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Abstract The host microbiome is integral to metabolism, immune function, and pathogen resistance. Yet, reliance on relative abundance in microbiome studies introduces compositional biases that obscure ecological interpretation, while the absence of robust tools for absolute abundance quantification has limited biological discovery. Here, we apply absolute abundance profiling to uncover host-specific microbial patterns across herpetofauna orders that are masked in relative abundance data. Relative- and absolute abundance-derived bacterial and fungal microbiomes exhibit divergent profiles shaped by compositional bias and multifactorial effects. Absolute abundance identified key genera, Lactococcus, Parabacteroides, and Cetobacterium in salamanders, and Basidiobolus and Mortierella in lizards, turtles, snakes, and tortoises, that consistently emerged as core taxa, revealing host-associated patterns previously obscured by compositional constraints. In closely related Desmognathus species, where environmental and phylogenetic variation was minimized, absolute abundance enabled finer resolution of microbiome dynamics and significantly reduced false discovery rates. Absolute abundance-based network analyses further revealed distinct keystone taxa between the relative and absolute abundance datasets. Despite low redundancy, Basidiobolus exhibited high network betweenness, efficiency, and degree, suggesting its role as a key connector between microbial modules and a contributor to overall network robustness. This predicted structural role aligns with Burt’s structural hole theory, which suggests that nodes linking otherwise disconnected modules occupy influential network positions. These findings underscore the value of absolute abundance in resolving microbial dynamics and supporting meaningful interpretation of host-microbiome associations. This advance is made possible by DspikeIn, a flexible wet-lab and computational framework that enhances ecological resolution and cross-study comparability. 

Abstract MotivationHigh-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. ResultsAddressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. Availability and implementationAll source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna. 

Abstract Emerging infectious diseases are increasingly recognized as a significant threat to global biodiversity conservation. Elucidating the relationship between pathogens and the host microbiome could lead to novel approaches for mitigating disease impacts. Pathogens can alter the host microbiome by inducing dysbiosis, an ecological state characterized by a reduction in bacterial alpha diversity, an increase in pathobionts, or a shift in beta diversity. We used the snake fungal disease (SFD; ophidiomycosis), system to examine how an emerging pathogen may induce dysbiosis across two experimental scales. We used quantitative polymerase chain reaction, bacterial amplicon sequencing, and a deep learning neural network to characterize the skin microbiome of free‐ranging snakes across a broad phylogenetic and spatial extent. Habitat suitability models were used to find variables associated with fungal presence on the landscape. We also conducted a laboratory study of northern watersnakes to examine temporal changes in the skin microbiome following inoculation withOphidiomyces ophidiicola. Patterns characteristic of dysbiosis were found at both scales, as were nonlinear changes in alpha and alterations in beta diversity, although structural‐level and dispersion changes differed between field and laboratory contexts. The neural network was far more accurate (99.8% positive predictive value [PPV]) in predicting disease state than other analytic techniques (36.4% PPV). The genusPseudomonaswas characteristic of disease‐negative microbiomes, whereas, positive snakes were characterized by the pathobiontsChryseobacterium,Paracoccus, andSphingobacterium. Geographic regions suitable forO. ophidiicolahad high pathogen loads (>0.66 maximum sensitivity + specificity). We found that pathogen‐induced dysbiosis of the microbiome followed predictable trends, that disease state could be classified with neural network analyses, and that habitat suitability models predicted habitat for the SFD pathogen.