Pasch, B. | bioRxiv Science

bioRxiv2026

Conserved patterns of mammal-microbe codiversification and interspecific bacterial sharing

What determines whether a gut microbial lineage has fidelity to one host species or is shared freely across multiple species? Factors that predict microbial community composition, including host species, diet, and environmental variation, have been well documented in wild animals. However, the eco-evolutionary processes maintaining host species- or population-specific microbial lineages remain unresolved. Here, we analyze 519 gut microbiome samples collected from 15 species of wild rodents across two mountains in the Madrean Sky Islands of southeastern Arizona using full-length 16S rRNA sequencing. This montane system provides a natural experiment in which adjacent replicated elevational gradients bring different combinations of rodent species into sympatry, allowing host evolutionary history to be disentangled from shared environmental effects. We find that host phylogeny, rather than environment, is the primary driver of microbial strain sharing across host species. We recover replicated signatures of codiversification on both mountains, indicating shared evolutionary history between hosts and their microbes, despite ample opportunities for cross-species environmental transmission. This evolutionary coupling is associated with predicted differences in bacterial oxygen tolerance and optimal growth temperature: codiversifying lineages are predominantly obligate anaerobes with optimal growth near host body temperature, suggesting more limited out-of-host persistence than non-codiversifying taxa. This pattern mirrors findings from the human gut microbiome, suggesting conserved constraints on microbial dispersal across mammalian hosts. The cost-effectiveness of long-read amplicon sequencing for phylogenetic inference within bacterial species opens new opportunities for large-scale evolutionary and ecological studies of host-microbe interactions in wildlife.

ecology↗

bioRxiv2018

SNP Variable Selection by Generalized Graph Domination

High-throughput sequencing technology has revolutionized both medical and biological research by generating exceedingly large numbers of genetic variants. The resulting datasets share a number of common characteristics that might lead to poor generalization capacity. Concerns include noise accumulated due to the large number of predictors, sparse information regarding the p >> n problem, and overfitting and model mis-identification resulting from spurious collinearity. Additionally, complex correlation patterns are present among variables. As a consequence, reliable variable selection techniques play a pivotal role in predictive analysis, generalization capability, and robustness in clustering, as well as interpretability of the derived models.\n\nK-dominating set, a parameterized graph-theoretic generalization model, was used to model SNP (single nucleotide polymorphism) data as a similarity network and searched for representative SNP variables. In particular, each SNP was represented as a vertex in the graph, (dis)similarity measures such as correlation coefficients or pairwise linkage disequilibrium were estimated to describe the relationship between each pair of SNPs; a pair of vertices are adjacent, i.e. joined by an edge, if the pairwise similarity measure exceeds a user-specified threshold. A minimum K-dominating set in the SNP graph was then made as the smallest subset such that every SNP that is excluded from the subset has at least k neighbors in the selected ones. The strength of\n\nk-dominating set selection in identifying independent variables, and in culling representative variables that are highly correlated with others, was demonstrated by a simulated dataset. The advantages of k-dominating set variable selection were also illustrated in two applications: pedigree reconstruction using SNP profiles of 1,372 Douglas-fir trees, and species delineation for 226 grasshopper mouse samples. A C++ source code that implements SNP-SELECT and uses Gurobi optimization solver for the k-dominating set variable selection is available (https://github.com/transgenomicsosu/SNP-SELECT).

genetics↗