Dr. Ashok K Sharma: A Journey Through Computational Biology & Toxicology

Current Role:

📚 Scientist-II, Toxicology, Takeda, San Diego

• Leading initiatives within the Global Drug Safety Research Evaluation (DSRE) team, championing safety research and evaluation through collaboration and innovation
• Driving advancements in pharmaceutical research by leveraging computational modeling, AI/ML-based toxicological modeling, and thorough analysis of sequencing datasets, ultimately amplifying the safety and effectiveness of pharmaceutical modalities.

Previous Experience:

📚 Postdoctoral Researcher, Cedars Sinai Medical Center

• Thoroughly analyzed and integrated multi-omics datasets, aiming to reveal intricate microbiome-host interactions..
• Projects included:
• Analyzed Imaging Mass Cytometry Datasets for Bacterial Co-Localization in Crohn's Disease.
• Quantified Bacterial Growth Rates (PTRs) from metagenomic datasets.
• Investigated microbial role in SARS-COV-2 persistence in the GI Tract.
• Explored the impact of Human Milk Oligosaccharides on Gene Expression and Immune Response in the Intestines.

📚 Postdoctoral Associate, University of Minnesota Twin Cities

• Delved into understanding microbiome-host interactions in both animal and human models.
• Projects included:
• Primates Gut Bacteriome & Mycobiome.
• Gut microbial functional adaptations influenced by diet and lifestyle factors.
• Oral microbiome studies in HNSCC and ex-vivo dental caries.
• Animal microbiome research on Beef cattle, Horses and other domestic animals.

Education:

• Ph.D. in Computational Biology, IISER Bhopal
• This journey laid the foundation for my passion in computational biology and its applications in diverse fields.

CV / Bio / Email / Google Scholar / Twitter / Github

I was deeply engaged in the development and application of AI/machine learning and other data-driven methodologies to discern the impact of the microbiome on the general growth, health, and well-being of both animals and humans. A significant portion of my research focused on deducing diet-microbiome-host interactions from multi-omics libraries. The following is a list of my representative papers from that time:

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 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