Biology subjects

Summers, A. O.

Publications and source records attributed to Summers, A. O..

3 recordsLinked to original sources

bioRxiv2025

Knowledge Graph-Based Framework for Detecting Horizontal Gene Transfer Events Driving Antimicrobial Resistance

Horizontal gene transfer (HGT) is a key mechanism driving the rapid dissemination of antimicrobial resistance (AMR) among bacterial populations, posing a severe threat to global public health. Traditional detection methods for HGT, such as phylogenetic incongruence and compositional analysis, are often limited in their scope and accuracy. To address these challenges, we propose a comprehensive knowledge graph (KG)-based framework that integrates genomic, environmental, biochemical, and ontological datasets to detect and analyze HGT events contributing to AMR. Our KG captures relationships among genes, plasmids, species, mobile genetic elements, antibiotics, and ecological factors using curated sources like NCBI, CARD, ICEberg, MGnify, and KEGG. We enrich nodes and edges with biological (e.g., GC content, codon bias) and chemical (e.g., binding affinities, environmental exposure) signatures and apply ontology-based semantic enrichment using ARO and GO. A Graph Neural Network (GNN) trained on the KG achieves high accuracy (92%) and F1-score (0.90) in predicting HGT events, outperforming conventional methods. Community detection identifies AMR dissemination hubs, while environmental analyses reveal strong correlations between antibiotic concentrations and resistance gene prevalence. Our framework demonstrates strong biological and chemical validation and provides a scalable, interpretable, and data-rich tool for AMR surveillance, with implications for clinical, agricultural, and environmental microbiology.

bioinformatics↗

bioRxiv2022

Three Distinct Annotation Platforms Differ in Detection of Antimicrobial Resistance Genes in Long-Read, Short-Read, and Hybrid Sequences Derived from Total Genomic DNA or from Purified Plasmid DNA

Recent advances and lower costs in rapid high-throughput sequencing have engendered hope that whole genome sequencing (WGS) might afford complete resistome characterization in clinical bacterial isolates. Despite its potential, several challenges should be addressed before adopting WGS to detect antimicrobial resistance (AMR) genes in the clinical laboratory. Here, with three distinct ESKAPE bacteria, we compared different approaches to identify best practices for detection of AMR genes, including: total genomic DNA and plasmid DNA extractions, solo assembly of Illumina short-reads and of ONT long-reads, two hybrid assembly pipelines, and three in silico AMR databases. We also determined the susceptibility of each strain to 21 antimicrobials. We found that all AMR genes detected in pure plasmid DNA were also detectable in total genomic DNA indicating that, at least in these three enterobacterial genera, purification of plasmid DNA was not necessary to detect plasmid-borne AMR genes. We also found that Illumina short-reads used with ONT long-reads in either hybrid or polished assemblies of total genomic DNA enhanced sensitivity and accuracy of AMR gene detection. Phenotypic susceptibility corresponded well with genotypes identified by sequencing, but the three AMR databases differed significantly in distinguishing mobile dedicated AMR genes from non-mobile chromosomal housekeeping genes in which rare spontaneous resistance mutations might occur. This study reveals the need for standardized biochemical and informatic procedures and database resources for consistent, reliable AMR genotyping to take full advantage of WGS to expedite patient treatment and to track AMR genes within the hospital and community.

microbiology↗

bioRxiv2017

Transcriptional responses of Escherichia coli during recovery from inorganic or organic mercury exposure

BackgroundThe protean chemical properties of mercury have long made it attractive for diverse applications, but its toxicity requires great care in its use, disposal, and recycling. Mercury occurs in multiple chemical forms, and the molecular basis for the distinct toxicity of its various forms is only partly understood. Global transcriptomics applied over time can reveal how a cell recognizes a toxicant and what cellular subsystems it marshals to repair and recover from the damage. The longitudinal effects on the transcriptome of exponential phase E. coli were compared during sub-acute exposure to mercuric chloride (HgCl2) or to phenylmercuric acetate (PMA) using RNA-Seq.\n\nResultsDifferential gene expression revealed common and distinct responses to the mercurials throughout recovery. Cultures exhibited growth stasis immediately after each mercurial exposure but returned to normal growth more quickly after PMA exposure than after HgCl2 exposure. Correspondingly, PMA rapidly elicited up-regulation of a large number of genes which continued for 30 min, whereas fewer genes were up-regulated early after HgCl2 exposure only some of which overlapped with PMA up-regulated genes. By 60 min gene expression in PMA-exposed cells was almost indistinguishable from unexposed cells, but HgCl2 exposed cells still had many differentially expressed genes. Relative expression of energy production and most metabolite uptake pathways declined with both compounds, but nearly all stress response systems were up-regulated by one or the other mercurial during recovery.\n\nConclusionsSub-acute exposure influenced expression of ~45% of all genes with many distinct responses for each compound, reflecting differential biochemical damage by each mercurial and the corresponding resources available for repair. This study is the first global, high-resolution view of the transcriptional responses to any common toxicant in a prokaryotic model system from exposure to recovery of active growth. The responses provoked by these two mercurials in this model bacterium also provide insights about how higher organisms may respond to these ubiquitous metal toxicants.

microbiology↗