Assessing causality is undoubtedly one of the key questions in microbiome studies for the upcoming years. Since randomized trials in human subjects are often unethical or difficult to pursue, analytical methods to derive causal effects from observational data deserve attention. As simple covariate adjustment is not likely to account for all potential confounders, the idea of instrumental variable (IV) analysis is worth exploiting. Here we propose a novel framework of antibiotic instrumental variable regression (AB-IVR) for estimating the causal relationships between microbiome and various diseases. We rely on the recent studies showing that antibiotic treatment has a cumulative long-term effect on the microbiome, resulting in individuals with higher antibiotic usage to have a more perturbed microbiome. We apply the AB-IVR method on the Estonian Biobank data and show that the microbiome has a causal role in numerous diseases including migraine, depression and irritable bowel syndrome. We show with a plethora of sensitivity analyses that the identified causal effects are robust and propose ways for further methodological developments.

The human gut microbiome, crucial in various diseases, can be utilized to develop diagnostic models through machine learning (ML). The specific tools and parameters used in model construction such as data preprocessing, batch effect removal and modeling algorithms can impact model performance and generalizability. To establish an generally applicable workflow, we divided the ML process into three above-mentioned steps and optimized each sequentially using 83 gut microbiome cohorts across 20 diseases. We tested a total of 156 tool-parameter-algorithm combinations and benchmarked them according to internal- and external- AUCs. At the data preprocessing step, we identified four data preprocessing methods that performed well for regression-type algorithms and one method that excelled for non-regression-type algorithms. At the batch effect removal step, we identified the "ComBat" function from the sva R package as an effective batch effect removal method and compared the performance of various algorithms. Finally, at the ML algorithm selection step, we found that Ridge and Random Forest ranked the best. Our optimized work flow performed similarly comparing with previous exhaustive methods for disease-specific optimizations, thus is generally applicable and can provide a comprehensive guideline for constructing diagnostic models for a range of diseases, potentially serving as a powerful tool for future medical diagnostics.

Heart failure is a global health issue with significant mortality and morbidity. There is increasing evidence that alterations in the gastrointestinal microbiome, gut epithelial permeability, and gastrointestinal disorders contribute to heart failure progression through various pathways, including systemic inflammation, metabolic dysregulation, and modulation of cardiac function. Moreover, several medications used to treat heart failure directly impact the microbiome. The relationship between the gastrointestinal tract and the heart is bidirectional, termed the gut-heart axis. It is increasingly understood that diet-derived microbial metabolites are key mechanistic drivers of the gut-heart axis. This includes, for example, trimethylamine N-oxide and short-chain fatty acids. This review discusses current insights into the interplay between heart failure, its associated risk factors, and the gut microbiome, focusing on key metabolic pathways, the role of dietary interventions, and the potential for gut-targeted therapies. Understanding these complex interactions could pave the way for novel strategies to mitigate heart failure progression and improve patient outcomes.

Spondyloarthritis (SpA) is a heterogenous group of diseases with an yet unknown exact pathogenesis. An interplay between genetic predispositionw (mainly HLA-B*27 positivity), a barrier leakage (of skin and gut) and environmental influences, such as the contact to certain microbiota are suspected to lead to an activation of the immune system and through this to the development and maintenance of the disease.

Similarity between candidate drug targets and human proteins is commonly assessed to minimize the occurrence of side effects. Although numerous drugs have been found to disrupt the health of the human microbiome, no comprehensive comparison between established drug targets and the human microbiome metaproteome has yet been conducted. Therefore, herein, sequence and structure alignments between human and pathogen drug targets and representative human gut, oral, and vaginal microbiome metaproteomes were performed. Both human and pathogen drug targets were found to be similar in sequence, function, structure, and drug binding capacity to proteins in diverse pathogenic and non-pathogenic bacteria from all three microbiomes. The gut metaproteome was identified as particularly susceptible overall to off-target effects. Certain symptoms, such as infections and immune disorders, may be more common among drugs that non-selectively target host microbiota. These findings suggest that similarities between human microbiome metaproteomes and drug target candidates should be routinely checked.

Enteric infections represent a prevalent global health issue and contribute significantly to the global disease burden. This study aims to investigate the patterns and trends of enteric infections from 1990 to 2021, providing valuable insights for health policy formulation, medical resource allocation, and the optimization of patient management plans.

We analyzed the Global Burden of Disease (GBD) 2021 for 21 regions and 204 countries to understand better the health burden using prevalence, incidence, mortality, and disability-adjusted life years (DALYs), subtype, risk factors, and etiology. We tested correlations with the Socio-demographic Index (SDI), and using decomposition analysis to dissect the reasons behind changes in epidemiological indicators of the disease.

In 2021, the age-standardized rates of prevalence, incidence, deaths, and DALYs per 100,000 population for enteric infections were 879.58, 577.21, 17.83, and 1020.15, respectively. Compared to 1990, these rates exhibited -0.18, -0.12, -0.73, and -0.72 changes. Gender and age analyses revealed a higher burden among females, those under 15 years old, and the elderly. Regions with low SDI had higher epidemiological indicators. The burden of Typhoid fever declines in high-development regions. Unsafe water sources were identified as the primary risk factor globally in both 1990 and 2021. Rotavirus was the leading cause of deaths and DALYs.

This study highlights the complex epidemiological landscape of enteric infections, revealing variations in burden, risk factors, and etiological characteristics across age, gender, and geographical regions. It underscores the urgent need for healthcare professionals and policymakers to develop innovative prevention and healthcare strategies based on the current and evolving burden of enteric infections, to alleviate the global disease burden.

The human gut microbiome, a community of microbes that plays a crucial role in our wellbeing, is highly adaptable but also vulnerable to drug treatments. This vulnerability can have serious consequences for the host, for example, increasing susceptibility to infections, immune, metabolic, and cognitive disorders. However, the microbiome's adaptability also provides opportunities to prevent, protect, or even reverse drug-induced damage. Recently, several innovative approaches have emerged aimed at minimizing the collateral damage of drugs on the microbiome. Here, we outline these approaches, discuss their applicability in different treatment scenarios, highlight current challenges, and suggest avenues that may lead to an effective protection of the microbiome.

The gut microbiota plays a pivotal role in influencing the metabolism and immune responses of the body. A balanced microbial composition promotes metabolic health through various mechanisms, including the production of beneficial metabolites, which help regulate inflammation and support immune functions. In contrast, imbalance in the gut microbiota, known as dysbiosis, can disrupt metabolic processes and increase the risk of developing diseases, such as obesity, type 2 diabetes, and inflammatory disorders. The composition of the gut microbiota is dynamic and can be influenced by environmental factors such as diet, medication, and the consumption of live bacteria. Since the early 1900s, bacteria isolated from food and have been used as probiotics. However, the human gut also offers an enormous reservoir of bacterial strains, and recent advances in microbiota research have led to the discovery of strains with probiotic potentials. These strains, derived from a broad spectrum of microbial taxa, differ in their ecological properties and how they interact with their hosts. For most probiotics bacterial structural components and metabolites, such as short-chain fatty acids, contribute to the maintenance of metabolic and immunological homeostasis by regulating inflammation and reinforcing gut barrier integrity. Metabolites produced by probiotic strains can also be used for bacterial cross-feeding to promote a balanced microbiota. Despite the challenges related to safety, stability, and strain-specific properties, several newly identified strains offer great potential for personalized probiotic interventions, allowing for targeted health strategies.

The disruptive effect of antibiotics on the composition and function of the human microbiota is well established. However, the hypothesis that probiotics can help restore the antibiotic-disrupted microbiota has been advanced, with little consideration of the strength of evidence supporting it. Some clinical data suggest that probiotics can reduce antibiotic-related side effects, including Clostridioides difficile-associated diarrhoea, but there are no data that causally link these clinical effects to microbiota protection or recovery. Substantial challenges hinder attempts to address this hypothesis, including the absence of consensus on the composition of a 'normal' microbiota, non-standardized and evolving microbiome measurement methods, and substantial inter-individual microbiota variation. In this Review, we explore these complexities. First, we review the known benefits and risks of antibiotics, the effect of antibiotics on the human microbiota, the resilience and adaptability of the microbiota, and how microbiota restoration might be defined and measured. Subsequently, we explore the evidence for the efficacy of probiotics in preventing disruption or aiding microbiota recovery post-antibiotic treatment. Finally, we offer insights into the current state of research and suggest directions for future research.

Kidney transplantation (KT) is the best treatment for end-stage kidney disease, with graft survival critically affected by the recipient's immune response. The role of the gut microbiome in modulating this immune response remains underexplored. Our study investigates how microbiome alterations might associate with allograft rejection by analyzing the gut microbiome using 16S rRNA gene amplicon sequencing of a multicenter prospective study involving 562 samples from 245 individuals of which 217 received KT. Overall, gut microbiome composition showed gradual recovery post-KT, mirroring CKD-to-health transition as indicated by an increase of Shannon diversity. Prior to graft rejection, we observed a decrease in microbial diversity and SCFA-producing taxa. Functional analysis highlighted a decreased potential for SCFA production in patients preceding the rejection event, validated by quantitative PCR for the production potential of propionate and butyrate. Post-rejection analysis revealed normalization of these microbiome features. Comparison to published microbiome signatures from CKD patients demonstrated a partial overlap of the microbiome alterations preceding graft rejection with the alterations typically found in CKD. Our findings suggest that alterations in gut microbiome composition and function may precede and influence KT rejection, suggesting potential implications as biomarkers or for early therapeutic microbiome-targeting interventions.

Hand-foot syndrome (HFS) and oral mucositis (OM) are common adverse events during cancer chemotherapy and can significantly decrease patients' quality of life and chemotherapy adaptation, however, prevention strategies of these complications yet to be established.

Patients with stages I-III breast cancer, who had surgery and needed pegylated liposomal doxorubicin (PLD)-based adjuvant chemotherapy were screened, recruited and randomly assigned to receive either probiotics or placebo (three capsules, twice/day) treatment during the course of chemotherapy from November 2019 to August 2020. The incidence and severity of PLD related HFS and OM, and patients' quality of life were assessed. Their plasma biomarkers, metabolites and fecal microbiota compositions were measured. And the results were further verified in animal experiments.

Probiotics supplement during PLD treatment significantly decreased the incidence and the severity of HFS and OM ( P < 0.001), improved patients' life quality ( P < 0.001), increased the relative abundance of intestinal Enterococcus ( P = 0.025) and mitigated the changes of seven plasma metabolites. Among these metabolites, the changes of p-Mentha-1,8-dien-7-ol (MDO) ( B = - 0.441, P = 0.02) and L-arginine ( B = - 0.586, P = 0.002) were negatively correlated with the occurrence of severe HFS and OM. MDO can partly reproduce the preventive effects of probiotics on PLD-related skin cell proliferating inhibition, DNA damage, and local inflammation in rats.

Probiotics supplement during PLD-based chemotherapy prevents the incidence and severity of HFS and OM, which may be associated with modulating plasma metabolites including the MDO.

The microbiota is emerging as a key factor in the predisposition to insulin resistance and obesity.

To understand the interplay among gut microbiota and insulin sensitivity in multiple tissues.

Integrative multiomics and multitissue approach across six studies, combining euglycaemic clamp measurements (used in four of the six studies) with other measurements of glucose metabolism and insulin resistance (glycated haemoglobin (HbA1c) and fasting glucose).

Several genera and species from the Proteobacteria phylum were consistently negatively associated with insulin sensitivity in four studies (ADIPOINST, n=15; IRONMET, n=121, FLORINASH, n=67 and FLOROMIDIA, n=24). Transcriptomic analysis of the jejunum, ileum and colon revealed T cell-related signatures positively linked to insulin sensitivity. Proteobacteria in the ileum and colon were positively associated with HbA1c but negatively with the number of T cells. Jejunal deoxycholic acid was negatively associated with insulin sensitivity. Transcriptomics of subcutaneous adipose tissue (ADIPOMIT, n=740) and visceral adipose tissue (VAT) (ADIPOINST, n=29) revealed T cell-related signatures linked to HbA1c and insulin sensitivity, respectively. VAT Proteobacteria were negatively associated with insulin sensitivity. Multiomics and multitissue integration in the ADIPOINST and FLORINASH studies linked faecal Proteobacteria with jejunal and liver deoxycholic acid, as well as jejunal, VAT and liver transcriptomic signatures involved in the actin cytoskeleton, insulin and T cell signalling. Fasting glucose was consistently linked to interferon-induced genes and antiviral responses in the intestine and VAT. Studies in Drosophila melanogaster validated these human insulin sensitivity-associated changes.

These data provide comprehensive insights into the microbiome-gut-adipose-liver axis and its impact on systemic insulin action, suggesting potential therapeutic targets.Cite Now.

Cardiovascular diseases (CVDs) present differently in women and men, influenced by host-microbiome interactions. The roles of sex hormones in CVD outcomes and gut microbiome in modifying these effects are poorly understood. The XCVD study examines gut microbiome mediation of sex hormone effects on CVD risk markers by observing transgender participants undergoing gender-affirming hormone therapy (GAHT), with findings expected to extrapolate to cisgender populations.

This observational, longitudinal cohort study includes baseline, 1- and 2-year follow-ups with transgender participants beginning GAHT. It involves comprehensive phenotyping and microbiome genotyping, integrating computational analyses of high-dimensional data. Microbial diversity will be assessed using gut, skin, and oral samples via 16S rRNA and shotgun metagenomic sequencing of gut samples. Blood measurements will include sex hormones, CVD risk markers, cardiometabolic parameters, cytokines, and immune cell counts. Hair samples will be analysed for cortisol. Participants will complete online questionnaires on physical activity, mental health, stress, quality of life, fatigue, sleep, pain, and gender dysphoria, tracking medication use and diet to control for confounders. Statistical analyses will integrate phenomic, lifestyle, and multi-omic data to model health effects, testing gut microbiome mediation of CVD risk as the endocrine environment shifts between that typical for cisgender men to women and vice versa.

The study adheres to Good Clinical Practice and the Declaration of Helsinki. The protocol was approved by the Charité Ethical Committee (EA1/339/21). Signed informed consent will be obtained. Results will be published in peer-reviewed journals and conferences and shared as accessible summaries for participants, community groups, and the public, with participants able to view their data securely after public and patient involvement review for accessibility.

The XCVD study was registered on ClinicalTrials.gov (NCT05334888) as 'Sex-differential host-microbiome CVD risk - a longitudinal cohort approach (XCVD)" on 4 April 2022. Data set link can be found at https://classic.

gov/ct2/show/NCT05334888.

The microbiota in individual habitats differ in both relative composition and absolute abundance. While sequencing approaches determine the relative abundances of taxa and genes, they do not provide information on their absolute abundances. Here, we developed a machine-learning approach to predict fecal microbial loads (microbial cells per gram) solely from relative abundance data. Applying our prediction model to a large-scale metagenomic dataset (n = 34,539), we demonstrated that microbial load is the major determinant of gut microbiome variation and is associated with numerous host factors, including age, diet, and medication. We further found that for several diseases, changes in microbial load, rather than the disease condition itself, more strongly explained alterations in patients' gut microbiome. Adjusting for this effect substantially reduced the statistical significance of the majority of disease-associated species. Our analysis reveals that the fecal microbial load is a major confounder in microbiome studies, highlighting its importance for understanding microbiome variation in health and disease.

The gut microbiota actually shares the host's physical space and affects the host's physiological functions and health indicators through a complex network of interactions with the host. However, its role as a determinant of host health and disease is often underestimated. With the emergence of new technologies including next-generation sequencing (NGS) and advanced techniques such as microbial community sequencing, people have begun to explore the interaction mechanisms between microorganisms and hosts at various omics levels such as genomics, transcriptomics, metabolomics, and proteomics. With the enrichment of multi-omics integrated analysis methods based on the microbiome, an increasing number of complex statistical analysis methods have also been proposed. In this review, we summarized the multi-omics research analysis methods currently used to study the interaction between the microbiome and the host. We analyzed the advantages and limitations of various methods and briefly introduced their application progress.

Antibiotics are fundamental in sepsis management; however, the optimal empirical treatment remains debated. Despite anaerobes rarely being the causative pathogen of sepsis, antibiotics targeting them are frequently used, which might lead to unintended consequences. Multiple studies have shown that depletion of commensal anaerobic gut microbes by anti-anaerobic antibiotics influences systemic immunity and is associated with increased mortality in patients with sepsis. However, this knowledge has not yet been translated into clinical practice. When considering empirical coverage of anaerobic pathogens in sepsis, most physicians advocate for a better-safe-than-sorry approach. In this Viewpoint, we argue that anti-anaerobic antibiotics could often result in being sorry rather than safe. We provide an overview of the limited necessity of anaerobic coverage and the potential detrimental effects of anaerobic depletion in sepsis. We aim to raise anaerobic awareness to reduce the unnecessary use of anti-anaerobic antibiotics in empirical sepsis treatment and improve patient outcomes.

Linking sequence-derived microbial taxa abundances to host (patho-)physiology or habitat characteristics in a reproducible and interpretable manner has remained a formidable challenge for the analysis of microbiome survey data. Here, we introduce a flexible probabilistic modeling framework, VI-MIDAS (variational inference for microbiome survey data analysis), that enables joint estimation of context-dependent drivers and broad patterns of associations of microbial taxon abundances from microbiome survey data. VI-MIDAS comprises mechanisms for direct coupling of taxon abundances with covariates and taxa-specific latent coupling, which can incorporate spatio-temporal information and taxon-taxon interactions. We leverage mean-field variational inference for posterior VI-MIDAS model parameter estimation and illustrate model building and analysis using Tara Ocean Expedition survey data. Using VI-MIDAS' latent embedding model and tools from network analysis, we show that marine microbial communities can be broadly categorized into five modules, including SAR11-, nitrosopumilus-, and alteromondales-dominated communities, each associated with specific environmental and spatiotemporal signatures. VI-MIDAS also finds evidence for largely positive taxon-taxon associations in SAR11 or Rhodospirillales clades, and negative associations with Alteromonadales and Flavobacteriales classes. Our results indicate that VI-MIDAS provides a powerful integrative statistical analysis framework for discovering broad patterns of associations between microbial taxa and context-specific covariate data from microbiome survey data.

This research investigates the determinants of circulating FGF21 levels in a cohort reflecting metabolic disease progression, examining the associations of circulating FGF21 with morphology and function of adipose tissue (AT), and with metabolic adjustments following metabolic surgery.

We measured serum FGF21 in 678 individuals cross-sectionally and in 189 undergoing metabolic surgery longitudinally. Relationships between FGF21 levels, AT histology, transcriptomes and proteomes, cardiometabolic risk factors, and post-surgery metabolic adjustments were assessed using univariate and multivariate analyses, causal mediation analysis, and network integration of AT transcriptomes and proteomes.

FGF21 levels were linked to central adiposity, subclinical inflammation, insulin resistance, and cardiometabolic risk, and were driven by circulating leptin and liver enzymes. Higher FGF21 were linked with AT dysfunction reflected in fibro-inflammatory and lipid dysmetabolism pathways. Specifically, visceral AT inflammation was tied to both FGF21 elevation and liver dysfunction. Post-surgery, FGF21 peaked transitorily at three months. Mediation analysis highlighted an underlying increased AT catabolic state with elevated free fatty acids (FFA), contributing to higher liver stress and FGF21 levels (total effect of free fatty acids on FGF21 levels: 0.38, p < 0.01; proportion mediation via liver 32%, p < 0.01). In line with this, histological AT fibrosis linked with less pronounced FGF21 responses and reduced fat loss post-surgery (FFA and visceral AT fibrosis: rho = -0.31, p = 0.030; FFA and fat-mass loss: rho = 0.17, p = 0.020).

FGF21 reflects the liver's disproportionate metabolic stress response in both central adiposity and after metabolic surgery, with its dynamics reflecting an AT-liver crosstalk.

This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through CRC 1052, project number 209933838, CRC1382 and a Walther-Benjamin Fellowship and by a junior research grant by the Medical Faculty, University of Leipzig, and by the Federal Ministry of Education and Research (BMBF), Germany, FKZ: 01EO1501. Part of this work was supported by the European Union's Seventh Framework Program for research, technological development and demonstration under grant agreement HEALTH-F4-2012-305312 and by the CRC1382 and the Novo Nordisk Foundation and by the Deutsche Forschungsgemeinschaft (DFG, German Research foundation) project number 530364326.

With the advancement of metagenomics and metabolomics techniques, the crucial role of the gut microbiome in intestinal, cardiovascular, and metabolic disorders has been extensively explored. Vascular calcification (VC) is common in atherosclerosis, hypertension, diabetes mellitus, and chronic kidney disease. Moreover, it is a significant cause of cardiovascular diseases and mortality. Roseburia intestinalis, as a promising candidate for the next generation of probiotics, plays a substantial role in inhibiting the systemic inflammatory response and holds great potential in the treatment of intestinal diseases, cardiovascular diseases, and metabolic disorders. Its primary metabolite, butyrate, acts on specific receptors (GPR43, GPR41, GPR109a). It enters cells via transporters (MCT1, SMCT1), affecting gene expression through HDACs, PPARγ and Nrf2, promoting energy metabolism and changing the concentration of other metabolites (including AGEs, LPS, BHB) in the circulation to affect the body's life activities. In this paper, we focus on the possible mechanism of the primary metabolite butyrate of Roseburia intestinalis in inhibiting VC, which may become a potential therapeutic target for the treatment of VC and the ways to enhance its effect.

With the rising prevalence of chronic liver diseases worldwide, there exists a need to diversify our artillery to incorporate a plethora of diagnostic and therapeutic methods to combat this disease. Currently, the most common causes of liver disease are non-alcoholic fatty liver disease, hepatitis, and alcoholic liver disease. Some of these chronic diseases have the potential to transform into hepatocellular carcinoma with advancing fibrosis. In this review, we analyse the relationship between the gut and liver and their significance in liver disease. This two-way relationship has interesting effects on each other in liver diseases. The gut microbiota, through its metabolites, influences the metabolism in numerous ways. Careful manipulation of its composition can lead to the discovery of numerous therapeutic potentials that can be applied in the treatment of various liver diseases. Numerous cohort studies with a pan-omics approach are required to understand the association between the gut microbiome and hepatic disease progression through which we can identify effective ways to deal with this issue.

Medication can affect the gut microbiota composition and function. The aim of this study was to investigate connections between use of common non-antibiotic medicines and the gut microbiota composition and function in a large Swedish cohort (N = 2223). Use of 67 medications and polypharmacy (≥ 5 medications), based on self-reported and prescription registry data, were associated with the relative abundance of 881 gut metagenomic species (> 5% prevalence) and 103 gut metabolic modules (GMMs). Altogether, 97 associations of 26 medications with 40 species and of four medications with five GMMs were observed (false discovery rate < 5%). Several earlier findings were replicated like the positive associations of proton pump inhibitors (PPIs) with numerous oral species, and those of metformin with Escherichia species and with lactate consumption I and arginine degradation II. Several new associations were observed between, among others, use of antidepressants, beta-blockers, nonsteroidal anti-inflammatory drugs and calcium channel blockers, and specific species. Polypharmacy was positively associated with Enterococcus faecalis, Bacteroides uniformis, Rothia mucilaginosa, Escherichia coli and Limosilactobacillus vaginalis, and with 13 GMMs. We confirmed several previous findings and identified numerous new associations between use of medications/polypharmacy and the gut microbiota composition and functional potential. Further studies are needed to confirm the new findings.

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

In microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance testing is lacking, and it remains unexplored how these cope with confounding. Previous differential abundance benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations.

Our simulation framework implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole meta-genome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies much more than in previous benchmarks. With extensively parametrized simulations, we benchmark the performance of nineteen differential abundance methods and further evaluate the best ones on confounded simulations. Only classic statistical methods (linear models, the Wilcoxon test, t-test), limma, and fastANCOM properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find that adjusted differential abundance testing can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications.

Tight error control is critical for microbiome association studies. The unsatisfactory performance of many differential abundance methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.

Defining what constitutes a healthy microbiome throughout our lives remains an ongoing challenge. Understanding to what extent host and environmental factors can influence it has been the primary motivation for large population studies worldwide. Here, we describe the fecal microbiome of 3,746 individuals (0-87 years of age) in a nationwide study in the Netherlands, in association with extensive questionnaires. We validate previous findings, such as infant-adult trajectories, and explore the collective impact of our variables, which explain over 40% of the variation in microbiome composition. We identify associations with less explored factors, particularly those ethnic related, which show the largest impact on the adult microbiome composition, diversity, metabolic profiles, and CAZy (carbohydrate-active enzyme) repertoires. Understanding the sources of microbiome variability is crucial, given its potential as a modifiable target with therapeutic possibilities. With this work, we aim to serve as a foundational element for the design of health interventions and fundamental research.

The human microbiome is a complex and dynamic system that plays important roles in human health and disease. However, there remain limitations and theoretical gaps in our current understanding of the intricate relationship between microbes and humans. In this narrative review, we integrate the knowledge and insights from various fields, including anatomy, physiology, immunology, histology, genetics, and evolution, to propose a systematic framework. It introduces key concepts such as the 'innate and adaptive genomes', which enhance genetic and evolutionary comprehension of the human genome. The 'germ-free syndrome' challenges the traditional 'microbes as pathogens' view, advocating for the necessity of microbes for health. The 'slave tissue' concept underscores the symbiotic intricacies between human tissues and their microbial counterparts, highlighting the dynamic health implications of microbial interactions. 'Acquired microbial immunity' positions the microbiome as an adjunct to human immune systems, providing a rationale for probiotic therapies and prudent antibiotic use. The 'homeostatic reprogramming hypothesis' integrates the microbiome into the internal environment theory, potentially explaining the change in homeostatic indicators post-industrialization. The 'cell-microbe co-ecology model' elucidates the symbiotic regulation affecting cellular balance, while the 'meta-host model' broadens the host definition to include symbiotic microbes. The 'health-illness conversion model' encapsulates the innate and adaptive genomes' interplay and dysbiosis patterns. The aim here is to provide a more focused and coherent understanding of microbiome and highlight future research avenues that could lead to a more effective and efficient healthcare system.

5-Aminosalicylic acid (5-ASA) is a first-line therapy for ulcerative colitis (UC). This study examined the mucosa-associated microbiota (MAM) in UC patients, distinguishing between those who were 5-ASA tolerant and intolerant.

Brushing samples were collected from the sigmoid and ileal end of patients with UC during endoscopic procedures. The samples were profiled by using the Illumina MiSeq platform. The V3-V4 regions of the 16S rRNA gene (460 bp) were amplified by using tailed PCR.

A total of 15 patients with 5-ASA intolerance, 38 patients with 5-ASA tolerance, and 19 healthy controls were recruited in this study. The α-diversity indices were remarkably different among the three groups in the ileum mucosa but not in the sigmoid colon. In the ileum mucosa, Alistipes, Ruminococcaceae, and Odoribacter were less abundant in the 5-ASA-intolerant group than in the control and 5-ASA-tolerant groups. On the contrary, Merdibacter, Brevundimonas, and Porphyromonas were more abundant in the 5-ASA-intolerant group than in other groups.

The present study showed that the changes in MAM were characterized by a decrease in mucoprotective bacteria rather than an increase in harmful bacteria.

Respiratory viral infections (RVIs) are a major global contributor to morbidity and mortality. The susceptibility and outcome of RVIs are strongly age-dependent and show considerable inter-population differences, pointing to genetically and/or environmentally driven developmental variability. The factors determining the age-dependency and shaping the age-related changes of human anti-RVI immunity after birth are still elusive.

We are conducting a prospective birth cohort study aiming at identifying endogenous and environmental factors associated with the susceptibility to RVIs and their impact on cellular and humoral immune responses against the influenza A virus (IAV), respiratory syncytial virus (RSV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The MIAI birth cohort enrolls healthy, full-term neonates born at the University Hospital Würzburg, Germany, with follow-up at four defined time-points during the first year of life. At each study visit, clinical metadata including diet, lifestyle, sociodemographic information, and physical examinations, are collected along with extensive biomaterial sampling. Biomaterials are used to generate comprehensive, integrated multi-omics datasets including transcriptomic, epigenomic, proteomic, metabolomic and microbiomic methods.

The results are expected to capture a holistic picture of the variability of immune trajectories with a focus on cellular and humoral key players involved in the defense of RVIs and the impact of host and environmental factors thereon. Thereby, MIAI aims at providing insights that allow unraveling molecular mechanisms that can be targeted to promote the development of competent anti-RVI immunity in early life and prevent severe RVIs.

https://drks.de/search/de/trial/, identifier DRKS00034278.

Animal models are regularly used to test the role of the gut microbiome in hypertension. Small-scale pre-clinical studies have investigated changes to the gut microbiome in the angiotensin II hypertensive model. However, the gut microbiome is influenced by internal and external experimental factors, which are not regularly considered in the study design. Once these factors are accounted for, it is unclear if microbiome signatures are reproduceable. We aimed to determine the influence of angiotensin II treatment on the gut microbiome using a large and diverse cohort of mice and to quantify the magnitude by which other factors contribute to microbiome variations.

We conducted a retrospective study to establish a diverse mouse cohort resembling large human studies. We sequenced the V4 region of the 16S rRNA gene from 538 samples across the gastrointestinal tract of 303 male and female C57BL/6J mice randomized into sham or angiotensin II treatment from different genotypes, diets, animal facilities, and age groups. Analysing over 17 million sequencing reads, we observed that angiotensin II treatment influenced α-diversity (P = 0.0137) and β-diversity (i.e. composition of the microbiome, P < 0.001). Bacterial abundance analysis revealed patterns consistent with a reduction in short-chain fatty acid producers, microbial metabolites that lower blood pressure. Furthermore, animal facility, genotype, diet, age, sex, intestinal sampling site, and sequencing batch had significant effects on both α- and β-diversity (all P < 0.001). Sampling site (6.8%) and diet (6%) had the largest impact on the microbiome, while angiotensin II and sex had the smallest effect (each 0.4%).

Our large-scale data confirmed findings from small-scale studies that angiotensin II impacted the gut microbiome. However, this effect was modest relative to most of the other factors studied. Accounting for these factors in future pre-clinical hypertensive studies will increase the likelihood that microbiome findings are replicable and translatable.

This study analyses whether first-line antihypertensive drugs ameliorate the dysbiosis state in hypertension, and to test if this modification contributes to their blood pressure (BP) lowering properties in a genetic model of neurogenic hypertension.

Twenty-week-old male Wistar Kyoto rats (WKY) and spontaneously hypertensive rats (SHR) were untreated or treated with captopril, amlodipine or hydrochlorothiazide. A faecal microbiota transplantation (FMT) experiment was also performed by gavage of faecal content from donor SHR-treated groups to SHR recipients for 3 weeks.

Faeces from SHR showed gut dysbiosis, characterized by lower acetate- and higher lactate-producing bacteria and lower strict anaerobic bacteria. All three drugs increased the anaerobic bacteria proportion, captopril and amlodipine restored the proportion of acetate-producing bacterial populations to WKY levels, whereas hydrochlorothiazide decreased butyrate-producing bacteria. Captopril and amlodipine decreased gut pathology and permeability and attenuated sympathetic drive in the gut. Both drugs decreased neuroinflammation and oxidative stress in the hypothalamic paraventricular nuclei. Hydrochlorothiazide was unable to reduce neuroinflammation, gut sympathetic tone and gut integrity. FMT from SHR-amlodipine to SHR decreased BP, ameliorated aortic endothelium-dependent relaxation to acetylcholine, lowered NADPH oxidase activity, aortic Th17 infiltration and reduced neuroinflammation, whereas FMT from SHR-hydrochlorothiazide did not have these effects.

First-line antihypertensive drugs induced different modifications of gut integrity and gut dysbiosis in SHR, which result in no contribution of microbiota in the BP lowering effects of hydrochlorothiazide, whereas the vasculo-protective effect induced by amlodipine involves gut microbiota reshaping and gut-immune system communication.