Nasar, M. I. | Search bioRxiv

bioRxiv2024

hAMRonization: Enhancing antimicrobial resistance prediction using the PHA4GE AMR detection specification and tooling

The detection of antimicrobial resistance (AMR) markers directly from genomic or metagenomic data is becoming a standard clinical and public health procedure. This has resulted in the development of a number of different bioinformatic AMR prediction tools. Although many may implement similar principles, these tools differ significantly in their supported inputs, search algorithms, parameterisation, and underlying reference databases. Each of these tools generates a report of detected AMR genes or variants in a distinct, non-standard, format. This presents a huge barrier to the comparison of results and to the modularity of tools for AMR gene prediction within bioinformatic workflows. In collaboration with 17 public health laboratories across 10 countries, the Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) data structures working group has developed and piloted a standardized output specification for the bioinformatic detection of AMR from microbial genomes. In this report, we discuss hAMRonization, a python package and command-line utility, which implements PHA4GEs AMR specification to combine the outputs of disparate antimicrobial resistance gene detection tools into a single unified format. hAMRonization can be easily extended and currently supports 18 different tools (both species-agnostic and species-specific) for the detection of genes and/or variants conferring AMR. The harmonized reports are available in tabular form, JSON format or through an interactive HTML file (e.g., https://maguire-lab.github.io/assets/interactive_report_demo.html) that can be opened within the browser for navigable data exploration. As of 2024-03-07 hAMRonization has been downloaded [~]12,500 times, incorporated into >9 public bioinformatic tools and workflows, and been internally adopted by several national and international public health groups. The hAMRonization tool and underlying specification are open-source and freely available through PyPI, conda and GitHub (https://github.com/pha4ge/hAMRonization).

bioinformatics↗

bioRxiv2023

An enzyme activation network provides evidence for extensive regulatory crosstalk between metabolic pathways

Enzyme activation by cellular metabolites plays a pivotal role in regulating metabolic processes. Nevertheless, our comprehension of such activation events on a global network scale remains incomplete. In this study, we conducted a comprehensive investigation into the optimization of cell-intrinsic activation interactions within Saccharomyces cerevisiae. To achieve this, we integrated a genome-scale metabolic model with enzyme kinetic data sourced from the BRENDA database. Our objective was to map the distribution of enzyme activators throughout the cellular network. Our findings indicate that virtually all biochemical pathways encompass enzyme activators, frequently originating from disparate pathways, thus revealing extensive regulatory crosstalk between metabolic pathways. Indeed, activators have short pathway lengths, indicating they are activated quickly upon nutrient shifts, and in most instances, these activators target key enzymatic reactions to facilitate downstream metabolic processes. Interestingly, non-essential enzymes exhibit a significantly higher degree of activation compared to their essential counterparts. This observation suggests that cells employ enzyme activators to finely regulate secondary metabolic pathways that are only required under specific conditions. Conversely, the activator metabolites themselves are more likely to be essential components, and their activation levels surpass those of non-essential activators. In summary, our study unveils the widespread importance of enzymatic activators, and suggests that feed-forward activation of conditional metabolic pathways through essential metabolites mediates metabolic plasticity.

systems biology↗