Diagnostic and prognostic signatures for sepsis

Abstract

The present disclosure relates to methods of diagnosing, determining a prognosis for and treating sepsis.

Description

Diagnostic and prognostic signatures for sepsis Cross-reference to related applications

[0001] The present application claims priority from Australian Provisional Patent Application No. 2024900696 filed on 15 March 2024, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0002] The present disclosure relates to methods of diagnosing, determining a prognosis for and treating sepsis. Background

[0003] Sepsis is defined as a dysregulated host response to infection leading to life-threatening organ dysfunction1. Sepsis remains a leading cause of mortality in paediatric age groups affecting millions of children every year with over 3 million deaths attributable to sepsis2. Millions survive sepsis each year, however with long-lasting neurodevelopmental, physical, or psychological sequelae3-6. In the United States alone, paediatric sepsis was estimated to account for $7.31 billion direct costs in 20167, and one in five survivors will develop new or progressive medical conditions after sepsis8. At the same time, given the predominance of viral infections in children and challenges in recognizing sepsis in this age group9, campaigns providing incentives for the early administration of antimicrobials have been criticized as they may potentially encourageunnecessary use of antibiotics, with unintended consequences10-12. Rapid diagnostics mayrepresent a hingepoint of sepsis management and have potential to improve timeliness and accuracy of sepsis treatment, as well as of reducing inadvertent antibiotic usage.

[0004] To date, the biological mechanisms underpinning dysregulated host responses characterizing the progression from uncomplicated infection towards infection with organ dysfunction remain poorly elucidated13-15. Despite progress in blood culture techniques and microbiological diagnostics, their turnaround time and accuracy remain inadequate to guide initial empiric treatment and lack the ability to predict disease severity16.

[0005] Accordingly, there remains an unmet clinical need for diagnostic and prognostic markers for sepsis, such as those capable of predicting the progression of a simple infection to one with organ dysfunction or characterizing the specific type of infection involved.

[0006] Summary

[0007] The present disclosure is based on the discovery of a biomarker signature, which showed significant diagnostic capability in paediatric sepsis patients. This diagnostic signature also demonstrated promise in classifying sepsis patients according to the broad microbial aetiology of disease. In this regard, the biomarkers described herein can be predictive of patient response to,for example, antimicrobial agents, such as antivirals and antibiotics, and guide appropriate treatment decisions by clinicians. The present disclosure is also based on the discovery of a prognostic biomarker signature, which may be helpful in classifying sepsis patients according to the risk of subsequent organ failure and / or need for timely additional supportive therapy.

[0008] In a first aspect, the present disclosure provides a method of diagnosing a subject with sepsis, said method including the step of measuring an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and wherein the expression level of the two or more biomarkers are diagnostic or indicative of the subject having sepsis.

[0009] Suitably, for the present diagnostic method: (a) an increased level of one or more of NCF1B, BATF, S100A11, NOD2 and PTGES3 and / or a decreased level of one or more of ZBED1 and HLX; (b) an increased level of one or more of USP18, NCF1B, BATF, S100A11, NOD2 and PTGES3 and / or a decreased level of one or more of CLC, ZBED1, HLX and ICAM1; (c) an increased level of one or more of NCF1B, BATF, CLC, S100A11, NOD2, PTGES3 and ICAM1 and / or a decreased level of one or more of USP18, ZBED1 and HLX; (d) an increased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1 and / or a decreased level of one or more of USP18, ZBED1 and PTGES3; (e) an increased level of one or more of USP18, ZBED1 and PTGES3 and / or a decreased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1; is diagnostic or indicative of the subject having sepsis.

[0010] Suitably, the present method further includes the step of determining an infection type in the subject based on the expression level of the two or more biomarkers. In some examples, an increased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1 and / or a decreased level of one or more of USP18, ZBED1 and PTGES3, is diagnostic or indicative of the subject having a bacterial infection. In alternative examples, an increased level of one or more of USP18, ZBED1 and PTGES3 and / or a decreased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, is diagnostic or indicative of the subject having a viral infection.

[0011] In a second aspect, the present disclosure provides a method of determining an infection type in a subject with sepsis, said method including the step of measuring an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, andwherein the expression level of the two or more biomarkers are indicative of the infection type in the subject.

[0012] Suitably, determining the infection type in the subject with sepsis comprises distinguishing between a bacterial infection and a viral infection. In some examples, an increased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1 and / or a decreased level of one or more of USP18, ZBED1 and PTGES3, is diagnostic or indicative of the subject having the bacterial infection. In other examples, an increased level of one or more of USP18, ZBED1 and PTGES3 and / or a decreased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, is diagnostic or indicative of the subject having the viral infection.

[0013] In a third aspect, the present disclosure provides a method of predicting the responsiveness of a subject with sepsis to a treatment, such as an antibiotic agent or an antiviral agent, said method including the step of determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, wherein the expression level of the two or more biomarkers indicates or correlates with increased or decreased responsiveness of the subject’s sepsis to the treatment.

[0014] The present method may further include the step of administering the treatment to the subject, such as if the expression level of the two or more biomarkers indicates or correlates with increased responsiveness of the subject’s sepsis to the treatment.

[0015] For the aforementioned aspects, the method may further include the step of determining an expression level of two or more further biomarkers in the biological sample from the subject, wherein the two or more further biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof, to determine a prognosis for the subject’s sepsis.

[0016] In a fourth aspect, the present disclosure provides a method for determining a prognosis for a subject with sepsis, said method including the step of: determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof, to thereby determine the prognosis of sepsis in the subject.

[0017] Suitably, the prognosis of the third and fourth aspects includes a likelihood or an estimated risk of the subject developing an organ dysfunction. In some examples, the organ dysfunction comprises or is associated with one or more of cardiac dysfunction, cardiovascular dysfunction, respiratory dysfunction, neurologic dysfunction, renal dysfunction, hepaticdysfunction and haematologic dysfunction. In some examples, the prognosis is used, at least in part, to develop a treatment strategy for the subject.

[0018] Suitably, the methods of the aforementioned aspects may further include the step of administering a treatment for sepsis, such as an antibiotic agent or an antiviral agent, to the subject.

[0019] In a fifth aspect, the present disclosure provides a method of treating sepsis in a subject, said method including the step of administering a therapeutically effective amount of a treatment for sepsis to the subject in which an expression level of two or more biomarkers has been determined in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and wherein the expression level of the two or more biomarkers indicates or correlates with the sepsis being at least partly responsive to the treatment.

[0020] In a sixth aspect, the present disclosure provides a method of treating sepsis in a subject, said method including the step of determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and based on the determination made, initiating, continuing, modifying or discontinuing a treatment of sepsis in the subject.

[0021] For the methods of the fifth and sixth aspect, the treatment suitably is or comprises an antibiotic agent or an antiviral agent.

[0022] Referring to the above aspects, the subject can be a paediatric subject.

[0023] For the aforementioned aspects, the biological sample is suitably one or more of a blood sample, a serum sample and a plasma sample.

[0024] In a seventh aspect, the present disclosure provides a kit for: (a) diagnosing a subject with sepsis; (b) determining an infection type in a subject with sepsis; and / or (c) predicting the responsiveness of a subject with sepsis to a treatment; the kit comprising one or more reagents for determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof.

[0025] The kit of this aspect may further include one or more further reagents for determining an expression level of two or more further biomarkers in the biological sample from the subject, wherein the two or more further biomarkers are selected from the group consisting of AATBC,MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof.

[0026] In an eighth aspect, the present disclosure provides a kit for determining a prognosis for a subject with sepsis, the kit comprising one or more reagents for determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof.

[0027] Suitably, the kits of the seventh and eighth aspects are for use in the methods of the first to sixth aspects. Brief description of the drawings

[0028] The following figures form part of the present specification and are included to further demonstrate certain aspects of the present disclosure. The disclosure may be better understood by reference to one or more of these figures in combination with the detailed description of specific embodiments presented herein. It will be appreciated by persons skilled in the art that numerous variations and / or modifications may be made to the above-described embodiments, without departing from the broad general scope of the present disclosure. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.

[0029] Figure 1. Schematic workflow of the multi-phenotype signature discovery using transcriptomics data. The discovery cohort was used for the signature discovery. The disease- class signature and disease-severity signature were discovered using the FSPLS method. These signatures were validated on two independent validation cohorts. First, the infection type of the sample was predicted using the disease-class signature, then the probability of developing organ dysfunction was predicted using the disease-severity signature. DB – Definite Bacterial; DV – Definite Viral; PV- Probable Viral; PB – Probable Bacterial; NI – Non-infectious; OD – Organ Dysfunction; ROC curve – Receiver Operating Characteristics curve; AUC – Area Under the Curve.

[0030] Figure 2. Performance of disease-class signature and disease-severity signature in distinguishing infection type and identifying organ dysfunction. Heat map showing the expression of (A) disease-class signature genes across patients in the discovery cohort with definite bacterial (n=172) and definite viral (n=110) infections; (B) disease-severity signature genes across patients in the discovery cohort with organ dysfunction (n=87) versus without organ dysfunction (n=508) at 24-hours after sampling; Receiver Operating Characteristics (ROC) curve for the performance of the signature in the discovery (red lines) and validation (blue lines) data to distinguish (C) definite bacterial versus definite viral infections; (D) with versus without organ dysfunction in all the patients; (E) with versus without organ dysfunction in patients with predicted definite bacterialinfections; and (F) with versus without organ dysfunction in patients with predicted definite viral infections. Continuous red and blue lines indicate AUC and the dashed lines shows the respective 95% confidence interval.

[0031] Figure 3. Classification of patients into diagnostic groups by type of infection and by organ dysfunction.

[0032] Figure 4. Sankey diagram showing the course of organ dysfunction from presentation to 24-hours after presentation in both discovery (A, B, C, D) and RAPIDS validation (E, F, G, H) cohorts; A, E - number of organs affected (number of OD); B, F - OD remote to the site of infection (0 – No, 1 – Yes); C, G - need of organ support (0 – No, 1 – Yes); D, H - administration of inotropes (0 – No, 1 – Yes).

[0033] Figure 5. Principal Component Analysis (PCA) performed on the discovery and validation cohort with the gene expression counts. Points represent samples and are coloured by RNA-seq run.

[0034] Figure 6. Volcano plots showing log2 fold change (LFC) values and adjusted -log10 p- values from differential expression analysis comparing (A) patients with definite bacterial (DB) (n=172 patients) versus definite viral (DV) (n=110 patients) infections; (B) patients with organ dysfunction (OD) at 24-hours after sampling (n = 87) versus patients without OD (n=508); (C) patients with definite bacterial infections with OD at 24-hours after sampling (n = 44) versus without OD (n=192); (D) patients with definite viral infections with OD at 24-hours after sampling (n = 11) versus patients without OD (n=186). Red points represents genes with adjusted p-values < 0.05 and absolute LFC > 1 or < -1; Blue points represents genes with adjusted p-values < 0.05 and absolute LFC < 1 or >-1; Green points represents genes with adjusted p-values > 0.05 and absolute LFC > 1 or < -1; Grey points represents genes which are not significant. Top 10 differentially expressed genes based on -log10 adjusted p-values are labelled for each comparison.

[0035] Figure 7. Gene counts distribution of the 10 genes in disease-class signature (top panel) and disease-severity signature (bottom panel) across the discovery and validation cohorts.

[0036] Figure 8. Weights of the genes in the disease-class signature for different phenotypes.

[0037] Figure 9. Distribution of the sample predictions into definite bacterial (DB), definite viral (DV) and non-infectious (NI) for each phenotype in the discovery and both validation cohorts. The three axes of the triangle are probability of DB, probability of DV and probability of NI. The upside-down triangle shows the lines which define greater than 0.5 probability for each infection type.

[0038] Figure 10. Enriched Gene Ontology terms in disease-class signature genes and disease- severity signature genes. Top 10 GO enriched terms are shown in the bar plots for (A) disease- class signature and (C) disease-severity signature. Count indicates the number of genes in thesignature associated with the GO term and the p-adjusted value shows the enrichment score of the GO term. Network of the signature genes for the top 10 GO enriched terms for (B) disease-class signature and (D) disease-severity signature are shown in the network pots. Grey circles are the genes, and the brown circles are the GO enriched terms. Size of the brown circle is based on the enrichment scores associated to the GO term.

[0039] Figure 11. Performance of gene signatures combined with clinical measurements. Disease- class signature predictions for definite bacterial vs definite viral combined with (A) CRP measurements and (B) Lymphocyte cell count measurements at baseline. Disease-severity signature predictions for presence of organ dysfunction at baseline combined with (C) CRP measurements and (D) Lymphocyte cell count measurements at baseline. Key to the Sequence Listing SEQ ID NO: 1 Nucleic acid sequence of USP18 coding sequence SEQ ID NO: 2 Amino acid sequence of USP18 protein SEQ ID NO: 3 Nucleic acid sequence of NCF1B coding sequence SEQ ID NO: 4 Amino acid sequence of NCF1B protein SEQ ID NO: 5 Nucleic acid sequence of BATF coding sequence SEQ ID NO: 6 Amino acid sequence of BATF protein SEQ ID NO: 7 Nucleic acid sequence of CLC coding sequence SEQ ID NO: 8 Amino acid sequence of CLC protein SEQ ID NO: 9 Nucleic acid sequence of S100A11 coding sequence SEQ ID NO: 10 Amino acid sequence of S100A11 protein SEQ ID NO: 11 Nucleic acid sequence of ZBED1 coding sequence SEQ ID NO: 12 Amino acid sequence of ZBED1 protein SEQ ID NO: 13 Nucleic acid sequence of PTGES3 coding sequence SEQ ID NO: 14 Amino acid sequence of PTGES3 protein SEQ ID NO: 15 Nucleic acid sequence of HLX coding sequence SEQ ID NO: 16 Amino acid sequence of HLX protein SEQ ID NO: 17 Nucleic acid sequence of NOD2 coding sequence SEQ ID NO: 18 Amino acid sequence of NOD2 protein SEQ ID NO: 19 Nucleic acid sequence of ICAM1 coding sequence SEQ ID NO: 20 Amino acid sequence of ICAM1 protein SEQ ID NO: 21 Nucleic acid sequence of AATBC coding sequence SEQ ID NO: 22 Nucleic acid sequence of MAFG coding sequence SEQ ID NO: 23 Amino acid sequence of MAFG proteinSEQ ID NO: 24 Nucleic acid sequence of VAV1 coding sequence SEQ ID NO: 25 Amino acid sequence of VAV1 protein SEQ ID NO: 26 Nucleic acid sequence of MS4A7 coding sequence SEQ ID NO: 27 Amino acid sequence of MS4A7 protein SEQ ID NO: 28 Nucleic acid sequence of IGHA1 coding sequence SEQ ID NO: 29 Amino acid sequence of IGHA1 protein SEQ ID NO: 30 Nucleic acid sequence of ATP6V0A1 coding sequence SEQ ID NO: 31 Amino acid sequence of ATP6V0A1 protein SEQ ID NO: 32 Nucleic acid sequence of RN7SL3 coding sequence SEQ ID NO: 33 Nucleic acid sequence of MPP7 coding sequence SEQ ID NO: 34 Amino acid sequence of MPP7 protein SEQ ID NO: 35 Nucleic acid sequence of DSC2 coding sequence SEQ ID NO: 36 Amino acid sequence of DSC2 protein SEQ ID NO: 37 Nucleic acid sequence of PHACTR2 coding sequence SEQ ID NO: 38 Amino acid sequence of PHACTR2 protein Detailed description General Techniques and Definitions

[0040] Unless specifically defined otherwise, all technical and scientific terms used herein shall be taken to have the same meaning as commonly understood by one of ordinary skill in the art (e.g. in genomics, immunology, molecular biology, immunohistochemistry, biochemistry, oncology, and pharmacology).

[0041] The present disclosure is performed using, unless otherwise indicated, conventional techniques of molecular biology, microbiology, recombinant DNA technology and immunology. Such procedures are described, for example in Sambrook, Fritsch & Maniatis, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratories, New York, Fourth Edition (2012), whole of Vols I, II, and III; DNA Cloning: A Practical Approach, Vols. I and II (D. N. Glover, Second Edition., 1995), IRL Press, Oxford, whole of text; Oligonucleotide Synthesis: A Practical Approach (M. J. Gait, ed, 1984) IRL Press, Oxford, whole of text, and particularly the papers therein by Gait, ppl-22; Atkinson et al, pp35-81; Sproat et al, pp 83-115; and Wu et al, pp 135- 151; 4. Nucleic Acid Hybridization: A Practical Approach (B. D. Hames & S. J. Higgins, eds., 1985) IRL Press, Oxford, whole of text; Immobilized Cells and Enzymes: A Practical Approach (1986) IRL Press, Oxford, whole of text; Perbal, B., A Practical Guide to Molecular Cloning (1984) and Methods In Enzymology (S. Colowick and N. Kaplan, eds., Academic Press, Inc.), whole of series.

[0042] Those skilled in the art will appreciate that the present disclosure is susceptible to variations and modifications other than those specifically described. It is to be understood that the disclosure includes all such variations and modifications. The disclosure also includes all of the steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations of any two or more of said steps or features.

[0043] The present disclosure is not to be limited in scope by the specific embodiments described herein, which are intended for the purpose of exemplification only. Functionally equivalent products, compositions and methods are clearly within the scope of the disclosure, as described herein.

[0044] Each feature of any particular aspect or embodiment of the present disclosure may be applied mutatis mutandis to any other aspect or embodiment of the present disclosure.

[0045] Throughout this specification, unless specifically stated otherwise or the context requires otherwise, reference to a single step, composition of matter, group of steps or group of compositions of matter shall be taken to encompass one and a plurality (i.e. one or more) of those steps, compositions of matter, groups of steps or group of compositions of matter.

[0046] As used herein, the singular forms of “a”, “and” and “the” include plural forms of these words, unless the context clearly dictates otherwise.

[0047] The term “and / or”, e.g., “X and / or Y” shall be understood to mean either “X and Y” or “X or Y” and shall be taken to provide explicit support for both meanings or for either meaning.

[0048] Throughout the present specification, various aspects and components of the disclosure can be presented in a range format. The range format is included for convenience and should not be interpreted as an inflexible limitation on the scope of the present disclosure. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub- ranges as well as individual numerical values within that range, unless specifically indicated. For example, description of a range such as from 1 to 5 should be considered to have specifically disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 5, from 3 to 5 etc., as well as individual and partial numbers within the recited range, for example, 1, 2, 3, 4, 5, 5.5 and 6, unless where integers are required or implicit from context. This applies regardless of the breadth of the disclosed range. Where specific values are required, these will be indicated in the specification.

[0049] As used herein, the term “about”, unless stated to the contrary, refers to + / - 10%, more particularly + / -5%, even more particularly + / -1%, of the designated value. The extent of such tolerances and variances are well understood by persons skilled in the art. Typically, suchtolerances and variances do not compromise the structure, function and / or implementation of the methods and kits described herein.

[0050] Throughout this specification, the word “comprise” or variations such as “comprises” or “comprising” will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.

[0051] All computer programs, algorithms, protein and nucleic acid sequences (e.g., accession numbers), patent and scientific literature referred to herein is incorporated herein by reference.

[0052] It will be appreciated by persons skilled in the art that numerous variations and / or modifications may be made to the above-described embodiments, without departing from the broad general scope of the present disclosure. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.

[0053] Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present disclosure as it existed before the priority date of each of the appended claims. Diagnostic methods

[0054] The inventors have surprisingly shown that the concentration or expression level of particular biomarkers in blood samples from sepsis patients, can be used to diagnose subjects as to the presence and also the type of the underlying microbial infection. Advantageously, such diagnostic methods may allow a physician to make appropriate, informed, and timely follow-up and treatment decisions (e.g., guide decisions on antibiotic use, and on treatment escalation) based on this information.

[0055] Accordingly, the inventors have developed methods of diagnosing sepsis. In this regard, the present methods may also be utilised to distinguish or diagnose patients with non-septic or non-infectious disease that may exhibit clinical signs or symptoms that resemble that of sepsis.

[0056] As such, in one broad form, the present disclosure provides a method for measuring a level, such as an expression level, or concentration of two or more biomarkers in a biological sample from a subject, said method including the steps of: (a) providing the biological sample; and (b) measuring the expression level of the two or more biomarkers in the biological sample, wherein the two or more biomarkers, such as protein biomarkers and / or mRNA biomarkers, are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof.

[0057] In a related broad form, the present disclosure provides a method of diagnosing a subject with sepsis, said method including the step of measuring an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and wherein the expression level of the two or more biomarkers are diagnostic or indicative of the subject having sepsis.

[0058] The above methods may further include the step of determining an infection type, such as a bacterial infection or a viral infection, in the subject based on the expression level of the two or more biomarkers.

[0059] With respect to the aspects described herein, the term “subject” includes, but is not limited to, mammals, inclusive of humans, performance animals (such as horses, camels, greyhounds), livestock (such as cows, sheep, horses) and companion animals (such as cats and dogs). In one example, the subject is a human. For particular examples, the subject is an adult human or an adult subject. In alternative examples, the subject is a paediatric human or a paediatric subject. The term “paediatric subject” as used herein refers to a subject under the age of 18 years (e.g., under the age of 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5 etc years). Included are subjects ranging from 0 to 17 years of age, 0 to 15 years of age, 11 to 16 years of age, 5 to 12 years of age, or 3 to 15 years of age.

[0060] As generally used herein, the terms “sepsis” and “septic” refer to their normal meaning in clinical medicine, which is a serious medical condition characterized by a whole-body inflammatory state caused by, for example, systemic and / or blood-borne infections, such as bacterial infections and viral infections. The typical clinical symptoms observed in septic patients are often related to the underlying infectious process. When the infection crosses into sepsis, the resulting symptoms are that of systemic inflammatory response syndrome (SIRS): general inflammation, fever, elevated white blood cell count (leukocytosis), and raised heart rate (tachycardia) and breathing rate (tachypnoea). In addition to the above, symptoms may also include flu-like chills. The immunological response that causes sepsis is a systemic inflammatory response causing widespread activation of inflammation and coagulation pathways. This may progress to dysfunction of the circulatory system and, even under optimal treatment, may result in the multiple organ dysfunction syndrome and eventually death.

[0061] It is contemplated that the subject’s sepsis may be at any stage of development or disease. For example, subjects may be diagnosed as having sepsis (e.g., a suspected or confirmed infection with organ dysfunction as a result of dysregulated host immune response; formerly called “severe sepsis”) or septic shock if they have sepsis plus compromised cardiovascular function, as evidenced by, for example, signs of systemic hypoperfusion and / or elevated serum lactate levels.

[0062] Moreover, patients may be defined as having septic shock if they have sepsis plus hypotension after administration of an appropriate fluid bolus. Patients with sepsis, including septic shock, may progress from having an infection with no organ dysfunction to single organ dysfunction or even multi-organ dysfunction over time, as discussed in more detail herein.

[0063] As used herein, the terms “diagnosis” and “diagnosing” refers to a method by which one of ordinary skill in the art can assess and / or determine whether a patient or subject is suffering from a given disease or condition, such as determining the presence or absence of sepsis. Those skilled in the art often make a diagnosis based on one or more diagnostic indicators or markers whose presence, absence, or amount (relative or absolute) indicates the presence or absence of the disease, disorder or condition. It will further be appreciated that these terms do not indicate the ability to determine the presence or absence of a particular disease with 100% accuracy, nor do they indicate that a given course or outcome is more likely to occur. Rather, one of ordinary skill in the art will understand that the terms “diagnosis” and “diagnosing” refer to an increased probability that a subject will have a certain disease, disorder or condition, such as sepsis.

[0064] Suitably, the methods described herein are performed in conjunction (e.g., before and / or after) with one or more further diagnostic tests or prognostic / diagnostic biomarkers as are known in the art (e.g., clinical evaluation, such as body temperature, heart rate and respiratory rate, complete blood count, chemistry panel, liver function test, biomarkers, such as blood lactate, C- reactive protein and procalcitonin, blood culture, or clinical screening tools, such as SIRS, qSOFA, SOFA, NEWS, MEWS, circulating lymphocyte levels). In this regard, the present method may be utilised as a preliminary screening test to identify subjects who may benefit from further diagnostic testing. To this end, the methods disclosed herein can be used in sepsis screening and the diagnosis of sepsis can be confirmed via further screening of the subject by the aforementioned methods. As such, the subject may have been previously diagnosed with sepsis or be suspected as having sepsis prior to application of the methods described herein.

[0065] Accordingly, in one form, the present disclosure provides a method of screening a subject to identify whether the subject requires further investigation by one or more further diagnostic tests, such as those provided herein, for sepsis, said method including the steps of: (i) measuring a level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof; (ii) based on the level obtained, providing a recommendation for further investigation by the one or more further diagnostic tests for sepsis.

[0066] Additionally, or alternatively, the present method may be utilised to confirm or rule out the presence or absence of sepsis as indicated by a previous diagnostic test. Accordingly, in some examples, the present method may include the initial or earlier step and / or subsequent step of performing one or more further diagnostic tests on the subject in question. In alternative examples, the methods described herein are performed without any further diagnostic testing as a primary diagnostic test for sepsis.

[0067] Suitably, if the level, such as a concentration level or an expression level, of the two or more biomarkers is altered or modulated in the biological sample from a subject, this can be diagnostic of sepsis in the subject. In one example, an increased level of expression or concentration of a first subset of the two or more biomarkers and / or a decreased level of expression or concentration of a second subset (e.g., not present in the first subset of the two or more biomarkers) of the two or more biomarkers is diagnostic or indicative of the subject having sepsis. In a related example, an increased level of expression or concentration of a second subset of the two or more biomarkers and / or a decreased level of expression or concentration of a first subset of the two or more biomarkers is diagnostic or indicative of the subject not having sepsis.

[0068] In certain examples, the measuring step includes determining the presence or absence of: (i) an increased level of expression of one or more of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and / or (ii) a decreased level of expression of one or more of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, in the biological sample of the subject. More particularly, the measuring step may include determining the presence or absence of an increased level of expression of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, and a decreased level of expression of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, in the biological sample of the subject. Even more particularly, the measuring step may include determining the presence or absence of a decreased level of expression of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, and / or an increased level of expression of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, in the biological sample of the subject.

[0069] According to some examples, the measuring step includes determining the presence or absence of an increased level of one or more of NCF1B, BATF, S100A11, NOD2 and PTGES3, or a fragment, variant or derivative thereof, and / or a decreased level of one or more of ZBED1 and HLX, or a fragment, variant or derivative thereof, in the biological sample of the subject. More particularly, the measuring step may include determining the presence or absence of an increased level of one or more of USP18, NCF1B, BATF, S100A11, NOD2 and PTGES3, or a fragment,variant or derivative thereof, and / or a decreased level of one or more of CLC, ZBED1, HLX and ICAM1, or a fragment, variant or derivative thereof, in the biological sample of the subject. Even more particularly, the measuring step may include determining the presence or absence of an increased level of one or more of NCF1B, BATF, CLC, S100A11, NOD2, PTGES3 and ICAM1, or a fragment, variant or derivative thereof, and / or a decreased level of one or more of USP18, ZBED1 and HLX, or a fragment, variant or derivative thereof, in the biological sample of the subject.

[0070] Suitably, an increased level of one or more of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and / or a decreased level of one or more of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, is diagnostic or indicative of the subject having sepsis. For such examples, the expression level of the respective biomarkers is suitably increased or decreased relative to a reference value or level of expression of the corresponding biomarker. Such a reference value or level of expression of the corresponding biomarker may be at least partly derived from a sepsis patient or population of sepsis patients having viral sepsis or bacterial sepsis. Alternatively, the reference value or level of expression of the corresponding biomarker may be at least partly derived from a population of patients not having sepsis (e.g., healthy controls), such as those patients having a non-infectious disease, disorder or condition, but also exhibiting sepsis-like symptoms.

[0071] More particularly, an increased level of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, and / or a decreased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, can be diagnostic or indicative of the subject having sepsis, such as a particular type of sepsis (e.g., viral sepsis). For such examples, the expression level of the respective biomarkers is suitably increased or decreased relative to a reference value or level of expression of the corresponding biomarker at least partly derived from a sepsis patient or population of sepsis patients having bacterial sepsis.

[0072] Even more particularly, a decreased level of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, and / or an increased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, can be diagnostic or indicative of the subject having sepsis, such as a particular type of sepsis (e.g., bacterial sepsis). For such examples, the expression level of the respective biomarkers is suitably increased or decreased relative to a reference value or level of expression of the corresponding biomarker at least partly derived from a sepsis patient or population of sepsis patients having viral sepsis.

[0073] Suitably, an increased level of one or more of NCF1B, BATF, S100A11, NOD2 and PTGES3, or a fragment, variant or derivative thereof, and / or a decreased level of one or more ofZBED1 and HLX, or a fragment, variant or derivative thereof, is diagnostic or indicative of the subject having sepsis. For such examples, the expression level of the respective biomarkers is suitably increased or decreased relative to a reference value or level of expression of the corresponding biomarker. Such a reference value or level of expression of the corresponding biomarker may be at least partly derived from a patient or population of patients having a non- infectious disease, disorder or condition, such as those described herein. Such patients suitably do not have sepsis, but may or may not exhibit one or more sepsis-like symptoms.

[0074] More particularly, an increased level of one or more of USP18, NCF1B, BATF, S100A11, NOD2 and PTGES3, or a fragment, variant or derivative thereof, and / or a decreased level of one or more of CLC, ZBED1, HLX and ICAM1, or a fragment, variant or derivative thereof, can be diagnostic or indicative of the subject having sepsis, and more particularly viral sepsis. For such examples, the expression level of the respective biomarkers is suitably increased or decreased relative to a reference value or level of expression of the corresponding biomarker at least partly derived from a patient or population of patients having a non-infectious disease, disorder or condition.

[0075] Even more particularly, an increased level of one or more of NCF1B, BATF, CLC, S100A11, NOD2, PTGES3 and ICAM1, or a fragment, variant or derivative thereof, and / or a decreased level of one or more of USP18, ZBED1 and HLX, or a fragment, variant or derivative thereof, can be diagnostic or indicative of the subject having sepsis, and more particularly bacterial sepsis. For such examples, the expression level of the respective biomarkers is suitably increased or decreased relative to a reference value or level of expression of the corresponding biomarker at least partly derived from a patient or population of patients having a non-infectious disease, disorder or condition.

[0076] In view of the foregoing, the inventors have also developed diagnostic methods that relate to determining or differentiating an infection type in subjects with sepsis.

[0077] As such, in another broad form, the present disclosure provides a method of determining an infection type in a subject with sepsis, said method including the step of measuring an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and wherein the expression level of the two or more biomarkers are indicative of the infection type in the subject.

[0078] The methods disclosed herein are suitably used to identify sepsis subjects with an infection or a specific infection type. By “infection type” or “type of infection” is meant to include bacterial infections, viral infections, mixed infections (bacterial and viral co-infection) and noinfection (i.e., non-infectious). In particular examples, the methods disclosed herein are used to rule in a bacterial infection or rule out a bacterial infection. In further examples, the methods disclosed herein are used to rule in a viral infection or rule out a viral infection. Some methods of the invention are used to distinguish subjects having one or more of a bacterial infection, a viral infection, a mixed infection (i.e., bacterial and viral co-infection), patients with a non-infectious disease and healthy individuals. Some methods of the present invention can also be used to monitor or select a treatment regimen for a subject who has an infection.

[0079] Suitably, the present method distinguishes a bacterially infected subject from either a subject with non-infectious disease or a healthy subject; a bacterially infected subject from a virally infected subject; a bacterially infected subject from a subject having both a viral and bacterial infection (mixed infection) and a virally infected subject from a subject having both a viral and bacterial infection. In particular examples, the aforementioned method comprises distinguishing between a bacterial infection and a viral infection in a subject with sepsis or suspected as having sepsis. In this regard, the subject may have already been diagnosed as having sepsis, such as by the method provided herein or those known in the art.

[0080] It is contemplated that the present methods may be used in conjunction with an assay to determine the presence of a specific virus or bacteria, such as those described herein below, in the subject with sepsis. Further, the present methods may be used in conjunction with an assay to determine a suitable antimicrobial agent (e.g., antiviral agent or antibiotic agent) for the specific virus or bacteria at least partly responsible for the subject’s sepsis (e.g., culture and sensitivity).

[0081] Accordingly, the present methods may be utilised to diagnosis bacterial sepsis in a subject. The term “bacterial sepsis”, as used herein, refers to life-threatening conditions resulting from the circulation of bacteria in the blood stream. Exemplary bacterial causes of sepsis may include Streptococcus pneumoniae, Staphylococcus aureus, a Group A streptococcus (e.g., Streptococcus pyogenes), a Group B streptococcus (e.g., Streptococcus agalactiae), Coagulase- negative staphylococci, Escherichia coli, Pseudomonas aeruginosa, Enterococcus sp. (e.g., Enterococcus faecalis), Klebsiella sp. (e.g., Klebsiella pneumoniae), Neisseria meningitidis, Haemophilus influenza, Mycoplasma sp., Bordetella pertussis and Mycobacteria sp., inclusive of any strain, subtype or serotype thereof.

[0082] Similarly, the present methods may be utilised to diagnosis viral sepsis in a subject. Further, exemplary viral causes of sepsis may include respiratory syncytial virus (RSV), influenza A, influenza B, parainfluenza 1, parainfluenza 2, parainfluenza 3, parainfluenza 4, human metapneumovirus (HMPV), adenovirus, herpes simplex virus (HSV), enterovirus, parechovirus and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), inclusive of any strain,subtype or serotype thereof. As such, determining the infection type in the subject with sepsis may comprise distinguishing between a bacterial infection and a viral infection.

[0083] Suitably, if the level, such as a concentration level or an expression level, of the two or more biomarkers is altered or modulated in the biological sample from a subject, this can be indicative of or correlate with a particular infection type in the subject with sepsis. In one example, an increased level of expression or concentration of a first subset of the two or more biomarkers and / or a decreased level of expression or concentration of a second subset (e.g., not present in the first subset of the two or more biomarkers) of the two or more biomarkers is diagnostic or indicative of the subject having a bacterial infection. In related examples, an increased level of expression or concentration of a first subset of the two or more biomarkers and / or a decreased level of expression or concentration of a second subset (e.g., not present in the first subset of the two or more biomarkers) of the two or more biomarkers is diagnostic or indicative of the subject not having a viral infection. In other examples, a decreased level of expression or concentration of a first subset of the two or more biomarkers and / or an increased level of expression or concentration of a second subset of the two or more biomarkers is diagnostic or indicative of the subject having a viral infection. In related examples, a decreased level of expression or concentration of a first subset of the two or more biomarkers and / or an increased level of expression or concentration of a second subset of the two or more biomarkers is diagnostic or indicative of the subject not having a bacterial infection.

[0084] Referring to certain examples, an increased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and / or a decreased level of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, is diagnostic or indicative of the subject having a bacterial infection. For such examples, the expression level of the respective biomarkers is suitably increased or decreased relative to a reference value or level of expression of the corresponding biomarker. More particularly, such a reference value or level of expression of the corresponding biomarker is at least partly derived from a sepsis patient or population of sepsis patients having viral sepsis.

[0085] In alternative examples, an increased level of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, and / or a decreased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and / or ICAM1, or a fragment, variant or derivative thereof, is diagnostic or indicative of the subject having a viral infection. For such examples, the expression level of the respective biomarkers is suitably increased or decreased relative to a reference value or level of expression of the corresponding biomarker. More particularly, such a reference value or level of expression of the corresponding biomarker is at least partly derived from a sepsis patient or population of sepsis patients having bacterial sepsis.

[0086] In certain examples, the measuring step includes determining the presence or absence of: (i) an increased level of one or more of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and / or (ii) a decreased level of expression of one or more of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, in the biological sample of the subject. More particularly, the measuring step may include determining the presence or absence of an increased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1 or a fragment, variant or derivative thereof, and / or a decreased level of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, in the biological sample of the subject. Even more particularly, the measuring step may include determining the presence or absence of an increased level of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, and / or a decreased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, in the biological sample of the subject.

[0087] Suitably, the diagnostic methods described herein may include the subsequent step of administering a treatment to the subject. By way of example, this can include administering to the subject a therapeutically effective amount of the treatment, such as those anti-microbial treatments and / or one or more other treatments for sepsis described herein, when the level of the two or more biomarkers (and / or a risk or diagnostic score derived therefrom) is diagnostic or indicative of the subject having sepsis. More particularly, this may include administering to the subject a therapeutically effective amount of an anti-bacterial agent, such as an antibiotic, when the level of the two or more biomarkers (and / or a risk or diagnostic score derived therefrom) is diagnostic or indicative of the subject having a bacterial infection or bacterial sepsis. Alternatively, this may include administering to the subject a therapeutically effective amount of an antiviral agent when the level of the two or more biomarkers (and / or a risk or diagnostic score derived therefrom) is diagnostic or indicative of the subject having a viral infection or viral sepsis.

[0088] It is further envisaged that the aforementioned diagnostic methods may be combined or include determining a prognosis for the subject with sepsis, such as by methods described herein. As such, the aforementioned methods may further include the step of determining an expression level of two or more further biomarkers in a biological sample from the subject (e.g., the same biological sample or a further biological sample obtained from the subject), wherein the two or more further biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof, to determine a prognosis for the subject’s sepsis, as described herein. Suitably, theprognosis includes a likelihood or an estimated risk of the subject developing an organ dysfunction, such as described herein.

[0089] As such, in another particular form, the present disclosure provides a method for measuring a level, such as an expression level, or concentration of two or more biomarkers in a biological sample from a subject, said method including the steps of: (a) providing the biological sample; and (b) measuring the expression level of the two or more biomarkers in the biological sample, wherein the two or more biomarkers, such as protein biomarkers and / or mRNA biomarkers, are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2, ICAM1, AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof.

[0090] This prognosis may then be used to further guide treatment decisions in relation to the subject with sepsis, such as described herein. In this regard, a subject’s prognosis and / or risk score can be utilised to determine whether said subject with sepsis can be, at least partly, treated or ameliorated with a high dose or increased dose of a treatment (e.g., an antiviral agent or an antibacterial agent), or additionally or alternatively, may benefit from being administered one or more additional sepsis treatments, such as those provided herein (e.g., a vasopressor agent, a positive inotrope, an anti-inflammatory agent), which may be administered in addition to, for example, an antiviral agent or an antibacterial agent in an attempt to inhibit or prevent, for example, progression of a subject’s sepsis (e.g., prevent subsequent organ dysfunction in the subject).

[0091] In certain examples, if (i) the prognosis is positive, the subject is to be administered a conventional or standard dose of a first treatment (e.g., an antimicrobial agent), and (ii) the prognosis is negative, the subject is to be administered a high, conventional or standard dose of a first treatment (e.g., an antimicrobial agent) and a second treatment (e.g., one or more of an intravenous fluid, an anti-inflammatory agent, a vasopressor agent, insulin or an insulin analogue, an analgesic agent, a sedative, a positive inotrope and an immune enhancing agent). More particularly, if (i) the prognosis is positive, the subject is to be administered a conventional or standard dose of an antimicrobial agent (e.g., an antiviral agent or an antibacterial agent) and optionally a further sepsis treatment, and (ii) the prognosis is negative, the subject is to be administered a conventional or standard dose of an antimicrobial agent (e.g., an antiviral agent or an antibacterial agent) and a further sepsis treatment (e.g., one or more of an intravenous fluid, an anti-inflammatory agent, a vasopressor agent, insulin or an insulin analogue, an analgesic agent, a sedative, a positive inotrope and an immune enhancing agent). Methods of predicting drug responsiveness

[0092] The inventors have also developed methods that relate to predicting the responsiveness of a subject’s sepsis to a treatment.

[0093] Accordingly, in another broad form, the present disclosure provides a method of predicting the responsiveness of a subject with sepsis to a treatment, said method including the step of determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, wherein the expression level of the two or more biomarkers indicates or correlates with increased or decreased responsiveness of the subject’s sepsis to the treatment.

[0094] In some examples of the present methods, an increased expression level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and / or a decreased expression level of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, indicates or correlates with relatively increased responsiveness of the subject’s sepsis to the treatment, such as an antibiotic agent. Conversely, a decreased expression level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and / or an increased expression level of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, suitably indicates or correlates with relatively decreased responsiveness of the subject’s sepsis to the treatment, such as an antibiotic agent.

[0095] According to other examples of the present methods, an increased expression level of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, and / or a decreased expression level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, indicates or correlates with relatively increased responsiveness of the subject’s sepsis to the treatment, such as an antiviral agent. Conversely, a decreased expression level of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, and / or an increased expression level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, suitably indicates or correlates with relatively decreased responsiveness of the subject’s sepsis to the treatment, such as an antiviral agent.

[0096] Similar to the diagnostic methods described herein, the present method may further include the step of determining a prognosis for the subject with sepsis, such as by determining an expression level of two or more further biomarkers in a biological sample from the subject, wherein the two or more further biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof, to determine a prognosis for the subject’s sepsis. Suitably, theprognosis includes a likelihood or an estimated risk of the subject developing an organ dysfunction, such as described herein. As noted above, this prognosis may then be used to further guide treatment decisions in relation to the subject with sepsis (e.g., administer cardiovascular supportive therapy to the subject), such as disclosed herein.

[0097] In view of the above, the present methods may further include the step of treating the subject’s sepsis. By way of example, this can include administering to the subject a therapeutically effective amount of the treatment, such as those described herein (e.g., an antimicrobial agent), when the expression level of the two or more biomarkers and / or the risk score indicates or correlates with relatively increased responsiveness of the subject’s sepsis to the treatment. In such examples, the therapeutically effective amount of the treatment may comprise a first treatment, such as an antimicrobial agent. To this end, the subject may be determined to be in a low risk or intermediate risk group based on the expression level of the two or more further biomarkers. In other examples, the therapeutically effective amount of the treatment may comprise a first treatment, such as an antimicrobial agent, and a second treatment, and more particularly a supportive therapy, such as one or more of an intravenous fluid, an anti-inflammatory agent, a vasopressor agent, insulin or an insulin analogue, an analgesic agent, a sedative, a positive inotrope and an immune enhancing agent. To this end, the subject may be determined to be in a high risk group based on the expression level of the two or more further biomarkers. Methods of treatment

[0098] Further to the above, the methods described herein may improve patient outcomes by diagnosing subjects with sepsis, such as bacterial sepsis or viral sepsis, who could potentially benefit from a treatment thereof.

[0099] Accordingly, the inventors have developed methods of treating sepsis in a subject.

[0100] In one broad form, the present disclosure provides a method of treating sepsis in a subject, said method including the step of administering a therapeutically effective amount of a treatment for sepsis to the subject in which an expression level of two or more biomarkers has been determined in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and wherein the expression level of the two or more biomarkers indicates or correlates with the subject’s sepsis being at least partly responsive to the treatment.

[0101] It is envisaged that in those instances in which the subject’s sepsis is diagnosed as a bacterial sepsis then the subject may be at least partly responsive to and benefit from receiving an antibiotic agent. Alternatively, in those instances in which the subject’s sepsis is diagnosed as a viral sepsis then the subject may be at least partly responsive to and benefit from receiving anantiviral agent. Accordingly, in some examples of the present methods, an increased expression level of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and / or ICAM1, or a fragment, variant or derivative thereof, and / or a decreased expression level of USP18, ZBED1 and / or PTGES3, or a fragment, variant or derivative thereof, indicates or correlates with relatively increased responsiveness of the subject’s to the treatment, such as an antibiotic agent. Conversely, a decreased expression level of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and / or ICAM1, or a fragment, variant or derivative thereof, and / or an increased expression level of USP18, ZBED1 and / or PTGES3, or a fragment, variant or derivative thereof, indicates or correlates with relatively decreased responsiveness of the subject’s sepsis to the treatment, such as an antibiotic agent. Such expression levels, however, may indicate or correlate with relatively increased responsiveness to the subject’s sepsis to an alternative treatment or therapeutic agent, such as one suitable for treating the underlying or microbial cause of the subject’s sepsis.

[0102] As such, the treatment, such as an antibiotic agent, for the subject’s sepsis may be administered to the subject in instances when an increased expression level of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and / or ICAM1, or a fragment, variant or derivative thereof, and / or a decreased expression level of USP18, ZBED1 and / or PTGES3, or a fragment, variant or derivative thereof, is determined in the biological sample obtained from the subject. Moreover, the treatment, such as an antiviral agent, for the subject’s sepsis may be administered to the subject in instances when a decreased expression level of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and / or ICAM1, or a fragment, variant or derivative thereof, and / or an increased expression level of USP18, ZBED1 and / or PTGES3, or a fragment, variant or derivative thereof, is determined in the biological sample obtained from the subject.

[0103] Moreover, the present method can include refraining from administering a first treatment, discontinuing administration of a first treatment, administering a high dose of a first treatment and / or administering a second treatment to the subject, such as when the expression level of the one or more protein biomarkers and / or a risk score derived therefrom indicates or correlates with relatively reduced responsiveness or resistance of the subject’s sepsis to said first treatment (e.g., a diagnosis of viral sepsis in the subject suitably indicates relatively reduced responsiveness or resistance of the subject’s sepsis to an antibiotic agent).

[0104] Suitably, the present method includes the initial step of measuring the expression level of the two or more biomarkers in the biological sample from the subject.

[0105] For the present method, an expression level of two or more further biomarkers may also have been determined in the biological sample from the subject, wherein the two or more further biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof.Suitably, the expression level of the two or more further biomarkers may be utilised to determine a prognosis for the subject sepsis. This prognosis may include a likelihood or an estimated risk of the subject’s sepsis progressing with treatment, such as developing an organ dysfunction, as described herein. As noted above, this prognosis may then be used to further guide treatment decisions in relation to the subject with sepsis, such as disclosed herein.

[0106] In view of the above, for the present methods, a risk score may have been determined using the expression level of the two or more further biomarkers and the risk score is indicative of the subject’s sepsis being at least partly responsive to the treatment or requiring a further treatment (e.g., a supportive therapy, such as a vasopressor agent or a positive inotrope) in addition to the treatment to, for example, prevent or inhibit progression of the subject’s sepsis. In such examples, the subject may be further classified, such as low risk, intermediate risk or high risk (e.g., a low, intermediate or high risk of disease progression and / or death), based on the risk score.

[0107] In examples in which the subject may be determined to be in a high risk or intermediate risk group based on the expression level of the two or more further biomarkers, the subject is suitably administered one or more further treatments for their sepsis. To this end, the subject can be administered a therapeutically effective amount of a further treatment, such as one or more of an intravenous fluid, an anti-inflammatory agent, a vasopressor agent, insulin or an insulin analogue, an analgesic agent, a sedative, a positive inotrope and an immune enhancing agent, in instances when a decreased expression level of one or more of AATBC, MS4A7 and IGHA1, or a fragment, variant or derivative thereof, and / or an increased expression level of one or more of MAFG, VAV1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof, is determined in the biological sample obtained from the subject.

[0108] In a related form, the present disclosure provides a method of treating sepsis in a subject, said method including the step of determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and based on the determination made, initiating, continuing, modifying or discontinuing a treatment of sepsis in the subject.

[0109] As such, the present method can include refraining from administering a first treatment, discontinuing administration of a first treatment, administering a high dose of a first treatment and / or administering a second treatment to the subject, such as when the expression level of the one or more protein biomarkers and / or a risk score derived therefrom indicates or correlates with relatively reduced responsiveness or resistance of the subject’s sepsis to said first treatment.

[0110] Accordingly, in instances when: (a) a decreased expression level of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, and / or an increasedexpression level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof; or (b) an increased expression level of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, and / or a decreased expression level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof; is determined in the biological sample obtained from the subject, which is indicative of responsiveness of the subject’s sepsis to a first treatment (e.g., an antibacterial agent for (a) or an antiviral agent for (b)), the present method may include the further step of administering the first treatment, continuing administration of the first treatment, administering a high dose of the first treatment and / or administering a second treatment in combination with the first treatment (e.g., a supportive therapy, such as a vasopressor agent, a positive inotrope) to the subject.

[0111] Moreover, in instances when: (a) a decreased expression level of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, and / or an increased expression level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof; or (b) an increased expression level of one or more of USP18, ZBED1 and PTGES3, or a fragment, variant or derivative thereof, and / or a decreased expression level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1 or a fragment, variant or derivative thereof; is determined in the biological sample obtained from the subject, which is indicative of a relatively decreased responsiveness of the subject’s sepsis to a first treatment (e.g., an antibacterial agent or an antiviral agent), the present method may include the further step of refraining from administering the first treatment (e.g., refraining from an antibacterial agent for (b) or refraining from an antiviral agent for (a)), discontinuing administration of the first treatment, administering a high dose of the first treatment and / or administering a second treatment (e.g., an antibacterial agent for (a) or an antiviral agent for (b)) to the subject.

[0112] The term “high dose” or “increased dose” as used herein refers to a therapeutically effective dose of a treatment, such as a sepsis treatment provided herein, whose dose is significantly or substantially more (e.g., at least about 2%, 3%, 4%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50% etc more) than the usual or the conventional dose required to produce a therapeutic effect.

[0113] Such a method may further include the step of determining a prognosis for the subject with sepsis, such as by determining an expression level of two or more further biomarkers in the biological sample from the subject, wherein the two or more further biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, to determine a prognosis for the subject’s sepsis. This prognosis may thenbe used to further guide treatment decisions in relation to the subject with sepsis, such as described herein.

[0114] Accordingly, in a related form, the present disclosure provides a method of treating sepsis in a subject, said method including the step of administering a therapeutically effective amount of a treatment for sepsis, such as a supportive therapy provided herein, to the subject in which an expression level of two or more biomarkers has been determined in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof, and wherein the expression level of the two or more biomarkers indicates or correlates with a prognosis, such as a negative or poor prognosis, for the subject’s sepsis.

[0115] In another broad form, the present disclosure provides a method of treating sepsis in a subject, said method including the step of administering a therapeutically effective amount of a treatment for sepsis to the subject in which an expression level of two or more biomarkers has been determined in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof, and wherein the expression level of the two or more biomarkers indicates or correlates with the subject’s sepsis being at least partly responsive to the treatment.

[0116] Suitably, the present method includes the initial step of measuring the expression level of the two or more biomarkers in the biological sample from the subject.

[0117] In a further form, the present disclosure provides a method of treating sepsis in a subject, said method including the step of determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof, and based on the determination made, initiating, continuing, modifying or discontinuing a treatment of sepsis, such as a supportive therapy, in the subject.

[0118] In instances when a decreased expression level of one or more of AATBC, MS4A7 and IGHA1, and / or an increased expression level of one or more of MAFG, VAV1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2 is determined in the biological sample obtained from the subject, which is indicative of poor prognosis for the subject’s sepsis (e.g., a high likelihood of subsequent organ dysfunction), the present method may include the further step of administering a supportive therapeutic agent, such as those provided herein (e.g., a vasopressor agent, a positive inotrope, an intravenous fluid, an anti-inflammatory agent, insulin or an insulin analogue, ananalgesic agent, a sedative, an immune enhancing agent), to the subject. The supportive therapeutic agent may be administered in combination with one or more additional treatments or therapeutic agents for sepsis to the subject, such as one or more antimicrobial agents described herein.

[0119] Alternatively, for examples when an increased expression level of one or more of AATBC, MS4A7 and IGHA1, and / or a decreased expression level of one or more of MAFG, VAV1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2 is determined in the biological sample obtained from the subject, which is indicative of good prognosis for the subject’s sepsis (e.g., a low likelihood of subsequent organ dysfunction), the present method may not include the further step of administering a supportive therapeutic agent to the subject.

[0120] Exemplary treatments for sepsis include an anti-microbial agent (e.g., an antibiotic agent or an antiviral agent), an intravenous fluid, an anti-inflammatory agent (e.g., a corticosteroid, a NSAID), a vasopressor agent (e.g., vasopressin, a catecholamine, like phenylephrine, norepinephrine, epinephrine, isoproterenol, dobutamine, and dopamine), insulin or an insulin analogue, an analgesic agent, a sedative, a positive inotrope (e.g., a cardiac glycoside, like digoxin; a beta agonist, like dobutamine; and a phosphodiesterase inhibitor, like milrinone), an immune enhancing agent and any combination thereof.

[0121] Exemplary antibacterials include aminoglycoside, ansamycin, carbacephem, carbapenems, cephalosporins, glycopeptides, lincosamides, lipopeptides, macrolides, monobactams, nitrofurans, oxazolidinones, penicillins, antimicrobial polypeptides, sulfonamides, tetracylcines, and any combination thereof. Specific nonlimiting examples of broad-spectrum antibiotics that may be utilized in the treatment of sepsis include vancomycin, ceftriaxone, piperacillin-tazobactam, cefepime, tobramycin, imipenem-cilastatin, gentamicin and any combination thereof. Exemplary antivirals include adamantane antivirals, antiviral boosters, antiviral interferons, chemokine receptor antagonists, integrase strand transfer inhibitors, neuraminidase inhibitors, non-nucleoside reverse transcriptase inhibitors, non-structural protein 5a (ns5a) inhibitors, nucleoside reverse transcriptase inhibitors, protease inhibitors, purine nucleosides, and any combination thereof.

[0122] As used herein, the term “therapeutically effective amount” describes a quantity of a specified agent or treatment sufficient to achieve a desired effect in a subject being treated with that agent. For example, this can be the amount of a composition comprising one or more agents that are necessary to reduce, alleviate and / or prevent sepsis (e.g., bacterial sepsis or viral sepsis) or a sepsis-associated disease, disorder or condition (e.g., SIRS, septic shock, septicaemia). In some examples, a “therapeutically effective amount” is sufficient to reduce or eliminate a symptom of sepsis, such as bacterial sepsis or viral sepsis. In other examples, a “therapeutically effectiveamount” is an amount sufficient to achieve a desired biological effect, for example an amount that is effective to decrease or prevent disease progression, such as organ dysfunction or failure.

[0123] Ideally, a therapeutically effective amount of an agent is an amount sufficient to induce the desired result without causing a substantial cytotoxic effect in the subject. The effective amount of an agent useful for reducing, alleviating and / or preventing sepsis will be dependent on the subject being treated, the type and severity of any associated disease, disorder and / or condition (e.g., the number and location of any associated infection sites, the causative microbe), and the manner of administration of the therapeutic composition.

[0124] Suitably, the various agents treatments described herein are administered to a subject as a pharmaceutical composition comprising a pharmaceutically-acceptable carrier, diluent or excipient. In this regard, any dosage form and route of administration, such as those provided therein, may be employed for providing a subject with the composition of the present disclosure.

[0125] By “pharmaceutically-acceptable carrier, diluent or excipient” is meant a solid or liquid filler, diluent or encapsulating substance that may be safely used in systemic administration. Depending upon the particular route of administration, a variety of carriers, well known in the art may be used. These carriers may be selected from a group including sugars, starches, cellulose and its derivatives, malt, gelatine, talc, calcium sulfate, liposomes and other lipid-based carriers, vegetable oils, synthetic oils, polyols, alginic acid, phosphate buffered solutions, emulsifiers, isotonic saline and salts such as mineral acid salts including hydrochlorides, bromides and sulfates, organic acids such as acetates, propionates and malonates and pyrogen-free water.

[0126] A useful reference describing pharmaceutically acceptable carriers, diluents and excipients is Remington’s Pharmaceutical Sciences (Mack Publishing Co. N.J. USA, 1991), which is incorporated herein by reference.

[0127] Any safe route of administration may be employed for providing a patient with the composition of the present disclosure. For example, oral, rectal, parenteral, sublingual, buccal, intravenous, intra-articular, intra-muscular, intra-dermal, subcutaneous, inhalational, intraocular, intraperitoneal, intracerebroventricular, transdermal and the like may be employed.

[0128] Dosage forms include tablets, dispersions, suspensions, injections, solutions, syrups, troches, capsules, suppositories, aerosols, transdermal patches and the like. These dosage forms may also include injecting or implanting controlled releasing devices designed specifically for this purpose or other forms of implants modified to act additionally in this fashion. Controlled release of the therapeutic agent may be effected by coating the same, for example, with hydrophobic polymers including acrylic resins, waxes, higher aliphatic alcohols, polylactic and polyglycolic acids and certain cellulose derivatives such as hydroxypropylmethyl cellulose. In addition, thecontrolled release may be effected by using other polymer matrices, liposomes and / or microspheres.

[0129] Compositions of the present disclosure suitable for oral or parenteral administration may be presented as discrete units such as capsules, sachets or tablets each containing a pre-determined amount of one or more therapeutic agents of the present disclosure, as a powder or granules or as a solution or a suspension in an aqueous liquid, a non-aqueous liquid, an oil-in-water emulsion or a water-in-oil liquid emulsion. Such compositions may be prepared by any of the methods of pharmacy, which may include the step of bringing into association one or more agents as described above with the carrier which constitutes one or more necessary ingredients. In general, the compositions are prepared by uniformly and intimately admixing the agents of the present disclosure with liquid carriers or finely divided solid carriers or both, and then, if necessary, shaping the product into the desired presentation.

[0130] The above compositions may be administered in a manner compatible with the dosage formulation, and in such amount as is pharmaceutically-effective. The dose administered to a patient, in the context of the present disclosure, should be sufficient to effect a beneficial response in a patient over an appropriate period of time. The quantity of agent(s) to be administered may depend on the subject to be treated inclusive of the age, sex, weight and general health condition thereof, factors that will depend on the judgement of the practitioner.

[0131] It is envisaged that the various agents described herein can be formulated as discrete doses, such as in the form of a kit. Such a kit may further comprise a package insert comprising printed instructions for simultaneous, concurrent, sequential, successive, alternate or separate use of the agents in the treatment, amelioration and / or prevention of sepsis, as described herein, in a patient in need thereof. Accordingly, the aforementioned kits are suitably for use in a method of treating, ameliorating and / or preventing sepsis, inclusive of one or more symptoms, consequences, sequelae or complications thereof, as described herein.

[0132] Alternatively, the various therapeutic agents described herein can be formulated together in a composition that optionally includes a pharmaceutically acceptable carrier, excipient or diluent.

[0133] Methods of treating sepsis may be prophylactic, preventative or therapeutic and suitable for treatment of sepsis in mammals, particularly humans. As used herein, “treating”, “treat” or “treatment” refers to a therapeutic intervention, course of action or protocol that at least ameliorates a symptom of sepsis after the sepsis and / or its symptoms have at least started to develop. The term “ameliorating”, with, reference to such diseases, disorders or conditions, refers to any observable beneficial effect of the treatment. Treatment need not be absolute to be beneficial to the subject. The beneficial effect can be determined using any methods or standards known tothe ordinarily skilled artisan. As used herein, “preventing”, “prevent” or “prevention” refers to therapeutic intervention, course of action or protocol initiated prior to the onset of sepsis and / or a symptom of sepsis so as to prevent, inhibit or delay or development or progression of sepsis or the symptom. It is to be understood that such preventing need not be absolute to be beneficial to a subject. Prognostic methods

[0134] The inventors have also surprisingly shown that the concentration or expression level of particular biomarkers in blood samples from sepsis patients can be used to determine the likelihood of patients subsequently developing organ dysfunction. Advantageously, such prognostic methods may guide healthcare providers in relation to personalised treatment decisions for septic patients (e.g., treatment escalation and resuscitation, such as fluids, inotropes, and intensive care unit (ICU) admission) based on this information.

[0135] Therefore, in another broad form, the present disclosure provides a method for determining a prognosis for a subject with sepsis, said method including the step of: determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, to thereby determine the prognosis of sepsis in the subject.

[0136] The terms “prognosis” and “prognostic” are used herein to include making a prognosis, which can provide for predicting a clinical outcome (with or without medical treatment), selecting an appropriate course of treatment (or whether treatment would be effective) and / or monitoring a current treatment and potentially changing the treatment. This may be at least partly based on determining a level of the two or more biomarkers by the methods of the present disclosure, which may be in combination with determining: (a) the expression and / or activity levels of additional protein and / or other nucleic acid biomarkers; and / or (b) one or more clinical variables, such as heart rate, temperature, white blood cell count, breathing rate, serum lactate levels, age and any co-morbidities. A prognosis may also include a prediction, forecast or anticipation of any lasting or permanent physical or psychological effects of sepsis suffered by the subject after the sepsis episode has been successfully treated or otherwise resolved. Furthermore, a prognosis may include one or more of determining a disease stage and / or grade, disease progression potential or occurrence (e.g., organ dysfunction), therapeutic responsiveness and implementing appropriate treatment regimes. A positive prognosis typically refers to a beneficial clinical outcome or outlook, such as responsive to therapy, low or no likelihood or probability (e.g., less than 50% probability) of disease progression (e.g., low or no likelihood or probability of subsequent organ failure) and survival (suitably without recurrence of the subject's causative infection), whereas a negativeprognosis typically refers to a negative clinical outcome or outlook, such as late stage disease, little or no response to treatment, high likelihood or probability (e.g., greater than 50% probability) of disease progression (e.g., high likelihood or probability of subsequent organ failure) and / or death.

[0137] Suitably, if the level of said two or more biomarkers is altered or modulated in the biological sample, the prognosis may be negative or positive. In one example, an increased level of expression of a first subset of the two or more biomarkers and / or a decreased level of expression of a second subset (e.g., not present in the first subset of the two or more biomarkers) of the two or more biomarkers indicates or correlates with a more favourable or positive prognosis for the subject’s sepsis (e.g., low probability of subsequent organ dysfunction). In a related example, a decreased level of expression of a first subset of the two or more biomarkers and / or an increased level of expression of a second subset of the two or more biomarkers indicates or correlates with a less favourable or negative prognosis for the subject’s sepsis (e.g., high probability of subsequent organ dysfunction).

[0138] Accordingly, in some examples of the present methods, an increased expression level of one or more of AATBC, MS4A7 and IGHA1, and / or a decreased expression level of one or more of MAFG, VAV1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, indicates or correlates with a positive prognosis, such as a low risk of developing an organ dysfunction and / or the subject dying from their sepsis. For such examples, the expression level of the respective biomarkers is suitably increased or decreased relative to a reference value or level of expression of the corresponding biomarker. More particularly, such a reference value or level of expression of the corresponding biomarker is at least partly derived from a sepsis patient or population of sepsis patients having a negative prognosis (e.g., an intermediate or high risk of subsequent organ dysfunction).

[0139] Conversely, a decreased expression level of one or more of AATBC, MS4A7 and IGHA1, and / or an increased expression level of one or more of MAFG, VAV1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, indicates or correlates with a negative prognosis, such as an intermediate or high risk of developing an organ dysfunction and / or the subject dying from their sepsis. For such examples, the expression level of the respective biomarkers is suitably increased or decreased relative to a reference value or level of expression of the corresponding biomarker. More particularly, such a reference value or level of expression of the corresponding biomarker is at least partly derived from a sepsis patient or population of sepsis patients having a positive prognosis (e.g., a low risk of subsequent organ dysfunction).

[0140] The present method is also useful for determining, for example, the risk of an event occurring, or the timing to an event occurring. For example, the present prognostic methods areuseful for determining the risk of a patient developing an organ dysfunction or dying, such as within a particular time period from when the biological sample was obtained from the subject.

[0141] The term “organ dysfunction”, as used herein, relates to a reduction or impairment, including failure thereof, in physical structure or function of an organ, including, without limitation, the cardiovascular vascular system (heart and lungs), digestive system (salivary glands, oesophagus, stomach, liver, gallbladder, pancreas, intestines, colon, rectum and anus), endocrine system (hypothalamus, pituitary gland, pineal body, thyroid, parathyroids and adrenals), excretory system (kidneys, ureters, bladder and urethra), immune system (lymphatic system, tonsils, adenoids, thymus and spleen), integumentary system (skin, hair and nails), muscular system, nervous system (brain and spinal cord), reproductive system (ovaries, fallopian tubes, uterus, vagina, mammary glands, prostate and penis), respiratory system (pharynx, larynx, trachea, bronchi and diaphragm) and the skeletal system (bones, cartilage, ligaments and tendons). Referring to certain examples, the organ dysfunction comprises or is associated with one or more of cardiac dysfunction, cardiovascular dysfunction, respiratory dysfunction, neurologic dysfunction, renal dysfunction, hepatic dysfunction and haematologic dysfunction.

[0142] Suitably, such methods can also be applicable to determining, for example, the risk of or time to disease progression and / or the likelihood of response of a subject to a therapeutic or prophylactic agent, such as those described herein. In some examples, the present method includes determining whether the subject has a low, intermediate or high risk of: (a) progression of their sepsis (e.g., development of organ dysfunction); and / or (b) dying, within a particular time frame, such as within about 6 hours, 12 hours, 18 hours, 24 hours, 30 hours, 36 hours, 42 hours or 48 hours of taking or obtaining the biological sample from the subject. Referring to certain examples, the present method includes determining whether the subject has a low, intermediate or high risk of developing organ dysfunction and / or dying within 24 hours of taking or obtaining the biological sample from the subject.

[0143] The term “low risk” in regards to patients diagnosed with sepsis refers to a septic patient with a lower probability (e.g., less than about a 25%, 20%, 15%, 10% or 5% probability, likelihood or chance) of disease progression (e.g., development of an organ dysfunction) and / or a lower probability (e.g., less than about a 25%, 20%, 15%, 10% or 5% probability, likelihood or chance) of causing death or dying within a particular time frame, such as within about 6 hours, 12 hours, 18 hours, 24 hours, 30 hours, 36 hours, 42 hours or 48 hours of taking or obtaining the biological sample from the subject, than all the septic patients within a given population. The term “low risk” may also refer to a septic patient with a higher probability (e.g., greater than about a 70%, 75%, 80%, 85%, 90% or 95% probability, likelihood or chance) of at least partly responding to a treatment for sepsis, such as those provided herein.

[0144] The term “high risk” in regards to patients diagnosed with sepsis refers to a septic patient with a higher probability (e.g., greater than about a 70%, 75%, 80%, 85%, 90% or 95% probability, likelihood or chance) of disease progression (e.g., development of an organ dysfunction) and / or higher probability (e.g., greater than about a 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% probability, likelihood or chance) of causing death or dying within a particular time frame, such as within about 6 hours, 12 hours, 18 hours, 24 hours, 30 hours, 36 hours, 42 hours or 48 hours of taking or obtaining the biological sample from the subject, than all the septic patients within a given population. The term “high risk” may also refer to a septic patient with a higher probability (e.g., greater than about a 70%, 75%, 80%, 85%, 90% or 95% probability, likelihood or chance) of substantially not or only minimally responding to a treatment for sepsis, such as those provided herein and / or may require supportive therapy.

[0145] The term “intermediate risk” in regards to patients diagnosed with sepsis refers to a septic patient with an intermediate probability (e.g., a probability, likelihood or chance of between about 25% to about 70%, such as 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70% or any range therein) of disease progression (e.g., development of an organ dysfunction) and / or higher probability (e.g., a probability, likelihood or chance of between about 25% to about 50%, such as 25%, 30%, 35%, 40%, 45%, 50% or any range therein) of causing death or dying within a particular time frame, such as within about 6 hours, 12 hours, 18 hours, 24 hours, 30 hours, 36 hours, 42 hours or 48 hours of taking or obtaining the biological sample from the subject, than all the septic patients within a given population. The term “intermediate risk” may also refer to a tumour or patient with an intermediate probability (e.g., a probability, likelihood or chance of between about 25% to about 70%, such as 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70% or any range therein) of at least partly responding or not responding to a treatment of sepsis and / or may require supportive therapy.

[0146] In various examples, the prognosis may be used, at least in part, to determine whether the subject would benefit from treatment of their sepsis, such as with one or more agents described herein. In other examples, the prognosis may be used, at least in part, to develop a treatment strategy for the subject. The methods disclosed herein may further comprise determining the suitability of the subject for a treatment, such as those hereinafter described, based, at least in part, on the prognosis. Moreover, the present methods may indicate whether the subject may benefit from additional supportive therapy (e.g., IV fluids, a vasopressor agent, a positive inotrope etc) or more particularly cardiovascular supportive therapy, such as in addition to an antimicrobial agent, to prevent or inhibit disease progression (e.g., subsequent development of organ dysfunction and / or death in the subject). On this point, the present methods may further include the step of administering a treatment for sepsis, such as those described herein, to the subject.

[0147] A level of the two or more biomarkers, or a risk score derived therefrom, can indicate or correlate with a level of severity or disease progression of the subject’s sepsis. For example, the higher the risk score calculated from the expression level of the two or more biomarkers, the more severe or advanced the subject’s sepsis. By “severity” is meant a property or propensity for a subject’s sepsis to have a relatively poor prognosis due to one or more of a combination of features or factors including: low or no responsiveness to treatment; high likelihood for or high rate of disease progression (e.g., organ dysfunction); and a low probability of patient survival, although without limitation thereto.

[0148] The term “survival” as used herein refers to survival of a subject having sepsis for a particular period of time, such as at least about 1 hour (e.g., about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 30, 36, 42, 48, 72, 96 etc hours), more particularly at least about 12 hours, even more particularly at least about 24 hours, and yet even more particularly at least about 48 hours, from the time of diagnosis or prognosis or taking of the biological sample from the subject. Biomarkers

[0149] As described herein, the inventors have found that the expression levels of particular gene biomarkers in blood or plasma samples from subjects can be diagnostic and prognostic for subjects with sepsis.

[0150] The term “biomarker” as used herein refers to a molecule, such as a protein or mRNA molecule whose levels are indicative or diagnostic of a subject having sepsis and additionally may be associated with, for example, prognosis and patient outcomes, such as disease progression and mortality. The term “biomarker” is intended to encompass all classes, forms (e.g., phosphorylated or glycosylated forms), fragments (e.g., peptide fragments, nucleic acid fragments) and variants of a biomarker, as are known in the art, such as those provided herein. In particular examples, the biomarkers provided herein are gene or genetic biomarkers (e.g., determine a gene or mRNA expression level of the respective biomarkers). For other examples, the biomarkers provided herein are protein, peptide or polypeptide biomarkers (e.g., determine a protein expression level of the respective biomarkers). It is further contemplated that a combination protein expression levels and gene expression levels may be determined for the methods described herein.

[0151] The USP18 gene encodes a type 1 interferon (IFN)-stimulated gene that had dual functions: a negative regulator of type 1 IFN signalling and an isopeptidase that is a member of the deubiquitinating protease family of enzymes. Gene and encoded protein sequences of USP18 are publicly available (see, e.g., NCBI Reference Sequence: NM_017414.4; UniProt Accession No. Q9UMW8). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 1 and 2 respectively. Thus, the USP18 nucleic acid sequence may be a polynucleotide which is atleast 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 1 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by USP18 may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 2 or a fragment or derivative thereof.

[0152] The NCF1B gene is predicted to enable superoxide-generating NADPH oxidase activator activity and to be involved in respiratory burst and superoxide anion generation. It is also predicted to be part of NADPH oxidase complex and to be active in the cytoplasm. Gene and encoded protein sequences of NCF1B are publicly available (see, e.g., EMBL Reference Sequence AC006995; UniProt Accession No. A6NI72). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 3 and 4 respectively. Thus, the NCF1B nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 3 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by NCF1B may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 4 or a fragment or derivative thereof.

[0153] The BATF gene encodes a transcription factor expressed in haematopoietic cells. Gene and encoded protein sequences of BATF are publicly available (see, e.g., NCBI Reference Sequence: NM_006399.5; UniProt Accession No. Q16520). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 5 and 6 respectively. Thus, the BATF nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 5 or a fragment or derivative thereof.

[0154] Moreover, the amino acid sequence encoded by BATF may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 6 or a fragment or derivative thereof.

[0155] The CLC gene encodes proteins that form Charcot-Leyden crystals, which are naturally occurring hexagonal bipyramidal crystals found in human tissues and secretions in association with increased numbers of peripheral blood or tissue eosinophils in parasitic and allergic processes. Gene and encoded protein sequences of CLC are publicly available (see, e.g., NCBI Reference Sequence: NM_001828.6; UniProt Accession No. Q05315). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 7 and 8 respectively. Thus, the CLC nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 7 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by CLC may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 8 or a fragment or derivative thereof.

[0156] The S100A11 gene encodes an S100 protein, which are 10- to 12-kD molecules that have a canonical EF hand at their C termini and a modified, S100-specific EF hand at their N termini. Binding of Ca(2+) to EF-hand motifs changes the conformation and hence the function of S100 proteins. Gene and encoded protein sequences of S100A11 are publicly available (see, e.g., NCBI Reference Sequence: NM_005620.1; UniProt Accession No. P31949). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 9 and 10 respectively. Thus, the S100A11 nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 9 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by S100A11 may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 10 or a fragment or derivative thereof.

[0157] The ZBED1 gene encodes a ZBED protein, which originated from domesticated hAT DNA transposons and encode regulatory proteins with diverse, fundamental functions in vertebrates. Gene and encoded protein sequences of ZBED1 are publicly available (see, e.g., NCBI Reference Sequence: NM_001171135.1; UniProt Accession No. O96006). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 11 and 12 respectively. Thus, the ZBED1 nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 11 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by ZBED1 may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 12 or a fragment or derivative thereof.

[0158] The PTGES3 gene encodes an enzyme that converts prostaglandin endoperoxide H2 (PGH2) to prostaglandin E2 (PGE2). This protein functions as a co-chaperone with heat shock protein 90 (HSP90), localizing to response elements in DNA and disrupting transcriptional activation complexes. Gene and encoded protein sequences of PTGES3 are publicly available (see, e.g., NCBI Reference Sequence: NM_001282601.1; UniProt Accession No. Q15185). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 13 and 14 respectively. Thus, the PTGES3 nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 13 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by PTGES3 may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 14 or a fragment or derivative thereof.

[0159] The HLX gene enables sequence-specific DNA binding activity. It is predicted to be involved in cell differentiation and regulation of transcription by RNA polymerase II. Also, predicted to act upstream of or within several processes, including animal organ development;enteric nervous system development; and regulation of T-helper cell differentiation. Gene and encoded protein sequences of HLX are publicly available (see, e.g., NCBI Reference Sequence: NM_021958.3; UniProt Accession No. Q14774). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 15 and 16 respectively. Thus, the HLX nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 15 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by HLX may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 16 or a fragment or derivative thereof.

[0160] The NOD2 gene belongs to the nucleotide-binding oligomerization domain (NOD)-like receptor family of pattern-recognition receptors (PRRs). Inflammatory responses are triggered when PRRs detect tissue damage or microbial infection. Gene and encoded protein sequences of NOD2 are publicly available (see, e.g., NCBI Reference Sequence: NM_001293557.1; UniProt Accession No. Q9HC29). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 17 and 18 respectively. Thus, the NOD2 nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 17 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by NOD2 may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 18 or a fragment or derivative thereof.

[0161] The ICAM1 gene encodes an inducible glycoprotein of the immunoglobulin (Ig) superfamily that contains 5 distinct Ig-like domains, a transmembrane domain, and a short cytoplasmic tail. It was first discovered as a ligand for LFA1 and then as a counter receptor for MAC1. Gene and encoded protein sequences of ICAM1 are publicly available (see, e.g., NCBI Reference Sequence: NM_000201.2; UniProt Accession No. P05362). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 19 and 20 respectively. Thus, the ICAM1 nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 19 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by ICAM1 may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 20 or a fragment or derivative thereof.

[0162] The AATBC (Apoptosis Associated Transcript In Bladder Cancer) is an RNA Gene, and is affiliated with the lncRNA class. Diseases associated with AATBC include bladder cancer. Gene and encoded protein sequences of AATBC are publicly available (see, e.g., HGNC: 51526). An exemplary nucleotide sequence is set forth in SEQ ID NOs: 21. Thus, the AATBC nucleic acidsequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 21 or a fragment or derivative thereof.

[0163] The MAFG gene encodes a protein that can chimerize with p45 nuclear factor erythroid- 2 (NFE2) and supports the expression of globin genes and promotes erythroid differentiation. MAFG is expressed in CNS neurons and involved in encephalomyelitis. Gene and encoded protein sequences of MAFG are publicly available (see, e.g., NCBI Reference Sequence: NM_002359.3; UniProt Accession No. O15525). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 22 and 23 respectively. Thus, the MAFG nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 22 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by MAFG may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 23 or a fragment or derivative thereof.

[0164] The VAV1 gene is a member of the VAV gene family. The VAV proteins are guanine nucleotide exchange factors (GEFs) for Rho family GTPases that activate pathways leading to actin cytoskeletal rearrangements and transcriptional alterations. The encoded protein is important in hematopoiesis, playing a role in T-cell and B-cell development and activation. The encoded protein has been identified as the specific binding partner of Nef proteins from HIV-1. Coexpression and binding of these partners initiates profound morphological changes, cytoskeletal rearrangements and the JNK / SAPK signaling cascade, leading to increased levels of viral transcription and replication. Gene and encoded protein sequences of VAV1 are publicly available (see, e.g., NCBI Reference Sequence: NM_001258207.1; UniProt Accession No. P15498). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 24 and 25 respectively. Thus, the VAV1 nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 24 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by VAV1 may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 25 or a fragment or derivative thereof.

[0165] The MS4A7 gene encodes a member of the membrane-spanning 4A gene family, members of which are characterized by common structural features and similar intron / exon splice boundaries and display unique expression patterns in hematopoietic cells and nonlymphoid tissues. This family member is associated with mature cellular function in the monocytic lineage, and it may be a component of a receptor complex involved in signal transduction. Gene and encoded protein sequences of MS4A7 are publicly available (see, e.g., NCBI Reference Sequence: NM_021201.4; UniProt Accession No. Q9GZW8). Exemplary nucleotide and amino acidsequences are set forth in SEQ ID NOs: 26 and 27 respectively. Thus, the MS4A7 nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 26 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by MS4A7 may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 27 or a fragment or derivative thereof.

[0166] The IGHA1 gene contributes to immunoglobulin receptor binding activity. It is involved in antibacterial humoral response; glomerular filtration; and positive regulation of respiratory burst. It is located in extracellular space and it is part of monomeric IgA immunoglobulin complex and secretory dimeric IgA immunoglobulin complex. Gene and encoded protein sequences of IGHA1 are publicly available (see, e.g., ENST00000641837.1; UniProt Accession No. P01876). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 28 and 29 respectively. Thus, the IGHA1 nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 28 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by IGHA1 may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 29 or a fragment or derivative thereof.

[0167] The ATP6V0A1 gene encodes a component of vacuolar ATPase (V-ATPase), a multisubunit enzyme that mediates acidification of eukaryotic intracellular organelles. V-ATPase dependent organelle acidification is necessary for such intracellular processes as protein sorting, zymogen activation, receptor-mediated endocytosis, and synaptic vesicle proton gradient generation. Gene and encoded protein sequences of ATP6V0A1 are publicly available (see, e.g., NCBI Reference Sequence: NM_001130020.1; UniProt Accession No. Q93050). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 30 and 31 respectively. Thus, the ATP6V0A1 nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 30 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by ATP6V0A1 may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 31 or a fragment or derivative thereof.

[0168] The RN7SL3 gene encodes the RNA component of the signal recognition particle (SRP), which is a cytoplasmic ribonucleoprotein complex that mediates co-translational insertion of secretory proteins into the lumen of the endoplasmic reticulum. Gene sequences of RN7SL3 are publicly available (see, e.g., ENST00000610674.1). An exemplary nucleotide sequence is set forth in SEQ ID NO: 32. Thus, the RN7SL3 nucleic acid sequence may be a polynucleotide which is atleast 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 32 or a fragment or derivative thereof.

[0169] The MPP7 gene encodes the protein which is a member of the p55 Stardust family of membrane-associated guanylate kinase (MAGUK) proteins. It is involved in the establishment of epithelial cell polarity. This family member forms a complex with the polarity protein DLG1 (discs, large homolog 1) and facilitates epithelial cell polarity and tight junction formation. Gene and encoded protein sequences of MPP7 are publicly available (see, e.g., NCBI Reference Sequence: NM_001318170.1; UniProt Accession No. Q5T2T1). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 33 and 34 respectively. Thus, the MPP7 nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 33 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by MPP7 may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 34 or a fragment or derivative thereof.

[0170] The DSC2 gene encodes a member of the desmocollin protein subfamily. Desmocollins, along with desmogleins, are cadherin-like transmembrane glycoproteins that are major components of the desmosome. Desmosomes are cell-cell junctions that help resist shearing forces and are found in high concentrations in cells subject to mechanical stress. Mutations in this gene are associated with arrhythmogenic right ventricular dysplasia-11, and reduced protein expression has been described in several types of cancer. Gene and encoded protein sequences of DSC2 are publicly available (see, e.g., NCBI Reference Sequence: NM_004949.4; UniProt Accession No. Q02487). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 35 and 36 respectively. Thus, the DSC2 nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 35 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by DSC2 may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 36 or a fragment or derivative thereof.

[0171] The protein encoded by the PHACTR2 gene is predicted to enable actin binding activity and to be involved in actin cytoskeleton organization. It is located in the plasma membrane and platelet alpha granule membrane. It is implicated in Parkinson's disease and multiple sclerosis and reported as a biomarker of Alzheimer's disease. Gene and encoded protein sequences of PHACTR2 are publicly available (see, e.g., NCBI Reference Sequence: NM_001100164.1; UniProt Accession No. O75167). Exemplary nucleotide and amino acid sequences are set forth in SEQ ID NOs: 37 and 38 respectively. Thus, the PHACTR2 nucleic acid sequence may be a polynucleotide which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identicalto SEQ ID NO: 37 or a fragment or derivative thereof. Moreover, the amino acid sequence encoded by PHACTR2 may be a protein which is at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5% or 100% identical to SEQ ID NO: 38 or a fragment or derivative thereof.

[0172] Suitably, the two or more biomarkers comprise or consist of USP18 and NCF1B. In such examples, the two or more biomarkers may optionally include one or more further biomarkers selected from the group consisting of BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1.

[0173] Referring to various examples, the two or more biomarkers are selected from the group consisting of USP18, NCF1B and BATF. More particularly, the two or more biomarkers may comprise or consist of USP18, NCF1B and BATF. In such examples, the two or more biomarkers may optionally include one or more further biomarkers selected from the group consisting of CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1.

[0174] In other examples, the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF and CLC. More particularly, the two or more biomarkers may comprise or consist of USP18, NCF1B, BATF and CLC. In such examples, the two or more biomarkers may optionally include one or more further biomarkers selected from the group consisting of S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1.

[0175] For various examples, the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC and S100A11. More particularly, the two or more biomarkers may comprise or consist of USP18, NCF1B, BATF, CLC and S100A11. In such examples, the two or more biomarkers may optionally include one or more further biomarkers selected from the group consisting of ZBED1, PTGES3, HLX, NOD2 and ICAM1.

[0176] In certain examples, the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11 and ZBED1. More particularly, the two or more biomarkers may comprise or consist of USP18, NCF1B, BATF, CLC, S100A11 and ZBED1. In such examples, the two or more biomarkers may optionally include one or more further biomarkers selected from the group consisting of PTGES3, HLX, NOD2 and ICAM1.

[0177] For particular examples, the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1 and PTGES3. More particularly, the two or more biomarkers may comprise or consist of USP18, NCF1B, BATF, CLC, S100A11, ZBED1 and PTGES3. In such examples, the two or more biomarkers may optionally include one or more further biomarkers selected from the group consisting of HLX, NOD2 and ICAM1.

[0178] Referring to other examples, the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3 and HLX. More particularly, the two or more biomarkers may comprise or consist of USP18, NCF1B, BATF, CLC,S100A11, ZBED1, PTGES3 and HLX. In such examples, the two or more biomarkers may optionally include one or more further biomarkers selected from the group consisting of NOD2 and ICAM1.

[0179] According to certain examples, the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX and NOD2. More particularly, the two or more biomarkers may comprise or consist of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX and NOD2. In such examples, the two or more biomarkers may optionally further include ICAM1.

[0180] In certain examples, the two or more biomarkers comprise or consist of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1.

[0181] Suitably, the two or more biomarkers comprise or consist of AATBC and MAFG. In such examples, the two or more biomarkers may optionally include one or more further biomarkers selected from the group consisting of VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2.

[0182] Referring to various examples, the two or more biomarkers are selected from the group consisting of AATBC, MAFG and VAV1. More particularly, the two or more biomarkers may comprise or consist of AATBC, MAFG and VAV1. In such examples, the two or more biomarkers may optionally include one or more further biomarkers selected from the group consisting of MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2.

[0183] In other examples, the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1 and MS4A7. More particularly, the two or more biomarkers may comprise or consist of AATBC, MAFG, VAV1 and MS4A7. In such examples, the two or more biomarkers may optionally include one or more further biomarkers selected from the group consisting of IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2.

[0184] For various examples, the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7 and IGHA1. More particularly, the two or more biomarkers may comprise or consist of AATBC, MAFG, VAV1, MS4A7 and IGHA1. In such examples, the two or more biomarkers may optionally include one or more further biomarkers selected from the group consisting of ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2.

[0185] In certain examples, the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1 and ATP6V0A1. More particularly, the two or more biomarkers may comprise or consist of AATBC, MAFG, VAV1, MS4A7, IGHA1 and ATP6V0A1. In such examples, the two or more biomarkers may optionally include one or more further biomarkers selected from the group consisting of RN7SL3, MPP7, DSC2 and PHACTR2.

[0186] For particular examples, the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1 and RN7SL3. More particularly, the two or more biomarkers may comprise or consist of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1 and RN7SL3. In such examples, the two or more biomarkers may optionally include one or more further biomarkers selected from the group consisting of MPP7, DSC2 and PHACTR2.

[0187] Referring to other examples, the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3 and MPP7. More particularly, the two or more biomarkers may comprise or consist of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3 and MPP7. In such examples, the two or more biomarkers may optionally include one or more further biomarkers selected from the group consisting of DSC2 and PHACTR2.

[0188] According to certain examples, the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7 and DSC2. More particularly, the two or more biomarkers may comprise or consist of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7 and DSC2. In such examples, the two or more biomarkers may optionally further include PHACTR2.

[0189] In certain examples, the two or more biomarkers comprise or consist of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2.

[0190] Suitably, the level (e.g., concentration or expression level) of two or more of the biomarkers (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 biomarkers) provided herein are determined for the methods described herein. In some examples, the methods described herein include the step of determining the level or concentration of three or more biomarkers described herein. In other examples, the methods described herein include the step of determining the level or concentration of four or more biomarkers described herein. In certain examples, the methods described herein include the step of determining the level or concentration of five or more biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of six or more biomarkers described herein. In various examples, the methods described herein include the step of determining the level or concentration of seven or more biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of eight or more biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of nine or more biomarkers described herein. In other examples, the methods described herein include the step of determining the level or concentration of ten or more biomarkers described herein. In certain examples, the methods described herein include the step of determining the level or concentration of eleven or morebiomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of twelve or more biomarkers described herein. In various examples, the methods described herein include the step of determining the level or concentration of thirteen or more biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of fourteen or more biomarkers described herein. In various examples, the methods described herein include the step of determining the level or concentration of fifteen or more biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of sixteen or more biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of seventeen or more biomarkers described herein. In certain examples, the methods described herein include the step of determining the level or concentration of eighteen or more biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of nineteen or more biomarkers described herein. In various examples, the methods described herein include the step of determining the level or concentration of twenty or more biomarkers described herein.

[0191] Suitably, the methods of the present disclosure include the step of determining a level of USP18 and at least one further biomarker described herein (e.g., at least one of NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1). In examples, the two or more biomarkers comprise or consist of USP18 and NCF1B. In examples, the two or more biomarkers comprise or consist of USP18 and BATF. In examples, the two or more biomarkers comprise or consist of USP18 and CLC. In examples, the two or more biomarkers comprise or consist of USP18 and S100A11. In examples, the two or more biomarkers comprise or consist of USP18 and ZBED1. In examples, the two or more biomarkers comprise or consist of USP18 and PTGES3. In examples, the two or more biomarkers comprise or consist of USP18 and HLX. In examples, the two or more biomarkers comprise or consist of USP18 and NOD2. In examples, the two or more biomarkers comprise or consist of USP18 and ICAM1.

[0192] Suitably, the methods of the present disclosure include the step of determining a level of NCF1B and at least one further biomarker described herein (e.g., at least one of USP18, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1). In examples, the two or more biomarkers comprise or consist of NCF1B and BATF. In examples, the two or more biomarkers comprise or consist of NCF1B and CLC. In examples, the two or more biomarkers comprise or consist of NCF1B and S100A11. In examples, the two or more biomarkers comprise or consist of NCF1B and ZBED1. In examples, the two or more biomarkers comprise or consist of NCF1B and PTGES3. In examples, the two or more biomarkers comprise or consist of NCF1B and HLX. Inexamples, the two or more biomarkers comprise or consist of NCF1B and NOD2. In examples, the two or more biomarkers comprise or consist of NCF1B and ICAM1.

[0193] Suitably, the methods of the present disclosure include the step of determining a level of BATF and at least one further biomarker described herein (e.g., at least one of USP18, NCF1B, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1). In examples, the two or more biomarkers comprise or consist of BATF and CLC. In examples, the two or more biomarkers comprise or consist of BATF and S100A11. In examples, the two or more biomarkers comprise or consist of BATF and ZBED1. In examples, the two or more biomarkers comprise or consist of BATF and PTGES3. In examples, the two or more biomarkers comprise or consist of BATF and HLX. In examples, the two or more biomarkers comprise or consist of BATF and NOD2. In examples, the two or more biomarkers comprise or consist of BATF and ICAM1.

[0194] Suitably, the methods of the present disclosure include the step of determining a level of CLC and at least one further biomarker described herein (e.g., at least one of USP18, NCF1B, BATF, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1). In examples, the two or more biomarkers comprise or consist of CLC and S100A11. In examples, the two or more biomarkers comprise or consist of CLC and ZBED1. In examples, the two or more biomarkers comprise or consist of CLC and PTGES3. In examples, the two or more biomarkers comprise or consist of CLC and HLX. In examples, the two or more biomarkers comprise or consist of CLC and NOD2. In examples, the two or more biomarkers comprise or consist of CLC and ICAM1.

[0195] Suitably, the methods of the present disclosure include the step of determining a level of S100A11 and at least one further biomarker described herein (e.g., at least one of USP18, NCF1B, BATF, CLC, ZBED1, PTGES3, HLX, NOD2 and ICAM1). In examples, the two or more biomarkers comprise or consist of S100A11 and ZBED1. In examples, the two or more biomarkers comprise or consist of S100A11 and PTGES3. In examples, the two or more biomarkers comprise or consist of S100A11 and HLX. In examples, the two or more biomarkers comprise or consist of S100A11 and NOD2. In examples, the two or more biomarkers comprise or consist of S100A11 and ICAM1.

[0196] Suitably, the methods of the present disclosure include the step of determining a level of ZBED1 and at least one further biomarker described herein (e.g., at least one of USP18, NCF1B, BATF, CLC, S100A11, PTGES3, HLX, NOD2 and ICAM1). In examples, the two or more biomarkers comprise or consist of ZBED1 and PTGES3. In examples, the two or more biomarkers comprise or consist of ZBED1 and HLX. In examples, the two or more biomarkers comprise or consist of ZBED1 and NOD2. In examples, the two or more biomarkers comprise or consist of ZBED1 and ICAM1.

[0197] Suitably, the methods of the present disclosure include the step of determining a level of PTGES3 and at least one further biomarker described herein (e.g., at least one of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, HLX, NOD2 and ICAM1). In examples, the two or more biomarkers comprise or consist of PTGES3 and HLX. In examples, the two or more biomarkers comprise or consist of PTGES3 and NOD2. In examples, the two or more biomarkers comprise or consist of PTGES3 and ICAM1.

[0198] Suitably, the methods of the present disclosure include the step of determining a level of HLX and at least one further biomarker described herein (e.g., at least one of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, NOD2 and ICAM1). In examples, the two or more biomarkers comprise or consist of HLX and NOD2. In examples, the two or more biomarkers comprise or consist of HLX and ICAM1.

[0199] Suitably, the methods of the present disclosure include the step of determining a level of NOD2 and at least one further biomarker described herein (e.g., at least one of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX and ICAM1). In examples, the two or more biomarkers comprise or consist of NOD2 and ICAM1.

[0200] Suitably, the methods of the present disclosure include the step of determining a level of ICAM1 and at least one further biomarker described herein (e.g., at least one of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX and NOD2).

[0201] Suitably, the methods of the present disclosure include the step of determining a level of AATBC and at least one further biomarker described herein (e.g., at least one of MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2). In examples, the two or more biomarkers comprise or consist of AATBC and MAFG. In examples, the two or more biomarkers comprise or consist of AATBC and VAV1. In examples, the two or more biomarkers comprise or consist of AATBC and MS4A7. In examples, the two or more biomarkers comprise or consist of AATBC and IGHA1. In examples, the two or more biomarkers comprise or consist of AATBC and ATP6V0A1. In examples, the two or more biomarkers comprise or consist of AATBC and RN7SL3. In examples, the two or more biomarkers comprise or consist of AATBC and MPP7. In examples, the two or more biomarkers comprise or consist of AATBC and DSC2. In examples, the two or more biomarkers comprise or consist of AATBC and PHACTR2.

[0202] Suitably, the methods of the present disclosure include the step of determining a level of MAFG and at least one further biomarker described herein (e.g., at least one of AATBC, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2). In examples, the two or more biomarkers comprise or consist of MAFG and VAV1. In examples, the two or more biomarkers comprise or consist of MAFG and MS4A7. In examples, the two or more biomarkers comprise or consist of MAFG and IGHA1. In examples, the two or more biomarkers comprise orconsist of MAFG and ATP6V0A1. In examples, the two or more biomarkers comprise or consist of MAFG and RN7SL3. In examples, the two or more biomarkers comprise or consist of MAFG and MPP7. In examples, the two or more biomarkers comprise or consist of MAFG and DSC2. In examples, the two or more biomarkers comprise or consist of MAFG and PHACTR2.

[0203] Suitably, the methods of the present disclosure include the step of determining a level of VAV1 and at least one further biomarker described herein (e.g., at least one of AATBC, MAFG, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2). In examples, the two or more biomarkers comprise or consist of VAV1 and MS4A7. In examples, the two or more biomarkers comprise or consist of VAV1 and IGHA1. In examples, the two or more biomarkers comprise or consist of VAV1 and ATP6V0A1. In examples, the two or more biomarkers comprise or consist of VAV1 and RN7SL3. In examples, the two or more biomarkers comprise or consist of VAV1 and MPP7. In examples, the two or more biomarkers comprise or consist of VAV1 and DSC2. In examples, the two or more biomarkers comprise or consist of VAV1 and PHACTR2.

[0204] Suitably, the methods of the present disclosure include the step of determining a level of MS4A7 and at least one further biomarker described herein (e.g., at least one of AATBC, MAFG, VAV1, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2). In examples, the two or more biomarkers comprise or consist of MS4A7 and IGHA1. In examples, the two or more biomarkers comprise or consist of MS4A7 and ATP6V0A1. In examples, the two or more biomarkers comprise or consist of MS4A7 and RN7SL3. In examples, the two or more biomarkers comprise or consist of MS4A7 and MPP7. In examples, the two or more biomarkers comprise or consist of MS4A7 and DSC2. In examples, the two or more biomarkers comprise or consist of MS4A7 and PHACTR2.

[0205] Suitably, the methods of the present disclosure include the step of determining a level of IGHA1 and at least one further biomarker described herein (e.g., at least one of AATBC, MAFG, VAV1, MS4A7, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2). In examples, the two or more biomarkers comprise or consist of IGHA1 and ATP6V0A1. In examples, the two or more biomarkers comprise or consist of IGHA1 and RN7SL3. In examples, the two or more biomarkers comprise or consist of IGHA1 and MPP7. In examples, the two or more biomarkers comprise or consist of IGHA1 and DSC2. In examples, the two or more biomarkers comprise or consist of IGHA1 and PHACTR2.

[0206] Suitably, the methods of the present disclosure include the step of determining a level of ATP6V0A1 and at least one further biomarker described herein (e.g., at least one of AATBC, MAFG, VAV1, MS4A7, IGHA1, RN7SL3, MPP7, DSC2 and PHACTR2). In examples, the two or more biomarkers comprise or consist of ATP6V0A1 and RN7SL3. In examples, the two or more biomarkers comprise or consist of ATP6V0A1 and MPP7. In examples, the two or more biomarkerscomprise or consist of ATP6V0A1 and DSC2. In examples, the two or more biomarkers comprise or consist of ATP6V0A1 and PHACTR2.

[0207] Suitably, the methods of the present disclosure include the step of determining a level of RN7SL3 and at least one further biomarker described herein (e.g., at least one of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, MPP7, DSC2 and PHACTR2). In examples, the two or more biomarkers comprise or consist of RN7SL3 and MPP7. In examples, the two or more biomarkers comprise or consist of RN7SL3 and DSC2. In examples, the two or more biomarkers comprise or consist of RN7SL3 and PHACTR2.

[0208] Suitably, the methods of the present disclosure include the step of determining a level of MPP7 and at least one further biomarker described herein (e.g., at least one of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, DSC2 and PHACTR2). In examples, the two or more biomarkers comprise or consist of MPP7 and DSC2. In examples, the two or more biomarkers comprise or consist of MPP7 and PHACTR2.

[0209] Suitably, the methods of the present disclosure include the step of determining a level of DSC2 and at least one further biomarker described herein (e.g., at least one of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7 and PHACTR2). In examples, the two or more biomarkers comprise or consist of DSC2 and PHACTR2.

[0210] Suitably, the methods of the present disclosure include the step of determining a level of PHACTR2 and at least one further biomarker described herein (e.g., at least one of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7 and DSC2).

[0211] Any of the methods disclosed herein may or may not include measuring any other biomarker. Thus, the methods disclosed herein may comprise excluding from analysis any other biomarker. Moreover, the methods disclosed herein may comprise include any other biomarker, such as described herein or known in the art. Determining the level of biomarkers

[0212] It will be understood by the person skilled in the art that the level of expression, abundance or concentration of the two or more biomarkers may be determined by any means known in the art. The terms “determining”, “measuring”, “evaluating”, “assessing”, “quantifying”, “calculating” and “assaying” are used interchangeably herein and may include any form of measurement known in the art, such as those described hereinafter. Such determining may include detecting the presence or absence of two or more of the biomarkers and / or determining a concentration level thereof in the biological sample obtained from the subject.

[0213] It is contemplated that determining the expression of the biomarkers described herein may include determining one or both of the nucleic acid levels thereof, such as by next generation sequencing, nucleic acid amplification and / or nucleic acid hybridization, and / or the protein levelsthereof. Utilising a combination of nucleic acid amplification and nucleic acid hybridization for the present methods is also envisaged.

[0214] Nucleic acid amplification techniques typically include repeated cycles of annealing one or more primers to a “template” nucleotide sequence under appropriate conditions and using a polymerase to synthesize a nucleotide sequence complementary to the target, thereby “amplifying” the target nucleotide sequence. Nucleic acid amplification techniques are well known to the skilled addressee, and include but are not limited to polymerase chain reaction (PCR); strand displacement amplification (SDA); rolling circle replication (RCR); nucleic acid sequence-based amplification (NASBA), Q-β replicase amplification; helicase-dependent amplification (HAD); loop-mediated isothermal amplification (LAMP); nicking enzyme amplification reaction (NEAR) and recombinase polymerase amplification (RPA), although without limitation thereto. As generally used herein, an “amplification product” refers to a nucleic acid product generated by a nucleic acid amplification technique.

[0215] PCR includes quantitative and semi-quantitative PCR, real-time PCR, allele-specific PCR, methylation-specific PCR, asymmetric PCR, nested PCR, multiplex PCR, touch-down PCR, digital PCR and other variations and modifications to “basic” PCR amplification.

[0216] Nucleic acid amplification techniques may be performed using DNA or RNA extracted, isolated or otherwise obtained from a cell or tissue source. In other examples, nucleic acid amplification may be performed directly on appropriately treated cell or tissue samples.

[0217] Nucleic acid hybridization typically includes hybridizing a nucleotide sequence, typically in the form of a probe, to a target nucleotide sequence under appropriate conditions, whereby the hybridized probe-target nucleotide sequence is subsequently detected. Non-limiting examples include Northern blotting, slot-blotting, in situ hybridization and fluorescence resonance energy transfer (FRET) detection, although without limitation thereto. Nucleic acid hybridization may be performed using DNA or RNA extracted, isolated, amplified or otherwise obtained from a cell or tissue source or directly on appropriately treated cell or tissue samples.

[0218] Referring to some examples, the methods described herein utilise at least in part a next- generation sequencing modality in determining an expression level of the biomarkers provided herein. In some examples, an expression level of the biomarkers of the present disclosure is determined at least in part by RNAseq. The term “RNAseq”, also known as “RNA-seq” and “Whole Transcriptome Shotgun Sequencing (WTSS)”, refers to the use of high throughput sequencing techniques to sequence and / or quantify cDNA to obtain information about the RNA content of a sample. Publications describing RNA-seq include Wang et al., Nature Reviews Genetics10 (1): 57-63, 2009; Ryan et al., bioTechniques 45 (1): 81-94, 2008; and Maher et al., Nature 458 (7234): 97-101, 2009, which are incorporated by reference herein.

[0219] Determining, assessing, evaluating, assaying or measuring protein levels of the biomarkers disclosed herein may be performed by any technique known in the art that is capable of detecting cell- or tissue-expressed proteins whether on the cell surface or intracellularly expressed, or proteins that are isolated, extracted or otherwise obtained from the cell or tissue source. These techniques include antibody-based detection that uses one or more antibodies which bind the protein, electrophoresis, surface plasmon resonance (SPR), isoelectric focussing, protein sequencing, chromatographic techniques and mass spectroscopy and combinations of these, although without limitation thereto. Antibody-based detection may include flow cytometry using fluorescently-labelled antibodies, ELISA, immunoblotting, immunoprecipitation, in situ hybridization, immunohistochemistry and immunocytochemistry, although without limitation thereto. Suitable techniques may be adapted for high throughput and / or rapid analysis such as using protein arrays such as a TissueMicroArrayTM(TMA), MSD MultiArraysTMand multiwell ELISA, although without limitation thereto.

[0220] By “protein” is meant an amino acid polymer. The amino acids may be natural or non- natural amino acids, D- or L- amino acids as are well understood in the art. As would be appreciated by the skilled person, the term “protein” also includes within its scope phosphorylated forms of a protein (i.e., a phosphoprotein) and / or glycosylated forms of a protein (i.e. a glycoprotein). A “peptide” is typically a protein having no more than fifty (50) amino acids. A “polypeptide” is typically a protein having more than fifty (50) amino acids.

[0221] Also provided are protein “variants” such as naturally occurring (e.g. allelic variants) and orthologs of the biomarkers provided herein. Suitably, protein variants share at least 70% or 75%, particularly at least 80% or 85% or more particularly at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% sequence identity with an amino acid sequence of a protein biomarker disclosed herein (e.g., any one of SEQ ID NOs: 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 23, 25, 27, 29, 31, 34, 36 or 38) or known in the art.

[0222] The term “nucleic acid” as used herein designates single- or double-stranded DNA and RNA. DNA includes genomic DNA and cDNA. RNA includes mRNA, RNA, RNAi, siRNA, cRNA and autocatalytic RNA. Nucleic acids may also be DNA-RNA hybrids. A nucleic acid comprises a nucleotide sequence which typically includes nucleotides that comprise an A, G, C, T or U base. However, nucleotide sequences may include other bases such as inosine, methylycytosine, methylinosine, methyladenosine and / or thiouridine, although without limitation thereto.

[0223] Variants, such as naturally occurring variants (e.g. allelic variants) and orthologs, of the nucleic acid biomarkers provided herein are also contemplated. Suitably, nucleic acid variants share at least 70% or 75%, particularly at least 80% or 85% or more particularly at least 90%,91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% sequence identity with a nucleotide sequence of a gene or mRNA biomarker disclosed herein (e.g., any one of SEQ ID NOs: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 22, 24, 26, 28, 30, 32, 33, 35 or 37) or known in the art.

[0224] In some examples, the methods of the present disclosure include the step of isolating or purifying the two or more biomarkers, inclusive of protein and / or nucleic acid biomarkers, prior to quantification or determination of the respective level thereof. Protein and nucleic acid biomarkers can be isolated and purified from biological samples by various methods, including the use of commercial kits as are well known in the art.

[0225] In some examples, the level, expression or concentration of a biomarker will be higher in a subject compared to a reference value determined from controls. However, for certain biomarkers, a level, expression or concentration of that biomarker is decreased relative to a reference value from controls.

[0226] As will be understood by the skilled person, the level, concentration or expression level of any one of the biomarkers described herein may be relatively (i) higher, increased or greater; or (ii) lower, decreased or reduced when compared to a level, concentration or expression level in a control or reference sample, or to a threshold level or expression level. In various examples, a level, concentration or expression level may be classified as higher, increased or greater if it exceeds a mean and / or median level, concentration or expression level of a reference population. In some examples, a level, concentration or expression level may be classified as lower, decreased or reduced if it is less than the mean and / or median level, concentration or expression level of the reference population. In this regard, a reference population may be a group of subjects who have sepsis. More particularly, a reference population may be a group of subjects who have bacterial sepsis. Even more particularly, a reference population may be a group of subjects who have viral sepsis. Alternatively, a reference population may be a group of subjects, such as age- and / or sex- matched subjects, who are known to be free of sepsis.

[0227] Terms such as “higher”, “increased” and “greater” as used herein refer to an elevated amount or level of a biomarker, such as in a biological sample, when compared to a control or reference level or amount. The concentration or expression level of the biomarker may be relative or absolute (i.e., relatively or absolutely higher, increased or greater). In some examples, the level of a biomarker is higher, increased or greater if its level of concentration or expression is more than about 0.5%, 1%, 2%, 3%, 4%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, 150%, 200%, 300%, 400% or at least about 500% above the level of concentration or expression of the biomarker in a control, threshold or reference level or amount.

[0228] The terms, “lower”, “reduced” and “decreased”, as used herein refer to a lower amount or level of a biomarker, such as in a biological sample, when compared to a control or reference level or amount. The concentration or expression level of the biomarker may be relative or absolute (i.e., relatively or absolutely lower, reduced or decreased). In some examples, the concentration or expression of a biomarker is lower, reduced or decreased if its level of concentration or expression is less than about 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20% or 10%, or even less than about 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.01%, 0.001% or 0.0001% of the level or amount of concentration or expression of the biomarker in a control, threshold or reference level or amount.

[0229] The term “control sample” typically refers to a biological sample from a (healthy) non- diseased individual not having sepsis. In certain examples, however, the control sample may be from a subject known to have bacterial sepsis. In some examples, the control sample may be from a subject known to not have bacterial sepsis. In various examples, the control sample may be from a subject known to have viral sepsis. In other examples, the control sample may be from a subject known to not have viral sepsis. Alternatively, the control sample may be from a subject that has recovered from sepsis. In other examples, the control sample may be from a subject with a non- infectious disease, disorder or condition, such as non-infectious Systemic Inflammatory Response Syndrome (e.g., SIRS caused by a non-infectious stressor, such as trauma, surgery, acute inflammation, ischemia or reperfusion, toxins or cancer) that may resemble or include one or more clinical symptoms or sequelae of sepsis. The control sample may be a pooled, average or an individual sample. An internal control is a marker from the same biological sample being tested.

[0230] In some examples, a reference level or amount is determined from measurements of the biomarkers in a corresponding panel of biomarkers from a population of healthy individuals. The term “healthy individual” as used herein refers to a person or populations of persons who are known not to have sepsis. In some examples, the control reference is determined from measurements of the corresponding biomarkers in a “typical population”. In some examples, a “typical population” may include a typical population of sepsis patients who exhibit a spectrum of disease at different stages of disease progression.

[0231] In another example, a reference level or amount may be derived from an established data set including one or more of: 1. a data set comprising measurements of the biomarkers for a population of subjects known to have sepsis (e.g., bacterial sepsis and / or viral sepsis), more particularly a population of subjects known to have viral sepsis, even more particularly a population of subjects known to have bacterial sepsis, or yet even more particularly a population of subjects known to have either viral sepsis or bacterial sepsis;2. a data set comprising measurements of the biomarkers for the subject being tested wherein said measurements have been made previously, such as, for example, when the subject was known to be healthy; 3. a data set comprising measurements of the biomarkers for a healthy individual or a population of healthy individuals; 4. a data set comprising measurements of the biomarkers for a population of subjects known to have sepsis, but without any organ dysfunction; 5. a data set comprising measurements of the biomarkers for a population of subjects known to have sepsis with organ dysfunction; 6. a data set comprising measurements of the biomarkers for a population of subjects known to have sepsis with a positive prognosis; and / or 7. a data set comprising measurements of the biomarkers for a population of subjects known to have sepsis with a negative prognosis.

[0232] As used herein, a level, such as an expression level, of a particular biomarker may be an absolute or relative amount thereof. Accordingly, in some examples, the concentration or expression level of any one of the two or more biomarkers is compared to a control level of concentration or expression, such as the level of or expression of one or a plurality of “housekeeping” molecules (e.g., housekeeping proteins and nucleic acids as are known in the art) in the biological sample of the subject.

[0233] In further examples, the concentration or expression level of any one of the two or more biomarkers is compared to a threshold level of concentration or expression, such as a level of biomarker concentration or expression in a biological sample from: (a) a control subject not having sepsis; (b) a control subject having sepsis; (c) a control subject having sepsis without organ dysfunction; (d) a control subject having sepsis with organ dysfunction; (e) a control subject with sepsis but not having bacterial sepsis; (f) a control subject with sepsis but not having viral sepsis; (g) a control subject having bacterial sepsis; (h) a control subject having viral sepsis; (i) a control subject having a non-infectious disease, disorder or condition; (j) a control subject having sepsis with a positive prognosis; or (k) a control subject having sepsis with a negative prognosis; and / or an average or median level of biomarker concentration or expression in biological samples derived from a population of patients or subjects: (a) not having sepsis; (b) having sepsis; (c) having sepsis without organ dysfunction; (d) having sepsis with organ dysfunction; (e) having sepsis but not having bacterial sepsis; (f) having sepsis but not having viral sepsis; (g) having bacterial sepsis; (h) having viral sepsis; (i) having a non-infectious disease, disorder or condition; (j) having sepsis with a positive prognosis; or (k) having sepsis with a negative prognosis.

[0234] A threshold level of biomarker concentration or expression is generally a quantified level of concentration or expression of a biomarker. Typically, a concentration level or an expression level of a biomarker in a sample that exceeds or falls below the threshold level of concentration or expression is predictive of a particular disease state or outcome, such as the presence or absence of sepsis, inclusive of being capable of distinguishing between the presence of bacterial sepsis or viral sepsis in a subject. The nature and numerical value (if any) of the threshold level of concentration or expression will typically vary based on the method chosen to determine the concentration or expression of the two or more biomarkers used in determining, for example, a diagnosis of sepsis, an infection type and / or a prognosis in the subject.

[0235] A person of skill in the art would be capable of determining the threshold level of any one of the two or more biomarkers in a sample that may be used in determining, for example, determining the presence or absence of sepsis, an infection type and / or a prognosis in the relevant subject, using any method of measuring biomarker concentration, abundance or expression known in the art, such as those described herein. In various examples, the threshold level is a mean and / or median concentration or expression level (relative or absolute) of the biomarker in a reference population that, for example, have or do not have sepsis or a particular type of sepsis (e.g., bacterial sepsis or viral sepsis, organ dysfunction) or have a particular prognosis (e.g., a positive prognosis or a negative prognosis). Additionally, the concept of a threshold level of concentration or expression should not be limited to a single value or result. In this regard, a threshold level of concentration or expression may encompass multiple threshold concentration or expression levels that could signify, for example, a high, medium, or low probability of, for example, the subject having sepsis, a particular infection type, developing organ dysfunction or dying from sepsis. Such multiple threshold levels could also be utilised to signify or determine disease progression, or a risk thereof, for a subject’s sepsis.

[0236] In view of the foregoing, any of the methods disclosed herein may comprise a step of establishing a reference level or threshold level of concentration or expression of the biomarkers provided herein.

[0237] Referring to certain examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is above a threshold level, the subject has sepsis. In other examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is below a threshold level, the subject does not have sepsis. For alternative examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is above a threshold level, the subject does not have sepsis. In other examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is below a threshold level, the subject has sepsis.

[0238] According to some examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is above a threshold level, the infection type is bacterial. In other examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is below a threshold level, the infection type is not bacterial. For alternative examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is above a threshold level, the infection type is not bacterial. In other examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is below a threshold level, the infection type is bacterial.

[0239] For some examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is above a threshold level, the infection type is viral. In other examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is below a threshold level, the infection type is not viral. For alternative examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is above a threshold level, the infection type is not viral. In other examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is below a threshold level, the infection type is viral.

[0240] In particular examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is above a threshold level, the subject has a positive prognosis. In other examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is below a threshold level, the subject has a negative prognosis. For alternative examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is above a threshold level, the subject has a negative prognosis. In other examples, when the expression level of two or more biomarkers (or a risk score or diagnostic score derived therefrom) is below a threshold level, the subject has a positive prognosis.

[0241] Suitably, the predictive accuracy of the methods described herein, as determined by an ROC AUC value, is at least about 0.65 (e.g., at least about 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99 or any range therein). More particularly, the predictive accuracy of the methods described is suitably at least about 0.70. Even more particularly, the predictive accuracy of the methods described is suitably at least about 0.75. Yet even more particularly, the predictive accuracy of the methods described is suitably at least about 0.80. Still even more particularly, the predictive accuracy of the methods described is suitably at least about 0.85. Yet still even more particularly, the predictive accuracy of the methods described is suitably at least about 0.90.

[0242] Suitably, the sensitivity of the methods described herein in terms of detecting or diagnosing sepsis in a subject or determining an infection type in a subject with sepsis is at least about 0.65 (e.g., at least about 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99 or any range therein). The term “sensitivity”, as used herein, relates to the percentage of subjects having sepsis or a particular infection type who are correctly identified as having sepsis or the particular infection type. More particularly, the sensitivity of the methods described is suitably at least about 0.70. Even more particularly, the sensitivity of the methods described is suitably at least about 0.75. Yet even more particularly, the sensitivity of the methods described is suitably at least about 0.80. Still even more particularly, the sensitivity of the methods described is suitably at least about 0.85. Yet still even more particularly, the sensitivity of the methods described is suitably at least about 0.90.

[0243] Suitably, the specificity of the methods described herein in terms of detecting or diagnosing sepsis, including determining an infection type, in a subject is at least about 0.65 (e.g., at least about 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99 or any range therein). The term “specificity”, as used herein, relates to the percentage of healthy subjects who are correctly identified as not having sepsis. More particularly, the specificity of the methods described is suitably at least about 0.70. Even more particularly, the specificity of the methods described is suitably at least about 0.75. Yet even more particularly, the specificity of the methods described is suitably at least about 0.80. Still even more particularly, the specificity of the methods described is suitably at least about 0.85. Yet still even more particularly, the specificity of the methods described is suitably at least about 0.90. Calculating risk scores

[0244] For the methods described herein, determining: (a) the presence or absence of sepsis in a subject; (b) an infection type in a subject with sepsis; (c) responsiveness of a subject with sepsis to a treatment; or (d) a prognosis for a subject with sepsis; may include the step of calculating a risk score or a diagnostic score.

[0245] The term “risk score”, “diagnostic score” or “disease risk score” refers to value calculated with one or more feature values or scores that indicates an undesirable physiological state of the patient, such as the presence of sepsis. The term “risk score” in certain instances refers to a numerical representation of the current degree of the risk or probability a patient is at for having a particular disease or condition.

[0246] A risk score or diagnostic score may be calculated using the concentration or expression levels or expression signature of the two or more biomarkers, such as in a panel (e.g., 2, 3, 4, 5 etc or more) of the biomarkers, inclusive of those hereinbefore described. To this end, the methods described herein include the step of obtaining a risk score for a biomarker combination hereinbefore described or set forth in the Examples. A concentration or expression signature of a biomarker may be determined using the normalized level of concentration or expression of the biomarker in a sample, and an independent diagnostic value of the biomarker based on the correlation of the concentration or expression of the biomarker with disease presence or absence. Any method of determining a concentration or expression signature for a biomarker known in the art may be utilised. After determining the concentration or expression levels or expression signatures of individual biomarkers, such as in a panel of two or more of the biomarkers described herein, a risk score may be calculated by combining the concentration or expression levels and / or the expression signatures of each biomarker in a panel thereof. Methods of calculating a risk score may be by any method or means known in the art.

[0247] Suitably, for the present methods, a risk or diagnostic score has been determined using the level, such as a concentration level or an expression level, of the two or more biomarkers and the risk or diagnostic score is diagnostic or indicative of the subject having sepsis and / or a particular infection type. To this end, the diagnostic score can be in the form of a probability of the subject having sepsis and / or a particular infection type, such that in the absence of additional information a score of 50% or above provides that the subject has a higher probability of having sepsis and / or a particular infection type than not having sepsis and / or the particular infection type. In some examples, a diagnostic score that correlates with a probability of 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% or higher that the subject has sepsis and / or a particular infection type was or has been determined for the subject.

[0248] Referring to other examples, the risk or diagnostic score is indicative of the subject having a poor prognosis and / or a high likelihood of subsequent organ dysfunction and / or death in the subject. To this end, the diagnostic score can be in the form of a probability of the subject having a poor prognosis, subsequent organ dysfunction and / or dying, such that in the absence of additional information a score of 50% or above provides that the subject has a higher probability of having a poor prognosis, subsequent organ dysfunction and / or dying than not having a poor prognosis, subsequent organ dysfunction and / or dying. In some examples, a diagnostic score that correlates with a probability of 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% or higher that the subject has a poor prognosis, subsequent organ dysfunction and / or dying was or has been determined for the subject.

[0249] Suitably, the diagnostic score is generated at least in part via a multivariate model. In particular examples, the risk score is calculated at least in part by Least absolute shrinkage and selection operator (LASSO)-penalised multivariate Cox regression and forward feature selection.

[0250] Accordingly, a risk score for the jth patient may be calculated according to the below formula:proteins included in the signature; i indicates the ith protein in the signature; ^i is the co-efficient of the ith protein and Xji the intensity of the ith protein, in the jth patient / sample.

[0251] In some examples, the expression level of the biomarkers disclosed herein is multiplied by a weighting score specific for the respective biomarker. By way of example, a weighted combination of the biomarkers is arrived at by, for example, a supervised classification technique which uses the expression data from the biomarkers within individual patients. The expression level of each biomarker in a patient can be multiplied by a weighting factor for that biomarker, and those weighted values calculated for each biomarker’s expression are summed for each individual patient, and, optionally, a separate coefficient specific for that comparison is added to the sum which gives a final risk score or diagnostic score. Each comparison set may include in its own specific set of weighting factors for each biomarker (e.g., bacterial sepsis vs viral sepsis; bacterial sepsis vs non-infectious disease; viral sepsis vs non-infectious disease). Weighting factors can also have either a positive value or a negative value, such that if the weighting factor is positive it increases the risk or diagnostic score, and if the weighting factor is negative it decreases the risk or diagnostic score. Further, a zero value weighting factor means that biomarker in question is not contributing to the comparison. Alternatively, the aforementioned weights or weighting factors for the expression levels of the biomarkers may be replaced or simplified by using a scale, such as +1, 0 or -1 to indicate their contribution to a risk or diagnostic score.

[0252] A calculated risk score of the present disclosure may be used to determine the likelihood of: (a) the presence or absence of sepsis in a subject; (b) the presence or absence of a particular infection type (e.g., a viral infection or a bacterial infection) in a subject with sepsis; (c) the subject’s sepsis responding to a treatment; (d) the subject’s with sepsis developing an organ dysfunction; and / or (e) the subject’s with sepsis dying. In general, a calculated risk score may be compared to a reference risk score or a threshold risk score.

[0253] In certain examples, if (i) the risk score is equal to or higher than the reference risk score, the subject has sepsis, and (ii) the risk score is lower than the reference risk score, the subject does not have sepsis. In alternative examples, if (i) the risk score is lower than the reference risk score,the subject has sepsis, and (ii) the risk score is equal to or higher than the reference risk score, the subject does not have sepsis.

[0254] In other examples, if (i) the risk score is equal to or higher than the reference risk score, the subject has a bacterial sepsis, and (ii) the risk score is lower than the reference risk score, the subject does not have a bacterial sepsis. In various examples, if (i) the risk score is lower than the reference risk score, the subject has a bacterial sepsis, and (ii) the risk score is equal to or higher than the reference risk score, the subject does not have a bacterial sepsis.

[0255] In other examples, if (i) the risk score is equal to or higher than the reference risk score, the subject has a viral sepsis, and (ii) the risk score is lower than the reference risk score, the subject does not have a viral sepsis. In alternative examples, if (i) the risk score is lower than the reference risk score, the subject has a viral sepsis, and (ii) the risk score is equal to or higher than the reference risk score, the subject does not have a viral sepsis.

[0256] It is envisaged that a subject’s diagnosis (e.g., infection type) and / or risk score can be utilised to determine whether said subject should be treated with a treatment for sepsis. Accordingly, in other examples, if (i) the risk score is equal to or higher than the reference risk score, the subject is to be administered a first treatment for sepsis, and (ii) the risk score is lower than the reference risk score, the subject is not to be administered the first treatment. In such examples in (ii), the subject may instead be administered a second treatment for sepsis.

[0257] It is further contemplated that a subject’s diagnosis and / or risk score can be utilised to determine whether said subject should be treated with a treatment for bacterial sepsis or viral sepsis. Accordingly, in other examples, if (i) the risk score is equal to or higher than the reference risk score, the subject is to be administered a treatment for bacterial sepsis, and (ii) the risk score is lower than the reference risk score, the subject is to be administered a treatment for viral sepsis. In alternative examples, if (i) the risk score is equal to or higher than the reference risk score, the subject is to be administered a treatment for viral sepsis, and (ii) the risk score is lower than the reference risk score, the subject is to be administered a treatment for bacterial sepsis.

[0258] In certain examples, if (i) the risk score is equal to or higher than a reference risk score, the subject is to be administered a conventional or standard dose of a first treatment, and (ii) the risk score is lower than the reference risk score, the subject is to be administered a high, conventional or standard dose of a first treatment and a second treatment. More particularly, if (i) the risk score is equal to or higher than the reference risk score, the subject is suitably to be administered a conventional or standard dose of an antimicrobial agent and optionally a further sepsis treatment, and (ii) the risk score is lower than the reference risk score, the subject is suitably to be administered a conventional or standard dose of an antimicrobial agent and a further sepsis treatment (e.g., a supportive therapy, such as a vasopressor agent, a positive inotrope). Inalternative examples, if (i) the risk score is lower than a reference risk score, the subject is to be administered a conventional or standard dose of a first treatment, and (ii) the risk score is equal to or higher than the reference risk score, the subject is to be administered a high, conventional or standard dose of a first treatment and a second treatment. More particularly, if (i) the risk score is lower than the reference risk score, the subject is suitably to be administered a conventional or standard dose of an antimicrobial agent and optionally a further sepsis treatment, and (ii) the risk score is equal to or higher than the reference risk score, the subject is suitably to be administered a conventional or standard dose of an antimicrobial agent and a further sepsis treatment (e.g., a supportive therapy, such as a vasopressor agent, a positive inotrope).

[0259] For some examples, the risk score is compared to a threshold risk score, such as a median or average risk score, to determine whether the subject with sepsis may benefit from more aggressive treatment of their sepsis (e.g., administration of a further treatment for sepsis), such as to prevent or inhibit disease progression. In other examples, if (i) the risk score is equal to or higher than the reference risk score, the subject is administered a first treatment of sepsis, and (ii) the risk score is lower than the reference risk score, the subject is administered a first treatment of sepsis (e.g., an antimicrobial agent) and a further treatment of sepsis (e.g., a vasopressor agent, a positive inotrope). In relation to (ii), the subject may be at risk of their sepsis progressing (e.g., developing organ dysfunction) or dying. In alternative examples, if (i) the risk score is lower than the reference risk score, the subject is administered a first treatment of sepsis (e.g., an antimicrobial agent) and a further treatment of sepsis (e.g., a vasopressor agent, a positive inotrope), and (ii) the risk score is equal to or higher than the reference risk score, the subject administered a first treatment of sepsis.

[0260] A threshold diagnostic score or risk score may be the respective median or average of the diagnostic scores or risk scores calculated for each subject in a population of subjects with sepsis, inclusive of discrete or combined populations of subjects have bacterial sepsis or viral sepsis, and / or the respective median or average of the risk scores calculated for each subject in a population of subjects without sepsis. Systems

[0261] The present disclosure also contemplates systems for the detection of biomarkers that may be suitable for use in the methods described herein.

[0262] In a broad form, the present disclosure provides a system for: (a) determining the presence or absence of sepsis in a subject; (b) determining an infection type in a subject with sepsis; and (c) predicting the responsiveness of a subject with sepsis to a treatment; the system comprising:an apparatus configured for determining an expression level of two or more biomarkers in a biological sample obtained from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1; and a processing unit configured for using or analysing the level of the two or more biomarkers.

[0263] In another broad form, the present disclosure provides a system for determining a prognosis for a subject with sepsis, the system comprising: an apparatus configured for determining an expression level of two or more biomarkers in a biological sample obtained from the subject, wherein the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2; and a processing unit configured for using or analysing the level of the two or more biomarkers to thereby determine the prognosis of sepsis in the subject.

[0264] It is noted that the step of determining a level of the two or more biomarkers may be performed by the apparatus and / or may be performed at least in part by a pre-processing unit. That pre-processing unit may be the same as or different to the processing unit performing the steps of analysing the level. For example, the pre-processing unit may receive data from the apparatus, such as a next generation sequencing unit, indicative of a number or concentration of mRNA molecules. The pre-processing unit may then process this data to determine the corresponding expression levels. In other examples, the pre-processing unit may receive data from the apparatus, such as a PCR unit, indicative of Ct values for one or more mRNA biomarkers and a housekeeping gene / mRNA. The pre-processing unit may then process this data to determine, for example, ΔΔCt values for respective mRNA biomarkers to thereby calculate the corresponding expression levels thereof.

[0265] Suitably, the pre-processing unit and the processing unit are that described herein.

[0266] Suitably, the system includes reference data, such as threshold levels, which may be accessed by the pre-processing unit and / or the processing unit. In particular examples, the reference data is on a computer-readable medium (e.g., software embodying or utilized by any one or more of the methodologies or functions described herein). The computer-readable medium can be included on a storage device, such as a computer memory (e.g., hard disk drives or solid state drives) and may comprise computer readable code components that when selectively executed by a processor implements one or more aspects of the present disclosure. Kits

[0267] The present disclosure also contemplates kits for the detection of biomarkers that may be suitable for use in the methods described herein.

[0268] In one broad form, the present disclosure provides a kit for: (a) determining the presence or absence of sepsis in a subject; (b) determining an infection type in a subject with sepsis; and (c) predicting the responsiveness of a subject with sepsis to a treatment; the kit comprising: one or more reagents for determining an expression level of two or more biomarkers in a biological sample obtained from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1.

[0269] Referring to some examples, the present kit may further including one or more further reagents for determining an expression level of two or more further biomarkers in the biological sample from the subject, wherein the two or more further biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2. In this regard, the kit may also be suitable for determining a prognosis for the subject with sepsis.

[0270] In another broad form, the present disclosure provides a kit for determining a prognosis for a subject with sepsis, the kit comprising: one or more reagents for determining an expression level of two or more biomarkers in a biological sample obtained from the subject, wherein the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2.

[0271] Any agent or probe capable of binding specifically to a biomarker of interest will be useful, such as a probe (e.g., an oligonucleotide probe), an aptamer, an antibody and / or an antibody fragment. Other components of the kits will typically include labels, secondary antibodies, inhibitors, co-factors and control product (e.g., protein and / or mRNA) preparations to allow the user to quantitate concentration or expression levels and / or to assess whether the measurement has worked correctly. Biosensors, including optical (e.g., SPR-based sensors, interferometry-based sensors, waveguide-based sensors), electrochemical and mechanical biosensors are particularly suitable assays that can be carried out easily by the skilled person using kit components.

[0272] In some examples, the kit may comprise a substrate, such as a microtitre plate, a microfluidic device or a bead, on which is immobilised capture probes or antibodies corresponding to the biomarkers being measured.

[0273] Optionally, the kit further comprises means for the detection of the binding of a probe, such as an antibody or oligonucleotide probe, to a biomarker. Such means include a reporter molecule such as, for example, an enzyme (such as horseradish peroxidase or alkaline phosphatase), a dye, a radionucleotide, a luminescent group, a chemiluminescent group, a fluorescent group, biotin or a colloidal particle, such as colloidal gold or selenium. Suitably, such a reporter molecule is directly linked to the probe.

[0274] In one example, a kit may additionally comprise a reference sample. Suitably, a reference sample comprises a biomarker that is detected by an antibody and / or may be labelled or modified so as to be distinguished from native biomarker. Suitably, the biomarker is of known concentration. Such a biomarker is suitably of particular use as a standard. Accordingly, various known concentrations of such a biomarker may be detected using a diagnostic assay described herein.

[0275] The kit may further include instructions for use thereof. Instructions supplied in the kits of the present disclosure are typically written instructions on a label or package insert (e.g., a paper sheet included in the kit), but machine-readable instructions (e.g., instructions carried on a magnetic or optical storage disk) are also acceptable. The instructions relating to the use of the reagents described herein, generally include information as to determining a concentration or expression level of the two or more biomarkers and guidance regarding dosage, dosing schedule, and route of administration for an indicated treatment. The kit may further comprise a description of selecting an individual having sepsis, and more particularly bacterial sepsis or viral sepsis, and thereby suitable for treatment. Computer-implemented methods

[0276] It is envisaged that one or more steps of the methods described herein may be automated or implemented by a computer in the sense that the disclosed methods are implemented as software code that is stored on a non-volatile data storage medium. The computer executes the software code, which causes the computer to perform the methods disclosed herein.

[0277] By way of example, comparing a concentration level or an expression level of the two or more biomarkers with, for example, a reference or threshold level or value may be carried by a computer executing software code describing the comparing step. Thus, the comparison may be carried out by a computer or computing device, such as by a pre-processing unit and / or a processing unit. The value of the determined or detected amount of the two or more biomarkers in the sample from the subject and the reference amount can be, for example, compared to each other and said comparison can be automatically carried out by a computer program executing an algorithm for the comparison. Additionally, the calculation of a risk or diagnostic score and / or its comparison to a reference risk or diagnostic score can be automatically carried out by a computerprogram executing an algorithm for the comparison. Suitably, such algorithms may be trained on one or more case and / or control samples. In some examples, a processor may utilize the concentration or expression level data and / or a risk or diagnostic score to calculate a likelihood of the subject in question having sepsis, such as bacterial sepsis or viral sepsis, a likelihood of the subject’s sepsis being responsive to a treatment for sepsis and / or a prognosis for the subject’s sepsis.

[0278] The computer program carrying out the evaluation will suitably provide the desired assessment in a suitable output format. For a computer-assisted comparison, the value of the determined amount may be compared to values corresponding to suitable references, which are stored in a database by a computer program. The computer program may further evaluate the result of the comparison, i.e. automatically provide the desired assessment in a suitable output format.

[0279] In some examples, the methods of the disclosure include one or more of the broad steps of: (i) optionally performing a measurement of the concentration or expression level of the two or more biomarkers described herein; (ii) inputting or receiving the values from (i) into a processing system that is configured to determine the presence or absence of sepsis, such as bacterial sepsis or viral sepsis, in a subject; (iii) optionally calculating a risk or diagnostic score from the level or expression level of the two or more biomarkers by the processing system; (iv) comparing the concentration or expression level and / or the risk or diagnostic score obtained in step (iii) with a threshold value by the processing system; (v) determining the presence or absence of sepsis in the subject and / or determining an infection type for the subject’s sepsis; and (vi) optionally providing a treatment for the sepsis if present in the subject.

[0280] The methods of the present disclosure suitably permit integration into existing or newly developed pathology architecture or platform systems. For example, the present disclosure contemplates a method of allowing a user to determine the status (e.g., the presence or absence of sepsis) of a subject, the method including the steps of: (a) receiving data in the form of concentration or expression levels of two or more biomarkers for a test sample, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1; (b) optionally receiving data in the form of concentration or expression levels of two or more further biomarkers for a test sample, wherein the two or more further biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2;(c) optionally processing the subject data, such as with a processing unit or system, via univariate and / or multivariate analysis and / or machine learning algorithms (e.g., LASSO-penalised multivariate Cox regression, logistic regression, partial least squares discriminant analysis, random forest, decision tree, gradient boosting) to provide a risk or diagnostic score; (d) determining a status of the subject in accordance with the results of the concentration or expression levels and / or the risk or diagnostic score in comparison with predetermined or reference concentration or expression levels and / or risk or diagnostic score values, such as with a processing unit or system; and (e) transferring or providing an indication of the status (e.g., a diagnosis and / or prognosis of sepsis, such as bacterial sepsis or viral sepsis) of the subject to the user.

[0281] Referring to other examples, the present disclosure contemplates a method of allowing a user to determine the status (e.g., the prognosis) of a subject with sepsis, the method including the steps of: (a) receiving data in the form of concentration or expression levels of two or more biomarkers for a test sample, wherein the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2; (b) optionally processing the subject data, such as with a processing unit or system, via univariate and / or multivariate analysis and / or machine learning algorithms (e.g., LASSO-penalised multivariate Cox regression, logistic regression, partial least squares discriminant analysis, random forest, decision tree, gradient boosting) to provide a risk or diagnostic score; (c) determining a status of the subject in accordance with the results of the concentration or expression levels and / or the risk or diagnostic score in comparison with predetermined or reference concentration or expression levels and / or risk or diagnostic score values, such as with a processing unit or system; and (d) transferring or providing an indication of the status (e.g., a prognosis for the subject’s sepsis, such as a likelihood of subsequent organ dysfunction and / or death) of the subject to the user.

[0282] In some examples, the above method further includes the step of producing or generating the concentration level or expression level data by determining a concentration level or an expression level of the above biomarkers in the biological sample obtained from the subject, such as by using one or more of those methodologies described herein.

[0283] In one example, the method additionally includes: (a) having a user determine the data using a remote end station; and (b) transferring the data from the end station to a base station via a communications network.

[0284] The base station can include first and second processing systems, in which case the method can include: (a) transferring the data to the first processing system; (b) causing the first processing system to perform a univariate or multivariate analysis function to generate the risk or diagnostic score.

[0285] The method may also include: (a) transferring the results of the univariate or multivariate analysis function and / or the determined concentration or expression levels of the biomarkers to the second processing system; and (b) causing the second processing system to determine the status of the subject.

[0286] The second processing system may be coupled to a database adapted to store predetermined data or reference data and / or the univariate or multivariate analysis function, such that the computer-implemented method may include: (a) querying the database to obtain at least selected predetermined data or access to the univariate or multivariate analysis function from the database; and (b) comparing the selected predetermined data to the subject data or generating a predicted probability index.

[0287] The second processing system can be coupled to a database, the method including storing the data in the database, such as by way of a memory unit.

[0288] The reference concentration or expression level data comprises a level, such as a concentration or expression level, determined for the above biomarkers within a biological sample selected from the group consisting of: (i) a biological sample from a normal or healthy subject, such as a normal or healthy subject without sepsis; (ii) a biological sample from a subject previously diagnosed or determined as having sepsis, inclusive of bacterial sepsis and viral sepsis; (iii) an extract of any one of (i) to (ii); (iv) a data set comprising levels of concentration or expression for the biomarkers within a normal or healthy individual or a population of normal or healthy individuals; (vi) a data set comprising levels of concentration or expression for the biomarkers in an individual or a population of individuals having sepsis; and (vii) a data set comprising levels of concentration or expression for the biomarkers in the subject being tested wherein the levels of concentration or expression are determined for a sample having been taken at an earlier time point when the subject was known to not have sepsis. Obtaining a sample from a subject

[0289] The methods disclosed herein may further include the initial or earlier step of providing or collecting a biological sample from the subject. Such a sample may be obtained by freshly collecting a sample, or may be obtained from a previously collected and stored sample. By way of example, a sample may be obtained from a previously collected and stored (e.g., refrigerated or frozen) blood or serum sample. Suitably, a sample is obtained by freshly collecting a sample from the subject. Alternatively, a sample can be obtained from a previously collected and stored sample from the subject.

[0290] The methods of the present disclosure can be performed on various biological samples. As used herein, the term “biological sample” is suitably a sample obtained from a subject. For example, the biological sample can be a bodily fluid of the subject. In certain examples, the biological sample is selected from a group consisting of blood, serum, plasma, urine, saliva, faeces, tears, broncho-alveolar lavage fluid (BALF), cerebrospinal fluid (CSF) and seminal fluid. In some examples, the biological sample is blood. In other examples, the biological sample is plasma. In various examples, the biological sample is serum. In further examples, the biological sample is selected from a group consisting of blood, plasma and serum. A serum sample may be purified to remove cells. In certain examples, other components (e.g. debris, albumin) originally within the biological sample are removed or partially removed from the biological sample before performing the methods of the present disclosure. In an example, the biological sample is substantially free of cells.

[0291] The sample includes extracts, derivatives, fractions or suspensions of an original biological sample obtained from a subject disclosed herein.

[0292] The biological sample may be subject to any suitable pre-treatment steps before measurement of the level of the two or more biomarkers is performed, in order to improve the accuracy and / or efficiency of the measurement. Such pre-treatment steps may include extraction, centrifugation (e.g., ultracentrifugation), lyophilization, fractionation, separation (e.g., using column or gel chromatography), concentration or evaporation. In some instances, this treatment can include one or more extractions with solutions comprising any suitable solvent or combinations of solvents, such as, but not limited to acetonitrile, water, chloroform, methanol, butylated hydroxytoluene, trichloroacetic acid, toluene, hexane, benzene, or combinations thereof. In some examples, the biological sample may undergo one or more treatment steps so as to at least partly isolate, purify, concentrate or enrich for mRNA and / or protein, therein.

[0293] As used herein, the term “isolate”, “isolating” or “isolation” refers to material that has been removed from its natural state or otherwise been subjected to human manipulation. Isolated material may be substantially or essentially free from components that normally accompany it in its natural state, or may be manipulated so as to be in an artificial state together with componentsthat normally accompany it in its natural state. These terms include gross physical separation of the material from their natural environment (e.g., removal / purification from a biological sample obtained from a subject suspected of having sepsis).

[0294] Thus, any of the methods disclosed herein may comprise a step of taking a biological sample from a subject and determining the level of expression, concentration or abundance of the two or more biomarkers in the sample. Alternatively, any of the methods disclosed herein may not comprise a step of taking a biological sample from a subject and determining the level of expression, concentration or abundance of the two or more biomarkers in the sample. Instead, the biological sample may have already been taken from the subject and the level of expression, concentration or abundance of the two or more biomarkers in the sample may have been determined previously.

[0295] So that preferred embodiments of the present disclosure may be fully understood and put into practical effect, reference is made to the following non-limiting examples. Examples

[0296] Example 1 – Host gene expression signatures to identify infection type and organ dysfunction in children evaluated for sepsis: a multicenter cohort study

[0297] Sepsis is generally defined as dysregulated host response to infection leading to life- threatening organ dysfunction. Biomarkers characterising dysregulated host response in sepsis are lacking. The present Example aimed to develop host gene expression signatures predicting organ dysfunction in children with bacterial versus viral infection. Methods Study design and oversight

[0298] This prospective multi-center cohort was performed at four hospitals in Queensland, Australia. The study reporting follows the Standards of Reporting of Diagnostic Accuracy Studies 2015 Update11. The institutional Human Research Ethics Committee approved the study. Written informed consent or delayed consent was obtained for all participants from their parents / carers. Patients

[0299] Children aged over 1 month to 17 years evaluated for sepsis at the participating Emergency Departments (ED) and ICUs were eligible if they underwent a diagnostic work-up for suspected sepsis, including blood cultures upon admission. Study procedures

[0300] 2.5mL of blood was obtained in PAXgene RNA tubes (PreAnalytix GMBH, Switzerland) simultaneously with routine clinical testing including blood cultures, blood counts, C-reactive protein, and microbiological investigations such as nasopharyngeal swabs as indicated clinically.A REDCap study database12prospectively captured information on demographics, symptoms, comorbidities, microbiology results, antimicrobial treatment, and severity. Disease severity was assessed at baseline (time of blood sampling) and at 24 hours using clinical, laboratory, and organ support criteria for organ dysfunction defined by the 2005 International Pediatric Sepsis Definition Consensus Conference13,14. Accordingly, presence of organ dysfunction (cardiovascular, respiratory, neurologic, renal, hepatic, haematologic) was adjudicated. The infection status was categorized into definite bacterial (DB), definite viral (DV), probable bacterial (PB), probable viral (PV), combined bacterial and viral (CBV), non-infectious (NI) and unknown based upon a previously validated approach9and outlined in more detail below (Figure 3). Two assessors experienced in paediatric critical care and infectious diseases independently verified the infection status using clinical records, microbiologic results, laboratory data and discharge reports. Adjudication of the final clinical phenotype required agreement of both assessors; in case of disagreement, a third senior assessor reviewed cases with the two assessors to ensure robust adjudication of clinical phenotypes. Clinical phenotyping algorithm.

[0301] The clinical phenotyping followed a two-step procedure (Figure 3). In a first step, the microbiologic etiology (bacterial versus viral infection) was classified. In a second step, the presence of organ dysfunction within 24 hours of sampling was assessed (organ dysfunction versus no organ dysfunction).

[0302] Presence of bacterial versus viral infection. Bacterial versus viral infections were classified on a 6-item order ranging from confirmed bacterial, probable bacterial, undefined infectious illness, probable viral, to confirmed viral infection, and non-infectious illness (including controls). Bacterial infections were confirmed by cultures of sterile sites by standard pathology services which must be compatible with the clinical presentation. Probable bacterial infections were microbiologic unconfirmed infections where the clinical presentation (bacterial syndrome, increased C-reactive protein, decision by the treating physician to treat for at least 5 days with antibiotics) was indicative of bacterial infection. Confirmed viral infection were based on routine diagnostics (influenza A and B, respiratory syncytial Virus (RSV), parainfluenza 1-3, human metapneumovirus (hMPV), adenovirus, enterovirus) and add-on viral diagnostics of specimens as clinically indicated (such as Enterovirus-PCR in infants with suspected sepsis or central nervous system infection). During the study it was routine practice to perform a standardized PCR panel for viral respiratory pathogens (influenzavirus A and B, parainfluenzavirus 1-3, RSV, hMPV, Rhino / Enterovirus, Adenovirus) using nasopharyngeal aspirates. respiratory PCR results were considered if compatible with the clinical phenotype. Probable viral infections were microbiologic unconfirmed infections where the clinical presentation (viral syndrome such as for examplebronchiolitis, low C-reactive protein) was indicative of a viral infection. Non-infectious illness was defined as patients with signs and symptoms of illness in the absence of signs and symptoms of infection, including surgical controls. Combined Bacterial / Viral infections were classified if a secondary infection with a different pathogen class (i.e., bacterial infection leading to presentation, with viral co-infection) was confirmed.

[0303] Presence of organ dysfunction. Severity was assessed at baseline (at time of blood sampling) and at 24 hours after blood sampling using clinical, laboratory, and organ support criteria for organ dysfunction as defined by the 2005 International Pediatric Sepsis Definition Consensus Conference3,4. Accordingly, presence of between one and six of the following organ dysfunctions (cardiovascular, respiratory, neurologic, renal, haematologic and hepatic) was adjudicated: • Cardiovascular: systemic hypotension (as per age-specific cut-offs for systolic blood pressure), OR need for vasoactive drugs, OR increased base excess / capillary refill / lactate • Respiratory: need for invasive or non-invasive mechanical ventilation, OR PaO2 / FiO2 <300, PR PaCo2 >65 mmHg, of need of FiO2 >50% to maintain oxygen saturations ≥92% • Neurologic: Glasgow Coma Score <11 • Renal: serum creatinine increase ≥ 2 times upper limit of normal for age • Haematologic: Platelet count <80,000 / mm3• Hepatic: total bilirubin ≥69 umol / l OR ALT 2 times upper limit

[0304] The inventors investigated in addition the following secondary severity outcomes: • Organ dysfunction remote to the site of infection: this outcome was chosen because organ dysfunction remote from the site of infection (i.e. renal failure or shock in a patient with pneumonia) may indicate more severe systemic processes; compared to organ dysfunction at the site of infection (i.e. respiratory failure in a patient with pneumonia; neurological failure in a child with meningitis). This is based on a recent global survey of the Pediatric Sepsis Definition Taskforce.5Shock was always considered as organ dysfunction remote of the site of infection) • Need for organ support: defined as inotropes, invasive or noninvasive ventilation, renal replacement therapy, or ECMO. This outcome was chosen as it indicates a higher severity, given the need for organ support. • Administration of inotropes: defined as vasoactives or inotropes (adrenaline, noradrenaline, milrinone, dobutamine, dopamine, vasopressin)• Cardiovascular, respiratory, or neurological organ dysfunction: presence of at least of these three organ dysfunctions, given that they were shown to be more relevant to outcomes compared to other organ failures.6• Multi-organ dysfunction (MOD): Presence of at least two organ dysfunctions7, which is associated with substantially higher mortality and worse short- and long-term outcomes compared to single organ dysfunction. • Improving / worsening organ dysfunction 24 hours after sampling (OD better; OD worse): using the count of organ dysfunctions at 24 hours after study blood sampling compared to the count of organ dysfunction at time of sampling to reflect disease dynamics. • Improving / worsening multi-organ dysfunction 24 hours after sampling (MOD better; MOD worse): using the count of organ dysfunctions at 24 hours after study blood sampling compared to the count of organ dysfunction at time of sampling to reflect disease dynamics. • Presence of individual organ dysfunction: specific for each of the six organs listed above.

[0305] Changes in organ dysfunction from baseline to 24-hours after sampling is shown in Figure 4. Endpoints

[0306] The primary outcomes were the presence of organ dysfunction at 24 hours of sampling in children with DB infection, and in children with DV infection. This outcome was constructed by combining the infection phenotype category (restricted to DB, DV, PB, PV, NI), with the adjudication by organ dysfunction at 24 hours (i.e. presence of any organ dysfunction versus no organ dysfunction at 24 hours). Given the lack of a gold standard for sepsis severity15, several secondary severity outcomes were defined: (i) organ dysfunction remote from the primary focus of infection (as a proxy of organ dysfunction caused by a systemic process related to infection16); (ii) need for organ support (invasive or non-invasive respiratory support, inotropes / vasopressors, renal replacement, extracorporeal membrane oxygenation); (iii) need for inotrope / vasopressors; (iv) multi-organ dysfunction; (v) presence of cardiovascular, respiratory, or neurologic dysfunction15,17; and (vi) type of organ dysfunction. These outcomes were assessed at 24 hours from sampling, as well as at time of sampling; with an additional secondary severity outcome created by the dynamics within the first 24 hours (worsening or improving) (Figure 4). RNA sequencing for discovery and validation cohort

[0307] Samples were stored at -80οC until extraction. RNA was purified from samples using PAXgene Blood miRNA kits (PreAnalytix). Library preparation and sequencing were conducted at Institute for Molecular Biosciences Sequencing Facility (University of Queensland, Australia). The TruSeq RNA Ribo Zero Kit (Illumina) was used for ribosomal RNA depletion and sequencinglibrary preparation. Libraries were sequenced on a NovaSeq Sequencer (Illumina) to generate at least 20 million sequencing reads per sample. The RNA sequencing configuration was 75bp single- end (50 samples), 100bp single-end (545 samples) and 100bp paired-end (316 samples), respectively. FastQC18and MultiQC19were used to assess the quality of sequencing reads. The first two batches of samples were used for discovery (n=595) and the third batch was used for validation (n=316). For the discovery cohort, the sample size was based on power to detect differential gene expression between conditions with 1.2-fold change, assuming 20 million reads per sample. According to RNASeqPower package in R, at least 78 samples were required per condition to achieve 80% power. This was achieved for the majority of comparisons, including organ dysfunction, and definite bacterial vs definite viral. For the validation cohort, the methodology described in Burderer et al20was used to estimate that a sample size of 315 would allow us to correctly estimate the sensitivity and specificity of the test within + / -0.05 at 95% confidence. Samples with completed phenotyping, monitoring, and RNA extraction by March 2020 were included in the discovery cohort, the rest of the samples which were recruited by October 2021 composed the validation cohort. Four samples in the validation cohort failed quality assessment and were excluded from analysis, leaving 595 samples in the discovery cohort, and 312 samples in the validation cohort.

[0308] Sequencing reads were mapped to the human reference genome (version hg38) usingSTAR aligner (version 2.7.6a)21. GENCODE version 35 gene transcript annotation was used forthe alignment. HTSeq count (version 0.13.5)22was used to ascertain the number of reads mapped per gene. Principal component analysis (PCA) was performed to identify any outliers (Figure 5). RNA Sequencing data

[0309] Sequencing was performed in three batches and the sequencing configuration was 75bp single-end (50 samples), 100bp single-end (545 samples) and 100bp paired-end (316 samples), respectively. The first two batches of samples were used for discovery (n=595) and the third batch was used for validation (n=316). Principal component analysis (PCA) was performed to identify any outliers (Figure 5). Principal components (PCs) which explain greater than 5% of variance were retained. There were only 2 PCs with >5% variance (PC1 – 72.52%, PC2 – 25.23%) and these are plotted in Figure 5. Run 3 clustered together, however this group was only used for the validation of the signatures. Hence, no corrections were performed in relation to the data. Differential Expression Analysis

[0310] DESeq223was used for differential expression analysis between different phenotypes (bacterial versus viral; with versus without organ dysfunction). Genes with <10 read counts wereexcluded from analyses. Genes which had absolute log2 fold-change (LFC) of >1 and adjusted p- value of <0.05 were considered as differentially expressed.

[0311] Genes which had absolute log2 fold-change (LFC) of >1 and adjusted p-value of <0.05 were considered as differentially expressed. Log2 fold change of >1 would identify genes which are 2 times different in expression levels between the comparison groups and this will assist to identify genes which are significantly differentially expressed between groups. DESeq2 adjusted P-values are from the Wald test using Benjamini and Hochberg method (BH-adjusted p values) and adjusted p-value of <0.05 will identify genes which are significantly differentially expressed between groups. Differential expression analysis was performed on infection type (definite bacterial versus definite viral) and organ dysfunction (with OD versus without OD) phenotypes. Outcomes are listed in Figure 6 and Table 7. External Validation Cohort

[0312] RNA sequencing gene expression count data were obtained from the European Childhood Life-threatening Infectious Disease Study (EUCLIDS24,25, n=362). This observational study recruited children with severe infection in nine European countries between 2012–2016. Patients were phenotyped based on the likelihood of bacterial or viral infection26and considering severity at time of sampling. Signature Discovery and Evaluation with FSPLS

[0313] Forward Selection Partial Least Squares (FSPLS), as outlined in more detail below, was used to discover gene signatures to first distinguish infection types and to then predict presence of organ dysfunction. The FSPLS approach enables simultaneous multiple comparisons to identify signatures which can be utilised to distinguish multiple phenotypes.

[0314] For disease-class signature analysis, FSPLS was run with five different comparisons (DB versus DV; DB versus PV; DV versus PB; DB versus NI; DV versus NI). Combined infections and unknown infections were not included in signature discovery. For severity signature analysis, FSPLS was run with those with versus those without organ dysfunction at 24-hours post sampling, and with those with versus those without organ dysfunction at the time of sampling. Disease-class stratified severity weights were obtained by running FSPLS on datasets stratified by predicted disease-class (viral, bacterial or non-infectious). To predict sepsis, firstly the disease-class signature was used to predict the infection types as either DB or DV or NI, as these groups have well-defined phenotypes (Figure 3). Then, the disease-severity signature was applied for each infection type to identify organ dysfunction (Figure 1).

[0315] In order to benchmark the present signatures, the inventors used their dataset to refit previously published gene-expression signatures reported in patients with infection and sepsis,specifically Herberg et al9, McHugh et al10, Tang et al27, Wong et al28, Sweeney et al29, Sampson et al30, Li et al31, Li et al32for disease-class and Lukaszewski et al33, Pena et al34, Irwin et al35and Baghela et al36for disease-severity. As the weights of the genes in the signatures were not publicly available, the inventors used their dataset to re-fit and generate the weights to use in the analysis. This allowed a comparison across all the signatures as they were all re-fitted similarly. The inventors did not correct for multiple comparisons. FSPLS methods

[0316] To identify gene signatures, the inventors applied an in-house forward selection algorithm Forward Selection – Partial Least Squares (FS-PLS) to discover a transcript signature. A previous iteration of the algorithm was reported in Herberg et al8and Gliddon et al9. The gene expression data are first converted from count data to transcripts per million mapped reads by dividing by the total library size for each sample. Next, a series of variance stabilising transformations were applied to the data, including log(x+1), Anscombe transformation and inverse binomial10. The inventors next divided the data into 10 random 10% chunks (by sample) for fitting the model using 10-fold cross validation. For each of these, the inventors used the remaining 90% of the data to fit a forward selection model, using a logistic link function. At each round of forward selection, it was predicted the held-out samples, and terminated the forward selection when a goodness of fit test on these held-out samples no longer increased. The area-under-the curve (AUC) was used as the goodness of fit function.

[0317] The inventors modified the FSPLS approach to enable simultaneous comparison of multiple disease groups. This allowed us to discover a single set of gene signature to distinguish various phenotypes. For disease-class signature discovery, FSPLS was run with 5 different comparisons, including DB versus DV; DB versus PV; DV versus PB; DB versus NI and DV versus NI comparisons. For OD signature analysis, FSPLS was run with two comparisons, those with versus those without OD at time of sampling (0 hours) and those with versus those without OD at 24-hours post sampling.

[0318] To minimize the signature size, maximum gene numbers in the signature was set at 10. The expression levels of the individual genes in the signatures were comparable across the discovery and validation cohorts (Figure 7).

[0319] Weights for each gene in the signature for each phenotype differed (Figure 8) and these gene weights were used for the validation of the signature. To test the performance of previous reported signatures, these signatures were trained on the present data and determined the weights for each gene in the signature and used these to determine the performance for each phenotype.

[0320] For the combined disease-class and disease-severity prediction model, the inventors first applied the disease-class signature and predict the probability of being definite bacterial or definiteviral or non-infectious, as these phenotypes were well-defined (Figure 9 shows the distribution of samples in the ternary plot for each phenotype in the corresponding cohorts). Then, for each predicted disease-class group, the disease-severity signature was applied to predict the likelihood of developing organ dysfunction.

[0321] Model calibration:

[0322] FS-PLS has been carefully designed so that model normalisation is not required prior to running (e.g. centralisation or standardisation), other than library size normalisation. Moreover, all the variance stabilising transformations are written in a functional form and do not rely on any calculation on the data (e.g. standard deviation). The model weights obtained from model fitting are derived in this space of library size normalised count data, and hence can be applied directly to validation datasets normalised by library size in the same way. Computational requirements:

[0323] The model fitting was carried out on a single 8 CPU node of HPC cluster utilising 16GB of memory. The model fitting for the disease-class signature required 492 seconds and the disease- severity signature required 462 seconds. Statistical significance:

[0324] Previous version of FS-PLS would terminate model fitting if the orthogonal component of the next variable selected was not significantly associated with the outcome (at alpha of 0.05). However, in the current version a cross-validation scheme was used instead. In more detail, the training set was randomly split into 10. At each stage of the forward iteration, the entire process of selection of the next best variable, and model fitting was carried out on the 10 different 90% subsets, with a prediction made on each remaining 10%. In this way each sample had an out-of- sample prediction made (for each of the different multi-way comparisons). Based on these out-of- sample predictions, a receiver operating curve was generated, and the area-under-the curve (AUC) was calculated and summed across the comparisons. The iterations terminate if the summed is less or equal to the previous iteration. Gene Ontology

[0325] The enriched Gene Ontology (GO) terms in disease-class and disease-severity signature genes using ClusterProfiler were assessed (Figure 10). Disease-class signature genes have immune response GO terms enriched indicating the immune response involved in different infection types. Disease-severity signature genes have immunoglobulin complex, signal recognition and proton transporting GO terms enriched, explaining the involvement of various organ dysfunctions and the subsequent biological response pathways. Statistical Analysis:

[0326] All analyses were performed with Stata / SE version 17.0 (StataCorp Pty Ltd, College Station, Texas) and R (R version 4.0.2)37. The pROC package38was used to calculate the AUCs to report the performance of signatures and the DeLong method39to compare the AUC values between signatures. Results

[0327] From January 2018 to October 2021, 907 children evaluated for sepsis were enrolled with 595 constituting the discovery, and 312 the RAPIDS validation cohort (Tables 1 and 5). Study samples were obtained at a median of 2.3 (interquartile range (IQR) 1.4, 4.1) hours, and 3.0 (IQR 1.8, 7.5) hours after hospital admission in the discovery, and validation cohort, respectively. Overall, 87 (14.6%) patients in the discovery, and 65 (20.8%) in the validation cohort had organ dysfunction 24-hours after sampling (Figure 4). Of these, 76 (87.4%) patients in the discovery cohort and 57 (87.7%) patients in the validation cohort had organ dysfunction at baseline sampling. 24 (27.6%) patients in the discovery, and 22 (33.8%) patients in the validation cohort developed new or additional organ dysfunction within 24 hours of sampling compared to sampling baseline. 172 (28.9%) and 110 (18.5%) patients in the discovery cohort had DB and DV infections, compared with 63 (20.2%) and 100 (32.1%) in the validation cohort (Tables 5 and 6).

[0328] Differential gene expression was assessed in the discovery cohort, first for disease-class, then for disease-severity. Differential expression analysis based on the infection type identified 886 differentially expressed genes (adjusted p-value <0.05) between patients with DV and DB infections (Figure 6A; Table 7). Comparing patients with versus without organ dysfunction at 24 hours after sampling, 1028 genes were differentially expressed (Figure 6B; Table 7). Differentially expressed genes differed based upon whether patients with organ dysfunction had DB or DV infections (Figure 6C and 6D; Table 7).

[0329] Using FSPLS, the inventors discovered a 10-gene disease-class signature to distinguish type of infection, which is comprised of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1 genes (Figure 2A). This disease-class signature achieved an AUC of 93.5% (95%-CI: 90.5% - 96.6%) in distinguishing DB versus DV in the discovery cohort, an AUC of 94.1% (95%-CI: 90.6% - 97.7%) in the RAPIDS validation cohort (Figure 2C), and an AUC of 90.9% (95%-CI: 85.0% - 96.9%) in the EUCLIDS validation cohort (Table 2). Similar performances were achieved for other disease-class phenotype comparisons. Compared with previously reported disease-class signatures, this signature demonstrated better performance for most classifications (Table 2, Table 8). The disease-class signature also distinguished patients with CBV infection and unknown infection status (Table 9). Gene Ontology enrichment analysis of the disease-class signature genes showed enrichment of immune response GO terms (Figure 10).

[0330] Using FSPLS, the inventors discovered a 10-gene disease-severity signature to identify presence of organ dysfunction 24 hours after sampling which is comprised of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2 (Figure 2B). This signature achieved an AUC of 92.4% (95%-CI: 89.2% - 95.6%) in distinguishing patients with and without organ dysfunction at 24 hours in the discovery cohort and an AUC of 82.2% (95%- CI: 76.3% - 88.1%) in the RAPIDS validation cohort (Figure 2D; Table 3). Compared with previously reported gene expression signatures for disease severity the signature demonstrated comparable or superior performance (Tables 3 and 10). Gene Ontology enrichment analysis of the disease-severity signature genes showed enrichment of immunoglobulin complex, signal recognition and proton transporting GO terms indicating biological pathways involved in the development of organ dysfunctions (Figure 10).

[0331] To enable prediction of sepsis, disease-class prediction was combined with disease-class stratified severity weights which achieved an AUC of 90.5% (95%-CI 83.3% - 97.6%) and 94.7% (95%-CI 87.9% - 100.0%) in identifying organ dysfunction in patients with predicted DB infection, and predicted DV infection, respectively, in the RAPIDS validation cohort (Figure 2E and 2F; Table 3). The disease-class and disease-severity signatures were then assessed using the EUCLIDS cohort, however severity information was only available at the time of sampling. The disease-class stratified severity signatures achieved an AUC of 70.1% (95%-CI 44.1% - 96.2%), and 69.6% (95%-CI 53.1% - 86.0%) in identifying organ dysfunction in children with predicted DB and DV infection, respectively in the EUCLIDS cohort (Table 3).

[0332] The severity signature performed comparably with identifying secondary severity outcomes at baseline and within 24 hours of sampling, including organ dysfunction remote from the site of infection, type of organ dysfunction, need for organ support, and need for inotrope support (Table 4). In the RAPIDS validation cohort, the signature identified progressive multi- organ dysfunction within 24 hours of sampling with an AUC of 75.8% (95% CI 67.3% - 84.3%) (Table 4).

[0333] Adding clinical information such as C-reactive protein levels and leukocyte counts to the gene signatures failed to improve the prediction of disease-class and disease-severity, which was superior to routine clinical markers (Figure 11). Both the disease-class and disease-severity signatures in the discovery and validation cohorts performed similarly across the age ranges included (Tables 11 and 12). Discussion

[0334] In this multi-center prospective study involving 912 children evaluated for suspected sepsis, the present inventors derived and validated gene expression signatures to identify children with confirmed viral versus bacterial infection and organ dysfunction. The two signatures provideactionable information on the likelihood of bacterial (versus viral) infection, and on the likelihood of life-threatening organ dysfunction in 24 hours.

[0335] In the past years, several infectious disease studies in adult and paediatric patients have investigated host gene expression analyses to differentiate patients with bacterial versus viral infection9,32,40,41. At the same time, ICU-based studies revealed pathways and differentially regulated genes associated with mortality in critically ill patients, which may identify patients more likely to suffer harm from specific interventions such as corticosteroids42,43. Until recently, however, the integration of the two key dimensions which constitute sepsis (i.e. presence of infection and development of organ dysfunction1) by a unifying measure of dysregulated host response has been lacking.

[0336] Compared with eight previously reported signatures9,10,27-32to diagnose the type of infection, the performance of the disease-class signature described herein was similar or higher in terms of AUC in both validation cohorts. The disease-class signature included 10 transcripts, a number which has become feasible to implement in rapid point-of-care platforms. Compared with seven previously reported signatures33-36to diagnose disease severity, the performance of the present signature was higher in terms of AUCs in the RAPIDS validation cohort for organ dysfunction 24 hours after sampling associated with bacterial and viral infection. The signature was less complex (i.e., 10 genes versus 40 genes) than the best performing previously publishedseverity signature36. When assessing other severity outcomes 24 hours after sampling, such asorgan dysfunction remote from the site of infection, multi-organ dysfunction, or need for inotropes, the disease-severity signature performed well with AUCs above 80%.

[0337] Both disease-class and disease-severity gene signatures discovered in this Example were shown to predict multiple phenotypes successfully. The disease-class signature identified the infection type in patients evaluated for sepsis. The disease-severity signature identified the presence of organ dysfunction and several other severity phenotypes including whether the organ dysfunction was likely to worsen within 24 hours of sampling. In combination, given the high negative predictive value (Table 10), the information provided by this sepsis signature has the potential to guide clinical decision-making (rule-out) on use of antimicrobials and escalation of care.

[0338] Mortality and other severity outcomes in paediatric sepsis relate directly to delays between presentation and delivery of a sepsis treatment bundle48. Sepsis quality improvement programs usually focus on presumed infection in the presence of clinical indicators of altered physiology. However, it is well recognized that clinical features of sepsis are often subtle and non- specific, in particular in children where viral aetiologies predominate. Therefore, initiatives to promote early treatment with intravenous antibiotics have been met with criticism as they riskinappropriate use of antibiotics, potentially promoting antimicrobial resistance. In this context, a direct marker of a dysregulated host response to bacterial versus viral infection remains highly desirable and can serve to identify treatable traits early upon presentation.

[0339] In conclusion, in this large cohort of children evaluated for sepsis encompassing a broad range of disease severity, pathogens, and comorbidities, two transcriptomic signatures were able to discriminate patients with bacterial versus viral infection and those who were likely to manifest organ dysfunction within the next 24 hours at high accuracy.).s2).4) ) ) ) - - - - - - - - - - - - - - - - - -.0 6.1 7isS n 2 pDoi2 5 7.2 6.4.5 ( 2(4(1(1(,8eI t .sL a 63 98 99 2 0 1 0rC d oil= 1 51 6 5(fU a N6.E 2 V detaul ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) )a7.8.30.0.83.73.75.993154654178). . . . . .16.1.65.25.2.6.9.2.7.9.9.veS n 9 o 3 1 15 1 1,2( ( ( ( (2 97 1 1 11 8(13 13 9 0 18n 2( ( ( ( ( 4.(2 6 0 4 2( ( ( ( (5( ( (2 3 8( ( (7erDIitP a 1 4 3 2 34 95 65 45 1(29 1 1 2 1 2 86 24 94 83 53 2 69 79 2 36 79 2 d dliAil= 1 1 2 a N4.3hRcVfostr )o7) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ).62.65.60.03.77.72.25.45.34.30.0 3( 5.30.89.14.95.7.1.6.1.7.8.2.h yor4 2 3 2 1,2( ( ( (0 1 7 1(51 5(62 03 21 3 4 8(ce5 v 9(5 8( ( ( ( 0.(7 1 0 8( ( (6(4( ( (2(3(9 7 65 71 91 30 1 23 2 2 2 1 08 46 17 5 2 3 5 2 2 1 7 4 noc= 2 1 2 1 1(9 5 8 7 4 0 oitsiN8.1 4 1 1 1 2 a D 2 dilavdna re syr drseeonsaev ntsin u ooncoiit ce eD ois yr aferecitioipdcs a / ed d oegmrD veten nss o ehtoftrSneoocgetal r sifCaae ,y yshCfntGi trcio tisipecoscsrsrMlaHlala aP p o nlemd itoeorvoyroit esilrsra aeaaeae eyatitni llaenearh m helytiosV rC delier / aaeo ot naharihyrey y8 m g gbep e drerehrb au esrrp gc / m y01- y htte sn nrcnhtv setl tizini u a suelcF 1 <5- 11- n o oen 5 01 A A C C C E y S Oe a rF R ArI eSa a i eo P N D R CaParahcytirevesdna,lacciitgsiolre )oitc%(baorr a)ncR nih Q)o CIitm (%( at,lnnn aainescid oiernil ) emtip%)dntaC()sros.n%aeccim1 rel e(d n ey(noto p bnegegrm a h G y T A A C S- - - )0).60) ) ) ) ).0) ) ) ) - ) ) )2)9)4)6)3) )5 91.2.6.7.9.0.1.4.9.6.3.9 3 8 5 83.8 1 2 1(81 3(4(6(3(1(1(41 851- 1 1,=,5 1 2,1 ((4(3 7 5 1 4 5( (,= =9= 8 5 6 1 1 2 1 4 21.N3N(3.5 0 6 5 17(7( ( N(0( N(1 2 -(7 54 4.94.1 4 -)6).20).4)1)6)3)9)4)4) ) ) ) ) ) ) ) ) )8)5)9) )7(4. . . . . . .2.6.6.5.8.0.5.5.3.7 5 02.0 ( 32 03 72 01 2(6(1(2 1 0 3 5 8 7,9 6)1 = 1 2,2 8 2( ( ( (9 0 3(7(5(2(1(8(2 2 58 ( ( 2.=)=0= 1 1 4 6 2 2 1 1 2.2 2 0N( ) .7 9 8 3 1(9 8,N(6N(1N(.30 6(0.1 4 34- , ,4.( 9 1 . 6 6 8(3(1- 18 04)2).21).2).7).8).7).0).9) ) ) ) ) ) ) ) ) ).5.7.5.0.9.0.2.1.1.0. ) )9)7 9)8 9) )83.49(6(62 7 0 7 3 6(2(3( (3 6(7 8(2 7(4 2 1 4 3 4 4 5(8(5(6(9(8(,92 322.3 = = 2 5( ( 0- =)0,3= 1 3 65 5 3 4 1 4 1 2 1 2 1 24.1 3 7,N(3N()5 N(1.1N(1 61 81(7 37.1,4,(3.1 142 -(0 1.7 45.( 3(1 2 - 99 83 n oin net oisoiccteitr f ctinsiti e scu o nefliniahoitclemse]lyorcs / lseef sinayrtiitoyrp oecefm n d b o n na / m]]l / ld utatan Ei otetnoity csnsitm[]g g omen oriu h prip / c saoit efkcoitcirssH H mrtotisxwwer sesirti rgt Oy / s citefnioed hef tecm m m nnexm m[e / sr r n r ins ioe[ [ etdln reis e epinanrhin n uciTrtre e2 2 OatoiC khtpS Oewo peirtr iS L U M U A k o xo NsahtsesaOS W T E G O Y Bap CcpaL n aidem) ) )sr%(R un QIoh( sr(n g u n oaiilh dep 4 m m2reasednioltneusa)nofobt%ia(ss)ya snim %tscisu d(ftsca iol n ohrefaatUtgtclicipsCIPne alrah n o ilhothticcm noyry o riswarf )sistotarmen R meio irmPiQ dt bTI(AaPaL)9)3)0)6)9)0- )7)8- - )5) ) ) ) - ) ) ) )4 0 0 0.1 9 4 3.26.68.03.33.6.5.3.5.,3 1 2,= 3 3 5 1 0 6 2 5 97 =,=,=( (1( (1(5 42 42.N(3 0(2 1(9( (( N(( N(1 6N0 9 1 0 0 963 6(1(4.(91 6 3 6 02 71 1 552)2)3)2)7)9)9)3)1) )5)9)7)2)5)1)3) ) ) ) ) ) )4 8 1 8 6 6 6 99.9 8. . . .6.9.9.3.4.8.7.,2,2 = = 3 2 5 2,62 02 21 23 01 9(11 2(1 4 0 303 = 5 =( N (7N )7. 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Only cases where microbiologic results were positive are shown. Pathogen Type Discovery RAPIDS EUCLIDS N=595 Validation Validation N=312 N= 362 Bacterial n (%) 209 (35.1) 77 (24.7) 189 (52.2) Streptococcus pneumoniae 22 (3.7) 8 (2.6) 29 (8.0) Staphylococcus aureus 53 (8.9) 19 (6.1) 33 (9.1) Group A streptococcus 26 (4.4) 2 (0.6) 16 (4.4) Group B streptococcus 1 (0.2) 0 (0) 3 (0.8) Coagulase-negative 2 (0.3) 2 (0.6) 1 (0.3) staphylococci Escherichia coli 40 (6.7) 16 (5.1) 6 (1.7) Pseudomonas aeruginosa 4 (0.7) 12 (3.8) 4 (1.1) Enterococcus species 4 (0.7) 3 (1.0) 3 (0.8) Klebsiella species 8 (1.3) 7 (2.2) 1 (0.3) Neisseria meningitidis 2 (0.3) 0 (0) 75 (20.7) Haemophilus influenza 8 (1.3) 2 (0.6) 2 (0.6) Other Gram-positive 13 (2.2) 9 (2.9) 5 (1.4) pathogens Other Gram-negative 10 (1.7) 10 (3.2) 6 (1.7) pathogens Mycoplasma 22 (3.7) 1 (0.3) 2 (0.6) Bordetella pertussis 0 (0) 0 (0) 1 (0.3) Mycobacteria 1 (0.2) 0 (0) 0 (0) Other 19 (3.2) 22 (7.1) 2 (0.6) Viral n (%) 156 (26.2) 123 (39.4) 39 (10.8) respiratory syncytial virus 49 (8.2) 24 (7.7) 4 (1.1) influenza A 37 (6.2) 22 (7.1) 2 (0.6) influenza B 8 (1.3) 8 (2.6) 0 (0) parainfluenza 1 5 (0.8) 1 (0.3) 0 (0) parainfluenza 2 4 (0.7) 0 (0) 0 (0) parainfluenza 3 10 (1.7) 13 (4.2) 1 (0.3) parainfluenza 4 0 (0) 0 (0) 0(0) human metapneumovirus 14 (2.4) 16 (5.1) 3 (0.8) (HMPV) adenovirus 28 (4.7) 18 (5.8) 6 (1.7) herpes simplex virus (HSV) 2 (0.3) 0 (0) 0 (0) enterovirus 3 (0.5) 1 (0.3) 9 (2.5) parechovirus 1 (0.2) 1 (0.3) 2 (0.6) Severe acute respiratory coronavirus-2 0 (0) 0 (0) 0 (0) Other 20 (3.4) 28 (9.0) 12 (3.3) Fungal n (%) 3 (0.5) 2 (0.6) Candida 3 (0.5) 0 (0) 0 Aspergillus 0 (0) 1 (0.3) 0 Other 0 (0) 1 (0.3) 0Parasitic n (%) 0 (0) 1 (0.3) 0 Blastocystis hominis 0 (0) 1 (0.3) 0 Table 7: Number of differentially expressed (DE) genes and top 10 DE genes for infection types and disease severity in the discovery cohort. Comparison Number of Top 10 DE genes* DE genes#DV versus DB CNP, CHROMR, PNPT1, CCL2, CCL8, HERC6, 886 PPM1K, LY6E-DT, AXL, USP18 With versus without OD at 24- LGR4, HBEGF, CD24, BCL2L15, CEACAM8, 1028 hours MPO, AMOTL1, IL3RA, INHBA, TMEM236 With versus without OD at 24- ZC3H12C, MYB, MYO1B, TNFRSF9, SATB2, 86 hours with DV infections CD38, IGHV4-59, CCSER1, IGKV1D-12, MSC With versus without OD at 24- MRC1, HBEGF, MAST4, CYB561, MPO, hours with DB infections 366 PLEKHA7, AC091117.2, KCNK5, ARHGEF10L, MIR210HG#- Differentially expressed genes with adjusted p-values < 0.05 and absolute LFC > 1 or < -1 *- Top 10 differentially expressed genes with low adjusted p-values DB – Definite Bacterial, DV – Definite Viral, OD – Organ Dysfunction.slea)1 5 - 5 2)7 2 2 - 0 3)3 7 6 - 9 4)3 8 4 - 3 8)-)-)-)-)4 7 0 4 6 2 5 6 7 0 7 3 6 3pte208.7.1 98.7.5 88.8.3 97.6.0 7 8.8 7.2 6 7.3 6.5 8 7.6 6.7 5 8.6 7.1yti2(00.00.00.08.09.08.08.09.n L(0(0(00(00(00(00(00(0oitc lefat4 - 6 6)6 9 7 - 8 0)1 1 2 - 3 9)4 2 7 - 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1- - - -n gi%- - - - -n y 5 7 35 99 30 50sy 5 7 6 5 2 9girse9 v( 8.8.7.9.9.y C 0(0(0(0(0( tire9 3 v( 7.3 7.6 8.9 7.0 8.C 0(0 0 0 0ssocU 9 7 8 4 5reocU 8( ( ( (alsicA 2 -D 9.1 09.0 09.5 9.3 09.vesiA 2 1 0s-D 8.2 9 08.1 3 09.7 2 08.5 08.esS 0 S 0aeDIesDsiP)ae IP)d A R%) ) ) )sid A R) ) ) )fo(r%6 %6 %0 %7f%(%6 %6 %0 %ece 2 b(3 2 1 59ore 2 3 2 71 59 6(7(9(3 5ecb(6( ( (5n m51 12 1 1 n m 7 9 3a u 1 0 a u 51 12 11 01mrNmNorforferP p:u1orsrsr s ep aaesrelP u p:orsrsr saesrelp1grlaey ae2 1 graaey aeeegeby y0y maeegeyy0y maa A1 5 10s<–1– 1 ll lba A1 5 1– –0s1 llT 5 > AT< 1 5 > ATable 13 – Fold change in gene expression in non-infectious disease vs bacterial sepsis samples Marker Fold change BATF 0.53 CLC 0.85 HLX 1.09 ICAM1 0.71 NCF1B 0.91 NOD2 0.57 PTGES3 0.87 S100A11 0.58 USP18 1.49 ZBED1 1.32 *a fold change > 1 indicates increased expression in non-infectious disease patients Table 14 – Fold change in gene expression in non-infectious disease vs viral sepsis samples Marker Fold change BATF 0.67 CLC 3.23 HLX 1.27 ICAM1 1.20 NCF1B 0.98 NOD2 0.67 PTGES3 0.73 S100A11 0.76 USP18 0.12 ZBED1 1.20 *a fold change > 1 indicates increased expression in non-infectious disease patientsTable 15 – Fold change in gene expression in definite viral sepsis vs definite bacterial sepsis samples Marker Fold change Viral Sepsis Bacterial Sepsis USP18 12.03 Increased expression Decreased expression NCF1B 0.92 Decreased expression Increased expression BATF 0.76 Decreased expression Increased expression CLC 0.29 Decreased expression Increased expression S100A11 0.76 Decreased expression Increased expression ZBED1 1.14 Increased expression Decreased expression PTGES3 1.15 Increased expression Decreased expression HLX 0.86 Decreased expression Increased expression NOD2 0.92 Decreased expression Increased expression ICAM1 0.62 Decreased expression Increased expression *a fold change > 1 indicates increased expression in viral sepsis patients Table 16 – Fold change in gene expression in organ dysfunction (OD) at 24hrs vs no organ dysfunction samples Marker Fold change OD (-ve prognosis) No OD (+ve prognosis) AATBC 0.54 Decreased Increased MAFG 1.94 Increased Decreased VAV1 1.01 Increased Decreased MS4A7 0.73 Decreased Increased IGHA1 0.60 Decreased Increased ATP6V0A1 1.28 Increased Decreased RN7SL3 1.56 Increased Decreased MPP7 1.07 Increased Decreased DSC2 1.26 Increased Decreased PHACTR2 1.08 Increased Decreased *a fold change > 1 indicates increased expression in sepsis patients with negative prognosisReferences 1. Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 2016; 315(8): 801-10. 2. Rudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet 2020; 395(10219): 200-11. 3. Carlton EF, Barbaro RP, Iwashyna TJ, Prescott HC. Cost of Pediatric Severe Sepsis Hospitalizations. JAMA Pediatr 2019; 173(10): 986-7. 4. Maddux AB, Zimmerman JJ, Banks RK, et al. Health Resource Use in Survivors of Pediatric Septic Shock in the United States. Pediatr Crit Care Med 2022; 23(6): e277-e88. 5. Schlapbach LJ, Weiss SL, Wolf J. Reducing Collateral Damage From Mandates for Time to Antibiotics in Pediatric Sepsis-Primum Non Nocere. JAMA Pediatr 2019; 173(5): 409-10. 6. Angus DC. Opening the Debate on the New Sepsis Definition Defining Sepsis: A Case of Bounded Rationality and Fuzzy Thinking? 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Critical Care 2012; 16(5).29. Sweeney TE, Wong HR, Khatri P. Robust classification of bacterial and viral infections via integrated host gene expression diagnostics. Sci Transl Med 2016; 8(346): 346ra91. 30. Sampson DL, Fox BA, Yager TD, et al. A Four-Biomarker Blood Signature Discriminates Systemic Inflammation Due to Viral Infection Versus Other Etiologies. Sci Rep 2017; 7(1): 2914. 31. Li Y, Li Y, Bai Z, Pan J, Wang J, Fang F. Identification of potential transcriptomic markers in developing pediatric sepsis: a weighted gene co-expression network analysis and a case-control validation study. Journal of translational medicine 2017; 15(1): 254. 32. Li HK, Kaforou M, Rodriguez-Manzano J, et al. Discovery and validation of a three-gene signature to distinguish COVID-19 and other viral infections in emergency infectious disease presentations: a case-control and observational cohort study. Lancet Microbe 2021; 2(11): e594- e603. 33. Lukaszewski RA, Jones HE, Gersuk VH, et al. Presymptomatic diagnosis of postoperative infection and sepsis using gene expression signatures. Intensive Care Med 2022; 48(9): 1133-43. 34. Pena OM, Hancock DG, Lyle NH, et al. An Endotoxin Tolerance Signature Predicts Sepsis and Organ Dysfunction at Initial Clinical Presentation. EBioMedicine 2014; 1(1): 64-71. 35. Irwin AD, Grant A, Williams R, et al. Predicting Risk of Serious Bacterial Infections in Febrile Children in the Emergency Department. Pediatrics 2017; 140(2): e20162853. 36. Baghela A, Pena OM, Lee AH, et al. Predicting sepsis severity at first clinical presentation: The role of endotypes and mechanistic signatures. EBioMedicine 2022; 75: 103776. 37. Team RC. R: A Language and Environment for Statistical Computing 2022.A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011; 12: 77. 39. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44(3): 837-45. 40. Tsalik EL, Henao R, Montgomery JL, et al. Discriminating Bacterial and Viral Infection Using a Rapid Host Gene Expression Test. Crit Care Med 2021; 49(10): 1651-63. 41. Sweeney TE, Azad TD, Donato M, et al. Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters. Crit Care Med 2018; 46(6): 915-25. 42. Sweeney TE, Perumal TM, Henao R, et al. A community approach to mortality prediction in sepsis via gene expression analysis. Nat Commun 2018; 9(1): 694. 43. Wong HR, Cvijanovich NZ, Anas N, et al. Developing a clinically feasible personalized medicine approach to pediatric septic shock. Am J Respir Crit Care Med 2015; 191(3): 309-15.44. Maslove DM, Tang B, Shankar-Hari M, et al. Redefining critical illness. Nature Medicine 2022; 28(6): 1141-8. 45. Komorowski M, Green A, Tatham KC, Seymour C, Antcliffe D. Sepsis biomarkers and diagnostic tools with a focus on machine learning. eBioMedicine 2022; 86. 46. Seymour CW, Kennedy JN, Wang S, et al. Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis. JAMA 2019; 321(20): 2003-17. 47. Sanchez-Pinto LN, Bennet TD, Stroup EK, et al. Derivation, Validation, and Clinical Relevance of a Pediatric Sepsis Phenotype With Persistent Hypoxemia, Encephalopathy, and Shock. Pediatr Crit Care Med 2023; 24(10): 795-806. 48. Weiss SL, Peters MJ, Alhazzani W, et al. Surviving sepsis campaign international guidelines for the management of septic shock and sepsis-associated organ dysfunction in children. Intensive Care Med 2020; 46(Suppl 1): 10-67.Itemized Listing of Embodiments 1. A method of diagnosing a subject with sepsis, said method including the step of measuring an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, and wherein the expression level of the two or more biomarkers are diagnostic or indicative of the subject having sepsis. 2. The method of Embodiment 1, wherein: (a) an increased level of one or more of NCF1B, BATF, S100A11, NOD2 and PTGES3 and / or a decreased level of one or more of ZBED1 and HLX; (b) an increased level of one or more of USP18, NCF1B, BATF, S100A11, NOD2 and PTGES3 and / or a decreased level of one or more of CLC, ZBED1, HLX and ICAM1; (c) an increased level of one or more of NCF1B, BATF, CLC, S100A11, NOD2, PTGES3 and ICAM1 and / or a decreased level of one or more of USP18, ZBED1 and HLX; (d) an increased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1 and / or a decreased level of one or more of USP18, ZBED1 and PTGES3; (e) an increased level of one or more of USP18, ZBED1 and PTGES3 and / or a decreased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1; is diagnostic or indicative of the subject having sepsis. 3. The method of Embodiment 1 or Embodiment 2, further including the step of determining an infection type in the subject based on the expression level of the two or more biomarkers. 4. The method of Embodiment 3, wherein an increased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1 and / or a decreased level of one or more of USP18, ZBED1 and PTGES3, is diagnostic or indicative of the subject having a bacterial infection. 5. The method of Embodiment 3 or Embodiment 4, wherein an increased level of one or more of USP18, ZBED1 and PTGES3 and / or a decreased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, is diagnostic or indicative of the subject having a viral infection. 6. A method of determining an infection type in a subject with sepsis, said method including the step of measuring an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, and wherein the expression level of the two or more biomarkers are indicative of the infection type in the subject. 7. The method of Embodiment 6, wherein determining the infection type in the subject with sepsis comprises distinguishing between a bacterial infection and a viral infection.8. The method of Embodiment 7, wherein an increased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1 and / or a decreased level of one or more of USP18, ZBED1 and PTGES3, is diagnostic or indicative of the subject having the bacterial infection. 9. The method of Embodiment 7 or Embodiment 8, wherein an increased level of one or more of USP18, ZBED1 and PTGES3 and / or a decreased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, is diagnostic or indicative of the subject having the viral infection. 10. A method of predicting the responsiveness of a subject with sepsis to a treatment, said method including the step of determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, wherein the expression level of the two or more biomarkers indicates or correlates with increased or decreased responsiveness of the subject’s sepsis to the treatment. 11. The method of any one of the preceding embodiments, further including the step of determining an expression level of two or more further biomarkers in the biological sample from the subject, wherein the two or more further biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, to determine a prognosis for the subject’s sepsis. 12. A method for determining a prognosis for a subject with sepsis, said method including the step of: determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, to thereby determine the prognosis of sepsis in the subject. 13. The method of Embodiment 11 or Embodiment 12, wherein the prognosis includes a likelihood or an estimated risk of the subject developing an organ dysfunction. 14. The method of Embodiment 13, wherein the organ dysfunction comprises or is associated with one or more of cardiac dysfunction, cardiovascular dysfunction, respiratory dysfunction, neurologic dysfunction, renal dysfunction, hepatic dysfunction and haematologic dysfunction. 15. The method of any one of Embodiments 11 to 14, wherein the prognosis is used, at least in part, to develop a treatment strategy for the subject. 16. The method of any one of Embodiments 1 to 9 and 11 to 14, further including the step of administering a treatment for sepsis to the subject. 17. The method of Embodiment 10 or Embodiment 16, wherein the treatment is or comprises an antibiotic agent or an antiviral agent.18. A method of treating sepsis in a subject, said method including the step of administering a therapeutically effective amount of a treatment for sepsis to the subject in which an expression level of two or more biomarkers has been determined in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, and wherein the expression level of the two or more biomarkers indicates or correlates with the sepsis being at least partly responsive to the treatment. 19. A method of treating sepsis in a subject, said method including the step of determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, and based on the determination made, initiating, continuing, modifying or discontinuing a treatment of sepsis in the subject. 20. The method of Embodiment 18 or Embodiment 19, wherein the treatment is or comprises an antibiotic agent or an antiviral agent. 21. The method of any one of the preceding embodiments, wherein the subject is a paediatric subject. 22. The method of any one of the preceding embodiments, wherein the biological sample is one or more of a blood sample, a serum sample and a plasma sample. 23. A kit for: (a) diagnosing a subject with sepsis; (b) determining an infection type in a subject with sepsis; and / or (c) predicting the responsiveness of a subject with sepsis to a treatment; the kit comprising one or more reagents for determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1. 24. The kit of Embodiment 23, further including one or more further reagents for determining an expression level of two or more further biomarkers in the biological sample from the subject, wherein the two or more further biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2. 25. A kit for determining a prognosis for a subject with sepsis, the kit comprising one or more reagents for determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2.

Claims

CLAIMS:

1. A method of diagnosing a subject with sepsis, said method including the step of measuring an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and wherein the expression level of the two or more biomarkers are diagnostic or indicative of the subject having sepsis.

2. The method of Claim 1, wherein: (a) an increased level of one or more of NCF1B, BATF, S100A11, NOD2 and PTGES3 and / or a decreased level of one or more of ZBED1 and HLX; (b) an increased level of one or more of USP18, NCF1B, BATF, S100A11, NOD2 and PTGES3 and / or a decreased level of one or more of CLC, ZBED1, HLX and ICAM1; (c) an increased level of one or more of NCF1B, BATF, CLC, S100A11, NOD2, PTGES3 and ICAM1 and / or a decreased level of one or more of USP18, ZBED1 and HLX; (d) an increased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1 and / or a decreased level of one or more of USP18, ZBED1 and PTGES3; (e) an increased level of one or more of USP18, ZBED1 and PTGES3 and / or a decreased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1; is diagnostic or indicative of the subject having sepsis.

3. The method of Claim 1 or Claim 2, further including the step of determining an infection type in the subject based on the expression level of the two or more biomarkers.

4. The method of Claim 3, wherein an increased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1 and / or a decreased level of one or more of USP18, ZBED1 and PTGES3, is diagnostic or indicative of the subject having a bacterial infection and / or wherein an increased level of one or more of USP18, ZBED1 and PTGES3 and / or a decreased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, is diagnostic or indicative of the subject having a viral infection.

5. A method of determining an infection type in a subject with sepsis, said method including the step of measuring an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variantor derivative thereof, and wherein the expression level of the two or more biomarkers are indicative of the infection type in the subject.

6. The method of Claim 5, wherein determining the infection type in the subject with sepsis comprises distinguishing between a bacterial infection and a viral infection.

7. The method of Claim 6, wherein an increased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1 and / or a decreased level of one or more of USP18, ZBED1 and PTGES3, is diagnostic or indicative of the subject having the bacterial infection and / or wherein an increased level of one or more of USP18, ZBED1 and PTGES3 and / or a decreased level of one or more of NCF1B, BATF, CLC, S100A11, HLX, NOD2 and ICAM1, is diagnostic or indicative of the subject having the viral infection.

8. A method of predicting the responsiveness of a subject with sepsis to a treatment, said method including the step of determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, wherein the expression level of the two or more biomarkers indicates or correlates with increased or decreased responsiveness of the subject’s sepsis to the treatment.

9. The method of any one of the preceding claims, further including the step of determining an expression level of two or more further biomarkers in the biological sample from the subject, wherein the two or more further biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof, to determine a prognosis for the subject’s sepsis.

10. A method for determining a prognosis for a subject with sepsis, said method including the step of: determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof, to thereby determine the prognosis of sepsis in the subject.

11. The method of Claim 9 or Claim 10, wherein the prognosis includes a likelihood or an estimated risk of the subject developing an organ dysfunction.

12. The method of Claim 11, wherein the organ dysfunction comprises or is associated with one or more of cardiac dysfunction, cardiovascular dysfunction, respiratory dysfunction, neurologic dysfunction, renal dysfunction, hepatic dysfunction and haematologic dysfunction.

13. A method of treating sepsis in a subject, said method including the step of administering a therapeutically effective amount of a treatment for sepsis to the subject in which an expression level of two or more biomarkers has been determined in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and wherein the expression level of the two or more biomarkers indicates or correlates with the sepsis being at least partly responsive to the treatment.

14. A method of treating sepsis in a subject, said method including the step of determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof, and based on the determination made, initiating, continuing, modifying or discontinuing a treatment of sepsis in the subject.

15. The method of Claim 13 or Claim 14, wherein the treatment is or comprises an antibiotic agent or an antiviral agent.

16. The method of any one of the preceding claims, wherein the subject is a paediatric subject.

17. The method of any one of the preceding claims, wherein the biological sample is one or more of a blood sample, a serum sample and a plasma sample.

18. A kit for: (a) diagnosing a subject with sepsis; (b) determining an infection type in a subject with sepsis; and / or (c) predicting the responsiveness of a subject with sepsis to a treatment; the kit comprising one or more reagents for determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of USP18, NCF1B, BATF, CLC, S100A11, ZBED1, PTGES3, HLX, NOD2 and ICAM1, or a fragment, variant or derivative thereof.

19. The kit of Claim 18, further including one or more further reagents for determining an expression level of two or more further biomarkers in the biological sample from the subject, wherein the two or more further biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof.

20. A kit for determining a prognosis for a subject with sepsis, the kit comprising one or more reagents for determining an expression level of two or more biomarkers in a biological sample from the subject, wherein the two or more biomarkers are selected from the group consisting of AATBC, MAFG, VAV1, MS4A7, IGHA1, ATP6V0A1, RN7SL3, MPP7, DSC2 and PHACTR2, or a fragment, variant or derivative thereof.

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