A detailed understanding of gut microbial ecology is essential to engineer effective microbial therapeutics and to model microbial community assembly and succession in health and disease. However, establishing generalizable insights into the functional determinants of microbial fitness in the human gut has been a formidable challenge. Here we employ fecal microbiota transplantation (FMT) as an in natura experimental model to identify determinants of microbial colonization and resilience. Our long-term sampling strategy and high-resolution multi-omics analyses of FMT donors and recipients reveal adaptive ecological processes as the main driver of microbial colonization outcomes after FMT. We also show that high-fitness donor microbial populations are significantly enriched in metabolic pathways that are responsible for the biosynthesis of nucleotides, essential amino acids, and micronutrients, independent of taxonomy. To determine whether such metabolic competence can explain the microbial ecology of human disease states, we analyzed genomes reconstructed from healthy humans and humans with inflammatory bowel disease (IBD). Our data reveal that such traits are also significantly enriched in microbial genomes recovered from IBD patients, linking presence of superior metabolic competence in bacteria to their expansion in IBD. Overall, these findings suggest that the transfer of gut microbes from a healthy donor to a disrupted recipient environment initiates an environmental filter that selects for populations that can self-sustain. Such ecological processes that select for self-sustenance under stress offer a model to explain why common yet typically rare members of healthy gut environments can become dominant in inflammatory conditions without any need for them to be causally associated with, or contribute to, such disease states.

Plasmids play a critical role in rapid bacterial adaptation by encoding accessory functions that may increase the host's fitness. However, the diversity and ecology of plasmids is poorly understood due to computational and experimental challenges in plasmid identification. Here, we report the Plasmid Classification System (PCS), a machine learning classifier that recognizes plasmid sequences based on gene functions. To train PCS, we performed a large-scale discovery and comparison of gene functions in a reference set of >16,000 plasmids and >14,000 chromosomes. PCS accurately recognizes a diverse range of plasmid subtypes, and it outperforms the previous state-of-the-art approach based on k-mer decomposition of sequences. Armed with this model, we conducted, to our knowledge, the largest search for naturally occurring human gut plasmids in 406 publicly available metagenomes representing 5 countries. This search yielded 6,257 high-confidence predicted plasmids, of which 576 had evidence of a circular conformation based on pair-end mapping. These predicted plasmids were found to be highly prevalent across the metagenomes compared to the reference set of known plasmids, suggesting there is extensive and uncharacterized plasmid diversity in the human gut microbiome.