrome (MOFS) and multiple organ failure (MOF), respectively, is the most frequent cause of death in patients in intensive care and is continuously increasing worldwide. The consequences of this development are not only considerable to the individual patient but they also have enormous effects on the costs of the public health care systems and the medical progress in many fields of medicine.
Multiple organ failure is defined as the failure of two or more vital organ systems occurring simultaneously or within a short time period. The multiple organ failure syndrome (MOFS) precedes the MOF as initial organ insufficiency [1]. Today's definition of multiple organ failure is the dysfunction of two or more organs occurring simultaneously or within a short period of time, whereas a chronically persistent organ failure can be ruled out [2]. The prognosis of MOF is closely related to the number of the involved organ systems. If one organ fails, the mortality rate within 24 hours is 22%; after 7 days it is 41%. In the case of failure of three organ systems, the mortality increases on the first day to 80% and after 4 days to 100% [3].
For the clinical scoring of the degree of severity in MOFS and MOF, the multiple organ failure score (MOF-score) of GORIS et al. [4] or, alternatively, the sepsis related organ failure assessment (SOFA) score are routinely used [5]. The MOF score renders a quick and clinically simple classification of the organ function in three grades possible. In the clinical literature, a MOF score>4 is routinely described as MOF [6]. SOFA score is a point system quickly scoring the clinical assessment of the function, of the following organ systems: respiration (lung), coagulation, liver, cardiovascular system, central nervous system and kidney. Four grades are used in this scoring system.
Clinically, the MOF runs in three stages [7]:
1. Organ in shock: The triggering pathophysiological mechanism is a perfusion deficiency of very different genesis. This happens within hours and does not yet lead to permanent damages.
2. Organ dysfunction: If the persistent perfusion deficit persists for the next few days, this will lead to the development of SIRS (Systemic inflammatory Response Syndrome, classified according to [8]) with local oedema and cell damages. This stage is called multiple organ dysfunction syndrome (MODS).

3. Organ failure: The persistent perfusion deficit leads to stasis in the splanchnic area which leads to a superinfection and translocation of endotoxines from the intestines. This leads to a potentiation of the clinical symptoms and to the complete picture of the sepsis. The organ dysfunction becomes an organ failure.

MODS and MOF are clinical pictures with a complex pathophysiology. The exact molecular causes for the development and the complexity of the immunological-inflammatory host response to severe infection and trauma that can trigger SIRS and the corresponding cardiocirculatory effects are not completely understood up to the present day [9].
MODS and MOF can be both of infectiologic and non-infectiologic genesis. MODS and MOF routinely develop as a clinical important complication in patients with sepsis, after a shock that was caused by trauma, with patients after surgeries where the heart-lung machine was used, after organ transplantation, and others (FIG. 1). An important pathogenetic mechanism for the development of MODS and MOF is the development of a systemic inflammatory syndrome (SIRS, [8]). The pathophysiological processes initiated in the framework of SIRS do not only involve all components of the immune system, but interfere with all levels of the cardiocirculatory system and are not restricted to myocardial depression and vasodilation. The cardiocirculatory changes in particular on the microcirculation level form the common final distance and result in a tissue hypoxia which is considered an important cofactor in the pathogenesis of multiple organ failure.
FIG. 1 shows an exemplary description of the most important mechanisms of the development of MODS and MOF by today's standards [10]: It seems that an overactive immune system plays a decisive role in the development of multiple organ failure. In this context, the endothelium plays a central key role by secretion of cytokines and by imparting leukocyte adhesion. Signal transduction cascades are activated in the endothelial cells leading to the expression and activation of transcription factors.
The reason why there is still no sensitive/specific diagnostic being able to differentiate between infectious and non-infectious causes is the still incomplete knowledge of the early stage processes in MODS and MOF. New types of biomarkers and diagnostics, now even on a gene expression level, may provide the essential diagnostic information for early diagnosis of multiple organ failure as well as for the differentiation between infectious and non-infectious causes of MODS and MOF. Additionally, they are important in contributing to the clarification of the pathophysiologic mechanisms of systemic inflammations.
The precursory symptoms that are often used in clinical practice, as fever, leukocytosis, tachycardia and tachypnea are completely unspecific for the diagnosis of MODS or MOF as well as for differentiating between infectious and non-infectious causes of MODS and MOF. Parameters detecting irregularities in microcirculations at an early stage, as for example changes in the pH of the intestinal mucosa [11] and lactate level in the capillary bed [12, 13], emerging of a respiratory insufficiency the cause of which is not in the lung [2], the ascent of the leukocyte elastase [14,15], the height of the neopterine level [16], the activation of polymorphnuclear leukocytes and the height of the IL-6-level [17] are suitable as early parameters for the later development of MODS and MOF only to a limited extend, but they cannot contribute to the differentiation between infectious and non-infectious causes of MODS and MOF. Thus, there is urgent need for novel diagnostic methods for improving the capacity of the person skilled in the art to differentiate at an early stage between non-infectious and infectious MODS or MOF and to make predictions on how the patient will respond to specific treatments.
However, it is exactly the differentiation between infectious and non-infectious causes of MODS and MOF which is of utmost medicinal importance, as for example antibiotics may be used more efficiently with this differentiation, this contributing to considerable cost savings as well as to the avoidance of side effects caused by the unspecific application of antibiotics. In the case of non-infectious MODS or MOF it is, moreover, possible to avoid time and people-intensive diagnostic measures that are very stressful for the patient (e.g. transport to CT/MRI) for identification of the respective site of infection, the realization of comprehensive microbiological methods (e.g. examination of blood cultures for which the patient also must deliver great amounts of blood) but also the risky exchange of all plastics material connected with the patient, such as venous catheter, etc. Vice versa the quick identification of infectious causes of MODS or MOF can ensure that these measures are taken quickly and mortality can, therefore, be reduced.
Technological advances, in particular the development of microarray technology, make it now possible for the person skilled in the art to simultaneously compare 10 000 or more genes and their gene products. The use of such microarray technologies can provide information regarding the status of health, regulatory mechanisms, biochemical interactions and signal transmitter networks. As the comprehension how an organism reacts to infections is improved this way, this should facilitate the development of enhanced modalities of detection, diagnosis and therapy of infectious disorders.
Microarrays have their origin in “Southern blotting” [19], which represented the first approach to immobilizing DNA-molecules so that it can be addressed three-dimensionally on a solid matrix. The first micro arrays consisted of DNA-fragments, frequently with unknown sequence, and were applied dotwise onto a porous membrane (normally nylon). Routinely, cDNA, genomic DNA or plasmid libraries were employed and the hybridized material was labelled with a radioactive group [20-22].
Nowadays, the use of glass as substrate and fluorescence for detection together with the development of new technologies for the synthesis and for the application of nucleic acids in very high densities makes it possible to miniaturize the nucleic acid arrays. At the same time, the experimental throughput and the information content were increased [23-25].
The first explanation for the applicability of microarray technology was obtained through clinical trials in the field of cancer research. Here, expression profiles proofed to be valuable with regard to identification of activities of individual genes or groups of genes, which correlate with certain clinical phenotypes [26]. Many samples of individuals with or without acute leukaemia or diffuse B-cell lymphoma were analyzed and gene expression labels (RNA) were found and subsequently employed for the clinically relevant classification of these types of cancer [26,27]. Golub et al. found out that an individual gene is not enough to make reliable predictions, while, however, predictions based on the change in transcription of 53 genes (selected from more than 6000 genes, which were present on the arrays) are highly accurate [26].
It is known from WO 03/002763 that determination of gene expression profiles using microarrays basically can be used for the diagnosis of sepsis and sepsis-like conditions.
The Applicant's German Patent Applications DE 103 40 395.7, DE 103 36 511.7, DE 103 150 31.5 and 10 2004 009 952.9 describe that gene expression profiles, which are for example obtainable by means of the microarray technology, are, in principle, usable for the diagnosis of SIRS, generalized inflammatory inflammations, sepsis and severe sepsis. These applications are herein incorporated by reference.
It is known from Feezor et al. [28] that the gene activities of patients which developed SIRS with multiple organ dysfunction syndrome (MODS) as a consequence of their surgical treatment differ from those of patients who developed SIRS without MODS as a consequence of the same surgical treatment. However, these studies do not allow a statement on the differentiation of non-infectious MOF compared to infectious MOF, as no infection was detected in these patients.
For the classification of gene expression profiles, various methods and their use for gene expression data, for example linear and quadratic discrimination analyses, Compound Covariant Predictor, Nearest Neighbor Classification, Classification Trees or Support Vector Machines have already been described [26, 29, 30, 31, 32]. A general survey on the use of classification methods for the analysis of gene expression data is shown in [33].
It is the object of the classification methods to develop multivariant classificators which allow predictions on whether a new data set belongs to a class. Thus, patients may, for example, be classified by means of classificators into responders or non-responders regarding their response to a special treatment.
Generally, classificators are developed in three steps:
1. Selection of statistically relevant features from a large data set. For gene expression analyses, univariant tests are used as a first step to select the statistically relevant genes from various classes, based on their expression pattern.
2. Determination of the classificators by means of different classification methods, at the end of which a training set of classificators is provided.
3. Validation of this training set by means of new, non-classified test sets of gene expression profiles, and optimization of the training set.
WO 2004/108957 generally describes the classification of biomarkers (nucleic acids) and their use for the diagnosis of SIRS and sepsis, respectively. The classification and/or use of biomarkers for the diagnosis of infectious and non-infectious multiple organ failure, respectively, is not described.
The prior German Patent Application No. 102004 049897.041 describes for the first time gene activity markers for differentiating between infectious and non-infectious multiple organ failure. This application describes the use of 1297 different genes for in vitro diagnosis of patients suffering from infectious and non-infectious multiple organ failure, respectively.
BRIEF SUMMARY OF THE INVENTION
The present invention goes beyond the state of the art described in DE 102004049897.041, in that it was found by means of specific tests of the gene expression profiles from patient samples, which gene activities are suitable as classificators for the in vitro diagnosis for differentiating between non-infectious and infectious multiple organ failure.
The invention disclosed in the present patent application is based on the perception that gene activity classificators can be used to classify the gene expression profiles of patients with non-infectious and infectious MOF, respectively. The use of these classificators is not possible with the clinical parameters conventionally used for diagnosis, however, it is very important for the initiation of a specialized therapy in intensive care.
Thus, it is the object of the present invention to use gene activity classificators for differentiating between non-infected patients without multiple organ failure, non-infectious multiple organ failure and infectious multiple organ failure.
The object of the present infection is achieved by gene activity markers, a method for classification of patients, a microarray according and a device which will now be explained in greater detail.
In the following, the term “ITS-control” is used for patients treated in intensive care, who, however, had no infection detected and multiple organ failure diagnosed.
The present invention in particular relates to the use of gene activity classificators, based on which gene expression profiles obtained in vitro from a patient sample are classified into non-infectious and infectious multiple organ failure.
Using the classificators, the present invention is further usable for assessing the course of patients suffering from non-infectious and infectious causes of multiple organ failure during therapy.
The gene activity classificators according to the present invention are further usable as inclusion or exclusion criterion of patients with non-infectious or infectious causes of multiple organ failure in clinical trials of the stages 2-4.



BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
The present invention is illustrated by way of example and not limitation in the accompanying drawings in which:
FIG. 1 shows the pathologic course of multiple organ failure starting from different medical conditions.
FIG. 2 shows classification of gene expression patterns into the classes infectious MOF and non-infectious MOF by means of the MediPred (Biocontrol Jena GmbH) software,
FIG. 3 shows thresholds determined for high and low expression for each gene.



DETAILED DESCRIPTION OF THE INVENTION
A preferred embodiment of the present invention relates to the provision of gene activity classificators for further electronic processing as well as for the production of software for the description of the individual prognosis for a patient, for diagnosis and/or patient data management systems. In this context, the gene activity classificators are used as basis for automatically assessing the gene expression profiles to be examined in vitro in diagnostic devices. Here, the gene expression classificators are stored as value parameters in software, an integrated circuit, an EPROM or other technical means known to the person skilled in the art for storage of value parameters.
Another preferred embodiment of the invention relates to a device using the gene activity classificators stored as value parameters to allow for an in vitro diagnosis of patients with non-infectious and infectious multiple organ failure. In addition to the gene activity classificators stored as value parameters, said device includes a means to compare non-classified gene expression profiles of patient samples with the stored gene activity classificators and to output the corresponding result as technical display. This can, for example, be realized by electronic processing of the value parameters—transformed to electronic signals—of the gene activity classificators and comparing with the electronic signals won from the gene expression profiles to be examined. The result of this comparison is the classification of the gene expression profile to be examined into one of the classes of non-infectious multiple organ failure and infectious multiple organ failure, respectively. Analog and/or digital displays, such as score systems (similar to the APACHE or SOFA Score already used in in vitro diagnostics) or in silico gels, acoustic signals or other methods known to the person skilled in the art are used as the technical display.
In a preferred embodiment, this device also enables the generation of gene expression profiles for comparison with the stored gene activity classificators. For this purpose, this device consists of a module for the sample preparation of the patient sample obtained in vitro, a module for the hybridization of the patient sample with gene activity probes derived from the gene activity classificators, a module for reading out the hybridization signals, another module for image analysis of the readout hybridization signals, a module enabling the automatic comparison with the stored gene activity classificators, as well as a module allowing the display of the resulting comparison. It is self-evident for the person skilled in the art that not all modules have to be combined in said device, depending on the level of automation. Examples for devices already enabling an automatic/semi-automatic creation of gene expression profiles are the Light Cycler of the company Roche, the Smart Cycler of Cepheid or the AP system of Clondiag Chip Technologies.
Further, the person skilled in the art knows that all other methods for analyses of differential gene expression may be used as an alternative to the method for generating gene expression profiles by the microarray technology described in the present patent application.
The gene activity classificators according to the present invention may also be used for the creation of “in silico” expert systems and/or for “in silico” modulation of cellular ways of signal transfer.
As gene activity classificators according to the present invention, gene and/or gene fragments are used, selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 9, as well as gene fragments thereof with 5-2000 or more, preferably 20-200, more preferably 20-80 nucleotides.
These sequences with SEQ ID NO: 1 to SEQ ID NO: 9 are incorporated by the scope of the present invention and they are in detail disclosed in the enclosed sequence listing comprising 9 sequences which is, thus, part of the description of the present invention and, therefore, also part of the disclosure of the invention. In the sequence listing the individual sequences with SEQ ID NO: 1 to SEQ ID NO: 9 are further assigned to their GenBank Accession No. (website: www.ncbi.nlm.nih.gov/).
In this context, also hybridizable synthetic analogues of the listed probes may be used.
The use of insertion-, deletion or nucleotide replacement mutants of sequences SEQ ID NO: 1 to SEQ ID NO: 9 is also possible for the purposes of the present invention, as long as these mutations do not substantially change the sequences' hybridizing behavior for the purposes of the present invention. Whenever reference is made to gene activity markers in the present invention, such mutations are also included.
In another embodiment, the gene activity classificators SEQ ID NO: 1 to SEQ ID NO: 9 are linked to logical selection rules for the classification of gene expression profiles of patient samples with non-infectious and infectious multiple organ failure, respectively, according to table 1.







TABLE 1







Selection rules for the classification ITS-control, non-infectious and infectious


multiple organ failure, respectively












Non-infectious multiple
Infectious multiple organ


Class
ITS-control
organ failure
failure





Classificators
(SEQ-ID No. I.1)↑
(Seq-ID I.3)↑
(SEQ-ID I.5)↑



and
and
and



(SEQ-ID No. I.3)↓
(Seq-ID I.6)↓
(SEQ-ID I.7)↓



or
or
or



(SEQ-ID I.2)↑
(SEQ-ID No. I.4)↑
(SEQ-ID No. I.8)↑



and
and
and



(SEQ-ID No. I.4)↓
(SEQ-ID No. I.5)↓
(SEQ-ID No. I.9)↑





↑overexpressed gene activity,


↓underexpressed gene activity






Another embodiment of the present invention is characterized in that the genes or gene fragments and/or sequences derived from their RNA are replaced by synthetic analogues, aptamers, as well as peptide nucleic acids.
Another embodiment of the present invention is characterized in that the sample is selected from: body fluids, in particular blood, liquor, urine, ascitic fluid, seminal fluid, saliva, puncture fluid, cell content, or a mixture thereof.
Another embodiment of the present invention is characterized in that cell samples are subjected to a lytic treatment, if necessary, in order to release their cell contents.
It is obvious to the person skilled in the art that the individual features of the present invention presented in the claims can be combined with each other in any desired way.
Gene activity classificators as used in the present invention include all derived DNA-sequences, partial sequences and synthetic analogues (for example peptido-nucleic acids, PNA). The description of the invention referring to the determination of the gene expression on RNA level is not supposed to be a restriction but only an exemplary application of the present invention.
The description of the invention referring to blood is only an exemplary embodiment of the present invention. The term biological fluids as used in the present invention is meant to include all human body fluids.
Further advantages and features of the present invention will become apparent from the description of a working example as well as from the drawing.
FIG. 1 shows the pathologic course of multiple organ failure starting from different medical conditions.
Working Example
Study on the generation and validation of gene activity classificators for classification of gene expression profiles of patient samples into one of the following classes: ITS-control, non-infectious multiple organ failure or infectious multiple organ failure.
Measurement of Differential Gene Expression as Basis for the Training Set:
Whole blood samples of a total of 57 patients treated in surgical intensive care units were tested for measuring differential gene expression to differentiate between non-infectious and infectious causes of multiple organ failure. The complete gene expression data formed the basis for generating the training set of the gene activity classificators.
Whole blood samples were taken from 31 patients who developed an infectious MOF [classified according to 8] during intensive care.
Furthermore, whole blood samples were taken from 26 patients who developed a non-infectious MOF [classified according to 8] during intensive care.
Additionally, whole blood samples were taken from 18 patients who were subject to intensive care (in the following: ITS-controls).
Reference samples were total RNA from SIG-M5 cell lines.
Selected characteristics of the three patient groups are shown in table 2. Information includes age, sex, as well as the SOFA-score as a measure for the function of the organ systems. In addition, the plasma protein levels of procalcitonine (PCT) and CRP as well as the number of leukocytes of the patients are given.
Each patient sample was co-hybridized with the reference sample on a microarray.







TABLE 2







Data of the group of patients











ITS-





controls
Non-infectious MOF
Infectious MOF














Number of Patients
18
26
31


Sex m/f
16/2
15/11
17/14


Age [years]
 65 (15)
69 (10)
60 (17)


APACHE-II Score
 10* (2.8)
14.9 (3.4) 
14 (10)


[points]


SOFA Score [points]
3.4* (1.7)
8* (3) 
10* (3) 


Number of organ
—
3 (1)
3 (1)


dysfunctions


PCT [ng/ml]
0.56* (0.8) 
3.8 (6.7)
3.1 (7.7)


CRP [μg/l]
68.5* (27.5)
80.2* (90.2) 
188* (168) 


WBC [no/l]
  8,088* (3,554.2)
12,300 (6,925) 
13,200 (8,150) 





*p < 0.05






Experimental Description:

After drawing whole blood, the total RNA of the samples was isolated using the PAXGene Blood RNA kit according to the manufacturer's (Qiagen) instructions.
Cell Cultivation
For cell cultivation (control samples) 19 cryo cell cultures (SIGM5) (frozen in liquid nitrogen) were used. The cells were each inoculated with 2 ml Iscove's medium (Biochrom AG) supplemented with 20% fetal calf serum (FCS). Subsequently, the cell cultures were incubated in 12 well plates for 24 hours at 37° C. in 5% CO2. Subsequently, the content of the 18 wells was parted in 2 parts with the same volume so that finally 3 plates of the same format (36 wells in total) were available. Afterwards, the cultivation was continued under the same conditions for 24 hours. Afterwards, the resulting cultures of 11 wells of each plate were combined and centrifuged (1000×g, 5 min, ambient temperature). The supernatant was removed and the cell pellet was dissolved in 40 ml of the above mentioned medium. These 40 ml of dissolved cells were distributed in equal shares in two 250 ml flasks and incubated after adding 5 ml of the above-mentioned medium. 80 μl of the remaining 2 ml of the two remaining plates were placed in empty wells of the same plates that had previously been prepared with 1 ml of the above-mentioned medium. After 48 hours of incubation, only one of the 12 well plates was processed as follows: 500 μl were extracted from each well and combined. The resulting 6 ml were introduced into a 250 ml flask comprising approximately 10 ml of fresh medium. This mixture was centrifuged 5 minutes with 1000×g at ambient temperature and dissolved in 10 ml of the above-mentioned medium. The following results were obtained by subsequent counting of cells: 1.5×107 cells per ml, 10 ml total volume, total number of cells: 1.5×108. As the number of cells was not yet sufficient, 2.5 ml of the above-mentioned cell suspension was introduced into 30 ml of the above-mentioned medium in a 250 ml (75 cm2) flask (4 flasks in total). After 72 hours of incubation 20 ml of fresh medium were added to each flask. After the subsequent incubation of 24 hours, the cells were counted as described above. The total amount of cells was 3.8×108 cells. In order to obtain the desired number of cells of 2×106 cells, the cells were resuspended in 47.5 ml of the above mentioned medium in 4 flasks. After the incubation time of 24 hours, the cells were centrifuged and washed two times with phosphate buffer in absence of Ca2+ and Mg2+ (Biochrom AG).
The isolation of the total RNA is performed by means of NucleoSpin RNA L Kits (Machery&Nagel) according to the manufacturer's instructions. The above described process was repeated until the necessary number of cells was obtained. This was necessary to obtain the necessary amount of 6 mg total RNA corresponding to an efficiency of 600 μg RNA per 108 cells.
Reverse Transcription/Labelling/Hybridization
After drawing whole blood, the total RNA of the samples was isolated and tested for quality using the PAXGene Blood RNA kit (PreAnalytiX) according to the manufacturer's instructions. 10 μg total RNA were aliquoted from each sample and transcribed with 10 μg total RNA from SIGM5 cells as reference RNA to complementary DNA (cDNA) by means of the reverse transcriptase Superscript II (Invitrogen). Subsequently, the RNA was removed from the mixture by alkaline hydrolysis. In the reaction mixture a part of the dTTP was replaced by aminoallyl-dUTP (AA-dUTP) in order to render the linkage of the fluorescent dye to the cDNA possible at a later point of time.
After the purification of the reaction mixture, the cDNA of the samples and the controls were covalently labelled with the fluorescent dyes Alexa 647 and Alexa 555 and hybridized on a microarray of the SIRS-Lab company. On the microarray used, 5308 different polynucleotides with lengths of 55 to 70 base pairs were immobilised. Each of the polynucleotides represents a human gene. Additionally there were control spots for quality assurance. One microarray is divided into 28 subarrays, each of the subarrays being arranged in a grid of 15×15 spots.
The hybridization and the subsequent washing and drying, respectively, were carried out using the hybridization station HS 400 (Tecan) according to the manufacturer's instructions for 10.5 hours at 42° C. The hybridization solution used was composed of the cDNA samples, each labelled, 3.5×SSC (1×SSC comprises 150 mM sodium chloride and 15 mM sodium citrate), 0.3% sodium lauryl sulfate (v/v) 25% formamide (v/v) and each 0.8 μg μl-1 cot-1 DNA, yeast t-RNA and poly-A RNA. The subsequent washing of the microarrays was carried out at ambient temperature according to the following scheme: Rinse 90 seconds with washing buffer 1 (2×SSC, 0.03% sodium lauryl sulfate), with washing buffer 2 (1×SSC) and finally with washing buffer 3 (0.2×SSC). Subsequently, the microarrays were dried under a nitrogen flow at a pressure of 2.5 bar for more than 150 seconds at 30° C.
The hybridization signals of the processed microarrays were subsequently read by means of the GenePix 4000B (Axon) scanner and the expression ratios of the different expressed genes were determined by means of the GenePix Pro 4.0 (Axon) software.
Evaluation:
For the analysis, the average intensity of one spot was determined as median value of the corresponding spot pixel.
Correction of Systematic Errors:
Systematic errors were corrected according to the approach of Huber et al. [34]. According to this approach, the additive and the multiplicative bias in a microarray was estimated on the basis of 70% of the gene samples present. For all further computations, the signal was transformed by means of arcus sinus hyperbolicus.
For the analysis, the normalized and transformed relative ratios of the signals of the patient samples were calculated with respect to the control. This means that the calculation for the gene no. j of the patient no. n revealed the data Gj,n=arcsin h(Scy5(j,n))−arcsin h(Scy3(j,n)), wherein [SCy3(j,n), SCy5(j,n)] is the associated signal pair. When a spot could not be analysed for a patient (e.g. scanned picture is stained), the associated value was marked as “missing value”.
Statistical Comparison:
For comparison the paired random student test was employed per gene. Both random samples contained the values of the patient groups of non-infectious MOF and infectious MOF, respectively. For choosing the differentially expressed genes, the associated p-value and the number of missing values were evaluated. It applied for the group of the selected genes that the associated p-vlaue was smaller than 0.05.







TABLE 3







Significantly increased gene activities in samples of patients with


infectious MOF, if compared with the gene activities of patients with non-


infectious MOF.











Mean normalised





and transformed





expression value
Standard deviation
















Group of patients
Group of
Group of patients
Group of



GenBank

with non-
patients with
with non-
patients with



Accession No.
p-value
infectious MOF
infectious MOF
infectious MOF
infectious MOF
SEQUENCE-ID
















N32857
0.00
−2.99
0.20
1.42
2.78
1


N32853
0.00
−0.85
1.60
2.15
2.89
2


N32495
0.00
−2.38
−0.56
1.37
0.40
3


AI701077
0.01
−0.33
1.46
0.17
3.08
4


M87790
0.00
1.18
2.93
1.13
1.24
5


AI559317
0.01
0.20
1.83
0.54
2.60
6


N34897
0.00
−2.60
−1.05
1.61
0.54
7


AA907084
0.02
0.53
1.94
0.49
2.58
8


N45223
0.00
−2.88
−1.54
1.24
0.57
9


H70430
0.03
0.12
1.42
0.70
2.73
10


R59591
0.01
−0.25
0.97
0.20
2.06
11


N47688
0.00
−2.47
−1.27
0.94
0.44
12


N52930
0.00
−1.49
−0.30
1.06
0.76
13


XM_004256
0.00
−3.10
−1.94
0.62
1.43
14


AJ010446
0.00
−0.22
0.93
0.66
1.11
15


N35225
0.00
−2.81
−1.75
1.21
0.52
16


N50680
0.00
−1.30
−0.29
1.58
0.46
17


BC018761
0.00
1.04
2.02
0.80
1.27
18


XM_009475
0.00
−2.54
−1.58
0.83
0.91
19


N53369
0.04
−0.37
0.55
1.62
1.39
20


AI420863
0.05
−0.17
0.74
0.47
1.99
21


N33423
0.05
−0.32
0.58
1.64
1.51
22


AA843281
0.05
0.27
1.15
0.54
1.88
23


X64641
0.02
0.26
1.11
1.09
1.23
24


N52545
0.00
−1.10
−0.28
1.02
0.55
25


X57817
0.01
0.19
1.00
0.58
1.21
26


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0.23
491


H06501
0.02
−0.10
0.10
0.32
0.28
492


R00259
0.03
−0.06
0.14
0.31
0.32
493


AI362368
0.00
−0.33
−0.13
0.17
0.19
494


AI635040
0.00
−0.14
0.06
0.18
0.26
495


AI354869
0.03
−0.48
−0.29
0.32
0.26
496


N71407
0.02
0.06
0.25
0.26
0.32
497


XM_038544
0.04
−0.12
0.07
0.38
0.29
498


NM_031910
0.04
0.18
0.38
0.27
0.38
499


AI862063
0.00
−0.28
−0.08
0.20
0.24
500


AA455638
0.03
0.07
0.27
0.28
0.32
501


AI697430
0.00
−0.36
−0.17
0.18
0.21
502


R42480
0.01
−0.49
−0.29
0.25
0.23
503


AI674115
0.01
0.02
0.21
0.24
0.29
504


AA968926
0.03
−0.32
−0.13
0.37
0.26
505


AI524694
0.00
−0.38
−0.19
0.18
0.23
506


AA609857
0.02
−0.08
0.12
0.30
0.29
507


AI913713
0.01
−0.46
−0.27
0.33
0.21
508


W04695
0.00
−0.28
−0.09
0.16
0.24
509


NM_033012
0.04
−0.12
0.07
0.35
0.31
510


T77048
0.02
−0.01
0.18
0.26
0.31
511


AI817381
0.01
−0.25
−0.06
0.23
0.24
512


AI624918
0.03
−0.02
0.17
0.28
0.32
513


AI888072
0.01
−0.26
−0.07
0.23
0.26
514


AA883759
0.00
−0.38
−0.20
0.21
0.22
515


AA478611
0.00
−0.34
−0.15
0.25
0.17
516


AI452862
0.03
−0.28
−0.09
0.34
0.26
517


AI277955
0.00
−0.46
−0.27
0.24
0.22
518


AI520967
0.00
−0.34
−0.15
0.17
0.20
519


T91937
0.05
0.36
0.54
0.27
0.37
520


AA993698
0.00
0.01
0.20
0.21
0.20
521


AI620374
0.00
−0.40
−0.22
0.18
0.22
522


AA707628
0.00
−0.27
−0.08
0.12
0.17
523


AI572545
0.01
−0.38
−0.19
0.17
0.28
524


AI801540
0.04
−0.16
0.03
0.36
0.28
525


AI354889
0.00
−0.11
0.07
0.22
0.18
526


NM_030751
0.03
−0.09
0.09
0.33
0.25
527


NM_000657
0.01
−0.50
−0.32
0.27
0.22
528


AA045139
0.02
−0.43
−0.24
0.34
0.23
529


AI912148
0.00
−0.25
−0.06
0.18
0.20
530


AA513806
0.04
−0.21
−0.03
0.29
0.32
531


H48440
0.00
−0.35
−0.17
0.16
0.23
532


AA114117
0.00
−0.38
−0.20
0.17
0.18
533


AI654471
0.00
−0.20
−0.02
0.19
0.20
534


AA423792
0.00
−0.16
0.02
0.14
0.25
535


AI926484
0.00
−0.08
0.10
0.25
0.14
536


T89979
0.00
−0.30
−0.12
0.16
0.19
537


AI889310
0.01
−0.25
−0.07
0.26
0.21
538


R11261
0.04
−0.27
−0.09
0.43
0.18
539


AI932551
0.00
−0.32
−0.14
0.16
0.23
540


NM_017626.1
0.01
−0.56
−0.38
0.22
0.26
541


AI381513
0.04
−0.29
−0.11
0.32
0.29
542


AA682407
0.02
−0.24
−0.07
0.25
0.29
543


AA954316
0.04
−0.75
−0.57
0.35
0.27
544


AI791500
0.02
0.16
0.34
0.18
0.31
545


T91881
0.00
−0.26
−0.08
0.18
0.23
546


AI149857
0.02
−0.06
0.12
0.18
0.30
547


AI370842
0.04
0.11
0.29
0.31
0.30
548


AA401205
0.00
−0.13
0.05
0.18
0.19
549


AA453267
0.02
−0.03
0.15
0.26
0.28
550


R88475
0.00
−0.35
−0.17
0.18
0.20
551


AI864919
0.01
−0.38
−0.20
0.19
0.25
552


NM_002169
0.04
−0.24
−0.07
0.33
0.27
553


R46801
0.05
0.27
0.44
0.35
0.27
554


AI277856
0.02
−0.12
0.06
0.22
0.27
555


H22921
0.00
−0.33
−0.15
0.19
0.22
556


AI763386
0.03
−0.37
−0.20
0.30
0.27
557


N78812
0.01
−0.23
−0.06
0.25
0.20
558


H83981
0.04
0.04
0.22
0.28
0.30
559


AA029887
0.00
−0.40
−0.22
0.19
0.21
560


AI192112
0.00
−0.11
0.06
0.15
0.24
561


W88960
0.01
0.10
0.28
0.21
0.24
562


W80744
0.00
−0.25
−0.08
0.15
0.21
563


AI521577
0.01
−0.31
−0.13
0.18
0.23
564


AA418572
0.01
−0.13
0.05
0.17
0.25
565


N73510
0.00
−0.38
−0.21
0.17
0.22
566


AI631299
0.03
−0.16
0.01
0.24
0.29
567


XM_012717
0.00
−0.44
−0.27
0.18
0.17
568


NM_000590
0.03
0.32
0.50
0.23
0.29
569


AI381910
0.01
−0.04
0.13
0.21
0.23
570


R87714
0.04
−0.18
−0.01
0.33
0.23
571


AA609628
0.00
−0.36
−0.19
0.17
0.19
572


AA634317
0.03
0.19
0.36
0.27
0.28
573


AI214830
0.04
−0.27
−0.10
0.23
0.32
574


AI203201
0.04
−0.26
−0.09
0.26
0.29
575


AI924806
0.00
−0.29
−0.12
0.20
0.18
576


AA701319
0.02
−0.07
0.10
0.22
0.28
577


N63628
0.03
−0.26
−0.09
0.26
0.27
578


R02742
0.04
−0.17
−0.01
0.32
0.25
579


H07860
0.02
−0.03
0.13
0.26
0.24
580


H77534
0.02
−0.35
−0.18
0.32
0.20
581


AI208537
0.02
−0.21
−0.04
0.34
0.17
582


AI184715
0.01
−0.03
0.13
0.23
0.20
583


R05816
0.00
−0.27
−0.10
0.19
0.20
584


AA961252
0.04
−0.14
0.02
0.26
0.31
585


AI801425
0.00
−0.21
−0.04
0.22
0.17
586


AA477776
0.01
−0.01
0.16
0.21
0.20
587


R06585
0.01
−0.40
−0.23
0.18
0.21
588


AA405788
0.01
−0.36
−0.19
0.15
0.25
589


R06107
0.01
−0.23
−0.07
0.24
0.19
590


AA923316
0.00
−0.20
−0.04
0.15
0.19
591


AI421397
0.02
−0.02
0.14
0.19
0.26
592


NM_006881
0.01
−0.40
−0.24
0.20
0.23
593


R43415
0.00
−0.24
−0.08
0.14
0.19
594


H11495
0.01
−0.29
−0.12
0.26
0.13
595


AI208772
0.04
−0.23
−0.07
0.29
0.27
596


AA479784
0.03
−0.06
0.10
0.29
0.24
597


AA485092
0.00
−0.36
−0.20
0.16
0.20
598


AA664688
0.00
−0.39
−0.23
0.18
0.20
599


H48230
0.01
−0.29
−0.13
0.19
0.21
600


AI248075
0.02
−0.12
0.04
0.25
0.22
601


AA418695
0.04
−0.01
0.15
0.21
0.31
602


AI673731
0.01
−0.41
−0.25
0.16
0.22
603


XM_008948
0.03
0.10
0.26
0.26
0.26
604


AI301257
0.00
−0.31
−0.15
0.19
0.19
605


NM_003823
0.04
−0.72
−0.56
0.31
0.24
606


AI744264
0.01
−0.14
0.02
0.16
0.22
607


AI809873
0.03
−0.45
−0.29
0.24
0.26
608


AI354243
0.01
−0.34
−0.18
0.17
0.21
609


NM_001553.1
0.04
−0.15
0.01
0.27
0.27
610


W86575
0.02
−0.34
−0.18
0.23
0.24
611


AA442720
0.03
−0.15
0.01
0.27
0.24
612


AA993597
0.03
0.17
0.33
0.26
0.24
613


AI433952
0.01
−0.30
−0.14
0.16
0.23
614


R56800
0.01
−0.09
0.06
0.17
0.21
615


AA417031
0.01
−0.21
−0.06
0.19
0.23
616


R53961
0.04
−0.45
−0.29
0.28
0.25
617


T86887
0.00
−0.23
−0.08
0.13
0.20
618


AA705808
0.01
−0.20
−0.04
0.25
0.18
619


AA426451
0.00
−0.28
−0.13
0.16
0.19
620


H06263
0.00
−0.28
−0.12
0.14
0.17
621


AA659421
0.00
−0.32
−0.17
0.14
0.16
622


AI801595
0.00
−0.28
−0.13
0.16
0.19
623


AI672318
0.04
−0.20
−0.05
0.31
0.24
624


AI762019
0.01
−0.25
−0.09
0.19
0.21
625


N92873
0.02
−0.11
0.05
0.28
0.19
626


NM_017442
0.04
0.08
0.23
0.28
0.25
627


H46164
0.03
0.03
0.18
0.21
0.27
628


T83946
0.01
−0.29
−0.14
0.20
0.21
629


AA868726
0.04
−0.42
−0.27
0.26
0.25
630


H88129
0.02
−0.37
−0.22
0.21
0.23
631


R88267
0.04
−0.12
0.03
0.30
0.23
632


AI798545
0.01
−0.32
−0.17
0.17
0.19
633


N57775
0.02
−0.14
0.01
0.22
0.22
634


AA425134
0.00
−0.21
−0.07
0.16
0.19
635


AI744807
0.01
−0.59
−0.44
0.20
0.22
636


AI702056
0.05
−0.27
−0.12
0.22
0.29
637


NM_000575
0.04
−0.27
−0.12
0.23
0.25
638


T98779
0.01
−0.38
−0.23
0.18
0.23
639


NM_000587
0.01
−0.43
−0.28
0.18
0.19
640


R92455
0.01
−0.36
−0.21
0.17
0.21
641


AI758473
0.01
−0.36
−0.21
0.18
0.22
642


AA398364
0.00
−0.31
−0.17
0.13
0.21
643


AI811774
0.05
0.20
0.35
0.23
0.27
644


AI299411
0.00
−0.24
−0.10
0.17
0.18
645


AA225138
0.00
−0.26
−0.11
0.12
0.17
646


AA418689
0.05
0.09
0.24
0.21
0.28
647


T77995
0.01
−0.18
−0.04
0.21
0.20
648


AA808788
0.04
−0.33
−0.18
0.18
0.25
649


AI677645
0.01
−0.25
−0.11
0.15
0.19
650


AA629306
0.04
−0.07
0.07
0.26
0.24
651


AA749151
0.00
−0.19
−0.05
0.13
0.17
652


AI679294
0.01
−0.41
−0.27
0.19
0.19
653


R45611
0.02
−0.24
−0.10
0.16
0.24
654


NM_000588
0.05
−0.18
−0.04
0.29
0.22
655


H99483
0.01
−0.29
−0.15
0.16
0.22
656


AI679923
0.01
−0.46
−0.32
0.17
0.20
657


AI077580
0.05
−0.04
0.10
0.27
0.23
658


D49410
0.01
−0.31
−0.17
0.19
0.19
659


AI692267
0.04
−0.42
−0.28
0.22
0.24
660


AI804001
0.02
0.00
0.14
0.19
0.23
661


T87188
0.01
−0.32
−0.18
0.19
0.19
662


AI368218
0.02
−0.24
−0.10
0.13
0.23
663


AI208749
0.02
−0.02
0.11
0.22
0.19
664


H61046
0.02
−0.21
−0.07
0.18
0.22
665


NM_001330.1
0.01
−0.05
0.08
0.19
0.20
666


XM_001322
0.01
−0.33
−0.19
0.18
0.17
667


NM_004195
0.04
0.23
0.37
0.16
0.27
668


AI285713
0.01
−0.32
−0.18
0.15
0.21
669


AA527369
0.00
−0.13
0.00
0.15
0.16
670


AI350069
0.01
−0.24
−0.11
0.15
0.21
671


AI493975
0.01
−0.24
−0.10
0.18
0.16
672


AI355007
0.03
−0.23
−0.10
0.22
0.21
673


AA225239
0.04
−0.40
−0.26
0.21
0.25
674


AA001392
0.03
−0.39
−0.26
0.24
0.19
675


AI933797
0.02
−0.28
−0.15
0.22
0.18
676


R43065
0.01
−0.21
−0.08
0.16
0.18
677


AA478621
0.03
−0.21
−0.08
0.21
0.20
678


AA012850
0.03
−0.32
−0.19
0.17
0.22
679


AI925035
0.03
−0.15
−0.02
0.17
0.23
680


AA995218
0.03
−0.22
−0.09
0.19
0.21
681


AA897716
0.04
−0.18
−0.06
0.23
0.21
682


AA983987
0.02
−0.28
−0.15
0.18
0.18
683


AI762202
0.03
−0.18
−0.05
0.22
0.20
684


T95909
0.02
−0.34
−0.22
0.18
0.19
685


N22551
0.03
−0.36
−0.24
0.17
0.22
686


AI769053
0.03
−0.28
−0.15
0.15
0.22
687


AF039955
0.01
−0.37
−0.24
0.20
0.16
688


AI935874
0.02
−0.26
−0.14
0.16
0.20
689


AI570779
0.01
−0.31
−0.19
0.15
0.18
690


AI240539
0.01
−0.22
−0.09
0.17
0.18
691


H54423
0.03
−0.32
−0.20
0.16
0.22
692


AA460136
0.02
−0.09
0.03
0.24
0.14
693


NM_033357
0.05
−0.24
−0.12
0.19
0.23
694


AI923479
0.04
−0.30
−0.18
0.20
0.22
695


H18944
0.04
−0.42
−0.30
0.21
0.19
696


NM_006509
0.03
0.01
0.13
0.12
0.23
697


AI865298
0.02
−0.31
−0.19
0.13
0.20
698


AI123502
0.04
−0.36
−0.24
0.17
0.22
699


AI885918
0.02
−0.24
−0.12
0.14
0.19
700


AA225023
0.02
−0.33
−0.21
0.12
0.20
701


AA421020
0.04
−0.25
−0.13
0.19
0.21
702


AJ297560
0.05
−0.29
−0.17
0.21
0.20
703


N95217
0.02
−0.30
−0.19
0.12
0.19
704


AA526032
0.04
−0.25
−0.13
0.20
0.19
705


AA496309
0.02
−0.32
−0.20
0.15
0.19
706


AI732958
0.03
−0.22
−0.11
0.18
0.18
707


AA410828
0.02
−0.29
−0.18
0.20
0.16
708


AA453993
0.02
−0.30
−0.19
0.18
0.16
709


R92993
0.02
−0.26
−0.15
0.12
0.19
710


NM_003921
0.04
−0.23
−0.13
0.20
0.18
711


AI379967
0.02
−0.34
−0.23
0.15
0.17
712


AI926656
0.04
−0.26
−0.15
0.18
0.19
713


AA935872
0.03
−0.31
−0.20
0.16
0.18
714


H08791
0.03
−0.27
−0.17
0.16
0.18
715


AI932884
0.03
−0.31
−0.21
0.16
0.18
716


AI926745
0.03
−0.33
−0.22
0.18
0.16
717


R99595
0.05
−0.29
−0.19
0.16
0.20
718


AI824579
0.03
−0.31
−0.21
0.13
0.18
719


AA427886
0.03
−0.27
−0.17
0.14
0.16
720


H42488
0.04
−0.33
−0.24
0.16
0.15
721
















TABLE 4







Significantly reduced gene activities in samples of patients with


infectious MOF, if compared with the gene activites of patients with non-


infectious MOF.











Mean normalised and





transformed expression value
Standard deviation

















Group of

Group of





Group of patients
patients with
Group of patients
patients with



GenBank

with non-
infectious
with non-
infectious
SEQUENCE-


Accession No.
p-value
infectious MOF
MOF
infectious MOF
MOF
ID
















NM_019111
0.00
1.41
0.21
0.73
0.56
722


N29761
0.00
−0.25
−1.35
0.61
0.92
723


NM_002124
0.00
1.60
0.54
0.62
0.52
724


R43910
0.00
2.51
1.49
1.25
0.88
725


NM_000570
0.00
3.66
2.67
0.70
1.31
726


NM_002923
0.00
2.03
1.07
0.83
0.89
727


X00457
0.00
1.46
0.50
0.84
0.60
728


NM_022555
0.00
1.86
0.91
0.58
0.54
729


NM_002125
0.00
1.38
0.46
0.55
0.45
730


AA620760
0.00
0.30
−0.62
0.47
0.60
731


NM_000569
0.01
3.13
2.26
0.86
1.21
732


NM_021983
0.00
1.38
0.52
0.48
0.41
733


R43203
0.00
2.03
1.18
1.16
0.74
734


NM_033554
0.00
1.42
0.60
0.60
0.52
735


AA626239
0.00
0.15
−0.65
0.74
0.73
736


NM_007328
0.00
−0.31
−1.10
0.49
0.64
737


M90746
0.02
3.67
2.89
0.74
1.37
738


T91086
0.00
−0.81
−1.59
0.63
0.67
739


AA151104
0.00
0.11
−0.64
0.46
0.46
740


H45298
0.01
1.84
1.09
0.90
0.97
741


NM_031311
0.00
0.78
0.03
0.52
0.39
742


AI590144
0.00
1.70
0.96
0.97
0.70
743


NM_001824.2
0.00
1.63
0.88
0.58
0.67
744


NM_018643
0.00
1.70
0.96
0.54
0.66
745


AA400790
0.00
0.93
0.20
0.59
0.52
746


NM_001251
0.00
1.18
0.47
0.42
0.48
747


NM_000887.2
0.00
0.24
−0.48
0.92
0.61
748


AI696291
0.00
0.74
0.04
0.64
0.39
749


NM_031477
0.02
1.89
1.19
1.02
1.06
750


AA910846
0.00
1.29
0.60
0.87
0.45
751


NM_005538
0.00
1.59
0.91
0.90
0.72
752


AA398331
0.00
0.13
−0.53
0.51
0.46
753


NM_025139
0.04
−1.79
−2.41
0.75
1.17
754


AA398611
0.00
1.16
0.54
0.77
0.43
755


NM_006682
0.00
−0.05
−0.67
0.46
0.39
756


X52473
0.00
1.71
1.09
0.59
0.74
757


AI859777
0.01
−1.02
−1.63
0.87
0.77
758


H18649
0.00
−0.41
−1.01
0.30
0.58
759


AI700444
0.00
1.57
0.97
0.70
0.63
760


XM_001472
0.00
−0.36
−0.95
0.61
0.59
761


XM_049959
0.01
2.10
1.53
0.73
0.78
762


AA863064
0.03
0.96
0.39
1.20
0.63
763


H88328
0.01
−1.27
−1.84
0.73
0.69
764


R40861
0.00
0.82
0.25
0.82
0.51
765


AI733498
0.00
−0.37
−0.93
0.36
0.58
766


NM_002621
0.01
1.16
0.61
0.63
0.70
767


AI732971
0.00
0.46
−0.09
0.55
0.35
768


AA813145
0.00
0.48
−0.07
0.40
0.41
769


NM_004221.2
0.02
2.05
1.51
0.75
0.79
770


AA740907
0.00
0.05
−0.49
0.44
0.32
771


NM_032022
0.00
0.90
0.36
0.42
0.50
772


XM_003789
0.00
−0.25
−0.78
0.37
0.47
773


AI357099
0.02
−1.06
−1.59
0.85
0.78
774


NM_003937
0.00
−0.43
−0.95
0.44
0.44
775


NM_002122
0.00
0.84
0.33
0.71
0.51
776


AI625626
0.01
0.90
0.40
0.87
0.52
777


H23819
0.00
0.97
0.46
0.62
0.47
778


AI797009
0.01
0.64
0.14
0.85
0.57
779


XM_031354
0.01
0.99
0.49
0.63
0.72
780


XM_051958
0.01
1.20
0.70
0.53
0.72
781


AI499173
0.01
0.11
−0.38
0.75
0.50
782


NM_000591
0.00
0.92
0.43
0.50
0.55
783


NM_057158
0.00
−0.03
−0.52
0.49
0.34
784


R71775
0.00
0.42
−0.07
0.61
0.55
785


AI924028
0.00
−0.35
−0.84
0.36
0.58
786


R39504
0.00
−0.50
−0.98
0.34
0.40
787


N66205
0.01
1.49
1.02
0.72
0.62
788


AI738831
0.00
0.09
−0.38
0.29
0.56
789


H18435
0.00
0.34
−0.14
0.43
0.32
790


R39782
0.00
−0.35
−0.82
0.44
0.35
791


R38717
0.00
−0.16
−0.63
0.43
0.48
792


H96798
0.00
0.19
−0.27
0.47
0.53
793


N72174
0.02
0.92
0.46
0.53
0.73
794


AI739381
0.00
−0.11
−0.57
0.24
0.50
795


AI654546
0.00
0.05
−0.41
0.31
0.46
796


AI097494
0.00
−0.72
−1.19
0.41
0.57
797


NM_000612.2
0.00
0.93
0.47
0.50
0.36
798


AI651536
0.00
0.62
0.16
0.50
0.29
799


AI804425
0.00
0.90
0.44
0.73
0.38
800


n67686
0.00
0.43
−0.03
0.62
0.38
801


NM_000062
0.01
−0.18
−0.63
0.75
0.45
802


R54442
0.00
0.69
0.24
0.53
0.39
803


AI475085
0.00
0.25
−0.20
0.67
0.29
804


AI700612
0.01
−0.85
−1.30
0.60
0.61
805


AA447615
0.00
0.18
−0.27
0.60
0.30
806


AI223092
0.00
−0.38
−0.82
0.30
0.56
807


AI262894
0.00
0.47
0.03
0.61
0.39
808


R52949
0.01
−1.08
−1.52
0.44
0.72
809


AA629034
0.00
−0.13
−0.57
0.37
0.33
810


R12559
0.00
0.72
0.29
0.45
0.37
811


AA910310
0.00
0.03
−0.40
0.35
0.27
812


NM_006850
0.01
0.19
−0.24
0.59
0.51
813


AI689080
0.05
1.34
0.91
0.74
0.78
814


R23755
0.00
0.16
−0.26
0.40
0.37
815


N95041
0.00
0.17
−0.25
0.30
0.40
816


AA443712
0.01
0.76
0.34
0.72
0.41
817


NM_033302
0.03
1.53
1.11
0.54
0.72
818


AI700810
0.00
0.59
0.18
0.72
0.25
819


XM_004011
0.00
0.52
0.11
0.44
0.35
820


H11433
0.00
0.38
−0.03
0.55
0.35
821


NM_006890
0.03
1.14
0.73
0.77
0.52
822


NM_138556
0.00
0.16
−0.25
0.23
0.36
823


XM_003937
0.00
0.13
−0.28
0.34
0.33
824


NM_000908.1
0.00
−0.05
−0.46
0.22
0.30
825


NM_017567
0.01
−0.52
−0.92
0.57
0.47
826


R89802
0.00
−0.21
−0.61
0.27
0.33
827


NM_000715
0.01
0.77
0.37
0.56
0.46
828


AI924733
0.00
−0.60
−1.00
0.37
0.50
829


AI859370
0.00
0.17
−0.23
0.16
0.24
830


AI023558
0.00
−0.41
−0.80
0.23
0.37
831


AA021303
0.00
0.19
−0.20
0.58
0.25
832


R69609
0.01
1.03
0.64
0.54
0.51
833


XM_057445
0.00
0.27
−0.12
0.35
0.39
834


AA046302
0.00
−0.10
−0.49
0.36
0.33
835


AI383451
0.01
0.26
−0.13
0.53
0.40
836


AA464191
0.00
−0.46
−0.84
0.32
0.41
837


AA425808
0.00
−0.22
−0.61
0.25
0.51
838


XM_038024
0.00
0.18
−0.21
0.28
0.47
839


AI016127
0.01
1.07
0.69
0.56
0.42
840


AA400144
0.03
−0.41
−0.79
0.60
0.62
841


R43074
0.00
−0.99
−1.36
0.28
0.51
842


AI628936
0.01
−0.65
−1.03
0.41
0.51
843


AA461499
0.00
−0.16
−0.54
0.39
0.38
844


AI668673
0.00
0.34
−0.03
0.35
0.50
845


AI539443
0.00
0.24
−0.13
0.39
0.43
846


AA404231
0.04
−0.14
−0.52
0.52
0.69
847


AI692869
0.01
0.72
0.34
0.30
0.53
848


AI822099
0.00
0.00
−0.37
0.51
0.34
849


R20616
0.00
0.12
−0.25
0.30
0.32
850


AA453406
0.01
−0.66
−1.03
0.42
0.49
851


AA282404
0.02
0.07
−0.29
0.46
0.58
852


AI023336
0.00
0.24
−0.13
0.28
0.27
853


NM_001964
0.02
−0.63
−0.99
0.56
0.53
854


N35603
0.04
−0.51
−0.87
0.51
0.67
855


AI632210
0.00
0.35
−0.01
0.60
0.26
856


AA156454
0.00
0.37
0.01
0.34
0.35
857


AA620836
0.02
0.24
−0.12
0.51
0.55
858


NM_020530
0.00
0.37
0.01
0.44
0.30
859


AA928277
0.00
−0.10
−0.46
0.34
0.36
860


NM_001559
0.04
0.37
0.01
0.73
0.50
861


AA401691
0.00
−0.08
−0.44
0.39
0.38
862


NM_015991
0.00
0.01
−0.34
0.46
0.33
863


N80764
0.00
−0.08
−0.43
0.33
0.43
864


L34657
0.00
0.12
−0.23
0.31
0.34
865


H98244
0.00
0.24
−0.11
0.39
0.35
866


AA894523
0.00
−0.24
−0.59
0.23
0.29
867


NM_013261.1
0.00
0.08
−0.26
0.32
0.37
868


H02254
0.01
−0.39
−0.73
0.40
0.45
869


NM_003781.2
0.01
−0.64
−0.98
0.50
0.36
870


NM_001243
0.05
0.78
0.44
0.51
0.66
871


AA442897
0.01
−0.44
−0.78
0.32
0.46
872


T85314
0.01
−0.29
−0.63
0.46
0.43
873


AI658519
0.05
0.50
0.16
0.70
0.50
874


AI207975
0.00
−0.28
−0.62
0.37
0.30
875


AI536602
0.00
0.28
−0.06
0.47
0.33
876


NM_001541.1
0.00
0.50
0.16
0.38
0.27
877


AA992540
0.00
0.14
−0.19
0.31
0.32
878


Z22971
0.01
0.62
0.29
0.51
0.39
879


AI560847
0.00
0.36
0.03
0.23
0.28
880


XM_008346
0.04
0.40
0.07
0.59
0.54
881


AA015795
0.02
−0.36
−0.69
0.57
0.42
882


R00742
0.00
0.37
0.04
0.34
0.33
883


H16774
0.00
0.02
−0.31
0.33
0.24
884


R51373
0.00
0.15
−0.18
0.31
0.24
885


AI479659
0.00
0.18
−0.14
0.34
0.29
886


W58195
0.00
−0.06
−0.39
0.27
0.39
887


NM_004437.1
0.05
1.06
0.73
0.47
0.65
888


AA479357
0.00
0.18
−0.15
0.30
0.21
889


AI423518
0.00
−0.25
−0.57
0.29
0.40
890


NM_002750
0.01
−0.52
−0.85
0.36
0.44
891


R26444
0.00
0.00
−0.32
0.27
0.36
892


AA136071
0.00
0.04
−0.29
0.25
0.34
893


AI554459
0.00
−0.02
−0.34
0.39
0.35
894


N51537
0.02
0.89
0.57
0.45
0.49
895


NM_006068
0.00
0.62
0.30
0.35
0.39
896


NM_016184
0.03
0.61
0.29
0.49
0.52
897


NM_000586
0.03
0.03
−0.29
0.40
0.54
898


NM_003102.1
0.01
−0.39
−0.71
0.49
0.43
899


AI264774
0.00
−0.11
−0.43
0.20
0.44
900


N90536
0.01
−0.45
−0.77
0.30
0.45
901


AA404342
0.00
−0.29
−0.61
0.36
0.36
902


AI373525
0.00
−0.16
−0.48
0.30
0.25
903


AI579907
0.00
0.07
−0.25
0.38
0.25
904


AA279410
0.00
0.11
−0.21
0.33
0.26
905


XM_038308
0.04
0.35
0.03
0.51
0.54
906


NM_000879
0.02
−0.01
−0.33
0.37
0.52
907


NM_001078)
0.00
0.38
0.07
0.38
0.32
908


AA781411
0.00
−0.24
−0.55
0.23
0.37
909


R07171
0.00
−0.16
−0.48
0.34
0.37
910


AA136273
0.00
−0.10
−0.41
0.26
0.32
911


AI565469
0.01
−0.06
−0.37
0.32
0.41
912


AI799767
0.00
−0.12
−0.44
0.35
0.36
913


AI889554
0.00
−0.08
−0.39
0.34
0.36
914


AA410301
0.01
0.77
0.46
0.35
0.42
915


AA995114
0.04
1.09
0.79
0.67
0.40
916


AI694444
0.00
−0.40
−0.71
0.26
0.35
917


T98940
0.00
0.05
−0.26
0.45
0.27
918


R16722
0.00
0.07
−0.23
0.42
0.23
919


H05436
0.00
0.40
0.10
0.34
0.33
920


R42778
0.01
0.39
0.09
0.45
0.33
921


AI378275
0.00
−0.02
−0.33
0.29
0.40
922


XM_083833
0.03
0.50
0.20
0.57
0.39
923


R94894
0.03
1.00
0.70
0.35
0.55
924


H15677
0.01
−0.24
−0.54
0.34
0.45
925


AI625523
0.04
0.75
0.45
0.47
0.51
926


AI627286
0.00
0.03
−0.27
0.35
0.26
927


NM_003807
0.01
0.08
−0.22
0.35
0.42
928


NM_002757
0.02
0.00
−0.30
0.50
0.41
929


XM_008411
0.02
−0.47
−0.77
0.31
0.51
930


AI379294
0.01
−0.06
−0.35
0.45
0.32
931


AI824470
0.00
−0.20
−0.49
0.19
0.42
932


N94525
0.00
0.15
−0.14
0.26
0.28
933


R38432
0.01
−0.03
−0.32
0.27
0.41
934


NM_017436.2
0.02
−0.44
−0.74
0.42
0.44
935


AA398968
0.00
−0.03
−0.33
0.37
0.35
936


U15085
0.03
−0.89
−1.18
0.47
0.47
937


AI734941
0.01
−0.14
−0.43
0.31
0.41
938


AI819159
0.00
0.44
0.15
0.39
0.28
939


AA426024
0.02
−0.11
−0.40
0.46
0.42
940


AA435854
0.00
−0.33
−0.62
0.21
0.28
941


NM_003264
0.00
0.28
−0.01
0.30
0.38
942


NM_001622.1
0.04
0.01
−0.28
0.41
0.53
943


AI828714
0.04
−0.25
−0.55
0.33
0.54
944


NM_006610
0.00
−0.04
−0.33
0.23
0.30
945


AI143013
0.00
−0.04
−0.33
0.38
0.31
946


AA428992
0.01
0.50
0.21
0.48
0.24
947


R40560
0.02
0.17
−0.12
0.33
0.44
948


AI203091
0.02
−0.44
−0.73
0.28
0.50
949


T92041
0.00
0.07
−0.22
0.28
0.22
950


AA453794
0.00
0.20
−0.09
0.22
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0.28
1180


AI912316
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1181


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1183


NM_024302.2
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1184


AA446463
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0.29
1185


NM_002747
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0.24
1186


AA446316
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0.30
0.29
1187


NM_052813)
0.05
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0.30
1188


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0.00
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1189


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1190


AI924296
0.03
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1191


AI017741
0.01
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1192


AI619681
0.01
−0.18
−0.37
0.17
0.29
1193


AA400967
0.01
0.25
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0.30
0.22
1194


NM_000680.1
0.01
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0.20
0.28
1195


AI732878
0.00
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0.16
0.16
1196


XM_006454
0.02
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0.34
0.24
1197


AI688916
0.03
0.02
−0.17
0.32
0.29
1198


T79834
0.01
0.09
−0.10
0.25
0.27
1199


AI015693
0.01
−0.01
−0.20
0.20
0.27
1200


R50755
0.00
−0.01
−0.20
0.19
0.19
1201


W44337
0.04
0.05
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0.18
0.39
1202


H23267
0.03
−0.37
−0.55
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AA101850
0.02
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0.34
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AI628322
0.05
−0.03
−0.22
0.37
0.28
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R94207
0.02
0.13
−0.06
0.26
0.28
1206


NM_004347
0.03
0.32
0.14
0.35
0.27
1207


AA960802
0.05
0.14
−0.04
0.37
0.29
1208


NM_052962
0.02
−0.42
−0.60
0.26
0.25
1209


T91946
0.04
0.14
−0.05
0.29
0.33
1210


AA531564
0.04
−0.14
−0.32
0.37
0.26
1211


R96155
0.01
0.00
−0.18
0.28
0.21
1212


AI825491
0.02
−0.07
−0.25
0.19
0.29
1213


N53973
0.02
0.01
−0.17
0.22
0.31
1214


NM_001544
0.01
0.10
−0.08
0.27
0.22
1215


AA702731
0.00
−0.16
−0.34
0.19
0.23
1216


AI554655
0.05
−0.04
−0.22
0.23
0.36
1217


H17495
0.04
0.50
0.32
0.29
0.31
1218


AI209185
0.02
−0.24
−0.42
0.16
0.31
1219


AA031813
0.03
−0.15
−0.33
0.29
0.27
1220


NM_004166
0.04
−0.37
−0.54
0.35
0.27
1221


AA461044
0.02
0.06
−0.11
0.21
0.31
1222


N45328
0.05
−0.12
−0.29
0.32
0.30
1223


N64446
0.03
−0.24
−0.42
0.24
0.32
1224


AI633617
0.01
−0.05
−0.22
0.23
0.24
1225


R45159
0.03
0.22
0.05
0.32
0.24
1226


R60898
0.00
0.13
−0.04
0.18
0.17
1227


AI621170
0.03
−0.05
−0.22
0.30
0.27
1228


N99049
0.01
0.16
−0.01
0.31
0.19
1229


H18651
0.01
0.19
0.02
0.24
0.22
1230


AA568582
0.04
0.02
−0.15
0.30
0.28
1231


AA026871
0.03
−0.02
−0.19
0.37
0.20
1232


AI559626
0.01
−0.11
−0.28
0.23
0.21
1233


AA443545
0.03
0.46
0.29
0.27
0.28
1234


R43339
0.04
0.22
0.06
0.36
0.23
1235


AA007369
0.04
−0.16
−0.33
0.28
0.30
1236


AA960982
0.01
0.25
0.08
0.26
0.22
1237


AA481399
0.01
0.01
−0.16
0.30
0.18
1238


AA280005
0.02
−0.17
−0.34
0.23
0.26
1239


NM_005666
0.01
0.36
0.19
0.26
0.20
1240


NM_000491
0.03
0.08
−0.09
0.32
0.24
1241


AA844053
0.03
−0.12
−0.28
0.22
0.26
1242


R49384
0.01
−0.05
−0.22
0.24
0.21
1243


AI698289
0.01
−0.16
−0.33
0.23
0.21
1244


AI680467
0.04
−0.09
−0.26
0.23
0.31
1245


M90391
0.03
−0.11
−0.28
0.27
0.23
1246


AF218727
0.05
0.18
0.02
0.25
0.31
1247


H22946
0.04
−0.44
−0.60
0.27
0.29
1248


N49285
0.03
−0.51
−0.67
0.23
0.28
1249


N74903
0.01
0.17
0.01
0.21
0.22
1250


NM_001066.2
0.04
0.14
−0.02
0.23
0.30
1251


NM_021805
0.02
0.05
−0.11
0.29
0.21
1252


NM_004590
0.04
0.26
0.10
0.27
0.27
1253


AA482392
0.01
−0.19
−0.35
0.25
0.20
1254


AA131826
0.01
−0.04
−0.20
0.26
0.17
1255


AA947111
0.02
0.07
−0.09
0.17
0.27
1256


AI159796
0.04
−0.13
−0.28
0.20
0.28
1257


AF086537
0.05
0.15
−0.01
0.32
0.24
1258


AI147932
0.00
0.13
−0.03
0.23
0.16
1259


AA460956
0.04
0.11
−0.04
0.30
0.24
1260


AA398249
0.03
−0.11
−0.27
0.23
0.26
1261


H08161
0.04
−0.23
−0.39
0.23
0.28
1262


AA281734
0.03
−0.12
−0.28
0.31
0.19
1263


AA628488
0.04
−0.18
−0.33
0.20
0.30
1264


AA430519
0.04
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0.22
0.26
1265


AA468113
0.05
−0.16
−0.31
0.29
0.25
1266


AI424466
0.04
0.06
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0.26
0.24
1267


AI190760
0.04
0.04
−0.11
0.29
0.23
1268


N89992
0.01
−0.06
−0.21
0.23
0.18
1269


AA046092
0.01
0.10
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0.16
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W35358
0.02
0.05
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0.22
0.20
1271


AA398341
0.04
−0.19
−0.33
0.27
0.23
1272


H01969
0.05
−0.09
−0.24
0.32
0.20
1273


AA970008
0.05
−0.34
−0.48
0.32
0.21
1274


R89846
0.01
0.14
−0.01
0.20
0.20
1275


H18639
0.04
0.13
−0.02
0.26
0.24
1276


AI016342
0.02
0.02
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0.22
1277


NM_002184
0.04
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−0.37
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0.28
1278


NM_001643.1
0.03
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−0.01
0.19
0.26
1279


AA280029
0.04
−0.14
−0.28
0.28
0.22
1280


AA927949
0.00
0.17
0.02
0.16
0.14
1281


AA625552
0.04
0.05
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0.28
0.20
1282


AA458912
0.03
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−0.37
0.24
0.23
1283


AI188025
0.02
0.29
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0.21
0.22
1284


XM_007417
0.02
0.00
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0.21
0.19
1285


AA019529
0.03
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0.22
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AA401542
0.04
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0.25
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AI478746
0.04
0.00
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0.22
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AA291522
0.01
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0.14
0.22
1289


AI493122
0.05
0.16
0.03
0.25
0.22
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AI203665
0.02
0.11
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0.05
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0.24
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AI185721
0.04
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AA437106
0.05
0.10
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NM_139208
0.04
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0.20
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AI922221
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AA412418
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1297









The GenBank Accession Numbers indicated in Tables 3 and 4 (Internet-access via http://www.ncbi.nlm.nih.gov/) of the individual sequences are associated with the attached sequence listing, itemized or in detail with respectively one sequence (Sequence ID: 1 up through Sequence ID: 1297).
Furthermore, the sequences are individually disclosed therein.
This sequence listing is part of the description of the present invention.
Additionally, these sequences are disclosed in the German patent application No. 102004049897.0, which is incorporated herein by reference.
Generation of Gene Activity Classificators (Training Set)
On the basis of all gene activities measured, gene activity classificators and selection rules were established by means of the MediPred (Biocontrol Jena GmbH) software, allowing the classification of gene expression patterns into the classes infectious MOF and non-infectious MOF (FIG. 2). Thresholds were determined for high and low expression for each gene (designated Cmin and Cmax in FIG. 3) and genes with typical and robust gene expression behaviors per patient class were extracted.
Testing of Gene Activity Classificators with Gene Expression Profiles not Yet Classified (Test Set)
The determined gene activity classificators were tested using the defined selection rules on a test set of altogether 190 not yet classified gene expression patterns of ITS-controls (56), patients with non-infectious MOF (75) and patients with infectious MOF (59), respectively, and validated by means of clinical data.
For this purpose, gene expression profiles were selected from patients clinically diagnosed as ITS-control or non-infectious MOF or infectious MOF.
If, in the subsequent comparison with the training set, the expression patterns to be classified were found to be the training set and within the expression limits of the corresponding gene activity classificators defined by the selection rules (and the selection criteria simultaneously applied), the expression pattern to be classified was assigned to the corresponding class.
Table 5 shows which test set belongs to which class. As will be seen, 86% of the ITS-controls, 80% of the patients with non-infectious multiple organ failure as well as 63% of the patients with infectious multiple organ failure could be correctly classified into the corresponding class.







TABLE 5







Percentage of 190 gene expression profiles to be classified into the


classes ITS-control, non-infectious MOF and infectious MOF.














Non-infectious





ITS-
multiple organ
Infectious multiple


Patient groups

controls
failure
organ failure













Number of gene expression
56
75
59


profiles to be classified

(several days of
(several days of




16 patients)
35 patients)











Corresponding to
ITS-controls
86
 4
14


classes
Non-infectious
13
80
16


[%]
multiple organ



failure



Infectious multiple
2
16
63



organ failure









This shows that the gene activity classificators in conjunction with the selection criteria are usable for the invention.
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## Claims

1. A method for classifying patients suffering from infectious and non-infectious multiple organ failure, respectively, comprising the following steps:
(a) isolation of mRNA from a patient sample;
(b) labeling the mRNA with a detectable unit;
(c) contacting the labeled mRNA at least two with gene activity markers, said gene activity markers being polynucleotides selected from the group consisting of SEQ ID NO: 1 to 9, or partial sequences thereof having at least 20 nucleotides in length, immobilized on a microarray, so that the individual gene activity markers are separated from each other even after the contacting step;
(d) washing of the hybridized gene activity markers to remove excess labeled mRNA;
(e) stimulating the detectable unit so as to emit a signal;
(f) reading out the hybridization signals for each of the individual gene activity markers and comparing them with a reference sample of a healthy patient, wherein
an overexpression of mRNA corresponding to SEQ ID NO: 1 and an underexpression of mRNA corresponding to SEQ ID NO: 3 or an overexpression of mRNA corresponding to SEQ ID NO: 2 and an underexpression of mRNA corresponding to SEQ ID NO: 4 indicates the classification “no infection and without multiple organ failure”;
an overexpression of mRNA corresponding to SEQ ID NO: 3 and an underexpression of mRNA corresponding to SEQ ID NO: 6 or an overexpression of mRNA corresponding to SEQ ID NO: 4 and an underexpression of mRNA corresponding to SEQ ID NO: 5 indicates the classification “non-infectious organ failure”; and
an overexpression of mRNA corresponding to SEQ ID NO: 5 and an underexpression of mRNA corresponding to SEQ ID NO: 7 or an overexpression of mRNA corresponding to SEQ ID NO: 8 and an underexpression of mRNA corresponding to SEQ ID NO: 9 indicates the classification “infectious multiple organ failure”.

2. The method according to claim 1, wherein the patient sample comprises body fluids.

3. The method according to claim 1, wherein the patient sample comprises body fluids selected from blood, liquor, urine, ascitic fluid, seminal fluid, saliva, puncture fluid, cell content, or a mixture thereof.