Microbiome Spillover Interface Project

Title: Microbiome insights into zoonotic risk at wildlife-human interfaces in a transitioning landscape in ThailandAbstract Land-use change is accelerating worldwide and is one of the strongest predictors of emerging zoonotic disease. These ecological transitions can disrupt host microbiomes, change pathogen carriage, and create novel opportunities for spillover at wildlife–livestock–human interfaces. Yet, little is known about how reforestation landscapes influence microbiome diversity and the distribution of zoonotic bacteria in key reservoir hosts such as bats, rodents, tree-shrews and domestic dogs. We characterized the rectal microbiome of bats, rodents, and domestic dogs sampled across a land-use gradient in Nan Province, Thailand, spanning caves, forests, reforestation, plantations, and village habitats. Full-length 16S rRNA sequencing was used to assess host- and habitat-specific patterns at the bacteria species-level. Pathogen-associated taxa were identified, and their potential transmission pathways were explored using network analysis and qPCR validation targeting Salmonella spp. From 102 samples, 1,816 taxa were identified, including 354 documented human pathogens. HMSC models confirmed that host species explained far more variation in pathogen occurrences than habitat type, with dogs, Menetus berdmorei, and Scotophilus heathii exhibiting particularly high pathogen diversity. Domestic dogs also displayed high network centrality and move freely across habitats, positioning them as key bridging host. Salmonella screening detected both S. enterica (serovars Newport/Typhimurium) and the reptile-associated S. bongori, the latter unexpectedly in bats and rodents, with variable concordance between metabarcoding and qPCR results. Our findings demonstrate that host identity, more than habitat type, structures pathogen-associated microbiomes across a reforestation landscape. Understanding these dynamics is essential to anticipate pathogen flow and strengthen One Health surveillance. This dataset provide microbiome and metadata information for phyloseq object generation for data analysis of the projet. The abundance reads are post-rarefaction at 8000 reads threshold.