Microbiota Datasets Research Articles

Association of gut microbiota with critical pneumonia: A two-sample Mendelian randomization study.

This study investigated the causal effect of gut microbiota on critical pneumonia. Data came from a large-scale gut microbiota data set (n = 18,340) and the critical pneumonia genome-wide genotyping array (cases n = 2758 and controls n = 42,8607). Inverse variance weighting was used as the primary Mendelian randomization (MR) analysis method. Weighted median, MR-Egger, simple model, weighted model, and MR-Egger, were used to evaluate robustness. Sensitivity analysis used Cochran Q test, MR-Egger intercept test, and MR-PRESSO. For critical pneumonia, inverse variance weighting estimates suggested that Class Verrucomicrobiae (OR = 0.415; 95% CI: 0.207, 0.833; P = .013), Family Verrucomicrobiaceae (OR = 0.415; 95% CI: 0.207, 0.833; P = .013), Genus Akkermansia (OR = 0.415; 95% CI: 0.207, 0.833; P = .013), Genus LachnospiraceaeFCS020group (OR = 0.449; 95% CI: 0.230, 0.890; P = .021), Genus Parasutterella (OR = 0.466; 95% CI: 0.233, 0.929; P = .030), Genus Prevotella7 (OR = 0.645; 95% CI: 0.432, 0.960; P = .031), Order Verrucomicrobiales (OR = 0.415; 95% CI: 0.207, 0.833; P = .013), and Phylum Cyanobacteria (OR = 0.510; 95% CI: 0.272, 0.956; P = .036) had a reduced risk, while Family Enterobacteriaceae (OR = 2.746; 95% CI: 1.008, 7.474; P = .048), Genus RuminococcaceaeUCG003 (OR = 2.811; 95% CI: 1.349, 5.851; P = .006) and Order Enterobacteriales (OR = 2.746; 95% CI: 1.008, 7.474; P = .048) were associated with an increased risk. Sensitivity analyses confirmed that the aforementioned correlations were robust.

A two-sample bidirectional Mendelian randomization analysis investigates associations between gut microbiota and type 2 diabetes mellitus.

This study sought to elucidate the causal association between gut microbiota (GM) composition and type 2 diabetes mellitus (T2DM) through a comprehensive two-sample bidirectional Mendelian randomization analysis. T2DM data were sourced from the IEU OpenGWAS Project database, complemented by 211 gut microbiota (GM) datasets from the MiBioGen Federation. The primary analytical approach employed was inverse variance weighted (IVW), supplemented by MR-Egger regression and weighted median (WME) methods to investigate their potential interplay. Results were assessed using odds ratios (OR) and 95% confidence intervals (CI). The robustness and reliability of the findings were confirmed through leave-one-out analysis, heterogeneity testing, and assessment of horizontal pleiotropy. Furthermore, we explored the potential mediating role of metabolites in the pathway linking GM to T2DM. A set of 11 Single Nucleotide Polymorphisms (SNPs) linked to GM were identified as instrumental variables (IVs). The IVW analysis revealed that increased abundance of the genus Actinomyces, genus Bilophila, genus Lachnoclostridium, genus Ruminococcus gnavus group, and genus Streptococcus corresponded to a heightened risk of T2DM. Conversely, higher levels of genus Eubacterium oxidoreducens group, genus Oscillospira, genus Ruminococcaceae UCG003, genus Ruminococcaceae UCG010, and genus Sellimonas were associated with a reduced risk of T2DM. However, following false discovery rate (FDR) correction, only the abundance of genus Lachnoclostridium retained a significant positive correlation with T2DM risk (OR = 1.22, q value = 0.09), while the other ten GM showed suggestive associations with T2DM. Reverse MR analysis did not reveal any causal relationship between T2DM and the increased risk associated with the identified GM. Additionally, metabolites did not exhibit mediating effects in this context. This study effectively pinpointed specific GM associated with T2DM, potentially paving the way for novel biomarkers in the prevention and treatment of this condition. The findings suggested that probiotics could emerge as a promising avenue for managing T2DM in the future. Furthermore, the analysis indicated that metabolites do not appear to act as mediators in the pathway from GM to T2DM.

Causal relationships of gut microbiota, plasma metabolites, and metabolite ratios with diffuse large B-cell lymphoma: a Mendelian randomization study.

Recent studies have revealed changes in microbiota constitution and metabolites associated with tumor progression, however, no causal relation between microbiota or metabolites and diffuse large B-cell lymphoma (DLBCL) has yet been reported. We download a microbiota dataset from the MiBioGen study, a metabolites dataset from the Canadian Longitudinal Study on Aging (CLSA) study, and a DLBCL dataset from Integrative Epidemiology Unit Open genome-wide association study (GWAS) project. Mendelian randomization (MR) analysis was conducted using the R packages, TwoSampleMR and MR-PRESSO. Five MR methods were used: MR-Egger, inverse variance weighting (IVW), weighted median, simple mode, and weighted mode. Reverse MR analyses were also conducted to explore the causal effects of DLBCL on the microbiome, metabolites, and metabolite ratios. Pleiotropy was evaluated by MR Egger regression and MR-PRESSO global analyses, heterogeneity was assessed by Cochran's Q-test, and stability analyzed using the leave-one-out method. 119 microorganisms, 1,091 plasma metabolite, and 309 metabolite ratios were analyzed. According to IVW analysis, five microorganisms were associated with risk of DLBCL. The genera Terrisporobacter (OR: 3.431, p = 0.049) andgenera Oscillibacter (OR: 2.406, p = 0.029) were associated with higher risk of DLBCL. Further, 27 plasma metabolites were identified as having a significant causal relationships with DLBCL, among which citrate levels had the most significant protective causal effect against DLBCL (p = 0.006), while glycosyl-N-tricosanoyl-sphingadienine levels was related to higher risk of DLBCL (p = 0.003). In addition, we identified 19 metabolite ratios with significant causal relationships to DLBCL, of which taurine/glutamate ratio had the most significant protective causal effect (p = 0.005), while the phosphoethanolamine/choline ratio was related to higher risk of DLBCL (p = 0.009). Reverse MR analysis did not reveal any significant causal influence of DLBCL on the above microbiota, metabolites, and metabolite ratios (p > 0.05). Sensitivity analyses revealed no significant heterogeneity or pleiotropy (p > 0.05). We present the first elucidation of the causal influence of microbiota and metabolites on DLBCL using MR methods, providing novel insights for potential targeting of specific microbiota or metabolites to prevent, assist in diagnosis, and treat DLBCL.

The regulation of intestinal flora on host's genes may play an essential role in the development of endometrial hyperplastic processes in yang deficiency individuals.

We recruited 202 EHPs patients including 147 with YADC (YEH group) and 55 with non-YADC (NYEH group). Fecal samples were collected from 8 YEH patients and 3 NYEH patients and analyzed using 16S rRNA V3-V4 sequencing for gut microbiota analysis. We obtained constitution survey data and a differential gut microbiota dataset from the literature for further analysis. Bioinformatics analysis was conducted using gut microbiota-related genes from public databases. YADC was significantly more prevalent in EHPs than non-YADC (P < 0.001), suggesting it as a potential risk factor for EHPs occurrence (ORpopulation survey = 13.471; ORhealthy women = 5.173). The YEH group had higher levels of inflammation, estrogen, and tamoxifen-related flora compared to NYEH and healthy YADC groups. There was an interaction between inflammation, estrogen, differential flora, and EHPs-related genes, particularly the TNF gene (related to inflammation) and the EGFR gene (related to estrogen), which may play a crucial role in EHPs development. YEH individuals exhibit significant changes in their gut microbiota compared to NYEH and healthy YADC. The interaction between specific microbiota and host genes is believed to play a critical role in the progression of EHPs.

Microbes as triggers and boosters of Type 1 Diabetes – Mediation by molecular mimicry

AimsType 1 diabetes is characterized by steadily increasing incidence and largely obscured pathogenesis. Molecular mimicry is well-established as trigger for different autoimmune pathologies, but obscurely explored in the context of T1D. The presented study explores the underestimated role of molecular mimicry in T1D-etiology/progression in search for etiologic factors among human pathogens and commensals. MethodsA comprehensive immunoinformatics analysis of T1D-specific experimental T-cell epitopes across bacterial, fungal, and viral proteomes was performed, coupled with MHC-restricted mimotope validation and docking of most potent epitopes/mimotopes to T1D-high-risk MHCII molecules. In addition, re-analysis of the publicly available T1D-microbiota dataset was performed, including samples at the pre-T1D disease stage. ResultsA number of bacterial pathogens/commensals were tagged as putative T1D triggers/boosters, including ubiquitous gut residents. The prediction of most likely mimicked epitopes revealed heat-shock proteins as most potent autoantigens for autoreactive T-cell priming via molecular mimicry. Docking revealed analogous interactions for predicted bacterial mimotopes and corresponding experimental epitopes. Finally, re-analysis of T1D gut microbiota datasets prompted pre-T1D as most significantly different/dysbiotic, compared to other explored categories (T1D stage/controls). ConclusionsObtained results support the unrecognized role of molecular mimicry in T1D, suggesting that autoreactive T-cell priming might be the triggering factor of disease development.

Association of gut microbiota with COVID-19 susceptibility and severity: A two-sample Mendelian randomization study.

Evidence supports the observational associations of gut microbiota with the risk of COVID-19; however, it is unclear whether these associations reflect a causal relationship. This study investigated the association of gut microbiota with COVID-19 susceptibility and severity. Data were obtained from a large-scale gut microbiota data set (n = 18 340) and the COVID-19 Host Genetics Initiative (n = 2 942 817). Causal effects were estimated with inverse variance weighted (IVW), MR-Egger, and weighted median, and sensitivity analyses were implemented with Cochran's Q test, MR-Egger intercept test, MR-PRESSO, leave-one-out analysis, and funnel plots. For COVID-19 susceptibility, IVW estimates suggested that Gammaproteobacteria (odds ratio [OR] = 0.94, 95% confidence interval [CI], 0.89-0.99, p = 0.0295] and Streptococcaceae (OR = 0.95, 95% CI, 0.92-1.00, p = 0.0287) had a reduced risk, while Negativicutes (OR = 1.05, 95% CI, 1.01-1.10, p = 0.0302), Selenomonadales (OR = 1.05, 95% CI, 1.01-1.10, p = 0.0302), Bacteroides (OR = 1.06, 95% CI, 1.01-1.12, p = 0.0283), and Bacteroidaceae (OR = 1.06, 95% CI, 1.01-1.12, p = 0.0283) were associated with an increased risk (all p < 0.05, nominally significant). For COVID-19 severity, Subdoligranulum (OR = 0.80, 95% CI, 0.69-0.92, p = 0.0018), Cyanobacteria (OR = 0.85, 95% CI, 0.76-0.96, p = 0.0062), Lactobacillales (OR = 0.87, 95% CI, 0.76-0.98, p = 0.0260), Christensenellaceae (OR = 0.87, 95% CI, 0.77-0.99, p = 0.0384), Tyzzerella3 (OR = 0.89, 95% CI, 0.81-0.97, p = 0.0070), and RuminococcaceaeUCG011 (OR = 0.91, 95% CI, 0.83-0.99, p = 0.0247) exhibited negative correlations, while RikenellaceaeRC9 (OR = 1.09, 95% CI, 1.01-1.17, p = 0.0277), LachnospiraceaeUCG008 (OR = 1.12, 95% CI, 1.00-1.26, p = 0.0432), and MollicutesRF9 (OR = 1.14, 95% CI, 1.01-1.29, p = 0.0354) exhibited positive correlations (all p < 0.05, nominally significant). Sensitivity analyses validated the robustness of the above associations. These findings suggest that gut microbiota might influence the susceptibility and severity of COVID-19 in a causal way, thus providing novel insights into the gut microbiota-mediated development mechanism of COVID-19.

Is there a placental microbiota? A critical review and re-analysis of published placental microbiota datasets

The existence of a placental microbiota is debated. The human placenta has historically been considered sterile and microbial colonization was associated with adverse pregnancy outcomes. Yet, recent DNA sequencing investigations reported a microbiota in typical human term placentas. However, this detected microbiota could represent background DNA or delivery-associated contamination. Using fifteen publicly available 16S rRNA gene datasets, existing data were uniformly re-analyzed with DADA2 to maximize comparability. While Amplicon Sequence Variants (ASVs) identified as Lactobacillus, a typical vaginal bacterium, were highly abundant and prevalent across studies, this prevalence disappeared after applying likely DNA contaminant removal to placentas from term cesarean deliveries. A six-study sub-analysis targeting the 16S rRNA gene V4 hypervariable region demonstrated that bacterial profiles of placental samples and technical controls share principal bacterial ASVs and that placental samples clustered primarily by study origin and mode of delivery. Contemporary DNA-based evidence does not support the existence of a placental microbiota.ImportanceEarly-gestational microbial influences on human development are unclear. By applying DNA sequencing technologies to placental tissue, bacterial DNA signals were observed, leading some to conclude that a live bacterial placental microbiome exists in typical term pregnancy. However, the low-biomass nature of the proposed microbiome and high sensitivity of current DNA sequencing technologies indicate that the signal may alternatively derive from environmental or delivery-associated bacterial DNA contamination. Here we address these alternatives with a re-analysis of 16S rRNA gene sequencing data from 15 publicly available placental datasets. After identical DADA2 pipeline processing of the raw data, subanalyses were performed to control for mode of delivery and environmental DNA contamination. Both environment and mode of delivery profoundly influenced the bacterial DNA signal from term-delivered placentas. Aside from these contamination-associated signals, consistency was lacking across studies. Thus, placentas delivered at term are unlikely to be the original source of observed bacterial DNA signals.

Transmission mode and dispersal traits correlate with host specificity in mammalian gut microbes.

Different host species associate with distinct gut microbes in mammals, a pattern sometimes referred to as phylosymbiosis. However, the processes shaping this host specificity are not well understood. One model proposes that barriers to microbial transmission promote specificity by limiting microbial dispersal between hosts. This model predicts that specificity levels measured across microbes is correlated to transmission mode (vertical vs. horizontal) and individual dispersal traits. Here, we leverage two large publicly available gut microbiota data sets (1490 samples from 195 host species) to test this prediction. We found that host specificity varies widely across bacteria (i.e., there are generalist and specialist bacteria) and depends on transmission mode and dispersal ability. Horizontally-like transmitted bacteria equipped with traits that facilitate switches between host (e.g., tolerance to oxygen) were found to be less specific (more generalist) than microbes without those traits, for example, vertically-like inherited bacteria that are intolerant to oxygen. Altogether, our findings are compatible with a model in which limited microbial dispersal abilities foster host specificity.

Diversity and Prevalence of Clostridium innocuum in the Human Gut Microbiota.

Clostridia are a polyphyletic group of Gram-positive, spore-forming anaerobes in the Firmicutes phylum that significantly impact metabolism and functioning of the human gastrointestinal tract. Recently, Clostridia were divided into two separate classes, Clostridia and Erysipelotrichia, based on phenotypic and 16S rRNA gene-based differences. While Clostridia include many well-known pathogenic bacteria, Erysipelotrichia remain relatively uncharacterized, particularly regarding their role as a pathogen versus commensal. Despite wide recognition as a commensal, the erysipelotrichial species Clostridium innocuum has recently been associated with various disease states. To further understand the ecological and potential virulent role of C. innocuum, we conducted a genomic comparison across 38 C. innocuum isolates and 194 publicly available genomes. Based on colony morphology, we isolated multiple C. innocuum cultivars from the feces of healthy human volunteers (n = 5). Comparison of the 16S rRNA gene of our isolates against publicly available microbiota data sets in healthy individuals suggests a high prevalence of C. innocuum across the human population (>80%). Analysis of single nucleotide polymorphisms (SNPs) across core genes and average nucleotide identify (ANI) revealed the presence of four clades among all available genomes (n = 232 total). Investigation of carbohydrate and protein utilization pathways, including comparison against the carbohydrate-activating enzyme (CAZyme) database, demonstrated inter- and intraclade differences that were further substantiated in vitro. Collectively, these data indicate genetic variance within the C. innocuum species that may help clarify its role in human disease and health. IMPORTANCE Clostridia are a group of medically important anaerobes as both commensals and pathogens. Recently, a new class of Erysipelotrichia containing a number of reassigned clostridial species has emerged, including Clostridium innocuum. Recent studies have implicated C. innocuum as a potential causative agent of diarrhea in patients from whom Clostridioides difficile could not be isolated. Using genomic and in vitro comparison, this study sought to characterize C. innocuum in the healthy human gut. Our analyses suggest that C. innocuum is a highly prevalent and diverse species, demonstrating clade-specific differences in metabolism and potential virulence. Collectively, this study is the first investigation into a broader description of C. innocuum as a human gut inhabitant.

MetaLab-MAG: A Metaproteomic Data Analysis Platform for Genome-Level Characterization of Microbiomes from the Metagenome-Assembled Genomes Database.

The studies of microbial communities have drawn increased attention in various research fields such as agriculture, environment, and human health. Recently, metaproteomics has become a powerful tool to interpret the roles of the community members by investigating the expressed proteins of the microbes. However, analyzing the metaproteomic data sets at genome resolution is still challenging because of the lack of efficient bioinformatics tools. Here we develop MetaLab-MAG, a specially designed tool for the characterization of microbiomes from metagenome-assembled genomes databases. MetaLab-MAG was evaluated by analyzing various human gut microbiota data sets and performed comparably or better than searching the gene catalog protein database directly. MetaLab-MAG can quantify the genome-level microbiota compositions and supports both label-free and isobaric labeling-based quantification strategies. MetaLab-MAG removes the obstacles of metaproteomic data analysis and provides the researchers with in-depth and comprehensive information from the microbiomes.

Compare the performance of multiple binary classification models in microbial high-throughput sequencing datasets.

The development of machine learning and deep learning provided solutions for predicting microbiota response on environmental change based on microbial high-throughput sequencing. However, there were few studies specifically clarifying the performance and practical of two types of binary classification models to find a better algorithm for the microbiota data analysis. Here, for the first time, we evaluated the performance, accuracy and running time of the binary classification models built by three machine learning methods - random forest (RF), support vector machine (SVM), logistic regression (LR), and one deep learning method - back propagation neural network (BPNN). The built models were based on the microbiota datasets that removed low-quality variables and solved the class imbalance problem. Additionally, we optimized the models by tuning. Our study demonstrated that dataset pre-processing was a necessary process for model construction. Among these 4 binary classification models, BPNN and RF were the most suitable methods for constructing microbiota binary classification models. Using these 4 models to predict multiple microbial datasets, BPNN showed the highest accuracy and the most robust performance, while the RF method was ranked second. We also constructed the optimal models by adjusting the epochs of BPNN and the n_estimators of RF for six times. The evaluation related to performances of models provided a road map for the application of artificial intelligence to assess microbial ecology.

The interplay of gut microbiota between donors and recipients determines the efficacy of fecal microbiota transplantation

ABSTRACT Fecal microbiota transplantation (FMT) is a promising treatment for microbiota dysbiosis associated diseases, such as Clostridioides difficile infection (CDI) and inflammatory bowel disease (IBD). The engraftment of donor bacteria is essential for the effectiveness of FMT, which to some extent depends on the matching of donors and recipients. However, how different types of donor-derived bacteria affect FMT efficacy has not been fully dissected. We recruited two longitudinal IBD cohorts of 103 FMT recipients and further analyzed 1,280 microbiota datasets from 14 public CDI and IBD studies to uncover the effect of donor-derived microbiota in recipients. We found that two enterotypes, RCPT/E and RCPT/B (dominated by Enterobacteriaceae and Bacteroides, respectively), consistently exist in both CDI and IBD patients. Based on a time-course-based multi-cohort analysis of FMT fecal samples, we observed the interplay between recipient and donor-derived microbiota during FMT, in which the FMT outcome was significantly associated with the enterotype and microbiota distance between donor and recipient after FMT. We proposed a new measurement, the ratio of colonizers to residents after FMT (C2R), to quantify the engraftment of donor-derived bacteria in the recipients, and then constructed an enterotype-based statistical model for donor-recipient matching, which was validated by both cross-validation and an additional IBD FMT cohort (n = 42). We believe that with the accumulation of FMT multi-omics datasets, machine learning-based methods will be helpful for rational donor selection for improving efficacy and precision FMT practices.

The Power of Microbiome Studies: Some Considerations on Which Alpha and Beta Metrics to Use and How to Report Results.

BackgroundSince sequencing techniques have become less expensive, larger sample sizes are applicable for microbiota studies. The aim of this study is to show how, and to what extent, different diversity metrics and different compositions of the microbiota influence the needed sample size to observe dissimilar groups. Empirical 16S rRNA amplicon sequence data obtained from animal experiments, observational human data, and simulated data were used to perform retrospective power calculations. A wide variation of alpha diversity and beta diversity metrics were used to compare the different microbiota datasets and the effect on the sample size.ResultsOur data showed that beta diversity metrics are the most sensitive to observe differences as compared with alpha diversity metrics. The structure of the data influenced which alpha metrics are the most sensitive. Regarding beta diversity, the Bray–Curtis metric is in general the most sensitive to observe differences between groups, resulting in lower sample size and potential publication bias.ConclusionWe recommend performing power calculations and to use multiple diversity metrics as an outcome measure. To improve microbiota studies, awareness needs to be raised on the sensitivity and bias for microbiota research outcomes created by the used metrics rather than biological differences. We have seen that different alpha and beta diversity metrics lead to different study power: because of this, one could be naturally tempted to try all possible metrics until one or more are found that give a statistically significant test result, i.e., p-value < α. This way of proceeding is one of the many forms of the so-called p-value hacking. To this end, in our opinion, the only way to protect ourselves from (the temptation of) p-hacking would be to publish a statistical plan before experiments are initiated, describing the outcomes of interest and the corresponding statistical analyses to be performed.

Kernel principal components based cascade forest towards disease identification with human microbiota

BackgroundNumerous pieces of clinical evidence have shown that many phenotypic traits of human disease are related to their gut microbiome, i.e., inflammation, obesity, HIV, and diabetes. Through supervised classification, it is feasible to determine the human disease states by revealing the intestinal microbiota compositional information. However, the abundance matrix of microbiome data is so sparse, an interpretable deep model is crucial to further represent and mine the data for expansion, such as the deep forest model. What’s more, overfitting can still exist in the original deep forest model when dealing with such “large p, small n” biology data. Feature reduction is considered to improve the ensemble forest model especially towards the disease identification in the human microbiota.MethodsIn this work, we propose the kernel principal components based cascade forest method, so-called KPCCF, to classify the disease states of patients by using taxonomic profiles of the microbiome at the family level. In detail, the kernel principal components analysis method is first used to reduce the original dimension of human microbiota datasets. Besides, the processed data is fed into the cascade forest to preliminarily discriminate against the disease state of the samples.ResultsThe proposed KPCCF algorithm can represent the small-scale and high-dimension human microbiota datasets with the sparse feature matrix. Systematic comparison experiments demonstrate that our method consistently outperforms the state-of-the-art methods with the comparative study on 4 datasets.ConclusionDespite sharing some common characteristics, a one-size-fits-all solution does not exist in any space. The traditional depth model has limitations in the biological application of the unbalanced scale between small samples and high dimensions. KPCCF distinguishes from the standard deep forest model for its excellent performance in the microbiota field. Additionally, compared to other dimensionality reduction methods, the kernel principal components analysis method is more suitable for microbiota datasets.

Understanding host-microbiota interactions in the commercial piglet around weaning

Weaning is a critical period in the life of pigs with repercussions on their health and welfare and on the economy of the swine industry. This study aimed to assess the effect of the commercial early weaning on gut microbiota, intestinal gene expression and serum metabolomic response via an integrated-omic approach combining 16S rRNA gene sequencing, the OpenArray gene expression technology and 1H-NMR spectroscopy. Fourteen piglets from different litters were sampled for blood, jejunum tissue and caecal content two days before (− 2d), and three days after (+ 3d) weaning. A clearly differential ordination of caecal microbiota was observed. Higher abundances of Roseburia, Ruminococcus, Coprococcus, Dorea and Lachnospira genera in weaned piglets compared to prior to weaning showed the quick microbial changes of the piglets’ gut microbiota. Downregulation of OCLN, CLDN4, MUC2, MUC13, SLC15A1 and SLC13A1 genes, also evidenced the negative impact of weaning on gut barrier and digestive functions. Metabolomic approach pinpointed significant decreases in choline, LDL, triglycerides, fatty acids, alanine and isoleucine and increases in 3-hydroxybutyrate after weaning. Moreover, the correlation between microbiota and metabolome datasets revealed the existence of metabolic clusters interrelated to different bacterial clusters. Our results demonstrate the impact of weaning stress on the piglet and give insights regarding the associations between gut microbiota and the animal gene activity and metabolic response.

Cross-feeding between intestinal pathobionts promotes their overgrowth during undernutrition

Child undernutrition is a global health issue associated with a high burden of infectious disease. Undernourished children display an overabundance of intestinal pathogens and pathobionts, and these bacteria induce enteric dysfunction in undernourished mice; however, the cause of their overgrowth remains poorly defined. Here, we show that disease-inducing human isolates of Enterobacteriaceae and Bacteroidales spp. are capable of multi-species symbiotic cross-feeding, resulting in synergistic growth of a mixed community in vitro. Growth synergy occurs uniquely under malnourished conditions limited in protein and iron: in this context, Bacteroidales spp. liberate diet- and mucin-derived sugars and Enterobacteriaceae spp. enhance the bioavailability of iron. Analysis of human microbiota datasets reveals that Bacteroidaceae and Enterobacteriaceae are strongly correlated in undernourished children, but not in adequately nourished children, consistent with a diet-dependent growth synergy in the human gut. Together these data suggest that dietary cross-feeding fuels the overgrowth of pathobionts in undernutrition.

Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model.

MotivationAccumulating evidence has highlighted the importance of microbial interaction networks. Methods have been developed for estimating microbial interaction networks, of which the generalized Lotka–Volterra equation (gLVE)-based method can estimate a directed interaction network. The previous gLVE-based method for estimating microbial interaction networks did not consider time-varying interactions.ResultsIn this study, we developed unsupervised learning-based microbial interaction inference method using Bayesian estimation (Umibato), a method for estimating time-varying microbial interactions. The Umibato algorithm comprises Gaussian process regression (GPR) and a new Bayesian probabilistic model, the continuous-time regression hidden Markov model (CTRHMM). Growth rates are estimated by GPR, and interaction networks are estimated by CTRHMM. CTRHMM can estimate time-varying interaction networks using interaction states, which are defined as hidden variables. Umibato outperformed the existing methods on synthetic datasets. In addition, it yielded reasonable estimations in experiments on a mouse gut microbiota dataset, thus providing novel insights into the relationship between consumed diets and the gut microbiota.Availability and implementationThe C++ and python source codes of the Umibato software are available at https://github.com/shion-h/Umibato.Supplementary information Supplementary data are available at Bioinformatics online.

Measuring and mitigating PCR bias in microbiota datasets.

PCR amplification plays an integral role in the measurement of mixed microbial communities via high-throughput DNA sequencing of the 16S ribosomal RNA (rRNA) gene. Yet PCR is also known to introduce multiple forms of bias in 16S rRNA studies. Here we present a paired modeling and experimental approach to characterize and mitigate PCR NPM-bias (PCR bias from non-primer-mismatch sources) in microbiota surveys. We use experimental data from mock bacterial communities to validate our approach and human gut microbiota samples to characterize PCR NPM-bias under real-world conditions. Our results suggest that PCR NPM-bias can skew estimates of microbial relative abundances by a factor of 4 or more, but that this bias can be mitigated using log-ratio linear models.

Early socialization and environmental enrichment of lactating piglets affects the caecal microbiota and metabolomic response after weaning

The aim of this study was to determine the possible impact of early socialization and an enriched neonatal environment to improve adaptation of piglets to weaning. We hypothesized that changes in the microbiota colonization process and in their metabolic response and intestinal functionality could help the animals face weaning stress. A total of 48 sows and their litters were allotted into a control (CTR) or an enriched treatment (ENR), in which piglets from two adjacent pens were combined and enriched with toys. The pattern of caecal microbial colonization, the jejunal gene expression, the serum metabolome and the intestinal physiology of the piglets were assessed before (-2 d) and after weaning (+ 3d). A differential ordination of caecal microbiota was observed after weaning. Serum metabolome suggested a reduced energetic metabolism in ENR animals, as evidenced by shifts in triglycerides and fatty acids, VLDL/LDL and creatine regions. The TLR2 gene showed to be downregulated in the jejunum of ENR pigs after weaning. The integration of gene expression, metabolome and microbiota datasets confirmed that differences between barren and enriched neonatal environments were evident only after weaning. Our results suggest that improvements in adaptation to weaning could be mediated by a better response to the post-weaning stress.

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

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