Biology subjects

Brinkman, F. S. L.

Publications and source records attributed to Brinkman, F. S. L..

3 recordsLinked to original sources

bioRxiv2026

Analysing antimicrobial resistance mobility patterns using a diverse dataset of over 8000 bacterial species

2.Advances in genomics have enhanced surveillance of antimicrobial resistance (AMR), yet the factors governing resistance gene mobility, and therefore their risk of spread, remain poorly characterized. Here, we analyzed AMR gene distributions across thousands of bacterial genomes from NCBI RefSeq, encompassing clinical, agricultural and environmental isolates, to quantify associations with plasmids and predicted mobile genomic islands. AMR genes were identified using the Resistance Gene Identifier, and in addition to plasmid identification, mobile chromosomal elements were predicted using IslandViewer 4. Analysing both the full dataset and subsets of data with corrections for sampling bias, we show that known AMR genes are significantly enriched in mobile regions overall. However, stratification by resistance mechanism supported marked heterogeneity: certain drug classes and mechanisms are strongly associated with mobile elements, whereas others are predominantly chromosomal and non-mobile. Notably, mechanisms with specialized functions showed higher mobility, consistent with their role as "ecological public goods" that need not be present in all cells to confer community-level benefit. Differences were also observed across bacteria with distinct cell envelope structures. Together, these findings lay the groundwork for predictive models of AMR gene mobility and provide a framework for incorporating gene-level mobility into AMR risk assessment and antimicrobial stewardship policy. 3. Impact statementUnderstanding which antimicrobial resistance (AMR) genes are most likely to spread is central to managing resistance in clinical, agricultural, and environmental settings. This study reports a large-scale, systematic analysis of antimicrobial resistance (AMR) gene mobility across >8000 bacterial species, spanning diverse clinical and other ecological sources. By linking resistance mechanisms to their genomic location (plasmids, genomic islands, or the rest of chromosomes), our study demonstrates that AMR genes are overall enriched in mobile elements, but that mobility varies substantially by mechanism and bacterial context. In particular, functionally specialized resistance mechanisms are more frequently associated with mobile elements, consistent with an "ecological public goods" model for AMR dissemination. These findings extend beyond gene-specific case studies to identify generalizable patterns that help explain why some resistance determinants spread more readily than others. The breadth of relevance spans clinical microbiology, evolutionary biology, environmental microbiology, agri-foods, and public health. The work provides evidence supporting an improved framework for AMR mobility risk assessment, and antimicrobial stewardship strategies, with direct implications for forecasting the durability of existing and future antibiotics. 4. Data summaryThe complete dataset and analysis code used to generate the results can be accessed via the Open Science Framework (OSF) repository under DOI:10.17605/OSF.IO/WE3TX

genomics↗

bioRxiv2025

VIRUS-MVP: A framework for comprehensive surveillance of viral mutations and their functional impacts

As viruses evolve, they accumulate genetic mutations that can influence disease severity, transmissibility, and the effectiveness of vaccines and therapeutics. Real-time tracking of viral mutations and their functional impacts is essential to understand these changes and assess their implications for public health responses. VIRUS-MVP is an interactive, portable platform designed for the comprehensive surveillance of viral mutations. Initially developed for SARS-CoV-2, it now fully supports mpox and is expanding to include influenza and RSV. The platform links viral mutations to functional annotations, providing insights into their predicted effects on viral infectivity, immune evasion, and protein functionality. It features an interactive interface for visualizing mutation distributions, a modular and reproducible genomics workflow, and a curated annotation resource that captures known impacts on viral proteins and host interactions. Users can also import custom functional annotations to tailor analyses to specific research needs or emerging pathogens. Developed collaboratively with public health and academic partners, VIRUS-MVP enhances understanding of viral evolution and its public health impact by bridging genomic data with biological insights. The platform is open-source, adaptable, and accessible on GitHub.

bioinformatics↗

bioRxiv2021

Similarities Between Bacterial GAD and Human GAD65:Implications in Gut Mediated Autoimmune Type 1 Diabetes

A variety of islet autoantibodies (AAbs) can predict and possibly dictate eventual type 1 diabetes (T1D) diagnosis. Upwards of 75% of those with T1D are positive for AAbs against glutamic acid decarboxylase (GAD65), a producer of gamma-aminobutyric acid (GABA) in human pancreatic beta cells. Interestingly, bacterial populations within the human gut also express GAD65 and produce GABA. Evidence suggests that dysbiosis of the microbiome may correlate with T1D pathogenesis and physiology. Therefore, autoimmune linkages between the gut microbiome and islets susceptible to autoimmune attack need to be further elucidated. Utilizing silico analyses, we show here that 25 GAD sequences from different human gut bacterial sources show sequence and motif similarities to human beta cell GAD65. Our motif analyses determined that a majority of gut GAD sequences contain the pyroxical dependent decarboxylase domain of human GAD65 which is important for its enzymatic activity. Additionally, we showed overlap with known human GAD65 T-cell receptor epitopes which may implicate the immune destruction of beta cells. Thus, we propose a physiological hypothesis in which changes in the gut microbiome in those with T1D result in a release of bacterial GAD, thus causing miseducation of the host immune system. Due to the notable similarities, we found between humans and bacterial GAD, these deputized immune cells may then go on to target human beta cells leading to the development of T1D.

genomics↗