RESUMO
The human gut microbiome is a promising therapeutic target, but interventions are hampered by our limited understanding of microbial ecosystems. Here, we present a platform to develop, evaluate, and score approaches to learn ecological interactions from microbiome time series data. The microbiome time series inference standardized test (MTIST) comprises: a simulation framework for the in silico generation of microbiome study data akin to what is obtained with quantitative next-generation sequencing approaches, a compilation of a large curated data set generated by the simulation framework representing 648 simulated microbiome studies containing 18,360 time series, with a total of 2,182,800 species abundance measurements, and a scoring method to rank ecological inference algorithms. We use the MTIST platform to rank five implementations of microbiome inference approaches, revealing that while all algorithms performed well on ecosystems with few species (3 and 10), all algorithms failed to infer most interaction in a large ecosystem with 100 member species. However, we do find that the strongest interactions within a large ecosystem are inferred with higher success by all algorithms. Finally, we use the MTIST platform to compare different microbiome study designs, characterizing tradeoffs between samples per subject and number of subjects. Interestingly, we find that when only few samples can be collected per subject, ecological inference is most successful when these samples are collected with highest feasible temporal frequency. Taken together, we provide a computational tool to aid the development of better microbiome ecosystem inference approaches, which will be crucial towards the development of reliable and predictable therapeutic approaches that target the microbiome ecosystem.
RESUMO
Depletion of microbiota increases susceptibility to gastrointestinal colonization and subsequent infection by opportunistic pathogens such as methicillin-resistant Staphylococcus aureus (MRSA). How the absence of gut microbiota impacts the evolution of MRSA is unknown. The present report used germ-free mice to investigate the evolutionary dynamics of MRSA in the absence of gut microbiota. Through genomic analyses and competition assays, we found that MRSA adapts to the microbiota-free gut through sequential genetic mutations and structural changes that enhance fitness. Initially, these adaptations increase carbohydrate transport; subsequently, evolutionary pathways largely diverge to enhance either arginine metabolism or cell wall biosynthesis. Increased fitness in arginine pathway mutants depended on arginine catabolic genes, especially nos and arcC, which promote microaerobic respiration and ATP generation, respectively. Thus, arginine adaptation likely improves redox balance and energy production in the oxygen-limited gut environment. Findings were supported by human gut metagenomic analyses, which suggest the influence of arginine metabolism on colonization. Surprisingly, these adaptive genetic changes often reduced MRSA's antimicrobial resistance and virulence. Furthermore, resistance mutation, typically associated with decreased virulence, also reduced colonization fitness, indicating evolutionary trade-offs among these traits. The presence of normal microbiota inhibited these adaptations, preserving MRSA's wild-type characteristics that effectively balance virulence, resistance, and colonization fitness. The results highlight the protective role of gut microbiota in preserving a balance of key MRSA traits for long-term ecological success in commensal populations, underscoring the potential consequences on MRSA's survival and fitness during and after host hospitalization and antimicrobial treatment.