WO2020096796A1 - Method for predicting severe dengue - Google Patents

Abstract

This disclosure provides a gene expression-based method for diagnosing or prognosing a patient with severe dengue. A kit for performing the method is also provided.

Description

METHOD FOR PREDICTING SEVERE DENGUE

CROSS-REFERENCING

This application claims the benefit of U.S. provisional application serial no.

62/756,482, filed on November 6, 2018, which application is incorporated by reference herein.

BACKGROUND

There is a need to identify biomarkers predictive of severe dengue. About 400 million individuals annually are infected with any of the dengue virus (DENV) serotypes. While the majority of symptomatic individuals present with acute dengue fever, a fraction (—5-20%) of these patients progresses to severe dengue manifested by bleeding, plasma leakage, shock, organ failure, and sometimes death. One of the risk factors for severe dengue is secondary infection with a heterologous DENV serotype causing antibody-dependent enhancement (ADE), with variable contribution of aberrant activation of cross-reactive T- cells. Early admission to an inpatient facility and administration of supportive care reduce severe dengue in patients with severe dengue. However, there are no usable prognostics to accurately predict which patients will progress to severe dengue. None of the existing gene sets have yet been shown to be generalizable.

The methods and kits disclosed herein address the above limitations and fulfill other needs.

SUMMARY

Provided herein, among other things, is a method for predicting whether a patient has a high risk of developing severe dengue. In some embodiments, the method of analyzing a sample may include: obtaining a biological sample from a patient; and detecting the levels of expression of two or more of DEFA4, GYG1, TOR3A, PTPRM, SPON2, GRAP2,

CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1, TRERF1, GBP2,

SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5 biomarkers in the biological sample. In some embodiments, the method may comprise analyzing three or more, four or more, five or more, or six or more of the biomarkers. In some embodiments, the method may comprise analyzing the expression of DEFA4, CACNA2D2, SPON2, CACNA2D3, CHD3, GRAP2, AK5, and PTPRM; GYG1, CX3CR1, TRERF1, GBP2, TMEM63C, SERINC5, and SOX13; or TOR3A, NCR3, ABI3, C3orfl8, and ENPP5. In some embodiments, the expression of up to 30, up to 40, up to 50, up to 60, up to 70, up to 80, up to 90 or up to 100 biomarkers or more may be measuring in this assay, including at least 2 (at least 3, at least 4 or at least 5, etc.) of the 20 biomarkers listed above. Based on transcriptomic data, a subject that has a strong likelihood of progressing to severe dengue may be identified. An at least 8-gene set is strongly associated with the progression to severe dengue and represents a predictive signature, generalizable across ages, host genetic factors, vims strains and sample sources. In some embodiments, a 20-gene set that is strongly associated with the progression to severe dengue is provided.

In certain embodiments, a panel of biomarkers is used for prognosis of severe dengue. Biomarker panels of any size can be used in the practice of the methods and/or kits described herein. Biomarker panels for prognosis of severe dengue typically includes at least 2 biomarkers and up to 30 biomarkers, including any number of biomarkers in between, such as 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 biomarkers. In certain embodiments, the methods and/or kits described herein includes a biomarker panel with at least 2, at least 3, at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 11 or more biomarkers.

Although smaller biomarker panels are usually more economical, larger biomarker panels (i.e., greater than 30 biomarkers) may have the advantage of providing more detailed information and can also be used in some cases.

In some embodiments, the two or more comprise DEFA4, CACNA2D2, SPON2,

CACNA2D3, CHD3, GRAP2, AK5, and PTPRM. In some embodiments, the two or more comprise GYG1, CX3CR1, TRERF1, GBP2, TMEM63C, SERINC5, and SOX13. In some embodiments, the two or more comprise TOR3A, NCR3, ABI3, C3orfl8, and ENPP5 biomarkers in the biological sample. In such embodiments, the method may further include generating a report indicating whether the patient has a high risk of severe dengue, wherein the report is based on the gene expression data obtained by detecting the levels of expression of two or more of DEFA4, GYG1, TOR3A, PTPRM, SPON2, GRAP2, CACNA2D2,

CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1, TRERF1, GBP2, SERINC5, SOX13,

NCR3, ABI3, C3orfl8, and ENPP5 biomarkers in the biological sample. In such

embodiments, the method may further include forwarding the report to a third party. In other embodiments, the method may include determining whether the patient is at high risk of severe dengue using the data from the detecting the levels of expression of a set of biomarkers in the biological sample. In such other embodiments, the method may further include monitoring the patient for a condition, wherein the condition includes kidney failure, bleeding, plasma leakage, shock, and organ failure. In some other embodiments, the method may further include admitting to a hospital only if the patient is at a high risk of severe dengue.

In some embodiments, a method of diagnosing or prognosing severe dengue in a patient is provided, said method including: obtaining a biological sample from a human patient; detecting levels of expression of two or more of DEFA4, GYG1, TOR3A, PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1,

TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5 biomarkers in the biological sample; diagnosing the patient with severe dengue when increased levels of expression of DEFA4, GYG1 and TOR3A biomarkers, and decreased levels of expression of PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1, TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5 biomarkers, compared to the reference value ranges for the biomarkers for a control subject are detected. In some embodiments, the methods described herein include administering an effective amount of a pharmaceutical drug to the patient. In some embodiments, the methods described herein include determining a dengue score for each biological sample by subtracting the mean expression of the levels of expression of PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1, TRERF1, GBP2,

SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5 biomarkers from the mean expression of the levels of expression of DEFA4, GYG1 and TOR3A biomarkers.

In some embodiments, provided herein is a method for treating a patient having a high risk of severe dengue, comprising: receiving a report indicating whether the patient has a high risk of severe dengue, wherein the report is based on the gene expression data obtained by measuring the amount of RNA transcripts encoded by two or more of DEFA4,

GYG1, TOR3A, PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5,

CHD3, CX3CR1, TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5 in a biological sample of RNA obtained from the patient, wherein increased DEFA4, GYG1 and/or TOR3A and/or decreased PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3,

C3orfl8, and/or ENPP5 indicates that the patient has a high risk of severe dengue; and treating the patient based on whether the patient is indicated as having a high risk of severe dengue. In such embodiments, the treating includes admitting to a hospital only if the patient is at a high risk of severe dengue

Kits for performing the methods described herein are also provided.

BRIEF DESCRIPTION OF THE FIGURES

The methods and/or kits described herein are best understood from the following detailed description when read in conjunction with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawings are not to-scale. On the contrary, the dimensions of the various features are arbitrarily expanded or reduced for clarity. Included in the drawings are the following figures:

FIG. 1, Panels A-D depicts a 20-gene set predictive of severe dengue.

FIG. 2 depicts publicly available datasets used for the discovery and validation of the

20-gene set.

FIG. 3, Panels A-T depicts forest plots of the over-expressed and under-expressed genes derived in the forward searches.

FIG. 4 depicts the over-expressed and under-expressed genes identified in the discovery cohort via the multi-cohort analysis.

FIG. 5, Panels A-B depicts violin plots showing the performance of the 20-gene set to separate Dengue Hemorrhagic Fever/Dengue Shock Syndrome (DHF/DSS) from Dengue Fever (DF) in the 7 datasets of the discovery cohort (FIG. 5, Panel A) and 3 datasets of the validation cohort (FIG. 5, Panel B).

FIG. 6, Panels A-H depicts in silico and prospective validation of the 20-gene set

FIG. 7, Panels A-B demonstrates that routine laboratory parameters are ineffective in predicting development of severe dengue and at most part do not correlate with the dengue severity score. DETAIFED DESCRIPTION

The practice of the methods and/or kits described herein will employ, unless otherwise indicated, conventional methods of pharmacology, chemistry, biochemistry, recombinant DNA techniques and immunology, within the skill of the art. Such techniques are explained fully in the literature. See, e.g., Handbook of Experimental Immunology, Vols. I-IV (D.M. Weir and C.C. Blackwell eds., Blackwell Scientific Publications); A.L.

Lehninger, Biochemistry (Worth Publishers, Inc., current addition); Sambrook, et ak, Molecular Cloning: A Laboratory Manual (2nd Edition, 1989); Methods In Enzymology (S. Colowick and N. Kaplan eds., Academic Press, Inc.).

All publications, patents and patent applications cited herein, whether supra or infra, are hereby incorporated by reference in their entireties.

Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limits of that range is also specifically disclosed. Each smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in that stated range is encompassed within the methods and/or kits described herein. The upper and lower limits of these smaller ranges may independently be included or excluded in the range, and each range where either, neither or both limits are included in the smaller ranges is also encompassed within the methods and/or kits described herein, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the methods and/or kits described herein.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which these methods and/or kits described herein belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the methods and/or kits described herein, some potential and preferred methods and materials are now described. All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the

publications are cited. It is understood that the present disclosure supercedes any disclosure of an incorporated publication to the extent there is a contradiction.

As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the methods and/or kits described herein. Any recited method can be carried out in the order of events recited or in any other order which is logically possible.

It must be noted that, as used in this specification and the appended claims, the singular forms“a”,“an” and“the” include plural referents unless the content clearly dictates otherwise. Thus, for example, reference to“an agonist” includes a mixture of two or more such agonists, and the like.

The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the methods and/or kits described herein are not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed.

I. DEFINITIONS

In describing the methods and/or kits described herein, the following terms will be employed, and are intended to be defined as indicated below.

It must be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the content clearly dictates otherwise. Thus, for example, reference to "a biomarker" includes a mixture of two or more biomarkers, and the like.

As used herein, the term "dengue virus" refers to members of the Flaviviridae family of enveloped viruses with a single-stranded positive-sense RNA genome (see, e.g., Frontiers in Dengue Virus Research, Hanley and Weaver (editors), Caister Academic Press, 2010). The term dengue vims may include any serotype of dengue vims, such as serotypes 1-4, which is capable of causing disease in an animal or human subject. In particular, the term encompasses any subtype of dengue virus that causes disease in humans, including strains DEN 1 Hawaii 1944, Den 2 New Guinea C strain, DEN 3 strain H87, and DEN 4 strain H241. A large number of dengue isolates have been partially or completely sequenced. See, e.g., the Broad Institute Dengue Vims Portal (website at

broadinstitute.org/annotation/viral/Dengue/); the Dengue Virus Database (website at denguedb.org); the Virus Pathogen Resource (website at

viprbrc.org/brc/home.do?decorator=flavi_dengue) and the GenBank database, which contain complete sequences for dengue vimses, including serotypes 1-4.

As used herein, the term“severe dengue” refers to a disease or condition classified according to WHO dengue classification methods, including but not limited to the 2009 WHO criteria (Alexander et al„ 2011; WHO, 2009) and 1997 WHO criteria (WHO, 1997).

Classic warning signs of severe dengue include a decrease in temperature (below 38°C); severe abdominal pain; rapid breathing; persistent vomiting; blood in vomit; fluid accumulation in the body; mucosal (gums and nose) bleeding; liver enlargement; rapid decrease in platelet count; lethargy; and restlessness. Classic symptoms of severe dengue include severe plasma leakage leading to shock (DSS) or fluid accumulation with respiratory distress; severe bleeding as evaluated by a clinician; severe organ involvement such as a liver having AST or ALT > 1000, a central nervous system with impaired consciousness or failure of heart and other organs; kidney failure; shock for 5-7 days; severe skin bleeding with spots of blood on the skin (petechiae) and large patches of blood under the skin (ecchymoses); black stools; blood in urine (hematuria); and respiratory distress.

The term "about," particularly in reference to a given quantity, is meant to encompass deviations of plus or minus five percent.

A "biomarker" in the context of the methods and/or kits described herein refers to a biological compound, such as a polynucleotide which is differentially expressed in a sample taken from a dengue patient as compared to a comparable sample taken from a patient without dengue or severe dengue. The biomarker can be a nucleic acid, a fragment of a nucleic acid, a polynucleotide, or an oligonucleotide that can be detected and/or quantified. Biomarkers include polynucleotides including nucleotide sequences from genes or RNA transcripts of genes, including but not limited to, DEFA4, GYG1, TOR3A, PTPRM,

SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1,

TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5.

The terms "polypeptide" and "protein" refer to a polymer of amino acid residues and are not limited to a minimum length. Thus, peptides, oligopeptides, dimers, multimers, and the like, are included within the definition. Both full-length proteins and fragments thereof are encompassed by the definition. The terms also include postexpression modifications of the polypeptide, for example, glycosylation, acetylation, phosphorylation, hydroxylation, oxidation, and the like.

The terms "polynucleotide," "oligonucleotide," "nucleic acid" and "nucleic acid molecule" are used herein to include a polymeric form of nucleotides of any length, either ribonucleotides or deoxyribonucleotides. This term refers only to the primary structure of the molecule. Thus, the term includes triple-, double- and single-stranded DNA, as well as triple-, double- and single- stranded RNA. It also includes modifications, such as by methylation and/or by capping, and unmodified forms of the polynucleotide. More particularly, the terms "polynucleotide," "oligonucleotide," "nucleic acid" and "nucleic acid molecule" include polydeoxyribonucleotides (containing 2-deoxy-D-ribose),

polyribonucleotides (containing D-ribose), and any other type of polynucleotide which is an N- or C-glycoside of a purine or pyrimidine base. There is no intended distinction in length between the terms "polynucleotide," "oligonucleotide," "nucleic acid" and "nucleic acid molecule," and these terms are used interchangeably.

The phrase "differentially expressed" refers to differences in the quantity and/or the frequency of a biomarker present in a sample taken from patients having a high risk of severe dengue as compared to a control subject at low risk of severe dengue. For example, a biomarker can be a polynucleotide which is present at an elevated level or at a decreased level in samples of patients with severe dengue compared to samples of control subjects. Alternatively, a biomarker can be a polynucleotide which is detected at a higher frequency or at a lower frequency in samples of patients at high risk of severe dengue compared to samples of control subjects. A biomarker can be differentially present in terms of quantity, frequency or both.

A polynucleotide is differentially expressed between two samples if the amount of the polynucleotide in one sample is statistically significantly different from the amount of the polynucleotide in the other sample. For example, a polynucleotide is differentially expressed in two samples if it is present at least about 120%, at least about 130%, at least about 150%, at least about 180%, at least about 200%, at least about 300%, at least about 500%, at least about 700%, at least about 900%, or at least about 1000% greater than it is present in the other sample, or if it is detectable in one sample and not detectable in the other.

Alternatively or additionally, a polynucleotide is differentially expressed in two sets of samples if the frequency of detecting the polynucleotide in samples of patients at high risk of severe dengue is statistically significantly higher or lower than in the control samples.

For example, a polynucleotide is differentially expressed in two sets of samples if it is detected at least about 120%, at least about 130%, at least about 150%, at least about 180%, at least about 200%, at least about 300%, at least about 500%, at least about 700%, at least about 900%, or at least about 1000% more frequently or less frequently observed in one set of samples than the other set of samples.

A "similarity value" is a number that represents the degree of similarity between two things being compared. For example, a similarity value may be a number that indicates the overall similarity between a patient's expression profile using specific phenotype-related biomarkers and reference value ranges for the biomarkers in one or more control samples or a reference expression profile. The similarity value may be expressed as a similarity metric, such as a correlation coefficient, or may simply be expressed as the expression level difference, or the aggregate of the expression level differences, between levels of biomarkers in a patient sample and a control sample or reference expression profile.

The terms "subject," "individual," and "patient," are used interchangeably herein and refer to any mammalian subject for whom diagnosis, prognosis, treatment, or therapy is desired, particularly humans. Other subjects may include cattle, dogs, cats, guinea pigs, rabbits, rats, mice, horses, and so on. In some cases, the methods and/or kits described herein find use in experimental animals, in veterinary application, and in the development of animal models for disease, including, but not limited to, rodents including mice, rats, and hamsters; and primates.

As used herein, a“hospital” refers to an institution, observation ward, clinic, hospice, emergency room, or area providing medical and/or surgical treatment and nursing care for sick or injured individuals.

As used herein, a "biological sample" refers to a sample of tissue, cells, or fluid isolated from a subject, including but not limited to, for example, blood, buffy coat, plasma, serum, blood cells (e.g., peripheral blood mononucleated cells (PBMCS), band cells, neutrophils, metamyelocytes, monocytes, or T cells), fecal matter, urine, bone marrow, bile, spinal fluid, lymph fluid, samples of the skin, external secretions of the skin, respiratory, intestinal, and genitourinary tracts, tears, saliva, milk, organs, biopsies and also samples of in vitro cell culture constituents, including, but not limited to, conditioned media resulting from the growth of cells and tissues in culture medium, e.g., recombinant cells, and cell components.

A "test amount" of a biomarker refers to an amount of a biomarker present in a sample being tested. A test amount can be either an absolute amount (e.g., mg/ml) or a relative amount (e.g., relative intensity of signals).

A "control amount" of a biomarker can be any amount or a range of amount which is to be compared against a test amount of a biomarker. For example, a control amount of a biomarker can be the amount of a biomarker in a person without a life-threatening condition (e.g., person without dengue or severe dengue) or healthy person. A control amount can be either in absolute amount (e.g., mg/ml) or a relative amount (e.g., relative intensity of signals).

The term "antibody" encompasses polyclonal and monoclonal antibody preparations, as well as preparations including hybrid antibodies, altered antibodies, chimeric antibodies and, humanized antibodies, as well as: hybrid (chimeric) antibody molecules (see, for example, Winter et al. (1991) Nature 349:293-299; and U.S. Pat. No. 4,816,567); F(ab')₂ and F(ab) fragments; F_v molecules (noncovalent heterodimers, see, for example, Inbar et al. (1972) Proc Natl Acad Sci USA 69:2659-2662; and Ehrlich et al. (1980) Biochem 19:4091-4096); single-chain Fv molecules (sFv) (see, e.g., Huston et al. (1988) Proc Natl Acad Sci USA 85:5879-5883); dimeric and trimeric antibody fragment constructs; minibodies (see, e.g., Pack et al. (1992) Biochem 31:1579-1584; Cumber et al. (1992) J Immunology 149B:120- 126); humanized antibody molecules (see, e.g., Riechmann et al. (1988) Nature 332:323- 327; Verhoeyan et al. (1988) Science 239:1534-1536; and U.K. Patent Publication No. GB 2,276,169, published 21 Sep. 1994); and, any functional fragments obtained from such molecules, wherein such fragments retain specific-binding properties of the parent antibody molecule.

"Detectable moieties" or "detectable labels" contemplated for use in the methods and/or kits described herein include, but are not limited to, radioisotopes, fluorescent dyes such as fluorescein, phycoerythrin, Cy-3, Cy-5, allophycoyanin, DAPI, Texas Red, rhodamine, Oregon green, Lucifer yellow, and the like, green fluorescent protein (GFP), red fluorescent protein (DsRed), Cyan Fluorescent Protein (CFP), Yellow Fluorescent Protein (YFP), Cerianthus Orange Fluorescent Protein (cOFP), alkaline phosphatase (AP), beta- lactamase, chloramphenicol acetyltransferase (CAT), adenosine deaminase (ADA), aminoglycoside phosphotransferase (neo^r, G4l8^r) dihydrofolate reductase (DHFR), hygromycin-B-phosphotransferase (HPH), thymidine kinase (TK), lacZ (encoding b- galactosidase), and xanthine guanine phosphoribosyltransferase (XGPRT), Beta- Glucuronidase (gus), Placental Alkaline Phosphatase (PLAP), Secreted Embryonic Alkaline Phosphatase (SEAP), or Firefly or Bacterial Luciferase (LUC). Enzyme tags are used with their cognate substrate. The terms also include color-coded microspheres of known fluorescent light intensities (see e.g., microspheres with xMAP technology produced by Luminex (Austin, TX); microspheres containing quantum dot nanocrystals, for example, containing different ratios and combinations of quantum dot colors (e.g., Qdot nanocrystals produced by Life Technologies (Carlsbad, CA); glass coated metal nanoparticles (see e.g., SERS nanotags produced by Nanoplex Technologies, Inc. (Mountain View, CA); barcode materials (see e.g., sub-micron sized striped metallic rods such as Nanobarcodes produced by Nanoplex Technologies, Inc.), encoded microparticles with colored bar codes (see e.g., CellCard produced by Vitra Bioscience, vitrabio.com), and glass microparticles with digital holographic code images (see e.g., CyVera microbeads produced by Illumina (San Diego, CA). As with many of the standard procedures associated with the practice of the methods and/or kits described herein, skilled artisans will be aware of additional labels that can be used.

"Diagnosis" as used herein generally includes determination as to whether a subject is likely affected by a given disease, disorder or dysfunction. The skilled artisan often makes a diagnosis on the basis of one or more diagnostic indicators, i.e., a biomarker, the presence, absence, or amount of which is indicative of the presence or absence of the disease, disorder or dysfunction.

"Prognosis" as used herein generally refers to a prediction of the probable course and outcome of a clinical condition or disease. A prognosis of a patient is usually made by evaluating factors or symptoms of a disease that are indicative of a favorable or unfavorable course or outcome of the disease (e.g., dengue fever, dengue hemorrhagic fever and dengue shock syndrome). It is understood that the term "prognosis" does not necessarily refer to the ability to predict the course or outcome of a condition with 100% accuracy. Instead, the skilled artisan will understand that the term "prognosis" refers to an increased probability that a certain course or outcome will occur; that is, that a course or outcome is more likely to occur in a patient exhibiting a given condition, when compared to those individuals not exhibiting the condition.

II. Modes of Carrying Out the Methods and/or Kits Described Herein

Before describing the methods and/or kits described herein in detail, it is to be understood that the methods and/or kits described herein are not limited to particular formulations or process parameters as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting. Although a number of methods and materials similar or equivalent to those described herein can be used in the practice of the methods and/or kits described herein, the preferred materials and methods are described herein.

The methods and/or kits described herein relate to the use of biomarkers either alone or in combination with clinical parameters for aiding diagnosis, prognosis, and treatment of patients. In particular, the inventors have discovered biomarkers whose expression profiles can be used for prognosis of severe dengue in patients with severe dengue.

In order to further an understanding of the methods and/or kits described herein, a more detailed discussion is provided below regarding the identified biomarkers and methods of using them in diagnosis, prognosis, and treatment of patients.

A. Biomarkers

Biomarkers that can be used in the practice of the methods and/or kits described herein include polynucleotides including nucleotide sequences from genes or RNA transcripts of genes, including but not limited to, DEFA4, GYG1, TOR3A, PTPRM,

SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1,

TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5. In some embodiments, biomarkers that can be used in the practice of the methods and/or kits described herein include polynucleotides including nucleotide sequences from genes or RNA transcripts of genes, including but not limited to, DEFA4, CACNA2D2, SPON2,

CACNA2D3, CHD3, GRAP2, AK5, and PTPRM. In other embodiments, biomarkers that can be used in the practice of the methods and/or kits described herein include

polynucleotides including nucleotide sequences from genes or RNA transcripts of genes, including but not limited to, GYG1, CX3CR1, TRERF1, GBP2, TMEM63C, SERINC5, and SOX13. In some other embodiments, biomarkers that can be used in the practice of the methods and/or kits described herein include polynucleotides including nucleotide sequences from genes or RNA transcripts of genes, including but not limited to, TOR3A, NCR3, ABI3, C3orfl8, and ENPP5. Differential expression of these biomarkers is associated with a high risk of severe dengue and therefore expression profiles of these biomarkers are useful for prognosis of severe dengue in patients.

Accordingly, in one aspect, provided herein is a method of determining severe dengue risk of a subject, including measuring the level of a plurality of biomarkers in a biological sample derived from a subject suspected of having a life-threatening condition, and analyzing the levels of the biomarkers and comparing with respective reference value ranges for the biomarkers, wherein differential expression of one or more biomarkers in the biological sample compared to one or more biomarkers in a control sample indicates that the subject is at high risk of severe dengue.

When analyzing the levels of biomarkers in a biological sample, the reference value ranges can represent the levels of one or more biomarkers found in one or more samples of one or more subjects without an illness (e.g., healthy subject or subject without infection). Alternatively, the reference values can represent the levels of one or more biomarkers found in one or more samples of one or more subjects with a critical illness (e.g., a subject with severe dengue). In certain embodiments, the levels of the biomarkers are compared to time- matched reference values ranges for non-inf ected and infected/dengue subjects.

The biological sample obtained from the subject to be diagnosed is typically whole blood, huffy coat, plasma, serum, or blood cells (e.g., peripheral blood mononucleated cells (PBMCS), band cells, metamyelocytes, neutrophils, monocytes, or T cells), but can be any sample from bodily fluids, tissue or cells that contain the expressed biomarkers. A "control" sample, as used herein, refers to a biological sample, such as a bodily fluid, tissue, or cells that are not diseased. That is, a control sample is obtained from a normal subject (e.g. an individual known to not have a life-threatening condition), a person who does not have severe dengue. A biological sample can be obtained from a subject by conventional techniques. For example, blood can be obtained by venipuncture, and solid tissue samples can be obtained by surgical techniques according to methods well known in the art.

In certain embodiments, a panel of biomarkers is used for prognosis of severe dengue risk. Biomarker panels of any size can be used in the practice of the methods and/or kits described herein. Biomarker panels for prognosis of severe dengue typically include at least 2 biomarkers and up to 30 biomarkers, including any number of biomarkers in between, such as 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 biomarkers. In certain embodiments, the methods and/or kits described herein include a biomarker panel including at least 2, or at least 3, or at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 11 or more biomarkers. Although smaller biomarker panels are usually more economical, larger biomarker panels (i.e., greater than 30 biomarkers) have the advantage of providing more detailed information and can also be used in the practice of the methods and/or kits described herein. In certain embodiments, the methods and/or kits described herein include a panel of biomarkers for prognosis of severe dengue risk including one or more polynucleotides including a nucleotide sequence from a gene or an RNA transcript of a gene selected from the group consisting of DEFA4, CACNA2D2, SPON2, CACNA2D3, CHD3, GRAP2, AK5, and PTPRM. In one embodiment, the panel of biomarkers includes a DEFA4

polynucleotide, a CACNA2D2 polynucleotide, a SPON2 polynucleotide, an CACNA2D3 polynucleotide, an CHD3 polynucleotide, a GRAP2 polynucleotide, a AK5 polynucleotide, and a PTPRM polynucleotide.

In certain embodiments, the methods and/or kits described herein include a panel of biomarkers for prognosis of severe dengue risk including one or more polynucleotides including a nucleotide sequence from a gene or an RNA transcript of a gene selected from the group consisting of GYG1, CX3CR1, TRERF1, GBP2, TMEM63C, SERINC5, and SOX13. In one embodiment, the panel of biomarkers includes a GYG1 polynucleotide, a CX3CR1 polynucleotide, a TRERF1 polynucleotide, an GBP2 polynucleotide, an

TMEM63C polynucleotide, a SERINC5 polynucleotide, and a SOXl3polynucleotide.

In certain embodiments, the methods and/or kits described herein include a panel of biomarkers for prognosis of severe dengue risk including one or more polynucleotides including a nucleotide sequence from a gene or an RNA transcript of a gene selected from the group consisting of TOR3A, NCR3, ABI3, C3orfl8, and ENPP5. In one embodiment, the panel of biomarkers includes a TOR3 A polynucleotide, a NCR3 polynucleotide, a ABI3 polynucleotide, an C3orf 18 polynucleotide, and a ENPP5 polynucleotide.

In certain embodiments, a severe dengue gene score is used for prognosis of severe dengue risk. The severe dengue gene score is calculated by subtracting the geometric mean of the expression levels of all measured biomarkers that are underexpressed compared to control reference values for the biomarkers from the geometric mean of the expression levels of all measured biomarkers that are overexpressed compared to control reference values for the biomarkers, and multiplying the difference by the ratio of the number of biomarkers that are overexpressed to the number of biomarkers that are underexpressed compared to control reference values for the biomarkers. A higher severe dengue gene score for the subject compared to reference value ranges for control subjects indicates that the subject has a high risk of severe dengue.

The methods described herein may be used to identify patients at high risk of severe dengue who should be monitored. For example, patients identified as having a high risk of severe dengue by the methods described herein can be sent immediately to a hospital for treatment, whereas patients identified as having a low risk of severe dengue may be further monitored and/or treated in a regular hospital ward, or potentially discharged. Both patients and clinicians can benefit from better estimates of severe dengue risk, which allows timely discussions of patients’ preferences and their choices regarding life-saving measures. Better molecular phenotyping of patients also makes possible improvements in clinical trials, both in 1) patient selection for drugs and interventions and 2) assessment of observed-to-expected ratios of subject severe dengue.

In certain embodiments, a patient diagnosed with a viral infection is further administered a therapeutically effective dose of an antiviral agent, such as a broad- spectrum antiviral agent, an antiviral vaccine, a neuraminidase inhibitor (e.g., zanamivir (Relenza) and oseltamivir (Tamiflu)), a nucleoside analogue (e.g., acyclovir, zidovudine (AZT), and lamivudine), an antisense antiviral agent (e.g., phosphorothioate antisense antiviral agents (e.g., Fomivirsen (Vitravene) for cytomegalovirus retinitis), morpholino antisense antiviral agents), an inhibitor of viral uncoating (e.g., Amantadine and rimantadine for influenza, Pleconaril for rhino viruses), an inhibitor of viral entry (e.g., Fuzeon for HIV), an inhibitor of viral assembly (e.g., Rifampicin), or an antiviral agent that stimulates the immune system (e.g., interferons). Exemplary antiviral agents include Abacavir, Aciclovir, Acyclovir, Adefovir, Amantadine, Amprenavir, Ampligen, Arbidol, Atazanavir, Atripla (fixed dose drug), Balavir, Cidofovir, Combivir (fixed dose drug), Dolutegravir, Darunavir, Delavirdine, Didanosine, Docosanol, Edoxudine, Efavirenz, Emtricitabine, Enfuvirtide, Entecavir, Ecoliever, Famciclovir, Fixed dose combination (antiretroviral), Fomivirsen, Fos amprenavir, Foscamet, Fosfonet, Fusion inhibitor, Ganciclovir, Ibacitabine, Imunovir, Idoxuridine, Imiquimod, Indinavir, Inosine, Integrase inhibitor, Interferon type III, Interferon type II, Interferon type I, Interferon, Lamivudine, Lopinavir, Loviride, Maraviroc, Moroxydine, Methisazone, Nelfinavir, Nevirapine, Nexavir, Nitazoxanide, Nucleoside analogues, Novir, Oseltamivir (Tamiflu), Peginterferon alfa-2a, Penciclovir, Peramivir, Pleconaril,

Podophyllotoxin, Protease inhibitor, Raltegravir, Reverse transcriptase inhibitor, Ribavirin, Rimantadine, Ritonavir, Pyramidine, Saquinavir, Sofosbuvir, Stavudine, Synergistic enhancer (antiretroviral), Telaprevir, Tenofovir, Tenofovir disoproxil, Tipranavir,

Trifluridine, Trizivir, Tromantadine, Truvada, Valaciclovir (Valtrex), Valganciclovir, Vicriviroc, Vidarabine, Viramidine, Zalcitabine, Zanamivir (Relenza), and Zidovudine. Antibiotics, if used, may include broad spectrum, bactericidal, or bacteriostatic antibiotics. Exemplary antibiotics include aminoglycosides such as Amikacin, Amikin,

Gentamicin, Garamycin, Kanamycin, Kantrex, Neomycin, Neo-Fradin, Netilmicin,

Netromycin, Tobramycin, Nebcin, Paromomycin, Humatin, Streptomycin,

Spectinomycin(Bs), and Trobicin; ansamycins such as Geldanamycin, Herbimycin,

Rifaximin, and Xifaxan; carbacephems such as Loracarbef and Lorabid; carbapenems such as Ertapenem, Invanz, Doripenem, Doribax, Imipenem/Cilastatin, Primaxin, Meropenem, and Merrem; cephalosporins such as Cefadroxil, Duricef, Cefazolin, Ancef, Cefalotin or

Cefalothin, Keflin, Cefalexin, Keflex, Cefaclor, Distaclor, Cefamandole, Mandol, Cefoxitin,

Mefoxin, Cefprozil, Cefzil, Cefuroxime, Ceftin, Zinnat, Cefixime, Cefdinir, Cefditoren,

Cefoperazone, Cefotaxime, Cefpodoxime, Ceftazidime, Ceftibuten, Ceftizoxime,

Ceftriaxone, Cefepime, Maxipime, Ceftaroline fosamil, Teflaro, Ceftobiprole, and Zeftera; glycopeptides such as Teicoplanin, Targocid, Vancomycin, Vancocin, Telavancin, Vibativ,

Dalbavancin, Dalvance, Oritavancin, and Orbactiv; lincosamides such as Clindamycin,

Cleocin, Lincomycin, and Lincocin; lipopeptides such as Daptomycin and Cubicin;

macrolides such as Azithromycin, Zithromax, Sumamed, Xithrone, Clarithromycin, Biaxin,

Dirithromycin, Dynabac, Erythromycin, Erythocin, Erythroped, Roxithromycin,

Troleandomycin, Tao, Telithromycin, Ketek, Spiramycin, and Rovamycine; monobactams such as Aztreonam and Azactam; nitrofurans such as Furazolidone, Furoxone,

Nitrofurantoin, Macrodantin, and Macrobid; oxazolidinones such as Linezolid, Zyvox,

VRSA, Posizolid, Radezolid, and Torezolid; penicillins such as Penicillin V, Veetids (Pen-

Vee-K), Piperacillin, Pipracil, Penicillin G, Pfizerpen, Temocillin, Negaban, Ticarcillin, and

Ticar; penicillin combinations such as Amoxicillin/clavulanate, Augmentin,

Ampicillin/sulbactam, Unasyn, Piperacillin/tazobactam, Zosyn, Ticarcillin/clavulanate, and

Timentin; polypeptides such as Bacitracin, Colistin, Coly-Mycin-S, and Polymyxin B;

quinolones/fluoroquinolones such as Ciprofloxacin, Cipro, Ciproxin, Ciprobay, Enoxacin,

Penetrex, Gatifloxacin, Tequin, Gemifloxacin, Factive, Levofloxacin, Levaquin,

Lomefloxacin, Maxaquin, Moxifloxacin, Avelox, Nalidixic acid, NegGram, Norfloxacin,

Noroxin, Ofloxacin, Floxin, Ocuflox Trovafloxacin, Trovan, Grepafloxacin, Raxar,

Sparfloxacin, Zagam, Temafloxacin, and Omniflox; sulfonamides such as Amoxicillin,

Novamox, Amoxil, Ampicillin, Principen, Azlocillin, Carbenicillin, Geocillin, Cloxacillin,

Tegopen, Dicloxacillin, Dynapen, Flucloxacillin, Floxapen, Mezlocillin, Mezlin, Methicillin,

Staphcillin, Nafcillin, Unipen, Oxacillin, Prostaphlin, Penicillin G, Pentids, Mafenide, Sulfamylon, Sulfacetamide, Sulamyd, Bleph-lO, Sulfadiazine, Micro-Sulfon, Silver sulfadiazine, Silvadene, Sulfadimethoxine Di-Methox, Albon, Sulfamethizole, Thiosulfil Forte, Sulfamethoxazole, Gantanol, Sulfanilimide, Sulfasalazine, Azulfidine, Sulfisoxazole, Gantrisin, Trimethoprim-Sulfamethoxazole (Co-trimoxazole) (TMP-SMX), Bactrim, Septra, Sulfonamidochrysoidine, and Prontosil; tetracyclines such as Demeclocycline, Declomycin, Doxycycline, Vibramycin, Minocycline, Minocin, Oxytetracycline, Terramycin,

Tetracycline and Sumycin, Achromycin V, and Steclin; drugs against mycobacteria such as Clofazimine, Lamprene, Dapsone, Avlosulfon, Capreomycin, Capastat, Cycloserine, Seromycin, Ethambutol, Myambutol, Ethionamide, Trecator, Isoniazid, I.N.H.,

Pyrazinamide, Aldinamide, Rifampicin, Rifadin, Rimactane, Rifabutin, Mycobutin,

Rifapentine, Priftin, and Streptomycin; others antibiotics such as Arsphenamine, Salvarsan, Chloramphenicol, Chloromycetin, Fosfomycin, Monurol, Monuril, Fusidic acid, Fucidin, Metronidazole, Flagyl, Mupirocin, Bactroban, Platensimycin, Quinupristin/Dalfopristin, Synercid, Thiamphenicol, Tigecycline, Tigacyl, Tinidazole, Tindamax Fasigyn,

Trimethoprim, Proloprim, and Trimpex.

In some embodiments, the methods described herein further include administering a treatment to the patient, an antimicrobial therapy to the patient, administering an immune- modulating therapy to the patient, or administering an organ- specific treatment to the patient. In such embodiments, the organ-specific treatment includes either or both of connecting the patient to any one or more of a mechanical ventilator, a pacemaker, a defibrillator, a dialysis or renal replacement therapy machine, an invasive monitor including a pulmonary artery catheter, arterial blood pressure catheter, or central venous pressure catheter, or

administering blood products, vasopressors, or sedatives. In some embodiments, treatment includes admitting to a hospital only if the patient is at a high risk of severe dengue.

In another embodiment, the methods and kits described herein include diagnosing or prognosing severe dengue in a patient, said method including: obtaining a biological sample from a human patient; detecting levels of expression of two or more of DEFA4, GYG1,

TOR3A, PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3,

CX3CR1, TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5 biomarkers in the biological sample; diagnosing the patient with severe dengue when increased levels of expression of DEFA4, GYG1 and TOR3A biomarkers, and decreased levels of expression of PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C,

ENPP5 biomarkers, compared to the reference value ranges for the biomarkers for a control subject are detected. In one embodiment, the methods further include diagnosing the patient with severe dengue when increased levels of expression of DEFA4 biomarker, and decreased levels of expression of CACNA2D2, SPON2, CACNA2D3, CHD3, GRAP2, AK5, and PTPRM biomarkers, compared to the reference value ranges for the biomarkers for a control subject are detected. In one embodiment, the methods further include diagnosing the patient with severe dengue when increased levels of expression of GYG1 biomarker, and decreased levels of expression of CX3CR1, TRERF1, GBP2, TMEM63C, SERINC5, and SOX13 biomarkers, compared to the reference value ranges for the biomarkers for a control subject are detected. In one embodiment, the methods further include diagnosing the patient with severe dengue when increased levels of expression of TOR3A biomarker, and decreased levels of expression of NCR3, ABI3, C3orfl8, and ENPP5 biomarkers, compared to the reference value ranges for the biomarkers for a control subject are detected.

In some embodiments, the method further includes calculating a severe dengue gene score for the patient based on the levels of the biomarkers, wherein a higher severe dengue gene score for the patient compared to a control subject indicates that the patient is at high risk of severe dengue; and administering a treatment to the diagnosed or prognosed patient.

Severe dengue treatment may include, for example, monitoring, administering antimicrobial therapy, supportive care, or an immune-modulating therapy, or a combination thereof. Antimicrobial therapy may include administration of one or more drugs against all pathogens the patient is likely to be infected with (e.g., bacterial and/or fungal and/or viral) with preferably broad-spectrum coverage using combinations of antimicrobial agents.

Combination antimicrobial therapy may include at least two different classes of antibiotics

(e.g., a beta-lactam agent with a macrolide, fluoroquinolone, or aminoglycoside). Broad spectrum antibiotics may be administered in combination with antifungal and/or antiviral agents. Supportive therapy for severe dengue may include administration of oxygen, blood transfusions, mechanical ventilation, fluid therapy (e.g., fluid administration with crystalloids and/or albumin continued until the patient shows hemodynamic improvement), nutrition (e.g., oral or enteral feedings), blood glucose management, vasopressor therapy

(e.g. administration of norepinephrine, epinephrine, and/or vasopressin to maintain adequate blood pressure), inotropic therapy (e.g., dobutamine), renal replacement therapy (e.g., dialysis), bicarbonate therapy, pharmacoprophylaxis against venous thromboembolism (e.g., treatment with heparin or intermittent pneumatic compression device), stress ulcer prophylaxis, sedation, analgesia, neuromuscular blockade, insulin (e.g., to maintain stable blood sugar levels), or corticosteroids (e.g., hydrocortisone), or any combination thereof. Immune-modulating therapy may include administration of activated protein C, immunoglobulin therapy, anti-platelet therapy, cytokine-blocking therapy, dialysis for pathogenic proteins or with antibiotic cartridges, or any combination thereof.

B. Detecting and Measuring Biomarkers

It is understood that the biomarkers in a sample can be measured by any suitable method known in the art. Measurement of the expression level of a biomarker can be direct or indirect. For example, the abundance levels of RNAs or proteins can be directly quantitated. Alternatively, the amount of a biomarker can be determined indirectly by measuring abundance levels of cDNAs, amplified RNAs or DNAs, or by measuring quantities or activities of RNAs, proteins, or other molecules (e.g., metabolites) that are indicative of the expression level of the biomarker. The methods for measuring biomarkers in a sample have many applications. For example, one or more biomarkers can be measured to aid in the prognosis of severe dengue risk, to determine the appropriate treatment for a subject, to monitor responses in a subject to treatment, or to identify therapeutic compounds that modulate expression of the biomarkers in vivo or in vitro.

Detecting Biomarker Polynucleotides

In one embodiment, the expression levels of the biomarkers are determined by measuring polynucleotide levels of the biomarkers. The levels of transcripts of specific biomarker genes can be determined from the amount of mRNA, or polynucleotides derived therefrom, present in a biological sample. Polynucleotides can be detected and quantitated by a variety of methods including, but not limited to, microarray analysis, polymerase chain reaction (PCR), reverse transcriptase polymerase chain reaction (RT-PCR), Northern blot, and serial analysis of gene expression (SAGE). See, e.g., Draghici Data Analysis Tools for

DNA Microarrays, Chapman and Hall/CRC, 2003; Simon et al. Design and Analysis ofDNA

Microarray Investigations, Springer, 2004; Real-Time PCR: Current Technology and

Applications, Logan, Edwards, and Saunders eds., Caister Academic Press, 2009; Bustin A-Z of Quantitative PCR (IUL Biotechnology, No. 5), International University Line, 2004;

Velculescu et al. (1995) Science 270: 484-487; Matsumura et al. (2005) Cell. Microbiol. 7:

11-18; Serial Analysis of Gene Expression (SAGE): Methods and Protocols (Methods in Molecular Biology), Humana Press, 2008; herein incorporated by reference in their entireties.

In one embodiment, microarrays are used to measure the levels of biomarkers. An advantage of microarray analysis is that the expression of each of the biomarkers can be measured simultaneously, and microarrays can be specifically designed to provide a diagnostic expression profile for a particular disease or condition (e.g., severe dengue).

Microarrays are prepared by selecting probes which include a polynucleotide sequence, and include immobilizing such probes to a solid support or surface. For example, the probes may include DNA sequences, RNA sequences, or copolymer sequences of DNA and RNA. The polynucleotide sequences of the probes may also include DNA and/or RNA analogues, or combinations thereof. For example, the polynucleotide sequences of the probes may be full or partial fragments of genomic DNA. The polynucleotide sequences of the probes may also be synthesized nucleotide sequences, such as synthetic oligonucleotide sequences. The probe sequences can be synthesized either enzymatically in vivo, enzymatically in vitro (e.g., by PCR), or non-enzymatically in vitro.

Probes used in the methods and/or kits described herein are preferably immobilized to a solid support which may be either porous or non-porous. For example, the probes may be polynucleotide sequences which are attached to a nitrocellulose or nylon membrane or filter covalently at either the 3' or the 5' end of the polynucleotide. Such hybridization probes are well known in the art (see, e.g., Sambrook, et ak, Molecular Cloning: A

Laboratory Manual (3rd Edition, 2001). Alternatively, the solid support or surface may be a glass or plastic surface. In one embodiment, hybridization levels are measured to microarrays of probes consisting of a solid phase on the surface of which are immobilized a population of polynucleotides, such as a population of DNA or DNA mimics, or, alternatively, a population of RNA or RNA mimics. The solid phase may be a nonporous or, optionally, a porous material such as a gel.

In one embodiment, the microarray includes a support or surface with an ordered array of binding (e.g., hybridization) sites or "probes" each representing one of the biomarkers described herein. Preferably the microarrays are addressable arrays, and more preferably positionally addressable arrays. More specifically, each probe of the array is preferably located at a known, predetermined position on the solid support such that the identity (i.e., the sequence) of each probe can be determined from its position in the array (i.e., on the support or surface). Each probe is preferably covalently attached to the solid support at a single site.

Microarrays can be made in a number of ways, of which several are described below. However they are produced, microarrays share certain characteristics. The arrays are reproducible, allowing multiple copies of a given array to be produced and easily compared with each other. Preferably, microarrays are made from materials that are stable under binding (e.g., nucleic acid hybridization) conditions. Microarrays are generally small, e.g., between 1 cm² and 25 cm²; however, larger arrays may also be used, e.g., in screening arrays. Preferably, a given binding site or unique set of binding sites in the microarray will specifically bind (e.g., hybridize) to the product of a single gene in a cell (e.g., to a specific mRNA, or to a specific cDNA derived therefrom). However, in general, other related or similar sequences will cross hybridize to a given binding site.

As noted above, the "probe" to which a particular polynucleotide molecule specifically hybridizes contains a complementary polynucleotide sequence. The probes of the microarray typically consist of nucleotide sequences of no more than 1,000 nucleotides. In some embodiments, the probes of the array consist of nucleotide sequences of 10 to 1,000 nucleotides. In one embodiment, the nucleotide sequences of the probes are in the range of 10-200 nucleotides in length and are genomic sequences of one species of organism, such that a plurality of different probes is present, with sequences complementary and thus capable of hybridizing to the genome of such a species of organism, sequentially tiled across all or a portion of the genome. In other embodiments, the probes are in the range of 10-30 nucleotides in length, in the range of 10-40 nucleotides in length, in the range of 20-50 nucleotides in length, in the range of 40-80 nucleotides in length, in the range of 50-150 nucleotides in length, in the range of 80-120 nucleotides in length, or are 60 nucleotides in length.

The probes may include DNA or DNA "mimics" (e.g., derivatives and analogues) corresponding to a portion of an organism's genome. In another embodiment, the probes of the microarray are complementary RNA or RNA mimics. DNA mimics are polymers composed of subunits capable of specific, Watson-Crick-like hybridization with DNA, or of specific hybridization with RNA. The nucleic acids can be modified at the base moiety, at the sugar moiety, or at the phosphate backbone (e.g., phosphorothioates).

Isothermal amplification methods do not require changing or cycling the reaction temperature and, as such, can be done by incubating a reaction at a constant temperature. Isothermal amplification methods include, but are not limited to, loop-mediated isothermal amplification (LAMP), strand displacement amplification (SDA), helicase-dependent amplification (HD A) and nicking enzyme amplification reaction (NEAR), as well as others.

DNA can be obtained, e.g., by polymerase chain reaction (PCR) amplification of genomic DNA or cloned sequences. PCR primers are preferably chosen based on a known sequence of the genome that will result in amplification of specific fragments of genomic DNA. Computer programs that are well known in the art are useful in the design of primers with the required specificity and optimal amplification properties, such as Oligo version 5.0 (National Biosciences). Typically each probe on the microarray will be between 10 bases and 50,000 bases, usually between 300 bases and 1,000 bases in length. PCR methods are well known in the art, and are described, for example, in Innis et ak, eds., PCR Protocols: A Guide To Methods And Applications, Academic Press Inc., San Diego, Calif. (1990); herein incorporated by reference in its entirety. It will be apparent to one skilled in the art that controlled robotic systems are useful for isolating and amplifying nucleic acids.

An alternative, preferred means for generating polynucleotide probes is by synthesis of synthetic polynucleotides or oligonucleotides, e.g., using N-phosphonate or

phosphoramidite chemistries (Froehler et ak, Nucleic Acid Res. 14:5399-5407 (1986); McBride et ak, Tetrahedron Lett. 24:246-248 (1983)). Synthetic sequences are typically between about 10 and about 500 bases in length, more typically between about 20 and about 100 bases, and most preferably between about 40 and about 70 bases in length. In some embodiments, synthetic nucleic acids include non-natural bases, such as, but by no means limited to, inosine. As noted above, nucleic acid analogues may be used as binding sites for hybridization. An example of a suitable nucleic acid analogue is peptide nucleic acid (see, e.g., Egholm et ak, Nature 363:566-568 (1993); U.S. Pat. No. 5,539,083).

Probes are preferably selected using an algorithm that takes into account binding energies, base composition, sequence complexity, cross-hybridization binding energies, and secondary structure. See Friend et ak, International Patent Publication WO 01/05935, published Jan. 25, 2001; Hughes et ak, Nat. Biotech. 19:342-7 (2001).

A skilled artisan will also appreciate that positive control probes, e.g., probes known to be complementary and hybridizable to sequences in the target polynucleotide molecules, and negative control probes, e.g., probes known to not be complementary and hybridizable to sequences in the target polynucleotide molecules, should be included on the array. In one embodiment, positive controls are synthesized along the perimeter of the array. In another embodiment, positive controls are synthesized in diagonal stripes across the array. In still another embodiment, the reverse complement for each probe is synthesized next to the position of the probe to serve as a negative control. In yet another embodiment, sequences from other species of organism are used as negative controls or as "spike-in" controls.

The probes are attached to a solid support or surface, which may be made, e.g., from glass, plastic (e.g., polypropylene, nylon), polyacrylamide, nitrocellulose, gel, or other porous or nonporous material. One method for attaching nucleic acids to a surface is by printing on glass plates, as is described generally by Schena et al, Science 270:467-470 (1995). This method is especially useful for preparing microarrays of cDNA (See also, DeRisi et al, Nature Genetics 14:457-460 (1996); Shalon et al., Genome Res. 6:639-645 (1996); and Schena et al., Proc. Natl. Acad. Sci. U.S.A. 93:10539-11286 (1995); herein incorporated by reference in their entireties).

A second method for making microarrays produces high-density oligonucleotide arrays. Techniques are known for producing arrays containing thousands of

oligonucleotides complementary to defined sequences, at defined locations on a surface using photolithographic techniques for synthesis in situ (see, Fodor et al., 1991, Science 251:767-773; Pease et al., 1994, Proc. Natl. Acad. Sci. U.S.A. 91:5022-5026; Lockhart et al., 1996, Nature Biotechnology 14:1675; U.S. Pat. Nos. 5,578,832; 5,556,752; and 5,510,270; herein incorporated by reference in their entireties) or other methods for rapid synthesis and deposition of defined oligonucleotides (Blanchard et al., Biosensors & Bioelectronics 11:687-690; herein incorporated by reference in its entirety). When these methods are used, oligonucleotides (e.g., 60-mers) of known sequence are synthesized directly on a surface such as a derivatized glass slide. Usually, the array produced is redundant, with several oligonucleotide molecules per RNA.

Other methods for making microarrays, e.g., by masking (Maskos and Southern, 1992, Nuc. Acids. Res. 20:1679-1684; herein incorporated by reference in its entirety), may also be used. In principle, any type of array, for example, dot blots on a nylon hybridization membrane (see Sambrook, et al., Molecular Cloning: A Laboratory Manual, 3rd Edition, 2001) could be used. However, as will be recognized by those skilled in the art, very small arrays will frequently be preferred because hybridization volumes will be smaller.

Microarrays can also be manufactured by means of an inkjet printing device for oligonucleotide synthesis, e.g., using the methods and systems described by Blanchard in

U.S. Pat. No. 6,028,189; Blanchard et al., 1996, Biosensors and Bioelectronics 11:687-690; Blanchard, 1998, in Synthetic DNA Arrays in Genetic Engineering, Vol. 20, J. K. Setlow, Ed., Plenum Press, New York at pages 111-123; herein incorporated by reference in their entireties. Specifically, the oligonucleotide probes in such microarrays are synthesized in arrays, e.g., on a glass slide, by serially depositing individual nucleotide bases in

"microdroplets" of a high surface tension solvent such as propylene carbonate. The microdroplets have small volumes (e.g., 100 pL or less, more preferably 50 pL or less) and are separated from each other on the microarray (e.g., by hydrophobic domains) to form circular surface tension wells which define the locations of the array elements (i.e., the different probes). Microarrays manufactured by this ink-jet method are typically of high density, preferably having a density of at least about 2,500 different probes per 1 cm². The polynucleotide probes are attached to the support covalently at either the 3' or the 5' end of the polynucleotide.

Biomarker polynucleotides which may be measured by microarray analysis can be expressed RNA or a nucleic acid derived therefrom (e.g., cDNA or amplified RNA derived from cDNA that incorporates an RNA polymerase promoter), including naturally occurring nucleic acid molecules, as well as synthetic nucleic acid molecules. In one embodiment, the target polynucleotide molecules include RNA, including, but by no means limited to, total cellular RNA, poly(A)⁺ messenger RNA (mRNA) or a fraction thereof, cytoplasmic mRNA, or RNA transcribed from cDNA (i.e., cRNA; see, e.g., Linsley & Schelter, U.S. patent application Ser. No. 09/411,074, filed Oct. 4, 1999, or U.S. Pat. No. 5,545,522, 5,891,636, or 5,716,785). Methods for preparing total and poly(A)⁺ RNA are well known in the art, and are described generally, e.g., in Sambrook, et ak, Molecular Cloning: A Laboratory Manual (3rd Edition, 2001). RNA can be extracted from a cell of interest using guanidinium thiocyanate lysis followed by CsCl centrifugation (Chirgwin et ak, 1979, Biochemistry 18:5294-5299), a silica gel-based column (e.g., RNeasy (Qiagen, Valencia, Calif.) or StrataPrep (Stratagene, La Jolla, Calif.)), or using phenol and chloroform, as described in Ausubel et ak, eds., 1989, Current Protocols In Molecular Biology, Vol. Ill, Green

Publishing Associates, Inc., John Wiley & Sons, Inc., New York, at pp. 13.12.1-13.12.5). Poly(A)⁺ RNA can be selected, e.g., by selection with oligo-dT cellulose or, alternatively, by oligo-dT primed reverse transcription of total cellular RNA. RNA can be fragmented by methods known in the art, e.g., by incubation with ZnCh, to generate fragments of RNA.

In one embodiment, total RNA, mRNA, or nucleic acids derived therefrom, are isolated from a sample taken from a patient suspected of having a life-threatening condition (e.g., severe dengue). Biomarker polynucleotides that are poorly expressed in particular cells may be enriched using normalization techniques (Bonaldo et al., 1996, Genome Res. 6:791-806).

As described above, the biomarker polynucleotides can be detectably labeled at one or more nucleotides. Any method known in the art may be used to label the target polynucleotides. Preferably, this labeling incorporates the label uniformly along the length of the RNA, and more preferably, the labeling is carried out at a high degree of efficiency. For example, polynucleotides can be labeled by oligo-dT primed reverse transcription. Random primers (e.g., 9-mers) can be used in reverse transcription to uniformly incorporate labeled nucleotides over the full length of the polynucleotides. Alternatively, random primers may be used in conjunction with PCR methods or T7 promoter-based in vitro transcription methods in order to amplify polynucleotides.

The detectable label may be a luminescent label. For example, fluorescent labels, bioluminescent labels, chemiluminescent labels, and colorimetric labels may be used in the practice of the methods and/or kits described herein. Fluorescent labels that can be used include, but are not limited to, fluorescein, a phosphor, a rhodamine, or a polymethine dye derivative. Additionally, commercially available fluorescent labels including, but not limited to, fluorescent phosphoramidites such as FluorePrime (Amersham Pharmacia, Piscataway, N.J.), Fluoredite (Miilipore, Bedford, Mass.), FAM (ABI, Foster City, Calif.), and Cy3 or Cy5 (Amersham Pharmacia, Piscataway, N.J.) can be used. Alternatively, the detectable label can be a radiolabeled nucleotide.

In one embodiment, biomarker polynucleotide molecules from a patient sample are labeled differentially from the corresponding polynucleotide molecules of a reference sample. The reference can include polynucleotide molecules from a normal biological sample (i.e., control sample, e.g., blood from a subject not having severe dengue) or from a reference biological sample, (e.g., blood from a subject having severe dengue).

Nucleic acid hybridization and wash conditions are chosen so that the target polynucleotide molecules specifically bind or specifically hybridize to the complementary polynucleotide sequences of the array, preferably to a specific array site, wherein its complementary DNA is located. Arrays containing double-stranded probe DNA situated thereon are preferably subjected to denaturing conditions to render the DNA single- stranded prior to contacting with the target polynucleotide molecules. Arrays containing single- stranded probe DNA (e.g., synthetic oligodeoxyribonucleic acids) may need to be denatured prior to contacting with the target polynucleotide molecules, e.g., to remove hairpins or dimers which form due to self-complementary sequences.

Optimal hybridization conditions will depend on the length (e.g., oligomer versus polynucleotide greater than 200 bases) and type (e.g., RNA, or DNA) of probe and target nucleic acids. One of skill in the art will appreciate that as the oligonucleotides become shorter, it may become necessary to adjust their length to achieve a relatively uniform melting temperature for satisfactory hybridization results. General parameters for specific (i.e., stringent) hybridization conditions for nucleic acids are described in Sambrook, et al., Molecular Cloning: A Laboratory Manual (3rd Edition, 2001), and in Ausubel et al., Current Protocols In Molecular Biology, vol. 2, Current Protocols Publishing, New York (1994). Typical hybridization conditions for the cDNA microarrays of Schena et al. are hybridization in 5.times.SSC plus 0.2% SDS at 65°C for fthe hours, followed by washes at 25°C in low stringency wash buffer (lxSSC plus 0.2% SDS), followed by 10 minutes at 25°C in higher stringency wash buffer (O.lxSSC plus 0.2% SDS) (Schena et al., Proc. Natl. Acad. Sci. U.S.A. 93:10614 (1993)). Useful hybridization conditions are also provided in, e.g., Tijessen, 1993, Hybridization With Nucleic Acid Probes, Elsevier Science Publishers B.V.; and Kricka, 1992, Nonisotopic Dna Probe Techniques, Academic Press, San Diego, Calif. Particularly preferred hybridization conditions include hybridization at a temperature at or near the mean melting temperature of the probes (e.g., within 5l°C, more preferably within 21 °C) in 1 M NaCl, 50 mM MES buffer (pH 6.5), 0.5% sodium sarcosine and 30% formamide.

When fluorescently labeled gene products are used, the fluorescence emissions at each site of a microarray may be, preferably, detected by scanning confocal laser microscopy. In one embodiment, a separate scan, using the appropriate excitation line, is carried out for each of the two fluorophores used. Alternatively, a laser may be used that allows simultaneous specimen illumination at wavelengths specific to the two fluorophores and emissions from the two fluorophores can be analyzed simultaneously (see Shalon et al., 1996, "A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization," Genome Research 6:639-645, which is incorporated by reference in its entirety for all purposes). Arrays can be scanned with a laser fluorescent scanner with a computer controlled X-Y stage and a microscope objective. Sequential excitation of the two fluorophores is achieved with a multi-line, mixed gas laser and the emitted light is split by wavelength and detected with two photomultiplier tubes. Fluorescence laser scanning devices are described in Schena et al., Genome Res. 6:639-645 (1996), and in other references cited herein. Alternatively, the fiber-optic bundle described by Ferguson et al., Nature Biotech. 14:1681-1684 (1996), may be used to monitor mRNA abundance levels at a large number of sites simultaneously.

In one embodiment, the methods described herein include detecting the amount of RNA transcripts encoded by two or more of DEFA4, GYG1, TOR3A, PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1, TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5 in a sample of RNA obtained from the patient. In one embodiment, the methods described herein include detecting the amount of RNA transcripts encoded by two or more of DEFA4, CACNA2D2, SPON2, CACNA2D3, CHD3, GRAP2, AK5, and PTPRM in a sample of RNA obtained from the patient. In one embodiment, the methods described herein include detecting the amount of RNA transcripts encoded by two or more of GYG1, CX3CR1, TRERF1, GBP2, TMEM63C, SERINC5, and SOX13 in a sample of RNA obtained from the patient. In one embodiment, the methods described herein include detecting the amount of RNA transcripts encoded by two or more of TOR3A, NCR3, ABI3, C3orfl8, and ENPP5 in a sample of RNA obtained from the patient.

In one embodiment, the methods and/or kits described herein include two or more of: an oligonucleotide that hybridizes to a DEFA4 polynucleotide, an oligonucleotide that hybridizes to a GYG1 polynucleotide, an oligonucleotide that hybridizes to a TOR3A polynucleotide, an oligonucleotide that hybridizes to a PTPRM polynucleotide, an oligonucleotide that hybridizes to an SPON2 polynucleotide, an oligonucleotide that hybridizes to a GRAP2 polynucleotide, an oligonucleotide that hybridizes to a CACNA2D2 polynucleotide, an oligonucleotide that hybridizes to a CACNA2D3 polynucleotide, an oligonucleotide that hybridizes to a TMEM63C polynucleotide, an oligonucleotide that hybridizes to a AK5 polynucleotide, an oligonucleotide that hybridizes to an CHD3 polynucleotide, an oligonucleotide that hybridizes to an CX3CR1 polynucleotide, an oligonucleotide that hybridizes to an TRERF1 polynucleotide, an oligonucleotide that hybridizes to an GBP2 polynucleotide, an oligonucleotide that hybridizes to an SERINC5 polynucleotide, an oligonucleotide that hybridizes to an SOX13 polynucleotide, an oligonucleotide that hybridizes to an NCR3 polynucleotide, an oligonucleotide that hybridizes to an ABI3 polynucleotide, an oligonucleotide that hybridizes to an C3orfl8 polynucleotide, and an oligonucleotide that hybridizes to a ENPP5 polynucleotide. In one embodiment, the methods and/or kits described herein include two or more of: an oligonucleotide that hybridizes to a DEFA4 polynucleotide, an oligonucleotide that hybridizes to a CACNA2D2 polynucleotide, an oligonucleotide that hybridizes to a SPON2 polynucleotide, an oligonucleotide that hybridizes to a CACNA2D3 polynucleotide, an oligonucleotide that hybridizes to an CHD3 polynucleotide, an oligonucleotide that hybridizes to a GRAP2 polynucleotide, an oligonucleotide that hybridizes to a AK5 polynucleotide, and an oligonucleotide that hybridizes to a PTPRM polynucleotide.

In one embodiment, the methods and/or kits described herein include two or more of: an oligonucleotide that hybridizes to a GYG1 polynucleotide, an oligonucleotide that hybridizes to a CX3CR1 polynucleotide, an oligonucleotide that hybridizes to a TRERF1 polynucleotide, an oligonucleotide that hybridizes to a GBP2 polynucleotide, an

oligonucleotide that hybridizes to an TMEM63C polynucleotide, an oligonucleotide that hybridizes to a SERINC5 polynucleotide, and an oligonucleotide that hybridizes to a SOX13 polynucleotide.

In one embodiment, the methods and/or kits described herein includetwo or more of: an oligonucleotide that hybridizes to a TOR3A polynucleotide, an oligonucleotide that hybridizes to a NCR3 polynucleotide, an oligonucleotide that hybridizes to a ABI3 polynucleotide, an oligonucleotide that hybridizes to a C3orfl8 polynucleotide, and an oligonucleotide that hybridizes to an ENPP5 polynucleotide.

In some embodiments, detecting the levels of expression of the two or more biomarkers includes performing microarray analysis, polymerase chain reaction (PCR), reverse transcriptase polymerase chain reaction (RT-PCR), a Northern blot, a serial analysis of gene expression (SAGE), isothermal amplification such as LAMP or RPA, or next generation sequencing (NGS).

Various methods are known for sequencing polymers composed of two essential biological building-blocks, amino acids, carbohydrates and nucleotides. For example, existing methods for peptide sequence determination include the N-terminal chemistry of the Edman degradation, N- and C-terminal enzymatic methods, and C-terminal chemical methods. Existing methods for sequencing oligonucleotides include the Maxam- Gilbert base-specific chemical cleavage method and the enzymatic ladder synthesis with dideoxy base-specific termination method. Any method known in the art may be used for sequencing.

Polynucleotides can also be analyzed by other methods including, but not limited to, northern blotting, nuclease protection assays, RNA fingerprinting, polymerase chain reaction, ligase chain reaction, Qbeta replicase, isothermal amplification method, strand displacement amplification, transcription based amplification systems, nuclease protection (Sl nuclease or RNAse protection assays), SAGE as well as methods disclosed in

International Publication Nos. WO 88/10315 and WO 89/06700, and International

Applications Nos. PCT/US87/00880 and PCT/US89/01025; herein incorporated by reference in their entireties.

A standard Northern blot assay can be used to ascertain an RNA transcript size, identify alternatively spliced RNA transcripts, and the relative amounts of mRNA in a sample, in accordance with conventional Northern hybridization techniques known to those persons of ordinary skill in the art. In Northern blots, RNA samples are first separated by size by electrophoresis in an agarose gel under denaturing conditions. The RNA is then transferred to a membrane, cross-linked, and hybridized with a labeled probe. Nonisotopic or high specific activity radiolabeled probes can be used, including random-primed, nick- translated, or PCR-generated DNA probes, in vitro transcribed RNA probes, and

oligonucleotides. Additionally, sequences with only partial homology (e.g., cDNA from a different species or genomic DNA fragments that might contain an exon) may be used as probes. The labeled probe, e.g., a radiolabelled cDNA, either containing the full-length, single stranded DNA or a fragment of that DNA sequence may be at least 20, at least 30, at least 50, or at least 100 consecutive nucleotides in length. The probe can be labeled by any of the many different methods known to those skilled in this art. The labels most commonly employed for these studies are radioactive elements, enzymes, chemicals that fluoresce when exposed to ultraviolet light, and others. A number of fluorescent materials are known and can be utilized as labels. These include, but are not limited to, fluorescein, rhodamine, auramine, Texas Red, AMCA blue and Lucifer Yellow. A particular detecting material is anti-rabbit antibody prepared in goats and conjugated with fluorescein through an isothiocyanate. Proteins can also be labeled with a radioactive element or with an enzyme.

The radioactive label can be detected by any of the currently available counting procedures.

Isotopes that can be used include, but are not limited to, ³H, ¹⁴C, ³²P, ³⁵S, ³⁶Cl, ³⁵Cr, ⁵⁷Co,

⁵⁸Co, ⁵⁹Fe, ⁹⁰Y, ¹²⁵I, ^{1 1}1, and ¹⁸⁶Re. Enzyme labels are likewise useful, and can be detected by any of the presently utilized colorimetric, spectrophotometric, fluorospectrophotometric, amperometric or gasometric techniques. The enzyme is conjugated to the selected particle by reaction with bridging molecules such as carbodiimides, diisocyanates, glutaraldehyde and the like. Any enzymes known to one of skill in the art can be utilized. Examples of such enzymes include, but are not limited to, peroxidase, beta-D-galactosidase, urease, glucose oxidase plus peroxidase and alkaline phosphatase. U.S. Pat. Nos. 3,654,090, 3,850,752, and 4,016,043 are referred to by way of example for their disclosure of alternate labeling material and methods.

Nuclease protection assays (including both ribonuclease protection assays and Sl nuclease assays) can be used to detect and quantitate specific mRNAs. In nuclease protection assays, an antisense probe (labeled with, e.g., radiolabeled or nonisotopic) hybridizes in solution to an RNA sample. Following hybridization, single-stranded, unhybridized probe and RNA are degraded by nucleases. An acrylamide gel is used to separate the remaining protected fragments. Typically, solution hybridization is more efficient than membrane-based hybridization, and it can accommodate up to 100 qg of sample RNA, compared with the 20-30 qg maximum of blot hybridizations.

The ribonuclease protection assay, which is the most common type of nuclease protection assay, requires the use of RNA probes. Oligonucleotides and other single- stranded DNA probes can only be used in assays containing S 1 nuclease. The single- stranded, antisense probe must typically be completely homologous to target RNA to prevent cleavage of the probe: target hybrid by nuclease.

Serial Analysis Gene Expression (SAGE) can also be used to determine RNA abundances in a cell sample. See, e.g., Velculescu et al., 1995, Science 270:484-7; Carulli, et al., 1998, Journal of Cellular Biochemistry Supplements 30/31:286-96; herein

incorporated by reference in their entireties. SAGE analysis does not require a special device for detection, and is one of the preferable analytical methods for simultaneously detecting the expression of a large number of transcription products. First, poly A⁺ RNA is extracted from cells. Next, the RNA is converted into cDNA using a biotinylated oligo (dT) primer, and treated with a four-base recognizing restriction enzyme (Anchoring Enzyme:

AE) resulting in AE-treated fragments containing a biotin group at their 3' terminus. Next, the AE-treated fragments are incubated with streptavidin for binding. The bound cDNA is divided into two fractions, and each fraction is then linked to a different double- stranded oligonucleotide adapter (linker) A or B. These linkers are composed of: (1) a protruding single strand portion having a sequence complementary to the sequence of the protruding portion formed by the action of the anchoring enzyme, (2) a 5' nucleotide recognizing sequence of the IIS-type restriction enzyme (cleaves at a predetermined location no more than 20 bp away from the recognition site) serving as a tagging enzyme (TE), and (3) an additional sequence of sufficient length for constructing a PCR-specific primer. The linker- linked cDNA is cleaved using the tagging enzyme, and only the linker- linked cDNA sequence portion remains, which is present in the form of a short-strand sequence tag. Next, pools of short-strand sequence tags from the two different types of linkers are linked to each other, followed by PCR amplification using primers specific to linkers A and B. As a result, the amplification product is obtained as a mixture including myriad sequences of two adjacent sequence tags (ditags) bound to linkers A and B. The amplification product is treated with the anchoring enzyme, and the free ditag portions are linked into strands in a standard linkage reaction. The amplification product is then cloned. Determination of the clone's nucleotide sequence can be used to obtain a read-out of consecutive ditags of constant length. The presence of mRNA corresponding to each tag can then be identified from the nucleotide sequence of the clone and information on the sequence tags.

Quantitative reverse transcriptase PCR (qRT-PCR) can also be used to determine the expression profiles of biomarkers (see, e.g., U.S. Patent Application Publication No.

2005/0048542A1; herein incorporated by reference in its entirety). The first step in gene expression profiling by RT-PCR is the reverse transcription of the RNA template into cDNA, followed by its exponential amplification in a PCR reaction. The two most commonly used reverse transcriptases are avilo myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia vims reverse transcriptase (MLV-RT). The reverse transcription step is typically primed using specific primers, random hexamers, or oligo-dT primers, depending on the circumstances and the goal of expression profiling. For example, extracted RNA can be reverse-transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, Calif., USA), following the manufacturer's instructions. The derived cDNA can then be used as a template in the subsequent PCR reaction.

Although the PCR step can use a variety of thermostable DNA-dependent DNA polymerases, it typically employs the Taq DNA polymerase, which has a 5'-3' nuclease activity but lacks a 3'-5' proofreading endonuclease activity. Thus, TAQMAN PCR typically utilizes the 5 '-nuclease activity of Taq or Tth polymerase to hydrolyze a hybridization probe bound to its target amplicon, but any enzyme with equivalent 5' nuclease activity can be used. Two oligonucleotide primers are used to generate an amplicon typical of a PCR reaction. A third oligonucleotide, or probe, is designed to detect nucleotide sequence located between the two PCR primers. The probe is non-extendible by Taq DNA polymerase enzyme, and is labeled with a reporter fluorescent dye and a quencher fluorescent dye. Any laser-induced emission from the reporter dye is quenched by the quenching dye when the two dyes are located close together as they are on the probe. During the amplification reaction, the Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The resultant probe fragments disassociate in solution, and signal from the released reporter dye is free from the quenching effect of the second fluorophore. One molecule of reporter dye is liberated for each new molecule synthesized, and detection of the unquenched reporter dye provides the basis for quantitative interpretation of the data.

TAQMAN RT-PCR can be performed using commercially available equipment, such as, for example, ABI PRISM 7700 sequence detection system. (Perkin-Elmer- Applied Biosystems, Foster City, Calif., USA), or Lightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In a preferred embodiment, the 5' nuclease procedure is ran on a real-time quantitative PCR device such as the ABI PRISM 7700 sequence detection system. The system consists of a thermocycler, laser, charge-coupled device (CCD), camera and computer. The system includes software for running the instrument and for analyzing the data. 5 '-Nuclease assay data are initially expressed as Ct, or the threshold cycle.

Fluorescence values are recorded during every cycle and represent the amount of product amplified to that point in the amplification reaction. The point when the fluorescent signal is first recorded as statistically significant is the threshold cycle (Ct).

To minimize errors and the effect of sample-to-sample variation, RT-PCR is usually performed using an internal standard. The ideal internal standard is expressed at a constant level among different tissues, and is unaffected by the experimental treatment. RNAs most frequently used to normalize patterns of gene expression are mRNAs for the housekeeping genes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and beta-actin.

A more recent variation of the RT-PCR technique is the real time quantitative PCR, which measures PCR product accumulation through a dual-labeled fluorigenic probe (i.e., TAQMAN probe). Real time PCR is compatible both with quantitative competitive PCR, where internal competitor for each target sequence is used for normalization, and with quantitative comparative PCR using a normalization gene contained within the sample, or a housekeeping gene for RT-PCR. For further details see, e.g. Held et ak, Genome Research 6:986-994 (1996). Analysis of Biomarker Data

Biomarker data may be analyzed by a variety of methods to identify biomarkers and determine the statistical significance of differences in observed levels of biomarkers between test and reference expression profiles in order to evaluate whether a patient is at risk of severe dengue. In certain embodiments, patient data is analyzed by one or more methods including, but not limited to, multivariate linear discriminant analysis (LDA), receiver operating characteristic (ROC) analysis, principal component analysis (PCA), ensemble data mining methods, significance analysis of microarrays (SAM), cell specific significance analysis of microarrays (csSAM), spanning-tree progression analysis of density-normalized events (SPADE), and multi-dimensional protein identification technology (MUDPIT) analysis. (See, e.g., Hilbe (2009) Logistic Regression Models, Chapman & Hall/CRC Press; McLachlan (2004) Discriminant Analysis and Statistical Pattern Recognition. Wiley Interscience; Zweig et al. (1993) Clin. Chem. 39:561-577; Pepe (2003) The statistical evaluation of medical tests for classification and prediction, New York, NY: Oxford; Sing et al. (2005) Bioinformatics 21:3940-3941; Tusher et al. (2001) Proc. Natl. Acad. Sci. U.S.A. 98:5116-5121; Oza (2006) Ensemble data mining, NASA Ames Research Center, Moffett Field, CA, USA; English et al. (2009) J. Biomed. Inform. 42(2):287-295; Zhang (2007) Bioinformatics 8: 230; Shen-Orr et al. (2010) Journal of Immunology 184: 144-130; Qiu et al. (2011) Nat. Biotechnol. 29(l0):886-89l; Ru et al. (2006) J. Chromatogr. A. 1111(2): 166- 174, Jolliffe Principal Component Analysis (Springer Series in Statistics, 2^nd edition,

Springer, NY, 2002), Koren et al. (2004) IEEE Trans Vis Comput Graph 10:459-470; herein incorporated by reference in their entireties.)

C. Kits

In yet another aspect, provided herein are kits for prognosis of severe dengue in a subject, wherein the kits can be used to detect the biomarkers described herein. For example, the kits can be used to detect any one or more of the biomarkers described herein, which are differentially expressed in samples from patients. The kit may include one or more agents for detection of biomarkers, a container for holding a biological sample isolated from a human subject suspected of having a life-threatening condition; and printed instructions for reacting agents with the biological sample or a portion of the biological sample to detect the presence or amount of at least one biomarker in the biological sample.

The agents may be packaged in separate containers. The kit may further include one or more control reference samples and reagents for performing an immunoassay or microarray analysis.

In certain embodiments, the kit includes agents for measuring the levels of two or more of two or more of DEFA4, GYG1, TOR3A, PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1, TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5. In some embodiments, the two or more include DEFA4, CACNA2D2, SPON2, CACNA2D3, CHD3, GRAP2, AK5, and PTPRM. In other embodiments, the two or more include GYG1, CX3CR1, TRERF1, GBP2, TMEM63C, SERINC5, and SOX13. The kit may comprise reagents for measuring up to 30, up to 40, up to 50, up to 60, up to 70, up to 80, up to 90 or up to 100 biomarkers, including the reagents required for the analysis of DEFA4, GYG1, TOR3A, PTPRM, SPON2, GRAP2,

CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1, TRERF1, GBP2,

SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5. In some other embodiments, the two or more include TOR3A, NCR3, ABI3, C3orfl8, and ENPP5. For example, the kit may include agents for detecting biomarkers of a panel including a DEFA4 polynucleotide, a CACNA2D2 polynucleotide, a SPON2 polynucleotide, a CACNA2D3 polynucleotide, an CHD3 polynucleotide, a GRAP2 polynucleotide, a AK5 polynucleotide, and a PTPRM polynucleotide. In certain other embodiments, the kit may include agents for detecting biomarkers of a panel including two or more of a DEFA4 polynucleotide, a GYG1 polynucleotide, a TOR3A polynucleotide, a PTPRM polynucleotide, a SPON2

polynucleotide, a GRAP2 polynucleotide, a CACNA2D2 polynucleotide, a CACNA2D3 polynucleotide, a TMEM63C polynucleotide, a AK5 polynucleotide, a CHD3

polynucleotide, a CX3CR1 polynucleotide, a TRERF1 polynucleotide, a GBP2

polynucleotide, a SERINC5 polynucleotide, a SOX13 polynucleotide, a NCR3

polynucleotide, a ABI3 polynucleotide, a C3orfl8 polynucleotide, and a ENPP5

polynucleotide.

Also provided by this disclosure are kits for practicing the subject methods, as described above. In some embodiments, the kit may reagents for measuring the amount of

RNA transcripts encoded by two or more of, three or more of, five or more of, ten or more of, fifteen or more of, twenty or more of, thirty or more of or all of DEFA4, GYG1, TOR3A,

PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1,

TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5. In some embodiments, the kit may include, for each RNA transcript, a sequence-specific oligonucleotide that hybridizes to the transcript. In some embodiments, the sequence- specific oligonucleotide may be biotinylated and/or labeled with an optically-detectable moiety. In some embodiments, the kit may include, for each RNA transcript, a pair of PCR primers that amplify a sequence from the RNA transcript, or cDNA made from the same. In some embodiments, the kit may include an array of oligonucleotide probes, wherein the array includes, for each RNA transcript, at least one sequence-specific oligonucleotide that hybridizes to the transcript. The oligonucleotide probes may be spatially addressable on the surface of a planar support, or tethered to optically addressable beads, for example.

The various components of the kit may be present in separate containers or certain compatible components may be precombined into a single container, as desired.

In addition to the above-mentioned components, the subject kit may further include instructions for using the components of the kit to practice the subject method.

D. Diagnostic System and Computerized Methods for Determining Severe

Dengue Risk

In a further aspect, the methods and/or kits described herein include a computer implemented method for determining severe dengue risk of a patient suspected of having a life-threatening condition. The computer performs steps including: receiving inputted patient data including values for the levels of one or more biomarkers in a biological sample from the patient; analyzing the levels of one or more biomarkers and comparing with respective reference value ranges for the biomarkers; calculating a severe dengue gene score for the patient based on the levels of the biomarkers, wherein a higher severe dengue gene score for the patient compared to a control subject indicates that the patient is at high risk of severe dengue; and displaying information regarding the severe dengue risk of the patient.

In certain embodiments, the inputted patient data includes values for the levels of a plurality of biomarkers in a biological sample from the patient. In one embodiment, the inputted patient data includes values for the levels of two or more of DEFA4, GYG1, TOR3A, PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1, TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5 polynucleotides. In some embodiments, the methods described herein further include information, in electronic or paper form, comprising instructions to correlate the detected levels of each biomarker with severe dengue.

In some embodiments, the methods described herein may involve creating a report that shows the severe dengue gene score of the subject, e.g., in an electronic form, and forwarding the report to a doctor or other medical professional to help identify a suitable course of action, e.g., to identify a suitable therapy for the subject. The report may be used along with other metrics as a diagnostic to determine whether the subject has a high risk of a disease or condition (e.g. severe dengue).

In any embodiment, a report can be forwarded to a“remote location”, where“remote location,” means a location other than the location at which the image is examined. For example, a remote location could be another location (e.g., office, lab, etc.) in the same city, another location in a different city, another location in a different state, another location in a different country, etc. As such, when one item is indicated as being "remote" from another, what is meant is that the two items can be in the same room but separated, or at least in different rooms or different buildings, and can be at least one mile, ten miles, or at least one hundred miles apart. "Communicating" information references transmitting the data representing that information as electrical signals over a suitable communication channel (e.g., a private or public network). "Forwarding" an item refers to any means of getting that item from one location to the next, whether by physically transporting that item or otherwise (where that is possible) and includes, at least in the case of data, physically transporting a medium carrying the data or communicating the data. Examples of communicating media include radio or infra-red transmission channels as well as a network connection to another computer or networked device, and the internet or including email transmissions and information recorded on websites and the like. In certain embodiments, the report may be analyzed by an MD or other qualified medical professional, and a report based on the results of the analysis of the image may be forwarded to the subject from which the sample was obtained.

In computer-related embodiments, a diagnostic system is provided for performing the computer implemented methods described herein. A diagnostic system may include a computer containing a processor, a storage component (i.e., memory), a display component, and other components typically present in general purpose computers. The storage component stores information accessible by the processor, including instructions that may be executed by the processor and data that may be retrieved, manipulated or stored by the processor.

The storage component includes instructions for determining the severe dengue risk of the subject. For example, the storage component includes instructions for calculating the severe dengue gene score for the subject based on biomarker expression levels, as described herein. In addition, the storage component may further include instructions for performing multivariate linear discriminant analysis (LDA), receiver operating characteristic (ROC) analysis, principal component analysis (PCA), ensemble data mining methods, cell specific significance analysis of microarrays (csSAM), or multi-dimensional protein identification technology (MUDPIT) analysis. The computer processor is coupled to the storage component and configured to execute the instructions stored in the storage component in order to receive patient data and analyze patient data according to one or more algorithms. The display component displays information regarding the diagnosis and/or prognosis (e.g., severe dengue risk) of the patient.

The storage component may be of any type capable of storing information accessible by the processor, such as a hard-drive, memory card, ROM, RAM, DVD, CD-ROM, USB Flash drive, write-capable, and read-only memories. The processor may be any well-known processor, such as processors from Intel Corporation. Alternatively, the processor may be a dedicated controller such as an ASIC.

The instructions may be any set of instructions to be executed directly (such as machine code) or indirectly (such as scripts) by the processor. In that regard, the terms "instructions," "steps" and "programs" may be used interchangeably herein. The instructions may be stored in object code form for direct processing by the processor, or in any other computer language including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance.

Data may be retrieved, stored or modified by the processor in accordance with the instructions. For instance, although the diagnostic system is not limited by any particular data structure, the data may be stored in computer registers, in a relational database as a table having a plurality of different fields and records, XML documents, or flat files. The data may also be formatted in any computer-readable format such as, but not limited to, binary values, ASCII or Unicode. Moreover, the data may include any information sufficient to identify the relevant information, such as numbers, descriptive text, proprietary codes, pointers, references to data stored in other memories (including other network locations) or information which is used by a function to calculate the relevant data.

In certain embodiments, the processor and storage component may include multiple processors and storage components that may or may not be stored within the same physical housing. For example, some of the instructions and data may be stored on removable CD- ROM and others within a read-only computer chip. Some or all of the instructions and data may be stored in a location physically remote from, yet still accessible by, the processor. Similarly, the processor may actually include a collection of processors which may or may not operate in parallel.

In one aspect, computer is a server communicating with one or more client computers. Each client computer may be configured similarly to the server, with a processor, storage component and instructions. Each client computer may be a personal computer, intended for use by a person, having all the internal components normally found in a personal computer such as a central processing unit (CPU), display (for example, a monitor displaying information processed by the processor), CD-ROM, hard-drive, user input device (for example, a mouse, keyboard, touch-screen or microphone), speakers, modem and/or network interface device (telephone, cable or otherwise) and all of the components used for connecting these elements to one another and permitting them to communicate (directly or indirectly) with one another. Moreover, computers in accordance with the systems and methods described herein may include any device capable of processing instructions and transmitting data to and from humans and other computers including network computers lacking local storage capability.

Although the client computers and may include a full-sized personal computer, many aspects of the system and method are particularly advantageous when used in connection with mobile devices capable of wirelessly exchanging data with a server over a network such as the Internet. For example, client computer may be a wireless-enabled PDA such as a Blackberry phone, Apple iPhone, Android phone, or other Internet-capable cellular phone.

In such regard, the user may input information using a small keyboard, a keypad, a touch screen, or any other means of user input. The computer may have an antenna for receiving a wireless signal.

The server and client computers are capable of direct and indirect communication, such as over a network. Although only a few computers may be used, it should be appreciated that a typical system can include a large number of connected computers, with each different computer being at a different node of the network. The network, and intervening nodes, may include various combinations of devices and communication protocols including the Internet, World Wide Web, intranets, virtual private networks, wide area networks, local networks, cell phone networks, private networks using communication protocols proprietary to one or more companies, Ethernet, WiFi and HTTP. Such communication may be facilitated by any device capable of transmitting data to and from other computers, such as modems (e.g., dial-up or cable), networks and wireless interfaces. The server may be a web server.

Although certain advantages are obtained when information is transmitted or received as noted above, other aspects of the system and method are not limited to any particular manner of transmission of information. For example, in some aspects, information may be sent via a medium such as a disk, tape, flash drive, DVD, or CD-ROM. In other aspects, the information may be transmitted in a non-electronic format and manually entered into the system. Yet further, although some functions are indicated as taking place on a server and others on a client, various aspects of the system and method may be implemented by a single computer having a single processor.

EXAMPLES

The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the methods and/or kits described herein, and are not intended to limit the scope of the methods and/or kits described herein nor are they intended to represent that the experiments below are all or the only experiments performed. Efforts have been made to ensure accuracy with respect to numbers used (e.g. amounts, temperature, etc.) but some experimental errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, molecular weight is weight average molecular weight, temperature is in degrees Celsius, and pressure is at or near atmospheric. Standard abbreviations may be used, e.g., room temperature (RT); base pairs (bp); kilobases (kb); picoliters (pl); seconds (s or sec); minutes (m or min); hours (h or hr); days (d); weeks (wk or wks); nanoliters (nl); microliters (ul); milliliters (ml); liters (L); nanograms (ng); micrograms (ug); milligrams (mg); grams ((g), in the context of mass); kilograms (kg); equivalents of the force of gravity ((g), in the context of centrifugation); nanomolar (nM); micromolar (uM), millimolar (mM); molar (M); amino acids (aa);

kilobases (kb); base pairs (bp); nucleotides (nt); intramuscular (i.m.); intraperitoneal (i.p.); subcutaneous (s.c.); and the like.

Summary

It is proposed that integration of gene expression data from heterogeneous patient populations with dengue infection across a wide variety of ages, countries, and inclusion criteria would yield a set of conserved genes that is predictive of severe dengue and generalizable across cohorts. Blood samples of dengue patients were analyzed from seven gene expression datasets (446 samples, five countries) using an integrated multi-cohort analysis framework and identified a 20-gene set that predicted progression to severe dengue. The predictive power of the 20-gene set was validated in three retrospective dengue datasets (84 samples, three countries) and a prospective Colombia cohort (34 patients), with area under the receiver operating characteristic curve of 0.89, 100% sensitivity and 76% specificity. Therefore, the 20-gene gene set was validated both in existing cohorts and in a prospective cohort from Colombia and demonstrated that it performed well under both the current and former WHO dengue classification methods. The discovery and validation cohorts were from 8 countries on 3 continents, providing strong evidence that the 20-gene set is not modulated by the underlying genetic background of the patients or the vims strains. The 20-gene dengue severity scores declined during the disease course, suggesting that infection triggered this host response. The gene set was strongly associated with the progression to severe dengue and represents a predictive signature, generalizable across ages, host genetic factors, vims strains and sample sources, with potential implications for the development of a host response-based dengue prognostic assay.

Materials and Methods

Systematic search and analysis

Two public gene expression microarray repositories (NIH GEO and ArrayExpress) were searched for all human gene expression dengue datasets. Datasets that examined clinical cohorts of dengue infection in whole blood or PBMCs were retained for further study, and datasets that examined only dengue with no severe dengue, were done in patients on steroid treatment, were non-clinical (e.g. cell culture studies), or used on-chip two-sample arrays were excluded. The remaining 10 datasets contained 530 samples from 7 countries from both adult and pediatric patients (FIG. 2).

Gene expression in patients with dengue fever versus patients with DHF and/or DSS was compared using the validated multi-cohort analysis framework, as previously described

(Andres-Terre et ak, 2015; Khatri et ak, 2013; Lofgren et ak, 2016; Sweeney et ak, 20l6a;

Sweeney et ak, 2015; Sweeney et ak, 20l6b). \Seven datasets were used as the discovery cohort, and three datasets were left out for independent validation. The discovery/validation split was made such that there was a similar proportion of whole blood to PBMCs, and similar spread across years. GC Robust Multi-array Average (GCRMA) normalization was used for Affymetrix chips and a normal-exponential correction followed by quantile normalization for all other chip types. All arrays were log2 normalized prior to analysis. No inter-dataset normalization was performed since different technologies were used in the various datasets. A DerSimonian- Laird random-effects model was applied to combine gene expression effect sizes via Hedges’g. DerSimonian- Laird was used because of the previously published analysis of various random effects inverse variance models across a range of diseases (Sweeney et al., 2017) that showed DerSimonian- Laird provided compromise to identify differentially expressed genes while reducing false positives.

Significance thresholds were set for differential expression at FDR less than 10% and an effect size greater than 1.3 fold (in non- log space). These thresholds for gene selection came from the prior analysis of different meta-analysis models (Sweeney et al., 2017).

Derivation of dengue score

To identify a parsimonious gene set maximized for diagnostic power, a forward search was first ran, using the Metaintegrator R package, as previously described (Haynes et al., 2017; Sweeney et al., 20l6a). Briefly, the algorithm started with the single gene with the best discriminatory power, and then at each subsequent step added the gene with the best possible increase in weighted AUC (area under the curve; the sum of the AUC for each dataset times the number of samples in that dataset) to the set of genes, until no further additions can increase the weighted AUC more than some threshold amount (here 0.005 x the total number of samples). At each iteration of the greedy forward search, when adding a new gene, a dengue score was defined as follows: for each sample, the mean expression of the down-regulated genes is subtracted from the mean expression of the up-regulated genes to yield a dengue score.

Since there was a substantial amount of clinical heterogeneity present, it was desired to maximize the diagnostic performance rather than aiming for extreme parsimony. Thus, the forward search was ran exhaustively, such that once a gene set had been identified, those genes were removed from the remaining pool and the forward search was ran again. An arbitrary minimum threshold was set for performance of a mean AUC of 0.75 in the discovery data, which yielded three gene sets with a total of 20 genes. The entire list of 20 genes was then pooled to make a single dengue score.

This dengue score was tested for diagnostic power using receiver operating characteristic (ROC) curves. Validation of dengue score

The 20-gene set was validated in three independent clinical dengue gene expression datasets, comparing its ability to differentiate DF from DHF/DSS. Between-groups dengue score comparisons were done with the Wilcoxon rank sum test. Significance levels were set at two-tailed p<0.05, unless specified otherwise. All computation and calculations were done in the R language for statistical computing (version 3.0.2).

Colombia cohort ethics statement

All work with human subjects was approved by the Stanford University

Administrative Panel on Human Subjects in Medical Research (Protocol # 35460) and the Fundacion Valle del Lili Ethics committee in biomedical research (Cali/Colombia). All Subjects, their parents or legal guardians provided written informed consent, and subjects between 6 to 17 years of age and older provided assent.

Study population and sample collection

Blood samples were collected from individuals presenting to the emergency room or clinics of the Fundacion Valle del Lili in Cali (Colombia) between March 2016 and June 2017. Enrollment criteria consisted of: i) age greater than 2 years; ii) presentation with an acute febrile illness of less than 7 day duration associated with one or more of the following symptoms or signs: headache, rash, arthralgia, myalgia, retroorbital pain, abdominal pain, positive tourniquet test, petechiae, and bleeding; and iii) a positive dengue IgM antibody and/or NS1 antigen by the SD BIO LINE Dengue Duo combo device (Standard Diagnostic Inc., Korea) test (Wang and Sekaran, 2010). Two patients with a clinical presentation highly consistent with dengue were enrolled in face of having negative DENV IgM and NS1 antigen.

Patients were classified by infectious diseases specialists as having dengue, dengue with warning signs or severe dengue according to 2009 WHO criteria (Alexander et ak,

2011; WHO, 2009) upon both presentation and prior to their discharge (FIG. 6, Panel B).

Patients meeting criteria of severe dengue upon presentation were excluded from the study.

41 patients were enrolled. Discharge diagnoses were also blindly classified by infectious diseases specialists according to the 1997 WHO criteria into DF, DHF, and/or DSS criteria.

Demographics and clinical information were collected at the time of presentation. The first day of fever (fever day 0) was defined by the patients or their relatives. Symptoms, signs, and laboratory studies (including complete blood count, chemistry, and liver function tests) were documented by healthcare professionals (Table 2).

The first venous blood sample was collected upon enrollment on the first day of presentation (FIG. 6, Panel B). Patients presenting with dengue with warning signs provided additional blood samples every 48 to 72 hours during their hospital admission. When possible, an additional sample was obtained from all patients following defervescence (1-17 weeks after the initial presentation) during a routine visit to the infectious diseases clinic (FIG. 6, Panel B). 2.5 ml of whole blood were collected in Paxgene tubes (PreAnalytiX) and stored at -80 °C. Serum samples were obtained for additional assays. Samples transport, reception, and processing were strictly controlled using personal data assistants (PDAs) with barcode scanners.

Establishment of dengue diagnosis

Detection of DENV NS1 antigen and IgG/IgM

The SD BIOLINE Dengue Duo combo test (Standard Diagnostic Inc., Korea)(Wang and Sekaran, 2010), which is routinely used in the Department of Pathology (Fundacion Valle del Lili) was used to identify dengue patients for enrollment to the study. qRT-PCR assays for detection of dengue and other microbial pathogens

To confirm the diagnosis of dengue and distinguish from infection with the co circulating arboviruses, Zika vims and chikungunya virus, serum samples were screened with a qualitative, single-reaction, multiplex real-time reverse transcriptase PCR (rRT-PCR) that detects Zika, chikungunya, and dengue virus RNA (Waggoner et ah, 2016). To identify the specific DENV serotype and determine the vims load, samples positive for DENV in the screening assay were serotyped and quantitated using a separate DENV multiplex rRT-PCR (Waggoner et ak, 2013). A single sample was also subjected to rRT-PCR for leptospira.

Multiplexed serological assays on a plasmonic-gold platform ( Zhang et al, 2017).

Multiplexed antigen microarrays including DENV-2 vims-like particles spotted in triplicate were fabricated on pGOLD slides (Nirmidas Biotech, California) and serologic testing performed, as described (Zhang et ak, 2017). Briefly, for DENV IgG and IgM testing, each well was incubated with human sera (400 times dilution) for 40 min, followed by incubation of a mixture of anti-human IgG-IRDye680 conjugate and anti-human IgM- IRDye800 conjugate for 15 min (Vector-Laboratories, Burlingame, CA). Each well was washed between incubation procedures. The biochip was then scanned with a MidaScan-IR near-infrared scanner. IRDye680 and IRDye800 fluorescence images were generated, and the median fluorescence signal for each channel on each microarray spot was quantified by MidaScan software. For each sample, each antigen and each channel, the average of the three median fluorescence signals for three spots was calculated and normalized by positive and negative reference samples through a two-point calibration. Previously defined cutoffs based on mean levels +3 S.D. were used (Zhang et al., 2017). DENV IgG avidity was performed as above in duplicate wells, except that following primary incubation, one well was incubated with 10 M urea for 10 min. Then, anti-human IgG-IRDye680 conjugate was applied to each well and incubated for 15 min.

DENV IgG avidity was calculated by dividing the normalized DENV IgG result of the sample tested with urea treatment by the normalized DENV IgG result of the sample without urea treatment. High avidity (>0.6) is indicative of a past infection, whereas low avidity (<0.6) is consistent with a recent infection.

RNA extraction

RNA was extracted from PAXgene tubes using the PAXgene blood RNA extraction kit (Qiagen) and analyzed for RNA quality using the Agilent bioanalyzer QC analysis.

High-throughput microfluidic qRT-PCR assays

The Biomark Microfluidic qPCR Array was used to quantify the individual transcripts of the signature at the Stanford Human Immune Monitoring Center, as previously described(Cheow et ak, 2015). 50 ng of total RNA was reverse transcribed at 50°C for 15 minutes using the High Capacity Reverse Transcription kit (ABI). Preamplification was performed on a thermocycler following addition of the TaqMan PreAmp Master Mix Kit

(Invitrogen) to the pooled Taqman assays and cDNA. RT enzyme was inactivated and the

Taq polymerase reaction was initiated by bringing the sample to 95 °C for 2 minutes. The cDNA was preamplified by denaturing for 10 cycles at 95 °C for 15 seconds and annealing at

60°C for 4 minutes. The resulting cDNA product was diluted 1:2 with IX TE buffer

(Invitrogen). 2X Applied Biosystems Taqman Master Mix, Fluidigm Sample Loading

Reagent, and preamplified cDNA were mixed and loaded into the 48.48 Dynamic Array (Fluidigm) sample inlets, followed by loading 10X Taqman gene expression assays into the assay inlets. Manufacturer’s instructions for chip priming, pipetting, mixing, and loading onto the BioMark system were followed. RT-PCR was carried out at the following conditions: 10 min at 95 °C followed by 50 cycles of 15 sec at 95°C and 1 min at 60°C. Data were analyzed using software. All reactions were performed in duplicate and Ct values were normalized to 18S RNA and beta-actin. TaqMan reagents are listed below.

Results

Example 1: In silico discovery and validation of a 20-gene set predictive of severe dengue in existing cohorts

A systematic search was performed for whole-genome expression datasets that examined whole blood or PBMCs from patients with acute dengue infection. Ten datasets were identified and they were divided into 7‘discovery’ (Hoang et al., 2010; Kwissa et al.; Loke et al., 2010; Long et al., 2009; Popper et al., 2012; Sun et al., 2013; van de Weg et al., 2015) and 3‘validation’ (Devignot et al., 2010; Nascimento et al., 2009; Simmons et al., 2007) datasets according to chronological order (newer studies held as validation), using samples obtained at admission prior to the development of severe dengue (FIG. 2). Using the multi-cohort analysis framework, 59 significantly differentially expressed genes were identified (false discovery rate (FDR) <10%, effect size >1.3 fold) between patients that progress to DHF and/or DSS (DHF/DSS) vs. patients with an uncomplicated course (DF) in the seven discovery datasets (N=446) (Fig. 1A). Of the 59 differentially expressed genes, the following were up regulated in severe dengue: CTSG, DEFA4, ERP29, ANG, SLC7A5, IFI27L1, CKAP4, GYG1, TM9SF2 and TOR3A whereas the following were down-regulated in severe dengue: EPHX4, CACNA2D2, ENPP5, RAB11FIP5, PDGFD, NCR3, ABI3, C20orfl03, SBK1, SPON2, PTPN4, SOCS2, PDGFRB, CACNA2D3, TMEM63C,

B3GAT1, S1PR5, CX3CR1, SOX13, PAFAH2, TTC16, MLLT3, BZRAP1, AK5, C3orfl8, GFI1B, TRERF1, OLIG1, NEFL, ACVR2A, CHD3, PCSK5, AMIGOl, PTPRM,

JAKMIP2, PGAP3, ZNF575, GBP2, CD96, GAT A3, ARL4C, VILL, TTC39C, GRAP2, SERINC5, FLT3LG, MAP3K14, RELA, and C5orf25. Different combinations of at least 2, at least 3, at least 4, at least 5, etc. of the above genes may be used in certain cases.

An iterative greedy forward search (Sweeney et al., 2015) was applied to the 59 genes and a set of 20 differentially expressed genes (3 over-expressed, 17 under-expressed; see below) was identified in DHF/DSS that was optimized for prognostic power (FIG. 1, Panel B, FIG. 2, and FIG. 4). A dengue score was calculated for each sample by subtracting the mean expression of the 17 under-expressed genes from the mean expression of the 3 over-expressed genes. The 20-gene dengue severity scores distinguished DHF/DSS from DF upon presentation and prior to the onset of severe complications with summary AUC=0.79 [95% Cl: 0.71-0.85]) in the discovery datasets (FIG. 1, Panels C-D and FIG. 5, Panel A).

This 20-gene signature was validated in the validation datasets (N=84) (Devignot et al., 2010; Nascimento et al., 2009; Simmons et al., 2007) (FIG. 2). Despite the significant clinical heterogeneity in these datasets, including in age, host genetic factors represented by country of origin, source of sample, and inclusion criteria, the 20-gene dengue scores accurately identified dengue patients who would develop DHF/DSS in all three datasets (summary AUC=0.78, 95% Cl 0.63-0.88) (FIG. 6, Panel A). Additionally dengue scores were significantly higher in DHF/DSS patients than in those with DF in two of the datasets (Wilcoxon p values: GSE17924, p=8.7e-4; GSE18090, p=3.4e-2), albeit in the third dataset, the dengue scores did not reach statistical significance because of small sample size for the control group (GSE40628, p=l.0e-0l) (FIG. 5, Panel B)

FIG. 1, Panel A depicts a schematic of the multi-cohort analysis workflow for the discovery and validation of the 20-gene set. FIG. 1 , Panel B depicts representative forest plots of an over-expressed (DEFA4, left) and under-expressed (PTPRM, right) genes derived in the forward searches. The x-axis represents standardized mean difference between DHF/DSS and DF. The size of the blue rectangles is inversely proportional to the standard error of mean in the study. Whiskers represent the 95% Cl. The orange diamonds represent overall, combined mean difference for a given gene. Width of the diamonds represents the 95% Cl of overall combined mean difference. FIG. 1, Panel C depicts ROC curves comparing patients with DF with patients with DHF and/or DSS in the 7 discovery data sets. FIG. 1, Panel D depicts a representative violin plot showing the performance of the 20-gene set for separating DHF and/or DSS from DF in one of the discovery cohorts (GSE13052- GPL2700). Wilcoxon P value is shown. ROC=receiver operating characteristic. FDR=false discovery rate; AUC=area under the curve. FIG. 2 depicts publicly available datasets used for the discovery and validation of the 20-gene set.

FIG. 3, Panels A-T depicts forest plots of the over-expressed and under-expressed genes derived in the forward searches. The x axis represents standardized mean difference between DHF/DSS and DF. The size of the blue rectangles is inversely proportional to the standard error of mean in the study. Whiskers represent the 95% Cl. The orange diamonds represent overall, combined mean difference for a given gene. Width of the diamonds represents the 95% Cl of overall combined mean difference. FIG. 4 depicts over-expressed and under-expressed genes identified in the discovery cohort via the multi-cohort analysis.

FIG. 5, Panels A-B depict violin plots showing the performance of the 20-gene set to separate DHF/DSS from DF in the 7 datasets of the discovery cohort (FIG. 5, Panel A) and 3 datasets of the validation cohort (FIG. 5, Panel B).

Example 2: Validation of the 20-gene set in a prospective new cohort of dengue patients

To further validate this signature, a cohort of prospectively enrolled dengue patients in Colombia (“Colombia cohort”) was established (Tables 1, 2). Disease severity was classified on-site using 2009 WHO criteria upon presentation and discharge (WHO, 2009). Forty-one patients were enrolled based on clinical presentation compatible with dengue or dengue with warning signs (patients classified as having severe dengue upon presentation were excluded) and positive NS1 antigen and/or anti-DENV IgM antibody. Whole blood and serum samples were obtained upon presentation and at various time points during the disease course and/or at convalescence (FIG. 6, Panel B). qRT-PCR (Waggoner et al., 2013) and serological assays (Zhang et al., 2017) confirmed the diagnosis of DENV infection in 34 patients. Upon discharge, 9 of these patients were diagnosed with dengue, 17 with dengue with warning signs (including one with dengue-Zika co-infection) and 8 with severe dengue (including one with dengue -pseudomonas co-infection). IgG avidity testing (Zhang et al., 2017) distinguished primary (N=l2) from secondary (N=2l) dengue. Seven patients were excluded from the study due to establishment of alternative diagnoses (Zika (5), leptospirosis (1), and acute viral illness with prior dengue exposure (1)). One patient with severe dengue had degraded RNA leading to PCR failure and was removed from further analyses.

The transcripts for individual genes were quantified by high-throughput microfluidic qRT-PCR assays (Cheow et al., 2015) in samples of confirmed dengue patients. The 20-gene dengue score distinguished severe dengue from dengue with or without warning signs (AUC=0.89, 95% Cl 0.81-0.97) and even severe dengue from dengue with warning signs (AUC=0.85, 95% Cl 0.75-0.94) (FIG. 6, Panels C-E).

The 1997 WHO criteria (WHO, 1997) were used for dengue classification in the publicly available datasets, whereas the 2009 criteria (WHO, 2009) were used in the

Colombia cohort. To account for this difference in diagnosis, the Colombia cohort data was re-analyzed after blindly classifying patients based on the 1997 WHO criteria. The 20-gene dengue score had an AUC=0.97 [95% Cl 0.93-1.0] to distinguish DF from DHF/DSS (FIG. 6, Panels F-G), when using the 1997 WHO criteria.

Because different technologies were used in the various datasets, it was not possible to define a single diagnostic threshold to be used across different cohorts. Nevertheless, since it was desired to identify a gene set that would not miss any of the patients who would progress to severe dengue, a sensitivity of 100% was chosen in each cohort, and computed the corresponding specificity (76-79% in the Colombia cohort) (Table 3).

Next, the utility of laboratory parameters included in the WHO dengue classification was assessed to predict progression to severe dengue in the Colombia cohort. High hematocrit and/or low platelet count upon presentation failed to identify patients at risk to develop severe dengue (Hematocrit: AUC=0.73 [95%CI 0.61-0.84]; Platelets: AUC=0.65 [95%CI 0.53-0.77]; Hematocrit and platelets: AUC=0.77 [95%CI 0.66-0.87]) (FIG. 7,

Panel A), in line with prior publications demonstrating no, low or time-limited predictive power of these parameters (Lam et al., 2017; Leo et al., 2013). Combining these two laboratory values with the 20-gene set did not significantly increase the prognostic power of the latter (AUC=0.9l [95%CI 0.83-0.98]) (FIG. 7, Panel A).

The dengue severity scores negatively correlated with platelet count (R2=0.202, P=0.008), but did not correlate with hematocrit peak, total leukocytes and their subtypes nadir, viral load or dengue exposure (FIG. 7, Panel B). A single patient (#10) with serological evidence for primary infection presented with severe dengue. The DENV serotype did not appear to affect the dengue severity score, albeit the sample number for some serotypes was small (FIG. 7, Panel B).

To determine whether this transcriptomic signature preceded the infection or was triggered by it, its dynamics in longitudinal samples collected from the Colombia cohort were monitored during the disease course and after clinical recovery. The dengue scores progressively declined over time (FIG. 6, Panel H), suggesting that DENV infection itself triggered the higher scores measured in severe dengue patients.

The 20-gene set performed equally in DENV infected children and adults, suggesting that it is not affected by age-dependent variations in immune responses. It also performed well in several immunosuppressed patients and a pregnant patient with severe dengue, yet it unnecessarily predicted severe dengue in two early postpartum patients (Table S5). Larger cohort studies are required to determine its utility in these special populations.

FIG. 6, Panel A depicts ROC curves comparing patients with DHF and/or DSS with DF patients in the 3 existing validation data sets. FIG. 6, Panel B depicts a schematic of patient enrollment and sample collection in the prospective Colombia cohort. In brackets are the number of samples available for each disease category/the number of patients in each disease category. FIG. 6, Panel C depicts a ROC curve comparing patients with severe dengue (SD) with patients with dengue with (DWS) or without (D) warning signs in the Colombia cohort. FIG. 6, Panel D depicts a ROC curve comparing patients with SD with patients with DWS in the Colombia cohort. FIG. 6, Panel E depicts violin plots showing the performance of the 20-gene set to separate SD from D and DWS in the Colombia cohort. FIG. 6, Panel F depicts a ROC curve comparing patients with DHF and/or DSS with DF (1997 WHO criteria). FIG. 6, Panel G depicts violin plots showing the performance of the

20-gene set to separate DF from DHF and DSS in the Colombia cohort (1997 WHO criteria). FIG. 6, Panel H depicts dengue severity scores in longitudinal samples from individuals in the Colombia cohort over time.

FIG. 7, Panel A depicts ROC curves comparing patients with SD with patients with D and DWS based on hematocrit level (FIG. 7, Panel A, top left), platelet count (FIG. 7, Panel A, top right), hematocrit level and platelet count (FIG. 7, Panel A, bottom left) upon presentation in the Colombia cohort. FIG. 7, Panel A, bottom right depicts a ROC curve comparing patients with SD with patients with D and DWS based on the combination of the 20-gene set with hematocrit level and platelet count upon presentation in the Colombia cohort. FIG. 7, Panel B depicts a correlation of the dengue severity score with nadir platelet count, peak hematocrit, nadir of total leukocytes, neutrophils, lymphocytes, monocytes, viral load in serum, prior exposure to dengue, and dengue serotype via linear regression analysis. Mean+SD are shown in the lower mid and right panels.

Table 1. Colombia cohort. Demographic, clinical, and laboratory characteristics of study population.

Table 2. Severe dengue patients in the Columbia cohort.

Table 3. Sensitivity and specificity of the 20 gene-set in the pre-existing cohorts and new Colombia cohort.

Dataset

Specificity

Discovery GSE13052gpl2700 1.00 0.778

GSE25001gpl6104 0.938 0.290

GSE38246gpll5615 0.952 0.326

GSE43777gpl201 1.00 0.670

GSE43777gpl570 0.962 0.690

GSE51808gpll3158 0.900 0.556

EMTAB3162gpl570 0.933 0.323

Mean 0.955 0.519

SD 0.036 0.203

Validation GSE 17924 0.969 0.250

GSE18090 1.00 0.250

GSE40628 0.929 0.500

Mean 0.966 0.333

SD 0.035 0.144

Colombia D.DWS.SD 1.00 0.760

DWS.SD 1.00 0.688

DF.DHF.DSS 1.00 0.792

The 20-gene set predicted the progression to severe dengue early in the course of dengue infection with high sensitivity and specificity (100% and 76-79% in the Colombia cohort, respectively) (Table 3) and was robust to clinical heterogeneity of DENY infection. Example 3: The 20-gene set is enriched in NK and NKT cells

Whether the 20-gene signature was enriched in certain immune cell types was examined using a previously reported cell type enrichment analysis that used publicly available whole genome expression profiles from 25 different types of immune cells (Sweeney et al., 20l6a; Sweeney et al., 2015; Sweeney et al., 20l6b). It was found that the 20-gene set was significantly down regulated (P<0.05) in NK and NKT cells and trended towards enrichment in band neutrophils and metamyelocytes. Applying immunoStates with support vector regression (Bongen et al., 2018; Roy Chowdhury et al., 2018; Vallania et al., 2017) in the discovery and validation cohorts revealed no statistically consistent and reproducible differences in the proportions of the 20 estimated immune cell types, including NK cells, between dengue-infected patients who progressed to severe dengue and those experiencing an uncomplicated course.

These results suggested that the enrichment of NK cells observed for the 20-gene signature was likely due to changes in their expression level, rather than in NK cell population abundance.

The following gene sets (which are subsets of the 20 genes) can be used to predict severe dengue. The 20 gene set is made up of the combination of these gene sets:

pos neg

DEFA4 CACNA2D2, SPON2, CACNA2D3, CHD3, GRAP2, AK5, PTPRM

mean

0.778 0.837 0.744 0.875 0.861 0.656 0.78 0.79 pos neg

GYG1 CX3CR1, TRERF1, GBP2, TMEM63C, SERINC5, SOX13 gse437

gsel3052g gse25001g gse38246g gse43777 gse51808g EMTAB316

77gpl2 mean pl2700 pl6104 pi 15615 gpl570 pll3158 2gpl570

01

0.815 0.746 0.727 0.833 0.841 0.7 0.735 0.771

pos neg

TOR3A NCR3, ABI3, C3orfl8, ENPP5 gsel3052g gse25001g gse38246g gse43777 gse51808g EMTAB316

777gpl mean pl2700 pl6104 pl!5615 gpl570 pi 13158 2gpl570

201

0.815 0.637 0.717 0.847 0.857 0.739 0.645

The preceding merely illustrates the principles of the invention. It will be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the invention and are included within its spirit and scope. Furthermore, all examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the invention and the concepts contributed by the inventors to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. The scope of the present invention, therefore, is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present invention is embodied by the appended claims.

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Claims

CLAIMS That which is claimed is:

1. A method of analyzing a sample, the method comprising:

a. obtaining a biological sample from a patient; and

b. detecting the levels of expression of two or more of DEFA4, GYG1, TOR3A, PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1, TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5 biomarkers in the biological sample.

2. The method of claim 1, wherein the two or more comprise DEFA4, CACNA2D2, SPON2, CACNA2D3, CHD3, GRAP2, AK5, and PTPRM.

3. The method of claim 1, wherein the two or more comprise GYG1, CX3CR1,

TRERF1, GBP2, TMEM63C, SERINC5, and SOX13.

4. The method of claim 1, wherein the two or more comprise TOR3A, NCR3, ABI3, C3orfl8, and ENPP5.

5. The method of claim 1, further comprising generating a report indicating whether the patient has a high risk of severe dengue, wherein the report is based on the gene expression data obtained by step b.

6. The method of claim 1, further comprising forwarding the report to a third party.

7. The method of claim 1, further comprising determining whether the patient is at high risk of severe dengue using the data from step b.

8. The method of any of claims 1-7, further comprising monitoring the patient for a condition.

9. The method of claim 8, wherein the condition comprises kidney failure, bleeding, plasma leakage, shock, and organ failure.

10. The method of claims 1-9, further comprising administering an antiviral treatment and/or supportive care to the patient if the patient is at high risk of severe dengue.

11. The method of claim 10, wherein the organ-specific treatment comprises either or both of connecting the patient to any one or more of a mechanical ventilator, a pacemaker, a defibrillator, a dialysis or renal replacement therapy machine, an invasive monitor including a pulmonary artery catheter, arterial blood pressure catheter, or central venous pressure catheter, or administering blood products, vasopressors, or sedatives.

12. The method of any of claims 1-11, wherein detecting the levels of expression of the two or more biomarkers comprises performing microarray analysis, polymerase chain reaction (PCR), reverse transcriptase polymerase chain reaction (RT-PCR), a Northern blot, a serial analysis of gene expression (SAGE), isothermal amplification such as LAMP or RPA, or next generation sequencing (NGS).

13. The method of claims 1-12, further comprising admitting to a hospital only if the patient is at a high risk of severe dengue.

14. The method of any of claims 1-13, wherein the biological sample comprises blood, huffy coat, band cells, or metamyelocytes.

15. The method of any of claims 1-14, wherein the levels of the two or more biomarkers are compared to time-matched reference values for infected or non-infected subjects.

16. The method of any of claims 1-15, wherein the method comprises detecting the amount of RNA transcripts encoded by two or more of DEFA4, GYG1, TOR3A, PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1, TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5 in a sample of RNA obtained from the patient.

17. The method of claim 1, further comprising diagnosing the patient with severe dengue when increased levels of expression of DEFA4, GYG1 and TOR3A biomarkers, and decreased levels of expression of PTPRM, SPON2, GRAP2, CACNA2D2,

CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1, TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5 biomarkers, compared to the reference value ranges for the biomarkers for a control subject are detected.

18. The method of any of claims 1-17, further comprising determining a dengue score for each biological sample by subtracting the mean expression of the levels of expression of PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1, TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5 biomarkers from the mean expression of the levels of expression of DEFA4, GYG1 and TOR3A biomarkers.

19. A method for treating a patient having a high risk of severe dengue, comprising:

a. receiving a report indicating whether the patient has a high risk of severe dengue, wherein the report is based on the gene expression data obtained by measuring the amount of RNA transcripts encoded by two or more of DEFA4, GYG1, TOR3A, PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1, TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5 in a biological sample of RNA obtained from the patient, wherein increased DEFA4, GYG1 and/or TOR3A and/or decreased PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1, TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and/or ENPP5 indicates that the patient has a high risk of severe dengue; and b. treating the patient based on whether the patient is indicated as having a high risk of severe dengue.

20. The method of claim 19, wherein the treating comprises admitting to a hospital only if the patient is at a high risk of severe dengue.

21. A kit comprising agents for detecting the levels of expression of two or more of DEFA4, GYG1, TOR3A, PTPRM, SPON2, GRAP2, CACNA2D2, CACNA2D3, TMEM63C, AK5, CHD3, CX3CR1, TRERF1, GBP2, SERINC5, SOX13, NCR3, ABI3, C3orfl8, and ENPP5.

22. The kit of claim 21, wherein the two or more comprise DEFA4, CACNA2D2,

SPON2, CACNA2D3, CHD3, GRAP2, AK5, and PTPRM.

23. The kit of claim 21, wherein the two or more comprise GYG1, CX3CR1, TRERF1, GBP2, TMEM63C, SERINC5, and SOX13.

24. The kit of claim 21, wherein the two or more comprise TOR3A, NCR3, ABI3,

C3orfl8, and ENPP5.

25. The kit of claim 21, wherein the kit comprises two or more of: an oligonucleotide that hybridizes to a DEFA4 polynucleotide, an oligonucleotide that hybridizes to a GYG1 polynucleotide, an oligonucleotide that hybridizes to a TOR3A

polynucleotide, an oligonucleotide that hybridizes to a PTPRM polynucleotide, an oligonucleotide that hybridizes to an SPON2 polynucleotide, an oligonucleotide that hybridizes to a GRAP2 polynucleotide, an oligonucleotide that hybridizes to a CACNA2D2 polynucleotide, an oligonucleotide that hybridizes to a CACNA2D3 polynucleotide, an oligonucleotide that hybridizes to a TMEM63C polynucleotide, an oligonucleotide that hybridizes to a AK5 polynucleotide, an oligonucleotide that hybridizes to an CHD3 polynucleotide, an oligonucleotide that hybridizes to an CX3CR1 polynucleotide, an oligonucleotide that hybridizes to an TRERF1 polynucleotide, an oligonucleotide that hybridizes to an GBP2 polynucleotide, an oligonucleotide that hybridizes to an SERINC5 polynucleotide, an oligonucleotide that hybridizes to an SOX13 polynucleotide, an oligonucleotide that hybridizes to an NCR3 polynucleotide, an oligonucleotide that hybridizes to an ABI3 polynucleotide, an oligonucleotide that hybridizes to an C3orfl8 polynucleotide, and an

oligonucleotide that hybridizes to a ENPP5 polynucleotide.

26. The kit of any of claims 21-25, further comprising information, in electronic or paper form, comprising instructions to correlate the detected levels of each biomarker with severe dengue.