Dirichlet multinomial mixtures: generative models for microbial metagenomics

Using machine learning to identify environmental factors that collectively determine microbial community structure of activated sludge

Activated sludge (AS) microbial communities are influenced by various environmental variables. However, a comprehensive analysis of how these variables jointly and nonlinearly shape the AS microbial community remains challenging. In this study, we employed advanced machine learning techniques to elucidate the collective effects of environmental variables on the structure and function of AS microbial communities. Applying Dirichlet multinomial mixtures analysis to 311 global AS samples, we identified four distinct microbial community types (AS-types), each characterized by unique microbial compositions and metabolic profiles. We used 14 classical linear and nonlinear machine learning methods to select a baseline model. The extremely randomized trees demonstrated optimal performance in learning the relationship between environmental factors and AS types (with an accuracy of 71.43%). Feature selection identified critical environmental factors and their importance rankings, including latitude (Lat), longitude (Long), precipitation during sampling (Precip), solids retention time (SRT), effluent total nitrogen (Effluent TN), average temperature during sampling month (Avg Temp), mixed liquor temperature (Mixed Temp), influent biochemical oxygen demand (Influent BOD), and annual precipitation (Annual Precip). Significantly, Lat, Long, Precip, Avg Temp, and Annual Precip, influenced metabolic variations among AS types. These findings emphasize the pivotal role of environmental variables in shaping microbial community structures and enhancing metabolic pathways within activated sludge. Our study encourages the application of machine learning techniques to design artificial activated sludge microbial communities for specific environmental purposes.

Clinical sequelae of gut microbiome development and disruption in hospitalized preterm infants

Aberrant preterm infant gut microbiota assembly predisposes to early-life disorders and persistent health problems. Here, we characterize gut microbiome dynamics over the first 3 months of life in 236 preterm infants hospitalized in three neonatal intensive care units using shotgun metagenomics of 2,512 stools and metatranscriptomics of 1,381 stools. Strain tracking, taxonomic and functional profiling, and comprehensive clinical metadata identify Enterobacteriaceae, enterococci, and staphylococci as primarily exploiting available niches to populate the gut microbiome. Clostridioides difficile lineages persist between individuals in single centers, and Staphylococcus epidermidis lineages persist within and, unexpectedly, between centers. Collectively, antibiotic and non-antibiotic medications influence gut microbiome composition to greater extents than maternal or baseline variables. Finally, we identify a persistent low-diversity gut microbiome in neonates who develop necrotizing enterocolitis after day of life 40. Overall, we comprehensively describe gut microbiome dynamics in response to medical interventions in preterm, hospitalized neonates.

Primary succession of Bifidobacteria drives pathogen resistance in neonatal microbiota assembly

Human microbiota assembly commences at birth, seeded by both maternal and environmental microorganisms. Ecological theory postulates that primary colonizers dictate microbial community assembly outcomes, yet such microbial priority effects in the human gut remain underexplored. Here using longitudinal faecal metagenomics, we characterized neonatal microbiota assembly for a cohort of 1,288 neonates from the UK. We show that the pioneering neonatal gut microbiota can be stratified into one of three distinct community states, each dominated by a single microbial species and influenced by clinical and host factors, such as maternal age, ethnicity and parity. A community state dominated by Enterococcus faecalis displayed stochastic microbiota assembly with persistent high pathogen loads into infancy. In contrast, community states dominated by Bifidobacterium, specifically B. longum and particularly B. breve, exhibited a stable assembly trajectory and long-term pathogen colonization resistance, probably due to strain-specific functional adaptions to a breast milk-rich neonatal diet. Consistent with our human cohort observation, B. breve demonstrated priority effects and conferred pathogen colonization resistance in a germ-free mouse model. Our findings solidify the crucial role of Bifidobacteria as primary colonizers in shaping the microbiota assembly and functions in early life.

Networks as tools for defining emergent properties of microbiomes and their stability

The potential promise of the microbiome to ameliorate a wide range of societal and ecological challenges, from disease prevention and treatment to the restoration of entire ecosystems, hinges not only on microbiome engineering but also on the stability of beneficial microbiomes. Yet the properties of microbiome stability remain elusive and challenging to discern due to the complexity of interactions and often intractable diversity within these communities of bacteria, archaea, fungi, and other microeukaryotes. Networks are powerful tools for the study of complex microbiomes, with the potential to elucidate structural patterns of stable communities and generate testable hypotheses for experimental validation. However, the implementation of these analyses introduces a cascade of dichotomies and decision trees due to the lack of consensus on best practices. Here, we provide a road map for network-based microbiome studies with an emphasis on discerning properties of stability. We identify important considerations for data preparation, network construction, and interpretation of network properties. We also highlight remaining limitations and outstanding needs for this field. This review also serves to clarify the varying schools of thought on the application of network theory for microbiome studies and to identify practices that enhance the reproducibility and validity of future work. Video Abstract.

The temporal dynamics of the gut mycobiome and its association with cardiometabolic health in a nationwide cohort of 12,641 Chinese adults

The dynamics of the gut mycobiome and its association with cardiometabolic health remain largely unexplored. Here, we employ internal transcribed spacer (ITS) sequencing to capture the gut mycobiome composition and dynamics within a nationwide human cohort of 12,641 Chinese participants, including 1,946 participants with repeated measurements across three years. We find that the gut mycobiome is associated with cardiometabolic diseases and related biomarkers in both cross-sectional and dynamic analyses. Fungal alpha diversity indices and 19 mycobiome genera are the major contributors to the mycobiome-cardiometabolic disease link. Particularly, Saccharomyces emerges as an effect modifier of traditional risk factors in promoting type 2 diabetes risk. Further integration of multi-omics data reveals key metabolites such as γ-linolenic acid and L-valine linking the gut mycobiome to type 2 diabetes. This study advances our understanding of the potential roles of the gut mycobiome in cardiometabolic health.

Effect of intrapartum azithromycin on gut microbiota development in early childhood: A post hoc analysis of a double-blind randomized trial

Intrapartum azithromycin prophylaxis has shown the potential to reduce maternal infections but showed no effect on neonatal sepsis and mortality. Antibiotic exposure early in life may affect gut microbiota development, leading to undesired consequences. Therefore, we here assessed the impact of 2 g oral intrapartum azithromycin on gut microbiota development from birth to the age of 3 years, by 16S-rRNA gene profiling of rectal samples from 127 healthy Gambian infants selected from a double-blind randomized placebo-controlled clinical trial (PregnAnZI-2). Microbiota trajectories showed, over the first month of life, a slower community transition and increase of Enterobacteriaceae (p = 0.001) and Enterococcaceae (p = 0.064) and a decrease of Bifidobacterium (p < 0.001) in the azithromycin compared to the placebo arm. Intrapartum azithromycin alters gut microbiota development and increases proinflammatory bacteria in the first month of life, which may have undesirable effects on the child.

Reproductive Tract Microbial Transitions from Late Gestation to Early Postpartum Using 16S rRNA Metagenetic Profiling in First-Pregnancy Heifers

Studies in recent years indicate that reproductive tract microbial communities are crucial for shaping mammals' health and reproductive outcomes. Following parturition, uterine bacterial contamination often occurs due to the open cervix, which may lead to postpartum uterine inflammatory diseases, especially in primiparous individuals. However, investigations into spatio-temporal microbial transitions in the reproductive tract of primigravid females remain limited. Our objective was to describe and compare the microbial community compositions in the vagina at late gestation and in the vagina and uterus at early postpartum in first-pregnancy heifers. Three swab samples were collected from 33 first-pregnancy Holstein Friesian heifers: one vaginal sample at gestation day 258 ± 4, and vaginal and uterine samples at postpartum day 7 ± 2. Each sample underwent 16S rRNA V4 region metagenetic analysis via Illumina MiSeq, with bioinformatics following Mothur MiSeq SOP. The reproductive tract bacterial communities were assigned to 1255 genus-level OTUs across 30 phyla. Dominant phyla, accounting for approximately 90% of the communities, included Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, and Fusobacteria. However, the results revealed distinct shifts in microbial composition between the prepartum vagina (Vag-pre), postpartum vagina (Vag-post), and postpartum uterus (Utr-post). The Vag-pre and Utr-post microbial profiles were the most distinct. The Utr-post group had lower relative abundances of Proteobacteria but higher abundances of Bacteroidetes, Fusobacteria, and Tenericutes compared to Vag-pre, while Vag-post displayed intermediate values for these phyla, suggesting a transitional profile. Additionally, the Utr-post group exhibited lower bacterial richness and diversity compared to both Vag-pre and Vag-post. The unsupervised probabilistic Dirichlet Multinomial Mixtures model identified two distinct community types: most Vag-pre samples clustered into one type and Utr-post samples into another, while Vag-post samples were distributed evenly between the two. LEfSe analysis revealed distinct microbial profiles at the genus level. Overall, specific microbial markers were associated with anatomical and temporal transitions, revealing a dynamic microbial landscape during the first pregnancy and parturition. These differences highlight the complexity of these ecosystems and open new avenues for research in reproductive biology and microbial ecology.

Gut community structure as a risk factor for infection in Klebsiella pneumoniae-colonized patients

The primary risk factor for infection with members of the Klebsiella pneumoniae species complex is prior gut colonization, and infection is often caused by the colonizing strain. Despite the importance of the gut as a reservoir for infectious K. pneumoniae, little is known about the association between the gut microbiome and infection. To explore this relationship, we undertook a case-control study comparing the gut community structure of K. pneumoniae-colonized intensive care and hematology/oncology patients. Cases were K. pneumoniae-colonized patients infected by their colonizing strain (N = 83). Controls were K. pneumoniae-colonized patients who remained asymptomatic (N = 149). First, we characterized the gut community structure of K. pneumoniae-colonized patients agnostic to case status. Next, we determined that gut community data is useful for classifying cases and controls using machine learning models and that the gut community structure differed between cases and controls. K. pneumoniae relative abundance, a known risk factor for infection, had the greatest feature importance, but other gut microbes were also informative. Finally, we show that integration of gut community structure with bacterial genotype data enhanced the ability of machine learning models to discriminate cases and controls. Interestingly, inclusion of patient clinical variables failed to improve the ability of machine learning models to discriminate cases and controls. This study demonstrates that including gut community data with K. pneumoniae-derived biomarkers improves our ability to classify infection in K. pneumoniae-colonized patients.IMPORTANCEColonization is generally the first step in pathogenesis for bacteria with pathogenic potential. This step provides a unique window for intervention since a given potential pathogen has yet to cause damage to its host. Moreover, intervention during the colonization stage may help alleviate the burden of therapy failure as antimicrobial resistance rises. Yet, to understand the therapeutic potential of interventions that target colonization, we must first understand the biology of colonization and if biomarkers at the colonization stage can be used to stratify infection risk. The bacterial genus Klebsiella includes many species with varying degrees of pathogenic potential. Members of the K. pneumoniae species complex have the highest pathogenic potential. Patients colonized in their gut by these bacteria are at higher risk of subsequent infection with their colonizing strain. However, we do not understand if other members of the gut microbiota can be used as a biomarker to predict infection risk. In this study, we show that the gut microbiota differs between colonized patients who develop an infection versus those who do not. Additionally, we show that integrating gut microbiota data with bacterial factors improves the ability to classify infections. Surprisingly, patient clinical factors were not useful for classifying infections alone or when added to microbiota-based models. This indicates that the bacterial genotype and the microbial community in which it exists may determine the progression to infection. As we continue to explore colonization as an intervention point to prevent infections in individuals colonized by potential pathogens, we must develop effective means for predicting and stratifying infection risk.

Microbial Ecology and Metabolism of Emerging Adulthood: Gut Microbiome Insights from a College Freshman Cohort

The human gut microbiome (GM) undergoes dynamic changes throughout life, transitioning from infancy to adulthood. Despite improved understanding over the past years about how genetics, lifestyle, and the external environment impact the GM, limited research has explored the GM's evolution during late-stage adolescence, especially among college students. This study addresses this gap by investigating the longitudinal dynamics of fecal microbial, functional, and metabolomic signatures in a diverse group of first-year, dormitory-housed college students. A total of 485 stool samples from 246 participants were analyzed, identifying four primary GM community types, predominantly led by Bacteroides (66.8% of samples), as well as Blautia and Prevotella. The Prevotella/Bacteroides (P/B) ratio emerged as a robust GM composition indicator, predictively associated with 15 metabolites. Notably, higher P/B ratios correlated negatively with p-cresol sulfate and cholesterol sulfate, implying potential health implications, while positively correlating with kynurenic acid. Distinct GM transition and stability patterns were found from a detailed longitudinal subset of 93 participants over an academic year. Parasutterella and the Ruminococcus gnavus group exhibited positive associations with compositional variability, whereas Faecalibacterium and Eubacterium ventriosum group displayed negative associations, the latter suggesting stabilizing roles in the GM. Most notably, nearly half of the longitudinal cohort experienced GM community shifts, emphasizing long-term GM adaptability. Comparing individuals with stable community types to those undergoing transitions, we observed significant differences in microbial composition and diversity, signifying substantial shifts in the microbiota during transitions. Although diet-related variables contributed to some observed variance, diet did not independently predict the probability of switching between community types within the study's timeframe via multi-state Markov modeling. Furthermore, exploration of stability within dynamic microbiomes among the longitudinal cohort experiencing shifts in community types revealed that microbiome taxa at the genus level exhibited significantly higher total variance than estimated functional and fecal metabolomic features. This suggests tight control of function and metabolism, despite community shifting. Overall, this study highlights the dynamic nature of the late-stage adolescent GM, the role of core taxa, metabolic pathways, the fecal metabolome, and lifestyle and dietary factors, contributing to our understanding of GM assembly and potential health implications during this life phase.

Gut microbiota and intestinal rehabilitation: a prospective childhood cohort longitudinal study of short bowel syndrome (the MIRACLS study): study protocol

INTRODUCTION

Short bowel syndrome (SBS) is the predominant cause of paediatric intestinal failure. Although life-saving, parenteral nutrition (PN) is linked to complications and may impact quality of life (QoL). Most children will experience intestinal rehabilitation (IR), but the mechanisms underpinning this remain to be understood. SBS is characterised by abnormal microbiome patterns, which might serve as predictive indicators for IR. We aim to characterise the microbiome profiles of children with SBS during IR, concurrently exploring how parental perspectives of QoL relate to IR.

METHODS AND ANALYSIS

This study will enrol a minimum of 20 paediatric patients with SBS (0-18 years). Clinical data and biological samples will be collected over a 2-year study period. We will apply 16S rRNA gene sequencing to analyse the microbiome from faecal and gut tissue samples, with additional shotgun metagenomic sequencing specifically on samples obtained around the time of IR. Gas chromatography with flame ionisation detection will profile faecal short-chain fatty acids. Plasma citrulline and urinary intestinal fatty acid binding proteins will be measured annually. We will explore microbiome-clinical covariate interactions. Furthermore, we plan to assess parental perspectives on QoL during PN and post-IR by inviting parents to complete the Paediatric Quality of Life questionnaire at recruitment and after the completion of IR.

ETHICS AND DISSEMINATION

Ethical approval was obtained from the East Midlands-Nottingham 2 Research Ethics Committee (22/EM/0233; 28 November 2022). Recruitment began in February 2023. Outcomes of the study will be published in peer-reviewed scientific journals and presented at scientific meetings. A lay summary of the results will be made available to participants and the public.

TRIAL REGISTRATION NUMBER

ISRCTN90620576.

Major depletion of insulin sensitivity-associated taxa in the gut microbiome of persons living with HIV controlled by antiretroviral drugs

BACKGROUND

Persons living with HIV (PWH) harbor an altered gut microbiome (higher abundance of Prevotella and lower abundance of Bacillota and Ruminococcus lineages) compared to non-infected individuals. Some of these alterations are linked to sexual preference and others to the HIV infection. The relationship between these lineages and metabolic alterations, often present in aging PWH, has been poorly investigated.

METHODS

In this study, we compared fecal metagenomes of 25 antiretroviral-treatment (ART)-controlled PWH to three independent control groups of 25 non-infected matched individuals by means of univariate analyses and machine learning methods. Moreover, we used two external datasets to validate predictive models of PWH classification. Next, we searched for associations between clinical and biological metabolic parameters with taxonomic and functional microbiome profiles. Finally, we compare the gut microbiome in 7 PWH after a 17-week ART switch to raltegravir/maraviroc.

RESULTS

Three major enterotypes (Prevotella, Bacteroides and Ruminococcaceae) were present in all groups. The first Prevotella enterotype was enriched in PWH, with several of characteristic lineages associated with poor metabolic profiles (low HDL and adiponectin, high insulin resistance (HOMA-IR)). Conversely butyrate-producing lineages were markedly depleted in PWH independently of sexual preference and were associated with a better metabolic profile (higher HDL and adiponectin and lower HOMA-IR). Accordingly with the worst metabolic status of PWH, butyrate production and amino-acid degradation modules were associated with high HDL and adiponectin and low HOMA-IR. Random Forest models trained to classify PWH vs. control on taxonomic abundances displayed high generalization performance on two external holdout datasets (ROC AUC of 80-82%). Finally, no significant alterations in microbiome composition were observed after switching to raltegravir/maraviroc.

CONCLUSION

High resolution metagenomic analyses revealed major differences in the gut microbiome of ART-controlled PWH when compared with three independent matched cohorts of controls. The observed marked insulin resistance could result both from enrichment in Prevotella lineages, and from the depletion in species producing butyrate and involved into amino-acid degradation, which depletion is linked with the HIV infection.

Fusobacterium species are distinctly associated with patients with Lynch syndrome colorectal cancer

Accumulating evidence demonstrates clear correlation between the gut microbiota and sporadic colorectal cancer (CRC). Despite this, there is limited understanding of the association between the gut microbiota and CRC in Lynch Syndrome (LS), a hereditary type of CRC. Here, we analyzed fecal shotgun metagenomic and targeted metabolomic of 71 Japanese LS subjects. A previously published Japanese sporadic CRC cohort, which includes non-LS controls, was utilized as a non-LS cohort (n = 437). LS subjects exhibited reduced microbial diversity and low-Faecalibacterium enterotypes compared to non-LS. Patients with LS-CRC had higher levels of Fusobacterium nucleatum and fap2. Differential fecal metabolites and functional genes suggest heightened degradation of lysine and arginine in LS-CRC. A comparison between LS and non-LS subjects prior to adenoma formation revealed distinct fecal metabolites of LS subjects. These findings suggest that the gut microbiota plays a more responsive role in CRC tumorigenesis in patients with LS than those without LS.

Prolonged hospitalization signature and early antibiotic effects on the nasopharyngeal resistome in preterm infants

Respiratory pathogens, commonly colonizing nasopharynx, are among the leading causes of death due to antimicrobial resistance. Yet, antibiotic resistance determinants within nasopharyngeal microbial communities remain poorly understood. In this prospective cohort study, we investigate the nasopharynx resistome development in preterm infants, assess early antibiotic impact on its trajectory, and explore its association with clinical covariates using shotgun metagenomics. Our findings reveal widespread nasopharyngeal carriage of antibiotic resistance genes (ARGs) with resistomes undergoing transient changes, including increased ARG diversity, abundance, and composition alterations due to early antibiotic exposure. ARGs associated with the critical nosocomial pathogen Serratia marcescens persist up to 8-10 months of age, representing a long-lasting hospitalization signature. The nasopharyngeal resistome strongly correlates with microbiome composition, with inter-individual differences and postnatal age explaining most of the variation. Our report on the collateral effects of antibiotics and prolonged hospitalization underscores the urgency of further studies focused on this relatively unexplored reservoir of pathogens and ARGs.

Mudskipper detects combinatorial RNA binding protein interactions in multiplexed CLIP data

The uncovering of protein-RNA interactions enables a deeper understanding of RNA processing. Recent multiplexed crosslinking and immunoprecipitation (CLIP) technologies such as antibody-barcoded eCLIP (ABC) dramatically increase the throughput of mapping RNA binding protein (RBP) binding sites. However, multiplex CLIP datasets are multivariate, and each RBP suffers non-uniform signal-to-noise ratio. To address this, we developed Mudskipper, a versatile computational suite comprising two components: a Dirichlet multinomial mixture model to account for the multivariate nature of ABC datasets and a softmasking approach that identifies and removes non-specific protein-RNA interactions in RBPs with low signal-to-noise ratio. Mudskipper demonstrates superior precision and recall over existing tools on multiplex datasets and supports analysis of repetitive elements and small non-coding RNAs. Our findings unravel splicing outcomes and variant-associated disruptions, enabling higher-throughput investigations into diseases and regulation mediated by RBPs.

Faecalibaterium prausnitzii strain EXL01 boosts efficacy of immune checkpoint inhibitors

Gut microbiota impacts responses to immune checkpoint inhibitors (ICI). A high level of Faecalibacterium prausnitzii have been associated with a positive response to ICI in multiple cancer types. Here, based on fecal shotgun metagenomics data, we show in two independent cohorts of patients with non-small cell lung cancer and advanced melanoma that a high level of F. prausnitzii at baseline is positively associated with a better clinical response to ICI. In MCA205 tumor-bearing mice, administration of F. prausnitzii strain EXL01, already in clinical development for Inflammatory Bowel Disease, restores the anti-tumor response to ICI in the context of antibiotic-induced microbiota perturbation at clinical and tumor transcriptomics level. In vitro, EXL01 strain enhances T cell activation in the presence of ICI. Interestingly, oral administration of EXL01 strain did not induce any change in fecal microbiota diversity or composition, suggesting a direct effect on immune cells in the small intestine. F. prausnitzii strain EXL01 will be evaluated as an adjuvant to ICI in multiple cancers in the near future.

Airway microbiome signature accurately discriminates Mycobacterium tuberculosis infection status

Mycobacterium tuberculosis remains one of the deadliest infectious agents globally. Amidst efforts to control TB, long treatment duration, drug toxicity, and resistance underscore the need for novel therapeutic strategies. Despite advances in understanding the interplay between microbiome and disease in humans, the specific role of the microbiome in predicting disease susceptibility and discriminating infection status in tuberculosis still needs to be fully investigated. We investigated the impact of M.tb infection and M.tb-specific IFNγ immune responses on airway microbiome diversity by performing TB GeneXpert and QuantiFERON-GOLD assays during the follow-up phase of a longitudinal HIV-Lung Microbiome cohort of individuals recruited from two large independent cohorts in rural Uganda. M.tb rather than IFNγ immune response mainly drove a significant reduction in airway microbiome diversity. A microbiome signature comprising Streptococcus, Neisseria, Fusobacterium, Prevotella, Schaalia, Actinomyces, Cutibacterium, Brevibacillus, Microbacterium, and Beijerinckiacea accurately discriminated active TB from Latent TB and M.tb-uninfected individuals.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease. Pulmonary exacerbations (PEx) in these conditions are associated with accelerated lung function decline and higher mortality rates. Understanding PEx ecology is challenged by high inter-patient variability in airway microbial community profiles. We analyze bacterial communities in 880 CF sputum samples collected during an observational prospective cohort study and develop microbiome descriptors to model community reorganization prior to and during 18 PEx. We identify two microbial dysbiosis regimes with opposing ecology and dynamics. Pathogen-governed PEx show hierarchical community reorganization and reduced diversity, whereas anaerobic bloom PEx display stochasticity and increased diversity. A simulation of antimicrobial treatment predicts better efficacy for hierarchically organized communities. This link between PEx, microbiome organization, and treatment success advances the development of personalized clinical management in CF and, potentially, other obstructive lung diseases.

Longitudinal multicompartment characterization of host-microbiota interactions in patients with acute respiratory failure

Critical illness can significantly alter the composition and function of the human microbiome, but few studies have examined these changes over time. Here, we conduct a comprehensive analysis of the oral, lung, and gut microbiota in 479 mechanically ventilated patients (223 females, 256 males) with acute respiratory failure. We use advanced DNA sequencing technologies, including Illumina amplicon sequencing (utilizing 16S and ITS rRNA genes for bacteria and fungi, respectively, in all sample types) and Nanopore metagenomics for lung microbiota. Our results reveal a progressive dysbiosis in all three body compartments, characterized by a reduction in microbial diversity, a decrease in beneficial anaerobes, and an increase in pathogens. We find that clinical factors, such as chronic obstructive pulmonary disease, immunosuppression, and antibiotic exposure, are associated with specific patterns of dysbiosis. Interestingly, unsupervised clustering of lung microbiota diversity and composition by 16S independently predicted survival and performed better than traditional clinical and host-response predictors. These observations are validated in two separate cohorts of COVID-19 patients, highlighting the potential of lung microbiota as valuable prognostic biomarkers in critical care. Understanding these microbiome changes during critical illness points to new opportunities for microbiota-targeted precision medicine interventions.

Temporal and spatial dynamics within the fungal microbiome of grape fermentation

Over 6 years, we conducted an extensive survey of spontaneous grape fermentations, examining 3105 fungal microbiomes across 14 distinct grape-growing regions. Our investigation into the biodiversity of these fermentations revealed that a small number of highly abundant genera form the core of the initial grape juice microbiome. Consistent with previous studies, we found that the region of origin had the most significant impact on microbial diversity patterns. We also discovered that certain taxa were consistently associated with specific geographical locations and grape varieties, although these taxa represented only a minor portion of the overall diversity in our dataset. Through unsupervised clustering and dimensionality reduction analysis, we identified three unique community types, each exhibiting variations in the abundance of key genera. When we projected these genera onto global branches, it suggested that microbiomes transition between these three broad community types. We further investigated the microbial community composition throughout the fermentation process. Our observations indicated that the initial microbial community composition could predict the diversity during the early stages of fermentation. Notably, Hanseniaspora uvarum emerged as the primary non-Saccharomyces species within this large collection of samples.

Effects of Probiotics on Preterm Infant Gut Microbiota Across Populations: A Systematic Review and Meta-Analysis

Microbiota in early life is closely associated with the health of infants, especially premature ones. Probiotics are important drivers of gut microbiota development in preterm infants; however, there is no consensus regarding the characteristics of specific microbiota in preterm infants receiving probiotics. In this study, we performed a meta-analysis of 5 microbiome data sets (1816 stool samples from 706 preterm infants) to compare the gut microbiota of preterm infants exposed to probiotics with that of preterm infants not exposed to probiotics across populations. Despite study-specific variations, we found consistent differences in gut microbial composition and predicted functional pathways between the control and probiotic groups across different cohorts of preterm infants. The enrichment of Acinetobacter, Bifidobacterium, and Lactobacillus spp and the depletion of the potentially pathogenic bacteria Finegoldia, Veillonella, and Klebsiella spp. were the most consistent changes in the gut microbiota of preterm infants supplemented with probiotics. Probiotics drove microbiome transition into multiple preterm gut community types, and notably, preterm gut community type 3 had the highest α-diversity, with enrichment of Bifidobacterium and Bacteroides spp. At the functional level, the major predicted microbial pathways involved in peptidoglycan biosynthesis consistently increased in preterm infants supplemented with probiotics; in contrast, the crucial pathways associated with heme biosynthesis consistently decreased. Interestingly, Bifidobacterium sp. rather than Lactobacillus sp. gradually became dominant in gut microbiota of preterm infants using mixed probiotics, although both probiotic strains were administered at the same dosage. Taken together, our meta-analysis suggests that probiotics contribute to reshaping the microbial ecosystem of preterm infants at both the taxonomic and functional levels of the bacterial community. More standardized and relevant studies may contribute to better understanding the crosstalk among probiotics, the gut microbiota, and subsequent disease risk, which could help to give timely nutritional feeding guidance to preterm infants. This systematic review and meta-analysis was registered at PROSPERO (https://www.crd.york.ac.uk/PROSPERO/) as CRD42023447901.

Lysogeny destabilizes computationally simulated microbiomes

Microbiomes are ecosystems, and their stability can impact the health of their hosts. Theory predicts that predators influence ecosystem stability. Phages are key predators of bacteria in microbiomes, but phages are unusual predators because many have lysogenic life cycles. It has been hypothesized that lysogeny can destabilize microbiomes, but lysogeny has no direct analog in classical ecological theory, and no formal theory exists. We studied the stability of computationally simulated microbiomes with different numbers of temperate (lysogenic) and virulent (obligate lytic) phage species. Bacterial populations were more likely to fluctuate over time when there were more temperate phages species. After disturbances, bacterial populations returned to their pre-disturbance densities more slowly when there were more temperate phage species, but cycles engendered by disturbances dampened more slowly when there were more virulent phage species. Our work offers the first formal theory linking lysogeny to microbiome stability.

Rigorous Donor Selection for Fecal Microbiota Transplantation in Active Ulcerative Colitis: Key Lessons From a Randomized Controlled Trial Halted for Futility

BACKGROUND & AIMS

Rigorous donor preselection on microbiota level, strict anaerobic processing, and repeated fecal microbiota transplantation (FMT) administration were hypothesized to improve FMT induction of remission in ulcerative colitis (UC).

METHODS

The RESTORE-UC trial was a multi-centric, double-blind, sham-controlled, randomized trial. Patients with moderate to severe UC (defined by total Mayo 4-10) were randomly allocated to receive 4 anaerobic-prepared allogenic or autologous donor FMTs. Allogenic donor material was selected after a rigorous screening based on microbial cell count, enterotype, and the abundance of specific genera. The primary endpoint was steroid-free clinical remission (total Mayo ≤2, no sub-score >1) at week 8. A pre-planned futility analysis was performed after 66% (n = 72) of intended inclusions (n = 108). Quantitative microbiome profiling (n = 44) was performed at weeks 0 and 8.

RESULTS

In total, 72 patients were included, of which 66 received at least 1 FMT (allogenic FMT, n = 30 and autologous FMT, n = 36). At week 8, respectively, 3 and 5 patients reached the primary endpoint of steroid-free clinical remission (P = .72), indicating no treatment difference of at least 5% in favor of allogenic FMT. Hence, the study was stopped due to futility. Microbiome analysis showed numerically more enterotype transitions upon allogenic FMT compared with autologous FMT, and more transitions were observed when patients were treated with a different enterotype than their own at baseline (P = .01). Primary response was associated with lower total Mayo scores, lower bacterial cell counts, and higher Bacteroides 2 prevalence at baseline.

CONCLUSION

The RESTORE-UC trial did not meet its primary endpoint of increased steroid-free clinical remission at week 8. Further research should additionally consider patient selection, sterilized sham-control, increased frequency, density, and viability of FMT prior to administration.

CLINICALTRIALS

gov, Number: NCT03110289.

Latent Tuberculosis Infection Is Associated with an Enrichment of Short-Chain Fatty Acid-Producing Bacteria in the Stool of Women Living with HIV

Latent tuberculosis infection (LTBI) is common in people living with HIV (PLHIV) in high-TB-burden settings. Active TB is associated with specific stool taxa; however, little is known about the stool microbiota and LTBI in PLHIV. We characterised the stool microbiota of PLHIV with [interferon-γ release assay (IGRA)- and tuberculin skin test (TST)-positive] or without (IGRA- and TST-negative) LTBI (n = 25 per group). The 16S rRNA DNA sequences were analysed using QIIME2, Dirichlet-Multinomial Mixtures, DESeq2, and PICRUSt2. No α- or β-diversity differences occurred by LTBI status; however, LTBI-positive people were Faecalibacterium-, Blautia-, Gemmiger-, and Bacteroides-enriched and Moryella-, Atopobium-, Corynebacterium-, and Streptococcus-depleted. Inferred metagenome data showed that LTBI-negative-enriched pathways included several metabolite degradation pathways. Stool from LTBI-positive people demonstrated differential taxa abundance based on a quantitative response to antigen stimulation. In LTBI-positive people, older people had different β-diversities than younger people, whereas in LTBI-negative people, no differences occurred across age groups. Amongst female PLHIV, those with LTBI were, vs. those without LTBI, Faecalibacterium-, Blautia-, Gemmiger-, and Bacteriodes-enriched, which are producers of short-chain fatty acids. Taxonomic differences amongst people with LTBI occurred according to quantitative response to antigen stimulation and age. These data enhance our understanding of the microbiome's potential role in LTBI.

Gut Microbiota Improves Prognostic Prediction in Critically Ill COVID-19 Patients Alongside Immunological and Hematological Indicators

The gut microbiota undergoes substantial changes in COVID-19 patients; yet, the utility of these alterations as prognostic biomarkers at the time of hospital admission, and its correlation with immunological and hematological parameters, remains unclear. The objective of this study is to investigate the gut microbiota's dynamic change in critically ill patients with COVID-19 and evaluate its predictive capability for clinical outcomes alongside immunological and hematological parameters. In this study, anal swabs were consecutively collected from 192 COVID-19 patients (583 samples) upon hospital admission for metagenome sequencing. Simultaneously, blood samples were obtained to measure the concentrations of 27 cytokines and chemokines, along with hematological and biochemical indicators. Our findings indicate a significant correlation between the composition and dynamics of gut microbiota with disease severity and mortality in COVID-19 patients. Recovered patients exhibited a higher abundance of Veillonella and denser interactions among gut commensal bacteria compared to deceased patients. Furthermore, the abundance of gut commensal bacteria exhibited a negative correlation with the concentration of proinflammatory cytokines and organ damage markers. The gut microbiota upon admission showed moderate prognostic prediction ability with an AUC of 0.78, which was less effective compared to predictions based on immunological and hematological parameters (AUC 0.80 and 0.88, respectively). Noteworthy, the integration of these three datasets yielded a higher predictive accuracy (AUC 0.93). Our findings suggest the gut microbiota as an informative biomarker for COVID-19 prognosis, augmenting existing immune and hematological indicators.

Identifying microbial drivers in biological phenotypes with a Bayesian network regression model

In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes has an effect on the response (main effects), not just the interactions. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy-to-use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl.

Microbial Signatures in COVID-19: Distinguishing Mild and Severe Disease via Gut Microbiota

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has significantly impacted global healthcare, underscoring the importance of exploring the virus's effects on infected individuals beyond treatments and vaccines. Notably, recent findings suggest that SARS-CoV-2 can infect the gut, thereby altering the gut microbiota. This study aimed to analyze the gut microbiota composition differences between COVID-19 patients experiencing mild and severe symptoms. We conducted 16S rRNA metagenomic sequencing on fecal samples from 49 mild and 43 severe COVID-19 cases upon hospital admission. Our analysis identified a differential abundance of specific bacterial species associated with the severity of the disease. Severely affected patients showed an association with Enterococcus faecium, Akkermansia muciniphila, and others, while milder cases were linked to Faecalibacterium prausnitzii, Alistipes putredinis, Blautia faecis, and additional species. Furthermore, a network analysis using SPIEC-EASI indicated keystone taxa and highlighted structural differences in bacterial connectivity, with a notable disruption in the severe group. Our study highlights the diverse impacts of SARS-CoV-2 on the gut microbiome among both mild and severe COVID-19 patients, showcasing a spectrum of microbial responses to the virus. Importantly, these findings align, to some extent, with observations from other studies on COVID-19 gut microbiomes, despite variations in methodologies. The findings from this study, based on retrospective data, establish a foundation for future prospective research to confirm the role of the gut microbiome as a predictive biomarker for the severity of COVID-19.

The multispecies microbial cluster of Fusobacterium, Parvimonas, Bacteroides and Faecalibacterium as a precision biomarker for colorectal cancer diagnosis

The incidence of colorectal cancer (CRC) has increased worldwide, and early diagnosis is crucial to reduce mortality rates. Therefore, new noninvasive biomarkers for CRC are required. Recent studies have revealed an imbalance in the oral and gut microbiomes of patients with CRC, as well as impaired gut vascular barrier function. In the present study, the microbiomes of saliva, crevicular fluid, feces, and non-neoplastic and tumor intestinal tissue samples of 93 CRC patients and 30 healthy individuals without digestive disorders (non-CRC) were analyzed by 16S rRNA metabarcoding procedures. The data revealed that Parvimonas, Fusobacterium, and Bacteroides fragilis were significantly over-represented in stool samples of CRC patients, whereas Faecalibacterium and Blautia were significantly over-abundant in the non-CRC group. Moreover, the tumor samples were enriched in well-known periodontal anaerobes, including Fusobacterium, Parvimonas, Peptostreptococcus, Porphyromonas, and Prevotella. Co-occurrence patterns of these oral microorganisms were observed in the subgingival pocket and in the tumor tissues of CRC patients, where they also correlated with other gut microbes, such as Hungatella. This study provides new evidence that oral pathobionts, normally located in subgingival pockets, can migrate to the colon and probably aggregate with aerobic bacteria, forming synergistic consortia. Furthermore, we suggest that the group composed of Fusobacterium, Parvimonas, Bacteroides, and Faecalibacterium could be used to design an excellent noninvasive fecal test for the early diagnosis of CRC. The combination of these four genera would significantly improve the reliability of a discriminatory test with respect to others that use a single species as a unique CRC biomarker.

The impact of daily supplementation with rhamnogalacturonan-I on the gut microbiota in healthy adults: A randomized controlled trial

Pectin and its derivatives have been shown to modulate immune signaling as well as gut microbiota in preclinical studies, which may constitute the mechanisms by which supplementation of specific pectic polysaccharides confers protection against viral respiratory infections. In a double-blind, placebo-controlled rhinovirus (RV16) challenge study, healthy volunteers were randomized to consume placebo (0.0 g/day) (N = 46), low-dose (0.3 g/day) (N = 49) or high-dose (1.5 g/day) (N = 51) of carrot derived rhamnogalacturonan-I (cRG-I) for eight weeks and they were subsequently challenged with RV-16. Here, the effect of 8-week cRG-I supplementation on the gut microbiota was studied. While the overall gut microbiota composition in the population was generally unaltered by this very low dose of fibre, the relative abundance of Bifidobacterium spp. (mainly B. adolescentis and B. longum) was significantly increased by both doses of cRG-1. Moreover, daily supplementation of cRG-I led to a dose-dependent reduction in inter- and intra-individual microbiota heterogeneity, suggesting a stabilizing effect on the gut microbiota. The severity of respiratory symptoms did not directly correlate with the cRG-I-induced microbial changes, but several dominant groups of the Ruminococcaceae family and microbiota richness were positively associated with a reduced and hence desired post-infection response. Thus, the present results on the modulation of the gut microbiota composition support the previously demonstrated immunomodulatory and protective effect of cRG-I during a common cold infection.

Microbiome confounders and quantitative profiling challenge predicted microbial targets in colorectal cancer development

Despite substantial progress in cancer microbiome research, recognized confounders and advances in absolute microbiome quantification remain underused; this raises concerns regarding potential spurious associations. Here we study the fecal microbiota of 589 patients at different colorectal cancer (CRC) stages and compare observations with up to 15 published studies (4,439 patients and controls total). Using quantitative microbiome profiling based on 16S ribosomal RNA amplicon sequencing, combined with rigorous confounder control, we identified transit time, fecal calprotectin (intestinal inflammation) and body mass index as primary microbial covariates, superseding variance explained by CRC diagnostic groups. Well-established microbiome CRC targets, such as Fusobacterium nucleatum, did not significantly associate with CRC diagnostic groups (healthy, adenoma and carcinoma) when controlling for these covariates. In contrast, the associations of Anaerococcus vaginalis, Dialister pneumosintes, Parvimonas micra, Peptostreptococcus anaerobius, Porphyromonas asaccharolytica and Prevotella intermedia remained robust, highlighting their future target potential. Finally, control individuals (age 22-80 years, mean 57.7 years, standard deviation 11.3) meeting criteria for colonoscopy (for example, through a positive fecal immunochemical test) but without colonic lesions are enriched for the dysbiotic Bacteroides2 enterotype, emphasizing uncertainties in defining healthy controls in cancer microbiome research. Together, these results indicate the importance of quantitative microbiome profiling and covariate control for biomarker identification in CRC microbiome studies.

The heterogeneous wound microbiome varies with wound care pain, dressing type, and inflammatory gene expression

Wound dressing changes are essential procedures for wound management. However, ~50% of patients experience severe pain during these procedures despite the availability of analgesic medications, indicating a need for novel therapeutics that address underlying causes of pain. Along with other clinical factors, wound pathogens and inflammatory immune responses have previously been implicated in wound pain. To test whether these factors could contribute to severe pain during wound dressing changes, we conducted an exploratory, cross-sectional analysis of patient-reported pain, inflammatory immune responses, and wound microbiome composition in 445 wounds at the time of a study dressing change. We profiled the bacterial composition of 406 wounds using 16S ribosomal RNA amplicon sequencing and quantified gene expression of 13 inflammatory markers in wound fluid using quantitative real-time polymerase chain reaction (qPCR). Neither inflammatory gene expression nor clinically observed inflammation were associated with severe pain, but Corynebacterium and Streptococcus were of lower relative abundance in wounds of patients reporting severe pain than those reporting little or no pain. Wound microbiome composition differed by wound location, and correlated with six of the inflammatory markers, including complement receptor C5AR1, pro-inflammatory cytokine interleukin (IL)1β, chemokine IL-8, matrix metalloproteinase MMP2, and the antimicrobial peptide encoding cathelicidin antimicrobial peptide. Interestingly, we found a relationship between the wound microbiome and vacuum-assisted wound closure (VAC). These findings identify preliminary, associative relationships between wound microbiota and host factors which motivate future investigation into the directional relationships between wound care pain, wound closure technologies, and the wound microbiome.

A pan-cancer analysis of the microbiome in metastatic cancer

Microbial communities are resident to multiple niches of the human body and are important modulators of the host immune system and responses to anticancer therapies. Recent studies have shown that complex microbial communities are present within primary tumors. To investigate the presence and relevance of the microbiome in metastases, we integrated mapping and assembly-based metagenomics, genomics, transcriptomics, and clinical data of 4,160 metastatic tumor biopsies. We identified organ-specific tropisms of microbes, enrichments of anaerobic bacteria in hypoxic tumors, associations between microbial diversity and tumor-infiltrating neutrophils, and the association of Fusobacterium with resistance to immune checkpoint blockade (ICB) in lung cancer. Furthermore, longitudinal tumor sampling revealed temporal evolution of the microbial communities and identified bacteria depleted upon ICB. Together, we generated a pan-cancer resource of the metastatic tumor microbiome that may contribute to advancing treatment strategies.

A generic approach to infer community-level fitness of microbial genes

The gene content in a metagenomic pool defines the function potential of a microbial community. Natural selection, operating on the level of genomes or genes, shapes the evolution of community functions by enriching some genes while depriving the others. Despite the importance of microbiomes in the environment and health, a general metric to evaluate the community-wide fitness of microbial genes remains lacking. In this work, we adapt the classic neutral model of species and use it to predict how the abundances of different genes will be shaped by selection, regardless of at which level the selection acts. We establish a simple metric that quantitatively infers the average survival capability of each gene in a microbiome. We then experimentally validate the predictions using synthetic communities of barcoded Escherichia coli strains undergoing neutral assembly and competition. We further show that this approach can be applied to publicly available metagenomic datasets to gain insights into the environment-function interplay of natural microbiomes.

Early life factors and oral microbial signatures define the risk of caries in a Swedish cohort of preschool children

The oral cavity harbors complex communities comprising bacteria, archaea, fungi, protozoa, and viruses. The oral microbiota is establish at birth and develops further during childhood, with early life factors such as birth mode, feeding practices, and oral hygiene, reported to influence this development and the susceptibility to caries. We here analyzed the oral bacterial composition in saliva of 260 Swedish children at two, three and five years of age using 16S rRNA gene profiling to examine its relation to environmental factors and caries development at five years of age. We were able to assign the salivary bacterial community in each child at each time point to one of seven distinct clusters. We observed an individual dynamic in the development of the oral microbiota related to early life factors, such as being first born, born by C-section, maternal perinatal antibiotics use, with a distinct transition between three and five years of age. Different bacterial signatures depending on age were related to increased caries risk, while Peptococcus consistently linked to reduced risk of caries development.

Latent tuberculosis infection is associated with an enrichment of short chain fatty acid producing bacteria in the stool of women living with HIV

Background

Latent tuberculosis infection (LTBI) is common in people living with HIV (PLHIV) in high TB burden settings. Active TB is associated with specific stool taxa; however, little is known about the stool microbiota and LTBI, including in PLHIV.

Method

Within a parent study that recruited adult females with HIV from Cape Town, South Africa into predefined age categories (18-25, 35-60 years), we characterised the stool microbiota of those with [interferon-γ release assay (IGRA)- and tuberculin skin test (TST)-positive] or without (IGRA- and TST- negative) LTBI (n=25 per group). 16S rRNA DNA sequences were analysed using QIIME2, Dirichlet Multinomial Mixtures, DESeq2 and PICRUSt2.

Results

No α- or β-diversity differences occurred by LTBI status; however, LTBI-positives were Faecalibacterium-, Blautia-, Gemmiger-, Bacteroides-enriched and Moryella-, Atopobium-, Corynebacterium-, Streptococcus-depleted. Inferred metagenome data showed LTBI-negative-enriched pathways included several involved in methylglyoxal degradation, L-arginine, putrescine, 4-aminobutanoate degradation and L-arginine and ornithine degradation. Stool from LTBI-positives demonstrated differential taxa abundance based on a quantitative response to antigen stimulation (Acidaminococcus-enrichment and Megamonas-, Alistipes-, and Paraprevotella-depletion associated with higher IGRA or TST responses, respectively). In LTBI-positives, older people had different β-diversities than younger people whereas, in LTBI-negatives, no differences occurred across age groups.

Conclusion

Amongst female PLHIV, those with LTBI had, vs. those without LTBI, Faecalibacterium, Blautia, Gemmiger, Bacteriodes-enriched, which are producers of short chain fatty acids. Taxonomic differences amongst people with LTBI occurred according to quantitative response to antigen stimulation and age. These data enhance our understanding of the microbiome's potential role in LTBI.

Food additives impair gut microbiota from healthy individuals and IBD patients in a colonic in vitro fermentation model

Intestinal fibrosis is a long-term complication of inflammatory bowel diseases (IBD). Changes in microbial populations have been linked with the onset of fibrosis and some food additives are known to promote intestinal inflammation facilitating fibrosis induction. In this study, we investigated how polysorbate 80, sucralose, titanium dioxide, sodium nitrite and maltodextrin affect the gut microbiota and the metabolic activity in healthy and IBD donors (patients in remission and with a flare of IBD). The Simulator of the Human Intestinal Microbial Ecosystem (SHIME®) with a static (batch) configuration was used to evaluate the effects of food additives on the human intestinal microbiota. Polysorbate 80 and sucralose decreased butyrate-producing bacteria such as Roseburia and Faecalibacterium prausnitzii. Both compounds, also increased bacterial species positively correlated with intestinal inflammation and fibrosis (i.e.: Enterococcus, Veillonella and Mucispirillum schaedleri), especially in donors in remission of IBD. Additionally, polysorbate 80 induced a lower activity of the aryl hydrocarbon receptor (AhR) in the three groups of donors, which can affect the intestinal homeostasis. Maltodextrin, despite increasing short-chain fatty acids production, promoted the growth of Ruminococcus genus, correlated with higher risk of fibrosis, and decreased Oscillospira which is negatively associated with fibrosis. Our findings unveil crucial insights into the potential deleterious effects of polysorbate 80, sucralose and maltodextrin on human gut microbiota in healthy and, to a greater extent, in IBD patients.

Infant age inversely correlates with gut carriage of resistance genes, reflecting modifications in microbial carbohydrate metabolism during early life

The infant gut microbiome is increasingly recognized as a reservoir of antibiotic resistance genes, yet the assembly of gut resistome in infants and its influencing factors remain largely unknown. We characterized resistome in 4132 metagenomes from 963 infants in six countries and 4285 resistance genes were observed. The inherent resistome pattern of healthy infants (N = 272) could be distinguished by two stages: a multicompound resistance phase (Months 0-7) and a tetracycline-mupirocin-β-lactam-dominant phase (Months 8-14). Microbial taxonomy explained 40.7% of the gut resistome of healthy infants, with Escherichia (25.5%) harboring the most resistance genes. In a further analysis with all available infants (N = 963), we found age was the strongest influencer on the resistome and was negatively correlated with the overall resistance during the first 3 years (p < 0.001). Using a random-forest approach, a set of 34 resistance genes could be used to predict age (R 2 = 68.0%). Leveraging microbial host inference analyses, we inferred the age-dependent assembly of infant resistome was a result of shifts in the gut microbiome, primarily driven by changes in taxa that disproportionately harbor resistance genes across taxa (e.g., Escherichia coli more frequently harbored resistance genes than other taxa). We performed metagenomic functional profiling and metagenomic assembled genome analyses whose results indicate that the development of gut resistome was driven by changes in microbial carbohydrate metabolism, with an increasing need for carbohydrate-active enzymes from Bacteroidota and a decreasing need for Pseudomonadota during infancy. Importantly, we observed increased acquired resistance genes over time, which was related to increased horizontal gene transfer in the developing infant gut microbiome. In summary, infant age was negatively correlated with antimicrobial resistance gene levels, reflecting a composition shift in the gut microbiome, likely driven by the changing need for microbial carbohydrate metabolism during early life.

Analysis of Microbiome Data

The microbiome represents a hidden world of tiny organisms populating not only our surroundings but also our own bodies. By enabling comprehensive profiling of these invisible creatures, modern genomic sequencing tools have given us an unprecedented ability to characterize these populations and uncover their outsize impact on our environment and health. Statistical analysis of microbiome data is critical to infer patterns from the observed abundances. The application and development of analytical methods in this area require careful consideration of the unique aspects of microbiome profiles. We begin this review with a brief overview of microbiome data collection and processing and describe the resulting data structure. We then provide an overview of statistical methods for key tasks in microbiome data analysis, including data visualization, comparison of microbial abundance across groups, regression modeling, and network inference. We conclude with a discussion and highlight interesting future directions.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease. Pulmonary exacerbations (PEx) in these conditions are associated with accelerated lung function decline and higher mortality rates. An understanding of the microbial underpinnings of PEx is challenged by high inter-patient variability in airway microbial community profiles. We analyzed bacterial communities in 880 CF sputum samples and developed microbiome descriptors to model community reorganization prior to and during 18 PEx. We identified two microbial dysbiosis regimes with opposing ecology and dynamics. Pathogen-governed PEx showed hierarchical community reorganization and reduced diversity, whereas anaerobic bloom PEx displayed stochasticity and increased diversity. A simulation of antimicrobial treatment predicted better efficacy for hierarchically organized communities. This link between PEx type, microbiome organization, and treatment success advances the development of personalized clinical management in CF and, potentially, other obstructive lung diseases.

Cluster-specific associations between the gut microbiota and behavioral outcomes in preschool-aged children

BACKGROUND

The gut microbiota is recognized as a regulator of brain development and behavioral outcomes during childhood. Nonetheless, associations between the gut microbiota and behavior are often inconsistent among studies in humans, perhaps because many host-microbe relationships vary widely between individuals. This study aims to stratify children based on their gut microbiota composition (i.e., clusters) and to identify novel gut microbiome cluster-specific associations between the stool metabolomic pathways and child behavioral outcomes.

METHODS

Stool samples were collected from a community sample of 248 typically developing children (3-5 years). The gut microbiota was analyzed using 16S sequencing while LC-MS/MS was used for untargeted metabolomics. Parent-reported behavioral outcomes (i.e., Adaptive Skills, Internalizing, Externalizing, Behavioral Symptoms, Developmental Social Disorders) were assessed using the Behavior Assessment System for Children (BASC-2). Children were grouped based on their gut microbiota composition using the Dirichlet multinomial method, after which differences in the metabolome and behavioral outcomes were investigated.

RESULTS

Four different gut microbiota clusters were identified, where the cluster enriched in both Bacteroides and Bifidobacterium (Ba2) had the most distinct stool metabolome. The cluster characterized by high Bifidobacterium abundance (Bif), as well as cluster Ba2, were associated with lower Adaptive Skill scores and its subcomponent Social Skills. Cluster Ba2 also had significantly lower stool histidine to urocanate turnover, which in turn was associated with lower Social Skill scores in a cluster-dependent manner. Finally, cluster Ba2 had increased levels of compounds involved in Galactose metabolism (i.e., stachyose, raffinose, alpha-D-glucose), where alpha-D-glucose was associated with the Adaptive Skill subcomponent Daily Living scores (i.e., ability to perform basic everyday tasks) in a cluster-dependent manner.

CONCLUSIONS

These data show novel associations between the gut microbiota, its metabolites, and behavioral outcomes in typically developing preschool-aged children. Our results support the concept that cluster-based groupings could be used to develop more personalized interventions to support child behavioral outcomes. Video Abstract.

Temporal dynamics of gut microbiomes in non-industrialized urban Amazonia

Increasing levels of industrialization have been associated with changes in gut microbiome structure and loss of features thought to be crucial for maintaining gut ecological balance. The stability of gut microbial communities over time within individuals seems to be largely affected by these changes but has been overlooked among transitioning populations from low- to middle-income countries. Here, we used metagenomic sequencing to characterize the temporal dynamics in gut microbiomes of 24 individuals living an urban non-industrialized lifestyle in the Brazilian Amazon. We further contextualized our data with 165 matching longitudinal samples from an urban industrialized and a rural non-industrialized population. We show that gut microbiome composition and diversity have greater variability over time among non-industrialized individuals when compared to industrialized counterparts and that taxa may present diverse temporal dynamics across human populations. Enterotype classifications show that community types are generally stable over time despite shifts in microbiome structure. Furthermore, by tracking genomes over time, we show that levels of bacterial population replacements are more frequent among Amazonian individuals and that non-synonymous variants accumulate in genes associated with degradation of host dietary polysaccharides. Taken together, our results suggest that the stability of gut microbiomes is influenced by levels of industrialization and that tracking microbial population dynamics is important to understand how the microbiome will adapt to these transitions.IMPORTANCEThe transition from a rural or non-industrialized lifestyle to urbanization and industrialization has been linked to changes in the structure and function of the human gut microbiome. Understanding how the gut microbiomes changes over time is crucial to define healthy states and to grasp how the gut microbiome interacts with the host environment. Here, we investigate the temporal dynamics of gut microbiomes from an urban and non-industrialized population in the Amazon, as well as metagenomic data sets from urban United States and rural Tanzania. We showed that healthy non-industrialized microbiomes experience greater compositional shifts over time compared to industrialized individuals. Furthermore, bacterial strain populations are more frequently replaced in non-industrialized microbiomes, and most non-synonymous mutations accumulate in genes associated with the degradation of host dietary components. This indicates that microbiome stability is affected by transitions to industrialization, and that strain tracking can elucidate the ecological dynamics behind such transitions.

Urinary microbiome community types associated with urinary incontinence severity in women

BACKGROUND

Urinary microbiome (urobiome) studies have previously reported on specific taxa and community differences in women with mixed urinary incontinence compared with controls. Therefore, a hypothesis was made that higher urinary and vaginal microbiome diversity would be associated with increased urinary incontinence severity.

OBJECTIVE

This study aimed to test whether specific urinary or vaginal microbiome community types are associated with urinary incontinence severity in a population of women with mixed urinary incontinence.

STUDY DESIGN

This planned secondary, cross-sectional analysis evaluated associations between the urinary and vaginal microbiomes and urinary incontinence severity in a subset of Effects of Surgical Treatment Enhanced With Exercise for Mixed Urinary Incontinence trial participants with urinary incontinence. Incontinence severity was measured using bladder diaries and Urinary Distress Inventory questionnaires collected at baseline. Catheterized urine samples and vaginal swabs were concurrently collected before treatment at baseline to assess the urinary and vaginal microbiomes. Of note, 16S rRNA V4 to V6 variable regions were sequenced, characterizing bacterial taxa to the genus level using the DADA2 pipeline and SILVA database. Using Dirichlet multinomial mixtures methods, samples were clustered into community types based on core taxa. Associations between community types and severity measures (Urinary Distress Inventory total scores, Urinary Distress Inventory subscale scores, and the number of urinary incontinence episodes [total, urgency, and stress] from the bladder diary) were evaluated using linear regression models adjusted for age and body mass index. In addition, alpha diversity measures for richness (total taxa numbers) and evenness (proportional distribution of taxa abundance) were analyzed for associations with urinary incontinence episodes and community type.

RESULTS

Overall, 6 urinary microbiome community types were identified, characterized by varying levels of common genera (Lactobacillus, Gardnerella, Prevotella, Tepidimonas, Acidovorax, Escherichia, and others). The analysis of urinary incontinence severity in 126 participants with mixed urinary incontinence identified a Lactobacillus-dominated reference group with the highest abundance of Lactobacillus (mean relative abundance of 76%). A community characterized by fewer Lactobacilli (mean relative abundance of 19%) and greater alpha diversity was associated with higher total urinary incontinence episodes (2.67 daily leaks; 95% confidence interval, 0.76-4.59; P=.007) and urgency urinary incontinence episodes (1.75 daily leaks; 95% confidence interval, 0.24-3.27; P=.02) than the reference group. No significant association was observed between community type and stress urinary incontinence episodes or Urogenital Distress Inventory total or subscores. The composition of vaginal community types and urinary community types were similar but composed of slightly different bacterial taxa. Vaginal community types were not associated with urinary incontinence severity, as measured by bladder diary or Urogenital Distress Inventory total and subscale scores. Alpha diversity indicated that greater sample richness was associated with more incontinence episodes (observed genera P=.01) in urine. Measures of evenness (Shannon and Pielou) were not associated with incontinence severity in the urinary or vaginal microbiomes.

CONCLUSION

In the urobiome of women with mixed urinary incontinence, a community type with fewer Lactobacilli and more diverse bacteria was associated with more severe urinary incontinence episodes (total and urgency) compared with a community type with high predominance of a single genus, Lactobacillus. Whether mixed urinary incontinence severity is due to lesser predominance of Lactobacillus, greater presence of other non-Lactobacillus genera, or the complement of bacteria consisting of urobiome community types remains to be determined.

Sputum metagenomics of people with bronchiectasis

Background

The microbiota in the sputum of people with bronchiectasis has repeatedly been investigated in cohorts of different geographic origin, but so far has not been studied to the species level in comparison to control populations including healthy adults and smokers without lung disease.

Methods

The microbial metagenome from sputa of 101 European Bronchiectasis Registry (EMBARC) study participants was examined by using whole-genome shotgun sequencing.

Results

Our analysis of the metagenome of people with bronchiectasis revealed four clusters characterised by a predominance of Haemophilus influenzae, Pseudomonas aeruginosa or polymicrobial communities with varying compositions of nonpathogenic commensals and opportunistic pathogens. The metagenomes of the severely affected patients showed individual profiles characterised by low alpha diversity. Importantly, nearly 50% of patients with severe disease were grouped in a cluster characterised by commensals. Comparisons with the sputum metagenomes of healthy smokers and healthy nonsmokers revealed a gradient of depletion of taxa in bronchiectasis, most often Neisseria subflava, Fusobacterium periodonticum and Eubacterium sulci.

Conclusion

The gradient of depletion of commensal taxa found in healthy airways is a key feature of bronchiectasis associated with disease severity.

Development of the gut microbiota in the first 14 years of life and its relations to internalizing and externalizing difficulties and social anxiety during puberty

Relations between the gut microbiota and host mental health have been suggested by a growing number of case-control and cross-sectional studies, while supporting evidence is limited in large community samples followed during an extended period. Therefore, the current preregistered study ( https://osf.io/8ymav , September 7, 2022) described child gut microbiota development in the first 14 years of life and explored its relations to internalizing and externalizing difficulties and social anxiety in puberty, a period of high relevance for the development of mental health problems. Fecal microbiota composition was analysed by 16S ribosomal RNA gene amplicon sequencing in a total of 1003 samples from 193 children. Through a clustering method, four distinct microbial clusters were newly identified in puberty. Most children within three of these clusters remained in the same clusters from the age of 12 to 14 years, suggesting stability in microbial development and transition during this period. These three clusters were compositionally similar to enterotypes (i.e., a robust classification of the gut microbiota based on its composition across different populations) enriched in Bacteroides, Prevotella, and Ruminococcus, respectively. Two Prevotella 9-predominated clusters, including one reported by us earlier in middle childhood and the other one in puberty, were associated with more externalizing behavior at age 14. One Faecalibacterium-depleted pubertal cluster was related to more social anxiety at age 14. This finding was confirmed by a negative cross-sectional relation between Faecalibacterium and social anxiety in the 14-year-olds. The findings of this study continue to map gut microbiota development in a relatively large community sample followed from birth onwards, importantly extending our knowledge to puberty. Results indicate that Prevotella 9 and Faecalibacterium may be relevant microbial taxa in relation to externalizing behavior and social anxiety, respectively. These correlational findings need validations from other similar cohort studies, as well as well-designed mechanistic pre-clinical investigations before inferring cause and effect.

The clinical outcome of COVID-19 is strongly associated with microbiome dynamics in the upper respiratory tract

OBJECTIVES

The respiratory tract is the portal of entry for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although a variety of respiratory pathogens other than SARS-CoV-2 have been associated with severe cases of COVID-19 disease, the dynamics of the upper respiratory microbiota during disease the course of disease, and how they impact disease manifestation, remain uncertain.

METHODS

We collected 349 longitudinal upper respiratory samples from a cohort of 65 COVID-19 patients (cohort 1), 28 samples from 28 recovered COVID-19 patients (cohort 2), and 59 samples from 59 healthy controls (cohort 3). All COVID-19 patients originated from the earliest stage of the epidemic in Wuhan. Based on a modified clinical scale, the disease course was divided into five clinical disease phases (pseudotimes): "Healthy" (pseudotime 0), "Incremental" (pseudotime 1), "Critical" (pseudotime 2), "Complicated" (pseudotime 3), "Convalescent" (pseudotime 4), and "Long-term follow-up" (pseudotime 5). Using meta-transcriptomics, we investigated the features and dynamics of transcriptionally active microbes in the upper respiratory tract (URT) over the course of COVID-19 disease, as well as its association with disease progression and clinical outcomes.

RESULTS

Our results revealed that the URT microbiome exhibits substantial heterogeneity during disease course. Two clusters of microbial communities characterized by low alpha diversity and enrichment for multiple pathogens or potential pathobionts (including Acinetobacter and Candida) were associated with disease progression and a worse clinical outcome. We also identified a series of microbial indicators that classified disease progression into more severe stages. Longitudinal analysis revealed that although the microbiome exhibited complex and changing patterns during COVID-19, a restoration of URT microbiomes from early dysbiosis toward more diverse status in later disease stages was observed in most patients. In addition, a group of potential pathobionts were strongly associated with the concentration of inflammatory indicators and mortality.

CONCLUSION

This study revealed strong links between URT microbiome dynamics and disease progression and clinical outcomes in COVID-19, implying that the treatment of severe disease should consider the full spectrum of microbial pathogens present.

Dysbiosis and Associated Stool Features Improve Prediction of Response to Biological Therapy in Inflammatory Bowel Disease

BACKGROUND & AIMS

Dysbiosis of the gut microbiota is considered a key contributor to inflammatory bowel disease (IBD) etiology. Here, we investigated potential associations between microbiota composition and the outcomes to biological therapies.

METHODS

The study prospectively recruited 296 patients with active IBD (203 with Crohn's disease, 93 with ulcerative colitis) initiating biological therapy. Quantitative microbiome profiles of pretreatment and posttreatment fecal samples were obtained combining flow cytometry with 16S amplicon sequencing. Therapeutic response was assessed by endoscopy, patient-reported outcomes, and changes in fecal calprotectin. The effect of therapy on microbiome variation was evaluated using constrained ordination methods. Prediction of therapy outcome was performed using logistic regression with 5-fold cross-validation.

RESULTS

At baseline, 65.9% of patients carried the dysbiotic Bacteroides2 (Bact2) enterotype, with a significantly higher prevalence among patients with ileal involvement (76.8%). Microbiome variation was associated with the choice of biological therapy rather than with therapeutic outcome. Only anti-tumor necrosis factor-α treatment resulted in a microbiome shift away from Bact2, concomitant with an increase in microbial load and butyrogen abundances and a decrease in potentially opportunistic Veillonella. Remission rates for patients hosting Bact2 at baseline were significantly higher with anti-tumor necrosis factor-α than with vedolizumab (65.1% vs 35.2%). A prediction model, based on anthropometrics and clinical data, stool features (microbial load, moisture, and calprotectin), and Bact2 detection predicted treatment outcome with 73.9% accuracy for specific biological therapies.

CONCLUSION

Fecal characterization based on microbial load, moisture content, calprotectin concentration, and enterotyping may aid in the therapeutic choice of biological therapy in IBD.

Gut microbiota modulation enhances the immune capacity of lizards under climate warming

BACKGROUND

Host-microbial interactions are expected to affect species' adaptability to climate change but have rarely been explored in ectothermic animals. Some studies have shown that short-term warming reduced gut microbial diversity that could hamper host functional performance.

RESULTS

However, our longitudinal experiments in semi-natural conditions demonstrated that warming decreased gut microbiota diversity at 2 months, but increased diversity at 13 and 27 months in a desert lizard (Eremias multiocellata). Simultaneously, long-term warming significantly increased the antibacterial activity of serum, immune responses (higher expression of intestinal immune-related genes), and the concentration of short-chain fatty acids (thereby intestinal barrier and immunity) in the lizard. Fecal microbiota transplant experiments further revealed that increased diversity of gut microbiota significantly enhanced antibacterial activity and the immune response of lizards. More specifically, the enhanced immunity is likely due to the higher relative abundance of Bacteroides in warming lizards, given that the bacteria of Bacteroides fragilis regulated IFN-β expression to increase the immune response of lizards under a warming climate.

CONCLUSIONS

Our study suggests that gut microbiota can help ectotherms cope with climate warming by enhancing host immune response, and highlights the importance of long-term studies on host-microbial interactions and their biological impacts.

Genomic attributes of airway commensal bacteria and mucosa

Microbial communities at the airway mucosal barrier are conserved and highly ordered, in likelihood reflecting co-evolution with human host factors. Freed of selection to digest nutrients, the airway microbiome underpins cognate management of mucosal immunity and pathogen resistance. We show here the initial results of systematic culture and whole-genome sequencing of the thoracic airway bacteria, identifying 52 novel species amongst 126 organisms that constitute 75% of commensals typically present in heathy individuals. Clinically relevant genes encode antimicrobial synthesis, adhesion and biofilm formation, immune modulation, iron utilisation, nitrous oxide (NO) metabolism and sphingolipid signalling. Using whole-genome content we identify dysbiotic features that may influence asthma and chronic obstructive pulmonary disease. We match isolate gene content to transcripts and metabolites expressed late in airway epithelial differentiation, identifying pathways to sustain host interactions with microbiota. Our results provide a systematic basis for decrypting interactions between commensals, pathogens, and mucosa in lung diseases of global significance.

A new approach to describe the taxonomic structure of microbiome and its application to assess the relationship between microbial niches

BACKGROUND

Data from microbiomes from multiple niches is often collected, but methods to analyse these often ignore associations between niches. One interesting case is that of the oral microbiome. Its composition is receiving increasing attention due to reports on its associations with general health. While the oral cavity includes different niches, multi-niche microbiome data analysis is conducted using a single niche at a time and, therefore, ignores other niches that could act as confounding variables. Understanding the interaction between niches would assist interpretation of the results, and help improve our understanding of multi-niche microbiomes.

METHODS

In this study, we used a machine learning technique called latent Dirichlet allocation (LDA) on two microbiome datasets consisting of several niches. LDA was used on both individual niches and all niches simultaneously. On individual niches, LDA was used to decompose each niche into bacterial sub-communities unveiling their taxonomic structure. These sub-communities were then used to assess the relationship between microbial niches using the global test. On all niches simultaneously, LDA allowed us to extract meaningful microbial patterns. Sets of co-occurring operational taxonomic units (OTUs) comprising those patterns were then used to predict the original location of each sample.

RESULTS

Our approach showed that the per-niche sub-communities displayed a strong association between supragingival plaque and saliva, as well as between the anterior and posterior tongue. In addition, the LDA-derived microbial signatures were able to predict the original sample niche illustrating the meaningfulness of our sub-communities. For the multi-niche oral microbiome dataset we had an overall accuracy of 76%, and per-niche sensitivity of up to 83%. Finally, for a second multi-niche microbiome dataset from the entire body, microbial niches from the oral cavity displayed stronger associations to each other than with those from other parts of the body, such as niches within the vagina and the skin.

CONCLUSION

Our LDA-based approach produces sets of co-occurring taxa that can describe niche composition. LDA-derived microbial signatures can also be instrumental in summarizing microbiome data, for both descriptions as well as prediction.

Gut microbiome and intestinal inflammation in preclinical stages of rheumatoid arthritis

BACKGROUND

Faecal Prevotellaceae, and other microbes, have been associated with rheumatoid arthritis (RA) and preclinical RA. We have performed a quantitative microbiome profiling study in preclinical stages of RA.

METHODS

First-degree relatives of patients with RA (RA-FDRs) from the SCREEN-RA cohort were categorised into four groups: controls, healthy asymptomatic RA-FDRs; high genetic risk, asymptomatic RA-FDRs with two copies of the shared epitope; autoimmunity, asymptomatic RA-FDRs with RA-associated autoimmunity; and symptomatic, clinically suspect arthralgias or untreated new-onset RA.Faecal samples were collected and frozen. 16S sequencing was performed, processed with DADA2 pipeline and Silva database. Cell counts (cytometry) and faecal calprotectin (enzyme-linked immunosorbent assay, ELISA) were also obtained. Microbial community analyses were conducted using non-parametric tests, such as permutational multivariate analysis of variance (PERMANOVA), Wilcoxon and Kruskal-Wallis, or Aldex2.

RESULTS

A total of 371 individuals were included and categorised according to their preclinical stage of the disease. Groups had similar age, gender and body mass index. We found no significant differences in the quantitative microbiome profiles by preclinical stages (PERMANOVA, R2=0.00798, p=0.56) and, in particular, no group differences in Prevotellaceae abundance. Results were similar when using relative microbiome profiling data (PERMANOVA, R2=0.0073, p=0.83) or Aldex2 on 16S sequence counts. Regarding faecal calprotectin, we found no differences between groups (p=0.3).

CONCLUSIONS

We could not identify microbiome profiles associated with preclinical stages of RA. Only in a subgroup of individuals with the most pronounced phenotypes did we modestly retrieve the previously reported associations.

Association of the Infant Gut Microbiome with Temperament at Nine Months of Age: A Michigan Cohort Study

Though studies in animals and humans link the gut microbiota to brain development and control of behavior, little research has examined this connection in healthy infants. This prospective study could determine associations between infant gut microbiota at 3 months, and infant temperament at 9 months, in a prospective pregnancy cohort (Michigan Archive for Research on Child Health; n = 159). Microbiota profiling with 16S rRNA gene sequencing was conducted on fecal samples obtained at 3 months of age. Based on the relative abundance of gut microbiotas, three groups were identified, and each group was characterized by different microbes. Infant temperament outcomes were reported by mothers using the Infant Behavior Questionnaire-Revised Very Short Form at a mean age of 9.4 months. Fully adjusted multivariate linear regression models showed that certain clusters were associated with higher negative emotionality scores, prominently among infants who had poor vitamin D intake. However, no associations were evident between gut microbiota clusters and temperament scales after FDR correction. After using three differential abundance tools, Firmicutes was associated with higher positive affect/surgency scores, whereas Clostridioides was associated with lower scores. An association between the gut microbiota and early infancy temperament was observed; thus, this study warrants replication, with a particular focus on vitamin D moderation.