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The colon epithelium plays a key role in the host-microbiome interactions, allowing uptake of various nutrients and driving important metabolic processes. To unravel detailed metabolic activities in the human colon epithelium, our present study focuses on the generation of the first cell-type specific genome-scale metabolic model (GEM) of human colonic epithelial cells, named iColonEpithelium. GEMs are powerful tools for exploring reactions and metabolites at systems level and predicting the flux distributions at steady state. Our cell-type-specific iColonEpithelium metabolic reconstruction captures genes specifically expressed in the human colonic epithelial cells. The iColonEpithelium is also capable of performing metabolic tasks specific to the cell type. A unique transport reaction compartment has been included to allow simulation of metabolic interactions with the gut microbiome. We used iColonEpithelium to identify metabolic signatures associated with inflammatory bowel disease. We integrated single-cell RNA sequencing data from Crohn's Diseases (CD) and ulcerative colitis (UC) samples with the iColonEpithelium metabolic network to predict metabolic signatures of colonocytes between CD and UC compared to healthy samples. We identified reactions in nucleotide interconversion, fatty acid synthesis and tryptophan metabolism were differentially regulated in CD and UC conditions, which were in accordance with experimental results. The iColonEpithelium metabolic network can be used to identify mechanisms at the cellular level, and our network has the potential to be integrated with gut microbiome models to explore the metabolic interactions between host and gut microbiota under various conditions.

Introduction: Medical educators feel ill equipped to respond to racism directed towards trainees. Available tools for responding to microaggressions rely on clarifying questions or aligning with the aggressor, which may add to the harm experienced by the target. We aimed to provide methods for faculty to recognize and respond to overt racism so that trainees feel supported in the clinical learning environment., Methods: We created a faculty development workshop with didactic and experiential learning components based on Kolb's theory for program leadership to teach how to respond when racism was directed towards trainees. In the didactic session, we shared the IRES (identify, respond, end, support) tool we created to respond to overt racism. Participants took part in two small-group case-based practice sessions with group debriefing, followed by a postsession survey., Results: Over 2 years, we held two sessions with a total of 43 faculty participants. Postsession survey response rate was 74% and showed a 1-point Likert-scale median increase ( p < .001) in ability to distinguish the strategies for addressing overt racism versus microaggressions, confidence in responding to microaggressions versus overt racism, and ability to debrief learners. The majority of participants noted that scripted language was a valuable tool to promote upstander behavior., Discussion: Participants appreciated this novel framework for responding to racism directed at trainees in the learning environment. Providing scripted language and being able to practice in a safe environment were particularly important. This training can be adapted to include residents, fellows, and other disciplines., (© 2024 Gibbons et al.)

The human gut pathogen Clostridioides difficile displays substantial inter-strain genetic variability and confronts a changeable nutrient landscape in the gut. We examined how human gut microbiota inter-species interactions influence the growth and toxin production of various C. difficile strains across different nutrient environments. Negative interactions influencing C. difficile growth are prevalent in an environment containing a single highly accessible resource and sparse in an environment containing C. difficile-preferred carbohydrates. C. difficile toxin production displays significant community-context dependent variation and does not trend with growth-mediated inter-species interactions. C. difficile strains exhibit differences in interactions with Clostridium scindens and the ability to compete for proline. Further, C. difficile shows substantial differences in transcriptional profiles in co-culture with C. scindens or Clostridium hiranonis. C. difficile exhibits massive alterations in metabolism and other cellular processes in co-culture with C. hiranonis, reflecting their similar metabolic niches. C. hiranonis uniquely inhibits the growth and toxin production of diverse C. difficile strains across different nutrient environments and robustly ameliorates disease severity in mice. In sum, understanding the impact of C. difficile strain variability and nutrient environments on inter-species interactions could help improve the effectiveness of anti-C. difficile strategies., (© 2024. The Author(s).)

Resource partitioning is central to the incredible productivity of microbial communities, including gigatons in annual methane emissions through syntrophic interactions. Previous work revealed how a sulfate reducer (Desulfovibrio vulgaris, Dv) and a methanogen (Methanococcus maripaludis, Mm) underwent evolutionary diversification in a planktonic context, improving stability, cooperativity, and productivity within 300-1000 generations. Here, we show that mutations in just 15 Dv and 7 Mm genes within a minimal assemblage of this evolved community gave rise to co-existing ecotypes that were spatially enriched within a few days of culturing in a fluidized bed reactor. The spatially segregated communities partitioned resources in the simulated subsurface environment, with greater lactate utilization by attached Dv but partial utilization of resulting H 2 by low affinity hydrogenases of Mm in the same phase. The unutilized H 2 was scavenged by high affinity hydrogenases of planktonic Mm, producing copious amounts of methane. Our findings show how a few mutations can drive resource partitioning amongst niche-differentiated ecotypes, whose interplay synergistically improves productivity of the entire mutualistic community., (© 2024. The Author(s).)

Bowel movement frequency (BMF) directly impacts the gut microbiota and is linked to diseases like chronic kidney disease or dementia. In particular, prior work has shown that constipation is associated with an ecosystem-wide switch from fiber fermentation and short-chain fatty acid production to more detrimental protein fermentation and toxin production. Here, we analyze multi-omic data from generally healthy adults to see how BMF affects their molecular phenotypes, in a pre-disease context. Results show differential abundances of gut microbial genera, blood metabolites, and variation in lifestyle factors across BMF categories. These differences relate to inflammation, heart health, liver function, and kidney function. Causal mediation analysis indicates that the association between lower BMF and reduced kidney function is partially mediated by the microbially derived toxin 3-indoxyl sulfate (3-IS). This result, in a generally healthy context, suggests that the accumulation of microbiota-derived toxins associated with abnormal BMF precede organ damage and may be drivers of chronic, aging-related diseases., Competing Interests: Declaration of interests L.H. is a former shareholder of Arivale. A.T.M. was a former employee of Arivale. Arivale is no longer a commercially operating company as of April 2019., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Microbially derived short-chain fatty acids (SCFAs) in the human gut are tightly coupled to host metabolism, immune regulation and integrity of the intestinal epithelium. However, the production of SCFAs can vary widely between individuals consuming the same diet, with lower levels often associated with disease. A systems-scale mechanistic understanding of this heterogeneity is lacking. Here we use a microbial community-scale metabolic modelling (MCMM) approach to predict individual-specific SCFA production profiles to assess the impact of different dietary, prebiotic and probiotic inputs. We evaluate the quantitative accuracy of our MCMMs using in vitro and ex vivo data, plus published human cohort data. We find that MCMM SCFA predictions are significantly associated with blood-derived clinical chemistries, including cardiometabolic and immunological health markers, across a large human cohort. Finally, we demonstrate how MCMMs can be leveraged to design personalized dietary, prebiotic and probiotic interventions aimed at optimizing SCFA production in the gut. Our model represents an approach to direct gut microbiome engineering for precision health and nutrition., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Nicotinamide adenine dinucleotide (NAD) is essential for many enzymatic reactions, including those involved in energy metabolism, DNA repair and the activity of sirtuins, a family of defensive deacylases. During aging, levels of NAD + can decrease by up to 50% in some tissues, the repletion of which provides a range of health benefits in both mice and humans. Whether or not the NAD + precursor nicotinamide mononucleotide (NMN) extends lifespan in mammals is not known. Here we investigate the effect of long-term administration of NMN on the health, cancer burden, frailty and lifespan of male and female mice. Without increasing tumor counts or severity in any tissue, NMN treatment of males and females increased activity, maintained more youthful gene expression patterns, and reduced overall frailty. Reduced frailty with NMN treatment was associated with increases in levels of Anerotruncus colihominis, a gut bacterium associated with lower inflammation in mice and increased longevity in humans. NMN slowed the accumulation of adipose tissue later in life and improved metabolic health in male but not female mice, while in females but not males, NMN increased median lifespan by 8.5%, possible due to sex-specific effects of NMN on NAD + metabolism. Together, these data show that chronic NMN treatment delays frailty, alters the microbiome, improves male metabolic health, and increases female mouse lifespan, without increasing cancer burden. These results highlight the potential of NAD + boosters for treating age-related conditions and the importance of using both sexes for interventional lifespan studies.

Background: The human upper respiratory tract (URT) microbiome, like the gut microbiome, varies across individuals and between health and disease states. However, study-to-study heterogeneity in reported case-control results has made the identification of consistent and generalizable URT-disease associations difficult., Results: In order to address this issue, we assembled 26 independent 16S rRNA gene amplicon sequencing data sets from case-control URT studies, with approximately 2-3 studies per respiratory condition and ten distinct conditions covering common chronic and acute respiratory diseases. We leveraged the healthy control data across studies to investigate URT associations with age, sex, and geographic location, in order to isolate these associations from health and disease states., Conclusions: We found several robust genus-level associations, across multiple independent studies, with either health or disease status. We identified disease associations specific to a particular respiratory condition and associations general to all conditions. Ultimately, we reveal robust associations between the URT microbiome, health, and disease, which hold across multiple studies and can help guide follow-up work on potential URT microbiome diagnostics and therapeutics., (© 2024. The Author(s).)

Prior work has shown a positive scaling relationship between vertebrate body size, human height, and gut microbiome alpha diversity. This observation mirrors commonly observed species area relationships (SARs) in many other ecosystems. Here, we expand these observations to several large datasets, showing that this size-diversity scaling relationship is independent of relevant covariates, like diet, body mass index, age, sex, bowel movement frequency, antibiotic usage, and cardiometabolic health markers. Island biogeography theory (IBT), which predicts that larger islands tend to harbor greater species diversity through neutral demographic processes, provides a simple mechanism for positive SARs. Using a gut-adapted IBT model, we demonstrated that increasing the length of a flow-through ecosystem led to increased species diversity, closely matching our empirical observations. We delve into the possible clinical implications of these SARs in the American Gut cohort. Consistent with prior observations that lower alpha diversity is a risk factor for Clostridioides difficile infection (CDI), we found that individuals who reported a history of CDI were shorter than those who did not and that this relationship was mediated by alpha diversity. We observed that vegetable consumption had a much stronger association with CDI history, which was also partially mediated by alpha diversity. In summary, we find that the positive scaling observed between body size and gut alpha diversity can be plausibly explained by a gut-adapted IBT model, may be related to CDI risk, and vegetable intake appears to independently mitigate this risk, although additional work is needed to validate the potential disease risk implications., (© The Author(s) 2024. Published by Oxford University Press on behalf of the International Society for Microbial Ecology.)

Many gut microorganisms critical to human health rely on nutrients produced by each other for survival; however, these cross-feeding interactions are still challenging to quantify and remain poorly characterized. Here, we introduce a Metabolite Exchange Score (MES) to quantify those interactions. Using metabolic models of prokaryotic metagenome-assembled genomes from over 1600 individuals, MES allows us to identify and rank metabolic interactions that are significantly affected by a loss of cross-feeding partners in 10 out of 11 diseases. When applied to a Crohn's disease case-control study, our approach identifies a lack of species with the ability to consume hydrogen sulfide as the main distinguishing microbiome feature of disease. We propose that our conceptual framework will help prioritize in-depth analyses, experiments and clinical targets, and that targeting the restoration of microbial cross-feeding interactions is a promising mechanism-informed strategy to reconstruct a healthy gut ecosystem., (© 2023. Springer Nature Limited.)

Introduction: Infants who are born from mothers with HIV (infants who are HIV exposed but uninfected; iHEU) are at higher risk of morbidity and display multiple immune alterations compared to infants who are HIV-unexposed (iHU). Easily implementable strategies to improve immunity of iHEU, and possibly subsequent clinical health outcomes, are needed. iHEU have altered gut microbiome composition and bifidobacterial depletion, and relative abundance of Bifidobacterium infantis has been associated with immune ontogeny, including humoral and cellular vaccine responses. Therefore, we will assess microbiological and immunological phenotypes and clinical outcomes in a randomized, double-blinded trial of B. infantis Rosell®-33 versus placebo given during the first month of life in South African iHEU., Methods: This is a parallel, randomised, controlled trial. Two-hundred breastfed iHEU will be enrolled from the Khayelitsha Site B Midwife Obstetric Unit in Cape Town, South Africa and 1:1 randomised to receive 8 × 10 9  CFU B. infantis Rosell®-33 daily or placebo for the first 4 weeks of life, starting on day 1-3 of life. Infants will be followed over 36 weeks with extensive collection of meta-data and samples. Primary outcomes include gut microbiome composition and diversity, intestinal inflammation and microbial translocation and cellular vaccine responses. Additional outcomes include biological (e.g. gut metabolome and T cell phenotypes) and clinical (e.g. growth and morbidity) outcome measures., Discussion: The results of this trial will provide evidence whether B. infantis supplementation during early life could improve health outcomes for iHEU., Ethics and Dissemination: Approval for this study has been obtained from the ethics committees at the University of Cape Town (HREC Ref 697/2022) and Seattle Children's Research Institute (STUDY00003679)., Trial Registration: Pan African Clinical Trials Registry Identifier: PACTR202301748714019., Trials: gov: NCT05923333., Protocol Version: Version 1.8, dated 18 July 2023., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Longitudinal sampling of the stool has yielded important insights into the ecological dynamics of the human gut microbiome. However, human stool samples are available approximately once per day, while commensal population doubling times are likely on the order of minutes-to-hours. Despite this mismatch in timescales, much of the prior work on human gut microbiome time series modeling has assumed that day-to-day fluctuations in taxon abundances are related to population growth or death rates, which is likely not the case. Here, we propose an alternative model of the human gut as a stationary system, where population dynamics occur internally and the bacterial population sizes measured in a bolus of stool represent a steady-state endpoint of these dynamics. We formalize this idea as stochastic logistic growth. We show how this model provides a path toward estimating the growth phases of gut bacterial populations in situ. We validate our model predictions using an in vitro Escherichia coli growth experiment. Finally, we show how this method can be applied to densely-sampled human stool metagenomic time series data. We discuss how these growth phase estimates may be used to better inform metabolic modeling in flow-through ecosystems, like animal guts or industrial bioreactors., (© 2023. Springer Nature Limited.)

A recent study (Johansen et al., 2023) shows how the ecological composition of the human gut virome changes with age, showing a decline in core taxa and an enrichment of subdominant taxa, similar to what has been observed in the gut bacteriomes of centenarians., Competing Interests: Declaration of interests No interests are declared., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The composition of the human gut microbiome is heterogeneous across people. However, if you squint, co-abundant microbial genera emerge, accounting for much of this ecological variability. In this issue of Cell Host & Microbe, Frioux et al. provide a workflow for identifying these bacterial guilds, or "enterosignatures.", Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Microbial consortia drive essential processes, ranging from nitrogen fixation in soils to providing metabolic breakdown products to animal hosts. However, it is challenging to translate the composition of microbial consortia into their emergent functional capacities. Community-scale metabolic models hold the potential to simulate the outputs of complex microbial communities in a given environmental context, but there is currently no consensus for what the fitness function of an entire community should look like in the presence of ecological interactions and whether community-wide growth operates close to a maximum. Transitioning from single-taxon genome-scale metabolic models to multitaxon models implies a growth cone without a well-specified growth rate solution for individual taxa. Here, we argue that dynamic approaches naturally overcome these limitations, but they come at the cost of being computationally expensive. Furthermore, we show how two nondynamic, steady-state approaches approximate dynamic trajectories and pick ecologically relevant solutions from the community growth cone with improved computational scalability.

Multiomic profiling can reveal population heterogeneity for both health and disease states. Obesity drives a myriad of metabolic perturbations and is a risk factor for multiple chronic diseases. Here we report an atlas of cross-sectional and longitudinal changes in 1,111 blood analytes associated with variation in body mass index (BMI), as well as multiomic associations with host polygenic risk scores and gut microbiome composition, from a cohort of 1,277 individuals enrolled in a wellness program (Arivale). Machine learning model predictions of BMI from blood multiomics captured heterogeneous phenotypic states of host metabolism and gut microbiome composition better than BMI, which was also validated in an external cohort (TwinsUK). Moreover, longitudinal analyses identified variable BMI trajectories for different omics measures in response to a healthy lifestyle intervention; metabolomics-inferred BMI decreased to a greater extent than actual BMI, whereas proteomics-inferred BMI exhibited greater resistance to change. Our analyses further identified blood analyte-analyte associations that were modified by metabolomics-inferred BMI and partially reversed in individuals with metabolic obesity during the intervention. Taken together, our findings provide a blood atlas of the molecular perturbations associated with changes in obesity status, serving as a resource to quantify metabolic health for predictive and preventive medicine., (© 2023. The Author(s).)

Variation in the blood metabolome is intimately related to human health. However, few details are known about the interplay between genetics and the microbiome in explaining this variation on a metabolite-by-metabolite level. Here, we perform analyses of variance for each of 930 blood metabolites robustly detected across a cohort of 1,569 individuals with paired genomic and microbiome data while controlling for a number of relevant covariates. We find that 595 (64%) of these blood metabolites are significantly associated with either host genetics or the gut microbiome, with 69% of these associations driven solely by the microbiome, 15% driven solely by genetics and 16% under hybrid genome-microbiome control. Additionally, interaction effects, where a metabolite-microbe association is specific to a particular genetic background, are quite common, albeit with modest effect sizes. This knowledge will help to guide targeted interventions designed to alter the composition of the human blood metabolome., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Humans often show variable responses to dietary, prebiotic, and probiotic interventions. Emerging evidence indicates that the gut microbiota is a key determinant for this population heterogeneity. Here, we provide an overview of some of the major computational and experimental tools being applied to critical questions of microbiota-mediated personalized nutrition and health. First, we discuss the latest advances in in silico modeling of the microbiota-nutrition-health axis, including the application of statistical, mechanistic, and hybrid artificial intelligence models. Second, we address high-throughput in vitro techniques for assessing interindividual heterogeneity, from ex vivo batch culturing of stool and continuous culturing in anaerobic bioreactors, to more sophisticated organ-on-a-chip models that integrate both host and microbial compartments. Third, we explore in vivo approaches for better understanding of personalized, microbiota-mediated responses to diet, prebiotics, and probiotics, from nonhuman animal models and human observational studies, to human feeding trials and crossover interventions. We highlight examples of existing, consumer-facing precision nutrition platforms that are currently leveraging the gut microbiota. Furthermore, we discuss how the integration of a broader set of the tools and techniques described in this piece can generate the data necessary to support a greater diversity of precision nutrition strategies. Finally, we present a vision of a precision nutrition and healthcare future, which leverages the gut microbiota to design effective, individual-specific interventions., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society for Nutrition.)

Background: Statins remain one of the most prescribed medications worldwide. While effective in decreasing atherosclerotic cardiovascular disease risk, statin use is associated with adverse effects for a subset of patients, including disrupted metabolic control and increased risk of type 2 diabetes., Methods: We investigated the potential role of the gut microbiome in modifying patient responses to statin therapy across two independent cohorts (discovery n = 1,848, validation n = 991). Microbiome composition was assessed in these cohorts using stool 16S rRNA amplicon and shotgun metagenomic sequencing, respectively. Microbiome associations with markers of statin on-target and adverse effects were tested via a covariate-adjusted interaction analysis framework, utilizing blood metabolomics, clinical laboratory tests, genomics, and demographics data., Findings: The hydrolyzed substrate for 3-hydroxy-3-methylglutarate-coenzyme-A (HMG-CoA) reductase, HMG, emerged as a promising marker for statin on-target effects in cross-sectional cohorts. Plasma HMG levels reflected both statin therapy intensity and known genetic markers for variable statin responses. Through exploring gut microbiome associations between blood-derived measures of statin effectiveness and adverse metabolic effects of statins, we find that heterogeneity in statin responses was consistently associated with variation in the gut microbiome across two independent cohorts. A Bacteroides-enriched and diversity-depleted gut microbiome was associated with more intense statin responses, both in terms of on-target and adverse effects., Conclusions: With further study and refinement, gut microbiome monitoring may help inform precision statin treatment., Funding: This research was supported by the M.J. Murdock Charitable Trust, WRF, NAM Catalyst Award, and NIH grant U19AG023122 awarded by the NIA., Competing Interests: Declaration of interests Arivale, which closed in May 2019, partially funded this study. At the time this study was conceived and designed, J.L., S.A.K., A.M., N.P., and L.H. held positions and/or held stock options in the company. The authors declare no ongoing financial interests in Arivale. The authors declare no other competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Recent human feeding studies have shown how the baseline taxonomic composition of the gut microbiome can determine responses to weight loss interventions. However, the functional determinants underlying this phenomenon remain unclear. We report a weight loss response analysis on a cohort of 105 individuals selected from a larger population enrolled in a commercial wellness program, which included healthy lifestyle coaching. Each individual in the cohort had baseline blood metabolomics, blood proteomics, clinical labs, dietary questionnaires, stool 16S rRNA gene sequencing data, and follow-up data on weight change. We generated additional targeted proteomics data on obesity-associated proteins in blood before and after intervention, along with baseline stool metagenomic data, for a subset of 25 individuals who showed the most extreme weight change phenotypes. We built regression models to identify baseline blood, stool, and dietary features associated with weight loss, independent of age, sex, and baseline body mass index (BMI). Many features were independently associated with baseline BMI, but few were independently associated with weight loss. Baseline diet was not associated with weight loss, and only one blood analyte was associated with changes in weight. However, 31 baseline stool metagenomic functional features, including complex polysaccharide and protein degradation genes, stress-response genes, respiration-related genes, and cell wall synthesis genes, along with gut bacterial replication rates, were associated with weight loss responses after controlling for age, sex, and baseline BMI. Together, these results provide a set of compelling hypotheses for how commensal gut microbiota influence weight loss outcomes in humans. IMPORTANCE Recent human feeding studies have shown how the baseline taxonomic composition of the gut microbiome can determine responses to dietary interventions, but the exact functional determinants underlying this phenomenon remain unclear. In this study, we set out to better understand interactions between baseline BMI, metabolic health, diet, gut microbiome functional profiles, and subsequent weight changes in a human cohort that underwent a healthy lifestyle intervention. Overall, our results suggest that the microbiota may influence host weight loss responses through variable bacterial growth rates, dietary energy harvest efficiency, and immunomodulation.

Colorectal cancer is a major health concern worldwide. Growing evidence for the role of the gut microbiota in the initiation of CRC has sparked interest in approaches that target these microorganisms. However, little is known about the composition and role of the microbiota associated with precancerous polyps. Here, we found distinct microbial signatures between patients with and without polyps and between polyp subtypes using sequencing and culturing techniques. We found a correlation between Bacteroides fragilis recovered and the level of inflammatory cytokines in the mucosa adjacent to the polyp. Additional analysis revealed that B. fragilis from patients with polyps are bft-negative, activate NF-κB through Toll-like receptor 4, induce a pro-inflammatory response, and are enriched in genes associated with LPS biosynthesis. This study provides fundamental insight into the microbial microenvironment of the pre-neoplastic polyp by highlighting strain-specific genomic and proteomic differences, as well as more broad compositional differences in the microbiome., Competing Interests: Declaration of interests The authors declare the following competing interests. R.W.D. is on the advisory board of MicrobiomX. W.M.G. is on the advisory board of Guardant Health, Freenome, SEngine, and consults for Diacarta and receives research support from Jannsen and Tempus. . C.W.K. receives research support from Freenome., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Longitudinal multi-omics measurements are highly valuable in studying heterogeneity in health and disease phenotypes. For thousands of people, we have collected longitudinal multi-omics data. To analyze, interpret and visualize this extremely high-dimensional data, we use the Pareto Task Inference (ParTI) method. We find that the clinical labs data fall within a tetrahedron. We then use all other data types to characterize the four archetypes. We find that the tetrahedron comprises three wellness states, defining a wellness triangular plane, and one aberrant health state that captures aspects of commonality in movement away from wellness. We reveal the tradeoffs that shape the data and their hierarchy, and use longitudinal data to observe individual trajectories. We then demonstrate how the movement on the tetrahedron can be used for detecting unexpected trajectories, which might indicate transitions from health to disease and reveal abnormal conditions, even when all individual blood measurements are in the norm.

Background: Aldosterone and renin are pivotal hormones in the regulation of salt and water homeostasis and blood pressure. Measurement of renin and aldosterone in serum/plasma is essential for the investigation of primary hyperaldosteronism (PA) and monitoring of glucocorticoid replacement therapy., Methods: We report 2 LC-MS/MS methods developed to measure aldosterone and plasma renin activity (PRA). PRA was determined by endogenous enzymatic generation of angiotensin I using 150 µL of sample. Generated angiotensin I was purified by solid phase extraction prior to chromatographic separation and mass spectrometry. Aldosterone measurement required 300 μL of sample extracted with MTBE prior to LC-MS/MS analysis., Results: The PRA method was linear (1.2-193 nmol/L), sensitive (LLOQ = 1.2 nmol/L), precise (CV = 4.1%), and specific (no cross reactivity for a number of structurally similar steroids). Dilutional linearity and recovery (84%) were acceptable. Accuracy was confirmed by comparison against our current RIA method. The aldosterone method had equally acceptable performance characteristics. Reference ranges in 110 healthy normotensive subjects were: PRA 0.2-3.7 nmol/L/h and aldosterone 50-950 pmol/L. Consecutive patients (n = 62) with adrenal incidentalomas shown to have no functional adrenal disease; their post overnight 1 mg dexamethasone test values were: PRA 0.2-2.6 nmol/L/h and aldosterone 55-480 pmol/L. Serum aldosterone values after 2 liter saline suppression were-normal subjects (n = 17): 78-238 pmol/L and confirmed primary hyperaldosteronism (n = 25): 131-1080 pmol/L., Conclusions: We have developed robust assays for PRA and aldosterone with appropriate clinical evaluation. These assays are now in routine practice in the UK., (© Crown copyright 2020.)

Industrialization has impacted the human gut ecosystem, resulting in altered microbiome composition and diversity. Whether bacterial genomes may also adapt to the industrialization of their host populations remains largely unexplored. Here, we investigate the extent to which the rates and targets of horizontal gene transfer (HGT) vary across thousands of bacterial strains from 15 human populations spanning a range of industrialization. We show that HGTs have accumulated in the microbiome over recent host generations and that HGT occurs at high frequency within individuals. Comparison across human populations reveals that industrialized lifestyles are associated with higher HGT rates and that the functions of HGTs are related to the level of host industrialization. Our results suggest that gut bacteria continuously acquire new functionality based on host lifestyle and that high rates of HGT may be a recent development in human history linked to industrialization., Competing Interests: Declaration of interests R.J.X. is a consultant to Novartis and Nestle. E.J.A. is a co-founder and shareholder of Finch Therapeutics, a company that specializes in microbiome-targeted therapeutics., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Broad spectrum antibiotics cause both transient and lasting damage to the ecology of the gut microbiome. Antibiotic-induced loss of gut bacterial diversity has been linked to susceptibility to enteric infections. Prior work on subtherapeutic antibiotic treatment in humans and non-human animals has suggested that entire gut communities may exhibit tolerance phenotypes. In this study, we validate the existence of these community tolerance phenotypes in the murine gut and explore how antibiotic treatment duration or a diet enriched in antimicrobial phytochemicals might influence the frequency of this phenotype. Almost a third of mice exhibited whole-community tolerance to a high dose of the β-lactam antibiotic cefoperazone, independent of antibiotic treatment duration or dietary phytochemical amendment. We observed few compositional differences between non-responder microbiota during antibiotic treatment and the untreated control microbiota. However, gene expression was vastly different between non-responder microbiota and controls during treatment, with non-responder communities showing an upregulation of antimicrobial tolerance genes, like efflux transporters, and a down-regulation of central metabolism. Future work should focus on what specific host- or microbiome-associated factors are responsible for tipping communities between responder and non-responder phenotypes so that we might learn to harness this phenomenon to protect our microbiota from routine antibiotic treatment.

Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical. Important contributions have been made in the development of community-driven metadata standards; however, these standards have not been uniformly embraced by the microbiome research community. To understand how these standards are being adopted, or the barriers to adoption, across research domains, institutions, and funding agencies, the National Microbiome Data Collaborative (NMDC) hosted a workshop in October 2019. This report provides a summary of discussions that took place throughout the workshop, as well as outcomes of the working groups initiated at the workshop.

The demand for food will outpace productivity of conventional agriculture due to projected growth of the human population, concomitant with shrinkage of arable land, increasing scarcity of freshwater, and a rapidly changing climate. While aquaponics has potential to sustainably supplement food production with minimal environmental impact, there is a need to better characterize the complex interplay between the various components (fish, plant, microbiome) of these systems to optimize scale up and productivity. Here, we investigated how the commonly-implemented practice of continued microbial community transfer from pre-existing systems might promote or impede productivity of aquaponics. Specifically, we monitored plant growth phenotypes, water chemistry, and microbiome composition of rhizospheres, biofilters, and fish feces over 61-days of lettuce (Lactuca sativa var. crispa) growth in nitrogen-limited aquaponic systems inoculated with bacteria that were either commercially sourced or originating from a pre-existing aquaponic system. Lettuce above- and below-ground growth were significantly reduced across replicates treated with a pre-existing aquaponic system inoculum when compared to replicates treated with a commercial inoculum. Reduced productivity was associated with enrichment in specific bacterial genera in plant roots, including Pseudomonas, following inoculum transfer from pre-existing systems. Increased productivity was associated with enrichment of nitrogen-fixing Rahnella in roots of plants treated with the commercial inoculum. Thus, we show that inoculation from a pre-existing system, rather than from a commercial inoculum, is associated with lower yields. Further work will be necessary to test the putative mechanisms involved., Competing Interests: The authors have declared that no competing interests exist.

Competition between nitrate-reducing bacteria (NRB) and sulfate-reducing bacteria (SRB) for resources in anoxic environments is generally thought to be governed largely by thermodynamics. It is now recognized that intermediates of nitrogen and sulfur cycling (e.g., hydrogen sulfide, nitrite, etc.) can also directly impact NRB and SRB activities in freshwater, wastewater, and sediment and therefore may play important roles in competitive interactions. Here, through comparative transcriptomic and metabolomic analyses, we have uncovered mechanisms of hydrogen sulfide- and cysteine-mediated inhibition of nitrate respiratory growth for the NRB Intrasporangium calvum C5. Specifically, the systems analysis predicted that cysteine and hydrogen sulfide inhibit growth of I. calvum C5 by disrupting distinct steps across multiple pathways, including branched-chain amino acid (BCAA) biosynthesis, utilization of specific carbon sources, and cofactor metabolism. We have validated these predictions by demonstrating that complementation with BCAAs and specific carbon sources relieves the growth inhibitory effects of cysteine and hydrogen sulfide. We discuss how these mechanistic insights give new context to the interplay and stratification of NRB and SRB in diverse environments. IMPORTANCE Nitrate-reducing bacteria (NRB) and sulfate-reducing bacteria (SRB) colonize diverse anoxic environments, including soil subsurface, groundwater, and wastewater. NRB and SRB compete for resources, and their interplay has major implications on the global cycling of nitrogen and sulfur species, with undesirable outcomes in some contexts. For instance, the removal of reactive nitrogen species by NRB is desirable for wastewater treatment, but in agricultural soils, NRB can drive the conversion of nitrates from fertilizers into nitrous oxide, a potent greenhouse gas. Similarly, the hydrogen sulfide produced by SRB can help sequester and immobilize toxic heavy metals but is undesirable in oil wells where competition between SRB and NRB has been exploited to suppress hydrogen sulfide production. By characterizing how reduced sulfur compounds inhibit growth and activity of NRB, we have gained systems-level and mechanistic insight into the interplay of these two important groups of organisms and drivers of their stratification in diverse environments.

The gut microbiome has important effects on human health, yet its importance in human ageing remains unclear. In the present study, we demonstrate that, starting in mid-to-late adulthood, gut microbiomes become increasingly unique to individuals with age. We leverage three independent cohorts comprising over 9,000 individuals and find that compositional uniqueness is strongly associated with microbially produced amino acid derivatives circulating in the bloodstream. In older age (over ~80 years), healthy individuals show continued microbial drift towards a unique compositional state, whereas this drift is absent in less healthy individuals. The identified microbiome pattern of healthy ageing is characterized by a depletion of core genera found across most humans, primarily Bacteroides. Retaining a high Bacteroides dominance into older age, or having a low gut microbiome uniqueness measure, predicts decreased survival in a 4-year follow-up. Our analysis identifies increasing compositional uniqueness of the gut microbiome as a component of healthy ageing, which is characterized by distinct microbial metabolic outputs in the blood.

Many studies link the composition of the human gut microbiome to aberrant health states. However, our understanding of what constitutes a 'healthy' gut ecosystem, and how to effectively monitor and maintain it, are only now emerging. Here, we review current approaches to defining and monitoring gut microbiome health, and outline directions for developing targeted ecological therapeutics. We emphasize the importance of identifying which ecological features of the gut microbiome are most resonant with host molecular phenotypes, and highlight certain gut microbial metabolites as potential biomarkers of gut microbiome health. We further discuss how multi-omic measurements of host phenotypes, dietary information, and gut microbiome profiles can be integrated into increasingly sophisticated host-microbiome mechanistic models that can be leveraged to design personalized interventions. Overall, we summarize current progress on defining microbiome health and highlight a number of paths forward for engineering the ecology of the gut to promote wellness.

Variation in the human gut microbiome can reflect host lifestyle and behaviors and influence disease biomarker levels in the blood. Understanding the relationships between gut microbes and host phenotypes are critical for understanding wellness and disease. Here, we examine associations between the gut microbiota and ~150 host phenotypic features across ~3,400 individuals. We identify major axes of taxonomic variance in the gut and a putative diversity maximum along the Firmicutes-to-Bacteroidetes axis. Our analyses reveal both known and unknown associations between microbiome composition and host clinical markers and lifestyle factors, including host-microbe associations that are composition-specific. These results suggest potential opportunities for targeted interventions that alter the composition of the microbiome to improve host health. By uncovering the interrelationships between host diet and lifestyle factors, clinical blood markers, and the human gut microbiome at the population-scale, our results serve as a roadmap for future studies on host-microbe interactions and interventions.

The Pioneer 100 Wellness Project involved quantitatively profiling 108 participants' molecular physiology over time, including genomes, gut microbiomes, blood metabolomes, blood proteomes, clinical chemistries, and data from wearable devices. Here, we present a longitudinal analysis focused specifically around the Pioneer 100 gut microbiomes. We distinguished a subpopulation of individuals with reduced gut diversity, elevated relative abundance of the genus Prevotella , and reduced levels of the genus Bacteroides We found that the relative abundances of Bacteroides and Prevotella were significantly correlated with certain serum metabolites, including omega-6 fatty acids. Primary dimensions in distance-based redundancy analysis of clinical chemistries explained 18.5% of the variance in bacterial community composition, and revealed a Bacteroides/Prevotella dichotomy aligned with inflammation and dietary markers. Finally, longitudinal analysis of gut microbiome dynamics within individuals showed that direct transitions between Bacteroides -dominated and Prevotella -dominated communities were rare, suggesting the presence of a barrier between these states. One implication is that interventions seeking to transition between Bacteroides - and Prevotella -dominated communities will need to identify permissible paths through ecological state-space that circumvent this apparent barrier., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn's disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM, a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization, we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates. We integrated adjustable dietary and taxon abundance constraints to generate personalized metabolic models for individual metagenomic samples. We applied MICOM to a balanced cohort of metagenomes from 186 people, including a metabolically healthy population and individuals with type 1 and type 2 diabetes. Model results showed that individual bacterial genera maintained conserved niche structures across humans, while the community-level production of short-chain fatty acids (SCFAs) was heterogeneous and highly individual specific. Model output revealed complex cross-feeding interactions that would be difficult to measure in vivo Metabolic interaction networks differed somewhat consistently between healthy and diabetic subjects. In particular, MICOM predicted reduced butyrate and propionate production in a diabetic cohort, with restoration of SCFA production profiles found in healthy subjects following metformin treatment. Overall, we found that changes in diet or taxon abundances have highly personalized effects. We believe MICOM can serve as a useful tool for generating mechanistic hypotheses for how diet and microbiome composition influence community function. All methods are implemented in an open-source Python package, which is available at https://github.com/micom-dev/micom IMPORTANCE The bacterial communities that live within the human gut have been linked to health and disease. However, we are still just beginning to understand how those bacteria interact and what potential interventions to our gut microbiome can make us healthier. Here, we present a mathematical modeling framework (named MICOM) that can recapitulate the growth rates of diverse bacterial species in the gut and can simulate metabolic interactions within microbial communities. We show that MICOM can unravel the ecological rules that shape the microbial landscape in our gut and that a given dietary or probiotic intervention can have widely different effects in different people., (Copyright © 2020 Diener et al.)

Correlation analyses are often included in bioinformatic pipelines as methods for inferring taxon-taxon interactions. In this perspective, we highlight the pitfalls of inferring interactions from covariance and suggest methods, study design considerations, and additional data types for improving high-throughput interaction inferences. We conclude that correlation, even when augmented by other data types, almost never provides reliable information on direct biotic interactions in real-world ecosystems. These bioinformatically inferred associations are useful for reducing the number of potential hypotheses that we might test, but will never preclude the necessity for experimental validation.

Depleted gut microbiome α-diversity is associated with several human diseases, but the extent to which this is reflected in the host molecular phenotype is poorly understood. We attempted to predict gut microbiome α-diversity from ~1,000 blood analytes (laboratory tests, proteomics and metabolomics) in a cohort enrolled in a consumer wellness program (N = 399). Although 77 standard clinical laboratory tests and 263 plasma proteins could not accurately predict gut α-diversity, we found that 45% of the variance in α-diversity was explained by a subset of 40 plasma metabolites (13 of the 40 of microbial origin). The prediction capacity of these 40 metabolites was confirmed in a separate validation cohort (N = 540) and across disease states, showing that our findings are robust. Several of the metabolite biomarkers that are reported here are linked with cardiovascular disease, diabetes and kidney function. Associations between host metabolites and gut microbiome α-diversity were modified in those with extreme obesity (body mass index ≥ 35), suggesting metabolic perturbation. The ability of the blood metabolome to predict gut microbiome α-diversity could pave the way to the development of clinical tests for monitoring gut microbial health.

Our understanding of how the gut microbiome interacts with its human host has been restrained by limited access to longitudinal datasets to examine stability and dynamics, and by having only a few isolates to test mechanistic hypotheses. Here, we present the Broad Institute-OpenBiome Microbiome Library (BIO-ML), a comprehensive collection of 7,758 gut bacterial isolates paired with 3,632 genome sequences and longitudinal multi-omics data. We show that microbial species maintain stable population sizes within and across humans and that commonly used 'omics' survey methods are more reliable when using averages over multiple days of sampling. Variation of gut metabolites within people over time is associated with amino acid levels, and differences across people are associated with differences in bile acids. Finally, we show that genomic diversification can be used to infer eco-evolutionary dynamics and in vivo selection pressures for strains within individuals. The BIO-ML is a unique resource designed to enable hypothesis-driven microbiome research.