Manika Sehgal

ZikaVR: An Integrated Zika Virus Resource for Genomics, Proteomics, Phylogenetic and Therapeutic Analysis.

Current Zika virus (ZIKV) outbreaks that spread in several areas of Africa, Southeast Asia, and in pacific islands is declared as a global health emergency by World Health Organization (WHO). It causes Zika fever and illness ranging from severe autoimmune to neurological complications in humans. To facilitate research on this virus, we have developed an integrative multi-omics platform; ZikaVR (http://bioinfo.imtech.res.in/manojk/zikavr/), dedicated to the ZIKV genomic, proteomic and therapeutic knowledge. It comprises of whole genome sequences, their respective functional information regarding proteins, genes, and structural content. Additionally, it also delivers sophisticated analysis such as whole-genome alignments, conservation and variation, CpG islands, codon context, usage bias and phylogenetic inferences at whole genome and proteome level with user-friendly visual environment. Further, glycosylation sites and molecular diagnostic primers were also analyzed. Most importantly, we also proposed potential therapeutically imperative constituents namely vaccine epitopes, siRNAs, miRNAs, sgRNAs and repurposing drug candidates.

Unc-51 like kinase 1 (ULK1) in silico analysis for biomarker identification: a vital component of autophagy.

Autophagy is a degradation pathway involving lysosomal machinery for degradation of damaged organelles like the endoplasmic reticulum and mitochondria into their building blocks to maintain homeostasis within the cell. ULK1, a serine/threonine kinase, is conserved across species, from yeasts to mammals, and plays a central role in autophagy pathway. It receives signals from upstream modulators such as TIP60, mTOR and AMPK and relays them to its downstream substrates like Ambra1 and ZIP kinase. The activity of this complex is regulated through protein-protein interactions and post-translational modifications. Applying in silico analysis we identified (i) conserved patterns of ULK1 that showed its evolutionary relationship between the species which were closely related in a family compared to others. (ii) A total of 23 TFBS distributed throughout ULK1 and nuclear factor (erythroid-derived) 2 (NFE2) is of utmost significance because of its high importance rate. NEF2 has already been shown experimentally to play a role in the autophagy pathway. Most of these were of zinc coordinating class and we suggest that this information could be utilized to modulate this pathway by modifying interactions of these TFs with ULK1. (iii) CATTT haplotype was prominently found with frequency 0.774 in the studied population and nsSNPs which could have harmful effect on ULK1 protein and these could further be tested. (iv) A total of 83 phosphorylation sites were identified; 26 are already known and 57 are new that include one at tyrosine residue which could further be studied for its involvement in ULK1 regulation and hence autophagy. Furthermore, 4 palmitoylation sites at positions 426, 927, 1003 and 1049 were also found which could further be studied for protein-protein interactions as well as in trafficking.

An Integrative Approach for Mapping Differentially Expressed Genes and Network Components Using Novel Parameters to Elucidate Key Regulatory Genes in Colorectal Cancer.

For examining the intricate biological processes concerned with colorectal cancer (CRC), a systems biology approach integrating several biological components and other influencing factors is essential to understand. We performed a comprehensive system level analysis for CRC which assisted in unravelling crucial network components and many regulatory elements through a coordinated view. Using this integrative approach, the perceptive of complexity hidden in a biological phenomenon is extensively simplified. The microarray analyses facilitated differential expression of 631 significant genes employed in the progression of disease and supplied interesting associated up and down regulated genes like jun, fos and mapk1. The transcriptional regulation of these genes was deliberated widely by examining transcription factors such as hnf4, nr2f1, znf219 and dr1 which directly influence the expression. Further, interactions of these genes/proteins were evaluated and crucial network motifs were detected to associate with the pathophysiology of CRC. The available standard statistical parameters such as z-score, p-value and significance profile were explored for the identification of key signatures from CRC pathway whereas a few novel parameters representing over-represented structures were also designed in the study. The applied approach revealed 5 key genes i.e. kras, araf, pik3r5, ralgds and akt3 via our novel designed parameters illustrating high statistical significance. These novel parameters can assist in scrutinizing candidate markers for diseases having known biological pathways. Further, investigating and targeting these proposed genes for experimental validations, instead being spellbound by the complicated pathway will certainly endow valuable insight in a well-timed systematic understanding of CRC.

Gene expression-based biomarkers for discriminating early and late stage of clear cell renal cancer

In this study, an attempt has been made to identify expression-based gene biomarkers that can discriminate early and late stage of clear cell renal cell carcinoma (ccRCC) patients. We have analyzed the gene expression of 523 samples to identify genes that are differentially expressed in the early and late stage of ccRCC. First, a threshold-based method has been developed, which attained a maximum accuracy of 71.12% with ROC 0.67 using single gene NR3C2. To improve the performance of threshold-based method, we combined two or more genes and achieved maximum accuracy of 70.19% with ROC of 0.74 using eight genes on the validation dataset. These eight genes include four underexpressed (NR3C2, ENAM, DNASE1L3, FRMPD2) and four overexpressed (PLEKHA9, MAP6D1, SMPD4, C11orf73) genes in the late stage of ccRCC. Second, models were developed using state-of-art techniques and achieved maximum accuracy of 72.64% and 0.81 ROC using 64 genes on validation dataset. Similar accuracy was obtained on 38 genes selected from subset of genes, involved in cancer hallmark biological processes. Our analysis further implied a need to develop gender-specific models for stage classification. A web server, CancerCSP, has been developed to predict stage of ccRCC using gene expression data derived from RNAseq experiments.

Identification and analysis of biomarkers for mismatch repair proteins: A bioinformatic approach.

Introduction: Mismatch repair is a highly conserved process from prokaryotes to eukaryotes. Defects in mismatch repair can lead to mutations in human homologues of the Mut proteins and affect genomic stability which can result in microsatellite instability (MI). MI is implicated in most human cancers and majority of hereditary nonpolyposis colorectal cancers (HNPCCs) are attributed to defects in MLH1. Materials and Methods: In our study we analyzed MLH1 protein and the associated nucleotide and other protein sequences. The protein sequences involved in mismatch repair in different organisms have been found to be evolutionary related. Several other related proteins to MLH1 have also been identified through protein–protein interactions. All associated proteins are either mismatch repair proteins or associated with MLH1 in various pathways. Pathways information was also confirmed through MMR and other pathways in KEGG. QSite Finder showed that the active site of MLH1 protein involves residues from the conserved pattern and is involved in ligand–protein interactions and could be a useful site. To analyze linkage disequilibrium (LD) and common haplotype patterns in disease association, we performed statistical haplotype analysis on HapMap genotype data of SNPs genotyped in population CEU on chromosome 3 for MLH1. Results: Various markers have been found and LD plot was also generated. Two distinct blocks have been identified in LD plot which can be independent region of action, and there is involvement of 7 and 17 markers in first and second blocks, respectively. Conclusion: Overall correlation of 0.95 has been found among all interactions of genotyped SNPs which is significant.

Systems biology approach for mutational and site-specific structural investigation of DNA repair genes for xeroderma pigmentosum

Xeroderma pigmentosum (XP) is a rare genetic skin disorder caused due to the extreme sensitivity for ultraviolet (UV) radiations. On its exposure, DNA acquires damages leading to skin and often neurological abnormalities. The DNA repair implicated in fixing UV-induced damages is NER and mutations in genes involved in NER and TLS form the basis of XP. The analyses of such mutations are vital for understanding XP and involved cancer genetics to facilitate the identification of crucial biomarkers and anticancer therapeutics. We detected the deleterious nsSNPs and examined them at structure-level by altering the structure, estimating secondary structure, solvent accessibility and performing site specific analysis. Crucial phosphorylation sites were also identified for their role in the disorder. These mutational and structural analyses offer valuable insight to the fundamental association of genetic mutations with phenotypic variations in XP and will assist experimental biologists to evaluate the mutations and their impact on genome.

DR-GAS: A database of functional genetic variants and their phosphorylation states in human DNA repair systems

We present DR-GAS(1), a unique, consolidated and comprehensive DNA repair genetic association studies database of human DNA repair system. It presents information on repair genes, assorted mechanisms of DNA repair, linkage disequilibrium, haplotype blocks, nsSNPs, phosphorylation sites, associated diseases, and pathways involved in repair systems. DNA repair is an intricate process which plays an essential role in maintaining the integrity of the genome by eradicating the damaging effect of internal and external changes in the genome. Hence, it is crucial to extensively understand the intact process of DNA repair, genes involved, non-synonymous SNPs which perhaps affect the function, phosphorylated residues and other related genetic parameters. All the corresponding entries for DNA repair genes, such as proteins, OMIM IDs, literature references and pathways are cross-referenced to their respective primary databases. DNA repair genes and their associated parameters are either represented in tabular or in graphical form through images elucidated by computational and statistical analyses. It is believed that the database will assist molecular biologists, biotechnologists, therapeutic developers and other scientific community to encounter biologically meaningful information, and meticulous contribution of genetic level information towards treacherous diseases in human DNA repair systems. DR-GAS is freely available for academic and research purposes at: http://www.bioinfoindia.org/drgas.

A Platform for Designing Genome-Based Personalized Immunotherapy or Vaccine against Cancer

Due to advancement in sequencing technology, genomes of thousands of cancer tissues or cell-lines have been sequenced. Identification of cancer-specific epitopes or neoepitopes from cancer genomes is one of the major challenges in the field of immunotherapy or vaccine development. This paper describes a platform Cancertope, developed for designing genome-based immunotherapy or vaccine against a cancer cell. Broadly, the integrated resources on this platform are apportioned into three precise sections. First section explains a cancer-specific database of neoepitopes generated from genome of 905 cancer cell lines. This database harbors wide range of epitopes (e.g., B-cell, CD8+ T-cell, HLA class I, HLA class II) against 60 cancer-specific vaccine antigens. Second section describes a partially personalized module developed for predicting potential neoepitopes against a user-specific cancer genome. Finally, we describe a fully personalized module developed for identification of neoepitopes from genomes of cancerous and healthy cells of a cancer-patient. In order to assist the scientific community, wide range of tools are incorporated in this platform that includes screening of epitopes against human reference proteome (http://www.imtech.res.in/raghava/cancertope/).