Rudnicki, W. | Search bioRxiv

bioRxiv2024

Antimicrobial Resistance in Diverse Urban Microbiomes: Uncovering Patterns and Predictive Markers

Antimicrobial resistance (AMR) poses a significant global health threat, exacerbated by urbanization and anthropogenic activities. This study investigates the distribution and dynamics of AMR within urban microbiomes from six major U.S. cities using metagenomic data provided by the CAMDA 2023 challenge. We employed a range of analytical tools to investigate sample resistome, virome, and mobile genetic elements (MGEs) across these urban environments. Our results demonstrate that AMR++ and Bowtie outperform other tools in detecting diverse and abundant AMR genes, with binarization of data enhancing classification performance. The analysis revealed that a portion of resistome markers is closely associated with MGEs, and their removal drastically impacts the resistome profile and the accuracy of resistome modeling. These findings highlight the importance of preserving key MGEs in resistome studies to maintain the integrity and predictive power of AMR profiling models. This study underscores the heterogeneous nature of AMR in urban settings and the critical role of MGEs, providing valuable insights for future research and public health strategies.

microbiology↗

bioRxiv2023

Healthy microbiome - moving towards functional interpretation

Microbiome-based disease prediction has significant potential as an early, non-invasive marker of multiple health conditions linked to dysbiosis of the human gut microbiota, thanks in part to decreasing sequencing and analysis costs. Microbiome health indices and other computational tools currently proposed in the field often are based on a microbiomes species richness and are completely reliant on taxonomic classification. A resurgent interest in a metabolism-centric, ecological approach has led to an increased understanding of microbiome metabolic and phenotypic complexity revealing substantial restrictions of taxonomy-reliant approaches. In this study, we introduce a new metagenomic health index developed as an answer to recent developments in microbiome definitions, in an effort to distinguish between healthy and unhealthy microbiomes, here in focus, inflammatory bowel disease (IBD). The novelty of our approach is a shift from a traditional Linnean phylogenetic classification towards a more holistic consideration of the metabolic functional potential underlining ecological interactions between species. Based on well-explored data cohorts, we compare our method and its performance with the most comprehensive indices to date, the taxonomy-based Gut Microbiome Health Index (GMHI), and the high dimensional principal component analysis (hiPCA)methods, as well as to the standard taxon-, and function-based Shannon entropy scoring. After demonstrating better performance on the initially targeted IBD cohorts, in comparison with other methods, we retrain our index on an additional 27 datasets obtained from different clinical conditions and validate our indexs ability to distinguish between healthy and disease states using a variety of complementary benchmarking approaches. Finally, we demonstrate its superiority over the GMHI and the hiPCA on a longitudinal COVID-19 cohort and highlight the distinct robustness of our method to sequencing depth. Overall, we emphasize the potential of this metagenomic approach and advocate a shift towards functional approaches in order to better understand and assess microbiome health as well as provide directions for future index enhancements. Our method, q2-predict-dysbiosis (Q2PD), is freely available (https://github.com/Kizielins/q2-predict-dysbiosis).

bioinformatics↗