Biology subjects

Brettin, T.

Publications and source records attributed to Brettin, T..

7 recordsLinked to original sources

bioRxiv2026

A Spatio-Temporal Analysis Framework for Characterizing Radiation-Induced Genomic Instability

Chronic low-dose ionizing radiation induces complex genomic instability encompassing both structural variants and point mutations, yet these alterations are typically analyzed as independent events, limiting detection of mechanistic coupling between rearrangement formation and localized mutagenesis at breakpoint junctions. This gap is particularly consequential given the widespread occupational and environmental exposure contexts such as nuclear energy, medical imaging, and environmental contamination, where coupled genomic alterations may contribute to cancer risk through mechanisms invisible to type-agnostic analyses. We developed an integrated analytical framework combining temporal pattern tracking, breakpoint-proximal mutation enrichment analysis, and systematic testing across all structural variant types to resolve these coupled dynamics across dose and time. Applying this framework to whole-genome sequencing data from primary human endothelial cells (HUVEC) exposed to chronic low-dose gamma radiation (0.20-2.62 mGy/hr) over three weeks, we identified inversion-specific mutagenic coupling; doublet base substitutions (DBS) were 7.13-fold enriched within 10bp of inversion breakpoints, a signal absent from other structural variant types, with sharp distance-dependent decay indicating localized mutagenesis at these junctions. Temporal analysis further revealed divergent fates of co-occurring alterations: inversions appeared transiently while DBS mutations showed greater persistence. These results illustrate how systematic integration of dose, time, and variant-type dimensions can uncover coupled mutagenic mechanisms that remain invisible in static or type-agnostic analyses. The framework is broadly applicable to longitudinal sequencing studies of genotoxic exposures, with applications to cancer genomics, radiation risk assessment, and mechanistic studies of DNA repair fidelity.

bioinformatics↗

bioRxiv2026

Interpretable transcriptome-to-phenotype modeling of cell-painting nuclear morphology features from RNA-seq under low-dose radiation exposure

With rapid advancements in high-throughput multi-modal profiling techniques across molecular, cellular to tissue scales, translating such multi-modal data into knowledge discovery for foundational understanding of cellular mechanisms is central in modern biomedical sciences. In this study, we focus on understanding how low-dose radiation exposure perturbs cellular morphology by linking high-dimensional transcriptomic responses to quantitative cell-phenotype readouts over time. We present a time-resolved inverse modeling framework that associates gene expression changes via RNA-sequencing with nuclear morphology features obtained from cell-painting imaging. Morphology responses were defined as treated-control differences for multiple nuclear features including size, shape, intensity, and textures, indexed by radiation dose and week. To capture time-dependent associations while maintaining interpretability, both RNA-sequencing and cell-painting data were stratified into four temporal phases (weeks 1-2, 3-4, 5-6, 7-9) and phase-dependent effects are encoded via gene-phase interaction predictors. To reduce confounding by dose trends and to evaluate generalization across time, we used a two-stage leave-one-week-out procedure: (i) a dose-only baseline model produced out-of-week residuals for each morphology feature, and (ii) elastic-net regression on phase-aware predictors modeled residual variation not explained by dose. Hyperparameters were selected via an exhaustive grid search scored by the correlation between observed residuals and out-of-week residual predictions, with additional sparsity diagnostics based on nonzero coefficient counts per fold. Stable predictors were identified by selection frequency and sign consistency across folds, then pruned further for multicollinearity and parsimony. Final reduced models were fit using ordinary least squares with heteroskedasticity-consistent standard errors to report effect estimates robust to non-constant variance. This workflow yields a transparent, time-stratified set of transcriptomic predictors associated with longitudinal nuclear morphology changes and provides a reproducible foundation for downstream biological interpretation and validation.

bioinformatics↗

bioRxiv2024

Data Imbalance in Drug Response Prediction - Multi-Objective Optimization Approach in Deep Learning Setting

Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of small molecules with the specific genetic makeup of the patient. Anti-cancer DRP is a particularly challenging task requiring costly experiments as underlying pathogenic mechanisms are broad and associated with multiple genomic pathways. The scientific community has exerted significant efforts to generate public drug screening datasets, giving a path to various machine learning (ML) models that attempt to reason over complex data space of small compounds and biological characteristics of tumors. However, the data depth is still lacking compared to computer vision or natural language processing domains, limiting current learning capabilities. To combat this issue and increase the generalizability of the DRP models, we are exploring strategies that explicitly address the imbalance in the DRP datasets. We reframe the problem as a multi-objective optimization across multiple drugs to maximize deep learning model performance. We implement this approach by constructing Multi-Objective Optimization Regularized by Loss Entropy (MOORLE) loss function and plugging it into a Deep Learning model. We demonstrate the utility of proposed drug discovery methods and make suggestions for further potential application of the work to promote equitable outcomes in the healthcare field. Availabilityhttps://github.com/AlexandrNP/MOORLE Contactonarykov@anl.gov

cancer biology↗

bioRxiv2022

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics.

We seek to transform how new and emergent variants of pandemiccausing viruses, specifically SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pretraining on over 110 million prokaryotic gene sequences and finetuning a SARS-CoV-2-specific model on 1.5 million genomes, we show that GenSLMs can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLMs represents one of the first whole genome scale foundation models which can generalize to other prediction tasks. We demonstrate scaling of GenSLMs on GPU-based supercomputers and AI-hardware accelerators utilizing 1.63 Zettaflops in training runs with a sustained performance of 121 PFLOPS in mixed precision and peak of 850 PFLOPS. We present initial scientific insights from examining GenSLMs in tracking evolutionary dynamics of SARS-CoV-2, paving the path to realizing this on large biological data.

bioinformatics↗

bioRxiv2021

High Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Non-Covalent Inhibitor

Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel non-covalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (Mpro) by employing a scalable high throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this Mpro inhibitor with an inhibition constant (Ki) of 2.9 {micro}M [95% CI 2.2, 4.0]. Further, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of Mpro forming stable hydrogen bond and hydrophobic interactions. We then used multiple {micro}s-timescale molecular dynamics (MD) simulations, and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by Mpro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits Mpro and offers a springboard for further therapeutic design. O_TEXTBOXSignificance StatementThe ongoing novel coronavirus pandemic (COVID-19) has prompted a global race towards finding effective therapeutics that can target the various viral proteins. Despite many virtual screening campaigns in development, the discovery of validated inhibitors for SARS-CoV-2 protein targets has been limited. We discover a novel inhibitor against the SARS-CoV-2 main protease. Our integrated platform applies downstream biochemical assays, X-ray crystallography, and atomistic simulations to obtain a comprehensive characterization of its inhibitory mechanism. Inhibiting Mpro can lead to significant biomedical advances in targeting SARS-CoV-2 treatment, as it plays a crucial role in viral replication. C_TEXTBOX

biophysics↗

bioRxiv2017

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae

Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to apidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within {+/-}1 two-fold dilution factor, is 92%. Individual accuracies are[≥]90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.

bioinformatics↗

bioRxiv2017

Discovery and whole genome sequencing of a human clinical isolate of the novel species Klebsiella quasivariicola sp. nov.

Originally thought to be a single species, Klebsiella pneumoniae has been divided into three distinct species: K. pneumoniae, K. quasipneumoniae and K. variicola. In a recent study of 1,777 extended-spectrum beta-lactamase (ESBL)-producing Klebsiella strains recovered from human infections in Houston, we discovered one strain (KPN1705) causing a wound infection that was phylogenetically distinct from all currently recognized Klebsiella species. Whole genome sequencing of strain KPN1705 revealed that it was single locus variant of the multilocus sequence type ST-1155. This sequence type was reported only once previously. To further investigate the phylogeny of these two organisms, we sequenced the genome of strain KPN1705 to closure and compared its genetic features to Klebsiella reference strains. Results demonstrated strain KPN1705 extensively shares core gene content, antimicrobial resistance genes, and plasmids with K. pneumoniae, K. quasipneumoniae and K. variicola. Since strain KPN1705 and the previously reported novel strain are phylogenetically most closely related to K. variicola, we propose the name K. quasivariicola sp. nov.

microbiology↗