Ashok K. Sharma

I am a Bioinformatics Scientist at Cedars Sinai Medical Center, focusing on the analysis and integration of multi-omics, single-cell, and spatial datasets to understand disease mechanisms, particularly in Crohn’s disease. My work spans host-microbiome interactions, microbial co-localization, gene regulation, and immune profiling using high-dimensional data, including imaging mass cytometry. I also collaborate on projects addressing key questions in inflammatory bowel diseases to identify novel biomarkers and therapeutic targets.

Previously, I worked as Scientist-II, Computational Biology, with the Global Drug Safety Research Evaluation (DSRE) team at Takeda Pharmaceuticals , San Diego. I am collaborating with multidisciplinary teams to expand our predictive and retrospective safety capabilities. My work included leveraging AI/ML approaches for biomarker discovery, developing splicing analysis pipelines for oligonucleotide projects, and enhancing transcriptomic data analysis pipelines to support drug discovery.

During my postdoctoral training at Cedars Sinai Medical Center, I worked on SARS-COV-2 Persistence in the GI Tract | Imaging Mass Cytometry Datasets for Bacterial Co-Localization in Crohn's Disease's , Impact of Human Milk Oligosaccharides Gene Expression and Immune Response in Small and Large Intestine, Quantification of Bacterial Growth Rates (PTRs) from metagenomic datasets (*Ongoing). Before joining Cedars, I also worked as a Postdoctoral Associate at the University of Minnesota Twin Cities, where I worked on various projects to understand microbiome-host interactions in animal and human models under diverse physiological conditions. Which includes Primates Gut Bacteriome , Primates Gut Mycobiome , Gut microbial functional adaptations , Oral microbiome in ( oral/head and neck cancer (HNSCC) , and ex-vivo dental caries), and Animal microbiome of ( Beef cattle , and Horse).

I did my PhD in Computaional Biology from Metagenomics and Systems Biology Laboratory at IISER Bhopal and funded by the MHRD.

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Most of my research has focused on developing and implementing advanced computational approaches including AI/ML models to uncover the microbiome's role in the overall growth, health, and well-being of both animals and humans. I have extensively studied diet-microbiome-host interactions using multi-omics data. Below is a list of my representative papers.

We evaluated an in vitro panel of 11 ion channel targets linked to seizure risk using automated electrophysiology, identifying Cav2.1 as the most predictive target for seizurogenic activity. The study demonstrates the value of expanding target panels to improve early prediction of seizure risks in drug discovery.

Intestine-specific ATP7B deficiency affected both intestinal and systemic response to a high-fat challenge but not the microbiome profile, at least at early stages.

We investigated the impact of lacto-fermented vegetable (LFV) consumption on the gut microbiome and metabolome by comparing faecal microbial diversity, composition, and metabolite profiles between LFV consumers and non-consumers. Results revealed minor microbiome changes but significant alterations in the faecal metabolome, including increased abundances of butyrate, acetate, and valerate, highlighting the need for further research on LFV's health benefits.

We investigated whether the phenolic-rich aleurone and testa layers of red wheat contribute to its chemopreventive effects against colon cancer in a rat model. Red wheat diets reduced biomarkers of cancer risk and altered gut microbiota composition, including increased diversity and changes in key taxa, suggesting the potential roles of dietary fiber and phenolics in reducing colon cancer risk.

We optimize ex-vivo model integrated with the advanced imaging combined with multi-omics libraries to detect functional signatures associated with dental caries.

The heavy-dose antibiotic regimen administered to COVID-19 in-patients is associated with viral persistence of SARS-COV-2 in the GI tract, suggesting an important role of the gut microbiome in excluding SARS-COV-2 from the GI tract, perhaps by competitive exclusion or promoting interferon responses.

In this study, we have shown that HMO administration appears to promote immunoregulatory effects at the gene expression level in the absence of gut microbiota.

The main goal of the present study was to understand intestinal responses to nutritional Zn restriction and supplementation that could influence overall health of the animal and indicate early changes in response to Zn restriction.

In this study, we profiled oral microbiome 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.

This longitudinal study was designed to assess how BKG affects rumen bacterial communities and average daily gain (ADG) in beef cattle.

Our main findings indicate a strong influence of host-ecological factors including diet and lifestyle in shaping the fungal community composition in the primate’s gut. This is in contrast with the gut bacterial fraction, which seems to be more influenced by the host genetics.

A population-wide analysis was carried out on 586 healthy samples from western and non-western populations including the largest Indian cohort comprising of 200 samples, and 189 Inflammatory Bowel Disease samples from western populations.

The results of this study highlight parallel gut microbiome traits in human and nonhuman primates, depending on subsistence strategy.

Results in this study indicate that the horse gut microbiome responds in an individualized manner to changes in the amount of acid detergent lignin in alfalfa hay, potentially impacting several feed digestibility characteristics.

We developed the ‘Molib’ tool to predict the biofilm inhibitory activity of small molecules.

To determine microbial associations with oral/head and neck cancer (HNSCC) among tobacco users, we characterized oral microbiome composition in smokers with and without HNSCC. 16S rRNA MiSeq sequencing was used to examine the oral mucosa microbiome of 27 smokers with (cases) and 24 without HNSCC (controls).

This study provides novel insights on the CRC-associated microbiome of a unique cohort in India, reveals the potential role of a new bacterium in CRC, and identifies cohort-specific biomarkers, which can potentially be used in noninvasive diagnosis of CRC.

The results of this study indicate a discordance between gut microbiome composition and evolutionary history in primates, calling into question previous notions about host genetic control of the primate gut microbiome.

Results in this study suggest that fecal RNA-seq, targeting gastrointestinal epithelial cells can be used to evaluate primate intestinal physiology and gut gene regulation, in samples obtained in challenging conditions in situ.

Objective of this study was to determine the associations between an individual's age and sex on the diversity and composition of the gut microbiome in wild western lowland gorillas.

We analyzed the gut microbiome of 110 healthy individuals from two distinct locations (North-Central and Southern) in India using multi-omics approaches, including 16S rRNA gene amplicon sequencing, whole-genome shotgun metagenomic sequencing, and metabolomic profiling of fecal and serum samples.

Here, we report the first genome sequence and comparative analysis of peacock with the high quality genomes of chicken, turkey, duck, flycatcher and zebra finch.

We report the atomistic details and energy economics binding and metabolism of Digoxin by the Cgr2 protein of Eggerthella lenta DSM 2243.

In this study, we have developed a prediction tool for the classification of peptides as anti-inflammatory epitopes or non anti-inflammatory epitopes.

DrugBug is a computational approach to predict the metabolic enzymes and gut bacterial species, which can potentially carry out the biotransformation of a xenobiotic/drug molecule

ToxiM is a machine-learning based binary classifier for the prediction of toxicity of molecules. Regression models for CaCo-2 permeability and Aqueous solubility has been provided to support the predicted toxicity for a given molecule. For the batch processing please download standalone version of ToxiM from Dataset page.

BioFin is a unique computational method for the prediction of biofilm inhibiting peptides. The experimentally validated biofilm inhibiting peptides sequences and their features were used for the training.

ProInFlam is a unique tool for the computational identification of proinflammatory peptide antigen/candidates and provides leads for experimental validations

The present tool helps in the identification and classification of novel peptidoglycan hydrolases from complete genomic or metagenomic ORFs. To our knowledge, this is the only tool available for the prediction of peptidoglycan hydrolases from genomic and metagenomic data.

In this work, Binning-Assembly approach has been proposed and demonstrated for the reconstruction of bacterial and viral genomes from 72 human gut metagenomic datasets.

Integrated approach for fast and accurate functional annotation of proteins in both genomic and metagenomic datasets. It is available as stand-alone tool as well as a publicly available web-server.

'16S Classifier' is a Random Forest based tool which is developed to carry out fast, efficient and accurate taxonomic classification of 16S rRNA sequences. It has the unique ability to classify small Hypervariable Regions of 16S rRNA.

Source code credit to Dr. Jon Barron