BRPI0903187A2 - prognostic markers of clinical response in dengue virus infected patients - Google Patents

Abstract

PROGNOSTIC MARKINGS OF CLINICAL RESPONSE OF PATIENTS INFECTED WITH DENGUE VIRUS The invention relates to a method to provide the prognosis of clinical response of patients infected with the dengue virus conducted by analyzing the expression of a group of genes, as well as allelic polymorphism and levels of proteins encoded by them using the following methods: DNA microarrays, nortbern blot, PCR, quantitative PCR, western blot, ELISA and flow cytometry. The use of any of the above methods, or others related, is the basis for development of kites predictive of clinical response, thus facilitating clinical management.

Description

PROGNOSTIC MARKERS OF CLINICAL RESPONSE OF DENGUE VIRUS INFECTED PATIENTS

FIELD OF INVENTION

This invention relates to prognoses for the clinical response of dengue virus infected patients based on the expression level of the genes listed in Tables 2-7, or their encoded proteins, as well as allelic polymorphism, in biological samples.

BACKGROUND OF THE INVENTION

Dengue virus is a member of the family Flaviviridae, genus flavivirus with four antigenically distinct serotypes (DENV-1 to DENV-4). Dengue virus infection is a worldwide public health problem with an estimated incidence of 50-100 million cases of dengue fever (FD or classical dengue) resulting in 500,000 clinical cases of life-threatening dengue hemorrhagic fever. (DHF or dengue hemorrhagic fever) and 24,000 deaths per year (Green and Rothman 2006). DHF is characterized by vasculopathy, which leads to sudden plasma leakage that reduces blood volume and can result in hypovolemic shock, which is known as Dengue Shock Syndrome (SCD). There is no antiviral therapy for the treatment of dengue infection and no means to prevent the development of DHF.

During the acute phase of infection, patients with PD and DHF have very similar clinical pictures. However, defervescence (2 to 7 days after symptom onset) is accompanied by signs of circulatory disturbance in patients with DHF (WHO 1999). Therefore, we believe that genetic signature differentiating the two different clinical forms of dengue could be a powerful tool to be used in endemic areas to predict patient response, facilitating clinical management.

The concept currently accepted as underlying the immunopathology of DHF relies on the sequence of infection and induction of non-neutralizing antibodies (Endy, Nisalak et al. 2004; Pangf Cardosa et al. 2007), as well as the activation of reactive T cell clones. -cruzada (MANGADA, Endy et al. 2002; Mongkolsapaya, Dejnirattisai et al. 2003; MANGADA Rothman and 2005; Mongkolsapaya, Duangchinda et al. 2006) both acquired after primary infection. Some of the severity markers found in peripheral blood that have been correlated with plasma extravasation in DHF include inflammatory cytokines, chemokines, and high-level adhesion molecules (Green, Vaughn et al. 1999; Juffrie, van der Meer et al. 2000; Mustafa, Elbishbishi et al 2001; Suharti, van Gorp et al 2002; Hatfield, Hung et al 2003; Nguyen, Lei et al 2004; Cardier, Marino et al 2005; Tseng, Lo et al. 2005), complement activation (Avirutnan, Punyadee et al. 2006), in addition to increasing the frequency of activated cells (Green, Vaughn et al. 1999; Azeredo, De Oliveira-Pinto et al. 2006) and occurrence of apoptosis ( Mongkolsapaya, et al. Dejnirattisai (2003; Myint, Endy et al. 2006). These findings contributed to further understanding of the immunological mechanisms involved in the development of DHF, but neither has been proven to be reliable or useful as a biological marker for clinical use. DNA microarray has been used as a tool to locate "genetic signatures" and successfully predict the clinical response of the cancer patient (van de Vijver, He et al. 2002; Chen, Yu et al. 2007) as well as to bacterial and viral infections (Ramilo, Allman et al. 2007; Scherer, Magness et al. 2007). Van de Vijver et al (2002) reported the use of a 70-gene prognostic profile previously associated with the risk of early distant metastasis in young breast cancer patients whose lymph nodes were negative for cancer cells as a guiding tool. for adjuvant therapy in these patients (van de Vijver, He et al. 2002). This improves the selection of patients who would benefit from systemic adjuvant treatment, reducing both overtreatment and undertreatment rates.

In the dengue infection model, the pattern of gene expression was characterized along with the phase of infection in people with the most severe form of the disease in two different dengue cohorts: Simmons et al. (2007) showed a molecular signature on Peripheral Blood Mononuclear Cells (PBMCs) representing the acute and convalescent phases of Dengue Shock Syndrome and they reported that gene transcripts for 24 IFN-stimulated genes were less abundant in adults (6 patients). ) with dengue shock syndrome than in 8 dengue patients without SCD, whereas genes involved in apoptosis were expressed in greater numbers (Simmons, Popper et al. 2007). Recently Ubol et al (2008) in a Thai cohort conceptually confirmed the results shown in this report and the Vietnamese study, associating decreased innate immune response and increased apoptosis with the development of DHF (übol, Masrinoul et al. 2008), but the genetic pattern differs from what we show here. One possible reason may be the age range used, as it is known that phenotypically the repertoire of the immune response is different during the early years, where the innate response predominates compared to adults (Pettiford, Jason et al. 2002). In contrast, Kruif et al. (2008) reported a general association of dengue severity and greater expression of genes involved in the innate immune response (de Kruif, Setiati et al. 2008).

Several laboratory tests for dengue based serology or viral detection are available and are able to confirm the diagnosis of dengue infection. When used separately, each of these tests has limitations, but when used in the right combinations, the diagnosis of dengue virus infection can be safely confirmed or ruled out. However, the major limitation in the clinical management of dengue cases is the clinician's ability to assess the risk of a given dengue infected patient developing their fatal symptoms and their real need to be hospitalized. The incidence of DHF is approximately 1% and is fatal if not treated promptly. Even under medical care the death rate due to DHF can reach 10 to 20%. Unfortunately most of these deaths could be prevented with proper medical care. One of the main culprits of the lack of adequate assistance is the chaos caused by major outbreaks. The World Health Organization (WHO) has established clinical criteria for defining cases of DHF, but the difficulties both in meeting these criteria and in early identification of severe cases make clinical management of severe forms of the disease challenging even. even larger (Bandyopadhyay, Lum et al. 2006; Deen, Harris et al. 2006). Therefore, the search for new tools to predict the patient's clinical response is essential to facilitate the assessment of the need for medical interventions.

SUMMARY OF THE INVENTION

The present invention is a method of assessing the clinical response of the dengue virus infected patient during the acute phase of infection and before signs of vasculopathy appear. The method consists of analyzing the expression levels of a group of genes (Tables 2-7), or proteins encoded by them, as well as allelic polymorphisms.

In one aspect of the invention, the prediction of the clinical response of dengue virus infected patients is based on expression analysis of the 73 genes shown in Table 6, originated by combining the best 200 most significant genes (lowest p-value) and 200 greatest differences. in the expression (magnitude fold change) between classical dengue and hemorrhagic dengue.

In another aspect of the invention, the genes in Table 2 are used for characterization and differentiation of gene expression in people infected with dengue virus (FD + FHD) from other causes of febrile disease (non-dengue or ND). A further aspect of the invention relates to the use of Linear Discrimination Analysis as a classification rule and bolstered resubstitution as an error estimator of single, double, triple or more genes to predict clinical response. of patients infected with dengue virus.

In another aspect of the invention, expression of gene pairs based on the 1981 gene list (Table 7) is used as a classifier to predict clinical response of dengue virus infected patients.

In another aspect of the invention, gene trio expression based on the 1981 gene list (Table 7) is used as a classifier to predict clinical response of dengue virus infected patients.

In another aspect of the invention, gene quartet expression based on the 1981 gene list (Table 7) is used as a classifier to predict clinical response of dengue virus infected patients.

In yet another aspect of the invention, evaluation of gene expression may be performed by any of the techniques for detecting RNA, cDNA, cRNA (PCR, RT-PCR, quantitative PCR, DNA microarray, Northern blotting, and others).

In yet another aspect of the invention, assessment of the levels of protein products encoded by the genes shown in Table 7 can be performed by any technique for detecting denatured and native protein forms (ELISA, Western blotting, dot-blotting, flow cytometry). and others). BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows noise and efficiency measurements. The arrow indicates the only arrangement in the non-dengue group that showed reduced signal-to-noise ratio and percentage of calls present.

FIG. 2 is a graph of RNA degradation.

FIG. 3 shows exploratory analysis of data based on 1981 selected genes. (A) hierarchical grouping dendrogram. (B) 2-D MDS Chart. (C) 3-D MDS Chart. The ellipse in (B) represents the two largest sample groups.

FIG. 4 shows the 40 best differentially expressed genes between FD and FHD samples. (A) GeneBank annotated expression map. (B) Hierarchical clustering dendogram (only the top 40 genes). (C) MDS 3-D chart (only the top 40 genes).

FIG. 5 shows the classifier for the PSMB9 and MT2A gene pair in the discrimination of FD against FHD. Lower expression of both genes is characteristic of DHF, while higher expression of both genes is characteristic of DHF. The estimated probability of error for this classifier is only 5.38%.

FIG. 6 shows gene expression levels for discrimination between FD and FHD patients. (A) For all genes tested, pPCR assays were performed using a mixture of 8 FD and 8 FHD patients used in the microarray assay. (B) For all genes, qPCR assays were performed for a group of 8 FD and 8 FHD samples used in the microarray assay (white column), or a group of 8 independent FD and 8 FHD samples (gray column). The experiment was performed twice and each group was analyzed in triplicate.

FIG. 7 provides graphs Volcanoes showing the ρ values correlated with the magnitude of expression change (fold change) in FD versus FHD patients and dengue (FD + FHD) versus non-dengue patients. The genes highlighted in red and blue represent the top 40 genes according to the values ρ and magnitude of expression difference respectively.

DETAILED DESCRIPTION OF THE INVENTION

DNA microarray technology is an important tool for studying mRNA expression simultaneously of all genes in a genome. This technology consists of thousands of different DNA sequences representing different genes adsorbed in known regions of a solid matrix, such as a glass slide. The slides may be hybridized to cell or tissue mRNA-derived probes. Oligonucleotide arrays (composed of 25-mers DNA) is based on the probe match / mismatch strategy on the DNA chip, where perfectly matching probes and probes that differ from the first one are used. a base at its center (mismatch) and so do not hybridize to the DNA chip. In this type of microarray, only one fluorochrome is used for complementary RNA (cRNA) labeling and the signal is detected using optical scanning equipment. Quantitation of the hybridized probe is performed by comparing the signals from mismatch oligos (non-specific hybridization) and perfectly matched oligos (match). DNA microarrays have been used as a tool to find "gene signatures" and successfully predict the clinical response of cancer patients (van de Vijver, He et al. 2002; Chen, Yu et al. 2007) as well as for bacterial and bacterial infections. (Ramilo, Allman et al. 2007; Scherer, Magness et al. 2007). van de Vijver et al (2002) reported the use of prognostic profiling using 70 genes as a tool to guide adjuvant therapy in these patients by selecting those patients who would really benefit from this systemic adjuvant treatment and reducing either the overtreatment rate or undertreatment. The 70 genes used by these authors were previously associated with risk of distant metastasis in young breast cancer patients whose lymph nodes were negative for cancer cells (van de Vijver, He et al. 2002).

There is much evidence that genetic factors, involved in susceptibility / resistance to infectious diseases,

influence the immune response in humans. These factors are complex and genetically controlled and do not follow a Mendelian inheritance profile (Clementi and Di Gianantonio 2006). Some of these factors identified by DNA microarrays are involved with altered gene regulation and sometimes due to polymorphisms that interfere with gene expression in various ways, either by directly binding to the promoter or by interfering with the signal cascade that interferes with gene activation. . Host gene polymorphism related to immune response has been shown to correlate with altered gene expression and susceptibility to developing DHF, such as the TNF-308 allele, associated with elevated TNFα levels, has been correlated with increased risk of developing the most severe form. of the disease (Fernandez-Mestre, Gendzekhadze et al. 2004; Soundravally and Hoti 2007). An association has recently been reported between wild type AA MBL2 genotype, high MBL levels and high risk of developing thrombocytopenia associated with dengue virus infection (Acioli-Santos, Segat et al. 2008). On the other hand, CD209 promoter polymorphism (Sakuntabhai, Turbpaiboon et al. 2005) has been associated with reduced DC-SIGN expression and possibly reduced susceptibility of dendritic cells to dengue virus infection. Therefore, the search for alteration in gene expression among different clinical conditions of dengue using DNA microarray can elucidate the genetic factors and immunopathological mechanisms related to dengue severity.

Using this technology, the gene expression profile was characterized in people who developed DHF in two different cohorts: Simmons at al. (2007) showed a molecular signature on peripheral blood mononuclear cells (PBMC) representing the acute and convalescent phases of patients who developed dengue shock syndrome (SCD). In this study they reported that gene transcripts for 24 interferon-stimulated genes were less abundant in adults (6 patients) with SCD than and 8 patients with DHF patients who did not develop shock, while those genes involved in apoptosis were expressed in greater numbers. et al (2008) in a cohort of Thai children also observed reduced expression of innate immunity genes and increased expression of genes involved in severely apoptotic dengue virus infection (Ubol, Masrinoul et al. 2008).

During the acute phase of infection, patients affected with the benign (DF) and malignant (DHF) form of the disease develop the same clinical picture. However, during the defervescence period (4 to 7 days from the onset of symptoms) it is accompanied by circulatory disorders, the magnitude of which is directly proportional to the severity of the disease (WHO 1999). Thus, the events that precede defervescence are determinant for the patient's clinical response.

The present invention provides the use of gene group differential expression as a prognostic tool to predict clinical response of dengue infected patients based on the gene expression data obtained before circulatory symptoms begin using oligonucleotide microarrays. Heparinized blood samples (sodium citrate or EDTA may also be used as anticoagulant) collected from patients (Table 1), whose dengue virus infection was confirmed by IgM serology as well as viral isolation or detection of viral RNA by RT- PCR; was used. PBMC isolation using density gradient can be used to minimize "noise" caused by granulocytes. Consequently, moderate to small changes in gene expression between different clinical manifestations of dengue can be readily detected. Once samples containing the cells are obtained, messenger or total RNA can be extracted and amplified for evaluation of gene expression. The amplified RNA is then labeled so that it can be hybridized to a DNA microarray, which in turn is scanned and the obtained data processed to obtain gene expression profile data.

The quality of DNA microarray data is assessed using several criteria: visual inspection,

noise / efficiency ratio, amplified spiked in control genes (dap, thr, phe, lys) and hybridization (bioB, bioC, bioD, cre), constitutive gene expression (GAPDH - glyceraldehyde 3-phosphatase dehydrogenase) and beta-actin), and RNA degradation plots.

Visual inspection based on high definition DAT files should not reveal signs of scratches or stains and oligo B2 probes should be visible (chessboard profile as well as the name of the arrangement).

Measurement of the noise / efficiency ratio is made by the MAS 5.0 program. Noise factor Q, background, scaling factor and percentage of probes present should be similar between microarray.

The selection of predictive genes for clinical response of dengue virus infected patients, as well as those characteristic of dengue virus infection regardless of clinical manifestation [comparison between dengue cases (FD + FHD) and non-dengue group or ND, whose etiology of febrile illness was unknown but the possibility of dengue virus was ruled out], was based on 26 oligonucleotide microarrays distributed as follows: 7 from FD patients, 10 from DHF patients and 8 from ND patients. These microarrays were analyzed using Affymetrix MAS 5.0 software (Hubbell, Liu et al. 2002) to normalize data and produce signal and detect probes present among the 54,675 gene transcripts in each microarray. Of these, 12,299 transcripts were considered present (p <0.04) in all microarrays, while 20,365 transcripts were considered absent in either microarray. In order to keep only the most promising genes for future analysis, two nonspecific filters were employed: (i) detection filter to eliminate non-expressed genes, which kept only the genes considered present in at least 24 of the 26 microarrays, resulting in a total of 15,848 genes. Thus, 3,549 genes (15,848 - 12,299 = 3,549) that were considered absent in one or two microarrays were tolerated; (ii) variance filter to eliminate constitutive expression genes, which maintained the largest 1/8 of the 15,848 genes previously cited, resulting in 1981 genes with potential prognosis (Table 7). These genes were confirmed as prognostic potentials when hierarchical clustering and 2D and 3-D multidimensional scaling were performed (Figure 3), revealing the formation of two large groups, one group containing ND samples and half of the FD samples; and another group containing DHF patients, two ND samples and the other half of DH patients. The dissimilarity measure used was 1 - r, where r is Pearson correlation of log transformed values. The microarrays were colored according to the diagnostic class. It is noted that the DHF samples constitute a more cohesive group than the ND group, represented by patients with febrile diseases of unknown etiology. While FD samples can be divided into two groups, one similar and one different from the FHD samples, the latter being closer to the ND samples. The small stress value (13.52% in Graph 2-D and 7.32% in Graph 3-D), particularly in Graph 3-D means that the structure that the data represents is intrinsically small, indicating that a small number of gene signatures may represent the discriminatory content of the data.

Next, a statistical test between the means (Welch two-sample t-tests) was performed to find the genes that were differentially expressed between the different groups analyzed. Two comparisons were considered: FD verse FHD and Dengue (FD + FHD) verse ND. Volcano plots in Figure 4 show that the ρ values correlated well with the magnitude of fold change in both comparisons. The list of the highest fold-changing genes shown in the volcano graphs are shown in Tables 2 and 3.

Δ Figure 5 shows the expression map for the 40 most significant (lowest p-values) genes that discriminate against the DH FD groups according to the Welch t test criterion, as well as hierarchical grouping and MDS plots for the same 40 genes.

Among the 200 most significant differentially expressed genes between FD and FHD according to the Welch two-sample t-tests was compared with 200 genes with the largest fold-change in average expression of mRNA levels, there are 7 3 genes in common. (Table 6), which represents the best most significant genes and with the largest difference in the magnitude of expression (fold-change) between FD and FHD.

Among the differentially expressed genes between FD and FHD, 19 fall into the immune response category, including HLA-βα genes, complement factor D, CX3CR, MyD88, TNFSF17 (BCMA), TNFSF13 (APRIL). Seven genes were included in the defense response group, among which were the Myxovirus Resistance (Mx) gene, 2 ', 5'-oligoadenylate synthetase (0AS1 and 0AS2) and IFN-stimulated factors. These were expressed to a lesser extent in people who later developed DHF. These genes have been implicated in innate immune response, confirming that this arm of the immune response may be crucial to the clinical response of dengue virus infected patients. In addition, 6 genes (PDCD4, PACAP, Tumor protein D52, MAGED1, pro-apoptotic caspase adapter protein and TNF ligand super family) associated with apoptosis are expressed in larger numbers in DHF patients. In addition, CD53, a tetraspanin produced by monocytes and B lymphocytes, prevents these cells from suffering apoptosis (Yunta and Lazo 2003) was less expressed in the DHF group, reinforcing the premise that apoptotic cell augmentation is associated with development. of the most severe form of the disease.

Comparing with the study conducted by Simmons et al (2007) (Simmons, Popper et al. 2007), there is consistency in the proposed genes as prognostic for developing DHF. In this study, some IFN-stimulated genes whose expression was reduced in patients developing SCD, such as MX, IFIT, and OAS, as well as high levels of pro-apoptotic caspase adapter protein and PDCD5 (programmed cell death 5). part of our list of prognostic genes.

Thus, based on the 1981 identified genes, groups of DHF marker genes were selected for use in simpler assays, such as quantitative PCR. For this, an exhaustive selection was made with all possible combinations of genes isolated or combined in double or triple form, through linear discriminant analysis as a classification method, implemented by direct estimation of sample means and covariance matrices for each diagnostic class. (Braga-Neto 2005); and classification accuracy estimated by the potentiated resubstitution method (bolstead resubstitution, Braga-Neto 2004), which is ideal for gene group selection in a smaller sample situation (Sima, Attoor et al. 2005; Sima, Braga-Neto et al., 2005).

Table 5 shows the best genes in isolated (40) or combined [double (40) or trio (100)] form, ranked by the estimated error. There are 37 unique genes in the top 40 pairs, while there are 86 single genes in the top 100 triplets. The list of trios is dominated by the genes PSMB9, MT2A and LOC400368. The estimated error average for the best classifiers containing 1 gene (40) = 0.1639 ± 0.0286, 2 genes (40) = 0.0686 ± 0.0096, 3 genes (100) = 0.0395 ± 0.0040, which indicates that the classification using gene pairs is more accurate than 1 gene, whereas classification using 3 genes is more accurate than 2 genes (the above margin of error refers to a 95% confidence interval). The selection method described above with 2 or 3 genes has the potential to "select" genes that cannot be found using univariate methods such as t-test. This can be seen in the list with the best pair of classifiers. For example, the HLA-F gene (whose expression is reduced in FHD relative to FD).

Figure 6 shows the graph containing one of the classifier genes based on the PSMB9 and MT2A gene pair. The probability of error in future data for this classifier is 5.38%. In this case, reduced expression of both genes is characteristic of FHD, while increased expression is characteristic of FD. To confirm the potentiality of these genes as dengue severity predictors, we selected some (PSMB9, MT2A, HLA-F and C3aRl) to develop quantitative RT-PCR assays with FD and FHD samples. Genes were amplified and detected using the TaqMan® gene expression assay with commercially available primers and probes (Applied Biosystems). RNA was extracted with the aid of the RNeasy kit (Qiagen). Total RNA was reverse transcribed using the first SuperScript III ribbon synthesis system for quantitative PCR using random primers and quantitative PCR was performed on ABI PRISM 7500 (Applied Biosystems). Beta actin was used as an endogenous control and all data were normalized based on this constitutive gene. Samples used in this assay are described in Table 1 (ND patient samples as well as FHD 105 and 112 patients were not used). Quantitative RT-PCR results were comparable to those obtained in microarray assays (Figure 7). Furthermore, using a group of samples independent of those samples used in the microarray assays, the expression levels of the above mentioned genes were also confirmed (Figure 7). Thus, the results of the quantitative PCR assay confirmed the expression of the selected genes in samples collected at the first medical visit of a patient infected with dengue virus and can predict whether a person infected with dengue virus can develop or not DHF. Therefore, the object of this invention represents a useful means of guiding the clinical management of dengue patients.

Methods of choice for establishing gene expression levels to predict clinical response in dengue patients include DNA microarray, RT-PCR, northern blot, competitive RT-PCR, real-time RT-PCR, differential display RT-PCR. While it is possible to conduct these techniques using individual PCR reactions, it is best to amplify the complementary DNA (cDNA) and analyze it via quantitative PCR and then normalize the results using the expression of some constitutive gene. Then the analyzes can be done using both absolute and relative quantification so that the gene expression profile can be determined.

Levels of proteins encoded by dengue severity predictive genes, quantified from any body fluid (blood, plasma, serum, exudates) can be used to predict dengue severity. However, plasma samples are preferred. For these sample types, any immunoassay (ELISA, immunofluorescence, western blot, dot blot etc) may be employed. In addition, protein levels can be indirectly assessed by analyzing cell frequency (PBMC, for example) expressing a given protein in any cell compartment by flow cytometry.

The use of any of the above and related methods is a source for the development of dengue severity predictor kits during the acute phase of infection and before plasma extravasation occurs in DHF patients.

The invention is illustrated by the following examples which should not be construed as limiting the scope of protection.

EXAMPLES

The genes analyzed according to this invention are typically related to the complete sequence of nucleic acids that encode to produce a protein or peptide. However, identification of the complete sequence is not required from an analytical point of view. That is, portions of the sequence or ESTs may be selected according to well-known principles for which probes can be designed to evaluate gene expression for the corresponding gene.

Example 1 - Dengue Cohort Design and Strategy for Functional Genomics Studies

A cohort of febrile cases of suspected dengue virus infection cases seen at 3 hospitals in the city of Recife, Pernambuco, Brazil has been established and described (Cordeiro, Schatzmayr et al. 2007; Cordeiro, Silva et al. 2007). Briefly, only the full explanation of the study objectives, each participant in this cohort (or guardian) was asked to sign the consent form and was asked for a sequential blood donation within 30 days of the onset of symptoms. Serum, plasma and PBMC were separated and cryopreserved. Dengue diagnosis was made by viral isolation in C6 / 36 cells (Igarashi 1978) and RT-PCR (Lanciotti, Calisher et al. 1992) in samples collected on the day of admission (first sample) as well as by serology for identification. IgM (Bio-Manguinhos, Oswaldo Cruz Foundation, Brazil or PanBio, Pty., Ltd., Brisbane, Australia) and anti-dengue IgG (PanBio, Pty., Ltd., Brisbane, Australia) in the first and subsequent samples. The type of infection was characterized based on IgM / IgG production kinetics and antibody titers in hemagglutination inhibition (HI) assay (Clarke and Casais 1958).

Dengue cases (confirmed by serology, RT-PCR or viral isolation) were clinically classified according to World Health Organization criteria into two classes: dengue fever (FD or classical dengue) and dengue hemorrhagic fever (DHF). (PAHO 1995). The cases of PD were characterized by fever (39 ° C to 40 ° C) lasting up to 7 days accompanied by at least 2 of the symptoms, such as head odor, retrorbital pain, myalgia, arthralgia and rash. The cases of DHF were defined with the same symptoms found in DH, but with evidence of bleeding, thrombocytopenia (platelets <1000,000 / mm3) and plasma leakage following defervescence.

This study was reviewed and approved by the ethics committee of the Brazilian Ministry of Health CONEP: 4909; Case No. 25000.119007 / 2002-03; Zip Code: 68/02. In addition, the Johns Hopkins University Institutional Review Committee (USA) has also reviewed and approved this study as protocol JHM-IRB-3: 03-08-27-01.

Functional genomics studies were performed with PBMC samples collected at the time of the first medical visit. Samples from 18 confirmed dengue cases (RT-PCR, viral isolation and / or IgH positive), genotype III, and 8 control samples from febrile cases that were negative for dengue in all tests performed on 3 or more samples (here dengue or ND) were used in this assessment. The samples were divided into three groups: FD, FHD and ND. Samples from these three groups were selected to avoid significant differences regarding age, gender, history of dengue virus infection, and days of symptoms. None of the patients had symptoms of DHF at the time the samples used for this evaluation were collected. At the time of sample collection, patients reported that they had symptoms for less than 8 days (mean 5.3 days). Among confirmed dengue cases, 8 were characterized as DF and 10 were classified as DHF (Table 1). Bold in Table 1 are the samples used exclusively for qPCR assays. DF: dengue fever; DHF: dengue hemorrhagic fever; ND: Non-Dengue; M: male; F: female; Pos: positive; Neg: negative; -: no information.

Example 2 - Sample processing for chip hybridization. genics

Blood samples collected from patients enrolled in this assessment were collected in heparinized vacutainer tubes (BD Vacutainer) and within 2 hours of collection, PBMC samples were separated by density gradient using Ficoll-Paque (Amersham Biosciences) and cryopreserved in 10% (v / v) dimethyl sulfoxide (DMSO; Sigma-Aldrich) in inactivated fetal bovine serum (FBS; Hyclone). Four million cells were thawed and immediately lysed with Trizol (Invitrogen) for total RNA extraction with chloroform and isopropyl alcohol precipitation according to the manufacturer's instructions [expression using fresh or frozen samples were compared and found to be similar, ratio by which frozen cells were chosen for functional genomics studies (data not shown)].

Total RNA was then purified using the RNeasy MinElute Cleanup Kit (Qiagen) according to the manufacturer's protocol and quantified by spectrophotometry at 260nm and 280nm (ultraviolet spectrum). Total RNA was used for the synthesis of complementary RNA (cRNA) through the two-cycle targeting kit (Affymetrix). Briefly, RNA isolated from PBMC was labeled with biotin and hybridized to oligonucleotide microarrays (Affymetrix) using a two-cycle cDNA synthesis method. In the first cycle, the first DNA strand was prepared using a T7- (dT) primer and Superscript II reverse transcriptase (Invitrogen) from 10-100ng cellular RNA. Second strand synthesis was performed using Escherichia coli DNA polymerase I and double strand cDNA was used in the in vitro transcription assay (IVT) for cRNA amplification using the Megascript T7 kit (Ambion). The product of this first round of reactions was used for cDNA reverse transcription as described for the first round. This cDNA was used for IVT for synthesis of biotin-tagged cRNA, which was fragmented and sent to the Microarray Core Facility at Johns Hopkins University for cRNA hybridization to the U133 Plus 2.0 human DNA chip (Affymetrix), tagging and scanning.

Example 3 - Microarray Quality Control

The quality of microarray data was evaluated using the following criteria: visual inspection, noise / efficiency ratios, embedded controls, constitutive gene expression, and RNA degradation plots. Visual inspection based on high resolution DAT files revealed no scratches or smudges, and B2-oligo probes (in a chessboard format and the name of the microarray) were also visible. Noise / efficiency measurements were made by the Affymetrix MAS 5.0 program which can be used to assess the quality of microarray (Figure 1). Q noise factors, background, scaling factors and percentage of genes present were similar in all microarrays. Mean background values were below 100 for 20 of the 26 microarray; the scaling factors were within the order of 3x for 25 of the 26 microarrays; and the percentage of genes present was around 40% or more for 25 of the 26 microarrays. None of the 6 high background microarray had a gene rate significantly below 40%. All 3 'probes and intermediate probes for all inline control genes (dap, thr, phe, lys) were present in all microarrays, as well as for the prokaryotic gene group used for hybridization control (bioB, bioC, bioD, cre) and constitutive genes GAPDH (human glyceraldehyde 3-phosphatase dehydrogenase) and beta-actin. In addition, the RNA degradation graph showing the average intensity of the probes at the same numerical position in all probe groups (for all genes) demonstrates a consistent upward trend in signal intensity from the 5 'end to the 3' end, which is expected due to a decrease in cDNA synthesis efficiency (Figure 2).

Example 5 - Validation of DNA Microarray Data by Quantitative Real-Time PCR

Four differentially expressed genes between DF and FHD (MT2A, PSMB9, C3aR1 and HLA-F) were selected for quantitative real-time PCR (qPCR) quantitation. Genes were amplified and detected using TaqMan® gene expression assays with commercially available primers and probes (Applied Biosystems). RNA extraction was performed using the RNease kit (Qiagen) according to the manufacturer's instructions. Total RNA was reverse transcribed to cDNA using the Superscript III first strand synthesis system (Invitrogen) for qPCR using radom primers at the manufacturer's suggestion. qPCR was performed on the ABI PRISM 7500 apparatus (Applied Biosystems). The beta actin gene was selected as an endogenous control and all data were normalized against it. The reaction was performed in triplicate and included 2 μΐ cDNA, primers (20 μΜ each) and 6.25 μΜ from the specific or commercially pre-designed gene expression assay mix (Applied Biosystems), human beta-actin (Applied Biosystems), TaqMan universal PCR mix (Applied Biosystems) and water added to a final volume of 25 μl. Triplicates of non-template controls (NTC) were included each time qPCR was performed. The reaction cycles were: after incubation of 2 min at 50 ° C and 10 minutes at 95 ° C, the samples went through 40 cycles of 95 ° C for 15 seconds and 60 ° C for 1 min. The baseline and threshold used to determine the Cycle threshold were manually adjusted using Sequence Detection Software, version 1.4 (Applied Biosystems). The amplification efficiency (E) of the target molecule was calculated from the formula (E = 10Λ (-1 / Slope) - 1) where "slope" is slope of the straight line generated by Cts obtained from standard curve, automatically given by the program. . For the relative quantitation calculations, the Delta Ct-based method was used [ABI PRISM® 7000 Sequence Detection System and Applied Biosystems 7500 Real-Time PCR System - User Bulletin, Applied Biosystems] since the assays show amplification efficiency of 100% ± 10% [Application Note 127AP05-02]. Samples used in qPCR assays are described in Table 1 (samples from ND patients and DHF patients number 105 and 112 were not used).

Example 6 - Statistical Analysis

Quality control of patient data, statistical analysis and graphics were performed using the Affymetrix MAS 5.0 software (Hubbell, Liu et al. 2002) and the open access R statistical package, version 2.5.0 (Ihaka 1996) and additional library, in particular the BioConductor library, version 1.16 (Gentleman, Carey et al. 2004). Dendograms and MDS graphs were produced with the R functions "hclust" and "isoMDS", respectively, while expression maps (heatmaps) were obtained with the functions "hset.emap" and "heatmap". The ρ values corresponding to two-tailed Welch's two-sample t test were obtained with the "t-test" function. Functional category analysis was performed using the Expression Analysis Systematic Explorer (EASE) program on the DAVID Bioinformatic website (http://david.abcc.ncifcrf.gov) (Dennis, Sherman et al. 2003). The classification method used linear discriminant analysis implemented by direct estimation of mean samples and covariance matrices for each diagnostic class (Braga-Neto 2005). The accuracy of classification was estimated by the potentiated resubstitution method (bolsted resubstitution, Braga-Neto 2004), which is suitable for gene group selection when the sample number is low (Sima, Attoor et al. 2005; Sima, Braga- Neto et al., 2005).

Example 7 - DNA Microarray Data Processing Affymetrix MAS 5.0 program was used to scale data and produce signal and detection calls for the 54,675 genes in each of the 26 microarrays. Of these, 12,2999 were considered present (p <0.04) in all microarrays, while 20,365 were not present in any of the microarrays. To keep only promising genes for future analysis, two nonspecific filters were employed: (i) detection filter to eliminate non-expressed genes, which kept only the genes considered present in at least 24 of the 26 microarray, resulting in a total of 15,848 genes. Thus, 3,549 genes (15,848 - 12,299 = 3,549) that were considered absent in one or two microarrays were tolerated; (ii) variance filter to eliminate constitutive expression genes, which maintained the largest 1/8 of the 15,848 genes previously cited, resulting in 1981 genes with potential prognosis (Table 7).

Example 8 - Exploratory Analysis

Figure 3A shows the dendogram for hierarchical grouping of the 26 microarrays according to the expression of the 1981 genes selected at the preprocessing stage. "Average linkage" and Pearson correlation of log transformed expression values were used. It was observed that the samples fell into two large groups: the right one containing ND samples and half of the FD samples; while the left group contains all FHD samples, two ND samples (including the ND_199 outlier) and the other half of the FD patients. This fact is in agreement with the intuition that ND and FHD are the most different groups and FD samples forming an intermediate group. This is confirmed by the 2-D and 3-D multidimensional scaling (MDS) plots for the 1981 genes, shown in Figure 3B and C. The dissimilarity measure used was 1 - r, where r is Pearson's correlation of values. turned into log. Microarrays were colored according to the diagnostic class. It can be observed that the DHF samples constitute a more cohesive group than the ND samples, as expected since the ND group was obtained from individuals with feverish disease of unknown etiology, whereas the FD samples can be divided into two groups, a similar one. and one other than DHF, the latter being closer to the ND group (these are the two groups marked in Figure 5). The low stress value (13.52% in the 2-D graph and 7.32% in the 3-D case), particularly in the 3-D graph, means that the outlining structure of the data is intrinsically small, indicating that the small number of gene signatures is able to represent discriminatory content in the data.

Example 9 - Differential Expression Analysis

Statistical tests of differences between means (Welch two-sample t-tests) were performed to find differentially expressed genes between the different diagnostic groups. Two main comparisons were considered: DF versus DHF and dengue (DH + DHF) versus ND. The first comparison corresponds to 18 samples from patients with proven dengue virus infection and is the most important comparison for the present invention because it discriminates between the benign (FD) and malignant (DHF) form of the disease. Important genes used to characterize these two clinical manifestations as well as those specific for dengue virus infection are shown in Tables 2 and 3. Volcano graph in Figure 4 and the corresponding list of genes (Tables 2 and 3) show that the values ρ correlate well with magnitude of expression difference (fold-change) in both situations analyzed.

Figure 5 shows the heatmap for the top 40 genes that discriminate against FD FHD samples according to Welch t-test criteria, as well as hierarchical grouping and MDS plots for the top 40 discriminatory genes between FD and FHD. Δ Table 4 shows the results of the analysis of the functional category representation of the list of the best 40 discriminatory genes between FD and FHD with the EASE program as described in Example 6. The analysis indicated the enrichment of certain gene categories. Five categories showed significant scores (p <0.05) after Benferroni correction for multiple tests, including those involved in immune response and defense, response to biotic stimuli, copper / cadmium binding, and copper ion homeostasis (Table 4).

Example 10 - Identification of the classifying genes

In addition to the univariate selection performed by t-tests, exhaustive selection (all possible combinations) of isolated genes, as well as double and triple genes from the 10981 genes, was performed using linear discrimination analysis and potentiated replacement. Table 5 shows the classifiers based on 1, 2 or 3 genes, ranked by the estimated misclassification. There are 37 unique genes in the top 40 pairs, while there are 87 unique genes in the top 100 triplers. The list of gene triplets is dominated by the genes PSMB9, MT2A, and LOC400368. In fact, only one trio does not contain any of these three, this is SFRS5, PDCD4, MKNK2, whose estimated error is 0.0383. The averages for the estimated error of the best classifiers are: 1 gene classifier (40) = 0.1639 ± 0.0286, 2 gene classifier (40) = 0.0686 ± 0.0096, 3 gene classifier (100) = 0.0395 ± 0.0040, which indicates whereas gene pairing is more accurate than individual genes, whereas triple gene sorting is more accurate than paired (the above margin of error refers to the 95% confidence interval). Selection with two and three genes has the potential to retrieve genes that using univariate methods (such as test-t) cannot be recovered. This can be seen from the list of the best 2-gene classifiers, for example, the HLA-F gene (which is less expressed in FHD than FD). Figure 6 shows the graph for the 2 gene classifier PSMB9 and MT2A. The estimated probability of error in future data is 5.38%. In this case, the lowest expression of both genes is FHD signature, while the highest expression of both genes is characteristic of FD.

Example 11 - Validation of Microarray Data Using Quantitative Real-Time PCR

Quantitative Real Time PCR (qPCR) assays were performed to validate the results obtained in the microarray assays. The following genes were selected: PSMB9, MT2A, HLA-F and C3aRl, whose expression was predominant in FD samples compared to FHD samples. qPCRs of the above genes were performed using eight FD and eight FHD samples obtained from the same patients tested in the microarray assays. According to Figure 7A, qPCR quantification showed a good correlation with microarray data. In addition, qPCR was also performed on a sample group independent of the samples used in the microarray assays, collected within 11 days of symptom onset and prior to onset of vasculopathy symptoms. The qPCR results also indicated that the expression levels of the selected genes were lower in FHD than in FD, corroborating the results obtained with the 2D model shown in Figure 3. Thus, each of the four genes were expressed in lower amount in DHF and they were correctly classified in the analyzed samples (Figure 7B). REFERENCES

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Yunta, M. and P. A. Lazo (2003). "Apoptosis protection and survival signal by the CD53 tetraspanin antigen." Oncogene 22 (8): 1219-1224. Table 1. Patients selected for functional genomics study. <table> table see original document page 40 </column> </row> <table> Tctbela 2. List of genes shown in the "volcano chart" Dengue (FD + FHD) vs. ND according to fold change and p value.

<table> table see original document page 41 </column> </row> <table> <table> table see original document page 42 </column> </row> <table> <table> table see original document page 43 < / column> </row> <table> Table 3. List of genes shown in FD vs. "volcano chart" FHD according to "fold change" and p value.

<table> table see original document page 44 </column> </row> <table> <table> table see original document page 45 </column> </row> <table> <table> table see original document page 46 < / column> </row> <table> Table 4. EASE analysis of functional abundance from the list of 40 most significant discriminatory genes between FD and FHD. Statistically significant functional categories (p <0.05) without correction for laughsJiltirvIici ^ ade -f- ^ σ ή o

<table> table see original document page 47 </column> </row> <table> Table 5. Classifiers based on individual, double or triplet genes ranked by the estimated error.

<table> table see original document page 48 </column> </row> <table> <table> table see original document page 49 </column> </row> <table> <table> table see original document page 50 < / column> </row> <table> <table> table see original document page 51 </column> </row> <table> Table 6. List of 73 differentially expressed genes between FD and FHD cases that were common among those 200 most significant genes according to the Welch's t-test and the 200 genes with the largest fold-change in the average expression of mRNA levels.

<table> table see original document page 52 </column> </row> <table> <table> table see original document page 53 </column> </row> <table> <table> table see original document page 54 < / column> </row> <table> <table> table see original document page 55 </column> </row> <table> Table 7. List of 1981 genes after microarray data processing.

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Claims (50)

Method for predicting the clinical response of a dengue virus infected patient during the first days after symptom onset, characterized by identifying the differential expression of genes selected from a group of 1981 genes listed in Table 7, or allelic polymorphism. as well as its encoded protein. 2. Method for diagnosing dengue virus infection, differentiating from other febrile diseases, during the first days after symptom onset, characterized by identifying the differential expression of genes selected from a group of 1981 genes listed in Table 7; or allelic polymorphism, as well as its encoded protein. Method according to claim 1, characterized in that the identification comprises sequences of isolated nucleic acids, their complements, parts, allelic polymorphism or encoded product of an isolated gene. Method according to claim 1, characterized in that the identification comprises sequences of isolated nucleic acids, their complements, parts, allelic polymorphism or double-coded gene products. Method according to claim 1, characterized in that the identification comprises sequences of isolated nucleic acids, their complements, parts, allelic polymorphism or encoded trio gene proteins. Method according to claim 1, characterized in that the identification comprises sequences of isolated nucleic acids, their complements, parts, allelic polymorphism or coded gene proteins in foursome or larger groups. Method according to claim 1, characterized in that it is conducted on nucleic acids extracted from Peripheral Blood Mononuclear Cells (PBMC), as well as any of their cellular contents selected from B cells, T cells, Monocytes, Macrophages, NK and dendritic. A method according to claim 1, characterized in that it is conducted in any cell compartment as well as any body fluid such as blood, plasma, serum, exudate and the like. A method according to claim 1, characterized in that it uses Linear Discrimination Analysis as a classification rule, and bolstered resubstitution as an error estimator for exhaustive selection of gene groups predicting the clinical response of dengue virus infected patients using one, two or three or more differentially expressed genes. Method according to claim 1, characterized in that the gene group includes the 73 genes listed in Table 6. Method according to claim. 1, characterized in that the gene group includes the classifiers listed in Table 5. -12. Method according to claim 2, characterized in that the gene group includes the genes listed in Table 2. -11. Method according to claim 1, characterized in that the value ρ indicating differential modulation is less than 0.05. Method according to claim 1, characterized in that there is at least a 2-fold difference in expression of modulated genes. Method according to claim 1, characterized in that the infectious dengue virus serotypes are DENV1, DENV2, DENV3 or DENV4, as well as multiple infections with any combination thereof. Method according to Claim 1, characterized in that it comprises patients with primary infection. Method according to Claim 1, characterized in that it comprises patients with secondary infection. 16. Prognostic markers characterized by containing isolated cells, nucleic acid sequences, their complements, parts, allelic polymorphism or protein encoded by them, from a gene or gene combination selected from the 1981 transcript list. Markers according to claim 16, characterized in that the combination includes all genes individually. Markers according to claim 16, characterized in that the combination includes all genes in pairs. Markers according to claim 16, characterized in that the combination includes all genes in triplets. Markers according to claim 16, characterized in that the combination includes all genes in quartets or more. Markers according to claim 16, characterized in that it comprises the prognosis of two possible clinical responses of the patient: classical dengue and hemorrhagic dengue / Dengue Shock Syndrome. Markers according to claim 16, characterized in that it comprises the diagnosis of dengue infection. Markers according to claim 16, characterized in that they occur in an appropriate matrix to identify differential gene expression or allelic polymorphism, as well as the encoded protein contained therein. Markers according to claim 23, characterized in that said matrix is employed in a microarray. Markers according to claim 24, characterized in that said microarray is cDNA. Markers according to claim 24, characterized in that said microarray is of oligonucleotide. Markers according to claim 16, characterized in that it occurs in a quantitative PCR reaction to identify the differential expression of the genes contained therein. Markers according to claim 16, characterized in that it occurs in an RT-PCR reaction to identify the differential expression of the genes contained therein. Markers according to claim 16, characterized in that it occurs in a Northern blotting reaction to identify the differential expression of the genes contained therein. Markers according to claim 16, characterized in that it occurs in a PCR reaction to identify differential expression of allelic polymorphism of genes contained therein. Markers according to claim 16, characterized in that it occurs in the ELISA test to identify differential production of protein encoded by the genes. Markers according to claim 16, characterized in that it occurs in the Western blotting test to identify the differential production of protein encoded by the genes. Markers according to claim 16, characterized in that it occurs in the Dot blotting test to identify the differential production of protein encoded by the genes. Markers according to claim 16, characterized in that it occurs in the flow cytometry test to identify differential protein production encoded by the genes in any cell compartment. 35. Kit for determining the prognosis of the clinical response of a dengue infected patient, characterized by materials for detecting isolated cells, nucleic acid sequences, their complements, parts, allelic polymorphism or protein encoded by them, of a gene or gene combination. selected from the list of -1981 transcripts. 36. Dengue diagnosis kit, characterized in that materials for detecting single cells, nucleic acid sequence, their complements, parts, allelic polymorphism or protein encoded by them, from a gene or gene combination are selected from the list of -1981 transcribed. Kit according to claims 35 and 36, characterized in that the combination includes all genes individually. Kit according to claims 35 and 36, characterized in that the combination includes all genes in pairs. A kit according to claims 35 and 36, characterized in that the combination includes all genes in triplets. A kit according to claims 35 and 36, characterized in that the combination includes all genes in quartets or more. Kit according to claims 35 and 36, characterized in that it occurs in a microarray matrix to identify differential expression of genes. Kit according to claims 35 and 36, characterized in that it occurs in a quantitative PCR reaction to identify differential expression of genes. Kit according to claims 35 and 36, characterized in that it occurs in an RT-PCR reaction to identify differential expression of genes. A kit according to claims 35 and 36, characterized in that it occurs in a Northern blotting reaction to identify differential expression of genes. Kit according to claims 35 and 36, characterized in that it occurs in a PCR reaction to identify differential expression of allelic polymorphism of genes. Kit according to claims 35 and 36, characterized in that it occurs in an ELISA test to identify the differential production of protein encoded by the genes. Kit according to claims 35 and 36, characterized in that it occurs in Western blotting test to identify the differential production of protein encoded by the genes. Kit according to claims 35 and 36, characterized in that it occurs in a dot-blotting test to identify the differential protein production encoded by the genes. A kit according to claims 35 and 36, characterized in that it takes place in flow cytometry testing to identify differential protein production encoded by the genes or any cell compartment. 50. Method of treating dengue infected patient, characterized by identifying the patient as at high risk of developing hemorrhagic dengue / Dengue Shock Syndrome, based on the expression of one, two, three, four or more of the 1981 genes, and promptly performing the maintenance of the circulating liquid volume.