Biology subjects

Palaniappan, A.

Publications and source records attributed to Palaniappan, A..

3 recordsLinked to original sources

bioRxiv2018

Novel significant stage-specific differentially expressed genes in liver hepatocellular carcinoma

Liver cancer is among the top deadly cancers worldwide with a very poor prognosis, and the liver is a particularly vulnerable site for metastasis of other cancers. In this study, we developed a novel computational framework for the stage-specific analysis of hepatocellular carcinoma initiation and progression. Using publicly available clinical and RNA-Seq data of cancer samples and controls, we annotated the gene expression matrix with sample stages. We performed a linear modelling analysis of gene expression across all stages and found significant genome-wide changes in gene expression in cancer samples relative to control. Using a contrast against the control, we were able to identify differentially expressed genes (log fold change >2) that were significant at an adjusted p-value < 10E-3. In order to identify genes that were specific to each stage without confounding differential expression in other stages, we developed a full set of pairwise stage contrasts and enforced a p-value threshold (<0.05) for each such contrast. Genes were specific for a stage if they passed all the significance filters for that stage. Our analysis yielded two stage-I specific genes (CA9, WNT7B), two stage-II specific genes (APOBEC3B, FAM186A), ten stage-III specific genes including DLG5, PARI and GNMT, and ten stage-IV specific genes including GABRD, PGAM2 and PECAM1. Of these, only APOBEC3B is an established cancer driver gene. DLG5 was found to be tumor-promoting contrary to the cancer literature on this gene. Further, GABRD, well studied in literature on other cancers, emerged as a stage-IV specific gene. Our findings could be validated using multiple sources of omics data as well as experimentally. The biomarkers identified herein could potentially underpin diagnosis as well as pinpoint drug targets.

cancer biology↗

bioRxiv2018

PromoterPredict: sequence-based modelling of Escherichia coli σ70 promoter strength yields logarithmic dependence between promoter strength and sequence

We present PromoterPredict, a dynamic multiple regression approach to predict the strength of Escherichia coli promoters binding the {sigma}70 factor of RNA polymerase. {sigma}70 promoters are ubiquitously used in recombinant DNA technology, but characterizing their strength is demanding in terms of both time and money. Using a well-characterized set of promoters, we trained a multivariate linear regression model and found that the log of the promoter strength is significantly linearly associated with a weighted sum of the -10 and -35 sequence profile scores. It was found that the two regions contributed almost equally to the promoter strength. PromoterPredict accepts -10 and -35 hexamer sequences and returns the predicted promoter strength. It is capable of dynamic learning from user-supplied data to refine the model construction and yield more confident estimates of promoter strength.\n\nAvailabilityOpen source code and a standalone executable with both dynamic model-building and prediction are available (under GNU General Public License 3.0) at https://github.com/PromoterPredict, and require Python 2.7 or greater. PromoterPredict is also available as a web service at https://promoterpredict.com.\n\nContactapalania@scbt.sastra.edu

bioinformatics↗

bioRxiv2016

Computational studies of P-glycoprotein polymorphisms in antiepileptic drug resistance mechanisms

The treatment of epilepsy using antiepileptogenic drugs is complicated by drug re-sistance, resulting in treatment failure in more than one-third of cases. Human P-glycoprotein (hPGP; MDR1) is a known epileptogenic mediator. Given that experimental investigations have suggested a role for pharmacogenetics in this treatment failure, it would be of interest to study hPGP polymorphisms that might contribute to the emergence of drug resistance. Changes in protein functional activity could result from point mutations as well as altered abundance. Bioinformatics approaches were used to assess and rank the functional impact of 20 missense MDR1 polymorphisms and the top five were selected. The structures of the wildtype and mutant hPGP were modelled based on the mouse PGP structure. Docking studies of the wildtype and mutant hPGP with four standard anti-epileptic drugs were carried out. Our results revealed that the drug binding site with respect to the wildtype protein was uniform. However the mutant hPGP proteins displayed a repertoire of binding sites with stronger binding affinities towards the drug. Our studies indicated that specific polymorphisms in MDR1 could drive conformational changes of PGP structure, facilitating altered contacts with drug-substrates and resulting in drug extrusion. This suggests that MDR1 polymorphisms could play an active role in modifying drug bioavailability, leading to pharmacoresistance in antiepileptic chemotherapy.

bioinformatics↗