The gut microbiota is associated with immune cell dynamics in humans.

The gut microbiota influences development1-3 and homeostasis4-7 of the mammalian immune system, and is associated with human inflammatory8 and immune diseases9,10 as well as responses to immunotherapy11-14. Nevertheless, our understanding of how gut bacteria modulate the immune system remains limited, particularly in humans, where the difficulty of direct experimentation makes inference challenging. Here we study hundreds of hospitalized-and closely monitored-patients with cancer receiving haematopoietic cell transplantation as they recover from chemotherapy and stem-cell engraftment. This aggressive treatment causes large shifts in both circulatory immune cell and microbiota populations, enabling the relationships between the two to be studied simultaneously. Analysis of observed daily changes in circulating neutrophil, lymphocyte and monocyte counts and more than 10,000 longitudinal microbiota samples revealed consistent associations between gut bacteria and immune cell dynamics. High-resolution clinical metadata and Bayesian inference allowed us to compare the effects of bacterial genera in relation to those of immunomodulatory medications, revealing a considerable influence of the gut microbiota-together and over time-on systemic immune cell dynamics. Our analysis establishes and quantifies the link between the gut microbiota and the human immune system, with implications for microbiota-driven modulation of immunity.

The evolution of the host microbiome as an ecosystem on a leash.

The human body carries vast communities of microbes that provide many benefits. Our microbiome is complex and challenging to understand, but evolutionary theory provides a universal framework with which to analyse its biology and health impacts. Here we argue that to understand a given microbiome feature, such as colonization resistance, host nutrition or immune development, we must consider how hosts and symbionts evolve. Symbionts commonly evolve to compete within the host ecosystem, while hosts evolve to keep the ecosystem on a leash. We suggest that the health benefits of the microbiome should be understood, and studied, as an interplay between microbial competition and host control.

The microbe-derived short-chain fatty acids butyrate and propionate are associated with protection from chronic GVHD.

Studies of the relationship between the gastrointestinal microbiota and outcomes in allogeneic hematopoietic stem cell transplantation (allo-HCT) have thus far largely focused on early complications, predominantly infection and acute graft-versus-host disease (GVHD). We examined the potential relationship of the microbiome with chronic GVHD (cGVHD) by analyzing stool and plasma samples collected late after allo-HCT using a case-control study design. We found lower circulating concentrations of the microbe-derived short-chain fatty acids (SCFAs) propionate and butyrate in day 100 plasma samples from patients who developed cGVHD, compared with those who remained free of this complication, in the initial case-control cohort of transplant patients and in a further cross-sectional cohort from an independent transplant center. An additional cross-sectional patient cohort from a third transplant center was analyzed; however, serum (rather than plasma) was available, and the differences in SCFAs observed in the plasma samples were not recapitulated. In sum, our findings from the primary case-control cohort and 1 of 2 cross-sectional cohorts explored suggest that the gastrointestinal microbiome may exert immunomodulatory effects in allo-HCT patients at least in part due to control of systemic concentrations of microbe-derived SCFAs.

Antibiotic-Induced Shifts in Fecal Microbiota Density and Composition during Hematopoietic Stem Cell Transplantation.

Dramatic microbiota changes and loss of commensal anaerobic bacteria are associated with adverse outcomes in hematopoietic cell transplantation (HCT) recipients. In this study, we demonstrate these dynamic changes at high resolution through daily stool sampling and assess the impact of individual antibiotics on those changes. We collected 272 longitudinal stool samples (with mostly daily frequency) from 18 patients undergoing HCT and determined their composition by multiparallel 16S rRNA gene sequencing as well as the density of bacteria in stool by quantitative PCR (qPCR). We calculated microbiota volatility to quantify rapid shifts and developed a new dynamic systems inference method to assess the specific impact of antibiotics. The greatest shifts in microbiota composition occurred between stem cell infusion and reconstitution of healthy immune cells. Piperacillin-tazobactam caused the most severe declines among obligate anaerobes. Our approach of daily sampling, bacterial density determination, and dynamic systems modeling allowed us to infer the independent effects of specific antibiotics on the microbiota of HCT patients.

Gut microbiota dysbiosis and diarrhea in kidney transplant recipients.

Posttransplant diarrhea is associated with kidney allograft failure and death, but its etiology remains unknown in the majority of cases. Because altered gut microbial ecology is a potential basis for diarrhea, we investigated whether posttransplant diarrhea is associated with gut dysbiosis. We enrolled 71 kidney allograft recipients for serial fecal specimen collections in the first 3 months of transplantation and profiled the gut microbiota using 16S ribosomal RNA (rRNA) gene V4-V5 deep sequencing. The Shannon diversity index was significantly lower in 28 diarrheal fecal specimens from 25 recipients with posttransplant diarrhea than in 112 fecal specimens from 46 recipients without posttransplant diarrhea. We found a lower relative abundance of 13 commensal genera (Benjamini-Hochberg adjusted P ≤ .15) in the diarrheal fecal specimens including the same 4 genera identified in our prior study. The 28 diarrheal fecal specimens were also evaluated by a multiplexed polymerase chain reaction (PCR) assay for 22 bacterial, viral, and protozoan gastrointestinal pathogens, and 26 specimens were negative for infectious etiologies. Using PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) to predict metagenomic functions, we found that diarrheal fecal specimens had a lower abundance of metabolic genes. Our findings suggest that posttransplant diarrhea is not associated with common infectious diarrheal pathogens but with a gut dysbiosis.

The Evolution of Quorum Sensing as a Mechanism to Infer Kinship.

Bacteria regulate many phenotypes via quorum sensing systems. Quorum sensing is typically thought to evolve because the regulated cooperative phenotypes are only beneficial at certain cell densities. However, quorum sensing systems are also threatened by non-cooperative "cheaters" that may exploit quorum-sensing regulated cooperation, which begs the question of how quorum sensing systems are maintained in nature. Here we study the evolution of quorum sensing using an individual-based model that captures the natural ecology and population structuring of microbial communities. We first recapitulate the two existing observations on quorum sensing evolution: density-dependent benefits favor quorum sensing but competition and cheating will destabilize it. We then model quorum sensing in a dense community like a biofilm, which reveals a novel benefit to quorum sensing that is intrinsically evolutionarily stable. In these communities, competing microbial genotypes gradually segregate over time leading to positive correlation between density and genetic similarity between neighboring cells (relatedness). This enables quorum sensing to track genetic relatedness and ensures that costly cooperative traits are only activated once a cell is safely surrounded by clonemates. We hypothesize that under similar natural conditions, the benefits of quorum sensing will not result from an assessment of density but from the ability to infer kinship.

Importance of positioning for microbial evolution.

Microbes commonly live in dense surface-attached communities where cells layer on top of one another such that only those at the edges have unimpeded access to limiting nutrients and space. Theory predicts that this simple spatial effect, akin to plants competing for light in a forest, generates strong natural selection on microbial phenotypes. However, we require direct empirical tests of the importance of this spatial structuring. Here we show that spontaneous mutants repeatedly arise, push their way to the surface, and dominate colonies of the bacterium Pseudomonas fluorescens Pf0-1. Microscopy and modeling suggests that these mutants use secretions to expand and push themselves up to the growth surface to gain the best access to oxygen. Physically mixing the cells in the colony, or introducing space limitations, largely removes the mutant's advantage, showing a key link between fitness and the ability of the cells to position themselves in the colony. We next follow over 500 independent adaptation events and show that all occur through mutation of a single repressor of secretions, RsmE, but that the mutants differ in competitiveness. This process allows us to map the genetic basis of their adaptation at high molecular resolution and we show how evolutionary competitiveness is explained by the specific effects of each mutation. By combining population level and molecular analyses, we demonstrate how living in dense microbial communities can generate strong natural selection to reach the growing edge.

The evolution of mutualism in gut microbiota via host epithelial selection.

The human gut harbours a large and genetically diverse population of symbiotic microbes that both feed and protect the host. Evolutionary theory, however, predicts that such genetic diversity can destabilise mutualistic partnerships. How then can the mutualism of the human microbiota be explained? Here we develop an individual-based model of host-associated microbial communities. We first demonstrate the fundamental problem faced by a host: The presence of a genetically diverse microbiota leads to the dominance of the fastest growing microbes instead of the microbes that are most beneficial to the host. We next investigate the potential for host secretions to influence the microbiota. This reveals that the epithelium-microbiota interface acts as a selectivity amplifier: Modest amounts of moderately selective epithelial secretions cause a complete shift in the strains growing at the epithelial surface. This occurs because of the physical structure of the epithelium-microbiota interface: Epithelial secretions have effects that permeate upwards through the whole microbial community, while lumen compounds preferentially affect cells that are soon to slough off. Finally, our model predicts that while antimicrobial secretion can promote host epithelial selection, epithelial nutrient secretion will often be key to host selection. Our findings are consistent with a growing number of empirical papers that indicate an influence of host factors upon microbiota, including growth-promoting glycoconjugates. We argue that host selection is likely to be a key mechanism in the stabilisation of the mutualism between a host and its microbiota.

The MTIST platform: a microbiome time series inference standardized test.

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.

Microbiota and metabolic adaptation shape Staphylococcus aureus virulence and antimicrobial resistance during intestinal colonization.

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.

The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia.

Longitudinal microbiome data provide valuable insight into disease states and clinical responses, but they are challenging to mine and view collectively. To address these limitations, we present TaxUMAP, a taxonomically informed visualization for displaying microbiome states in large clinical microbiome datasets. We used TaxUMAP to chart a microbiome atlas of 1,870 patients with cancer during therapy-induced perturbations. Bacterial density and diversity were positively associated, but the trend was reversed in liquid stool. Low-diversity states (dominations) remained stable after antibiotic treatment, and diverse communities had a broader range of antimicrobial resistance genes than dominations. When examining microbiome states associated with risk for bacteremia, TaxUMAP revealed that certain Klebsiella species were associated with lower risk for bacteremia localize in a region of the atlas that is depleted in high-risk enterobacteria. This indicated a competitive interaction that was validated experimentally. Thus, TaxUMAP can chart comprehensive longitudinal microbiome datasets, enabling insights into microbiome effects on human health.

Pathogen Species Is Associated With Mortality in Nosocomial Bloodstream Infection in Patients With COVID-19.

Background: The epidemiology of nosocomial bloodstream infections (NBSIs) in patients with coronavirus disease 2019 (COVID-19) is poorly understood, due in part to substantial disease heterogeneity resulting from multiple potential pathogens. Methods: We identified risk factors for NBSIs and examined the association between NBSIs and mortality in a retrospective cohort of patients hospitalized with COVID-19 in 2 New York City hospitals during the height of the pandemic. We adjusted for the potential effects of factors likely to confound that association, including age, race, illness severity upon admission, and underlying health status. Results: Between January 1 and October 1, 2020, 1403 patients had a positive blood culture, and 79 and 101 met the stringent criteria for NBSI among non-COVID-19 and COVID-19 patients, respectively. NBSIs occurred almost exclusively among patients who were severely ill with COVID-19 at hospital admission. NBSIs were associated with elevated mortality, even after adjusting for baseline differences in COVID-19 illness (55% cases vs 45% controls; P = .13). Mortality was concentrated in patients with early-onset pneumonia caused by S. aureus and gram-negative bacteria. Less virulent Candida (49%) and Enterococcus (12%) species were the predominant cause of NBSI in the latter stages of hospitalization, after antibiotic treatment and COVID-19 treatments that attenuate immune response. Most Enterococcus and Candida infections did not have an identifiable source and were not associated with common risk factors for infection by these organisms. Conclusions: Pathogen species and mortality exhibited temporal differences. Early recognition of risk factors among COVID-19 patients could potentially decrease NBSI-associated mortality through early COVID-19 and antimicrobial treatment.

A compilation of fecal microbiome shotgun metagenomics from hematopoietic cell transplantation patients.

Hospitalized patients receiving hematopoietic cell transplants provide a unique opportunity to study the human gut microbiome. We previously compiled a large-scale longitudinal dataset of fecal microbiota and associated metadata, but we had limited that analysis to taxonomic composition of bacteria from 16S rRNA gene sequencing. Here we augment those data with shotgun metagenomics. The compilation amounts to a nested subset of 395 samples compiled from different studies at Memorial Sloan Kettering. Shotgun metagenomics describes the microbiome at the functional level, particularly in antimicrobial resistances and virulence factors. We provide accession numbers that link each sample to the paired-end sequencing files deposited in a public repository, which can be directly accessed by the online services of PATRIC to be analyzed without the users having to download or transfer the files. Then, we show how shotgun sequencing enables the assembly of genomes from metagenomic data. The new data, combined with the metadata published previously, enables new functional studies of the microbiomes of patients with cancer receiving bone marrow transplantation.

Gut microbiome dysbiosis during COVID-19 is associated with increased risk for bacteremia and microbial translocation.

The microbial populations in the gut microbiome have recently been associated with COVID-19 disease severity. However, a causal impact of the gut microbiome on COVID-19 patient health has not been established. Here we provide evidence that gut microbiome dysbiosis is associated with translocation of bacteria into the blood during COVID-19, causing life-threatening secondary infections. Antibiotics and other treatments during COVID-19 can potentially confound microbiome associations. We therefore first demonstrate in a mouse model that SARS-CoV-2 infection can induce gut microbiome dysbiosis, which correlated with alterations to Paneth cells and goblet cells, and markers of barrier permeability. Comparison with stool samples collected from 96 COVID-19 patients at two different clinical sites also revealed substantial gut microbiome dysbiosis, paralleling our observations in the animal model. Specifically, we observed blooms of opportunistic pathogenic bacterial genera known to include antimicrobial-resistant species in hospitalized COVID-19 patients. Analysis of blood culture results testing for secondary microbial bloodstream infections with paired microbiome data obtained from these patients indicates that bacteria may translocate from the gut into the systemic circulation of COVID-19 patients. These results are consistent with a direct role for gut microbiome dysbiosis in enabling dangerous secondary infections during COVID-19.

Gut microbiome dysbiosis in antibiotic-treated COVID-19 patients is associated with microbial translocation and bacteremia.

Although microbial populations in the gut microbiome are associated with COVID-19 severity, a causal impact on patient health has not been established. Here we provide evidence that gut microbiome dysbiosis is associated with translocation of bacteria into the blood during COVID-19, causing life-threatening secondary infections. We first demonstrate SARS-CoV-2 infection induces gut microbiome dysbiosis in mice, which correlated with alterations to Paneth cells and goblet cells, and markers of barrier permeability. Samples collected from 96 COVID-19 patients at two different clinical sites also revealed substantial gut microbiome dysbiosis, including blooms of opportunistic pathogenic bacterial genera known to include antimicrobial-resistant species. Analysis of blood culture results testing for secondary microbial bloodstream infections with paired microbiome data indicates that bacteria may translocate from the gut into the systemic circulation of COVID-19 patients. These results are consistent with a direct role for gut microbiome dysbiosis in enabling dangerous secondary infections during COVID-19.

Gut microbiome correlates of response and toxicity following anti-CD19 CAR T cell therapy.

Anti-CD19 chimeric antigen receptor (CAR) T cell therapy has led to unprecedented responses in patients with high-risk hematologic malignancies. However, up to 60% of patients still experience disease relapse and up to 80% of patients experience CAR-mediated toxicities, such as cytokine release syndrome or immune effector cell-associated neurotoxicity syndrome. We investigated the role of the intestinal microbiome on these outcomes in a multicenter study of patients with B cell lymphoma and leukemia. We found in a retrospective cohort (n = 228) that exposure to antibiotics, in particular piperacillin/tazobactam, meropenem and imipenem/cilastatin (P-I-M), in the 4 weeks before therapy was associated with worse survival and increased neurotoxicity. In stool samples from a prospective cohort of CAR T cell recipients (n = 48), the fecal microbiome was altered at baseline compared to healthy controls. Stool sample profiling by 16S ribosomal RNA and metagenomic shotgun sequencing revealed that clinical outcomes were associated with differences in specific bacterial taxa and metabolic pathways. Through both untargeted and hypothesis-driven analysis of 16S sequencing data, we identified species within the class Clostridia that were associated with day 100 complete response. We concluded that changes in the intestinal microbiome are associated with clinical outcomes after anti-CD19 CAR T cell therapy in patients with B cell malignancies.

Versatile metabolic adaptations of Ralstonia eutropha H16 to a loss of PdhL, the E3 component of the pyruvate dehydrogenase complex.

A previous study reported that the Tn5-induced poly(3-hydroxybutyric acid) (PHB)-leaky mutant Ralstonia eutropha H1482 showed a reduced PHB synthesis rate and significantly lower dihydrolipoamide dehydrogenase (DHLDH) activity than the wild-type R. eutropha H16 but similar growth behavior. Insertion of Tn5 was localized in the pdhL gene encoding the DHLDH (E3 component) of the pyruvate dehydrogenase complex (PDHC). Taking advantage of the available genome sequence of R. eutropha H16, observations were verified and further detailed analyses and experiments were done. In silico genome analysis revealed that R. eutropha possesses all five known types of 2-oxoacid multienzyme complexes and five DHLDH-coding genes. Of these DHLDHs, only PdhL harbors an amino-terminal lipoyl domain. Furthermore, insertion of Tn5 in pdhL of mutant H1482 disrupted the carboxy-terminal dimerization domain, thereby causing synthesis of a truncated PdhL lacking this essential region, obviously leading to an inactive enzyme. The defined ΔpdhL deletion mutant of R. eutropha exhibited the same phenotype as the Tn5 mutant H1482; this excludes polar effects as the cause of the phenotype of the Tn5 mutant H1482. However, insertion of Tn5 or deletion of pdhL decreases DHLDH activity, probably negatively affecting PDHC activity, causing the mutant phenotype. Moreover, complementation experiments showed that different plasmid-encoded E3 components of R. eutropha H16 or of other bacteria, like Burkholderia cepacia, were able to restore the wild-type phenotype at least partially. Interestingly, the E3 component of B. cepacia possesses an amino-terminal lipoyl domain, like the wild-type H16. A comparison of the proteomes of the wild-type H16 and of the mutant H1482 revealed striking differences and allowed us to reconstruct at least partially the impressive adaptations of R. eutropha H1482 to the loss of PdhL on the cellular level.

Microbial dysbiosis is associated with aggressive histology and adverse clinical outcome in B-cell non-Hodgkin lymphoma.

B-cell non-Hodgkin lymphoma cell survival depends on poorly understood immune evasion mechanisms. In melanoma, the composition of the gut microbiota (GMB) is associated with immune system regulation and response to immunotherapy. We investigated the association of GMB composition and diversity with lymphoma biology and treatment outcome. Patients with diffuse large B-cell lymphoma (DLBCL), marginal zone (MZL), and follicular lymphoma (FL) were recruited at Mayo Clinic, Minnesota, and Perlmutter Cancer Center, NYU Langone Health. The pretreatment GMB was analyzed using 16S ribosomal RNA gene sequencing. We examined GMB compositions in 3 contexts: lymphoma patients (51) compared with healthy controls (58), aggressive (DLBCL) (8) compared with indolent (FL, MZL) (18), and the association of GMB with immunochemotherapy treatment outcomes (8 responders, 6 nonresponders). Respectively, we found that the pretreatment GMB in lymphoma patients had a distinct composition compared with healthy controls (P < .001); GMB compositions in DLBCL patients were significantly different than indolent patients (P = .01) with a trend toward reduced microbial diversity in DLBCL patients (P = .08); and pretreatment GMB diversity and composition were significant predictors of treatment responses (P = .01). The impact of these pilot results is limited by our small sample size, and should be considered a proof of principle. If validated, our results could lead toward improved treatment outcomes by improving medication stewardship and informing which GMB-targeted therapies should be tested to improve patient outcomes.

Compilation of longitudinal microbiota data and hospitalome from hematopoietic cell transplantation patients.

The impact of the gut microbiota in human health is affected by several factors including its composition, drug administrations, therapeutic interventions and underlying diseases. Unfortunately, many human microbiota datasets available publicly were collected to study the impact of single variables, and typically consist of outpatients in cross-sectional studies, have small sample numbers and/or lack metadata to account for confounders. These limitations can complicate reusing the data for questions outside their original focus. Here, we provide comprehensive longitudinal patient dataset that overcomes those limitations: a collection of fecal microbiota compositions (>10,000 microbiota samples from >1,000 patients) and a rich description of the "hospitalome" experienced by the hosts, i.e., their drug exposures and other metadata from patients with cancer, hospitalized to receive allogeneic hematopoietic cell transplantation (allo-HCT) at a large cancer center in the United States. We present five examples of how to apply these data to address clinical and scientific questions on host-associated microbial communities.