Gut Microbiota Is Associated with Onset and Severity of Type 1 Diabetes in Nonobese Diabetic Mice Treated with Anti-PD-1.

Our bodies are home to individual-specific microbial ecosystems that have recently been found to be modified by cancer immunotherapies. The interaction between the gut microbiome and islet autoimmunity leading to type I diabetes (T1D) is well described and highlights the microbiome contribution during the onset and T1D development in animals and humans. As cancer immunotherapies induce gut microbiome perturbations and immune-mediated adverse events in susceptible patients, we hypothesized that NOD mice can be used as a predictive tool to investigate the effects of anti-PD-1 treatment on the onset and severity of T1D, and how microbiota influences immunopathology. In this longitudinal study, we showed that anti-PD-1 accelerated T1D onset, increased glutamic acid decarboxylase-reactive T cell frequency in spleen, and precipitated destruction of ß cells, triggering high glucose levels and pancreatic islet reduction. Anti-PD-1 treatment also resulted in temporal microbiota changes and lower diversity characteristic of T1D. Finally, we identified known insulin-resistance regulating bacteria that were negatively correlated with glucose levels, indicating that anti-PD-1 treatment impacts the early gut microbiota composition. Moreover, an increase of mucin-degrading Akkermansia muciniphila points to alterations of barrier function and immune system activation. These results highlight the ability of microbiota to readily respond to therapy-triggered pathophysiological changes as rescuers (Bacteroides acidifaciens and Parabacteroides goldsteinii) or potential exacerbators (A. muciniphila). Microbiome-modulating interventions may thus be promising mitigation strategies for immunotherapies with high risk of immune-mediated adverse events.

Machine learning approaches in microbiome research: challenges and best practices.

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.

Comparison of the relative impacts of acute consumption of an inulin-enriched diet, milk kefir or a commercial probiotic product on the human gut microbiome and metabolome.

It has been established that the human gut microbiota is central to health, and, consequently, there has been a growing desire to positively modulate its composition and/or function through, for example, the use of fermented foods, prebiotics or probiotics. Here, we compare the relative impact of the daily consumption of an inulin-enriched diet (n = 10), a commercial probiotic-containing fermented milk product (FMP) (n = 10), or a traditional kefir FMP (n = 9), over a 28-day period on the gut microbiome and urine metabolome of healthy human adults. None of the treatments resulted in significant changes to clinical parameters or biomarkers tested. However, shotgun metagenomic analysis revealed that kefir consumption resulted in a significant change in taxonomy, in the form of an increased abundance of the sub-dominant FMP-associated species Lactococcus raffinolactis, which further corresponded to shifts in the urine metabolome. Overall, our results indicated that daily consumption of a single portion of kefir alone resulted in detectable changes to the gut microbiota and metabolome of consumers.

Dietary-Induced Bacterial Metabolites Reduce Inflammation and Inflammation-Associated Cancer via Vitamin D Pathway.

Environmental factors, including westernised diets and alterations to the gut microbiota, are considered risk factors for inflammatory bowel diseases (IBD). The mechanisms underpinning diet-microbiota-host interactions are poorly understood in IBD. We present evidence that feeding a lard-based high-fat (HF) diet can protect mice from developing DSS-induced acute and chronic colitis and colitis-associated cancer (CAC) by significantly reducing tumour burden/incidence, immune cell infiltration, cytokine profile, and cell proliferation. We show that HF protection was associated with increased gut microbial diversity and a significant reduction in Proteobacteria and an increase in Firmicutes and Clostridium cluster XIVa abundance. Microbial functionality was modulated in terms of signalling fatty acids and bile acids (BA). Faecal secondary BAs were significantly induced to include moieties that can activate the vitamin D receptor (VDR), a nuclear receptor richly represented in the intestine and colon. Indeed, colonic VDR downstream target genes were upregulated in HF-fed mice and in combinatorial lipid-BAs-treated intestinal HT29 epithelial cells. Collectively, our data indicate that HF diet protects against colitis and CAC risk through gut microbiota and BA metabolites modulating vitamin D targeting pathways. Our data highlights the complex relationship between dietary fat-induced alterations of microbiota-host interactions in IBD/CAC pathophysiology.

The Athlete Gut Microbiome and its Relevance to Health and Performance: A Review.

The human gut microbiome is a complex ecosystem of microorganisms that play an important role in human health, influencing functions such as vitamin uptake, digestion and immunomodulation. While research of the gut microbiome has expanded considerably over the past decade, some areas such as the relationship between exercise and the microbiome remain relatively under investigated. Despite this, multiple studies have shown a potential bidirectional relationship between exercise and the gut microbiome, with some studies demonstrating the possibility of influencing this relationship. This, in turn, could provide a useful route to influence athletic performance via microbiome manipulation, a valuable prospect for many elite athletes and their teams. The evidence supporting the potential benefits of pursuing this route and associated future perspectives are discussed in this review.

Microbial bile salt hydrolase activity influences gene expression profiles and gastrointestinal maturation in infant mice.

The mechanisms by which early microbial colonizers of the neonate influence gut development are poorly understood. Bacterial bile salt hydrolase (BSH) acts as a putative colonization factor that influences bile acid signatures and microbe-host signaling pathways and we considered whether this activity can influence infant gut development. In silico analysis of the human neonatal gut metagenome confirmed that BSH enzyme sequences are present as early as one day postpartum. Gastrointestinal delivery of cloned BSH to immature gnotobiotic mice accelerated shortening of the colon and regularized gene expression profiles, with monocolonised mice more closely resembling conventionally raised animals. In situ expression of BSH decreased markers of cell proliferation (Ki67, Hes2 and Ascl2) and strongly increased expression of ALPI, a marker of cell differentiation and barrier function. These data suggest an evolutionary paradigm whereby microbial BSH activity potentially influences bacterial colonization and in-turn benefits host gastrointestinal maturation.

Interactions between Medications and the Gut Microbiome in Inflammatory Bowel Disease.

In view of the increasing evidence that commonly prescribed, non-antibiotic drugs interact with the gut microbiome, we re-examined the microbiota variance in inflammatory bowel disease (IBD) to determine the degree to which medication and supplement intake might account for compositional differences between disease subtypes and geographic location. We assessed the confounding effects of various treatments on the faecal microbiota composition (16S rRNA gene sequencing) in persons with Crohn's disease (CD; n = 188) or ulcerative colitis (UC; n = 161) from either Cork (Ireland) or Manitoba (Canada) sampled at three time points. The medication profiles between persons with UC and CD and from different countries varied in number and type of drugs taken. Among Canadian participants with CD, surgical resection and overall medication and supplement usage is significantly more common than for their Irish counterparts. Treatments explained more microbiota variance (3.5%) than all other factors combined (2.4%) and 40 of the 78 tested medications and supplements showed significant associations with at least one taxon in the gut microbiota. However, while treatments accounted for a relatively small proportion of the geographic contribution to microbiome variance between Irish and Canadian participants, additive effects from multiple medications contributed significantly to microbiome differences between UC and CD.

Strain level and comprehensive microbiome analysis in inflammatory bowel disease via multi-technology meta-analysis identifies key bacterial influencers of disease.

Objective: Inflammatory bowel disease (IBD) is a heterogenous disease in which the microbiome has been shown to play an important role. However, the precise homeostatic or pathological functions played by bacteria remain unclear. Most published studies report taxa-disease associations based on single-technology analysis of a single cohort, potentially biasing results to one clinical protocol, cohort, and molecular analysis technology. To begin to address this key question, precise identification of the bacteria implicated in IBD across cohorts is necessary. Methods: We sought to take advantage of the numerous and diverse studies characterizing the microbiome in IBD to develop a multi-technology meta-analysis (MTMA) as a platform for aggregation of independently generated datasets, irrespective of DNA-profiling technique, in order to uncover the consistent microbial modulators of disease. We report the largest strain-level survey of IBD, integrating microbiome profiles from 3,407 samples from 21 datasets spanning 15 cohorts, three of which are presented for the first time in the current study, characterized using three DNA-profiling technologies, mapping all nucleotide data against known, culturable strain reference data. Results: We identify several novel IBD associations with culturable strains that have so far remained elusive, including two genome-sequenced but uncharacterized Lachnospiraceae strains consistently decreased in both the gut luminal and mucosal contents of patients with IBD, and demonstrate that these strains are correlated with inflammation-related pathways that are known mechanisms targeted for treatment. Furthermore, comparative MTMA at the species versus strain level reveals that not all significant strain associations resulted in a corresponding species-level significance and conversely significant species associations are not always re-captured at the strain level. Conclusion: We propose MTMA for uncovering experimentally testable strain-disease associations that, as demonstrated here, are beneficial in discovering mechanisms underpinning microbiome impact on disease or novel targets for therapeutic interventions.

Improved gut microbiome recovery following drug therapy is linked to abundance and replication of probiotic strains.

Probiotics have been used for decades to alleviate the negative side-effects of oral antibiotics, but our mechanistic understanding on how they work is so far incomplete. Here, we performed a metagenomic analysis of the fecal microbiota in participants who underwent a 14-d Helicobacter pylori eradication therapy with or without consumption of a multi-strain probiotic intervention (L. paracasei CNCM I-1518, L. paracasei CNCM I-3689, L. rhamnosus CNCM I-3690, and four yogurt strains) in a randomized, double-blinded, controlled clinical trial. Using a strain-level analysis for detection and metagenomic determination of replication rate, ingested strains were detected and replicated transiently in fecal samples and in the gut during and following antibiotic administration. Consumption of the fermented milk product led to a significant, although modest, improvement in the recovery of microbiota composition. Stratification of participants into two groups based on the degree to which their microbiome recovered showed i) a higher fecal abundance of the probiotic L. paracasei and L. rhamnosus strains and ii) an elevated replication rate of one strain (L. paracasei CNCMI-1518) in the recovery group. Collectively, our findings show a small but measurable benefit of a fermented milk product on microbiome recovery after antibiotics, which was linked to the detection and replication of specific probiotic strains. Such functional insight can form the basis for the development of probiotic-based intervention aimed to protect gut microbiome from drug treatments.

Low-Molecular-Weight Seaweed-Derived Polysaccharides Lead to Increased Faecal Bulk but Do Not Alter Human Gut Health Markers.

Seaweeds are potentially sustainable crops and are receiving significant interest because of their rich bioactive compound content; including fatty acids, polyphenols, carotenoids, and complex polysaccharides. However, there is little information on the in vivo effects on gut health of the polysaccharides and their low-molecular-weight derivatives. Herein, we describe the first investigation into the prebiotic potential of low-molecular-weight polysaccharides (LMWPs) derived from alginate and agar in order to validate their in vivo efficacy. We conducted a randomized; placebo-controlled trial testing the impact of alginate and agar LWMPs on faecal weight and other markers of gut health and on composition of gut microbiota. We show that these LMWPs led to significantly increased faecal bulk (20-30%). Analysis of gut microbiome composition by sequencing indicated no significant changes attributable to treatment at the phylum and family level, although FISH analysis showed an increase in Faecalibacterium prausnitzii in subjects consuming agar LMWP. Sequence analysis of gut bacteria corroborated with the FISH data, indicating that alginate and agar LWMPs do not alter human gut microbiome health markers. Crucially, our findings suggest an urgent need for robust and rigorous human in vivo testing-in particular, using refined seaweed extracts.

Butyrate Inhibits Osteoclast Activity In Vitro and Regulates Systemic Inflammation and Bone Healing in a Murine Osteotomy Model Compared to Antibiotic-Treated Mice.

Short-chain fatty acids (SCFAs) produced by the gut microbiota have previously been demonstrated to play a role in numerous chronic inflammatory diseases and to be key mediators in the gut-bone signaling axis. However, the role of SCFAs in bone fracture healing and its impact on systemic inflammation during the regeneration process has not been extensively investigated yet. The aim of this study was to first determine the effects of the SCFA butyrate on key cells involved in fracture healing in vitro, namely, osteoclasts and mesenchymal stromal cells (MSCs), and second, to assess if butyrate supplementation or antibiotic therapy impacts bone healing, systemic immune status, and inflammation levels in a murine osteotomy model. Butyrate significantly reduced osteoclast formation and resorption activity in a dose-dependent manner and displayed a trend for increased calcium deposits in MSC cultures. Numerous genes associated with osteoclast differentiation were differentially expressed in osteoclast precursor cells upon butyrate exposure. In vivo, antibiotic-treated mice showed reduced SCFA levels in the cecum, as well as a distinct gut microbiome composition. Furthermore, circulating proinflammatory TNFα, IL-17a, and IL-17f levels, and bone preserving osteoprotegerin (OPG), were increased in antibiotic-treated mice compared to controls. Antibiotic-treated mice also displayed a trend towards delayed bone healing as revealed by reduced mineral apposition at the defect site and higher circulating levels of the bone turnover marker PINP. Butyrate supplementation resulted in a lower abundance of monocyte/macrophages in the bone marrow, as well as reduced circulating proinflammatory IL-6 levels compared to antibiotic- and control-treated mice. In conclusion, this study supports our hypothesis that SCFAs, in particular butyrate, are important contributors to successful bone healing by modulating key cells involved in fracture healing as well as systemic inflammation and immune responses.

Biopsy bacterial signature can predict patient tissue malignancy.

Considerable recent research has indicated the presence of bacteria in a variety of human tumours and matched normal tissue. Rather than focusing on further identification of bacteria within tumour samples, we reversed the hypothesis to query if establishing the bacterial profile of a tissue biopsy could reveal its histology / malignancy status. The aim of the present study was therefore to differentiate between malignant and non-malignant fresh breast biopsy specimens, collected specifically for this purpose, based on bacterial sequence data alone. Fresh tissue biopsies were obtained from breast cancer patients and subjected to 16S rRNA gene sequencing. Progressive microbiological and bioinformatic contamination control practices were imparted at all points of specimen handling and bioinformatic manipulation. Differences in breast tumour and matched normal tissues were probed using a variety of statistical and machine-learning-based strategies. Breast tumour and matched normal tissue microbiome profiles proved sufficiently different to indicate that a classification strategy using bacterial biomarkers could be effective. Leave-one-out cross-validation of the predictive model confirmed the ability to identify malignant breast tissue from its bacterial signature with 84.78% accuracy, with a corresponding area under the receiver operating characteristic curve of 0.888. This study provides proof-of-concept data, from fit-for-purpose study material, on the potential to use the bacterial signature of tissue biopsies to identify their malignancy status.

Differential Analysis of Longitudinal Methicillin-Resistant Staphylococcus aureus Colonization in Relation to Microbial Shifts in the Nasal Microbiome of Neonatal Piglets.

Methicillin-resistant Staphylococcus aureus (MRSA) is an important human pathogen and often colonizes pigs. To lower the risk of MRSA transmission to humans, a reduction of MRSA prevalence and/or load in pig farms is needed. The nasal microbiome contains commensal species that may protect against MRSA colonization and may be used to develop competitive exclusion strategies. To obtain a comprehensive understanding of the species that compete with MRSA in the developing porcine nasal microbiome, and the moment of MRSA colonization, we analyzed nasal swabs from piglets in two litters. The swabs were taken longitudinally, starting directly after birth until 6 weeks. Both 16S rRNA and tuf gene sequencing data with different phylogenetic resolutions and complementary culture-based and quantitative real-time PCR (qPCR)-based MRSA quantification data were collected. We employed a compositionally aware bioinformatics approach (CoDaSeq + rmcorr) for analysis of longitudinal measurements of the nasal microbiota. The richness and diversity in the developing nasal microbiota increased over time, albeit with a reduction of Firmicutes and Actinobacteria, and an increase of Proteobacteria. Coabundant groups (CAGs) of species showing strong positive and negative correlation with colonization of MRSA and S. aureus were identified. Combining 16S rRNA and tuf gene sequencing provided greater Staphylococcus species resolution, which is necessary to inform strategies with potential protective effects against MRSA colonization in pigs. IMPORTANCE The large reservoir of methicillin-resistant Staphylococcus aureus (MRSA) in pig farms imposes a significant zoonotic risk. An effective strategy to reduce MRSA colonization in pig farms is competitive exclusion whereby MRSA colonization can be reduced by the action of competing bacterial species. We complemented 16S rRNA gene sequencing with Staphylococcus-specific tuf gene sequencing to identify species anticorrelating with MRSA colonization. This approach allowed us to elucidate microbiome dynamics and identify species that are negatively and positively associated with MRSA, potentially suggesting a route for its competitive exclusion.

Paediatric Inflammatory Bowel Disease and its Relationship with the Microbiome.

Paediatric inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the digestive tract, comprising of Crohn's disease (CD), ulcerative colitis (UC), and, where classification is undetermined, inflammatory bowel disease unclassified (IBDU). Paediatric IBD incidence is increasing globally, with prevalence highest in the developed world. Though no specific causative agent has been identified for paediatric IBD, it is believed that a number of factors may contribute to the development of the disease, including genetics and the environment. Another potential component in the development of IBD is the microbiota in the digestive tract, particularly the gut. While the exact role that the microbiome plays in IBD is unclear, many studies acknowledge the complex relationship between the gut bacteria and pathogenesis of IBD. In this review, we look at the increasing number of studies investigating the role the microbiome and other biomes play in paediatric patients with IBD, particularly changes associated with IBD, varying disease states, and therapeutics. The paediatric IBD microbiome is significantly different to that of healthy children, with decreased diversity and differences in bacterial composition (such as a decrease in Firmicutes). Changes in the microbiome relating to various treatments of IBD and disease severity have also been observed in multiple studies. Changes in diversity and composition may also extend to other biomes in paediatric IBD, such as the virome and the mycobiome. Research into biome differences in IBD paediatric patients may help progress our understanding of the aetiology of the disease.

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Microbiota from young mice counteracts selective age-associated behavioral deficits.

The gut microbiota is increasingly recognized as an important regulator of host immunity and brain health. The aging process yields dramatic alterations in the microbiota, which is linked to poorer health and frailty in elderly populations. However, there is limited evidence for a mechanistic role of the gut microbiota in brain health and neuroimmunity during aging processes. Therefore, we conducted fecal microbiota transplantation from either young (3-4 months) or old (19-20 months) donor mice into aged recipient mice (19-20 months). Transplant of a microbiota from young donors reversed aging-associated differences in peripheral and brain immunity, as well as the hippocampal metabolome and transcriptome of aging recipient mice. Finally, the young donor-derived microbiota attenuated selective age-associated impairments in cognitive behavior when transplanted into an aged host. Our results reveal that the microbiome may be a suitable therapeutic target to promote healthy aging.

Ranking microbiome variance in inflammatory bowel disease: a large longitudinal intercontinental study.

OBJECTIVE: The microbiome contributes to the pathogenesis of inflammatory bowel disease (IBD) but the relative contribution of different lifestyle and environmental factors to the compositional variability of the gut microbiota is unclear. DESIGN: Here, we rank the size effect of disease activity, medications, diet and geographic location of the faecal microbiota composition (16S rRNA gene sequencing) in patients with Crohn's disease (CD; n=303), ulcerative colitis (UC; n = 228) and controls (n=161), followed longitudinally (at three time points with 16 weeks intervals). RESULTS: Reduced microbiota diversity but increased variability was confirmed in CD and UC compared with controls. Significant compositional differences between diseases, particularly CD, and controls were evident. Longitudinal analyses revealed reduced temporal microbiota stability in IBD, particularly in patients with changes in disease activity. Machine learning separated disease from controls, and active from inactive disease, when consecutive time points were modelled. Geographic location accounted for most of the microbiota variance, second to the presence or absence of CD, followed by history of surgical resection, alcohol consumption and UC diagnosis, medications and diet with most (90.3%) of the compositional variance stochastic or unexplained. CONCLUSION: The popular concept of precision medicine and rational design of any therapeutic manipulation of the microbiota will have to contend not only with the heterogeneity of the host response, but also with widely differing lifestyles and with much variance still unaccounted for.