RESUMEN
Every step in common microbiome profiling protocols has variable efficiency for each microbe. For example, different DNA extraction kits may have different efficiency for Gram-positive and -negative bacteria. These variable efficiencies, combined with technical variation, create strong processing biases, which impede the identification of signals that are reproducible across studies and the development of generalizable and biologically interpretable prediction models. "Batch-correction" methods have been used to alleviate these issues computationally with some success. However, many make strong parametric assumptions which do not necessarily apply to microbiome data or processing biases, or require the use of an outcome variable, which risks overfitting. Lastly and importantly, existing transformations used to correct microbiome data are largely non-interpretable, and could, for example, introduce values to features that were initially mostly zeros. Altogether, processing bias currently compromises our ability to glean robust and generalizable biological insights from microbiome data. Here, we present DEBIAS-M (Domain adaptation with phenotype Estimation and Batch Integration Across Studies of the Microbiome), an interpretable framework for inference and correction of processing bias, which facilitates domain adaptation in microbiome studies. DEBIAS-M learns bias-correction factors for each microbe in each batch that simultaneously minimize batch effects and maximize cross-study associations with phenotypes. Using benchmarks of HIV and colorectal cancer classification from gut microbiome data, and cervical neoplasia prediction from cervical microbiome data, we demonstrate that DEBIAS-M outperforms batch-correction methods commonly used in the field. Notably, we show that the inferred bias-correction factors are stable, interpretable, and strongly associated with specific experimental protocols. Overall, we show that DEBIAS-M allows for better modeling of microbiome data and identification of interpretable signals that are reproducible across studies.
RESUMEN
Antimicrobial resistance (AMR) is a significant threat to public health. Plasmids are principal vectors of AMR genes, significantly contributing to their spread and mobility across hosts. Nevertheless, little is known about the dynamics of plasmid genetic exchange across animal hosts. Here, we use theory and methodology from network and disease ecology to investigate the potential of gene transmission between plasmids using a data set of 21 plasmidomes from a single dairy cow population. We constructed a multilayer network based on pairwise plasmid genetic similarity. Genetic similarity is a signature of past genetic exchange that can aid in identifying potential routes and mechanisms of gene transmission within and between cows. Links between cows dominated the transmission network, and plasmids containing mobility genes were more connected. Modularity analysis revealed a network cluster where all plasmids contained a mobM gene, and one where all plasmids contained a beta-lactamase gene. Cows that contain both clusters also share transmission pathways with many other cows, making them candidates for super-spreading. In support, we found signatures of gene super-spreading in which a few plasmids and cows are responsible for most gene exchange. An agent-based transmission model showed that a new gene invading the cow population will likely reach all cows. Finally, we showed that edge weights contain a non-random signature for the mechanisms of gene transmission, allowing us to differentiate between dispersal and genetic exchange. These results provide insights into how genes, including those providing AMR, spread across animal hosts.