Mendes, 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↗

bioRxiv2021

Preliminary analysis of a neotropical plant extract antiviral potential against Chikungunya and Mayaro viruses

Chikungunya and Mayaro fevers are viral infectious diseases, without vaccine or treatment, that causes fever and arthralgia. Establishing novel antiviral tools capable of preventing or treating Chikungunya virus (CHIKV), and Mayaro virus (MAYV) infections are needed. The use of plant-based compounds that affect the replication cycles of these viruses has been proposed as a promising strategy. Chiococca alba (L.) Hitchc. is a neotropical plant used by Yucatec Mayas traditional healers, mainly, as antipyretic and antirheumatic. To evaluate the potential of C. alba methanolic extracts against CHIKV and MAYV through preliminary analysis in vitro and in silico. The cytotoxicity profile of two C. alba roots methanolic extracts in Vero cells was performed by lysosomal viability using the neutral red assay, and the antiviral potential was determined by plaque assay. We further assessed, through in silico computational predictions, the possible interactions between the active site of the nsP2 proteases of these viruses with some secondary metabolites present in C. alba extracts, identified by High-Performance Liquid Chromatography (HPLC). Our partial phytochemical analysis revealed the presence of flavonoids, and phenolic acids in the C. alba extracts. Our in vitro assays showed that both C. alba extracts inhibited more than 70% of CHIKV and MAYV activities at 60 {micro}g/mL concentration. Based on our in silico computational predictions, the flavonoids naringin and vitexin showed the greatest affinity energies with the CHIKV and MAYV nsP2 proteases, revealing the great potential of these compounds as viral inhibitors. The findings described here indicates that C. alba extracts, or their secondary metabolites, as a potential source of novel antiviral compounds.

microbiology↗