Meta-analysis of gut microbiome studies identifies disease-specific and shared responses

Mouse adaptation of human inflammatory bowel diseases microbiota enhances colonization efficiency and alters microbiome aggressiveness depending on the recipient colonic inflammatory environment

BACKGROUND

Understanding the cause vs consequence relationship of gut inflammation and microbial dysbiosis in inflammatory bowel diseases (IBD) requires a reproducible mouse model of human-microbiota-driven experimental colitis.

RESULTS

Our study demonstrated that human fecal microbiota transplant (FMT) transfer efficiency is an underappreciated source of experimental variability in human microbiota-associated (HMA) mice. Pooled human IBD patient fecal microbiota engrafted germ-free (GF) mice with low amplicon sequence variant (ASV)-level transfer efficiency, resulting in high recipient-to-recipient variation of microbiota composition and colitis severity in HMA Il-10-/- mice. In contrast, mouse-to-mouse transfer of mouse-adapted human IBD patient microbiota transferred with high efficiency and low compositional variability resulting in highly consistent and reproducible colitis phenotypes in recipient Il-10-/- mice. Engraftment of human-to-mouse FMT stochastically varied with individual transplantation events more than mouse-adapted FMT. Human-to-mouse FMT caused a population bottleneck with reassembly of microbiota composition that was host inflammatory environment specific. Mouse-adaptation in the inflamed Il-10-/- host reassembled a more aggressive microbiota that induced more severe colitis in serial transplant to Il-10-/- mice than the distinct microbiota reassembled in non-inflamed WT hosts.

CONCLUSIONS

Our findings support a model of IBD pathogenesis in which host inflammation promotes aggressive resident bacteria, which further drives a feed-forward process of dysbiosis exacerbated by gut inflammation. This model implies that effective management of IBD requires treating both the dysregulated host immune response and aggressive inflammation-driven microbiota. We propose that our mouse-adapted human microbiota model is an optimized, reproducible, and rigorous system to study human microbiome-driven disease phenotypes, which may be generalized to mouse models of other human microbiota-modulated diseases, including metabolic syndrome/obesity, diabetes, autoimmune diseases, and cancer. Video Abstract.

Meta-analysis identifies common gut microbiota associated with multiple sclerosis

BACKGROUND

Previous studies have identified a diverse group of microbial taxa that differ between patients with multiple sclerosis (MS) and the healthy population. However, interpreting findings on MS-associated microbiota is challenging, as there is no true consensus. It is unclear whether there is gut microbiota commonly altered in MS across studies.

METHODS

To answer this, we performed a meta-analysis based on the 16S rRNA gene sequencing data from seven geographically and technically diverse studies comprising a total of 524 adult subjects (257 MS and 267 healthy controls). Analysis was conducted for each individual study after reprocessing the data and also by combining all data together. The blocked Wilcoxon rank-sum test and linear mixed-effects regression were used to identify differences in microbial composition and diversity between MS and healthy controls. Network analysis was conducted to identify bacterial correlations. A leave-one-out sensitivity analysis was performed to ensure the robustness of the findings.

RESULTS

The microbiome community structure was significantly different between studies. Re-analysis of data from individual studies revealed a lower relative abundance of Prevotella in MS across studies, compared to controls. Meta-analysis found that although alpha and beta diversity did not differ between MS and controls, a higher abundance of Actinomyces and a lower abundance of Faecalibacterium were reproducibly associated with MS. Additionally, network analysis revealed that the recognized negative Bacteroides-Prevotella correlation in controls was disrupted in patients with MS.

CONCLUSIONS

Our meta-analysis identified common gut microbiota associated with MS across geographically and technically diverse studies.

Meta-analysis of the human gut microbiome uncovers shared and distinct microbial signatures between diseases

Microbiome studies have revealed gut microbiota's potential impact on complex diseases. However, many studies often focus on one disease per cohort. We developed a meta-analysis workflow for gut microbiome profiles and analyzed shotgun metagenomic data covering 11 diseases. Using interpretable machine learning and differential abundance analysis, our findings reinforce the generalization of binary classifiers for Crohn's disease (CD) and colorectal cancer (CRC) to hold-out cohorts and highlight the key microbes driving these classifications. We identified high microbial similarity in disease pairs like CD vs ulcerative colitis (UC), CD vs CRC, Parkinson's disease vs type 2 diabetes (T2D), and schizophrenia vs T2D. We also found strong inverse correlations in Alzheimer's disease vs CD and UC. These findings, detected by our pipeline, provide valuable insights into these diseases.

IMPORTANCE

Assessing disease similarity is an essential initial step preceding a disease-based approach for drug repositioning. Our study provides a modest first step in underscoring the potential of integrating microbiome insights into the disease similarity assessment. Recent microbiome research has predominantly focused on analyzing individual diseases to understand their unique characteristics, which by design excludes comorbidities in individuals. We analyzed shotgun metagenomic data from existing studies and identified previously unknown similarities between diseases. Our research represents a pioneering effort that utilizes both interpretable machine learning and differential abundance analysis to assess microbial similarity between diseases.

The oral-gut microbiome axis in health and disease

The human body hosts trillions of microorganisms throughout many diverse habitats with different physico-chemical characteristics. Among them, the oral cavity and the gut harbour some of the most dense and diverse microbial communities. Although these two sites are physiologically distinct, they are directly connected and can influence each other in several ways. For example, oral microorganisms can reach and colonize the gastrointestinal tract, particularly in the context of gut dysbiosis. However, the mechanisms of colonization and the role that the oral microbiome plays in causing or exacerbating diseases in other organs have not yet been fully elucidated. Here, we describe recent advances in our understanding of how the oral and intestinal microbiota interplay in relation to their impact on human health and disease.

All (remains) in the family? Using healthy relatives to define Crohn's gut microbiome alterations

Gut microbial imbalance is noted in Crohn's disease (CD), but the specific bacteria associated with CD vary between studies. Chen et al.1 pair CD patients with their healthy first-degree relatives to mitigate some of the environmental and genetic effects.

Advances and prospects of biomarkers for immune checkpoint inhibitors

Immune checkpoint inhibitors (ICIs) activate anti-cancer immunity by blocking T cell checkpoint molecules such as programmed death 1 (PD-1) and cytotoxic T lymphocyte-associated protein 4 (CTLA-4). Although ICIs induce some durable responses in various cancer patients, they also have disadvantages, including low response rates, the potential for severe side effects, and high treatment costs. Therefore, selection of patients who can benefit from ICI treatment is critical, and identification of biomarkers is essential to improve the efficiency of ICIs. In this review, we provide updated information on established predictive biomarkers (tumor programmed death-ligand 1 [PD-L1] expression, DNA mismatch repair deficiency, microsatellite instability high, and tumor mutational burden) and potential biomarkers currently under investigation such as tumor-infiltrated and peripheral lymphocytes, gut microbiome, and signaling pathways related to DNA damage and antigen presentation. In particular, this review aims to summarize the current knowledge of biomarkers, discuss issues, and further explore future biomarkers.

Aberrant bowel movement frequencies coincide with increased microbe-derived blood metabolites associated with reduced organ function

Bowel movement frequency (BMF) directly impacts the gut microbiota and is linked to diseases like chronic kidney disease or dementia. In particular, prior work has shown that constipation is associated with an ecosystem-wide switch from fiber fermentation and short-chain fatty acid production to more detrimental protein fermentation and toxin production. Here, we analyze multi-omic data from generally healthy adults to see how BMF affects their molecular phenotypes, in a pre-disease context. Results show differential abundances of gut microbial genera, blood metabolites, and variation in lifestyle factors across BMF categories. These differences relate to inflammation, heart health, liver function, and kidney function. Causal mediation analysis indicates that the association between lower BMF and reduced kidney function is partially mediated by the microbially derived toxin 3-indoxyl sulfate (3-IS). This result, in a generally healthy context, suggests that the accumulation of microbiota-derived toxins associated with abnormal BMF precede organ damage and may be drivers of chronic, aging-related diseases.

Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare

The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022-2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine.

Integrating functional metagenomics to decipher microbiome-immune interactions

Microbial metabolites can be viewed as the cytokines of the microbiome, transmitting information about the microbial and metabolic environment of the gut to orchestrate and modulate local and systemic immune responses. Still, many immunology studies focus solely on the taxonomy and community structure of the gut microbiota rather than its functions. Early sequencing-based microbiota profiling approaches relied on PCR amplification of small regions of bacterial and fungal genomes to facilitate identification of the microbes present. However, recent microbiome analysis methods, particularly shotgun metagenomic sequencing, now enable culture-independent profiling of microbiome functions and metabolites in addition to taxonomic characterization. In this review, we showcase recent advances in functional metagenomics methods and applications and discuss the current limitations and potential avenues for future development. Importantly, we highlight a few examples of key areas of opportunity in immunology research where integrating functional metagenomic analyses of the microbiome can substantially enhance a mechanistic understanding of microbiome-immune interactions and their contributions to health and disease states.

Unlocking the therapeutic potential of Huoxiang Zhengqi San in cold and high humidity-induced diarrhea: Insights into intestinal microbiota modulation and digestive enzyme activity

Huoxiang Zhengqi San (HXZQS), a traditional Chinese herbal formula, enjoys widespread use in Chinese medicine to treat diarrhea with cold-dampness trapped spleen syndrome (CDSS), which is induced by exposure to cold and high humidity stress. This study aimed to explore its therapeutic mechanisms in mice, particularly focusing on the intestinal microbiota. Forty male SPF-grade KM mice were allocated into two groups: the normal control group (H-Cc, n = 10) and the CDSS group (H-Mc, n = 30). After modeling, H-Mc was subdivided into H-Mc (n = 15) and HXZQS treatment (H-Tc, n = 15) groups. Intestinal samples were analyzed for enzyme activity and microbiota composition. Our findings demonstrated a notable reduction in intestinal lactase activity post-HXZQS treatment (P < 0.05). Lactobacillus johnsonii, Lactobacillus reuteri, and Lactobacillus murinus emerged as the main dominant species across most groups. However, in the H-Mc group, Clostridium sensu stricto 1 was identified as the exclusive dominant bacteria. LEfSe analysis highlighted Clostridiales vadinBB60 group and Corynebacterium as differential bacteria in the H-Tc group, and Cyanobacteria unidentified specie in the H-Mc group. Predicted microbiota functions aligned with changes in abundance, notably in cofactors and vitamins metabolism. The collinear results of the intestinal microbiota interaction network showed that HXZQS restored cooperative interactions among rare bacteria by mitigating their mutual promotion. The HXZQS decoction effectively alleviates diarrhea with CDSS by regulating intestinal microbiota, digestive enzyme activity, and microbiota interaction. Notably, it enhances Clostridium vadinBB60 and suppresses Cyanobacteria unidentified specie, warranting further study.

Sequential Sampling of the Gastrointestinal Tract to Characterize the Entire Digestive Microbiome in Japanese Subjects

The gastrointestinal (GI) tract harbors trillions of microorganisms known to influence human health and disease, and next-generation sequencing (NGS) now enables the in-depth analysis of their diversity and functions. Although a significant amount of research has been conducted on the GI microbiome, comprehensive metagenomic datasets covering the entire tract are scarce due to cost and technical challenges. Despite the widespread use of fecal samples, integrated datasets encompassing the entire digestive process, beginning at the mouth and ending with feces, are lacking. With this study, we aimed to fill this gap by analyzing the complete metagenome of the GI tract, providing insights into the dynamics of the microbiota and potential therapeutic avenues. In this study, we delved into the complex world of the GI microbiota, which we examined in five healthy Japanese subjects. While samples from the whole GI flora and fecal samples provided sufficient bacteria, samples obtained from the stomach and duodenum posed a challenge. Using a principal coordinate analysis (PCoA), clear clustering patterns were identified; these revealed significant diversity in the duodenum. Although this study was limited by its small sample size, the flora in the overall GI tract showed unwavering consistency, while the duodenum exhibited unprecedented phylogenetic diversity. A visual heat map illustrates the discrepancy in abundance, with Fusobacteria and Bacilli dominating the upper GI tract and Clostridia and Bacteroidia dominating the fecal samples. Negativicutes and Actinobacteria were found throughout the digestive tract. This study demonstrates that it is possible to continuously collect microbiome samples throughout the human digestive tract. These findings not only shed light on the complexity of GI microbiota but also provide a basis for future research.

Quantitative and dynamic profiling of human gut core microbiota by real-time PCR

The human gut microbiota refers to a diverse community of microorganisms that symbiotically exist in the human intestinal system. Altered microbial communities have been linked to many human pathologies. However, there is a lack of rapid and efficient methods to assess gut microbiota signatures in practice. To address this, we established an appraisal system containing 45 quantitative real-time polymerase chain reaction (qPCR) assays targeting gut core microbes with high prevalence and/or abundance in the population. Through comparative genomic analysis, we selected novel species-specific genetic markers and primers for 31 of the 45 core microbes with no previously reported specific primers or whose primers needed improvement in specificity. We comprehensively evaluated the performance of the qPCR assays and demonstrated that they showed good sensitivity, selectivity, and quantitative linearity for each target. The limit of detection ranged from 0.1 to 1.0 pg/µL for the genomic DNA of these targets. We also demonstrated the high consistency (Pearson's r = 0.8688, P < 0.0001) between the qPCR method and metagenomics next-generation sequencing (mNGS) method in analyzing the abundance of selected bacteria in 22 human fecal samples. Moreover, we quantified the dynamic changes (over 8 weeks) of these core microbes in 14 individuals using qPCR, and considerable stability was demonstrated in most participants, albeit with significant individual differences. Overall, this study enables the simple and rapid quantification of 45 core microbes in the human gut, providing a promising tool to understand the role of gut core microbiota in human health and disease. KEY POINTS: • A panel of original qPCR assays was developed to quantify human gut core microbes. • The qPCR assays were evaluated and compared with mNGS using real fecal samples. • This method was used to dynamically profile the gut core microbiota in individuals.

Regulations of Citrus Pectin Oligosaccharide on Cholesterol Metabolism: Insights from Integrative Analysis of Gut Microbiota and Metabolites

(1) Background: Recently, academic studies are demonstrating that the cholesterol-lowering effects of pectin oligosaccharides (POSs) are correlated to intestinal flora. However, the mechanisms of POS on cholesterol metabolisms are limited, and the observations of intestinal flora are lacking integrative analyses. (2) Aim and methods: To reveal the regulatory mechanisms of POS on cholesterol metabolism via an integrative analysis of the gut microbiota, the changes in gut microbiota structure and metabolite composition after POS addition were investigated using Illumina MiSeq sequencing and non-targeted metabolomics through in vitro gut microbiota fermentation. (3) Results: The composition of fecal gut flora was adjusted positively by POS. POS increased the abundances of the cholesterol-related bacterial groups Bacteroidetes, Bifidobacterium and Lactobacillus, while it decreased conditional pathogenic Escherichia coli and Enterococcus, showing good prebiotic activities. POS changed the composition of gut microbiota fermentation metabolites (P24), causing significant changes in 221 species of fermentation metabolites in a non-targeted metabolomics analysis and promoting the production of short-chain fatty acids. The abundances of four types of cholesterol metabolism-related metabolites (adenosine monophosphate, cyclic adenosine monophosphate, guanosine and butyrate) were significantly higher in the P24 group than those in the control group without POS addition. (4) Conclusion: The abovementioned results may explain the hypocholesterolemic effects of POS and promotion effects on cholesterol efflux of P24. These findings indicated that the potential regulatory mechanisms of citrus POS on cholesterol metabolism are modulated by cholesterol-related gut microbiota and specific metabolites.

The gut microbiota and diabetes: research, translation, and clinical applications - 2023 Diabetes, Diabetes Care, and Diabetologia Expert Forum

This article summarises the state of the science on the role of the gut microbiota (GM) in diabetes from a recent international expert forum organised by Diabetes, Diabetes Care, and Diabetologia, which was held at the European Association for the Study of Diabetes 2023 Annual Meeting in Hamburg, Germany. Forum participants included clinicians and basic scientists who are leading investigators in the field of the intestinal microbiome and metabolism. Their conclusions were as follows: (1) the GM may be involved in the pathophysiology of type 2 diabetes, as microbially produced metabolites associate both positively and negatively with the disease, and mechanistic links of GM functions (e.g. genes for butyrate production) with glucose metabolism have recently emerged through the use of Mendelian randomisation in humans; (2) the highly individualised nature of the GM poses a major research obstacle, and large cohorts and a deep-sequencing metagenomic approach are required for robust assessments of associations and causation; (3) because single time point sampling misses intraindividual GM dynamics, future studies with repeated measures within individuals are needed; and (4) much future research will be required to determine the applicability of this expanding knowledge to diabetes diagnosis and treatment, and novel technologies and improved computational tools will be important to achieve this goal.

Age over sex: evaluating gut microbiota differences in healthy Chinese populations

Age and gender have been recognized as two pivotal covariates affecting the composition of the gut microbiota. However, their mediated variations in microbiota seem to be inconsistent across different countries and races. In this study, 613 individuals, whom we referred to as the "healthy" population, were selected from 1,018 volunteers through rigorous selection using 16S rRNA sequencing. Three enterotypes were identified, namely, Escherichia-Shigella, mixture (Bacteroides and Faecalibacterium), and Prevotella. Moreover, 11 covariates that explain the differences in microbiota were determined, with age being the predominant factor. Furthermore, age-related differences in alpha diversity, beta diversity, and core genera were observed in our cohort. Remarkably, after adjusting for 10 covariates other than age, abundant genera that differed between age groups were demonstrated. In contrast, minimal differences in alpha diversity, beta diversity, and differentially abundant genera were observed between male and female individuals. Furthermore, we also demonstrated the age trajectories of several well-known beneficial genera, lipopolysaccharide (LPS)-producing genera, and short-chain fatty acids (SCFAs)-producing genera. Overall, our study further elucidated the effects mediated by age and gender on microbiota differences, which are of significant importance for a comprehensive understanding of the gut microbiome spectrum in healthy individuals.

Short-term dietary fiber interventions produce consistent gut microbiome responses across studies

The composition of the human gut microbiome varies tremendously among individuals, making the effects of dietary or treatment interventions difficult to detect and characterize. The consumption of fiber is important for gut health, yet the specific effects of increased fiber intake on the gut microbiome vary across studies. The variation in study outcomes might be due to inter-individual (or inter-population) variation or to the details of the interventions including the types of fiber, length of study, size of cohort, and molecular approaches. Thus, to identify generally (on average) consistent fiber-induced responses in the gut microbiome of healthy individuals, we re-analyzed 16S rRNA sequencing data from 21 dietary fiber interventions from 12 human studies, which included 2,564 fecal samples from 538 subjects across all interventions. Short-term increases in dietary fiber consumption resulted in highly consistent gut bacterial community responses across studies. Increased fiber consumption explained an average of 1.5% of compositional variation (vs 82% of variation attributed to the individual), reduced alpha-diversity, and resulted in phylogenetically conserved responses in relative abundances among bacterial taxa. Additionally, we identified bacterial clades, at approximately the genus level, that were highly consistent in their response (on average, increasing or decreasing in their relative abundance) to dietary fiber interventions across the studies.

IMPORTANCE

Our study is an example of the power of synthesizing and reanalyzing 16S rRNA microbiome data from many intervention studies. Despite high inter-individual variation of the composition of the human gut microbiome, dietary fiber interventions cause a consistent response both in the degree of change and the particular taxa that respond to increased fiber.

From the Microbiome to the Electrome: Implications for the Microbiota-Gut-Brain Axis

The gut microbiome plays a fundamental role in metabolism, as well as the immune and nervous systems. Microbial imbalance (dysbiosis) can contribute to subsequent physical and mental pathologies. As such, interest has been growing in the microbiota-gut-brain brain axis and the bioelectrical communication that could exist between bacterial and nervous cells. The aim of this study was to investigate the bioelectrical profile (electrome) of two bacterial species characteristic of the gut microbiome: a Proteobacteria Gram-negative bacillus Escherichia coli (E. coli), and a Firmicutes Gram-positive coccus Enterococcus faecalis (E. faecalis). We analyzed both bacterial strains to (i) validate the fluorescent probe bis-(1,3-dibutylbarbituric acid) trimethine oxonol, DiBAC4(3), as a reliable reporter of the changes in membrane potential (Vmem) for both bacteria; (ii) assess the evolution of the bioelectric profile throughout the growth of both strains; (iii) investigate the effects of two neural-type stimuli on Vmem changes: the excitatory neurotransmitter glutamate (Glu) and the inhibitory neurotransmitter γ-aminobutyric acid (GABA); (iv) examine the impact of the bioelectrical changes induced by neurotransmitters on bacterial growth, viability, and cultivability using absorbance, live/dead fluorescent probes, and viable counts, respectively. Our findings reveal distinct bioelectrical profiles characteristic of each bacterial species and growth phase. Importantly, neural-type stimuli induce Vmem changes without affecting bacterial growth, viability, or cultivability, suggesting a specific bioelectrical response in bacterial cells to neurotransmitter cues. These results contribute to understanding the bacterial response to external stimuli, with potential implications for modulating bacterial bioelectricity as a novel therapeutic target.

Microbiome compositional data analysis for survival studies

The growing interest in studying the relationship between the human microbiome and our health has also extended to time-to-event studies where researchers explore the connection between the microbiome and the occurrence of a specific event of interest. The analysis of microbiome obtained through high throughput sequencing techniques requires the use of specialized Compositional Data Analysis (CoDA) methods designed to accommodate its compositional nature. There is a limited availability of statistical tools for microbiome analysis that incorporate CoDA, and this is even more pronounced in the context of survival analysis. To fill this methodological gap, we present coda4microbiome for survival studies, a new methodology for the identification of microbial signatures in time-to-event studies. The algorithm implements an elastic-net penalized Cox regression model adapted to compositional covariates. We illustrate coda4microbiome algorithm for survival studies with a case study about the time to develop type 1 diabetes for non-obese diabetic mice. Our algorithm identified a bacterial signature composed of 21 genera associated with diabetes development. coda4microbiome for survival studies is integrated in the R package coda4microbiome as an extension of the existing functions for cross-sectional and longitudinal studies.

MicroPredict: predicting species-level taxonomic abundance of whole-shotgun metagenomic data using only 16S amplicon sequencing data

BACKGROUND

The importance of the human microbiome in the analysis of various diseases is emerging. The two main methods used to profile the human microbiome are 16S rRNA gene sequencing (16S sequencing) and whole-genome shotgun sequencing (WGS). Owing to the full coverage of the genome in sequencing, WGS has multiple advantages over 16S sequencing, including higher taxonomic profiling resolution at the species-level and functional profiling analysis. However, 16S sequencing remains widely used because of its relatively low cost. Although WGS is the standard method for obtaining accurate species-level data, we found that 16S sequencing data contained rich information to predict high-resolution species-level abundances with reasonable accuracy.

OBJECTIVE

In this study, we proposed MicroPredict, a method for accurately predicting WGS-comparable species-level abundance data using 16S taxonomic profile data.

METHODS

We employed a mixed model using two key strategies: (1) modeling both sample- and species-specific information for predicting WGS abundances, and (2) accounting for the possible correlations among different species.

RESULTS

We found that MicroPredict outperformed the other machine learning methods.

CONCLUSION

We expect that our approach will help researchers accurately approximate the species-level abundances of microbiome profiles in datasets for which only cost-effective 16S sequencing has been applied.

Host factors are associated with vaginal microbiome structure in pregnancy in the ECHO Cohort Consortium

Using pooled vaginal microbiota data from pregnancy cohorts (N = 683 participants) in the Environmental influences on Child Health Outcomes (ECHO) Program, we analyzed 16S rRNA gene amplicon sequences to identify clinical and demographic host factors that associate with vaginal microbiota structure in pregnancy both within and across diverse cohorts. Using PERMANOVA models, we assessed factors associated with vaginal community structure in pregnancy, examined whether host factors were conserved across populations, and tested the independent and combined effects of host factors on vaginal community state types (CSTs) using multinomial logistic regression models. Demographic and social factors explained a larger amount of variation in the vaginal microbiome in pregnancy than clinical factors. After adjustment, lower education, rather than self-identified race, remained a robust predictor of L. iners dominant (CST III) and diverse (CST IV) (OR = 8.44, 95% CI = 4.06-17.6 and OR = 4.18, 95% CI = 1.88-9.26, respectively). In random forest models, we identified specific taxonomic features of host factors, particularly urogenital pathogens associated with pregnancy complications (Aerococcus christensenii and Gardnerella spp.) among other facultative anaerobes and key markers of community instability (L. iners). Sociodemographic factors were robustly associated with vaginal microbiota structure in pregnancy and should be considered as sources of variation in human microbiome studies.

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.

Inulin mitigated antibiotic-induced intestinal microbiota dysbiosis - a comparison of different supplementation stages

Antibiotics are unavoidable to be prescribed to subjects due to different reasons, and they decrease the relative abundance of beneficial microbes. Inulin, a fructan type of polysaccharide carbohydrate, on the contrary, could promote the growth of beneficial microbes. In this study, we investigated the effect of inulin on antibiotic-induced intestinal microbiota dysbiosis and compared their overall impact at different supplementation stages, i.e., post-antibiotic, at the time of antibiotic administration or prior to antibiotic treatment, in the C57BL/6 mice model. Although supplementation of inulin after antibiotic treatment could aid in the reconstruction of the intestinal microbial community its overall impact was limited and no remarkable differences were identified as compared to the spontaneous restoration. On the contrary, the effect of simultaneous and pre-supplementation was more remarkable. Simultaneous inulin supplementation significantly mitigated the antibiotic-induced dysbiosis based on alterations as evaluated using weighted and unweighted UniFrac distance between baseline and after treatment. Moreover, comparing the effect of simultaneous supplementation, pre-supplemented inulin further mitigated the antibiotic-induced dysbiosis, especially on the relative abundance of dominant microbes. Collectively, the current study found that the use of inulin could alleviate antibiotic-induced microbiota dysbiosis, and the best supplementation stage (overall effect as evaluated by beta diversity distance changes) was before the antibiotic treatment, then simultaneous supplementation and supplementation after the antibiotic treatment.

The MTIST platform: a microbiome time series inference standardized test

The human gut microbiome is a promising therapeutic target, but interventions are hampered by our limited understanding of microbial ecosystems. Here, we present a platform to develop, evaluate, and score approaches to learn ecological interactions from microbiome time series data. The microbiome time series inference standardized test (MTIST) comprises: a simulation framework for the in silico generation of microbiome study data akin to what is obtained with quantitative next-generation sequencing approaches, a compilation of a large curated data set generated by the simulation framework representing 648 simulated microbiome studies containing 18,360 time series, with a total of 2,182,800 species abundance measurements, and a scoring method to rank ecological inference algorithms. We use the MTIST platform to rank five implementations of microbiome inference approaches, revealing that while all algorithms performed well on ecosystems with few species (3 and 10), all algorithms failed to infer most interaction in a large ecosystem with 100 member species. However, we do find that the strongest interactions within a large ecosystem are inferred with higher success by all algorithms. Finally, we use the MTIST platform to compare different microbiome study designs, characterizing tradeoffs between samples per subject and number of subjects. Interestingly, we find that when only few samples can be collected per subject, ecological inference is most successful when these samples are collected with highest feasible temporal frequency. Taken together, we provide a computational tool to aid the development of better microbiome ecosystem inference approaches, which will be crucial towards the development of reliable and predictable therapeutic approaches that target the microbiome ecosystem.

mi-Mic: a novel multi-layer statistical test for microbiota-disease associations

mi-Mic, a novel approach for microbiome differential abundance analysis, tackles the key challenges of such statistical tests: a large number of tests, sparsity, varying abundance scales, and taxonomic relationships. mi-Mic first converts microbial counts to a cladogram of means. It then applies a priori tests on the upper levels of the cladogram to detect overall relationships. Finally, it performs a Mann-Whitney test on paths that are consistently significant along the cladogram or on the leaves. mi-Mic has much higher true to false positives ratios than existing tests, as measured by a new real-to-shuffle positive score.

Probiotic formulations and gastro-intestinal diseases in the paediatric population: a narrative review

Background/Aim

Probiotics are live microbial supplements that improve the microbial balance in the host animal when administered in adequate amounts. They play an important role in relieving symptoms of many diseases associated with gastrointestinal tract, for example, in necrotizing enterocolitis (NEC), antibiotic-associated diarrhea, relapsing Clostridium difficile colitis, Helicobacter pylori infections, and inflammatory bowel disease (IBD). In this narrative review, the authors aim to evaluate the role of different probiotic formulations in treating gastrointestinal diseases in pediatric population aged 18 years or younger and highlight the main considerations for selecting probiotic formulations for use in this population.

Methodology

The authors searched PubMed and Clinicaltrials.gov from inception to 24th July 2022, without any restrictions. Using an iterative process, the authors subsequently added papers through hand-searching citations contained within retrieved articles and relevant systematic reviews and meta-analyses.

Results

The effectiveness of single-organism and composite probiotics in treating gastrointestinal disorders in pediatric patients aged 18 or under were analyzed and compared in this study. A total of 39 studies were reviewed and categorized based on positive and negative outcomes, and compared with a placebo, resulting in 25 studies for single-organism and 14 studies for composite probiotics. Gastrointestinal disorders studied included NEC, acute gastroenteritis (AGE), Acute Diarrhea, Ulcerative Colitis (UC), and others. The results show that probiotics are effective in treating various gastrointestinal disorders in children under 18, with single-organism probiotics demonstrating significant positive outcomes in most studies, and composite probiotics showing positive outcomes in all studies analyzed, with a low incidence of negative outcomes for both types.

Conclusion

This study concludes that single-organism and composite probiotics are effective complementary therapies for treating gastrointestinal disorders in the pediatric population. Hence, healthcare professionals should consider using probiotics in standard treatment regimens, and educating guardians can enhance the benefits of probiotic therapy. Further research is recommended to identify the optimal strains and dosages for specific conditions and demographics. The integration of probiotics in clinical practice and ongoing research can contribute to reducing the incidence and severity of gastrointestinal disorders in pediatric patients.

Ageing, microbes and health

The human gut microbiome is a modifier of the risk for many non-communicable diseases throughout the lifespan. In ageing, the effect of the microbiome appears to be more pronounced because of the lower physiological reserve. Microbial metabolites and other bioactive products act upon some of the key physiological processes involved in the Hallmarks of Ageing. Dietary interventions that delay age-related change in the microbiome have also led to delayed onset of ageing-related health loss, and improved levels of cognitive function, inflammatory status and frailty. Cross-sectional analysis of thousands of gut microbiome datasets from around the world has identified key taxa that are depleted during accelerated health loss, and other taxa that become more abundant, but these signatures differ in some geographical regions. The key challenges for research in this area are to experimentally prove that particular species or strains directly contribute to health-related ageing outcomes, and to develop practical ways of retaining or re-administering them on a population basis. The promotion of a health-associated gut microbiome in ageing mirrors the challenge of maintaining planetary microbial ecosystems in the face of anthropogenic effects and climate change. Lessons learned from acting at the individual level can inform microbiome-targeting strategies for achieving Sustainable Development Goals at a global level.

Informed development of a multi-species biofilm in chronic obstructive pulmonary disease

Recent evidence indicates that microbial biofilm aggregates inhabit the lungs of COPD patients and actively contribute towards chronic colonization and repeat infections. However, there are no contextually relevant complex biofilm models for COPD research. In this study, a meta-analysis of the lung microbiome in COPD was used to inform development of an optimized biofilm model composed of genera highly associated with COPD. Bioinformatic analysis showed that although diversity matrices of COPD microbiomes were similar to healthy controls, and internal compositions made it possible to accurately differentiate between these cohorts (AUC = 0.939). Genera that best defined these patients included Haemophilus, Moraxella and Streptococcus. Many studies fail to account for fungi; therefore, Candida albicans was included in the creation of an interkingdom biofilm model. These organisms formed a biofilm capable of tolerating high concentrations of antimicrobial therapies with no significant reductions in viability. However, combined therapies of antibiotics and an antifungal resulted in significant reductions in viable cells throughout the biofilm (p < 0.05). This biofilm model is representative of the COPD lung microbiome and results from in vitro antimicrobial challenge experiments indicate that targeting both bacteria and fungi in these interkingdom communities will be required for more positive clinical outcomes.

Meta-analysis of the human upper respiratory tract microbiome reveals robust taxonomic associations with health and disease

BACKGROUND

The human upper respiratory tract (URT) microbiome, like the gut microbiome, varies across individuals and between health and disease states. However, study-to-study heterogeneity in reported case-control results has made the identification of consistent and generalizable URT-disease associations difficult.

RESULTS

In order to address this issue, we assembled 26 independent 16S rRNA gene amplicon sequencing data sets from case-control URT studies, with approximately 2-3 studies per respiratory condition and ten distinct conditions covering common chronic and acute respiratory diseases. We leveraged the healthy control data across studies to investigate URT associations with age, sex, and geographic location, in order to isolate these associations from health and disease states.

CONCLUSIONS

We found several robust genus-level associations, across multiple independent studies, with either health or disease status. We identified disease associations specific to a particular respiratory condition and associations general to all conditions. Ultimately, we reveal robust associations between the URT microbiome, health, and disease, which hold across multiple studies and can help guide follow-up work on potential URT microbiome diagnostics and therapeutics.

Bacterial interactome disturbance in chronic obstructive pulmonary disease clinical stability and exacerbations

RATIONALE

Our understanding of airway dysbiosis in chronic obstructive pulmonary disease (COPD) remains incomplete, which may be improved by unraveling the complexity in microbial interactome.

OBJECTIVES

To characterize reproducible features of airway bacterial interactome in COPD at clinical stability and during exacerbation, and evaluate their associations with disease phenotypes.

METHODS

We performed weighted ensemble-based co-occurrence network analysis of 1742 sputum microbiomes from published and new microbiome datasets, comprising two case-control studies of stable COPD versus healthy control, two studies of COPD stability versus exacerbation, and one study with exacerbation-recovery time series data.

RESULTS

Patients with COPD had reproducibly lower degree of negative bacterial interactions, i.e. total number of negative interactions as a proportion of total interactions, in their airway microbiome compared with healthy controls. Evaluation of the Haemophilus interactome showed that the antagonistic interaction networks of this established pathogen rather than its abundance consistently changed in COPD. Interactome dynamic analysis revealed reproducibly reduced antagonistic interactions but not diversity loss during COPD exacerbation, which recovered after treatment. In phenotypic analysis, unsupervised network clustering showed that loss of antagonistic interactions was associated with worse clinical symptoms (dyspnea), poorer lung function, exaggerated neutrophilic inflammation, and higher exacerbation risk. Furthermore, the frequent exacerbators (≥ 2 exacerbations per year) had significantly reduced antagonistic bacterial interactions while exhibiting subtle compositional changes in their airway microbiota.

CONCLUSIONS

Bacterial interactome disturbance characterized by reduced antagonistic interactions, rather than change in pathogen abundance or diversity, is a reproducible feature of airway dysbiosis in COPD clinical stability and exacerbations, which suggests that we may target interactome rather than pathogen alone for disease treatment.

Interaction between Dietary Lactoferrin and Gut Microbiota in Host Health

The gut microbiota are known to play an important role in host health and disease. Alterations in the gut microbiota composition can disrupt the stability of the gut ecosystem, which may result in noncommunicable chronic diseases (NCCDs). Remodeling the gut microbiota through personalized nutrition is a novel therapeutic avenue for both disease control and prevention. However, whether there are commonly used gut microbiota-targeted diets and how gut microbiota-diet interactions combat NCCDs and improve health remain questions to be addressed. Lactoferrin (LF), which is broadly used in dietary supplements, acts not only as an antimicrobial in the defense against enteropathogenic bacteria but also as a prebiotic to propagate certain probiotics. Thus, LF-induced gut microbiota alterations can be harnessed to induce changes in host physiology, and the underpinnings of their relationships and mechanisms are beginning to unravel in studies involving humans and animal models.

Integrative metagenomic analysis reveals distinct gut microbial signatures related to obesity

Obesity is a metabolic disorder closely associated with profound alterations in gut microbial composition. However, the dynamics of species composition and functional changes in the gut microbiome in obesity remain to be comprehensively investigated. In this study, we conducted a meta-analysis of metagenomic sequencing data from both obese and non-obese individuals across multiple cohorts, totaling 1351 fecal metagenomes. Our results demonstrate a significant decrease in both the richness and diversity of the gut bacteriome and virome in obese patients. We identified 38 bacterial species including Eubacterium sp. CAG:274, Ruminococcus gnavus, Eubacterium eligens and Akkermansia muciniphila, and 1 archaeal species, Methanobrevibacter smithii, that were significantly altered in obesity. Additionally, we observed altered abundance of five viral families: Mesyanzhinovviridae, Chaseviridae, Salasmaviridae, Drexlerviridae, and Casjensviridae. Functional analysis of the gut microbiome indicated distinct signatures associated to obesity and identified Ruminococcus gnavus as the primary driver for function enrichment in obesity, and Methanobrevibacter smithii, Akkermansia muciniphila, Ruminococcus bicirculans, and Eubacterium siraeum as functional drivers in the healthy control group. Additionally, our results suggest that antibiotic resistance genes and bacterial virulence factors may influence the development of obesity. Finally, we demonstrated that gut vOTUs achieved a diagnostic accuracy with an optimal area under the curve of 0.766 for distinguishing obesity from healthy controls. Our findings offer comprehensive and generalizable insights into the gut bacteriome and virome features associated with obesity, with the potential to guide the development of microbiome-based diagnostics.

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.

Multi-omic integration of microbiome data for identifying disease-associated modules

Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists of disease-associated features (e.g., species, pathways, or metabolites), without capturing the multi-layered structure of the data. Here, we address this challenge by introducing "MintTea", an intermediate integration-based approach combining canonical correlation analysis extensions, consensus analysis, and an evaluation protocol. MintTea identifies "disease-associated multi-omic modules", comprising features from multiple omics that shift in concord and that collectively associate with the disease. Applied to diverse cohorts, MintTea captures modules with high predictive power, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome study, MintTea identifies a module with serum glutamate- and TCA cycle-related metabolites, along with bacterial species linked to insulin resistance. In another dataset, MintTea identifies a module associated with late-stage colorectal cancer, including Peptostreptococcus and Gemella species and fecal amino acids, in line with these species' metabolic activity and their coordinated gradual increase with cancer development. This work demonstrates the potential of advanced integration methods in generating systems-level, multifaceted hypotheses underlying microbiome-disease interactions.

Dopamine Polymerization-Mediated Surface Functionalization toward Advanced Bacterial Therapeutics

Bacteria-based therapy has spotlighted an unprecedented potential in treating a range of diseases, given that bacteria can be used as both drug vehicles and therapeutic agents. However, the use of bacteria for disease treatment often suffers from unsatisfactory outcomes, due largely to their suboptimal bioavailability, dose-dependent toxicity, and low targeting colonization. In the past few years, substantial efforts have been devoted to tackling these difficulties, among which methods capable of integrating bacteria with multiple functions have been extensively pursued. Different from conventional genetic engineering and modern synthetic bioengineering, surface modification of bacteria has emerged as a simple yet flexible strategy to introduce different functional motifs. Polydopamine, which can be easily formed via in situ dopamine oxidation and self-polymerization, is an appealing biomimetic polymer that has been widely applied for interfacial modification and functionalization. By virtue of its catechol groups, polydopamine can be efficiently codeposited with a multitude of functional elements on diverse surfaces.In this Account, we summarize the recent advances from our group with a focus on the interfacial polymerization-mediated functionalization of bacteria for advanced microbial therapy. First, we present the optimized strategy for bacterial surface modification under cytocompatible conditions by in situ dopamine polymerization. Taking advantage of the hydrogen bonding, π-π stacking, Michael addition, and Schiff base reaction with polydopamine, diverse functional small molecules and macromolecules are facilely codeposited onto the bacterial surface. Namely, monomodal, dual-modal, and multimodal surface modification of bacteria can be achieved by dopamine self-deposition, codeposition with a unitary composition, and codeposition with a set of multiple components, respectively. Second, we outline the regulation of bacterial functions by surface modification. The formed polydopamine surface endows bacteria with the ability to resist in vivo insults, such as gastrointestinal tract stressors and immune clearance, resulting in greatly improved bioavailability. Integration with specific ligands or therapeutic components enables the modified bacteria to increase targeting accumulation and colonization at lesion sites or play synergistic effects in disease treatment. Bacteria codeposited with different bioactive moieties, such as protein antigens, antibodies, and immunoadjuvants, are even able to actively interact with the host, particularly to elicit immune responses by either suppressing immune overactivation to promote the reversion of pathological inflammations or provoking protective innate and/or adaptive immunity to inhibit pathogenic invaders. Third, we highlight the applications of surface-modified bacteria as multifunctional living therapeutics in disease treatment, especially alleviating inflammatory bowel diseases via oral delivery and intervening in different types of cancer through systemic or intratumoral injection. Finally, we discuss the challenges and prospects of dopamine polymerization-mediated multifunctionalization for preparing advanced bacterial therapeutics as well as their bench to bedside translation. We anticipate that this Account can provide an insightful overview of bacterial therapy and inspire innovative thinking and new efforts to develop next-generation living therapeutics for treating various diseases.

Past, present, and future of microbiome-based therapies

Technological advances in studying the human microbiome in depth have enabled the identification of microbial signatures associated with health and disease. This confirms the crucial role of microbiota in maintaining homeostasis and the host health status. Nowadays, there are several ways to modulate the microbiota composition to effectively improve host health; therefore, the development of therapeutic treatments based on the gut microbiota is experiencing rapid growth. In this review, we summarize the influence of the gut microbiota on the development of infectious disease and cancer, which are two of the main targets of microbiome-based therapies currently being developed. We analyze the two-way interaction between the gut microbiota and traditional drugs in order to emphasize the influence of gut microbial composition on drug effectivity and treatment response. We explore the different strategies currently available for modulating this ecosystem to our benefit, ranging from 1st generation intervention strategies to more complex 2nd generation microbiome-based therapies and their regulatory framework. Lastly, we finish with a quick overview of what we believe is the future of these strategies, that is 3rd generation microbiome-based therapies developed with the use of artificial intelligence (AI) algorithms.

Paralytic shellfish toxins producing dinoflagellates cause dysbacteriosis in scallop gut microbial biofilms

Filter-feeding bivalves could accumulate paralytic shellfish toxins (PSTs) produced by harmful dinoflagellates through diet. Despite that bivalves are resistant to these neurotoxins due to possessing PST-resistant sodium channel, exposure to PSTs-producing dinoflagellates impair bivalve survival. We hypothesized that ingesting PSTs-producing dinoflagellates may influence the gut microbiota, and then the health of bivalves. To test this idea, we compared the gut microbiota of the scallop Patinopecten yessoensis, after feeding with PST-producing or non-toxic dinoflagellates. Exposure to PSTs-producing dinoflagellates resulted in a decline of gut microbial diversity and a disturbance of community structure, accompanied by a significant increase in the abundance and richness of pathogenic bacteria, represented by Vibrio. Moreover, network analysis demonstrated extensive positive correlations between pathogenic bacteria abundances and PSTs concentrations in the digestive glands of the scallops. Furthermore, isolation of a dominant Vibrio strain and its genomic analysis revealed a variety of virulence factors, including the tolC outer membrane exporter, which were expressed in the gut microbiota. Finally, the infection experiment demonstrated scallop mortality caused by the isolated Vibrio strain; further, the pathogenicity of this Vibrio strain was attenuated by a mutation in the tolC gene. Together, these findings demonstrated that the PSTs may affect gut microbiota via direct and taxa-specific interactions with opportunistic pathogens, which proliferate after transition from seawater to the gut environment. The present study has revealed novel mechanisms towards deciphering the puzzles in environmental disturbances-caused death of an important aquaculture species.

Generally-healthy individuals with aberrant bowel movement frequencies show enrichment for microbially-derived blood metabolites associated with reduced kidney function

Bowel movement frequency (BMF) has been linked to changes in the composition of the human gut microbiome and to many chronic conditions, like metabolic disorders, neurodegenerative diseases, chronic kidney disease (CKD), and other intestinal pathologies like irritable bowel syndrome and inflammatory bowel disease. Lower BMF (constipation) can lead to compromised intestinal barrier integrity and a switch from saccharolytic to proteolytic fermentation within the microbiota, giving rise to microbially-derived toxins that may make their way into circulation and cause damage to organ systems. However, the connections between BMF, gut microbial metabolism, and the early-stage development and progression of chronic disease remain underexplored. Here, we examined the phenotypic impact of BMF variation in a cohort of generally-healthy, community dwelling adults with detailed clinical, lifestyle, and multi-omic data. We showed significant differences in microbially-derived blood plasma metabolites, gut bacterial genera, clinical chemistries, and lifestyle factors across BMF groups that have been linked to inflammation, cardiometabolic health, liver function, and CKD severity and progression. We found that the higher plasma levels of 3-indoxyl sulfate (3-IS), a microbially-derived metabolite associated with constipation, was in turn negatively associated with estimated glomerular filtration rate (eGFR), a measure of kidney function. Causal mediation analysis revealed that the effect of BMF on eGFR was significantly mediated by 3-IS. Finally, we identify self-reported diet, lifestyle, and psychological factors associated with BMF variation, which indicate several common-sense strategies for mitigating constipation and diarrhea. Overall, we suggest that aberrant BMF is an underappreciated risk factor in the development of chronic diseases, even in otherwise healthy populations.

Autoimmune diseases exhibit shared alterations in the gut microbiota

OBJECTIVE

Accumulating evidence from microbial studies have highlighted the modulatory roles of intestinal microbes in numerous human diseases, however, the shared microbial signatures across different diseases remain relatively unclear.

METHODS

To consolidate existing knowledge across multiple studies, we performed meta-analyses of 17 disease types, covering 34 case-control datasets of 16S rRNA sequencing data, to identify shared alterations among different diseases. Furthermore, the impact of a microbial species, Lactobacillus salivarius, was established in a dextran sodium sulphate-induced colitis model and a collagen type II-induced arthritis mouse model.

RESULTS

Microbial alterations among autoimmune diseases were substantially more consistent compared with that of other diseases (cancer, metabolic disease and nervous system disease), with microbial signatures exhibiting notable discriminative power for disease prediction. Autoimmune diseases were characterized by the enrichment of Enterococcus, Veillonella, Streptococcus and Lactobacillus and the depletion of Ruminococcus, Gemmiger, Oscillibacter, Faecalibacterium, Lachnospiracea incertae sedis, Anaerostipes, Coprococcus, Alistipes, Roseburia, Bilophila, Barnesiella, Dorea, Ruminococcus2, Butyricicoccus, Phascolarctobacterium, Parabacteroides and Odoribacter, among others. Functional investigation of L. salivarius, whose genus was commonly enriched in numerous autoimmune diseases, demonstrated protective roles in two separate inflammatory mouse models.

CONCLUSION

Our study highlights a strong link between autoimmune diseases and the gut microbiota, with notably consistent microbial alterations compared with that of other diseases, indicating that therapeutic strategies that target the gut microbiome may be transferable across different autoimmune diseases. Functional validation of L. salivarius highlighted that bacterial genera associated with disease may not always be antagonistic, but may represent protective or adaptive responses to disease.

Carrimycin ameliorates lipopolysaccharide and cecal ligation and puncture-induced sepsis in mice

Carrimycin (CA), sanctioned by China's National Medical Products Administration (NMPA) in 2019 for treating acute bronchitis and sinusitis, has recently been observed to exhibit multifaceted biological activities, encompassing anti-inflammatory, antiviral, and anti-tumor properties. Despite these applications, its efficacy in sepsis treatment remains unexplored. This study introduces a novel function of CA, demonstrating its capacity to mitigate sepsis induced by lipopolysaccharide (LPS) and cecal ligation and puncture (CLP) in mice models. Our research employed in vitro assays, real-time quantitative polymerase chain reaction (RT-qPCR), and RNA-seq analysis to establish that CA significantly reduces the levels of pro-inflammatory cytokines, namely tumor necrosis factor-alpha (TNF-α), interleukin 1 beta (IL-1β), and interleukin 6 (IL-6), in response to LPS stimulation. Additionally, Western blotting and immunofluorescence assays revealed that CA impedes Nuclear Factor Kappa B (NF-κB) activation in LPS-stimulated RAW264.7 cells. Complementing these findings, in vivo experiments demonstrated that CA effectively alleviates LPS- and CLP-triggered organ inflammation in C57BL/6 mice. Further insights were gained through 16S sequencing, highlighting CA's pivotal role in enhancing gut microbiota diversity and modulating metabolic pathways, particularly by augmenting the production of short-chain fatty acids in mice subjected to CLP. Notably, a comparative analysis revealed that CA's anti-inflammatory efficacy surpasses that of equivalent doses of aspirin (ASP) and TIENAM. Collectively, these findings suggest that CA exhibits significant therapeutic potential in sepsis treatment. This discovery provides a foundational theoretical basis for the clinical application of CA in sepsis management.

Functional antagonism and insights into the biosynthetic potential of human gut-derived microbes

The specialised small molecules encoded by commensal microbes mediate distinct functional interactions. However, there is a landscape of antagonistic interactions mediated by specialised strains and their small molecules. Herein, the antagonistic landscape within a collection of 330 human gut-derived commensal microbial strains was elucidated to evaluate antimicrobial interactions as a defensive contributor, and gain new insights into structure-related functions. The potential antagonistic gut-derived strains displayed strain-specific selective inhibition. This is in contrast to common antimicrobial drugs, which typically wipe out a broad range of species and are usually found in environmental microbes. Genome sequencing of representative gut strains revealed the presence of significant biosynthetic gene clusters (BGCs) encoding compound families that contribute to antagonistic activities, and are important in host defence and maintaining gut homeostasis. Subsets of these BGCs were abundant in metagenomic sequencing data from healthy individuals. Furthermore, the cell culture secretome of these strains revealed potential biomarkers linked to hallmark pathways. These microorganisms have biosynthetic novelty and are a source of biologically significant natural products. Such natural products are essential in the development of new antimicrobial agents to reduce the usage of broad-spectrum antibiotics and combat antimicrobial resistance.

Intestinal microbiome and maternal mental health: preventing parental stress and enhancing resilience in mothers

The number of mothers suffering from mental illness is increasing steadily, particularly under conditions of the coronavirus pandemic. The identification of factors that contribute to resilience in mothers is urgently needed to decrease the risks of poor physical and psychological health. We focused on the risk of parenting stress and psychological resilience in healthy mothers with no psychiatric and physical disorders and conducted two studies to examine the relationships between intestinal microbiota, physical condition, and psychological state. Our results showed that alpha diversity and beta diversity of the microbiome are related to high parenting stress risk. Psychological resilience and physical conditions were associated with relative abundances of the genera Blautia, Clostridium, and Eggerthella. This study helps further understand the gut-brain axis mechanisms and supports proposals for enhancing resilience in mothers.

Meta-analysis of the human gut microbiome uncovers shared and distinct microbial signatures between diseases

Microbiome studies have revealed gut microbiota's potential impact on complex diseases. However, many studies often focus on one disease per cohort. We developed a meta-analysis workflow for gut microbiome profiles and analyzed shotgun metagenomic data covering 11 diseases. Using interpretable machine learning and differential abundance analysis, our findings reinforce the generalization of binary classifiers for Crohn's disease (CD) and colorectal cancer (CRC) to hold-out cohorts and highlight the key microbes driving these classifications. We identified high microbial similarity in disease pairs like CD vs ulcerative colitis (UC), CD vs CRC, Parkinson's disease vs type 2 diabetes (T2D), and schizophrenia vs T2D. We also found strong inverse correlations in Alzheimer's disease vs CD and UC. These findings detected by our pipeline provide valuable insights into these diseases.

Exploring the Dynamic Relationship between the Gut Microbiome and Body Composition across the Human Lifespan: A Systematic Review

This systematic review aimed to identify different gut microbiome profiles across the human lifespan and to correlate such profiles with the body composition. PubMed, Scopus, and Cochrane were searched from inception to March 2022. Sixty studies were included in this systematic review. Overall, the gut microbiome composition in overweight participants exhibited decreased α-diversity, decreased levels of the phylum Bacteroidetes and its taxa, and increased levels of the phylum Firmicutes, its taxa, and the Firmicutes/Bacteroidetes ratio, in comparison to normal-weight participants. Other body composition parameters showed similar correlations. Fat mass and waist circumference were found to correlate positively with the Firmicutes taxa and negatively with the Bacteroidetes taxa. In contrast, lean body mass and muscle mass demonstrated a positive correlation with the Bacteroidetes taxa. Notably, these correlations were more pronounced in athletes than in obese and normal-weight individuals. The composition of the gut microbiome is evidently different in overweight individuals or athletes of all age groups, with the former tending towards decreased Bacteroidetes taxa and increased Firmicutes taxa, while a reversed relationship is observed concerning athletes. Further studies are needed to explore the dynamic relationship between energy intake, body composition, and the gut microbiome across the human lifespan.

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.

Integrated microbiome and metabolomics revealed the protective effect of baicalin on alveolar bone inflammatory resorption in aging

BACKGROUND

With the growing aging population and longer life expectancy, periodontitis and tooth loss have become major health concerns. The gut microbiota, as a key regulator in bone homeostasis, has gathered immense interest. Baicalin, a flavonoid compound extracted from Scutellaria baicalensis Georgi, has shown antioxidant and anti-inflammatory activities.

PURPOSE

This study investigated, for the first time, the protective mechanism of baicalin against alveolar bone inflammatory resorption in aging mice by regulating intestinal flora and metabolites, as well as intestinal barrier function.

METHODS

A ligature-induced periodontitis model was established in d-galactose (D-gal)-induced aging mice, and baicalin was administered at different dosages for 13 weeks. Body weight was measured weekly. The antioxidant and anti-inflammatory activity of baicalin were evaluated using serum superoxide dismutase (SOD), malonaldehyde (MDA), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) levels. The immune capability was assessed by thymus and spleen indices. Histopathological changes were observed in the heart, liver, ileum, and periodontal tissues. Alveolar bone absorption of maxillary second molars was examined, and osteoclasts were counted by tartrate-resistant acid phosphatase (TRAP) staining. Furthermore, fecal samples were analyzed using 16S rRNA sequencing and non-targeted metabolomics to identify differences in intestinal bacterial composition and metabolites.

RESULTS

Baicalin exhibited anti-aging properties, as evidenced by increased SOD activity and decreased levels of MDA, IL-6, and TNF-α in serum compared to the control group. Baicalin also ameliorated alveolar bone loss in the d-gal-induced aging-periodontitis group (p < 0.05). Furthermore, baicalin restored ileal permeability by up-regulating the expression of ZO-1 and occludin in aging-periodontitis groups (p < 0.05). Alpha diversity analysis indicated that baicalin-treated mice harbored a higher diversity of gut microbe. PCoA and ANOSIM results revealed significant dissimilarity between groups. The Firmicutes/Bacteroidetes (F/B) ratio, which decreased in periodontitis mice, was restored by baicalin treatment. Additionally, medium-dosage baicalin promoted the production of beneficial flavonoids, and enriched short-chain fatty acids (SCFAs)-producing bacteria.

CONCLUSION

Intestinal homeostasis is a potential avenue for treating age-related alveolar bone loss. Baicalin exerts anti-inflammatory, antioxidant, and osteo-protective properties by regulating the gut microbiota and metabolites.

Multi-scale topology and position feature learning and relationship-aware graph reasoning for prediction of drug-related microbes

MOTIVATION

The human microbiome may impact the effectiveness of drugs by modulating their activities and toxicities. Predicting candidate microbes for drugs can facilitate the exploration of the therapeutic effects of drugs. Most recent methods concentrate on constructing of the prediction models based on graph reasoning. They fail to sufficiently exploit the topology and position information, the heterogeneity of multiple types of nodes and connections, and the long-distance correlations among nodes in microbe-drug heterogeneous graph.

RESULTS

We propose a new microbe-drug association prediction model, NGMDA, to encode the position and topological features of microbe (drug) nodes, and fuse the different types of features from neighbors and the whole heterogeneous graph. First, we formulate the position and topology features of microbe (drug) nodes by t-step random walks, and the features reveal the topological neighborhoods at multiple scales and the position of each node. Second, as the features of nodes are high-dimensional and sparse, we designed an embedding enhancement strategy based on supervised fully connected autoencoders to form the embeddings with representative features and the more discriminative node distributions. Third, we propose an adaptive neighbor feature fusion module, which fuses features of neighbors by the constructed position- and topology-sensitive heterogeneous graph neural networks. A novel self-attention mechanism is developed to estimate the importance of the position and topology of each neighbor to a target node. Finally, a heterogeneous graph feature fusion module is constructed to learn the long-distance correlations among the nodes in the whole heterogeneous graph by a relationship-aware graph transformer. Relationship-aware graph transformer contains the strategy for encoding the connection relationship types among the nodes, which is helpful for integrating the diverse semantics of these connections. The extensive comparison experimental results demonstrate NGMDA's superior performance over five state-of-the-art prediction methods. The ablation experiment shows the contributions of the multi-scale topology and position feature learning, the embedding enhancement strategy, the neighbor feature fusion, and the heterogeneous graph feature fusion. Case studies over three drugs further indicate that NGMDA has ability in discovering the potential drug-related microbes.

AVAILABILITY AND IMPLEMENTATION

Source codes and Supplementary Material are available at https://github.com/pingxuan-hlju/NGMDA.

Gut microbiome of the sole surviving member of reptile order Rhynchocephalia reveals biogeographic variation, influence of host body condition and a substantial core microbiota in tuatara across New Zealand

Tuatara are the sole extant species in the reptile order Rhynchocephalia. They are ecologically and evolutionarily unique, having been isolated geographically for ~84 million years and evolutionarily from their closest living relatives for ~250 million years. Here we report the tuatara gut bacterial community for the first time. We sampled the gut microbiota of translocated tuatara at five sanctuaries spanning a latitudinal range of ~1000 km within Aotearoa New Zealand, as well as individuals from the source population on Takapourewa (Stephens Island). This represents a first look at the bacterial community of the order Rhynchocephalia and provides the opportunity to address several key hypotheses, namely that the tuatara gut microbiota: (1) differs from those of other reptile orders; (2) varies among geographic locations but is more similar at sites with more similar temperatures and (3) is shaped by tuatara body condition, parasitism and ambient temperature. We found significant drivers of the microbiota in sampling site, tuatara body condition, parasitism and ambient temperature, suggesting the importance of these factors when considering tuatara conservation. We also derived a 'core' community of shared bacteria across tuatara at many sites, despite their geographic range and isolation. Remarkably, >70% of amplicon sequence variants could not be assigned to known genera, suggesting a largely undescribed gut bacterial community for this ancient host species.

Gut dysbiosis in patients with chronic pain: a systematic review and meta-analysis

Introduction

Recent evidence supports the contribution of gut microbiota dysbiosis to the pathophysiology of rheumatic diseases, neuropathic pain, and neurodegenerative disorders. The bidirectional gut-brain communication network and the occurrence of chronic pain both involve contributions of the autonomic nervous system and the hypothalamic pituitary adrenal axis. Nevertheless, the current understanding of the association between gut microbiota and chronic pain is still not clear. Therefore, the aim of this study is to systematically evaluate the existing knowledge about gut microbiota alterations in chronic pain conditions.

Methods

Four databases were consulted for this systematic literature review: PubMed, Web of Science, Scopus, and Embase. The Newcastle-Ottawa Scale was used to assess the risk of bias. The study protocol was prospectively registered at the International prospective register of systematic reviews (PROSPERO, CRD42023430115). Alpha-diversity, β-diversity, and relative abundance at different taxonomic levels were summarized qualitatively, and quantitatively if possible.

Results

The initial database search identified a total of 3544 unique studies, of which 21 studies were eventually included in the systematic review and 11 in the meta-analysis. Decreases in alpha-diversity were revealed in chronic pain patients compared to controls for several metrics: observed species (SMD= -0.201, 95% CI from -0.04 to -0.36, p=0.01), Shannon index (SMD= -0.27, 95% CI from -0.11 to -0.43, p<0.001), and faith phylogenetic diversity (SMD -0.35, 95% CI from -0.08 to -0.61, p=0.01). Inconsistent results were revealed for beta-diversity. A decrease in the relative abundance of the Lachnospiraceae family, genus Faecalibacterium and Roseburia, and species of Faecalibacterium prausnitzii and Odoribacter splanchnicus, as well as an increase in Eggerthella spp., was revealed in chronic pain patients compared to controls.

Discussion

Indications for gut microbiota dysbiosis were revealed in chronic pain patients, with non-specific disease alterations of microbes.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42023430115.

Mouse Adaptation of Human Inflammatory Bowel Diseases Microbiota Enhances Colonization Efficiency and Alters Microbiome Aggressiveness Depending on Recipient Colonic Inflammatory Environment

Understanding the cause vs consequence relationship of gut inflammation and microbial dysbiosis in inflammatory bowel diseases (IBD) requires a reproducible mouse model of human-microbiota-driven experimental colitis. Our study demonstrated that human fecal microbiota transplant (FMT) transfer efficiency is an underappreciated source of experimental variability in human microbiota associated (HMA) mice. Pooled human IBD patient fecal microbiota engrafted germ-free (GF) mice with low amplicon sequence variant (ASV)-level transfer efficiency, resulting in high recipient-to-recipient variation of microbiota composition and colitis severity in HMA Il-10-/- mice. In contrast, mouse-to-mouse transfer of mouse-adapted human IBD patient microbiota transferred with high efficiency and low compositional variability resulting in highly consistent and reproducible colitis phenotypes in recipient Il-10-/- mice. Human-to-mouse FMT caused a population bottleneck with reassembly of microbiota composition that was host inflammatory environment specific. Mouse-adaptation in the inflamed Il-10-/- host reassembled a more aggressive microbiota that induced more severe colitis in serial transplant to Il-10-/- mice than the distinct microbiota reassembled in non-inflamed WT hosts. Our findings support a model of IBD pathogenesis in which host inflammation promotes aggressive resident bacteria, which further drives a feed-forward process of dysbiosis exacerbated gut inflammation. This model implies that effective management of IBD requires treating both the dysregulated host immune response and aggressive inflammation-driven microbiota. We propose that our mouse-adapted human microbiota model is an optimized, reproducible, and rigorous system to study human microbiome-driven disease phenotypes, which may be generalized to mouse models of other human microbiota-modulated diseases, including metabolic syndrome/obesity, diabetes, autoimmune diseases, and cancer.

Multi-omic integration of microbiome data for identifying disease-associated modules

The human gut microbiome is a complex ecosystem with profound implications for health and disease. This recognition has led to a surge in multi-omic microbiome studies, employing various molecular assays to elucidate the microbiome's role in diseases across multiple functional layers. However, despite the clear value of these multi-omic datasets, rigorous integrative analysis of such data poses significant challenges, hindering a comprehensive understanding of microbiome-disease interactions. Perhaps most notably, multiple approaches, including univariate and multivariate analyses, as well as machine learning, have been applied to such data to identify disease-associated markers, namely, specific features (e.g., species, pathways, metabolites) that are significantly altered in disease state. These methods, however, often yield extensive lists of features associated with the disease without effectively capturing the multi-layered structure of multi-omic data or offering clear, interpretable hypotheses about underlying microbiome-disease mechanisms. Here, we address this challenge by introducing MintTea - an intermediate integration-based method for analyzing multi-omic microbiome data. MintTea combines a canonical correlation analysis (CCA) extension, consensus analysis, and an evaluation protocol to robustly identify disease-associated multi-omic modules. Each such module consists of a set of features from the various omics that both shift in concord, and collectively associate with the disease. Applying MintTea to diverse case-control cohorts with multi-omic data, we show that this framework is able to capture modules with high predictive power for disease, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome (MS) study, we found a MS-associated module comprising of a highly correlated cluster of serum glutamate- and TCA cycle-related metabolites, as well as bacterial species previously implicated in insulin resistance. In another cohort, we identified a module associated with late-stage colorectal cancer, featuring Peptostreptococcus and Gemella species and several fecal amino acids, in agreement with these species' reported role in the metabolism of these amino acids and their coordinated increase in abundance during disease development. Finally, comparing modules identified in different datasets, we detected multiple significant overlaps, suggesting common interactions between microbiome features. Combined, this work serves as a proof of concept for the potential benefits of advanced integration methods in generating integrated multi-omic hypotheses underlying microbiome-disease interactions and a promising avenue for researchers seeking systems-level insights into coherent mechanisms governing microbiome-related diseases.