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Pärnänen, K.

Publications and source records attributed to Pärnänen, K..

3 recordsLinked to original sources

Shared antimicrobial resistance gene families across gut microbiomes in indigenous Orang Asli communities of Peninsular Malaysia

Indigenous groups across the world have been underrepresented in the ongoing efforts to map human microbiome diversity. This study investigates the gut microbiome and resistome diversities of Indigenous Orang Asli communities in Malaysia with different lifestyles and degree of urbanisation. Healthy adult participants (>18 years old) were recruited from three Indigenous communities - Temuan (urban), Temiar (semi-urban), and Jahai (rural hunter-gatherer), as well as urban Malays. Stool samples were collected on dry ice and analysed using shotgun metagenomic sequencing. Microbial alpha diversity (Shannon) decreased but antibiotic resistance genes (ARGs) diversity increased as the degree of urbanisation in the groups increased (P < 0.05). The groups contributed to 13% of the variation observed in the microbial composition (PERMANOVA, P = 0.001), and 14.5% in resistome composition (PERMANOVA, P = 0.001). Romboutsia timonensis was significantly depleted in Jahai compared to Malay (FDR = 0.04). Shared ARGs conferring resistance to beta-lactams (cfxA), tetracyclines (tet), and macrolides (erm) were observed across all groups, irrespective of geographical location, ethnicity and lifestyle. This study provides initial characterisation of the gut microbiome and resistome of three Indigenous Orang Asli communities in Malaysia. Our findings offer foundational evidence of antimicrobial resistance patterns and underscores the need for broader inclusion of underrepresented populations in national surveillance and stewardship efforts. Impact statementOur study generated one of the first integrated microbiome-resistome analyses across an urbanisation gradient of Malaysian gut microbiome. Shotgun metagenomic profiling of hard-to-access Indigenous cohorts enabled taxonomic classification for most reads, supporting high-resolution microbial community and ARG analysis. Increasing urbanisation was associated with reduced microbial alpha diversity alongside increased ARG diversity, with population grouping explaining an insightful proportion of both microbiome and resistome variation. Core resistance determinants to beta-lactams (cfxA), tetracyclines (tet), and macrolides (erm) were consistently detected across lifestyles and geographies. These results establish a baseline framework for urbanisation-linked microbial shifts in the under-studied Malaysian gut microbiome and provide actionable direction for investigating national AMR drivers, surveillance design, and targeted stewardship strategies in underrepresented populations. Data summary and code availabilitySupporting data are provided in the supplementary data files. Specific accession numbers of the sequence data are provided as Supplementary Material. Codes produced for this analysis can be found at https://zenodo.org/records/18208358.

genomics↗

Orchestrating Microbiome Analysis with Bioconductor

The expansion of microbiome research has led to the accumulation of interlinked datasets encompassing versatile taxonomic and functional assays. The analysis of increasingly large and heterogeneous multi-modal microbiome data would benefit from unified approaches supporting the design of modular data science workflows through interoperable methods. The Bioconductor project has recently developed an optimized statistical programming framework for multi-assay data integration. Building on this foundation, we introduce a community-developed open source ecosystem for microbiome data science. In contrast to the previous alternatives, the methodology is specifically designed to support joint analysis of hierarchical, interlinked, and heterogeneous multi-table datasets that are increasingly common in modern microbiome research. This data science ecosystem encompasses open data, methods, tutorials, and an active online community. These resources support standardized and reproducible data wrangling, joint analysis, and reporting. We have detailed the functionality and usage in the online book https://microbiome.github.io/OMA, which offers guidance for prospective users and contributors.

bioinformatics↗

Fecal pollution explains antibiotic resistance gene abundances in anthropogenically impacted environments

Discharge of treated sewage leads to release of antibiotic resistant bacteria, resistance genes and antibiotic residues to the environment. Such pollution can directly contribute to increased morbidity caused by the transmission of resistant fecal pathogens. Residual antibiotics in wastewaters have been speculated to select for resistant bacteria and thereby promote the evolution and emergence of new resistance factors. Increased abundance of antibiotic resistance genes in sewage and sewage-impacted environments may, however, simply be a result of fecal contamination with resistant bacteria rather than caused by an on-site selection pressure. In this study we have disentangled these two alternative scenarios by relating the relative resistance gene abundance to the accompanying extent of fecal pollution in publicly available metagenomic data. This was possible by analyzing the abundance of a newly discovered phage which is exceptionally abundant in, and specific to, human feces. The presence of resistance genes could largely be explained by fecal pollution, with no clear signs of selection in the environment, the only exception being environments polluted by very high levels of antibiotics from manufacturing where selection is evident. Our results demonstrate the necessity to take in to account the fecal pollution levels to avoid making erroneous assumptions regarding environmental selection of antibiotic resistance. The presence or absence of selection pressure has major implications for what the risk scenarios are (transmission versus evolution) and for what mitigations (reducing pathogenic bacteria or selective agents) should be prioritized to reduce health risks related to antibiotic resistance in the environment.

microbiology↗