Rohit Gupta

Molecular alterations associated with chronic exposure to cigarette smoke and chewing tobacco in normal oral keratinocytes.

ABSTRACT Tobacco usage is a known risk factor associated with development of oral cancer. It is mainly consumed in two different forms (smoking and chewing) that vary in their composition and methods of intake. Despite being the leading cause of oral cancer, molecular alterations induced by tobacco are poorly understood. We therefore sought to investigate the adverse effects of cigarette smoke/chewing tobacco exposure in oral keratinocytes (OKF6/TERT1). OKF6/TERT1 cells acquired oncogenic phenotype after treating with cigarette smoke/chewing tobacco for a period of 8 months. We employed whole exome sequencing (WES) and quantitative proteomics to investigate the molecular alterations in oral keratinocytes chronically exposed to smoke/ chewing tobacco. Exome sequencing revealed distinct mutational spectrum and copy number alterations in smoke/ chewing tobacco treated cells. We also observed differences in proteomic alterations. Proteins downstream of MAPK1 and EGFR were dysregulated in smoke and chewing tobacco exposed cells, respectively. This study can serve as a reference for fundamental damages on oral cells as a consequence of exposure to different forms of tobacco.

A validated pipeline for detection of SNVs and short InDels from RNA Sequencing

Effective identification of genomic variations is a crucial step to understand the relationship between genotype and phenotype and it can yield important insights into rare diseases and cancer biology. Exome and whole-genome sequencing are now routinely used in clinics for detection of disease causing variants in Mendelian disorders. Despite great success achieved using these methods, the diagnostic rate for identifying the right variant is ~25-50%. Recent studies have shown that using RNA sequencing (RNA-seq) the diagnostic rate can be improved and can be used as a complementary method for clinical diagnostic. In this study, we have developed a pipeline to detect germline variants from RNA-seq data. The pipeline steps include: preprocessing, alignment, GATK best practices for RNA-seq and variant filtering. The pre-processing step includes base and adapter trimming and removal of contamination reads from rRNA, tRNA, mitochondrial DNA and repeat regions. The read alignment of the pre-processed reads is performed using STAR/HiSAT. After this we used GATK best practices for the RNA-seq dataset to call germline variants. We benchmarked our pipeline on NA12878 RNA-seq data downloaded from SRA (SRR1258218). After variant calling, the quality passed variants were compared against the gold standard variants provided by GIAB consortium. Of the total ~3.6 million high quality variants reported as gold standard variants for this sample (considering whole genome), our pipeline identified ~58,104 variants to be expressed in RNA-seq. Our pipeline achieved more than 99% of sensitivity in detection of germline variants. Citation: Mandloi, N., Gupta, R. and Gupta, R. A validated pipeline for detection of SNVs and short InDels from RNA Sequencing [Abstract]. In: Abstracts of the NGBT conference; Oct 02-04, 2017; Bhubaneswar, Odisha, India: Can J Biotech, Volume 1, Special Issue (Supplement), Page 225. https://doi.org/10.24870/cjb.2017-a210