Person:

Bry, Lynn

The human gut microbiota comprise a complex and dynamic ecosystem that profoundly affects host development and physiology. Standard approaches for analyzing time-series data of the microbiota involve computation of measures of ecological community diversity at each time-point, or measures of dissimilarity between pairs of time-points. Although these approaches, which treat data as static snapshots of microbial communities, can identify shifts in overall community structure, they fail to capture the dynamic properties of individual members of the microbiota and their contributions to the underlying time-varying behavior of host ecosystems. To address the limitations of current methods, we present a computational framework that uses continuous-time dynamical models coupled with Bayesian dimensionality adaptation methods to identify time-dependent signatures of individual microbial taxa within a host as well as across multiple hosts. We apply our framework to a publicly available dataset of 16S rRNA gene sequences from stool samples collected over ten months from multiple human subjects, each of whom received repeated courses of oral antibiotics. Using new diversity measures enabled by our framework, we discover groups of both phylogenetically close and distant bacterial taxa that exhibit consensus responses to antibiotic exposure across multiple human subjects. These consensus responses reveal a timeline for equilibration of sub-communities of micro-organisms with distinct physiologies, yielding insights into the successive changes that occur in microbial populations in the human gut after antibiotic treatments. Additionally, our framework leverages microbial signatures shared among human subjects to automatically design optimal experiments to interrogate dynamic properties of the microbiota in new studies. Overall, our approach provides a powerful, general-purpose framework for understanding the dynamic behaviors of complex microbial ecosystems, which we believe will prove instrumental for future studies in this field.

Longitudinal studies of the microbiota are important for discovering changes in microbial communities that affect the host. The complexity of these ecosystems requires rigorous integrated experimental and computational methods to identify temporal signatures that promote physiologic or pathophysiologic responses in vivo. Employing a murine model of infectious colitis with the pathogen Citrobacter rodentium, we generated a 2-month time-series of 16S rDNA gene profiles, and quantitatively cultured commensals, from multiple intestinal sites in infected and uninfected mice. We developed a computational framework to discover time-varying signatures for individual taxa, and to automatically group signatures to identify microbial sub-communities within the larger gut ecosystem that demonstrate common behaviors. Application of this model to the 16S rDNA dataset revealed dynamic alterations in the microbiota at multiple levels of resolution, from effects on systems-level metrics to changes across anatomic sites for individual taxa and species. These analyses revealed unique, time-dependent microbial signatures associated with host responses at different stages of colitis. Signatures included a Mucispirillum OTU associated with early disruption of the colonic surface mucus layer, prior to the onset of symptomatic colitis, and members of the Clostridiales and Lactobacillales that increased with successful resolution of inflammation, after clearance of the pathogen. Quantitative culture data validated findings for predominant species, further refining and strengthening model predictions. These findings provide new insights into the complex behaviors found within host ecosystems, and define several time-dependent microbial signatures that may be leveraged in studies of other infectious or inflammatory conditions.

Background: Increasing evidence links altered intestinal flora in infancy to eczema and asthma. No studies have investigated the influence of maternal intestinal flora on wheezing and eczema in early childhood.

Objective: To investigate the link between maternal intestinal flora during pregnancy and development of wheeze and eczema in infancy.

Methods: Sixty pregnant women from the Boston area gave stool samples during the third trimester of their pregnancy and answered questions during pregnancy about their own health, and about their children’s health when the child was 2 and 6 months of age. Quantitative culture was performed on stool samples and measured in log10colony-forming units(CFU)/gram stool. Primary outcomes included infant wheeze and eczema in the first 6 months of life. Atopic wheeze, defined as wheeze and eczema, was analyzed as a secondary outcome.

Results: In multivariate models adjusted for breastfeeding, daycare attendance and maternal atopy, higher counts of maternal total aerobes (TA) and enterococci (E) were associated with increased risk of infant wheeze (TA: OR 2.32 for 1 log increase in CFU/g stool [95% CI 1.22, 4.42]; E: OR 1.57 [95% CI 1.06, 2.31]). No organisms were associated with either eczema or atopic wheeze.

Conclusions & Clinical Relevance: In our cohort, higher maternal total aerobes and enterococci were related to increased risk of infant wheeze. Maternal intestinal flora may be an important environmental exposure in early immune system development.

ABSTRACT Carbapenem-resistant Enterobacteriaceae (CRE) are an urgent public health concern. Rapid identification of the resistance genes, their mobilization capacity, and strains carrying them is essential to direct hospital resources to prevent spread and improve patient outcomes. Whole-genome sequencing allows refined tracking of both chromosomal traits and associated mobile genetic elements that harbor resistance genes. To enhance surveillance of CREs, clinical isolates with phenotypic resistance to carbapenem antibiotics underwent whole-genome sequencing. Analysis of 41 isolates of Klebsiella pneumoniae and Enterobacter cloacae, collected over a 3-year period, identified K. pneumoniae carbapenemase (KPC) genes encoding KPC-2, −3, and −4 and OXA-48 carbapenemases. All occurred within transposons, including multiple Tn4401 transposon isoforms, embedded within more than 10 distinct plasmids representing incompatibility (Inc) groups IncR, -N, -A/C, -H, and -X. Using short-read sequencing, draft maps were generated of new KPC-carrying vectors, several of which were derivatives of the IncN plasmid pBK31551. Two strains also had Tn4401 chromosomal insertions. Integrated analyses of plasmid profiles and chromosomal single-nucleotide polymorphism (SNP) profiles refined the strain patterns and provided a baseline hospital mobilome to facilitate analysis of new isolates. When incorporated with patient epidemiological data, the findings identified limited outbreaks against a broader 3-year period of sporadic external entry of many different strains and resistance vectors into the hospital. These findings highlight the utility of genomic analyses in internal and external surveillance efforts to stem the transmission of drug-resistant strains within and across health care institutions.

Leveraging systems biology approaches, we illustrate how metabolically distinct species of Clostridia protect against or worsen Clostridioides difficile infection in mice by modulating the pathogen's colonization, growth, and virulence to impact host survival. Gnotobiotic mice colonized with the amino acid fermenter Paraclostridium bifermentans survive infection with reduced disease severity, while mice colonized with the butyrate-producer, Clostridium sardiniense, succumb more rapidly. Systematic in vivo analyses revealed how each commensal alters the gut-nutrient environment to modulate the pathogen's metabolism, gene regulatory networks, and toxin production. Oral administration of P. bifermentans rescues conventional, clindamycin-treated mice from lethal C. difficile infection in a manner similar to that of monocolonized animals, thereby supporting the therapeutic potential of this commensal species. Our findings lay the foundation for mechanistically informed therapies to counter C. difficile disease using systems biology approaches to define host-commensal-pathogen interactions in vivo.