Biology subjects

Kaufman, J. H.

Publications and source records attributed to Kaufman, J. H..

3 recordsLinked to original sources

bioRxiv2025

Macroscopic Analyses of RNA-Seq Data to Reveal Chromatin Modifications in Aging and Disease

Regulation of gene expression is fundamental for proper cellular function, and is constrained by the local chromatin environment of each gene, which varies spatially along the chromosome and is shaped by epigenetic modifications. Epigenetic modifications induce changes in the local chromatin structure, which can influence gene expression, by affecting the accessibility of DNA to transcription factors. Such changes are particularly relevant in aging and genetic disorders like Hutchinson-Gilford Progeria Syndrome (HGPS) and Werner Syndrome (WRN), where altered chromatin structure contributes to disease pathology. In this study, we analyze RNA-seq data using macroscopic metrics designed to be explicitly sensitive to chromatin modifications. The first metric, intra-chromosomal gene correlation length, measures spatial correlations in gene expressions along the chromosome. The second metric employs an energy landscape model based on the Arrhenius equation to estimate the energetic barriers associated with chromatin state transitions. We apply these metrics to various aging-related datasets, demonstrating their sensitivity to changes in the chromatin structure and the interpretability of the resulting outputs. The intra-chromosomal gene correlation length is particularly effective in quantifying changes in RNA-seq profiles due to increased chromatin accessibility during aging (and conversely, reduced accessibility due to treatment). This metric not only accurately distinguishes cell states, but also provides insight into the direction of aging. For instance, our observations on the effects of anti-sense oligonucleotide (ASO) treatment align with the existing literature, demonstrating that ASO partially restores chromatin structure in diseased cells. They additionally quantify the more pronounced effects in HGPS compared to WRN. The barrier energy landscape further extends this capability by offering a framework for understanding the progressive degradation of the regulatory mechanisms. Together, these metrics provide robust screening tools that enhance our ability to exploit common measurements such as RNA-seq to derive new phenotypes such as chromatin dynamics on aging and disease, offering an alternative perspective that complements traditional analytical techniques and enriches our understanding of cellular states.

bioinformatics↗

bioRxiv2024

Commensal or pathogen: computational vectorizing microbial genomes for de novo risk assessment and virulence feature discovery in Klebsiella pneumoniae

Bacterial pathogenicity has traditionally focused on gene-level content with experimentally-confirmed functional properties. Hence, significant inferences are made based on similarity to known pathotypes and DNA-based genomic subtyping for risk. Herein, we achieved de novo prediction of human virulence in Klebsiella pneumoniae by expanding known virulence genes with spatially proximal gene discoveries linked by functional domain architectures across all prokaryotes. This approach identified gene ontology functions not typically associated with virulence sensu stricto. By leveraging machine learning models with these expanded discoveries, public genomes were assessed for virulence prediction using categorizations derived from isolation sources captured in available metadata. Performance for de novo strain-level virulence prediction achieved 0.81 F1-Score. Virulence predictions using expanded "discovered" functional genetic content were superior to that restricted to extant virulence database content. Additionally, this approach highlighted the incongruence in relying on traditional phylogenetic subtyping for categorical inferences. Our approach represents an improved deconstruction of genome-scale datasets for functional predictions and risk assessment intended to advance public health surveillance of emerging pathogens.

microbiology↗

bioRxiv2021

Semi-supervised identification of SARS-CoV-2 molecular targets

SARS-CoV-2 genomic sequencing efforts have scaled dramatically to address the current global pandemic and aid public health. In this work, we analyzed a corpus of 66,000 SARS-CoV-2 genome sequences. We developed a novel semi-supervised pipeline for automated gene, protein, and functional domain annotation of SARS-CoV-2 genomes that differentiates itself by not relying on use of a single reference genome and by overcoming atypical genome traits. Using this method, we identified the comprehensive set of known proteins with 98.5% set membership accuracy and 99.1% accuracy in length prediction compared to proteome references including Replicase polyprotein 1ab (with its transcriptional slippage site). Compared to other published tools such as Prokka (base) and VAPiD, we yielded an 6.4- and 1.8-fold increase in protein annotations. Our method generated 13,000,000 molecular target sequences-- some conserved across time and geography while others represent emerging variants. We observed 3,362 non-redundant sequences per protein on average within this corpus and describe key D614G and N501Y variants spatiotemporally. For spike glycoprotein domains, we achieved greater than 97.9% sequence identity to references and characterized Receptor Binding Domain variants. Here, we comprehensively present the molecular targets to refine biomedical interventions for SARS-CoV-2 with a scalable high-accuracy method to analyze newly sequenced infections.

bioinformatics↗