Biology subjects

Gayvert, K.

Publications and source records attributed to Gayvert, K..

4 recordsLinked to original sources

bioRxiv2021

Viral population genomics reveals host and infectivity impact on SARS-CoV-2 adaptive landscape

Public health surveillance, drug treatment development, and optimization of immunological interventions all depend on understanding pathogen adaptation, which differ for specific pathogens. SARS-CoV-2 is an exceptionally successful human pathogen, yet complete understanding of the forces driving its evolution is lacking. Here, we leveraged almost four million SARS-CoV-2 sequences originating mostly from non-vaccinated naive patients to investigate the impact of functional constraints and natural immune pressures on the sequence diversity of the SARS-CoV-2 genome. Overall, we showed that the SARS-CoV-2 genome is under strong and intensifying levels of purifying selection with a minority of sites under diversifying pressure. With a particular focus on the spike protein, we showed that sites under selection were critical for protein stability and virus fitness related to increased infectivity and/or reduced neutralization by convalescent sera. We investigated the genetic diversity of SARS-CoV-2 B and T cell epitopes and determined that the currently known T cell epitope sequences were highly conserved. Outside of the spike protein, we observed that mutations under selection in variants of concern can be associated to beneficial outcomes for the virus. Altogether, the results yielded a comprehensive map of all sites under selection across the entirety of SARS-CoV-2 genome, highlighting targets for future studies to better understand the virus spread, evolution and success.

evolutionary biology↗

bioRxiv2019

A machine learning approach predicts essential genes and pharmacological targets in cancer

Loss-of-function (LoF) screenings have the potential to reveal novel cancer-specific vulnerabilities, prioritize drug treatments, and inform precision medicine therapeutics. These screenings were traditionally done using shRNAs, but with the recent emergence of CRISPR technology there has been a shift in methodology. However, recent analyses have found large inconsistencies between CRISPR and shRNA essentiality results. Here, we examined the DepMap project, the largest cancer LoF effort undertaken to date, and find a lack of correlation between CRISPR and shRNA LoF results; we further characterized differences between genes found to be essential by either platform. We then introduce ECLIPSE, a machine learning approach, which combines genomic, cell line, and experimental design features to predict essential genes and platform specific essential genes in specific cancer cell lines. We applied ECLIPSE to known drug targets and found that our approach strongly differentiated drugs approved for cancer versus those that have not, and can thus be leveraged to identify potential cancer repurposing opportunities. Overall, ECLIPSE allows for a more comprehensive analysis of gene essentiality and drug development; which neither platform can achieve alone.

cancer biology↗

bioRxiv2018

A Machine Learning Approach Predicts Tissue-Specific Drug Adverse Events

One of the main causes for failure in the drug development pipeline or withdrawal post approval is the unexpected occurrence of severe drug adverse events. Even though such events should be detected by in vitro, in vivo, and human trials, they continue to unexpectedly arise at different stages of drug development causing costly clinical trial failures and market withdrawal. Inspired by the \"moneyball\" approach used in baseball to integrate diverse features to predict player success, we hypothesized that a similar approach could leverage existing adverse event and tissue-specific toxicity data to learn how to predict adverse events. We introduce MAESTER, a data-driven machine learning approach that integrates information on a compounds structure, targets, and phenotypic effects with tissue-wide genomic profiling and our toxic target database to predict the probability of a compound presenting with different types of tissue-specific adverse events. When tested on 6 different types of adverse events MAESTER maintains a high accuracy, sensitivity, and specificity across both the training data and new test sets. Additionally, MAESTER scores could flag a number of drugs that were approved, but later withdrawn due to unknown adverse events - highlighting its potential to identify events missed by traditional methods. MAESTER can also be used to identify toxic targets for each tissue type. Overall MAESTER provides a broadly applicable framework to identify toxic targets and predict specific adverse events and can accelerate the drug development pipeline and drive the design of new safer compounds.

pharmacology and toxicology↗

bioRxiv2017

A New Big-Data Paradigm For Target Identification And Drug Discovery

Drug target identification is one of the most important aspects of pre-clinical development yet it is also among the most complex, labor-intensive, and costly. This represents a major issue, as lack of proper target identification can be detrimental in determining the clinical application of a bioactive small molecule. To improve target identification, we developed BANDIT, a novel paradigm that integrates multiple data types within a Bayesian machine-learning framework to predict the targets and mechanisms for small molecules with unprecedented accuracy and versatility. Using only public data BANDIT achieved an accuracy of approximately 90% over 2000 different small molecules - substantially better than any other published target identification platform. We applied BANDIT to a library of small molecules with no known targets and generated [~]4,000 novel molecule-target predictions. From this set we identified and experimentally validated a set of novel microtubule inhibitors, including three with activity on cancer cells resistant to clinically used anti-microtubule therapies. We next applied BANDIT to ONC201 - an active anti- cancer small molecule in clinical development - whose target has remained elusive since its discovery in 2009. BANDIT identified dopamine receptor 2 as the unexpected target of ONC201, a prediction that we experimentally validated. Not only does this open the door for clinical trials focused on target-based selection of patient populations, but it also represents a novel way to target GPCRs in cancer. Additionally, BANDIT identified previously undocumented connections between approved drugs with disparate indications, shedding light onto previously unexplained clinical observations and suggesting new uses of marketed drugs. Overall, BANDIT represents an efficient and highly accurate platform that can be used as a resource to accelerate drug discovery and direct the clinical application of small molecule therapeutics with improved precision.

pharmacology and toxicology↗