WO2012125125A1

WO2012125125A1 - Nucleic acids and methods for determining the outcome of dengue

Info

Publication number: WO2012125125A1
Application number: PCT/SG2012/000087
Authority: WO
Inventors: Mark SHREIBER; Anna LINBLOM; Martin L. Hibberd
Original assignee: Agency For Science, Technology And Research; Novartis Ag
Priority date: 2011-03-15
Filing date: 2012-03-15
Publication date: 2012-09-20

Abstract

The present invention is directed to a method of determining the outcome of dengue in a patient suffering from dengue. The present invention also refers to nucleic acids used in such a method.

Description

NUCLEIC ACIDS AND METHODS FOR DETERMINING THE OUTCOME OF

DENGUE

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority of Singapore patent application No. 201101841-3, filed March 15, 201 1 the contents of it being hereby incorporated by reference in its entirety for all purposes.

FIELD OF THE INVENTION

[0002] The present invention is directed to the field of biochemistry and bioinformatics.

BACKGROUND OF THE INVENTION

[0003] Dengue or Dengue fever is an acute, self-limiting, febrile disease caused by the mosquito-borne dengue virus. The disease is endemic in the tropical region and, as the most common vector-borne viral disease, results in considerable morbidity and economic burden to tropica] countries. The disease is characterized by high fever, severe arthralgia (joint and bone pain) skin rash, retro-orbital pain and vascular leakage. In some cases, the disease will progress and cause the patient to present with one or more serious complications including Dengue Hemorrhagic Fever (DHF) and Dengue Shock Syndrome (DSS). There are currently no vaccines or drugs approved for the clinical treatment of the disease.

[0004] Management of the disease generally involves the palliative treatment of symptoms and close clinical observation for the onset of warning signs or complications. If suitable clinical management is available, most serious complications can be resolved. However, good clinical management of dengue does require experienced clinicians and the need to observe patients closely, in order to catch rare complications. This consumes valuable health resources; when complications do not manifest these resources are wasted.

[0005] Thus, there is a need for improved methods and materials suitable to assist in management of dengue and suitable to assist in effective allocation of health resources. SUMMARY OF THE INVENTION

[0006] In a first aspect, the present invention is directed to a method of determining the outcome of dengue in a patient suffering from dengue. This method can comprise the steps of (a) determining the level of viral dengue RNA in a patient-derived sample,

(b) determining the expression levels of two or more genes in the sample, wherein the genes are selected from a group consisting of CCL2, CDKN1C, CPVL, CYP27A1, LIME1, LYPD2, PDZK4, SLC03A1, TSR1, VSIG1, CCL8, DEFB1, TCF7, STMN3, SIT1, TNFRSF25, HLA-DPB1 , CTSH, GOLGA8A, ELF2, AHNAK, VPS13C, EN02, LRFN3, ATBF1, and CDC2L2;

(c) performing a comparison using the level measured under (a) and the expression levels measured under (b) with the expression of these genes and viral dengue RNA in one group of patients which were already diagnosed with severe outcome of dengue and another group of patients which were already diagnosed with mild outcome of dengue;

(d) classifying the patient as having mild outcome or severe outcome of dengue depending on the comparison performed in step (c).

[0007] In another aspect, the present invention is directed to a method of treating a patient classified according to the method of the present invention as having severe outcome of dengue by subjecting the patient to a dengue immunotherapy or by administering to the patient a medicament used for the treatment of dengue.

[0008] In still another aspect, the present invention is directed to the use of immunotherapy specific, for dengue in the preparation of a medicament for the treatment of patients having a severe outcome of dengue as classified by the method of the present invention.

[0009] In another aspect, the present invention is directed to a primer or probe comprising the nucleotide sequence of any of SEQ ID NO: 1 to 20, or complements thereof.

[0010] In still another aspect, the present invention is directed to a set of primers for amplifying Dengue virus in a test sample. This set of primers can comprise the following pair of forward and reverse primers, in which the nucleotide sequences of the primers comprise or consist of the following sequences, or complements thereof:

a) SEQ ID NO: 1 and 2;

b) SEQ ID NO:7 and 2; and/or

c) SEQ ID NO:8 and 2.

[0011] In another aspect, the present invention is directed to a set of primers and a probe for detecting Dengue virus in a test sample. This set of primers can comprise the forward primers SEQ ID NO:l, SEQ ID NO: 7 or SEQ ID NO: 8 or complements thereof; the reverse primer SEQ ID NO:2 or complements thereof; and one or more of the probe Tof SEO1D^~N0^~:^~ 3, 4, 5, 6 or 9 to 20 or complements thereof.

[0012] In a further aspect, the present invention is directed to a method for determining the presence or absence of a Dengue virus serotype in a biological sample. The method can comprise the step of contacting a nucleotide sequence obtained or derived from the biological sample with at least one primer or probe or set according to the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The invention will be better understood with reference to the detailed description when considered in conjunction with the non-limiting examples and the accompanying drawings, in which:

[0014] Fig. 1 represents an exemplary three layer Artificial Neuronal Network for the prognosis of Mild or Severe Dengue Outcome.

[0015] Fig. 2 represents the statistically significant probes that prognose the onset of severe Dengue.

[0016] Fig. 3 represents a Confusion Matrix and ROC.

DETAILED DESCRIPTION OF THE PRESENT INVENTION

[0017] The availability of biomarkers that are prognostic of the onset of serious disease could assist in the triage of dengue patients and could positively affect the allocation of health resources. A prognostic assay would have both a) a clinical Impact, which means ai) doctors could predict which patients will develop severe outcomes; aii) inform triage of early DF patients; and aiii) reduce patient inconvenience and cost; and b) a drug discovery impact, which means bi) biomarkers of severity should reduce with anti-viral treatment; and bii) recruitment and/or treatment of patients with a higher probability of severity.

[0018] To save lives but also to avoid unnecessary hospital admissions optimal initial management of patients with dengue infection is crucial. Today, biomarkers predicting severe dengue disease are missing and the initial management and classification of the patients is based on the clinical presentation.

[0019] The inventors of the present invention identified a new group of known genes suitable to provide a prognosis/determination of the outcome of dengue in a patient showing the first signs of dengue. Therefore, in a first aspect the present invention is directed to a method of determining the outcome of dengue in a patient suffering from dengue. This method can comprise or consists of the steps of:

(a) determining the level of viral dengue RNA in a patient-derived sample,

(b) determining the expression levels of two or more genes in the sample, wherein the genes are selected from a group consisting of CCL2, CDKNIC, CPVL, CYP27A1, LIMEl,

LYPD2, PDZK4, SLC03A1, TSR1, VSIG1, CCL8, DEFB1, TCF7, STMN3, SIT1, TNFRSF25, HLA-DPB1, CTSH, GOLGA8A, ELF2, AHNAK, VPS13C, EN02, LRFN3, ATBF1, and CDC2L2;

[0020] In one example, there is provided a method as described herein in which the genes are CCL2, CDKNIC, CPVL, CYP27A1, LIMEl, LYPD2, PDZK4, SLC03A1, TSR1 and VSIG1 and/or any combination thereof.

[0021] In one example, the group of genes used for comparison and classification is the group of genes as described in table 1 below.

[0022] Further, the inventors have also identified that some of the genes in the group that is suitable for prognosis may be interchangeable with other genes, based on the positive correlation between these genes. The list of the interchangeable genes is presented in table 2.

[0023] Thus, in another example,there is provided a method as described herein, wherein CCL2 is replaced by a gene selected from the group consisting of CCL8 and DEFB1.

[0024] In a further example, there is provided a method as described herein, wherein LIMEl is replaced by a gene selected from the group consisting of TNDRSF25, SITl and STMN3.

[0025] In another example, there is provided a method as described herein, wherein CDKN1C is replaced by ATBF1.

[0026] In one example, there is provided a method as described herein, wherein PDZK4 is replaced by STMN3.

[0027] In a further example, there is provided a method as described herein, wherein SLC03A1 is replaced by a gene selected from the group consisting of AHNAK, CTSH, ATBF1, ELF2, CDC2L2, HLA-DPB1 and VPS13C.

[0028] In another example, there is provided a method as described herein, wherein TSR1 is replaced by a gene selected from the group consisting of AFTNAK, CTSH, ATBF1, ELF2, CDC2L2, HLA-DPB1, VPS13C, EN02, LRFN3 and GOLGA8A.

[0029] In one example, there is provided a method as described herein, wherein VSIG1 is replaced by a gene selected from the group consisting of TNDRSF25, TCF7, STMN3 and SITl .

[0030] This method allows determining whether the course of the disease is going to have a mild outcome or a severe outcome of dengue. "Severe outcome", also referred to as "poor prognosis", as known in the art and used herein refers to a prediction/determination that indicates the likelihood of a patient requiring strict observation and medical intervention. In one example severe outcome means that the patient would need hospitalization. Patients identified to have severe outcome would typically have or would develop at least one of the World Health Organization (WHO) defined dengue warning signs including fever, abdominal pain or tenderness, persistent vomiting, clinical fluid accumulation, mucosal bleed, lethargy, restlessness, liver enlargement (more than 2 cm) and laboratory increase in hematocrit (HCT/pack cell volume) with concurrent rapid decrease in platelet count. Patients identified to have poor prognosis may also develop severe dengue, such as Dengue Hemorrhagic Fever (DHF) or Dengue Shock Syndrome (DSS), with clinical symptoms such as severe plasma leakage, which may lead to shock and fluid accumulation with respiratory distress, severe bleeding and severe multiple organ impairment, such as liver AST or ALT enzyme level of > 1000, central nervous system impairment/impaired consciousness, heart or other organ failure.

[0031] On the other hand "mild outcome" as used herein refers to a dengue positive patient that does not develop warning signs such as those described above for severe outcome.

[0032] In general, dengue is caused by Dengue virus (DENV), a mosquito-borne flavivirus. DENV is a single stranded RNA positive-strand virus of the family Flaviviridae, genus Flavivirus. Dengue virus causes a wide range of diseases in humans, from a self limited Dengue Fever (DF) to a life-threatening syndrome called Dengue Hemorrhagic Fever (DHF) or Dengue Shock Syndrome (DSS).

[0033] The life cycle of dengue involves endocytosis via a cell surface receptor. The virus uncoats intracellularly via a specific process. In the infectious form of the virus, the envelope protein lays flat on the surface of the virus, forming a smooth coat with icosahedral symmetry. However, when the virus is carried into the cell and into lysozomes, the acidic environment causes the protein to snap into a different shape, assembling into trimeric spike. Several hydrophobic amino acids at the tip of this spike insert into the lysozomal membrane and cause the virus membrane to fuse with lysozome. This releases the Dengue virus RNA into the cell and infection starts. In an infected person the viral RNA can not only be detected in cells but also in bodily fluids, such as blood.

[0034] Thus, determining the level of (total) viral dengue RNA means measuring the viral dengue RNA which can be found in the sample of a patient. The sample can be a blood sample or blood plasma sample. RNA stands for ribonucleic acid while for example DNA stands for deoxyribonucleic acid. Methods to determine the viral dengue RNA in a sample obtained from a patient are known in the art, such as nucleic acid based tests. Nucleic acid based tests can include, but are not limited to reverse transcription polymerase chain reaction (RT-PCR), nucleic acid sequence based amplification (NASBA) or an reverse transcription- loop-mediated isothermal amplification (RT-LAMP) assay targeting the 3' non-coding region for the rapid detection of the dengue virus. Primers and probes that can be used for such methods are described herein.

[0035] The term probe as used herein refers to a short sequence of deoxyribonucleic acid (DNA) that can specifically hybridise to the target DNA without exhibiting non-specific hybridisation to uninfected DNA and the term primer as used herein refers to an oligonucleotide capable of acting as a point of initiation of synthesis of a primer extension product that is complementary to a nucleic acid strand (template or target sequence), when placed under suitable conditions (e.g., salt concentration, temperature, and pH) in the presence of nucleotides and other reagents for nucleic acid polymerization (e.g., a DNA dependent or RNA dependent polymerase).

[0036] It is known in the art that a primer must be of a sufficient length to prime the synthesis of extension products. A typical primer contains at least 10 nucleotides, and is substantially complementary or homologous to the target sequence. The dengue virus-specific primers of the present invention can be a nucleic acid of 10 to 30, or 15 to 30, or 15 to 20, or 18 to 22 nucleotides in length and including, for example, nucleotides of SEQ ID NO: 1 to 20. The lengths of the two primers in a pair may not be the same. In one example, the primers and probes employed in determining the total viral dengue RNA in a patient sample comprise or consist of the nucleotide sequence of any one of SEQ ID NO: 1 to 20, or complement thereof.

[0037] The probe as used herein may be conjugated to a detectable label at the 5' end and/or a quencher at the 3' end. A detectable label attached to a probe may be in the form of a fluorophore as known in the art. In one example, the detectable label used may be SYTOX- Blue.

[0038] In one example, there is provided a method as described herein, wherein step (a) is performed using (i) a primer or a probe having nucleotide sequence comprising or consisting any of SEQ ID NO: 1 to 20, or complement thereof, and/or (ii) a set of primers having nucleotide sequences comprising or consisting of any one of SEQ ID NO: 1 and 2, SEQ ID NO: 7 and 2, or SEQ ID NO: 8 and 2 and/or complement thereof; and/or (iii) a probe having a nucleotide sequence comprising or consisting of SEQ ID NO: 3, 4, 5, 6, 9, 10, 1 1, 12, 13, 14, 15, 16,17, 18, 19 or 20.

[0039] In a further example, the set or probe as described herein, in which the detectable labels for each of the probe sequences of SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5 and SEQ ID NO: 6 is such that the probe sequences are independently detectable. The probes and primer sequences for the detection of dengue viral infection as employed is shown in Tables 3 to 7.

[0040] In one example, a primer or probe comprising the nucleotide sequence of any of SEQ ID NO: 1 to 20 or complements thereof is employed.

[0041] In another example, a forward primer comprising of the nucleotide sequence of any of SEQ ID NO: 1, 7 or 8 or complements thereof is used for amplifying a nucleotide sequence of a Dengue virus serotype in a test sample.

[0042] In a further example, a reverse primer comprising the nucleotide sequence of SEQ ID NO: 2 or complement thereof is used for amplifying a nucleotide sequence of a Dengue virus serotype in a test sample.

[0043] In yet another example, a probe comprising or consisting the primer sequence of nucleotide sequence of any one of SEQ ID NO: 3, 4, 5, 6, or 9 to 20, or complements thereof, is employed.

[0044] In one example, there is provided a set of primers and a probe for detecting Dengue virus in a test sample comprising the forward primers SEQ ID NO: 1, SEQ ID NO: 7 or SEQ ID NO: 8 or complements thereof, the reverse primers SEQ ID NO: 2 or complements thereof; and one or more of the probes of SEQ ID NO: 3, 4, 5, 6 or 9 to 20, or complements thereof.

[0045] In another example, there is provided a set of primers, comprising a forward primer and a reverse primer, and a probe, as described herein comprising the following sequences or complements thereof:

(i) SEQ ID NO:l ; SEQ ID NO:2; and SEQ ID NO:3; (ii) SEQlD NO:1; SEQ ID NO:2; and SEQ ID NO:4;

(iii) SEQ ID NO:1; SEQ ID NO:2; and SEQ ID NO:5; and/or (iv) SEQ ID NO: 1 ; SEQ ID NO:2; and SEQ ID NO:6 in which Y represents C or T; and R represents A or G.

[0046] In one example, there is provided a method for determining the presence or absence of a Dengue virus serotype in a biological sample, comprising the step of contacting a nucleotide sequence obtained or derived from the biological sample with at least one primer or probe or set according to any of the methods as described herein.

[0047] In a further example, there is provided a method as described herein in which the amplification conditions comprise an amplification reaction, and in which the amplification reaction is a polymerase chain reaction (PCR).

[0048] In one example, there is provided a method as described herein further comprising the step of determining whether the nucleotide sequence hybridises to the at least one primer or probe under stringent conditions, thereby detecting whether the sample contains a Dengue virus serotype.

[0049] In another example, there is provided a method as described herein comprising the steps of:

(a) contacting a nucleotide sequence obtained or derived from a biological sample with at least one forward primer and at least one reverse primer, or at least one set, under amplification conditions to generate an amplicon of a region of a dengue serotype nucleotide sequence; and

(b) detecting hybridization between the amplicon and at least one probe according to any of the methods as described herein.

[0050] Other primers may be designed and employed to determine the dengue viral RNA in a patient sample. For example, primers can be designed using appropriate software programs known in the art and prepared by synthetic or recombinant methods such as RT- PCR assay to determine whether any of them can be used to practice the method of detecting or quantifying dengue virus.

[0051] It is known that it often takes some time before the first viral RNA can be detected after an infection. Thus, in one example a sample from a patient can be taken during early fever outbreak after infection with the dengue virus. Depending on the individual patient the period until onset of early fever can vary. In one example, the sample can be obtained 72 hours of onset of dengue fever or within 72 hours post-onset of fever. A fever is defined by a body temperature of above 37.5°C for a human patient.

[0052] In still another example, the sample can be obtained from a patient during stage 2, stage 3 or stage 4. The term "stage of disease" as used herein refers to a particular stage of dengue progression in a patient.

[0053] Stages of dengue disease progression are well known in the art and can, for example, be characterized as follows: Stage 1 represents the pre-viraemia stage; stage 2 represents the blood viraemia stage (the period for administering anti -viral drugs); stage 3 represents the critical pre-or early hospitalization stage (the period for administering antiinflammatory drugs); stage 4 defines the hospitalization stage (the period for administering disease management clinical methods); while finally stage 5 represents the post disease stage.

[0054] A patient suffering from dengue is considered a dengue positive patient. A "dengue positive patient" as used herein refers to patients who are seropositive for dengue virus. So far four different serotypes of dengue virus are known, DENV-1 to DENV-4. Patients are seropositive for life following their first infection, but may have dengue a few more times, such as three more times. A dengue positive patient could be identified by a number of techniques, including clinical symptoms, viral protein or RNA detection, or anti -viral antibody detection level that is rapidly increasing. For example, Dengue positive patients may be diagnosed through methods known in the art, including methods, but not limited to viral isolation and serotype identification, nucleic acid detection, antigen detection, IgM enzyme- linked immunosorbent assay (ELISA), IgG paired sera by ELISA, hemaglutination inhibition assay or neutralization test.

[0055] In one example, the standard used for reference or comparison between a dengue positive patient and a dengue-negative patient is a patient-derived sample from dengue negative febrile patients.

[0056] A gene referred to herein comprises the code required to construct a protein. Thus a gene is a collection of deoxyribonucleic acid (DNA) in sequence. Methods for determining the expression levels of a gene are known in the art. Expression of a gene describes that every gene directs the production of a particular protein. Standard methods for measuring the gene expression level include, but are not limited to differential display, RNAse protection assay or Northern blotting, both methods which detect the amount of RNA in a cell or sample. Other methods include the use of microarrays.Oligonucleotide-based microarrays can either be produced by spotting pre-synthesised short oligonucleotides onto glass or synthesised in situ on the surface of silicon wafers by photo-lithography (often referred to as oligonucleotide chips). In contrast, cDNA microarrays consist of longer DNA fragments, either inserts from cDNA libraries or PCR products generated from gene-specific primers, which are printed onto glass slides or nylon membranes. Primers and probes that can be used in the exemplary methods referred to above and for microarray^'s are described herein.

[0057] The expression levels of the genes referred to in the above group which are measured in a sample obtained from a patient are compared with a standard created on the basis of a pool of patients which are known to have a mild outcome or poor outcome of the disease after dengue virus infection or recurrence.

[0058] For creating an exemplary standard which can be used as reference in the method of the present invention the expression levels of the group of genes of the present invention were determined a) in one group of patients which were already diagnosed with severe outcome of dengue and b) another group of patients which were already diagnosed with mild outcome of dengue.

[0059] Two tables referred to herein, i.e. Tables 9 and 10 represent examples of such reference groups. One for a mild outcome group and the other one for the severe outcome group. In Table 9 and 10 the results of measurement of the average expression level within one reference group are shown. These exemplary reference groups can be used for performing the comparison referred to in step (c) and for classifying the patient (step (d)). It will be understood that depending on the size of the group or the origin of the patients tested to create a reference group for mild outcome and severe outcome, the measured values referred to in Table 9 and 10 can vary. The values described in Table 9 and 10 illustrate the average of the measurement of the expression level for each of the genes in the group of genes of the present invention. In one Table the average values from a specific group of patients who are known to suffer from dengue and who had a mild outcome are shown while in the other Table the average values from a specific group of patients who are known to suffer from dengue and who had a severe outcome are shown.

[0060] The group of patients tested to establish the above referenced reference groups can vary and so can the values obtained from these measurements. However, this does not influence the usability of these references in the method of the present invention. [0061] The above described references can be used in the method of the present invention.- In brief, the method of the present invention would work as follows. Once the expression of genes of a test patient for which the outcome of the disease is to be determined has been measured, the expression level for each of the genes is allocated either to the severe outcome group or the mild outcome group (e.g. using Table 9 and 10) depending on how close the measured value of the test patient is to the average value of expression in either one of these two groups. If, for example, most of the measured values for each of the genes falls into the mild outcome group the patient is then classified to have a mild outcome of the disease.

[0062] For carrying out this comparison any kind of statistical analysis can be used. The method used can also comprise giving different weight to different genes thus influencing whether the final determination shifts, e.g. from mild outcome to severe outcome, even though the majority of the measured gene expression values falls into the group of mild outcome.

[0063] Suitable mathematical methods for this kind of analysis are known in the art. In one example an artificial neural network is employed. In one embodiment a multilayer artificial neural network, such as a three-fold artificial neural network can be used.

[0064] An artificial neural network can be used in the following exemplary manner. The disclosed biomarkers as. claimed can be used to produce a prognostic prediction of dengue patient outcome using samples taken, for example, up to 72 hours after fever onset. As an example it is shown an Artificial Neural Network (Fig. 1) that was used to produce reliable predictions from a certain set of patient data. The model can be re-used by those who have biomarker measurements and desire a prognostic prediction. The model is provided as an example and other techniques of machine learning or statistical inference could also be employed that make use of the claimed biomarkers. For each patient with dengue fever a blood sample was obtained during early fever (less than 72 hours). Following which, total RNA was extracted from the blood sample. R A measurements expressed as "Cross over thresholds" or Ct values were obtained for each of the biomarker genes. Such cross over values can be obrtained using Fluidigm Biomark quantitative PCR (qPCR). Cross over thresholds are normalized to the patients 16S RNA level (for example measured using Fluidigm Biomark qPCR) by subtracting the 16S RNA Ct value from each of the measure biomarker values. The copy number of the dengue viral RNA in a bodily fluid, such as blood, can also be measured. In one example the copy number of the dengue viral RNA in the bodily fluid is measured using Fluidigm biomark and normalized to the patients 16S RNA. Normalized Ct values for each patient can then be added to a spreadsheet with each row representing a single patient and each column representing a single biomarker. A neural Network software is used in one example to read along with the patient data. In one example, using the Rapid Miner software the Neural Network model is read along with the patient data. The data can then be fed into the model which results in a prediction of "severe" or "mild" for each patient. The software makes this prediction based on the relative activation of the "severe" and "mild" nodes of the artificial neural network model. The severe or mild prognosis is then recorded for each patient. Exemplary weighted connections assigned to each gene (biomarker) are listed, for example, in Table 1 1.

[0065] For example, in an artificial neural network while establishing the two reference groups (mild and severe outcome), the expression of different genes within the group of genes of the present invention is more prominent or characteristic for classification than other genes. Such variances can for example vary depending on the group of patients used to establish the reference groups. An artificial neural network can account for such differences and thus produce more accurate results than simpler statistical methods. For example, Fig.l illustrates an artificial neural network.

[0066] Further, RNA probes can be used with a neural network classifier to prognose the onset of severe symptoms of Dengue. For example, 11 RNA probes (CCL2, LIME1, VSIG1, PDZK4, CPVL, TSR1, SLC08A1, CYP27A1, LYPD2, CDKN1C, Viral RNA Ct Value) can be used with a Neural Network classifier to prognose the onset of severe symptoms of dengue; with a sensitivity of 90% (+/- 12.25%) and Specificity of 76.33%) (+/- 13.86) ; estimated using lOx cross-validation; as illustrated in Fig. 2.

[0067] The present invention can be advantageously utilized in primary healthcare and hospital settings to prognose Dengue. In addition, the present invention can also be used by drug companies to give evidence that a drug intervention has resulted in reduced disease severity (i.e. by comparing predicted and actual hospitalization rates) during drug trials. Further, the present invention can also be used as a therapeutic companion diagnostic, identifying patients at most in need of intervention and monitoring the success of the intervention.

[0068] The invention illustratively described herein may suitably be practiced in the absence of any element or elements, limitation or limitations, not specifically disclosed herein. Thus, for example, the terms "comprising", "including", "containing", etc. shall he read expansively and without limitation. Additionally, the terms and expressions employed herein have been used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the present invention has been specifically disclosed by preferred embodiments and optional features, modification and variation of the inventions embodied therein herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention.

[0069] The invention has been described broadly and generically herein. Each of the narrower species and subgeneric groupings falling within the generic disclosure also form part of the invention. This includes the generic description of the invention with a proviso or negative limitation removing any subject matter from the genus, regardless of whether or not the excised material is specifically recited herein.

[0070] Other embodiments are within the following claims and non-limiting examples. In addition, where features or aspects of the invention are described in terms of Markush groups, those skilled in the art will recognize that the invention is also thereby described in terms of any individual member or subgroup of members of the Markush group.

EXPERIMENTAL SECTION

[0071] PATIENT DEMOGRAPHICS

[0072] In a period of about 3 years, a total of 1,315 subjects were enrolled. Of the 1 ,315 patients, 212 (16%) had real-time PCR confirmed dengue infection. Clinical samples and/or data were missing in 51 cases resulting in a total study population of 161 cases. Of the 161 cases, 76 had at least one of the two warning signs. As it was desired to have daily follow up data on the clinical picture, only hospitalized patients were considered in the study resulting in a group of 47 cases referred to as "patients with warning signs". Of these, 43 (91%) had mucosal bleeding and 8 (17%) persistent vomiting. There were 85 patients that did not show any warning sign. Of these, only the non hospitalized patients were considered in the study due to an overrepresentation of other underlying conditions in the hospitalized group without warning signs (data not shown) given us a total number of 45 cases referred to as "patients without warning signs". There were no apparent difference age between the two groups with a median age of 37 (range 19-77) in the group of patients with warning signs and 41 (range 21-63) in the group without warning signs. There was also an equal gender distribution with 49 % females in both groups.

[0073] The patients with warning signs were admitted to hospital in median 4 days (range 1-7) after fever onset and hospitalized for in median 3 days (range 1-7). Of the 43 patients with mucosal bleeding, 31 (72%) had mucosal bleeding at time of hospital admission while 1 1 (26%) developed bleeding at any time during hospitalization. Six (14%) of the 43 patients had signs of mucosal bleeding at time of inclusion. The gum was the most common bleeding localization (n= 18) followed by skin (n=12), menstrual bleeding (n=8), nose bleeding (n=5), hematuria (n=3) and per rectum bleeding (n=2). Six of the eight patients with persistent vomiting reported that at the first and second visit and 2 of 8 patients at the second visit and during hospitalization. No patients presented with "severe dengue" as defined by the new WHO criteria.

[0074] It is noted that the old definitions of dengue fever (DF), dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS) by World Health Organization (WHO) have been criticized as they are excluding patients with severe clinical disease not fulfilling the criteria. Therefore, WHO recently published re-defined definitions together with treatment guidelines to be used in the management of acutely dengue infected patients This new definition classifies the patients into three different clinical groups, "probably dengue", "dengue with warning signs" and "severe dengue".

[0075] DETERMINING SIGNIFICANCE OF VIRAL COPY NUMBER IN PATIENTS SUFFERING FROM DENGUE KNOWN TO HA VE MILD OUTCOME AND SEVERE OUTCOME

[0076] A comparison of viral copy number, platelet levels and lymphocyte counts between the two groups are determined. The median viral genome copy numbers in blood were 100 fold higher in the group of patients with warning signs as compared with the patients without warning signs; 3,32E+09 and 2,88E+07 respective, p= 0.0027. There was also a statistical significant difference in platelet levels between the two groups at the first and second sampling time point, p=0,0001 and p=0,0001 respective. Lymphocyte counts did also statistically differ at the two first sampling points, p=0,0031 and p=0,0369 respectively. There was no statistical significance in lymphocyte and platelet counts between the two groups at the third study clinic visit (data not shown).

[0077] Afterwards, the frequency of secondary dengue infection between the two groups was examined. There were no statistical differences between the groups with 25 out of 47 (53%) of the patients with warning signs and 20 out of 45 (44%) seropositive at the first visit, p=0.4649. However, the viral genome copy numbers did differ within the warning sign group when the patients into were divided into primary and secondary dengue; the viral gene copy numbers were 6,33E+07 in the patients with secondary dengue and 6,33E+09 in the patients with primary infection, p=0.0038. No difference was noticed in the group of patients without warning signs with a median viral copy number of 3,26E+07 in the group with secondary dengue and 3,68E+07 in the group with primary infection, p=0.1345.

[0078] DETERMINING BIOMARKERS/PROGNOSTIC MARKERS BY CARRYING OUT A WHOLE GENOME GENE EXPRESSION PROFILING

[0079] To see if it is possible to identify prognostic markers for patients with warning signs the gene transcript abundance from the first sampling point was compared between the two groups. After analyzing by significant analysis of microarrays (SAM), 23 genes came out differently expressed and of which 17 were significantly enriched and 6 less abundant in the group of patients with warning signs when compared to the group without warning signs. Of these 23 genes, 15 have earlier been correlated to dengue infection as they have been shown to be differently expressed in a set of acutely dengue infected patients (sampled within 72 hours after fever onset) when compared to their convalescent samples (unpublished data). Additionally, 12 of the 23 genes were identified when comparing dengue positive patients with a group of dengue negative febrile patients both groups sampled within 72 hours after fever onset (unpublished data). Interestingly, CDKN1C came out down regulated in the group with warning signs as compared with the patients without warning signs but was up regulated in the acutely infected dengue patients as compared with their convalescent samples and in the group with acute dengue infection as compared to non-dengue patients.

[0080] To explore if there were any biological relationships between the differently expressed genes an unsupervised IP A^® network analysis (pathway-analysis) was performed (IP A^® software is provided by Ingenuity^® Systems, Inc., US). Of the 23 differently expressed genes, 8 clustered into the outlined network which indicates possible biological key functions of the chemokines CCL2 and CCL3. These two, together with CCL8 and CD69, are important mediators of the inflammatory response and are important in the recruitment and activation of other inflammatory cells. Pathway analyzes revealed two significant canonical pathways; TREM1 signaling and the glucocorticoid signaling pathway. However, only 2 and 3 genes from the list referred to in Table were identified in each pathway. CCL2 and CCL3 were included in the TREM1 signaling pathway and CCL2, CCL3 and CDK 1C in the glucocorticoid signaling pathway.

[0081] DISCUSSION OF INITIAL STUDY FOR IDENTIFYING BIOMARKERS

[0082] Dengue infection can cause a spectrum of illness ranging from asymptomatically to life threaten disease and is therefore an insidious illness. In addition, the global emergence of dengue in adults along with the difference in clinical outcome of dengue infection compared to children necessitate detailed clinical investigation into adult disease, particularly since no animal model adequately reflects the disease outcomes that are seen in humans. For this study 92 adult patients presenting with acute dengue infection were classified into two groups based on WHO new guidelines. By doing that it was possible to demonstrate that early laboratory parameters such as lymphocyte count, platelet counts and viral genome copy numbers could differentiate patients with warning signs from patients without warning signs.

[0083] Earlier studies have tried to clinically distinguish dengue fever from other acute febrile illnesses. In a detailed review of the dengue literature, Potts and Rothman highlighted that clinical laboratory variables such as low platelet counts, white blood cells and neutrophil counts could be useful in recognizing dengue disease early. Also several clinical signs such as hemorrhage, rash and myalgia could be connected to dengue disease but were age dependent. More recently, Binh and colleagues presented descriptive clinical and laboratory data and found that frequent vomiting (> 3 times a day), marked lymphopenia, thrombocytopenia, and elevated liver enzymes on day 3 after onset of fever were significantly associated with plasma leakage and gallbladder thickening in an adult population. That study, however, was limited by the short period of patient recruitment (four months) and the data biased by the limited circulating viral genotypes. For the present study we classified patients into two groups, patients with warning signs and patients without warning signs. For the classification, the two warning signs persistent vomiting and mucosal bleeding were used. Mucosal bleeding was a far more common clinical presentation than persistent vomiting. Mucosal bleeding was not an early symptom in the acutely infected patients and only 14% of the patients with mucosal bleeding showed signs of that at time of inclusion. Instead, 72% had mucosal bleeding at time of hospitalization which corresponds to time of deference. Persistent vomiting was seen earlier and presented already at the first and second study clinic visits in the majority of the cases. When laboratory data between the two groups was compared, it was possible to observe significant differences between the two groups already at the first study sampling time point with significant lower platelet and lymphocyte counts in the group with warning signs. Additionally, the viral genome copy numbers were 100 fold higher in the group with warning signs. Hence, the study could confirm that mucosal bleeding and persistent vomiting is associated with a more severe clinical picture marked by a high viral genome copy number, decreased platelet levels and lymphopenia and that these early parameters are seen before the development of mucosal bleeding.

[0084] High viral genome copy numbers have earlier been correlated with antibody- dependent enhancement (ADE) and seen in patients presenting with secondary dengue infections and severe dengue disease. In the present study, the patients with warning sign have a significantly higher viral load as compared to the group without warning sign which support the theory that severe dengue disease is driven by a high viral load. However, it was not possible to detect correlation between secondary dengue infection and a high viral load. The pathogenesis behind severe dengue infection is not fully understood and difficult to study as working animal models are missing. However, imbalances of inflammatory mediators driven by a high viral burden are believed to be responsible for dengue virus induced immune-mediated pathology. A range of cytokines have been correlated to dengue virus infection both in vitro and in vivo. CXCL10 have been detected in geographically different patient cohorts and also correlated to patients with hemorrhagic manifestations. Also cytokines correlated to dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS) have been identified. An evaluated RNA level of CCL2 was identified in a group of children with DSS/DHF as compared to a group of children with dengue fever (DF). Additionally, another study identified higher protein levels of CXCL9, CXCL10 and CXCL1 1 but not CCL2 in a group of children with DHF as compared to children with DF. CCL2 had also been shown to play a role in modulating vascular permeability. In the present study 23 genes were differently expressed in the group of patients with warning signs as compared to the group without warning signs. Interestingly, 8 of these 23 genes clustered into a network with genes involved in the innate immune response. Four of the genes, CCL2, CCL3, CCL8 and CD69 all play a role in the pro inflammatory response and it is interesting to speculate whether a stronger inflammatory activation could explain the more severe clinical picture in the warning sign group. These genes can thus serve as biomarkers predicting patients in need of clinical referral. For creating an exemplary standard which can be used as reference in the method of the present invention the expression levels of the group of genes of the present invention were determined a) in one group of patients which were already diagnosed with severe outcome of dengue and b) another group of patients which were already diagnosed with mild outcome of dengue.

Table 10. Profile of patients who do not develop warning signs (Mild Outcome)

[0086] EXAMPLARY METHOD OF PROGNOSIS USING A MACHINE LEARNING ALGORITHM

[0087] The disclosed biomarkers as claimed can be used to produce a prognostic prediction of dengue patient outcome using samples taken, for example, up to 72 hours after fever onset. As an example it is shown an Artificial Neural Network (Fig. 1) that was used to produce reliable predictions from a certain set of patient data. The model could be re-used by those who have biomarker measurements and desire a prognostic prediction. The model is provided as an example and other techniques of machine learning or statistical inference could also be employed that make use of the claimed biomarkers. For each patient with dengue fever a blood sample was obtained during early fever (less than 72 hours). Following which, total R A was extracted from the blood sample. RNA measurements expressed as "Cross over thresholds" or Ct values were obtained for each of the biomarker genes using Fluidigm Biomark quantitative PCR (qPCR). Cross over thresholds were normalized to the patients 16S RNA level (also measured using Fluidigm Biomark qPCR) by subtracting the 16S RNA Ct value from each of the measure biomarker values. The copy number of the dengue viral RNA in the blood was also measured using Fluidigm biomark and normalized to the patients 16S RNA. Normalized Ct values for each patient where added to a spreadsheet with each row representing a single patient and each column representing a single biomarker. Using the Rapid Miner software the Neural Network model is read along with the patient data. The data is fed into the model which results in a prediction of "severe" or "mild" for each patient. The software makes this prediction based on the relative activation of the "severe" and "mild" nodes of the model. The severe or mild prognosis is then recorded for each patient. The weighted connections assigned to each gene (biomarker) is listed in Table 11.

[0088] In Table 11, the source node indicates the source node of the connection and the destination indicates the destination node of the connection. The weight indicates the weighting of the connection between the source node and the destination node.

[0089] In this instance, a three layer artificial Neural Network for the prognosis of Mild or Severe Dengue outcome is employed. With reference to Fig. 1 , the first layer represents the input layer; each node is named for the biomarker input. Normalized cross-over threshold values obtained by Q-PCR and imputed into this layer. The second layer is the hidden layer and the final layer or output layer generates the mild or severe prognosis. Absolute connection weights between the layers (as shown in table 1 1) are represented by the thickness of the lines between the nodes with thicker lines represented more heavily weighted connections. Red lines indicate "positive" weights, blue present negative weights. The two "Threshold" nodes are not input nodes but represent node activation thresholds. In addition, an exemplary classifying algorithm that may be used in conjunction with the RapidMiner5 software in developing the artificial neural network is shown below:

Claims

1. A method of determining the outcome of dengue in a patient suffering from dengue comprising the steps of:

(a) determining the level of viral dengue RNA in a patient-derived sample,

(b) determining the expression levels of two or more genes in the sample, wherein the genes are selected from a group consisting of CCL2, CDKNIC, CPVL, CYP27A1, LIME1, LYPD2, PDZK4, SLC03A1, TSR1, VSIG1, CCL8, DEFB1, TCF7, STMN3, SIT1, TNFRSF25, HLA-DPB1, CTSH, GOLGA8A, ELF2, AHNAK, VPS13C, EN02, LRFN3, ATBF1, and CDC2L2;

2. The method according to claim 1 wherein an average expression of the genes in the group of patients which were already diagnosed with severe outcome of dengue is as shown in Table 3.

3. The method according to claim 1 wherein an average expression of the genes in the group of patients which were already diagnosed with mild outcome of dengue is as shown in Table 4.

4. The method according to claim 1 or 2, wherein step (c) and (d) is performed using a mathematical model.

5. The method according to claim 4, wherein the mathematical model is an Artificial Neural Network.

6. The method according to claim 5, wherein the neural network is a multilayer Artificial Neural Network.

7. A method according to any one of the preceding claims, in which the patient-derived sample is obtained at dengue fever stage 2, stage 3 or stage 4, or within 72 hours of onset of fever.

8. A method according to any one of the preceding claims, in which the two or more genes are CCL2, CDKNIC, CPVL, CYP27A1, LIMEl, LYPD2, PDZK4, SLC03A1, TSRl, VSIG1 and/or any combination thereof.

9. The method according to claim 8, wherein CCL2 is replaced by a gene selected from the group consisting of CCL8 and DEFB1.

10. The method according to claims 8 or 9, wherein CDKN 1 C is replaced by ATBF 1.

11. The method of any one of claims 8 to 10, wherein LIMEl is replaced by a gene selected from the group consisting of TNDRSF25, SITl and STMN3.

12. The method of any one of claims 8 to 11, wherein PDZK4 is replaced by STMN3.

13. The method of any one of claims 8 to 12, wherein SLC03A1 is replaced by a gene selected from the group consisting of AHNAK, CTSH, ATBF1, ELF2, CDC2L2, HLA- DPB1 and VPS 13C.

14. The method of any one of claims 8 to 13, wherein TSRl is replaced by a gene selected from the group consisting of AHNAK, CTSH, ATBF 1 , ELF2, CDC2L2, HLA-DPBl , VPS13C, EN02, LRFN3 and GOLGA8A.

15. The method of any one of claims 8 to 14, wherein VSIG1 is replaced by a gene selected from the group consisting of TNDRSF25, TCF7, STMN3 AND SITl .

16. A method according to any one of the preceding claims, wherein step (a) is performed using

(i) a primer or probe having a nucleotide sequence comprising or consisting of any one of SEQ ID NO: 1 to 20, and/or complements thereof, and/or

(ii) a set of primers having nucleotide sequences comprising or consisting of SEQ ID NO: 1 and 2, SEQ ID NO: 7 and 2, or SEQ ID NO: 8 and 2 and/or complements thereof; and/or (iii) a probe having a nucleotide sequence comprising or consisting of SEQ ID NO: 3, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20.

17. A method as defined in any of claims 1 to 16 comprising the further step of selecting a patient for therapy, after classifying the patient as having severe outcome of dengue.

18. A method of treating a patient classified according to the method referred to in any one of claims 1 to 17 as having severe outcome of dengue by subjecting the patient to a dengue immunotherapy , or by administering to the patient a medicament used for the treatment of dengue.

19. Use of immunotherapy specific for dengue in the preparation of a medicament for the treatment of patients having a severe outcome of dengue as classified by the method according to any one of claims 1 to 18.

20. A primer or probe comprising the nucleotide sequence of any of SEQ ID NO: 1 to 20, or complements thereof.

21. A forward primer according to claim 20 for amplifying a nucleotide sequence of a Dengue virus serotype in a test sample, in which the primer sequence comprises the nucleotide sequence of any of SEQ ID NO: 1, 7 or 8, or complements thereof.

22. A reverse primer according to claim 20 for amplifying a nucleotide sequence of a Dengue virus serotype in a test sample, in which the primer sequence comprises the nucleotide sequence of SEQ ID NO: 2, or complements thereof.

23. A probe according to claim 20 in which the probe sequence comprises the nucleotide sequence of any of SEQ ID NO: 3, 4, 5, 6 or 9 to 20, or complements thereof.

24. A set of primers for amplifying Dengue virus in a test sample comprising the following pair of forward and reverse primers, in which the nucleotide sequences of the primers comprise or consist of the following sequences, or complements thereof:

a) SEQ ID NO: 1 and 2;

b) SEQ ID NO:7 and 2; and/or

c) SEQ ID NO:8 and 2.

25. A set of primers and a probe for detecting Dengue virus in a test sample comprising the forward primers SEQ ID NO: l, SEQ ID NO:7 or SEQ ID NO:8 or complements thereof; the reverse primer SEQ ID NO:2 or complements thereof; and one or more of the probes of SEQ ID NO: 3, 4, 5, 6 or 9 to 20 or complements thereof.

26. A set of primers, comprising a forward primer and a reverse primer, and a probe, according to claim 25 comprising the following sequences or complements thereof:

(i) SEQ ID NO: l ; SEQ ID NO:2; and SEQ ID NO:3;

(ii) SEQ ID NO: 1 ; SEQ ID NO:2; and SEQ ID NO:4;

(iii) SEQ ID NO:l ; SEQ ID NO:2; and SEQ ID NO:5; and/or

(iv) SEQ ID NO: 1 ; SEQ ID NO:2; and SEQ ID NO:6

in which Y represents C or T; and R represents A or G.

27. A method for determining the presence or absence of a Dengue virus serotype in a biological sample, comprising the step of contacting a nucleotide sequence obtained or derived from the biological sample with at least one primer or probe or set according to any of claims 20 to 26.

28. A method according to claim 27 further comprising the step of determining whether the nucleotide sequence hybridises to the at least one primer or probe under stringent conditions, thereby detecting whether the sample contains a Dengue virus serotype.

29. A method according to claim 27 or 28, comprising the steps of:

(a) contacting a nucleotide sequence obtained or derived from a biological sample with at least one forward primer and at least one reverse primer, or at least one set, under

amplification conditions to generate an amplicon of a region of a dengue serotype nucleotide sequence; and

(b) detecting hybridization between the amplicon and at least one probe according to any of claim 20.

30. A method according to claim 29 in which the amplification conditions comprise an amplification reaction, and in which the amplification reaction is a polymerase chain reaction (PCR).