Longitudinal linked-read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment

Gut microbial communities can respond to antibiotic perturbations by rapidly altering their taxonomic and functional composition. However, little is known about the strain-level processes that drive this collective response. Here, we characterize the gut microbiome of a single individual at high temporal and genetic resolution through a period of health, disease, antibiotic treatment, and recovery. We used deep, linked-read metagenomic sequencing to track the longitudinal trajectories of thousands of single nucleotide variants within 36 species, which allowed us to contrast these genetic dynamics with the ecological fluctuations at the species level. We found that antibiotics can drive rapid shifts in the genetic composition of individual species, often involving incomplete genome-wide sweeps of pre-existing variants. These genetic changes were frequently observed in species without obvious changes in species abundance, emphasizing the importance of monitoring diversity below the species level. We also found that many sweeping variants quickly reverted to their baseline levels once antibiotic treatment had concluded, demonstrating that the ecological resilience of the microbiota can sometimes extend all the way down to the genetic level. Our results provide new insights into the population genetic forces that shape individual microbiomes on therapeutically relevant timescales, with potential implications for personalized health and disease.

The composition of the gut microbiome varies among human populations and individuals, and it is thought to play a key role in maintaining health and reducing susceptibility to different diseases (Gill et al. 2006; Feng et al. 2015; Sharon et al. 2016; Halfvarson et al. 2017). Understanding how this microbial ecosystem changes from week to week—through periods of health, disease, and treatment—is important for personalized health management and design of microbiome-aware therapies (Spanogiannopoulos et al. 2016).Many studies have investigated intra-host dynamics at the species or pathway level (Jernberg et al. 2010; Dethlefsen and Relman 2011; Keeney et al. 2014; Buffie et al. 2015; Yin et al. 2015; Zaura et al. 2015; Raymond et al. 2016; Yassour et al. 2016; Lloyd-Price et al. 2017; Palleja et al. 2018; Ng et al. 2019). Among other findings, these studies have shown that oral antibiotics can strongly influence the composition of the gut microbiome over a period of days, whereas the community often regains much of its initial composition in the weeks or months after antibiotics are removed (Dethlefsen and Relman 2011; Buffie et al. 2015; Ng et al. 2019). This suggests an intriguing hypothesis, in which the long-term composition of a healthy gut community is buffered against brief environmental perturbations.However, the mechanisms that enable this ecological resilience are still poorly understood. Does species composition recover because external strains are able to recolonize the host? Or do resident strains persist in refugia and expand again once antibiotics are removed? In the latter case, do resident populations also acquire genetic differences during this time, either due to population bottlenecks or to new selection pressures that are revealed during treatment? These questions can be addressed by quantifying fine-scale microbiome genetic variation below the species or pathway level and tracking how it changes during periods of health, disease, and treatment.Recent advances in strain-resolved metagenomics and isolate sequencing (Scholz et al. 2016; Ward et al. 2016; Truong et al. 2017) have made it possible to detect DNA sequence variants within species and to track how they change within and between hosts. These studies have shown that gut bacteria can acquire genetic differences over time even in healthy human hosts and that these differences arise from a mixture of external replacement events (Schloissnig et al. 2013; Truong et al. 2017; Garud et al. 2019) and the evolution of resident strains (Ghalayini et al. 2018; Garud et al. 2019; Zhao et al. 2019). However, because these previous studies have included relatively few time points per host, or relatively shallow sampling of their microbiota, the population genetic processes that drive these strain-level dynamics remain poorly characterized. Understanding how the forces of mutation, recombination, selection, and genetic drift operate within hosts is critical for efforts to forecast personalized responses to drugs or other therapies.To bridge this gap, we used deep metagenomic sequencing to follow the genetic diversity within a single host microbiome at approximately weekly intervals over a period of 5 mo, which included periods of infectious disease and the oral administration of broad-spectrum antibiotics. We used a linked-read sequencing technique to generate each of our metagenomic samples: large molecules of bacterial DNA were isolated in millions of emulsified droplets, digested into shorter fragments, and labeled with a corresponding DNA barcode to follow linked reads from the same droplet. Previous work has shown that the linkage information encoded in these barcoded “read clouds” can improve genome assembly (Bishara et al. 2018) and taxonomic assignment (Danko et al. 2019) in human gut metagenomes. Here, we took a different approach and developed new statistical methods that leverage longitudinal linked-read sequencing to detect and interpret fine-scale genetic changes that take place within the resident populations of individual bacterial species over time. This reference-based strategy simultaneously captures the ecological and evolutionary dynamics of multiple strains in many resident species, without requiring assembly of complete genomes.Here, we sought to use this approach to characterize the population genetic forces that shape native gut microbiota through periods of health, disease, antibiotic treatment, and recovery. By analyzing the temporal dynamics of thousands of single nucleotide polymorphisms in 36 abundant species, we obtain new insights into the strain-level mechanisms that govern the ecological resiliency of this community, which have important potential implications for personalized health and disease.