WO2021002792A1

WO2021002792A1 - Biomarkers in sepsis and trauma and uses thereof

Info

Publication number: WO2021002792A1
Application number: PCT/SE2020/050661
Authority: WO
Inventors: Heiko Herwald; Praveen Papareddy; Arne Egesten; Gopinath Kasetty
Original assignee: Inndefense Biotech Ab
Priority date: 2019-07-03
Filing date: 2020-06-25
Publication date: 2021-01-07

Abstract

A method for determining the presence of sepsis and/or trauma in a subject is described. The method comprises the steps of providing a test sample to be tested, earlier obtained from the subject; determining the level of expression in the test sample of one or more biomarkers; providing a first control sample, earlier obtained from a subject not afflicted with sepsis, and/or providing a second control sample, earlier obtained from a subject not afflicted with trauma; and determining the level of expression in the first and/or second control sample of the one or more biomarkers. A level of expression in the test sample of the one or more biomarkers that is different from the level of expression in the first control sample of the one or more biomarkers is indicative of the presence of sepsis, and a level of expression in the test sample of the one or more biomarkers that is different from the level of expression in the second control sample of the one or more biomarkers is indicative of the presence of trauma. The one or more biomarkers are selected from the group defined in Table 2.

Description

BIOMARKERS IN SEPSIS AND TRAUMA AND USES THEREOF

TECHNICAL FIELD

The present invention relates to a method for determining the presence of sepsis and/or trauma in a subject.

BACKGROUND

Severe infectious diseases, including sepsis, severe sepsis, and septic shock, remain a worldwide significant cause of mortality. Despite an improved intensive care system, complications are considered as a medical emergency. This is underlined by a global epidemiological assessment on the outcome for patients in intensive care units, which was published by the Intensive Care Over Nations (ICON) group in 2014. In their study, the authors found that about one third of the included patients suffered from a severe sepsis on admission or during their stay at the intensive care unit (ICU). Out of these patients, about 25% died, which is almost double the general ICU mortality rate. Another recent US study by Winters and colleagues revealed that 28% of autopsied ICU patients had at least one misdiagnosis, of which 8% were considered as "major and potentially lethal" (class I misdiagnosis), accounting for more than 40.000 patients who yearly die in American ICUs. Notably, infections and vascular events were found to be the most common class I misdiagnoses.

Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Organ dysfunction can be identified as an acute change in total SOFA score > 2 points, where SOFA stands for Sequential [Sepsis-related] Organ Failure Assessment. The baseline SOFA score is assumed to be zero in patients not known to have pre-existing organ dysfunction. A SOFA score > 2 reflects an overall mortality risk of approximately 10% in a general hospital population with suspected infection.

Patients with suspected infection who are likely to have a prolonged ICU stay or to die in the hospital can be promptly identified at the bedside with qSOFA (Quick SOFA), ie., alteration in mental status, systolic blood pressure <100 mm Hg, or respiratory rate >22/min. Patients with sepsis often experience a body temperature above 38.0 degree Celsius or below 36.0 degree Celsius, a heart rate above 90 beats/minute, a respiratory rate above 20 breaths per minute; and a white blood cell count higher than 12,000 or lower than 4,000 cells per microliter. The levels of inflammatory mediators such interleukin-6 (IL-6), IL-8, IL-18, tumor necrosis factor-a (TNF-a), and IL-10 often reach pathological and sometime-life-threatening concentration which in turn can lead to further systemic complications. Dependent on the hemostatic state, blood coagulation is activated leading to the formation of microclots that can cause the closure of small blood vessels. Because of the subsequent lack of oxygen supply the organs will then shut down which in the worst-case scenario will lead to irreparable multiorgan failure und ultimately to death. In addition the formation of microclots combined with an increased vascular permeability, will lead to a consumption of clotting factors in the circulation which may trigger severe bleeding complications. Apart from these symptoms patients may hallucinate or are unconscious.

Septic shock is a subset of sepsis in which underlying circulatory and cellular/metabolic abnormalities are profound enough to substantially increase mortality. Patients with septic shock can be identified with a clinical construct of sepsis with persisting hypotension requiring vasopressors to maintain a mean arterial pressure (MAP) > 65 mm Hg and having a serum lactate level > 2 mmol/L (18 mg/dL) despite adequate volume resuscitation.

Especially hospital acquired infections constitute a growing clinical problem. The most frequent pathogens isolated are methicillin-resistant Staphylococcus aureus (MRSA), Pseudomonas spp., vancomycin-resistant enterococci (VRE), Clostridium difficile, and Acinetobacter spp. The most common hospital acquired-infections are primary bloodstream infections, urinary tract infections, surgical wound infections, and ventilator-associated pneumonia. The outcome of hospital acquired infections often depends on the immunologic status of the patient which can be modulated by many factors such as immunosuppressive chemotherapies, antibiotic treatments for instance upon surgical wound infections, or other medications that can affect the host's immune system. The European Centre for Disease Prevention and Control has estimated that only in Europe more than 4.000.000 patients are annually diagnosed with at least one hospital acquired-infection infection. The costs involved for treating these patients are enormous and have been calculated to be as high as 7 billion Euros per year. These numbers clearly show that hospital acquired-infections are a significant clinical problem involving enormous healthcare costs. Timely monitoring is therefore an important issue that can lead to better survival outcome and prevent the occurrence of secondary infections. For instance, it has been reported that for every hour that antimicrobial treatment is delayed, mortality can increase by up to 7.6%. Thus, prompt recognition and reliable diagnostic tools are of essential importance to improve the outcomes of these life-threatening conditions.

Trauma is one of the leading causes of death worldwide with mortality rates exceeding 50 %. As seen in sepsis patients, trauma patients suffer from both pro- and anti-inflammatory responses leading to an increased risk of acute respiratory distress syndrome (ARDS) and multiple organ failure (MOF). Though the post-traumatic and sepsis pathologic processes are almost identical, different therapeutic approaches are needed. Because of the rapid progression of the disease process there is, like in sepsis, an urgent need for reliable and robust biomarkers. So far, such tools are not available and the global estimated cost can be as high as 518 billion dollars each year.

One major reason for misdiagnosis is that patients suffering from complications such as trauma and burn show similar clinical symptoms. As these patients need different treatments, rapid diagnostics is required. Decisions regarding where and how to treat the patients will mostly be taken at the ICU because of the severe health status of the patients.

The human body has a wide variety of defense strategies as part of its immune system to protect itself against the invasion and colonisation of pathogenic microorganisms. Innate and adaptive immune responses are cross-linked parts of the immunity. Whereas the innate immune defence reacts to evolutionarily conserved structures on pathogens, the adaptive response plays a role in the cell-mediated immunity. The various cells of the immune response need to build a tight network to protect the host from several different pathogens, and cell communication is one of the most important capacities within an inflammatory response.

Sepsis is the result of an extreme immune response to microbial infection that gets into the blood, and may, if not recognised early, lead to a septic shock, organ failure and death. Sepsis is caused by a hyper-inflammatory host response to microbial infections. Severe infectious diseases such as sepsis constitute a huge problem in hospitals. According to the World Health Organisation, sepsis effects about 30 million people a year world-wide. In general, therapy for sepsis is directed against the causative microorganism by antibiotics, surgery and fluid resuscitation, but with the increasing antibiotic resistance alternative therapies are developed. However, different clinical trials targeting the blockage of inflammatory responses or other processes have not successfully been increasing the survival rate, reflecting the complex pathophysiology of sepsis. The biggest problem of treating sepsis successfully is the lack of early diagnosis due to body's rapid immune responses.

Thus, in view of the above there is a need for new methods for the diagnosis of sepsis and trauma, methods which are practical and easy to perform, and which identify subjects for effective treatment of sepsis or trauma.

SUMMARY OF THE INVENTION

It is an object of at least certain aspects of the present invention to provide an improvement over known art; particularly to achieve a method of diagnosis that provides an easy, correct and patient friendly diagnosis of sepsis and/or trauma.

Accordingly, a first aspect of the present invention provides a method for determining the presence of sepsis and/or trauma in a subject, comprising the steps of:

a) providing a test sample to be tested, earlier obtained from the subject;

b) determining the level of expression in the test sample of one or more biomarkers;

cl) providing a first control sample, earlier obtained from a subject not afflicted with sepsis, and/or

c2) providing a second control sample, earlier obtained from a subject not afflicted with trauma,

d) determining the level of expression in the first and/or second control sample of the one or more biomarkers determined in step b);

wherein a level of expression in the test sample of the one or more biomarkers determined in step b) that is different from the level of expression in the first control sample of the one or more biomarkers determined in step d) is indicative of the presence of sepsis,

wherein a level of expression in the test sample of the one or more biomarkers determined in step b) that is different from the level of expression in the second control sample of the one or more biomarkers determined in step d) is indicative of the presence of trauma,

and wherein the one or more biomarkers are selected from the group defined in Table 2. According to one aspect, the method further comprises the steps of:

el) providing a third control sample, earlier obtained from a subject afflicted with sepsis, and/or

e2) providing a fourth control sample, earlier obtained from a subject afflicted with trauma, f) determining the level of expression of the one or more biomarkers determined in step b) in the third and/or fourth control sample;

wherein a level of expression in the test sample of the one or more biomarkers measured in step b) that corresponds to the level of expression in the third control sample of the one or more biomarkers determined in step f) is indicative of the presence of sepsis, and

wherein a level of expression in the test sample of the one or more biomarkers measured in step b) that corresponds to the level of expression in the fourth control sample of the one or more biomarkers determined in step f) is indicative of the presence of trauma.

According to one aspect the test sample provided in step a) and/or the first, second, third and/or fourth control sample provided in step cl), c2), el) and/or e2) is selected from the group consisting of blood plasma and microparticles.

According to one aspect the microparticles are obtained from blood plasma by separation.

According to one aspect the separation is done by centrifugation.

According to one aspect the separation is followed by lysis of the microparticles.

According to one aspect the one or more biomarkers is/are selected from the group consisting of IL-1 F10, GPI, S100A10, VIPR2, TREM1, MMP7, MMP20, Pepsinogen, Serpin A9, Trail R4 and A2M.

According to one aspect a level of expression in the test sample of the biomarkers IL-1 F10, GPI, S100A10, and/or VIPR2 determined in step b) that is different from the level of expression in the first control sample of the biomarkers IL-1 F10, GPI, S100A10, and/or VIPR2 determined in step d) is indicative of the presence of sepsis.

According to one aspect a level of expression in the test sample of the biomarkers TREM1, MMP7, MMP20, and/or Pepsinogen determined in step b) that is different from the level of expression in the second control sample of the biomarkers TREM1, MP7, MMP20, and/or Pepsinogen determined in step d) is indicative of trauma. According to one aspect step b) comprises measuring the expression in the test sample of all the biomarkers defined in Table 2.

According to one aspect step b), step d) and/or step f) is/are performed using a first binding agent capable of binding to the one or more biomarkers.

According to one aspect the first binding agent comprises or consists of an antibody or an antigen-binding fragment thereof.

According to one aspect the antibody or antigen-binding fragment thereof is a recombinant antibody or antigen-binding fragment thereof.

According to one aspect the one or more biomarkers in the sample to be tested are labelled with a detectable moiety.

According to one aspect the one or more biomarkers in the first, second, third and/or fourth control sample are labelled with a detectable moiety.

According to one aspect the detectable moiety is selected from the group consisting of: a fluorescent moiety, a luminescent moiety, a chemiluminescent moiety, a radioactive moiety, and an enzymatic moiety.

According to one aspect the subject shows one or more of the following symptoms: a body temperature above 38.0 °C or below 36.0 °C, a heart rate above 90 beats/minute, a respiratory rate above 20 breaths per minute, a white blood cell count higher than 12,000 or lower than 4,000 cells per microliter and increased blood levels of inflammatory mediators, such as interleukin-6 (IL-6), IL-8, IL-18, tumor necrosis factor-a (TNF-a), and IL-10.

According to one aspect the present invention provides use of one or more biomarkers selected from the group defined in Table 2 as diagnostic markers for determining the presence of sepsis in a subject in vitro in a sample earlier obtained from the subject.

According to one aspect the sample is selected from the group consisting of plasma and microparticles.

According to one aspect the microparticles are obtained from blood plasma by separation. According to one aspect the separation is done by centrifugation.

According to one aspect the present invention provides a kit for determining the presence of sepsis or trauma in a subject comprising:

i) one or more first binding agent as defined according to the present invention;

ii) instructions for performing the method as defined according to the present invention or the use according to the present invention.

According to one aspect, the present invention provides a method of treating a subject having sepsis or trauma, wherein the subject is identified as having sepsis or trauma using the method of the present invention. The patient is treated appropriately after determining the presence of sepsis or trauma in the subject. The treatment may e.g. involve giving antibiotics, and treating the source of infection when this can be identified.

As exemplified in the accompanying examples, the expression of certain proteins in a plasma or microparticle test sample may be indicative of sepsis or trauma in a subject.

According to one aspect, the biomarkers may be used for early or late detection of sepsis.

According to one aspect, a standard or reference value may be used instead of, or in addition, to said first, second, third, and/or fourth control sample. The standard or reference value(s) may be determined in separate procedures from the test value(s).

Step (b) may comprise or consist of measuring the expression of 1 or more biomarker from the biomarkers listed in Table 2, for example at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,

17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,

43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63 of the biomarkers listed in Table 2. Hence, step (b) may comprise or consist of measuring the expression of all of the biomarkers listed in Table 2.

At least some of the above identified and other objects and advantages that may be apparent from the description have been achieved by the methods in accordance with the above. BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects, features and advantages of which embodiments of the invention are capable of, will be apparent and elucidated from the following description of embodiments and aspects of the present invention, reference being made to the accompanying drawings, in which

Figure 1 shows the total protein concentration in microparticles of subjects afflicted with E. coli sepsis (lower curve) and polytrauma (upper curve).

Figure 2 is a TMT-MALDI cluster heatmap of 24 proteins selected in cell fusion data, where the lighter grey fields relate to strong expression levels and the darker grey fields relate to week expression levels.

Figure 3 is a TMT-MALDI cluster heatmap of 24 proteins selected in plasma fusion data, where the lighter grey fields relate to strong expression levels and the darker grey fields relate to week expression levels.

Figure 4 is an antibody microarray cluster heatmap of 19 proteins selected in cell fusion data, where the lighter grey fields relate to strong expression levels and the darker grey fields relate to week expression levels.

Figure 5 is an antibody microarray cluster heatmap of 12 proteins selected in plasma fusion data, where the lighter grey fields relate to strong expression levels and the darker grey fields relate to week expression levels.

Figure 6 shows a mutual information network presenting the correlation between the toplO differentially expressed genes in the plasma fusion data for E. coli.

Figure 7 shows a mutual information network presenting the correlation between the toplO differentially expressed genes in the plasma fusion data for polytrauma.

Figure 8 shows a mutual information network presenting the correlation between the toplO differentially expressed genes in the cell fusion data for E. coli.

Figure 9 shows a mutual information network presenting the correlation between the toplO differentially expressed genes in the cell fusion data for polytrauma.

Figure 10 shows a comparison of protein levels in microparticle vs plasma samples. Figure 11 shows a quantification of protein levels in plasma samples.

DETAILED DESCRIPTION OF THE INVENTION

Aspects of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings.

The terminology used herein is for the purpose of describing particular aspects of the disclosure only, and is not intended to limit the disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

In the drawings and specification, there have been disclosed exemplary aspects of the disclosure. However, many variations and modifications can be made to these aspects without substantially departing from the principles of the present disclosure. Thus, the disclosure should be regarded as illustrative rather than restrictive, and not as being limited to the particular aspects discussed above. Accordingly, although specific terms are employed, they are used in a generic and descriptive sense only and not for purposes of limitation.

It should be noted that the word "comprising" does not necessarily exclude the presence of other elements or steps than those listed and the words "a" or "an" preceding an element do not exclude the presence of a plurality of such elements.

By "biomarker" is intended a naturally occuring biological molecule, or component or fragment thereof, the measurement of which can provide information useful in the prognosis of sepsis. For example, the biomarker may be a naturally occuring protein or carbohydrate moiety, or an antigenic component of fragment thereof.

By "corresponds to the expression in the control sample" it is included that the expression of the one or more biomarkers in the sample to be tested is the same as or similar to the expression of the one or more biomarkers of the positive control sample. Preferably, the expression of the one or more biomarkers in the sample to be tested is identical to the expression of the one or more biomarkers of the positive control sample.

Step b) may comprise or consist of measuring the expression of 1 or more biomarkers from the biomarkers listed in Table 2, for example at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,

43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62 or 63 of the biomarkers listed in Table 2. Step b) may comprise or consist or measuring the expression of each of the biomarkers listed in Table 2.

Activation of immune cells triggers the inflammatory cross-talk that spreads through the body. A cross-talk between two immune cells can be also passed through microparticles. Microparticles display an abundant spectrum of bioactive substances, adhesion molecules and membrane-anchored receptors, allowing specific interaction with target cells; displaying a possible function in sepsis. The present inventors have surprisingly found that microparticles can be identified as biomarkers or mediators in early stages of sepsis, thus enabling a more premature treatment of sepsis treatment. In the present study it was shown that the depletion of microparticles in plasma improves potential biomarkers detection.

Important mediators of physiological and pathological cellular processes are membranous vesicles. Specialized vesicles including endosomes, lysosomes and transport vesicles are necessary for processes within the cell. Cells can also produce extracellular vesicles, which have a significant role in intercellular communication. Between the various types of extracellular vesicles, microparticles (MP) are produced in physiological and pathophysiological conditions. After activation of different cells in the immune system, the increasing production of microparticles is a potential biomarker for endothelial dysfunction, coagulation, inflammation and other pathological processes.

EXAMPLES

Embodiments of the present disclosure will be described and exemplified more fully hereinafter with reference to the accompanying drawings. The solutions disclosed herein can, however, be realized in many different forms and should not be construed as being limited to the embodiments set forth herein. Microparticle Isolation

The microparticles were extracted from 1 ml human plasma by centrifugation at 21 000 g (high speed) for 45 min. at 20 °C. The pelleted microparticles were lysed in 500 pL IP lysis buffer (+5 pL proteinase inhibitor). After full lysis for 30 min. at 4°C in shaking, the sample was stored in -80 °C.

Bradford Assay

By this assay, the protein concentration of a sample is captured by photometric measure ments. The Pierce 660 nm Protein Assay Reagent was used, which is based on the binding of a proprietary dye-metal complex to protein in acidic conditions that causes a shift in the dye's absorption maximum. The measured absorption is proportional to the bound protein quantity. To determine the protein concentration a standard series provided in the kit was used. The standards contain bovine serum albumin (BSA) in different concentrations from 125 to 2000 pg/pL. As a blank, distilled water was used. The sample was diluted until it fitted into the standard series. For measurement 150 pL Assay Reagent was added to 10 pL diluted sample, blank or standard. The photometric measurement was performed directly after adding of the Assay Reagent at 660 nm.

Antibody Microarray

All samples were tested in RayBiotech Service Department. Briefly, Antibody microarray Lysed MPs and MPs separated plasma samples was biotinylated and hybridized to L-series 507 and 493-antibody arrays (Raybiotech). For each array, protein intensity values were background subtracted, scaled by the internal control, and floored at 1 unit. For each dilution, t-test p- values for triplicate arrays were used to rank proteins, and antibodies showing changes in different directions were excluded. The top 10% assayed proteins were selected by mean rank across three dilutions.

TMT-MALDI

All samples were tested in Proteomics Core Facility, Goteborg, Sweden. Samples preparation, nano LC-MS analysis, data search and TMT quantification were described in Anders E.

Henriksson e.tal, Proteomes 2108, 6(4), 43. Bioinformatics

Analysis was done equal for cell fusion and plasma fusion data. Raw MS intensities were normalized using quantile normalization [1] from R-package limma. Normalized data were first explored by principal component analysis (PCA) and hierarchical cluster analysis to identify groups of samples. Differential expression analysis was performed to identify differentially expressed proteins between experimental groups (E. coli versus polytrauma) and between time points (day 3, 5, 7 versus day 1) using the linear models proposed by Smyth, G. K (2005) [2] Gene-wise p-values were adjusted to control a false discovery rate (FDR) of 5% using the methods of Benjamini and Hochberg [3]. A gene was selected if its adjusted p-values was <0.05 and its absolute log2-FoldChange was >2. Enrichment of gene ontology (GO) terms was studied using the ROMER gene set enrichment test [4] Mutual information networks were generated using the R-package minet [5]

Data exploration, differential expression and network analysis

PCA and hierarchical clustering showed a clear separation of E. coli and polytrauma samples in the cell fusion data, as can be seen in Figure 1. The separation was not such clear when studying the plasma fusion data, which is shown in Figures 2-3. According to the differential expression analysis 721 proteins were differentially expressed between E. coli and polytrauma samples in the cell fusion data and 70 proteins in the plasma fusion data, based on FDR- adjusted p-values, as shown in table 1. Table 1. Number of differentially expressed proteins in the individual comparisons

With the additional fold change criterion, | logFC | >2, 24 proteins remained in each data set. Cluster heatmaps of the 24 proteins show several subsets of up- and down-regulated proteins in the two data sets, shown in Figures 2-3. There were nearly no differentially expressed proteins between the time points. Only 2 proteins remained in the cell fusion data that were altered between day 7 and day 1. The top 10 differentially expressed proteins between E. coli and polytrauma samples were studied by mutual network analysis (Figures 6-9). The figures show the losses and gains in correlations between proteins compared between E. coli and polytrauma samples.

Gene ontology enrichment analysis

In the cell fusion data, 311 GO terms were significantly enriched among the 721 differentially expressed proteins. No GO term enrichment could be found in the plasma fusion data.

ELISA

The concentration of specific proteins was measured in human microparticles depleted plasma according to manufacturer's protocols.

Conclusion

Total protein content of the microparticles derived from 1 ml plasma sample was measured. From the perspective of identifying unbiased biomarkers for sepsis, plasma samples from 5 patients suffering from E. coli infection or polytrauma were selected. Samples were collected before onset of treatment and following day 1, 3, 5 and 7 post treatment. Using TMT-MALDI and antibody microarray analysis, biomarkers were identified in circulating microparticles isolated from E. coli infected patents and polytrauma patients. This approach enabled the inventors to differentiate the protein patterns between E. coli infection and polytrauma. Cluster heatmap was generated using top hits of 24 significant proteins from microparticles and plasma. For the proof of concept, potential biomarkers were selected for developing accurate diagnostic to differentiate between healthy, fever, polytrauma, bacteremia and sepsis. Microparticles separated plasma samples from five patients, healthy or suffering from fever, polytrauma, bacteremia or sepsis, were used. Using ELSIA, the potential biomarkers for the detection of sepsis in patients were detected. Table 2. Selected candidate proteins as biomarkers

REFERENCES

[1] Bolstad, B. M., Irizarry, R. A., Astrand, M., & Speed, T. P. (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 19(2), 185-193. [2] Smyth, G. K. (2005). Limma: linear models for microarray data. In Bioinformatics and computational biology solutions using R and Bioconductor (pp. 397-420). Springer New York.

[3] Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the royal statistical society. Series B

(Methodological), 289-300. [4] Ritchie, ME, Phipson, B, Wu, D, Hu, Y, Law, CW, Shi, W, and Smyth, GK (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43, e47.

[5] Meyer, P. E., Lafitte, F., & Bontempi, G. (2008). minet: AR/Bioconductor package for inferring large transcriptional networks using mutual information. BMC bioinformatics, 9(1), 461.

Claims

1. A method for determining the presence of sepsis and/or trauma in a subject, comprising the steps of:

a) providing a test sample to be tested, earlier obtained from the subject;

wherein a level of expression in the test sample of the one or more biomarkers determined in step b) that is different from the level of expression in the first control sample of the one or more biomarkers determined in step d) is indicative of the presence of sepsis, wherein a level of expression in the test sample of the one or more biomarkers determined in step b) that is different from the level of expression in the second control sample of the one or more biomarkers determined in step d) is indicative of the presence of trauma,

and wherein the one or more biomarkers are selected from the group defined in

Table 2.

2. The method according to claim 1, further comprising the steps of:

wherein a level of expression in the test sample of the one or more biomarkers measured in step b) that corresponds to the level of expression in the third control sample of the one or more biomarkers determined in step f) is indicative of the presence of sepsis, and wherein a level of expression in the test sample of the one or more biomarkers measured in step b) that corresponds to the level of expression in the fourth control sample of the one or more biomarkers determined in step f) is indicative of the presence of trauma.

3. The method according to claim 1 or 2, wherein the test sample provided in step a) and/or the first, second, third and/or fourth control sample provided in step cl), c2), el) and/or e2) is selected from the group consisting of blood plasma and microparticles.

4. The method according to any one of the previous claims, wherein the one or more biomarkers is/are selected from the group consisting of IL-1 F10, GPI, S100A10, VIPR2, TREM1, MMP7, MMP20, Pepsinogen, Serpin A9, Trail R4 and A2M.

5. The method according to any one of the previous claims, wherein a level of expression in the test sample of the biomarkers IL-1 F10, GPI, S100A10, and/or VIPR2 determined in step b) that is different from the level of expression in the first control sample of the biomarkers IL-1 F10, GPI, S100A10, and/or VIPR2 determined in step d) is indicative of the presence of sepsis.

6. The method according to any one of the previous claims, wherein a level of expression in the test sample of the biomarkers TREM1, MMP7, MMP20, and/or Pepsinogen determined in step b) that is different from the level of expression in the second control sample of the biomarkers TREM1, MP7, MMP20, and/or Pepsinogen determined in step d) is indicative of trauma.

7. The method according to any one of the previous claims, wherein step b) comprises measuring the expression in the test sample of all the biomarkers defined in Table 2.

8. The method according to any one of the previous claims, wherein step b), step d) and/or step f) is/are performed using a first binding agent capable of binding to the one or more biomarkers.

9. The method according to claim 8, wherein the first binding agent comprises or consists of an antibody or an antigen-binding fragment thereof.

10. Use of one or more biomarkers selected from the group defined in Table 2 as diagnostic markers for determining the presence of sepsis in a subject in vitro in a sample earlier obtained from the subject.