Not all T cell epitopes are equally desired: a review of in silico tools for the prediction of cytokine-inducing potential of T-cell epitopes.

Assessment of protective or harmful T cell response induced by any antigenic epitope is important in designing any immunotherapeutic molecule. The understanding of cytokine induction potential also helps us to monitor antigen-specific cellular immune responses and rational vaccine design. The classical immunoinformatics tools served well for prediction of B cell and T cell epitopes. However, in the last decade, the prediction algorithms for T cell epitope inducing specific cytokines have also been developed and appreciated in the scientific community. This review summarizes the current status of such tools, their applications, background algorithms, their use in experimental setup and functionalities available in the tools/web servers.

Prediction of anti-inflammatory proteins/peptides: an insilico approach.

BACKGROUND: The current therapy for inflammatory and autoimmune disorders involves the use of nonspecific anti-inflammatory drugs and other immunosuppressant, which are often accompanied with potential side effects. As an alternative therapy, anti-inflammatory peptides are recently being exploited as anti-inflammatory agents for treatment of various inflammatory diseases such as Alzheimer's disease and rheumatoid arthritis. Thus, understanding the correlation between amino acid sequence and its potential anti-inflammatory property is of great importance for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics. METHODS: In this study, we have developed a prediction tool for the classification of peptides as anti-inflammatory epitopes or non anti-inflammatory epitopes. The training was performed using experimentally validated epitopes obtained from Immune epitope database and analysis resource database. Different sequence-based features and their hybrids with motif information were employed for development of support vector machine-based machine learning models. Similarly, machine learning models were also constructed using random forest. RESULTS: The composition and terminal residue conservation analysis of peptides revealed the dominance of leucine, serine, tyrosine and arginine residues in anti-inflammatory epitopes as compared to non anti-inflammatory epitopes. Similarly, the anti-inflammatory epitopes specific motifs were found to be rich in hydrophobic and polar residues. The hybrid of tripeptide composition-based support vector machine model and motif yielded the best performance on 10-fold cross validation with an accuracy of 78.1% and MCC of 0.58. The same displayed an accuracy of 72% and MCC of 0.45 on validation dataset, rejecting any possibility of over-fitting. The tripeptide composition-based random forest model displayed an accuracy of 0.8 and MCC of 0.59 on 10-fold cross validation, however, the accuracy (0.68) and MCC (0.31) was lower as compared to support vector machine model on validation dataset. Thus, the support vector machine model is implemented as the default model and an additional option of using the random forest model is provided. CONCLUSION: The prediction models along with tools for epitope mapping and similarity search have been provided as a web server which is freely accessible at http://metagenomics.iiserb.ac.in/antiinflam/ .

CancerPPD: a database of anticancer peptides and proteins.

CancerPPD (http://crdd.osdd.net/raghava/cancerppd/) is a repository of experimentally verified anticancer peptides (ACPs) and anticancer proteins. Data were manually collected from published research articles, patents and from other databases. The current release of CancerPPD consists of 3491 ACP and 121 anticancer protein entries. Each entry provides comprehensive information related to a peptide like its source of origin, nature of the peptide, anticancer activity, N- and C-terminal modifications, conformation, etc. Additionally, CancerPPD provides the information of around 249 types of cancer cell lines and 16 different assays used for testing the ACPs. In addition to natural peptides, CancerPPD contains peptides having non-natural, chemically modified residues and D-amino acids. Besides this primary information, CancerPPD stores predicted tertiary structures as well as peptide sequences in SMILES format. Tertiary structures of peptides were predicted using the state-of-art method, PEPstr and secondary structural states were assigned using DSSP. In order to assist users, a number of web-based tools have been integrated, these include keyword search, data browsing, sequence and structural similarity search. We believe that CancerPPD will be very useful in designing peptide-based anticancer therapeutics.

ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins.

BACKGROUND: Proinflammatory immune response involves a complex series of molecular events leading to inflammatory reaction at a site, which enables host to combat plurality of infectious agents. It can be initiated by specific stimuli such as viral, bacterial, parasitic or allergenic antigens, or by non-specific stimuli such as LPS. On counter with such antigens, the complex interaction of antigen presenting cells, T cells and inflammatory mediators like IL1α, IL1ß, TNFα, IL12, IL18 and IL23 lead to proinflammatory immune response and further clearance of infection. In this study, we have tried to establish a relation between amino acid sequence of antigen and induction of proinflammatory response. RESULTS: A total of 729 experimentally-validated proinflammatory and 171 non-proinflammatory epitopes were obtained from IEDB database. The A, F, I, L and V amino acids and AF, FA, FF, PF, IV, IN dipeptides were observed as preferred residues in proinflammatory epitopes. Using the compositional and motif-based features of proinflammatory and non-proinflammatory epitopes, we have developed machine learning-based models for prediction of proinflammatory response of peptides. The hybrid of motifs and dipeptide-based features displayed best performance with MCC = 0.58 and an accuracy of 87.6 %. CONCLUSION: The amino acid sequence-based features of peptides were used to develop a machine learning-based prediction tool for the prediction of proinflammatory epitopes. This is a unique tool for the computational identification of proinflammatory peptide antigen/candidates and provides leads for experimental validations. The prediction model and tools for epitope mapping and similarity search are provided as a comprehensive web server which is freely available at http://metagenomics.iiserb.ac.in/proinflam/ and http://metabiosys.iiserb.ac.in/proinflam/ .

Detectable Viremia at Presentation Is a Predictor of Disease Severity in Chikungunya.

Background Chikungunya is a mosquito-borne re-emerging disease that has caused a significant number of outbreaks recently in diverse geographic settings across the globe. It leads to severe debilitating illness in a significant proportion of persons who are infected. Measures to limit the impact produced by recurrent outbreaks of the disease are limited and there is an urgent clinical need for early identification of those predisposed to develop severe disease. A comprehensive understanding regarding the proportion of individuals predisposed to developing severe disease is lacking as its correlation with detectable viremia is hinted at by some studies. In this context, we hypothesized that detectable viremia reflected in the diagnostic RT-PCR assay could be significantly associated with the development of severe disease in Chikungunya among those diagnosed on the basis of seroconversion. Our study aims to confirm the same in relation to disease severity among the suspected patients of Chikungunya in the setting of a tertiary care center. Methods In a prospective observational study at a tertiary care center, a total number of 1021 Chikungunya suspects presenting within seven days of illness were screened with Chikungunya Virus IgM ELISA from 2021 to 2023. Those having positive IgM results were further tested with RT-PCR in a blinded manner. According to the information entered into the predesigned form and the hospital follow-up/discharge data, the cases where symptoms like fever and joint pain persisted beyond two weeks were classified as severe versus those resolving within two weeks as mild. The patients in each group were compared for their clinical symptoms and association with the disease severity with detectable viremia (RT-PCR positivity). Results We identified a total of 178 (17.4%) lab-confirmed Chikungunya IgM-positive cases amongst the recruited patients. Here a total of 31 (18.9%) cases could be classified as severe and 133 (74.7%) as mild illness, the remaining 14 patients were excluded from analysis due to insufficient clinical data. Severe illness was significantly higher in elderly individuals belonging to more than 60 years (p = 0.01). Viremia was detected in 16 (9%), those with detectable viremia had higher odds (OR = 4.1) of manifesting as severe disease. Among the severe cases, the proportion of cases with RT-PCR positivity (8, 25.8%) at presentation was significantly higher (P = 0.01) versus those who presented with mild disease (7, 5.5%). Conclusion Our study reveals a correlation between detectable viremia in Chikungunya virus (CHIKV) patients and an increased risk of manifesting into a severe disease, where severe cases exhibited a significantly higher proportion of viremia, indicated by RT-PCR positivity. This study hints at the presence of viremia, joint symptoms, and elderly age as potentially useful clinical predictors of disease outcomes, these may serve as indicators for closer monitoring among individuals seeking medical attention due to Chikungunya infection. However, we need to validate these findings in future longitudinal studies incorporating multiple, time-bound follow-up data on clinical outcomes, viral titers, and its long-term complications.

Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information.

BACKGROUND: The vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure. RESULTS: In this study, first we compared protein-interacting residues of vitamins with other ligands using Two Sample Logo (TSL). It was observed that ATP, GTP, NAD, FAD and mannose preferred {G,R,K,S,H}, {G,K,T,S,D,N}, {T,G,Y}, {G,Y,W} and {Y,D,W,N,E} residues respectively, whereas vitamins preferred {Y,F,S,W,T,G,H} residues for the interaction with proteins. Furthermore, compositional information of preferred and non-preferred residues along with patterns-specificity was also observed within different vitamin-classes. Vitamins A, B and B6 preferred {F,I,W,Y,L,V}, {S,Y,G,T,H,W,N,E} and {S,T,G,H,Y,N} interacting residues respectively. It suggested that protein-binding patterns of vitamins are different from other ligands, and motivated us to develop separate predictor for vitamins and their sub-classes. The four different prediction modules, (i) vitamin interacting residues (VIRs), (ii) vitamin-A interacting residues (VAIRs), (iii) vitamin-B interacting residues (VBIRs) and (iv) pyridoxal-5-phosphate (vitamin B6) interacting residues (PLPIRs) have been developed. We applied various classifiers of SVM, BayesNet, NaiveBayes, ComplementNaiveBayes, NaiveBayesMultinomial, RandomForest and IBk etc., as machine learning techniques, using binary and Position-Specific Scoring Matrix (PSSM) features of protein sequences. Finally, we selected best performing SVM modules and obtained highest MCC of 0.53, 0.48, 0.61, 0.81 for VIRs, VAIRs, VBIRs, PLPIRs respectively, using PSSM-based evolutionary information. All the modules developed in this study have been trained and tested on non-redundant datasets and evaluated using five-fold cross-validation technique. The performances were also evaluated on the balanced and different independent datasets. CONCLUSIONS: This study demonstrates that it is possible to predict VIRs, VAIRs, VBIRs and PLPIRs from evolutionary information of protein sequence. In order to provide service to the scientific community, we have developed web-server and standalone software VitaPred (http://crdd.osdd.net/raghava/vitapred/).

Prediction of IL4 inducing peptides.

The secretion of Interleukin-4 (IL4) is the characteristic of T-helper 2 responses. IL4 is a cytokine produced by CD4+ T cells in response to helminthes and other extracellular parasites. It has a critical role in guiding antibody class switching, hematopoiesis and inflammation, and the development of appropriate effector T-cell responses. In this study, it is the first time an attempt has been made to understand whether it is possible to predict IL4 inducing peptides. The data set used in this study comprises 904 experimentally validated IL4 inducing and 742 noninducing MHC class II binders. Our analysis revealed that certain types of residues are preferred at certain positions in IL4 inducing peptides. It was also observed that IL4 inducing and noninducing epitopes differ in compositional and motif pattern. Based on our analysis we developed classification models where the hybrid method of amino acid pairs and motif information performed the best with maximum accuracy of 75.76% and MCC of 0.51. These results indicate that it is possible to predict IL4 inducing peptides with reasonable precession. These models would be useful in designing the peptides that may induce desired Th2 response.

Real-Time Polymerase Chain Reaction-Based Detection and Quantification of Hepatitis B Virus DNA.

Hepatitis B virus (HBV) is a significant cause of liver disease worldwide. It can lead to acute or chronic infections, making individuals highly susceptible to fatal cirrhosis and liver cancer. Accurate detection and quantification of HBV DNA in the blood are essential for diagnosing and monitoring HBV infection. The most common method for detecting HBV DNA is real-time PCR, which can be used to detect the virus and assess the viral load to monitor the response to antiviral therapy. Here, we describe a detailed protocol for the detection and quantification of HBV DNA in human serum or plasma using an IVD-marked real-time PCR-based kit. The kit uses primers and probes that target the highly conserved core region of the HBV genome and can accurately quantify all HBV genotypes (A, B, C, D, E, F, G, H, I, and J). The kit also includes an endogenous internal control to monitor possible PCR inhibition. This assay runs for 40 cycles, and its cutoff is 38 Ct. For the quantification of HBV DNA in clinical samples, a set of 5 quantification standards is provided with the kit. The standards contain known concentrations of HBV-specific DNA that are calibrated against the 4th WHO International Standard for HBV DNA for the nucleic acid test (NIBSC code 10/266). The standards are used to validate the functionality of the HBV-specific DNA amplification and to generate a standard curve, allowing the quantification of HBV DNA in a sample. HBV DNA as low as 2.5 IU/mL was detected using the PCR kit. The high sensitivity and reproducibility of the kit make it a powerful tool in clinical laboratories, aiding healthcare professionals in effectively diagnosing and managing HBV infections.

Role of Streptococcus pneumoniae extracellular glycosidases in immune evasion.

Streptococcus pneumoniae (pneumococcus) typically colonizes the human upper airway asymptomatically but upon reaching other sites of the host body can cause an array of diseases such as pneumonia, bacteremia, otitis media, and meningitis. Be it colonization or progression to disease state, pneumococcus faces multiple challenges posed by host immunity ranging from complement mediated killing to inflammation driven recruitment of bactericidal cells for the containment of the pathogen. Pneumococcus has evolved several mechanisms to evade the host inflicted immune attack. The major pneumococcal virulence factor, the polysaccharide capsule helps protect the bacteria from complement mediated opsonophagocytic killing. Another important group of pneumococcal proteins which help bacteria to establish and thrive in the host environment is surface associated glycosidases. These enzymes can hydrolyze host glycans on glycoproteins, glycolipids, and glycosaminoglycans and consequently help bacteria acquire carbohydrates for growth. Many of these glycosidases directly or indirectly facilitate bacterial adherence and are known to modulate the function of host defense/immune proteins likely by removing glycans and thereby affecting their stability and/or function. Furthermore, these enzymes are known to contribute the formation of biofilms, the bacterial communities inherently resilient to antimicrobials and host immune attack. In this review, we summarize the role of these enzymes in host immune evasion.

Assessing the Effect of Smokeless Tobacco Consumption on Oral Microbiome in Healthy and Oral Cancer Patients.

Oral cancer is a globally widespread cancer that features among the three most prevalent cancers in India. The risk of oral cancer is elevated by factors such as tobacco consumption, betel-quid chewing, excessive alcohol consumption, unhygienic oral condition, sustained viral infections, and also due to dysbiosis in microbiome composition of the oral cavity. Here, we performed an oral microbiome study of healthy and oral cancer patients to decipher the microbial dysbiosis due to the consumption of smokeless-tobacco-based products and also revealed the tobacco-associated microbiome. The analysis of 196 oral microbiome samples from three different oral sites of 32 healthy and 34 oral squamous cell carcinoma (OSCC) patients indicated health status, site of sampling, and smokeless tobacco consumption as significant covariates associated with oral microbiome composition. Significant similarity in oral microbiome composition of smokeless-tobacco-consuming healthy samples and OSCC samples inferred the possible role of smokeless tobacco consumption in increasing inflammation-associated species in oral microbiome. Significantly higher abundance of Streptococcus was found to adequately discriminate smokeless-tobacco-non-consuming healthy samples from smokeless-tobacco-consuming healthy samples and contralateral healthy site of OSCC samples from the tumor site of OSCC samples. Comparative analysis of oral microbiome from another OSCC cohort also confirmed Streptococcus as a potential marker for healthy oral microbiome. Gram-negative microbial genera such as Prevotella, Capnocytophaga, and Fusobacterium were found to be differentially abundant in OSCC-associated microbiomes and can be considered as potential microbiome marker genera for oral cancer. Association with lipopolysaccharide (LPS) biosynthesis pathway further confirms the differential abundance of Gram-negative marker genera in OSCC microbiomes.

Genome-wide mutational analysis of Chikungunya strains from 2016 to 2017 outbreak of central India: An attempt to elucidate the immunological basis for outbreak.

Chikungunya re-emerged in India in 2016-2017, as the first major outbreak since 2006. In our previous study, we undertook partial E1 gene sequencing and phylogenetic/mutational analysis of strains from the 2016-2017 outbreak of Chikungunya in central India and reported important mutations associated with the outbreak. This study was performed to validate the previous findings and to identify key mutations that had emerged throughout the entire genome of Chikungunya virus that could be driving the enormity of this outbreak. The phylogenetic analysis revealed the closeness of our isolates with ECSA genotype, specifically with the Singapore 2015 strain. We found 2 mutations in C and E2 genes, which were present in our isolates but were non-existent during the period of 2010-2016. Furthermore, re-emergence of Arg amino acid in place of stop codon in nsP3 gene and Thr at E2:312 positions was observed after 2011. We also used computational tools to assess the effect of the identified mutations on the T cell and B cell epitopes that could influence the protective immune response against this infection.

Presence of entry receptors and viral markers suggest a low level of placental replication of hepatitis B virus in a proportion of pregnant women infected with chronic hepatitis B.

The transplacental route of vertical transmission of Hepatitis B Virus (HBV) has been known for over a decade. Here we present evidence which suggest HBV can replicate in placenta. Forty-one HBsAg positive and 10 control pregnant women were enrolled in the study after obtaining informed consent. HBV positives were further divided in the High Viral Load (HVL) Group and Low Viral Load (LVL) Group according to INASL guidelines 2018. The Presence of the HBV DNA and expression of NTCP in the placenta was analyzed by qPCR/RT-qPCR and/or immunohistochemistry (IHC). The presence of cccDNA was assessed using Digital Droplet PCR while the presence of pre-genomic (pg) RNA was assessed through qRT-PCR and sequencing. The presence of HBeAg and HBcAg in the placenta was assessed by IHC. Immunostaining of NTCP, HBeAg and HBcAg on trophoblasts along with the presence of total HBV DNA, cccDNA and pgRNA indicated, that these cells are not only susceptible to HBV infection but may also support viral replication. This is further supported by the finding that trophoblasts of the several HBeAg seronegative samples harbored the HBeAg. Although, we did not find any correlation in NTCP expression and viral markers with viral load indicates placental replication may not aping hepatocytes. The presence of the HBV receptor, NTCP along with the presence of cccDNA, pgRNA, and HBeAg in placenta of HBV infected females without circulating HBeAg suggest that placenta act as a replication host.

Laboratory diagnosis of COVID-19: current status and challenges.

The magnitude and pace of global affliction caused by Coronavirus Disease-19 (COVID-19) is unprecedented in the recent past. From starting in a busy seafood market in the Chinese city of Wuhan, the virus has spread across the globe in less than a year, infecting over 76 million people and causing death of close to 1.7 million individuals worldwide. As no specific antiviral treatment is currently available, the major strategy in containing the pandemic is focused on early diagnosis and prompt isolation of the infected individuals. Several diagnostic modalities have emerged within a relatively short period, which can be broadly classified into molecular and immunological assays. While the former category is centered around real-time PCR, which is currently considered the gold standard of diagnosis, the latter aims to detect viral antigens or antibodies specific to the viral antigens and is yet to be recommended as a stand-alone diagnostic tool. This review aims to provide an update on the different diagnostic modalities that are currently being used in diagnostic laboratories across the world as well as the upcoming methods and challenges associated with each of them. In a rapidly evolving diagnostic landscape with several testing platforms going through various phases of development and/or regulatory clearance, it is prudent that the clinical community familiarizes itself with the nuances of different testing modalities currently being employed for this condition.

Testing of four-sample pools offers resource optimization without compromising diagnostic performance of real time reverse transcriptase-PCR assay for COVID-19.

Quick identification and isolation of SARS-CoV-2 infected individuals is central to managing the COVID-19 pandemic. Real time reverse transcriptase PCR (rRT-PCR) is the gold standard for COVID-19 diagnosis. However, this resource-intensive and relatively lengthy technique is not ideally suited for mass testing. While pooled testing offers substantial savings in cost and time, the size of the optimum pool that offers complete concordance with results of individualized testing remains elusive. To determine the optimum pool size, we first evaluated the utility of pool testing using simulated 5-sample pools with varying proportions of positive and negative samples. We observed that 5-sample pool testing resulted in false negativity rate of 5% when the pools contained one positive sample. We then examined the diagnostic performance of 4-sample pools in the operational setting of a diagnostic laboratory using 500 consecutive samples in 125 pools. With background prevalence of 2.4%, this 4-sample pool testing showed 100% concordance with individualized testing and resulted in 66% and 59% reduction in resource and turnaround time, respectively. Since the negative predictive value of a diagnostic test varies inversely with prevalence, we re-tested the 4-sample pooling strategy using a fresh batch of 500 samples in 125 pools when the prevalence rose to 12.7% and recorded 100% concordance and reduction in cost and turnaround time by 36% and 30%, respectively. These observations led us to conclude that 4-sample pool testing offers the optimal blend of resource optimization and diagnostic performance across difference disease prevalence settings.

Molecular and phylogenetic analysis of Chikungunya virus in Central India during 2016 and 2017 outbreaks reveal high similarity with recent New Delhi and Bangladesh strains.

Central India witnessed Chikungunya virus (CHIKV) outbreaks in 2016 and 2017. The present report is a hospital based cross-sectional study on the serological and molecular epidemiology of the outbreak. Mutational and phylogenetic analysis was conducted to ascertain the genetic relatedness of the central Indian strains with other Indian and global strains. Chikungunya infection was confirmed in the clinically suspected patients by the detection of anti-CHIKV IgM antibody by ELISA and viral RNA by RT-PCR. A representative set of the RT-PCR positive samples were sequenced for E1 gene and analyzed to identify the emerging mutations and establish their phylogenetic relationship, particularly with other contemporary strains. Phylogenetic analysis revealed the present strains to be of East Central South African (ECSA) genotype. Emergence of a variant strain was observed in the year 2016, which became the predominant strain in this region in 2017. The strains showed significant identity with recent New Delhi strains of 2015 and 2016 and Bangladesh strains of 2017. The epidemic mutation A226V which emerged in 2006 outbreaks of India and Indian Ocean Islands was found to be absent in the current strains. Among the important mutations viz. K211E, M269 V, D284E, I317V & V322A observed in the recent strains. I317V is a novel mutation which has emerged very recently as it was found only in central Indian (2016, 2017), New Delhi strains (2015, 2016) and Bangladesh strains (2017). This study has identified a unique mutation E1:I317V in the Central Indian strains, which is present only in recent New Delhi and Bangladesh strains till date. This study highlights the need for continuous molecular surveillance of circulating CHIKV strains in order to facilitate the prompt identification of novel strains of this virus and enable the elucidation of their clinical correlates.

Co-circulation of dengue virus serotypes in Central India: Evidence of prolonged viremia in DENV-2.

In view of paucity of information on serotype distribution of Dengue virus (DENV) in Central India, we undertook a cross-sectional study to identify clinical and virological characteristics of DENV serotypes that circulated in this region during the 2016 outbreak. Suspected cases were screened by ELISA for NS1 antigen and anti-DENV IgM antibodies. Serologically confirmed cases were subjected to RT-PCR based detection and serotyping. The RT-PCR results were confirmed by nucleotide sequencing. Genome-wide association was undertaken with DENV sequences from ViPR database and the immune evasion potential of infecting serotypes was ascertained by computing antigenic variability in B cell and Cytotoxic T cell (CTL) epitopes of all DENV proteins. The immunological basis of more prolonged viremia in DENV2-infected patients was also addressed through sequencing of NS2a gene and comparing the CTL activity in NS2a sequences identified among patients with ≤5 days and >5 days of illness. Among 166 serologically confirmed Dengue patients, 75 were positive for DENV RNA. Serotyping revealed predominance of DENV-1 and DENV-2, followed by DENV-3. Co-infection with multiple serotypes was observed in 15.5% of cases. In ~40% cases, DENV RNA was detectable beyond 5 days, among whom majority were DENV-2 infected (p = .044). Highest prevalence of antigenic variability was observed in B cell and CTL epitopes of DENV-2. The potential association between prolonged viremia and higher ability for immune evasion in DENV-2 patients was further corroborated with the observation of poorer HLA-I binding affinity in CTL epitopes observed in NS2a sequences retrieved from patients with >5 days of illness, compared to those with ≤5 days. This is the first report from central India revealing circulation of all DENV serotypes and high prevalence of co-infection with multiple serotypes. We also observed prolonged viremia upon DENV-2 infection, which could be potentially associated with its superior immune evasion potential.

Host Genetic Polymorphisms Influencing Susceptibility to Dengue.

Dengue is a pandemic-prone viral disease which is endemic in more than 100 countries and which puts half of the world's population at risk. While the disease presents as subclinical infection or mild fever in the majority of cases, approximately a quarter of the infected individuals experience severe forms of disease like dengue hemorrhagic fever/dengue shock syndrome that cause significant rates of mortality and morbidity. The pathogenesis of this differential outcome of infection is determined by a complex interplay of factors associated with the virus, vector, and host; much of which is not completely understood. In this review, we present an update on the various host genetic polymorphisms that have been reported to influence the susceptibility to dengue. For the convenience of discussion, we have categorized the genetic factors according to the different arms of the immune system with which the corresponding immune determinants are associated.

Genome-Wide Analysis to Identify HLA Factors Potentially Associated With Severe Dengue.

The pathogenesis of dengue hemorrhagic fever (DHF), following dengue virus (DENV) infection, is a complex and poorly understood phenomenon. In view of the clinical need of identifying patients with higher likelihood of developing this severe outcome, we undertook a comparative genome-wide association analysis of epitope variants from sequences available in the ViPR database that have been reported to be differentially related to dengue fever and DHF. Having enumerated the incriminated epitope variants, we determined the corresponding HLA alleles in the context of which DENV infection could potentially precipitate DHF. Our analysis considered the development of DHF in three different perspectives: (a) as a consequence of primary DENV infection, (b) following secondary DENV infection with a heterologous serotype, (c) as a result of DENV infection following infection with related flaviviruses like Zika virus, Japanese Encephalitis virus, West Nile virus, etc. Subject to experimental validation, these viral and host markers would be valuable in triaging DENV-infected patients for closer supervision owing to the relatively higher risk of poor prognostic outcome and also for the judicious allocation of scarce institutional resources during large outbreaks.

BioFuelDB: a database and prediction server of enzymes involved in biofuels production.

BACKGROUND: In light of the rapid decrease in fossils fuel reserves and an increasing demand for energy, novel methods are required to explore alternative biofuel production processes to alleviate these pressures. A wide variety of molecules which can either be used as biofuels or as biofuel precursors are produced using microbial enzymes. However, the common challenges in the industrial implementation of enzyme catalysis for biofuel production are the unavailability of a comprehensive biofuel enzyme resource, low efficiency of known enzymes, and limited availability of enzymes which can function under extreme conditions in the industrial processes. METHODS: We have developed a comprehensive database of known enzymes with proven or potential applications in biofuel production through text mining of PubMed abstracts and other publicly available information. A total of 131 enzymes with a role in biofuel production were identified and classified into six enzyme classes and four broad application categories namely 'Alcohol production', 'Biodiesel production', 'Fuel Cell' and 'Alternate biofuels'. A prediction tool 'Benz' was developed to identify and classify novel homologues of the known biofuel enzyme sequences from sequenced genomes and metagenomes. 'Benz' employs a hybrid approach incorporating HMMER 3.0 and RAPSearch2 programs to provide high accuracy and high speed for prediction. RESULTS: Using the Benz tool, 153,754 novel homologues of biofuel enzymes were identified from 23 diverse metagenomic sources. The comprehensive data of curated biofuel enzymes, their novel homologs identified from diverse metagenomes, and the hybrid prediction tool Benz are presented as a web server which can be used for the prediction of biofuel enzymes from genomic and metagenomic datasets. The database and the Benz tool is publicly available at http://metabiosys.iiserb.ac.in/biofueldb& http://metagenomics.iiserb.ac.in/biofueldb.

IL17eScan: A Tool for the Identification of Peptides Inducing IL-17 Response.

IL-17 cytokines are pro-inflammatory cytokines and are crucial in host defense against various microbes. Induction of these cytokines by microbial antigens has been investigated in the case of ischemic brain injury, gingivitis, candidiasis, autoimmune myocarditis, etc. In this study, we have investigated the ability of amino acid sequence of antigens to induce IL-17 response using machine-learning approaches. A total of 338 IL-17-inducing and 984 IL-17 non-inducing peptides were retrieved from Immune Epitope Database. 80% of the data were randomly selected as training dataset and rest 20% as validation dataset. To predict the IL-17-inducing ability of peptides/protein antigens, different sequence-based machine-learning models were developed. The performance of support vector machine (SVM) and random forest (RF) was compared with different parameters to predict IL-17-inducing epitopes (IIEs). The dipeptide composition-based SVM-model displayed an accuracy of 82.4% with Matthews correlation coefficient = 0.62 at polynomial (t = 1) kernel on 10-fold cross-validation and outperformed RF. Amino acid residues Leu, Ser, Arg, Asn, and Phe and dipeptides LL, SL, LK, IL, LI, NL, LR, FK, SF, and LE are abundant in IIEs. The present tool helps in the identification of IIEs using machine-learning approaches. The induction of IL-17 plays an important role in several inflammatory diseases, and identification of such epitopes would be of great help to the immunologists. It is freely available at http://metagenomics.iiserb.ac.in/IL17eScan/ and http://metabiosys.iiserb.ac.in/IL17eScan/.