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

The intestinal microbiota and cellular therapy: implications for impact and mechanisms

ABSTRACT

The microbiota, comprising bacteria, fungi, and viruses residing within our bodies, functions as a key modulator in host health and states, including immune responses. Studies have linked microbiota and microbiota-derived metabolites to immune cell functions. In this review, we probe the complex relationship between the human microbiota and clinical outcomes of cellular therapies that leverage immune cells to fight various cancers. With a particular emphasis on hematopoietic cell transplantation and chimeric antigen receptor T-cell therapy, we explore the potential mechanisms underpinning this interaction. We also highlight the interventional applications of the microbiota in cellular therapy while outlining future research directions in the field.

Oral bacteria relative abundance in faeces increases due to gut microbiota depletion and is linked with patient outcomes

The detection of oral bacteria in faecal samples has been associated with inflammation and intestinal diseases. The increased relative abundance of oral bacteria in faeces has two competing explanations: either oral bacteria invade the gut ecosystem and expand (the 'expansion' hypothesis), or oral bacteria transit through the gut and their relative increase marks the depletion of other gut bacteria (the 'marker' hypothesis). Here we collected oral and faecal samples from mouse models of gut dysbiosis (antibiotic treatment and DSS-induced colitis) and used 16S ribosomal RNA sequencing to determine the abundance dynamics of oral bacteria. We found that the relative, but not absolute, abundance of oral bacteria increases, reflecting the 'marker' hypothesis. Faecal microbiome datasets from diverse patient cohorts, including healthy individuals and patients with allogeneic haematopoietic cell transplantation or inflammatory bowel disease, consistently support the 'marker' hypothesis and explain associations between oral bacterial abundance and patient outcomes consistent with depleted gut microbiota. By distinguishing between the two hypotheses, our study guides the interpretation of microbiome compositional data and could potentially identify cases where therapies are needed to rebuild the resident microbiome rather than protect against invading oral bacteria.

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.

Data-driven prediction of colonization outcomes for complex microbial communities

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.

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.

High-resolution analyses of associations between medications, microbiome, and mortality in cancer patients

Discerning the effect of pharmacological exposures on intestinal bacterial communities in cancer patients is challenging. Here, we deconvoluted the relationship between drug exposures and changes in microbial composition by developing and applying a new computational method, PARADIGM (parameters associated with dynamics of gut microbiota), to a large set of longitudinal fecal microbiome profiles with detailed medication-administration records from patients undergoing allogeneic hematopoietic cell transplantation. We observed that several non-antibiotic drugs, including laxatives, antiemetics, and opioids, are associated with increased Enterococcus relative abundance and decreased alpha diversity. Shotgun metagenomic sequencing further demonstrated subspecies competition, leading to increased dominant-strain genetic convergence during allo-HCT that is significantly associated with antibiotic exposures. We integrated drug-microbiome associations to predict clinical outcomes in two validation cohorts on the basis of drug exposures alone, suggesting that this approach can generate biologically and clinically relevant insights into how pharmacological exposures can perturb or preserve microbiota composition. The application of a computational method called PARADIGM to a large dataset of cancer patients' longitudinal fecal specimens and detailed daily medication records reveals associations between drug exposures and the intestinal microbiota that recapitulate in vitro findings and are also predictive of clinical outcomes.

Data-driven prediction of colonization outcomes for complex microbial communities

Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validated this approach using synthetic data, finding that machine learning models (including Random Forest and neural ODE) can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conducted colonization experiments for two commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approach can successfully predict the colonization outcomes. Furthermore, we found that while most resident species were predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., the presence of Enterococcus faecalis inhibits the invasion of E. faecium . The presented results suggest that the data-driven approach is a powerful tool to inform the ecology and management of complex microbial communities.

Leveraging the microbiome in the treatment of sepsis: potential pitfalls and new perspectives

PURPOSE OF REVIEW

This review aims to provide an overview of the current knowledge about microbiota-targeted therapies in sepsis, and calls out - despite recent negative studies - not to halt our efforts of translating these tools into regular medical practice.

RECENT FINDINGS

The intestinal microbiome has an important role in shaping our immune system, and microbiota-derived metabolites prime innate and adaptive inflammatory responses to infectious pathogens. Microbiota composition is severely disrupted during sepsis, which has been linked to increased risk of mortality and secondary infections. However, efforts of using these microbes as a tool for prognostic or therapeutic purposes have been unsuccessful so far, and recent trials studying the impact of probiotics in critical illness did not improve patient outcomes. Despite these negative results, researchers must continue their attempts of harnessing the microbiome to improve sepsis survival in patients with a high risk of clinical deterioration. Promising research avenues that could potentially benefit sepsis patients include the development of next-generation probiotics, use of the microbiome as a theranostic tool to direct therapy, and addressing the restoration of microbial communities following ICU discharge.

SUMMARY

Although research focused on microbiome-mediated therapy in critically ill patients has not yielded the results that were anticipated, we should not abandon our efforts to translate promising preclinical findings into clinical practice.

Protective Effect of Intestinal Blautia Against Neutropenic Fever in Allogeneic Transplant Recipients

BACKGROUND

Neutropenic fever (NF) occurs in >70% of hematopoietic cell transplant (HCT) recipients, without a documented cause in most cases. Antibiotics used to prevent and treat NF disrupt the gut microbiota; these disruptions predict a higher posttransplantation mortality rate. We hypothesized that specific features in the gut microbial community may mediate the risk of NF.

METHODS

We searched a large gut microbiota database in allogeneic HCT recipients (12 546 stool samples; 1278 patients) to find pairs with NF (cases) versus without NF (controls) on the same day relative to transplantation and with a stool sample on the previous day. A total of 179 such pairs were matched as to the underlying disease and graft source. Several other important clinical variables were similar between the groups.

RESULTS

The gut microbiota of cases on the day before NF occurrence had a lower abundance of Blautia than their matched controls on the same day after transplantation, suggesting a protective role for Blautia. Microbiota network analysis did not find any differences in community structure between the groups, suggesting a single-taxon effect. To identify putative mechanisms, we searched a gut microbiome and serum metabolome database of patients with acute leukemia receiving chemotherapy and identified 139 serum samples collected within 24 hours after a stool sample from the same patient. Greater Blautia abundances predicted higher levels of next-day citrulline, a biomarker of total enterocyte mass.

CONCLUSIONS

These findings support a model in which Blautia protects against NF by improving intestinal health. Therapeutic restoration of Blautia may help prevent NF, thus reducing antibiotic exposures and transplantation-related deaths.

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.

Linking gastrointestinal microbiota and metabolome dynamics to clinical outcomes in paediatric haematopoietic stem cell transplantation

BACKGROUND

Haematopoietic stem cell transplantation is a curative procedure for a variety of conditions. Despite major advances, a plethora of adverse clinical outcomes can develop post-transplantation including graft-versus-host disease and infections, which remain the major causes of morbidity and mortality. There is increasing evidence that the gastrointestinal microbiota is associated with clinical outcomes post-haematopoietic stem cell transplantation. Herein, we investigated the longitudinal dynamics of the gut microbiota and metabolome and potential associations to clinical outcomes in paediatric haematopoietic stem cell transplantation at a single centre.

RESULTS

On admission (baseline), the majority of patients presented with a different gut microbial composition in comparison with healthy control children with a significantly lower alpha diversity. A further, marked decrease in alpha diversity was observed immediately post-transplantation and in most microbial diversity, and composition did not return to baseline status whilst hospitalised. Longitudinal trajectories identified continuous fluctuations in microbial composition, with the dominance of a single taxon in a significant proportion of patients. Using pam clustering, three clusters were observed in the dataset. Cluster 1 was common pre-transplantation, characterised by a higher abundance of Clostridium XIVa, Bacteroides and Lachnospiraceae; cluster 2 and cluster 3 were more common post-transplantation with a higher abundance of Streptococcus and Staphylococcus in the former whilst Enterococcus, Enterobacteriaceae and Escherichia predominated in the latter. Cluster 3 was also associated with a higher risk of viraemia. Likewise, further multivariate analysis reveals Enterobacteriaceae, viraemia, use of total parenteral nutrition and various antimicrobials contributing towards cluster 3, Streptococcaceae, Staphylococcaceae, Neisseriaceae, vancomycin and metronidazole contributing towards cluster 2. Lachnospiraceae, Ruminococcaceae, Bifidobacteriaceae and not being on total parenteral nutrition contributed to cluster 1. Untargeted metabolomic analyses revealed changes that paralleled fluctuations in microbiota composition; importantly, low faecal butyrate was associated with a higher risk of viraemia.

CONCLUSIONS

These findings highlight the frequent shifts and dominations in the gut microbiota of paediatric patients undergoing haematopoietic stem cell transplantation. The study reveals associations between the faecal microbiota, metabolome and viraemia. To identify and explore the potential of microbial biomarkers that may predict the risk of complications post-HSCT, larger multi-centre studies investigating the longitudinal microbial profiling in paediatric haematopoietic stem cell transplantation are warranted. Video abstract.

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.

Listening in on the conversation between the human gut microbiome and its host

The gut microbiome is an ecosystem. Natural selection favored microbes fit for the gut, which can utilize and convert molecules produced by the host for their own benefit. But natural selection also favored the host's mechanisms to sense and respond to the microbial ecosystem for its own benefit. We can listen in on the host-microbiome 'conversation' in the simultaneous responses of the microbiome and the host to strong perturbations. In laboratory animals a perturbation can be done for research; in human patients a perturbation can be caused by disease or therapy. Advances in metagenomics, metabolomics and computation amplify our means to listen in on the conversation between the gut microbiome and its host.

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 that the gut microbiome is directly affected by SARS-CoV-2 infection in a dose-dependent manner in a mouse model, causally linking viral infection and gut microbiome dysbiosis. Comparison with stool samples collected from 97 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 suggest that bacteria 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.

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.