Biology subjects

Panda, B.

Publications and source records attributed to Panda, B..

6 recordsLinked to original sources

bioRxiv2018

Functional Genomics Screen with Pooled shRNA Library and Gene Expression Profiling with Extracts of Azadirachta indica Identify Potential Pathways for Therapeutic Targets in Head and Neck Squamous Cell Carcinoma

Tumor suppression by the extracts of Azadirachta indica (neem) works via anti-proliferation, cell cycle arrest, and apoptosis, demonstrated previously using cancer cell lines and live animal models. However, very little is known about the molecular targets and pathways that the neem extracts and the associated compounds act through. Here, we address this using a genome-wide functional pooled shRNA screen on head and neck squamous cell carcinoma cell line treated with crude neem leaf extracts, known for their anti-tumorigenic activity. By analyzing differences in global clonal sizes of the shRNA-infected cells cultured under no treatment and treatment with neem leaf extract conditions, assayed using next-generation sequencing, we found 225 genes affected the cancer cell growth in the shRNA-infected cells treated with neem extract. Pathway enrichment analyses of whole-genome gene expression data from cells temporally treated with neem extract revealed important roles played by the TGF-{beta} pathway and HSF-1-related gene network. Our results indicate that neem extract simultaneously affects various important molecular signaling pathways in head and neck cancer cells, some of which may be therapeutic targets for this devastating tumor.

genomics↗

bioRxiv2017

Comparative analyses of venom-associated genes from an Old World viper, Daboia russelii

Molecular basis of toxin gene diversity among snakes is poorly understood. Lack of whole genome sequence information for most snakes makes studies on toxin genes and their orthologous counterparts difficult. One of the challenges in studying snake genomes is the acquisition of biological material from live animals, especially from the venomous ones. Additionally, in certain geographies, Government permission is required to handle live snakes making the process cumbersome and time-consuming. Here, we report comparative sequence analyses of toxin genes from Russells viper (Daboia russelii) using whole-genome sequencing data obtained from the skin exuviate. In addition to the comparative analyses of 46 toxin-associated proteins, we present evidence of unique sequence motifs in five key toxin-associated protein domains; nerve growth factor (NGF), platelet derived growth factor (PDGF), Kunitz/Bovine pancreatic trypsin inhibitor (Kunitz BPTI), cysteine-rich secretory proteins, antigen 5, and pathogenesis-related 1 proteins (CAP) and cysteine-rich secretory protein (CRISP). We compared the venom-associated domains from Russells viper with those from both venomous and non-venomous vertebrates and invertebrates. The in silico study on structures identified V11 and T35 in the NGF domain; F23 and A29 in the PDGF domain; N69, K2 and A5 in the CAP domain; and Q17 in the CRISP domain to be responsible for differences in the largest pockets across the protein domain structures in New World vipers, Old World vipers and elapids. Similarly, residues F10, Y11 and E20 appear to play an important role in the protein structures across the kunitz protein domain of viperids and elapids. Our study sheds light on the uniqueness of these key toxin-associated proteins and their evolution in vipers.\n\nData deposition: Russells viper sequence data is deposited in the NCBI SRA database under the accession number SRR5506741 and the GenBank accession numbers for the individual venom-associated genes is provided in Table S1.

bioinformatics↗

bioRxiv2017

A minimal set of internal control genes for gene expression studies in head and neck squamous cell carcinoma

BackgroundSelection of the right reference gene(s) is crucial in the analysis and interpretation of gene expression data. In head and neck cancer, studies evaluating the efficacy of internal reference genes are rare. Here, we present data for a minimal set of candidates as internal control genes for gene expression studies in head and neck cancer.\n\nMethodsWe analyzed data from multiple sources (in house whole-genome gene expression microarrays, n=21; TCGA RNA-seq, n=42, and published gene expression studies in head and neck tumors from literature) to come up with a set of genes (discovery set) for their stable expression across tumor and normal tissues. We then performed independent validation of their expression using qPCR in 14 tumor:normal pairs. Genes in the discovery set were ranked using four different algorithms (BestKeeper, geNorm, NormFinder, and comparative delta Ct) and a web-based comparative tool, RefFinder, for their stability and variance in expression across tissues.\n\nResultsOur analyses resulted in 18 genes (discovery set) that had lowest variance and high level of expression across tumor and normal samples. Independent experimental validation and analyses with multiple tools resulted in top ranked five genes (RPL30, RPL27, PSMC5, OAZ1 and MTCH1) out of which, RPL30 (60S ribosomal protein L30) and RPL27 (60S ribosomal protein L27), performed best and were abundantly expressed across tumor and normal tissues.\n\nConclusionsRPL30 and RPL27 are stably expressed in HNSCC and should be used as internal control genes in gene expression in head and neck tumors studies.

cancer biology↗

bioRxiv2017

CAFE MOCHA: An Integrated Platform for Discovering Clinically Relevant Molecular Changes in Cancer; an Example of Distant Metastasis and Recurrence-linked Classifiers in Head and Neck Squamous Cell Carcinoma

BackgroundCAFE MOCHA (Clinical Association of Functionally Established MOlecular CHAnges) is an integrated GUI-driven computational and statistical framework to discover molecular signatures linked to a specific clinical attribute in a cancer type. We tested CAFE MOCHA in head and neck squamous cell carcinoma (HNSCC) for discovering a signature linked to distant metastasis and recurrence (MR) in 517 tumors from TCGA and validated the signature in 18 tumors from an independent cohort.\n\nMethodsThe platform integrates mutations and indels, gene expression, DNA methylation and copy number variations to discover a classifier first, predict an incoming tumour for the same by pulling defined class variables into a single framework that incorporates a coordinate geometry-based algorithm, called Complete Specificity Margin Based Clustering (CSMBC) with 100% specificity. CAFE MOCHA classifies an incoming tumour sample using either a matched normal or a built-in database of normal tissues. The application is packed and deployed using the install4j multi-platform installer.\n\nResultsWe tested CAFE MOCHA to discover a signature for distant metastasis and recurrence in HNSCC. The signature MR44 in HNSCC yielded 80% sensitivity and 100% specificity in the discovery stage and 100% sensitivity and 100% specificity in the validation stage.\n\nConclusionsCAFE MOCHA is a cancer type- and clinical attribute-agnostic computational and statistical framework to discover integrated molecular signature for a specific clinical attribute.\n\nCAFE MOCHA is available in GitHub (https://github.com/binaypanda/CAFEMOCHA).

cancer biology↗

bioRxiv2016

RNAtor: an Android-based application for biologists to plan RNA sequencing experiments.

RNA sequencing (RNA-seq) is a powerful technology for identification of novel transcripts (coding, non-coding and splice variants), understanding of transcript structures and estimation of gene and/or allelic expression. There are specific challenges that biologists face in determining the number of replicates to use, total number of sequencing reads to generate for detecting marginally differentially expressed transcripts and the number of lanes in a sequencing flow cell to use for the production of right amount of information. Although past studies attempted answering some of these questions, there is a lack of accessible and biologist-friendly mobile applications to answer these questions. Keeping this in mind, we have developed RNAtor, a mobile application for Android platforms, to aid biologists in correctly designing their RNA-seq experiments. The recommendations from RNAtor are based on simulations and real data.\n\nAvailability and ImplementationThe Android version of RNAtor is available on Google Play Store and the code from GitHub (https://github.com/binaypanda/RNAtor).

bioinformatics↗

bioRxiv2016

High-risk human papillomavirus in oral cavity squamous cell carcinoma

PurposeThe prevalence of human papillomavirus (HPV) in oral cavity squamous cell carcinoma (OSCC) varies significantly based on assay sensitivity and patient geography. Accurate detection is essential to understand the role of HPV in disease prognosis and management of patients with OSCC.\n\nMethodsWe generated and integrated data from multiple analytes (HPV DNA, HPV RNA, and p16), assays (immunohistochemistry, PCR, qPCR and digital PCR) and molecular changes (somatic mutations and DNA methylation) from 153 OSCC patients to correlate p16 expression, HPV DNA, and HPV RNA with HPV incidence and patient survival.\n\nResultsHigh prevalence (33-58%) of HPV16/18 DNA did not correlate with the presence of transcriptionally active viral genomes (15%) in tumors. Eighteen percent of the tumors were p16 positive. and only 6% were both HPV DNA and RNA positive. Most tumors with relatively high-copy HPV DNA, and/or HPV RNA, but not with HPV DNA alone (irrespective of copy number), were wild-type for TP53 and CASP8 genes. In our study, p16 protein, HPV DNA and HPV RNA, either alone or in combinations, did not correlate with patient survival. Nine HPV-associated genes stratified the virus +ve from the -ve tumor group with high confidence (p<0.008) when HPV DNA copy number and/or HPV RNA were considered to define HPV positivity and not HPV DNA alone irrespective of their copy number (p < 0.2).\n\nConclusionsIn OSCC, the presence of both HPV RNA and p16 are rare. HPV DNA alone is not an accurate measure of HPV positivity and therefore not informative. Moreover, HPV DNA, RNA or p16 dont correlate with outcome.

cancer biology↗