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

Performance of Gut Microbiome as an Independent Diagnostic Tool for 20 Diseases: Cross-Cohort Validation of Machine-Learning Classifiers

Cross-cohort validation is essential for gut-microbiome-based disease stratification but was only performed for limited diseases. Here, we systematically evaluated the cross-cohort performance of gut microbiome-based machine-learning classifiers for 20 diseases. Using single-cohort classifiers, we obtained high predictive accuracies in intra-cohort validation (~0.77 AUC), but low accuracies in cross-cohort validation, except the intestinal diseases (~0.73 AUC). We then built combined-cohort classifiers trained on samples combined from multiple cohorts to improve the validation of non-intestinal diseases, and estimated the required sample size to achieve validation accuracies of >0.7. In addition, we observed higher validation performance for classifiers using metagenomic data than 16S amplicon data in intestinal diseases. We further quantified the cross-cohort marker consistency using a Marker Similarity Index and observed similar trends. Together, our results supported the gut microbiome as an independent diagnostic tool for intestinal diseases and revealed strategies to improve cross-cohort performance based on identified determinants of consistent cross-cohort gut microbiome alterations.

In sickness and in health: the dynamics of the fruit bat gut microbiota under a bacterial antigen challenge and its association with the immune response

Introduction

Interactions between the gut microbiome (GM) and the immune system influence host health and fitness. However, few studies have investigated this link and GM dynamics during disease in wild species. Bats (Mammalia: Chiroptera) have an exceptional ability to cope with intracellular pathogens and a unique GM adapted to powered flight. Yet, the contribution of the GM to bat health, especially immunity, or how it is affected by disease, remains unknown.

Methods

Here, we examined the dynamics of the Egyptian fruit bats' (Rousettus aegyptiacus) GM during health and disease. We provoked an inflammatory response in bats using lipopolysaccharides (LPS), an endotoxin of Gram-negative bacteria. We then measured the inflammatory marker haptoglobin, a major acute phase protein in bats, and analyzed the GM (anal swabs) of control and challenged bats using high-throughput 16S rRNA sequencing, before the challenge, 24h and 48h post challenge.

Results

We revealed that the antigen challenge causes a shift in the composition of the bat GM (e.g., Weissella, Escherichia, Streptococcus). This shift was significantly correlated with haptoglobin concentration, but more strongly with sampling time. Eleven bacterial sequences were correlated with haptoglobin concentration and nine were found to be potential predictors of the strength of the immune response, and implicit of infection severity, notably Weissella and Escherichia. The bat GM showed high resilience, regaining the colony's group GM composition rapidly, as bats resumed foraging and social activities.

Conclusion

Our results demonstrate a tight link between bat immune response and changes in their GM, and emphasize the importance of integrating microbial ecology in ecoimmunological studies of wild species. The resilience of the GM may provide this species with an adaptive advantage to cope with infections and maintain colony health.

Ghrelin Alleviates Experimental Ulcerative Colitis in Old Mice and Modulates Colonocyte Metabolism via PPARγ Pathway

There is a growing prevalence of inflammatory bowel disease (IBD), a chronic inflammatory condition of the gastrointestinal tract, among the aging population. Ghrelin is a gut hormone that, in addition to controlling feeding and energy metabolism, has been shown to exert anti-inflammatory effects; however, the effect of ghrelin in protecting against colitis in old mice has not been assessed. Here, we subjected old female C57BL/6J mice to dextran sulfate sodium (DSS) in drinking water for six days, then switched back to normal drinking water, administered acyl-ghrelin or vehicle control from day 3 to 13, and monitored disease activities throughout the disease course. Our results showed that treatment of old mice with acyl-ghrelin attenuated DSS-induced colitis. Compared to the DSS group, ghrelin treatment decreased levels of the inflammation marker S100A9 in the colons collected on day 14 but not on day 8, suggesting that the anti-inflammatory effect was more prominent in the recovery phase. Ghrelin treatment also significantly reduced F4/80 and interleukin-17A on day 14. Moreover, acyl-ghrelin increased mitochondrial respiration and activated transcriptional activity of the peroxisome proliferator-activated receptor gamma (PPARγ) in Caco-2 cells. Together, our data show that ghrelin alleviated DSS-induced colitis, suggesting that ghrelin may promote tissue repair in part through regulating epithelial metabolism via PPARγ mediated signaling.

Machine learning-based feature selection to search stable microbial biomarkers: application to inflammatory bowel disease

BACKGROUND

Biomarker discovery exploiting feature importance of machine learning has risen recently in the microbiome landscape with its high predictive performance in several disease states. To have a concrete selection among a high number of features, recursive feature elimination (RFE) has been widely used in the bioinformatics field. However, machine learning-based RFE has factors that decrease the stability of feature selection. In this article, we suggested methods to improve stability while sustaining performance.

RESULTS

We exploited the abundance matrices of the gut microbiome (283 taxa at species level and 220 at genus level) to classify between patients with inflammatory bowel disease (IBD) and healthy control (1,569 samples). We found that applying an already published data transformation before RFE improves feature stability significantly. Moreover, we performed an in-depth evaluation of different variants of the data transformation and identify those that demonstrate better improvement in stability while not sacrificing classification performance. To ensure a robust comparison, we evaluated stability using various similarity metrics, distances, the common number of features, and the ability to filter out noise features. We were able to confirm that the mapping by the Bray-Curtis similarity matrix before RFE consistently improves the stability while maintaining good performance. Multilayer perceptron algorithm exhibited the highest performance among 8 different machine learning algorithms when a large number of features (a few hundred) were considered based on the best performance across 100 bootstrapped internal test sets. Conversely, when utilizing only a limited number of biomarkers as a trade-off between optimal performance and method generalizability, the random forest algorithm demonstrated the best performance. Using the optimal pipeline we developed, we identified 14 biomarkers for IBD at the species level and analyzed their roles using Shapley additive explanations.

CONCLUSION

Taken together, our work not only showed how to improve biomarker discovery in the metataxonomic field without sacrificing classification performance but also provided useful insights for future comparative studies.

Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi-omic analyses

BACKGROUND

Alterations to the gut microbiome have been linked to multiple chronic diseases. However, the drivers of such changes remain largely unknown. The oral cavity acts as a major route of exposure to exogenous factors including pathogens, and processes therein may affect the communities in the subsequent compartments of the gastrointestinal tract. Here, we perform strain-resolved, integrated meta-genomic, transcriptomic, and proteomic analyses of paired saliva and stool samples collected from 35 individuals from eight families with multiple cases of type 1 diabetes mellitus (T1DM).

RESULTS

We identified distinct oral microbiota mostly reflecting competition between streptococcal species. More specifically, we found a decreased abundance of the commensal Streptococcus salivarius in the oral cavity of T1DM individuals, which is linked to its apparent competition with the pathobiont Streptococcus mutans. The decrease in S. salivarius in the oral cavity was also associated with its decrease in the gut as well as higher abundances in facultative anaerobes including Enterobacteria. In addition, we found evidence of gut inflammation in T1DM as reflected in the expression profiles of the Enterobacteria as well as in the human gut proteome. Finally, we were able to follow transmitted strain-variants from the oral cavity to the gut at the individual omic levels, highlighting not only the transfer, but also the activity of the transmitted taxa along the gastrointestinal tract.

CONCLUSIONS

Alterations of the oral microbiome in the context of T1DM impact the microbial communities in the lower gut, in particular through the reduction of "mouth-to-gut" transfer of Streptococcus salivarius. Our results indicate that the observed oral-cavity-driven gut microbiome changes may contribute towards the inflammatory processes involved in T1DM. Through the integration of multi-omic analyses, we resolve strain-variant "mouth-to-gut" transfer in a disease context. Video Abstract.

Short-Chain Fatty Acids as Bacterial Enterocytes and Therapeutic Target in Diabetes Mellitus Type 2

Diabetes mellitus is a disease with multiple gastrointestinal symptoms (diarrhea or constipation, abdominal pain, bloating) whose pathogenesis is multifactorial. The most important of these factors is the enteric nervous system, also known as the "second brain"; a part of the peripheral nervous system capable of functioning independently of the central nervous system. Modulation of the enteric nervous system can be done by short-chain fatty acids, which are bacterial metabolites of the intestinal microbiota. In addition, these acids provide multiple benefits in diabetes, particularly by stimulating glucagon-like peptide 1 and insulin secretion. However, it is not clear what type of nutraceuticals (probiotics, prebiotics, and alimentary supplements) can be used to increase the amount of short-chain fatty acids and achieve the beneficial effects in diabetes. Thus, even if several studies demonstrate that the gut microbiota modulates the activity of the ENS, and thus, may have a positive effect in diabetes, further studies are needed to underline this effect. This review outlines the most recent data regarding the involvement of SCFAs as a disease modifying agent in diabetes mellitus type 2. For an in-depth understanding of the modulation of gut dysbiosis with SCFAs in diabetes, we provide an overview of the interplay between gut microbiota and ENS.

Is there a difference in fecal microbiota of children with and without voiding dysfunction?

OBJECTIVE

Voiding dysfunction (VD), which encompasses many urinary symptoms that are not caused by neurological or anatomical anomalies, is a frequently encountered functional urinary bladder disorder in children. It was reported that there was an association between lower urinary tract symptoms and fecal microbiota in adult patients. Therefore, we aimed to investigate the differences in fecal microbiota between children with or without VD.

METHODS

Two patient groups, including 30 patients, were compared. Group 1 included patients with VD, while Group 2 consisted of healthy children. All study participants were asked to fill lower urinary tract and voiding dysfunction symptom score forms with the assistance of their parents. Subsequently, uroflowmetry tests and postvoiding residual urine measurements were performed. Fresh stool samples were collected from all children and analyzed by polymerase chain reaction. General bacterial load and presence of Roseburia intestinalis, Clostridium difficile, Fusobacterium nucleatum, and Bacteroides clarus were tested.

RESULTS

The two groups were significantly different regarding general bacterial load; the presence of Fusobacterium nucleatum. Clostridium difficile and Bacteroides clarus was not detected in the fresh stool samples of the patients in Group 2; the counts of Roseburia intestinalis were less in Group 1 than in Group 2, although there was no statistically significant difference. There was a negative correlation between symptom scores, general bacterial load, and the presence of Fusobacterium nucleatum. However, there was no correlation between the presence of Roseburia intestinalis and symptom scores.

CONCLUSIONS

There is a potential relationship between VD and a deviation in the fecal microbiota in the pediatric population.

Stochastic variational variable selection for high-dimensional microbiome data

BACKGROUND

The rapid and accurate identification of a minimal-size core set of representative microbial species plays an important role in the clustering of microbial community data and interpretation of clustering results. However, the huge dimensionality of microbial metagenomics datasets is a major challenge for the existing methods such as Dirichlet multinomial mixture (DMM) models. In the approach of the existing methods, the computational burden of identifying a small number of representative species from a large number of observed species remains a challenge.

RESULTS

We propose a novel approach to improve the performance of the widely used DMM approach by combining three ideas: (i) we propose an indicator variable to identify representative operational taxonomic units that substantially contribute to the differentiation among clusters; (ii) to address the computational burden of high-dimensional microbiome data, we propose a stochastic variational inference, which approximates the posterior distribution using a controllable distribution called variational distribution, and stochastic optimization algorithms for fast computation; and (iii) we extend the finite DMM model to an infinite case by considering Dirichlet process mixtures and estimating the number of clusters as a variational parameter. Using the proposed method, stochastic variational variable selection (SVVS), we analyzed the root microbiome data collected in our soybean field experiment, the human gut microbiome data from three published datasets of large-scale case-control studies and the healthy human microbiome data from the Human Microbiome Project.

CONCLUSIONS

SVVS demonstrates a better performance and significantly faster computation than those of the existing methods in all cases of testing datasets. In particular, SVVS is the only method that can analyze massive high-dimensional microbial data with more than 50,000 microbial species and 1000 samples. Furthermore, a core set of representative microbial species is identified using SVVS that can improve the interpretability of Bayesian mixture models for a wide range of microbiome studies. Video Abstract.

TaxiBGC: a Taxonomy-Guided Approach for Profiling Experimentally Characterized Microbial Biosynthetic Gene Clusters and Secondary Metabolite Production Potential in Metagenomes

Biosynthetic gene clusters (BGCs) in microbial genomes encode bioactive secondary metabolites (SMs), which can play important roles in microbe-microbe and host-microbe interactions. Given the biological significance of SMs and the current profound interest in the metabolic functions of microbiomes, the unbiased identification of BGCs from high-throughput metagenomic data could offer novel insights into the complex chemical ecology of microbial communities. Currently available tools for predicting BGCs from shotgun metagenomes have several limitations, including the need for computationally demanding read assembly, predicting a narrow breadth of BGC classes, and not providing the SM product. To overcome these limitations, we developed taxonomy-guided identification of biosynthetic gene clusters (TaxiBGC), a command-line tool for predicting experimentally characterized BGCs (and inferring their known SMs) in metagenomes by first pinpointing the microbial species likely to harbor them. We benchmarked TaxiBGC on various simulated metagenomes, showing that our taxonomy-guided approach could predict BGCs with much-improved performance (mean F1 score, 0.56; mean PPV score, 0.80) compared with directly identifying BGCs by mapping sequencing reads onto the BGC genes (mean F1 score, 0.49; mean PPV score, 0.41). Next, by applying TaxiBGC on 2,650 metagenomes from the Human Microbiome Project and various case-control gut microbiome studies, we were able to associate BGCs (and their SMs) with different human body sites and with multiple diseases, including Crohn's disease and liver cirrhosis. In all, TaxiBGC provides an in silico platform to predict experimentally characterized BGCs and their SM production potential in metagenomic data while demonstrating important advantages over existing techniques. IMPORTANCE Currently available bioinformatics tools to identify BGCs from metagenomic sequencing data are limited in their predictive capability or ease of use to even computationally oriented researchers. We present an automated computational pipeline called TaxiBGC, which predicts experimentally characterized BGCs (and infers their known SMs) in shotgun metagenomes by first considering the microbial species source. Through rigorous benchmarking techniques on simulated metagenomes, we show that TaxiBGC provides a significant advantage over existing methods. When demonstrating TaxiBGC on thousands of human microbiome samples, we associate BGCs encoding bacteriocins with different human body sites and diseases, thereby elucidating a possible novel role of this antibiotic class in maintaining the stability of microbial ecosystems throughout the human body. Furthermore, we report for the first time gut microbial BGC associations shared among multiple pathologies. Ultimately, we expect our tool to facilitate future investigations into the chemical ecology of microbial communities across diverse niches and pathologies.

Short Chain Fatty Acid Metabolism in Relation to Gut Microbiota and Genetic Variability

It is widely accepted that the gut microbiota plays a significant role in modulating inflammatory and immune responses of their host. In recent years, the host-microbiota interface has gained relevance in understanding the development of many non-communicable chronic conditions, including cardiovascular disease, cancer, autoimmunity and neurodegeneration. Importantly, dietary fibre (DF) and associated compounds digested by the microbiota and their resulting metabolites, especially short-chain fatty acids (SCFA), were significantly associated with health beneficial effects, such as via proposed anti-inflammatory mechanisms. However, SCFA metabolic pathways are not fully understood. Major steps include production of SCFA by microbiota, uptake in the colonic epithelium, first-pass effects at the liver, followed by biodistribution and metabolism at the host's cellular level. As dietary patterns do not affect all individuals equally, the host genetic makeup may play a role in the metabolic fate of these metabolites, in addition to other factors that might influence the microbiota, such as age, birth through caesarean, medication intake, alcohol and tobacco consumption, pathogen exposure and physical activity. In this article, we review the metabolic pathways of DF, from intake to the intracellular metabolism of fibre-derived products, and identify possible sources of inter-individual variability related to genetic variation. Such variability may be indicative of the phenotypic flexibility in response to diet, and may be predictive of long-term adaptations to dietary factors, including maladaptation and tissue damage, which may develop into disease in individuals with specific predispositions, thus allowing for a better prediction of potential health effects following personalized intervention with DF.

Interpreting tree ensemble machine learning models with endoR

Tree ensemble machine learning models are increasingly used in microbiome science as they are compatible with the compositional, high-dimensional, and sparse structure of sequence-based microbiome data. While such models are often good at predicting phenotypes based on microbiome data, they only yield limited insights into how microbial taxa may be associated. We developed endoR, a method to interpret tree ensemble models. First, endoR simplifies the fitted model into a decision ensemble. Then, it extracts information on the importance of individual features and their pairwise interactions, displaying them as an interpretable network. Both the endoR network and importance scores provide insights into how features, and interactions between them, contribute to the predictive performance of the fitted model. Adjustable regularization and bootstrapping help reduce the complexity and ensure that only essential parts of the model are retained. We assessed endoR on both simulated and real metagenomic data. We found endoR to have comparable accuracy to other common approaches while easing and enhancing model interpretation. Using endoR, we also confirmed published results on gut microbiome differences between cirrhotic and healthy individuals. Finally, we utilized endoR to explore associations between human gut methanogens and microbiome components. Indeed, these hydrogen consumers are expected to interact with fermenting bacteria in a complex syntrophic network. Specifically, we analyzed a global metagenome dataset of 2203 individuals and confirmed the previously reported association between Methanobacteriaceae and Christensenellales. Additionally, we observed that Methanobacteriaceae are associated with a network of hydrogen-producing bacteria. Our method accurately captures how tree ensembles use features and interactions between them to predict a response. As demonstrated by our applications, the resultant visualizations and summary outputs facilitate model interpretation and enable the generation of novel hypotheses about complex systems.

Analysis of correlations between gut microbiota, stool short chain fatty acids, calprotectin and cardiometabolic risk factors in postmenopausal women with obesity: a cross-sectional study

BACKGROUND

Microbiota and its metabolites are known to regulate host metabolism. In cross-sectional study conducted in postmenopausal women we aimed to assess whether the microbiota, its metabolites and gut barrier integrity marker are correlated with cardiometabolic risk factors and if microbiota is different between obese and non-obese subjects.

METHODS

We analysed the faecal microbiota of 56 obese, postmenopausal women by means of 16S rRNA analysis. Stool short chain fatty acids, calprotectin and anthropometric, physiological and biochemical parameters were correlates to microbiome analyses.

RESULTS

Alpha-diversity was inversely correlated with lipopolysaccharide (Rho = - 0.43, FDR P (Q) = 0.004). Bray-Curtis distance based RDA revealed that visceral fat and waist circumference had a significant impact on metabolic potential (P = 0.003). Plasma glucose was positively correlated with the Coriobacteriaceae (Rho = 0.48, Q = 0.004) and its higher taxonomic ranks, up to phylum (Actinobacteria, Rho = 0.46, Q = 0.004). At the metabolic level, the strongest correlation was observed for the visceral fat (Q < 0.15), especially with the DENOVOPURINE2-PWY, PWY-841 and PWY0-162 pathways. Bacterial abundance was correlated with SCFAs, thus some microbiota-glucose relationships may be mediated by propionate, as indicated by the significant average causal mediation effect (ACME): Lachnospiraceae (ACME 1.25, 95%CI (0.10, 2.97), Firmicutes (ACME 1.28, 95%CI (0.23, 3.83)) and Tenericutes (ACME - 0.39, 95%CI (- 0.87, - 0.03)). There were significant differences in the distribution of phyla between this study and Qiita database (P < 0.0001).

CONCLUSIONS

Microbiota composition and metabolic potential are associated with some CMRF and fecal SCFAs concentration in obese postmenopausal women. There is no unequivocal relationship between fecal SCFAs and the marker of intestinal barrier integrity and CMRF. Further studies with appropriately matched control groups are warranted to look for causality between SCFAs and CMRF.

Reproducible and opposing gut microbiome signatures distinguish autoimmune diseases and cancers: a systematic review and meta-analysis

BACKGROUND

The gut microbiome promotes specific immune responses, and in turn, the immune system has a hand in shaping the microbiome. Cancer and autoimmune diseases are two major disease families that result from the contrasting manifestations of immune dysfunction. We hypothesized that the opposing immunological profiles between cancer and autoimmunity yield analogously inverted gut microbiome signatures. To test this, we conducted a systematic review and meta-analysis on gut microbiome signatures and their directionality in cancers and autoimmune conditions.

METHODOLOGY

We searched PubMed, Web of Science, and Embase to identify relevant articles to be included in this study. The study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements and PRISMA 2009 checklist. Study estimates were pooled by a generic inverse variance random-effects meta-analysis model. The relative abundance of microbiome features was converted to log fold change, and the standard error was calculated from the p-values, sample size, and fold change.

RESULTS

We screened 3874 potentially relevant publications. A total of 82 eligible studies comprising 37 autoimmune and 45 cancer studies with 4208 healthy human controls and 5957 disease cases from 27 countries were included in this study. We identified a set of microbiome features that show consistent, opposite directionality between cancers and autoimmune diseases in multiple studies. Fusobacterium and Peptostreptococcus were the most consistently increased genera among the cancer cases which were found to be associated in a remarkable 13 (+0.5 log fold change in 5 studies) and 11 studies (+3.6 log fold change in 5 studies), respectively. Conversely, Bacteroides was the most prominent genus, which was found to be increased in 12 autoimmune studies (+0.2 log fold change in 6 studies) and decreased in six cancer studies (-0.3 log fold change in 4 studies). Sulfur-metabolism pathways were found to be the most frequent pathways among the member of cancer-increased genus and species.

CONCLUSIONS

The surprising reproducibility of these associations across studies and geographies suggests a shared underlying mechanism shaping the microbiome across cancers and autoimmune diseases. Video Abstract.

Maintenance of gut microbiome stability for optimum intestinal health in pigs - a review

Pigs are exposed to various challenges such as weaning, environmental stressors, unhealthy diet, diseases and infections during their lifetime which adversely affects the gut microbiome. The inability of the pig microbiome to return to the pre-challenge baseline may lead to dysbiosis resulting in the outbreak of diseases. Therefore, the maintenance of gut microbiome diversity, robustness and stability has been influential for optimum intestinal health after perturbations. Nowadays human and animal researches have focused on more holistic approaches to obtain a robust gut microbiota that provides protection against pathogens and improves the digestive physiology and the immune system. In this review, we present an overview of the swine gut microbiota, factors affecting the gut microbiome and the importance of microbial stability in promoting optimal intestinal health. Additionally, we discussed the current understanding of nutritional interventions using fibers and pre/probiotics supplementation as non-antibiotic alternatives to maintain microbiota resilience to replace diminished species.

The gut microbiota and depressive symptoms across ethnic groups

The gut microbiome is thought to play a role in depressive disorders, which makes it an attractive target for interventions. Both the microbiome and depressive symptom levels vary substantially across ethnic groups. Thus, any intervention for depression targeting the microbiome requires understanding of microbiome-depression associations across ethnicities. Analysing data from the HELIUS cohort, we characterize the gut microbiota and its associations with depressive symptoms in 6 ethnic groups (Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish, Moroccan; N = 3211), living in the same urban area. Diversity of the gut microbiota, both within (α-diversity) and between individuals (β-diversity), predicts depressive symptom levels, taking into account demographic, behavioural, and medical differences. These associations do not differ between ethnic groups. Further, β-diversity explains 29%-18% of the ethnic differences in depressive symptoms. Bacterial genera associated with depressive symptoms belong to mulitple families, prominently including the families Christensenellaceae, Lachnospiraceae, and Ruminococcaceae. In summary, the results show that the gut microbiota are linked to depressive symptom levels and that this association generalizes across ethnic groups. Moreover, the results suggest that ethnic differences in the gut microbiota may partly explain parallel disparities in depression.

Microbiome and Metabolome Insights into the Role of the Gastrointestinal-Brain Axis in Parkinson's and Alzheimer's Disease: Unveiling Potential Therapeutic Targets

Neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's disease (AD), the prevalence of which is rapidly rising due to an aging world population and westernization of lifestyles, are expected to put a strong socioeconomic burden on health systems worldwide. Clinical trials of therapies against PD and AD have only shown limited success so far. Therefore, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome and metabolome studies have already revealed important insights into disease mechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the interplay between microbiota, metabolites, and neurodegeneration along the gastrointestinal-brain axis. We further illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude with therapeutic options to modulate microbiome composition to prevent or delay neurodegeneration and illustrate potential future research directions to fight PD and AD.

Distinct changes in the colonic microbiome associated with acute diverticulitis

AIM

The pathogenesis of acute diverticulitis (AD) remains incompletely understood, despite it being one of the most common gastrointestinal conditions worldwide. The aim of this study was to investigate the role of the colonic microbiome in the pathogenesis of AD.

METHOD

A prospective case-control study was performed, comparing the microbiome of AD patients with that of controls, using 16S rRNA sequencing of rectal swab samples.

RESULTS

The microbiome of individuals with AD showed lower diversity than that of controls. There were significant compositional differences observed, with a lower abundance of commensal bacterial families and genera such as Lachnospiraceae, Ruminococcus and Faecalibacterium in AD patients compared with controls, and there was an increase in several genera with known pathogenic roles including Fusobacteria, Prevotella and Paraprevotella.

CONCLUSION

This is the largest study to date to examine the microbiota of AD patients, and adds evidence to the proposed hypothesis that alterations in the colonic microbiome play a role in the pathogenesis of AD.

Meta-analysis of caries microbiome studies can improve upon disease prediction outcomes

As one of the most prevalent infective diseases worldwide, it is crucial that we not only know the constituents of the oral microbiome in dental caries but also understand its functionality. Herein, we present a reproducible meta-analysis to effectively report the key components and the associated functional signature of the oral microbiome in dental caries. Publicly available sequencing data were downloaded from online repositories and subjected to a standardized analysis pipeline before analysis. Meta-analyses identified significant differences in alpha and beta diversities of carious microbiomes when compared to healthy ones. Additionally, machine learning and receiver operator characteristic analysis showed an ability to discriminate between healthy and disease microbiomes. We identified from importance values, as derived from random forest analyses, a group of genera, notably containing Selenomonas, Aggregatibacter, Actinomyces and Treponema, which can be predictive of dental caries. Finally, we propose the most appropriate study design for investigating the microbiome of dental caries by synthesizing the studies, which had the most accurate differentiation based on random forest modelling. In conclusion, we have developed a non-biased, reproducible pipeline, which can be applied to microbiome meta-analyses of multiple diseases, but importantly we have derived from our meta-analysis a key group of organisms that can be used to identify individuals at risk of developing dental caries based on oral microbiome inhabitants.

Toward a Deeper Understanding of Gut Microbiome in Depression: The Promise of Clinical Applicability

The emergence of the coronavirus disease 2019 pandemic has dramatically increased the global prevalence of depression. Unfortunately, antidepressant drugs benefit only a small minority of patients. Thus, there is an urgent need to develop new interventions. Accumulating evidence supports a causal relationship between gut microbiota dysbiosis and depression. To advance microbiota-based diagnostics and therapeutics of depression, a comprehensive overview of microbial alterations in depression is presented to identify effector microbial biomarkers. This procedure generated 215 bacterial taxa from humans and 312 from animal models. Compared to controls, depression shows significant differences in β-diversity, but no changes in microbial richness and diversity. Additionally, species-specific microbial changes are identified like increased Eggerthella in humans and decreased Acetatifactor in rodent models. Moreover, a disrupted microbiome balance and functional changes, characterized by an enrichment of pro-inflammatory bacteria (e.g., Desulfovibrio and Escherichia/Shigella) and depletion of anti-inflammatory butyrate-producing bacteria (e.g., Bifidobacterium and Faecalibacterium) are consistently shared across species. Confounding effects of geographical region, depression type, and intestinal segments are also investigated. Ultimately, a total of 178 species and subspecies probiotics are identified to alleviate the depressive phenotypes. Current findings provide a foundation for developing microbiota-based diagnostics and therapeutics and advancing microbiota-oriented precision medicine for depression.

Meta-analysis of sputum microbiome studies identifies airway disease-specific taxonomic and functional signatures

Introduction. Studying taxonomic and functional signatures of respiratory microbiomes provide a better understanding of airway diseases.Gap Statement. Several human airway metagenomics studies have identified taxonomic and functional features restricted to a single disease condition and the findings are not comparable across airway diseases due to use of different samples, NGS platforms, and bioinformatics databases and tools.Aim. To study the microbial taxonomic and functional components of sputum microbiome across airway diseases and healthy smokers.Methodology. Here, 57 whole metagenome shotgun sequencing (WMSS) runs coming from the sputum of five airway diseases: asthma, bronchiectasis, chronic obstructive pulmonary diseases (COPD), cystic fibrosis (CF), tuberculosis (TB), and healthy smokers as the control were reanalysed using a common WMSS analysis pipeline.Results. Shannon's index (alpha diversity) of the healthy smoker group was the highest among all. The beta diversity showed that the sputum microbiome is distinct in major airway diseases such as asthma, COPD and cystic fibrosis. The microbial composition based on differential analysis showed that there are specific markers for each airway disease like Acinetobacter bereziniae as a marker for COPD and Achromobacter xylosoxidans as a marker of cystic fibrosis. Pathways and metabolites identified from the sputum microbiome of these five diseases and healthy smokers also show specific markers. 'ppGpp biosynthesis' and 'purine ribonucleosides degradation' pathways were identified as differential markers for bronchiectasis and COPD. In this meta-analysis, besides bacteria kingdom, Aspergillus fumigatus was detected in asthma and COPD, and Roseolovirus human betaherpesvirus 7 was detected in COPD. Our analysis showed that the majority of the gene families specific to the drug-resistant associated genes were detected from opportunistic pathogens across all the groups.Conclusion. In summary, the specific species in the sputum of airway diseases along with the microbial features like specific gene families, pathways, and metabolites were identified which can be explored for better diagnosis and therapy.

Machine learning for data integration in human gut microbiome

Recent studies have demonstrated that gut microbiota plays critical roles in various human diseases. High-throughput technology has been widely applied to characterize the microbial ecosystems, which led to an explosion of different types of molecular profiling data, such as metagenomics, metatranscriptomics and metabolomics. For analysis of such data, machine learning algorithms have shown to be useful for identifying key molecular signatures, discovering potential patient stratifications, and particularly for generating models that can accurately predict phenotypes. In this review, we first discuss how dysbiosis of the intestinal microbiota is linked to human disease development and how potential modulation strategies of the gut microbial ecosystem can be used for disease treatment. In addition, we introduce categories and workflows of different machine learning approaches, and how they can be used to perform integrative analysis of multi-omics data. Finally, we review advances of machine learning in gut microbiome applications and discuss related challenges. Based on this we conclude that machine learning is very well suited for analysis of gut microbiome and that these approaches can be useful for development of gut microbe-targeted therapies, which ultimately can help in achieving personalized and precision medicine.

Faecal microbiome-based machine learning for multi-class disease diagnosis

Systemic characterisation of the human faecal microbiome provides the opportunity to develop non-invasive approaches in the diagnosis of a major human disease. However, shared microbial signatures across different diseases make accurate diagnosis challenging in single-disease models. Herein, we present a machine-learning multi-class model using faecal metagenomic dataset of 2,320 individuals with nine well-characterised phenotypes, including colorectal cancer, colorectal adenomas, Crohn's disease, ulcerative colitis, irritable bowel syndrome, obesity, cardiovascular disease, post-acute COVID-19 syndrome and healthy individuals. Our processed data covers 325 microbial species derived from 14.3 terabytes of sequence. The trained model achieves an area under the receiver operating characteristic curve (AUROC) of 0.90 to 0.99 (Interquartile range, IQR, 0.91-0.94) in predicting different diseases in the independent test set, with a sensitivity of 0.81 to 0.95 (IQR, 0.87-0.93) at a specificity of 0.76 to 0.98 (IQR 0.83-0.95). Metagenomic analysis from public datasets of 1,597 samples across different populations observes comparable predictions with AUROC of 0.69 to 0.91 (IQR 0.79-0.87). Correlation of the top 50 microbial species with disease phenotypes identifies 363 significant associations (FDR < 0.05). This microbiome-based multi-disease model has potential clinical application in disease diagnostics and treatment response monitoring and warrants further exploration.

Toward an improved definition of a healthy microbiome for healthy aging

The gut microbiome is a modifier of disease risk because it interacts with nutrition, metabolism, immunity and infection. Aging-related health loss has been correlated with transition to different microbiome states. Microbiome summary indices including alpha diversity are apparently useful to describe these states but belie taxonomic differences that determine biological importance. We analyzed 21,000 fecal microbiomes from seven data repositories, across five continents spanning participant ages 18-107 years, revealing that microbiome diversity and uniqueness correlate with aging, but not healthy aging. Among summary statistics tested, only Kendall uniqueness accurately reflects loss of the core microbiome and the abundance and ranking of disease-associated and health-associated taxa. Increased abundance of these disease-associated taxa and depletion of a coabundant subset of health-associated taxa are a generic feature of aging. These alterations are stronger correlates of unhealthy aging than most microbiome summary statistics and thus help identify better targets for therapeutic modulation of the microbiome.

Meta-analysis of microbiome association networks reveal patterns of dysbiosis in diseased microbiomes

The human gut microbiome is composed of a diverse and dynamic population of microbial species which play key roles in modulating host health and physiology. While individual microbial species have been found to be associated with certain disease states, increasing evidence suggests that higher-order microbial interactions may have an equal or greater contribution to host fitness. To better understand microbial community dynamics, we utilize networks to study interactions through a meta-analysis of microbial association networks between healthy and disease gut microbiomes. Taking advantage of the large number of metagenomes derived from healthy individuals and patients with various diseases, together with recent advances in network inference that can deal with sparse compositional data, we inferred microbial association networks based on co-occurrence of gut microbial species and made the networks publicly available as a resource (GitHub repository named GutNet). Through our meta-analysis of inferred networks, we were able to identify network-associated features that help stratify between healthy and disease states such as the differentiation of various bacterial phyla and enrichment of Proteobacteria interactions in diseased networks. Additionally, our findings show that the contributions of taxa in microbial associations are disproportionate to their abundances and that rarer taxa of microbial species play an integral part in shaping dynamics of microbial community interactions. Network-based meta-analysis revealed valuable insights into microbial community dynamics between healthy and disease phenotypes. We anticipate that the healthy and diseased microbiome association networks we inferred will become an important resource for human-related microbiome research.

A novel therapeutic concern: Antibiotic resistance genes in common chronic diseases

Infections caused by multidrug-resistant bacteria carrying antibiotic resistance genes pose a severe threat to global public health and human health. In clinical practice, it has been found that human gut microbiota act as a “reservoir” of antibiotic resistance genes (ARGs) since gut microbiota contain a wide variety of ARGs, and that the structure of the gut microbiome is influenced by the profile of the drug resistance genes present. In addition, ARGs can spread within and between species of the gut microbiome in multiple ways. To better understand gut microbiota ARGs and their effects on patients with chronic diseases, this article reviews the generation of ARGs, common vectors that transmit ARGs, the characteristics of gut microbiota ARGs in common chronic diseases, their impact on prognosis, the current state of treatment for ARGs, and what should be addressed in future research.

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.

Psychological stress in inflammatory bowel disease: Psychoneuroimmunological insights into bidirectional gut–brain communications

Inflammatory bowel disease (IBD), mainly including ulcerative colitis (UC) and Crohn’s disease (CD), is an autoimmune gastrointestinal disease characterized by chronic inflammation and frequent recurrence. Accumulating evidence has confirmed that chronic psychological stress is considered to trigger IBD deterioration and relapse. Moreover, studies have demonstrated that patients with IBD have a higher risk of developing symptoms of anxiety and depression than healthy individuals. However, the underlying mechanism of the link between psychological stress and IBD remains poorly understood. This review used a psychoneuroimmunology perspective to assess possible neuro-visceral integration, immune modulation, and crucial intestinal microbiome changes in IBD. Furthermore, the bidirectionality of the brain–gut axis was emphasized in the context, indicating that IBD pathophysiology increases the inflammatory response in the central nervous system and further contributes to anxiety- and depression-like behavioral comorbidities. This information will help accurately characterize the link between psychological stress and IBD disease activity. Additionally, the clinical application of functional brain imaging, microbiota-targeted treatment, psychotherapy and antidepressants should be considered during the treatment and diagnosis of IBD with behavioral comorbidities. This review elucidates the significance of more high-quality research combined with large clinical sample sizes and multiple diagnostic methods and psychotherapy, which may help to achieve personalized therapeutic strategies for IBD patients based on stress relief.

Periodontitis-related salivary microbiota aggravates Alzheimer's disease via gut-brain axis crosstalk

The oral cavity is the initial chamber of digestive tract; the saliva swallowed daily contains an estimated 1.5 × 10¹² oral bacteria. Increasing evidence indicates that periodontal pathogens and subsequent inflammatory responses to them contribute to the pathogenesis of Alzheimer's disease (AD). The intestine and central nervous system jointly engage in crosstalk; microbiota-mediated immunity significantly impacts AD via the gut-brain axis. However, the exact mechanism linking periodontitis to AD remains unclear. In this study, we explored the influence of periodontitis-related salivary microbiota on AD based on the gut-brain crosstalk in APP^swe/PS1^ΔE9 (PAP) transgenic mice. Saliva samples were collected from patients with periodontitis and healthy individuals. The salivary microbiota was gavaged into PAP mice for two months. Continuous gavage of periodontitis-related salivary microbiota in PAP mice impaired cognitive function and increased β-amyloid accumulation and neuroinflammation. Moreover, these AD-related pathologies were consistent with gut microbial dysbiosis, intestinal pro-inflammatory responses, intestinal barrier impairment, and subsequent exacerbation of systemic inflammation, suggesting that the periodontitis-related salivary microbiota may aggravate AD pathogenesis through crosstalk of the gut-brain axis. In this study, we demonstrated that periodontitis might participate in the pathogenesis of AD by swallowing salivary microbiota, verifying the role of periodontitis in AD progression and providing a novel perspective on the etiology and intervention strategies of AD.

Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin

Microbiome studies of inflammatory bowel diseases (IBD) have achieved a scale for meta-analysis of dysbioses among populations. To enable microbial community meta-analyses generally, we develop MMUPHin for normalization, statistical meta-analysis, and population structure discovery using microbial taxonomic and functional profiles. Applying it to ten IBD cohorts, we identify consistent associations, including novel taxa such as Acinetobacter and Turicibacter, and additional exposure and interaction effects. A single gradient of dysbiosis severity is favored over discrete types to summarize IBD microbiome population structure. These results provide a benchmark for characterization of IBD and a framework for meta-analysis of any microbial communities.

Assessment of Energy and Nutrient Intake and the Intestinal Microbiome (ErNst study): Protocol and Methods of a Cross-sectional Human Observational Study (Preprint)

BACKGROUND

On the national level, nutritional monitoring requires the assessment of reliable representative dietary intake data. To achieve this, standardized tools need to be developed, validated, and kept up-to-date with recent developments in food products and the nutritional behavior of the population. Recently, the human intestinal microbiome has been identified as an essential mediator between nutrition and host health. Despite growing interest in this connection, only a few associations between the microbiome, nutrition, and health have been clearly established. Available studies paint an inconsistent picture, partly due to a lack of standardization.

OBJECTIVE

First, we aim to verify if food consumption, as well as energy and nutrient intake of the German population, can be recorded validly by means of the dietary recall software GloboDiet, which will be applied in the German National Nutrition Monitoring. Second, we aim to obtain high-quality data using standard methods on the microbiome, combined with dietary intake data and additional fecal sample material, and to also assess the functional activity of the microbiome by measuring microbial metabolites.

METHODS

Healthy female and male participants aged between 18 and 79 years were recruited. Anthropometric measurements included body height and weight, BMI, and bioelectrical impedance analysis. For validation of the GloboDiet software, current food consumption was assessed with a 24-hour recall. Nitrogen and potassium concentrations were measured from 24-hour urine collections to enable comparison with the intake of protein and potassium estimated by the GloboDiet software. Physical activity was measured over at least 24 hours using a wearable accelerometer to validate the estimated energy intake. Stool samples were collected in duplicate for a single time point and used for DNA isolation and subsequent amplification and sequencing of the 16S rRNA gene to determine microbiome composition. For the identification of associations between nutrition and the microbiome, the habitual diet was determined using a food frequency questionnaire covering 30 days.

RESULTS

In total, 117 participants met the inclusion criteria. The study population was equally distributed between the sexes and 3 age groups (18-39, 40-59, and 60-79 years). Stool samples accompanying habitual diet data (30-day food frequency questionnaire) are available for 106 participants. Current diet data and 24-hour urine samples for the validation of GloboDiet are available for 109 participants, of which 82 cases also include physical activity data.

CONCLUSIONS

We completed the recruitment and sample collection of the ErNst study with a high degree of standardization. Samples and data will be used to validate the GloboDiet software for the German National Nutrition Monitoring and to compare microbiome composition and nutritional patterns.

TRIAL REGISTRATION

German Register of Clinical Studies DRKS00015216; https://drks.de/search/de/trial/DRKS00015216.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/42529.

The Metabolic Role and Therapeutic Potential of the Microbiome

We are host to an assembly of microorganisms that vary in structure and function along the length of the gut and from the lumen to the mucosa. This ecosystem is collectively known as the gut microbiota and significant efforts have been spent during the past 2 decades to catalog and functionally describe the normal gut microbiota and how it varies during a wide spectrum of disease states. The gut microbiota is altered in several cardiometabolic diseases and recent work has established microbial signatures that may advance disease. However, most research has focused on identifying associations between the gut microbiota and human diseases states and to investigate causality and potential mechanisms using cells and animals. Since the gut microbiota functions on the intersection between diet and host metabolism, and can contribute to inflammation, several microbially produced metabolites and molecules may modulate cardiometabolic diseases. Here we discuss how the gut bacterial composition is altered in, and can contribute to, cardiometabolic disease, as well as how the gut bacteria can be targeted to treat and prevent metabolic diseases.

EXPERT: transfer learning-enabled context-aware microbial community classification

Microbial community classification enables identification of putative type and source of the microbial community, thus facilitating a better understanding of how the taxonomic and functional structure were developed and maintained. However, previous classification models required a trade-off between speed and accuracy, and faced difficulties to be customized for a variety of contexts, especially less studied contexts. Here, we introduced EXPERT based on transfer learning that enabled the classification model to be adaptable in multiple contexts, with both high efficiency and accuracy. More importantly, we demonstrated that transfer learning can facilitate microbial community classification in diverse contexts, such as classification of microbial communities for multiple diseases with limited number of samples, as well as prediction of the changes in gut microbiome across successive stages of colorectal cancer. Broadly, EXPERT enables accurate and context-aware customized microbial community classification, and potentiates novel microbial knowledge discovery.

Filtering ASVs/OTUs via mutual information-based microbiome network analysis

Microbial communities are widely studied using high-throughput sequencing techniques, such as 16S rRNA gene sequencing. These techniques have attracted biologists as they offer powerful tools to explore microbial communities and investigate their patterns of diversity in biological and biomedical samples at remarkable resolution. However, the accuracy of these methods can negatively affected by the presence of contamination. Several studies have recognized that contamination is a common problem in microbial studies and have offered promising computational and laboratory-based approaches to assess and remove contaminants. Here we propose a novel strategy, MI-based (mutual information based) filtering method, which uses information theoretic functionals and graph theory to identify and remove contaminants. We applied MI-based filtering method to a mock community data set and evaluated the amount of information loss due to filtering taxa. We also compared our method to commonly practice traditional filtering methods. In a mock community data set, MI-based filtering approach maintained the true bacteria in the community without significant loss of information. Our results indicate that MI-based filtering method effectively identifies and removes contaminants in microbial communities and hence it can be beneficial as a filtering method to microbiome studies. We believe our filtering method has two advantages over traditional filtering methods. First, it does not required an arbitrary choice of threshold and second, it is able to detect true taxa with low abundance.

Searching for a Consensus Among Inflammatory Bowel Disease Studies: A Systematic Meta-Analysis

BACKGROUND

Numerous studies have examined the gut microbial ecology of patients with Crohn's disease (CD) and ulcerative colitis, but inflammatory bowel disease-associated taxa and ecological effect sizes are not consistent between studies.

METHODS

We systematically searched PubMed and Google Scholar and performed a meta-analysis of 13 studies to analyze how variables such as sample type (stool, biopsy, and lavage) affect results in inflammatory bowel disease gut microbiome studies, using uniform bioinformatic methods for all primary data.

RESULTS

Reduced alpha diversity was a consistent feature of both CD and ulcerative colitis but was more pronounced in CD. Disease contributed significantly variation in beta diversity in most studies, but effect size varied, and the effect of sample type was greater than the effect of disease. Fusobacterium was the genus most consistently associated with CD, but disease-associated genera were mostly inconsistent between studies. Stool studies had lower heterogeneity than biopsy studies, especially for CD.

CONCLUSIONS

Our results indicate that sample type variation is an important contributor to study variability that should be carefully considered during study design, and stool is likely superior to biopsy for CD studies due to its lower heterogeneity.

Successful Dietary Therapy in Paediatric Crohn's Disease is Associated with Shifts in Bacterial Dysbiosis and Inflammatory Metabotype Towards Healthy Controls

BACKGROUND AND AIMS

Nutritional therapy with the Crohn's Disease Exclusion Diet + Partial Enteral Nutrition [CDED+PEN] or Exclusive Enteral Nutrition [EEN] induces remission and reduces inflammation in mild-to-moderate paediatric Crohn's disease [CD]. We aimed to assess if reaching remission with nutritional therapy is mediated by correcting compositional or functional dysbiosis.

METHODS

We assessed metagenome sequences, short chain fatty acids [SCFA] and bile acids [BA] in 54 paediatric CD patients reaching remission after nutritional therapy [with CDED + PEN or EEN] [NCT01728870], compared to 26 paediatric healthy controls.

RESULTS

Successful dietary therapy decreased the relative abundance of Proteobacteria and increased Firmicutes towards healthy controls. CD patients possessed a mixture of two metabotypes [M1 and M2], whereas all healthy controls had metabotype M1. M1 was characterised by high Bacteroidetes and Firmicutes, low Proteobacteria, and higher SCFA synthesis pathways, and M2 was associated with high Proteobacteria and genes involved in SCFA degradation. M1 contribution increased during diet: 48%, 63%, up to 74% [Weeks 0, 6, 12, respectively.]. By Week 12, genera from Proteobacteria reached relative abundance levels of healthy controls with the exception of E. coli. Despite an increase in SCFA synthesis pathways, remission was not associated with increased SCFAs. Primary BA decreased with EEN but not with CDED+PEN, and secondary BA did not change during diet.

CONCLUSION

Successful dietary therapy induced correction of both compositional and functional dysbiosis. However, 12 weeks of diet was not enough to achieve complete correction of dysbiosis. Our data suggests that composition and metabotype are important and change quickly during the early clinical response to dietary intervention. Correction of dysbiosis may therefore be an important future treatment goal for CD.

The gut microbiome molecular complex in human health and disease

The human gut microbiome produces a functional complex of biomolecules, including nucleic acids, (poly)peptides, structural molecules, and metabolites. This impacts human physiology in multiple ways, especially by triggering inflammatory pathways in disease. At present, much remains to be learned about the identity of key effectors and their causal roles.

The neurovascular unit and systemic biology in stroke - implications for translation and treatment

Ischaemic stroke is a leading cause of disability and death for which no acute treatments exist beyond recanalization. The development of novel therapies has been repeatedly hindered by translational failures that have changed the way we think about tissue damage after stroke. What was initially a neuron-centric view has been replaced with the concept of the neurovascular unit (NVU), which encompasses neuronal, glial and vascular compartments, and the biphasic nature of neural-glial-vascular signalling. However, it is now clear that the brain is not the private niche it was traditionally thought to be and that the NVU interacts bidirectionally with systemic biology, such as systemic metabolism, the peripheral immune system and the gut microbiota. Furthermore, these interactions are profoundly modified by internal and external factors, such as ageing, temperature and day-night cycles. In this Review, we propose an extension of the concept of the NVU to include its dynamic interactions with systemic biology. We anticipate that this integrated view will lead to the identification of novel mechanisms of stroke pathophysiology, potentially explain previous translational failures, and improve stroke care by identifying new biomarkers of and treatment targets in stroke.

The gut microbiome as a modulator of healthy ageing

The gut microbiome is a contributory factor in ageing-related health loss and in several non-communicable diseases in all age groups. Some age-linked and disease-linked compositional and functional changes overlap, while others are distinct. In this Review, we explore targeted studies of the gut microbiome of older individuals and general cohort studies across geographically distinct populations. We also address the promise of the targeted restoration of microorganisms associated with healthier ageing.

Microbiome and paediatric gut diseases

In the human gut resides a vast community of microorganisms which perform critical functions for the maintenance of whole body homeostasis. Changes in the composition and function of this community, termed microbiome, are believed to provoke disease onset, including non-communicable diseases. In this review, we debate the current evidence on the role of the gut microbiome in the pathogenesis, outcomes and management of paediatric gut disease. We conclude that even though the gut microbiome is altered in paediatric inflammatory bowel disease, coeliac disease, intestinal failure, necrotising enterocolitis and irritable bowel syndrome, there are currently very few implications for unravelling disease pathogenesis or guiding clinical practice. In the future, the gut microbiome may aid in disease differential diagnosis and prediction of clinical outcomes, and comprise a target for therapeutic interventions.

Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data

Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high dimensional, underdetermined, sparse and compositional. Great efforts have recently been made on developing new methods for feature selection that handle the above data characteristics, but almost all methods were evaluated based on performance of model predictions. However, little attention has been paid to address a fundamental question: how appropriate are those evaluation criteria? Most feature selection methods often control the model fit, but the ability to identify meaningful subsets of features cannot be evaluated simply based on the prediction accuracy. If tiny changes to the data would lead to large changes in the chosen feature subset, then many selected features are likely to be a data artifact rather than real biological signal. This crucial need of identifying relevant and reproducible features motivated the reproducibility evaluation criterion such as Stability, which quantifies how robust a method is to perturbations in the data. In our paper, we compare the performance of popular model prediction metrics (MSE or AUC) with proposed reproducibility criterion Stability in evaluating four widely used feature selection methods in both simulations and experimental microbiome applications with continuous or binary outcomes. We conclude that Stability is a preferred feature selection criterion over model prediction metrics because it better quantifies the reproducibility of the feature selection method.

Inside out: Relations between the microbiome, nutrition, and eye health

Age-related macular degeneration (AMD) is a complex disease with increasing numbers of individuals being afflicted and treatment modalities limited. There are strong interactions between diet, age, the metabolome, and gut microbiota, and all of these have roles in the pathogenesis of AMD. Communication axes exist between the gut microbiota and the eye, therefore, knowing how the microbiota influences the host metabolism during aging could guide a better understanding of AMD pathogenesis. While considerable experimental evidence exists for a diet-gut-eye axis from murine models of human ocular diseases, human diet-microbiome-metabolome studies are needed to elucidate changes in the gut microbiome at the taxonomic and functional levels that are functionally related to ocular pathology. Such studies will reveal new ways to diminish risk for progression of- or incidence of- AMD. Current data suggest that consuming diets rich in dark fish, fruits, vegetables, and low in glycemic index are most retina-healthful during aging.

Global research trends and hotspots of fecal microbiota transplantation: A bibliometric and visualization study

Introduction

Fecal microbiota transplantation (FMT) has gained considerable attention in a variety of clinical research areas, and an increasing number of articles are being published. It is very critical to reveal the global status, future research trends, and hotspots in the FMT research and application.

Methods

We searched the Web of Science Core Collection up to May 10, 2022, and only articles and review articles about FMT were included finally. CiteSpace 5.8.R3, VOSviewer 1.6.18, Scimago Graphica and Microsoft Office Excel 2019 were used for data analysis and visualization. The results included publication characteristics, Co-authorships analysis, Co-cited analysis, Co-occurrence analysis, and burst analysis.

Results

Eleven thousand nine hundred seventy-two records were used for the analysis and visualization finally, these records were published between 1980 and 2022, and the publication about FMT is increasing year by year. Co-authorship analysis shown that the USA played a key role in this field. After data analysis and visualization, a total of 57 hotspots about FMT were produced. We summarized these hotspots and classified them into 7 grades according to the number of evidence sources. The evidence sources included top 25 of Web of Science categories, top 30 most Co-cited references, top 10 clusters of references, top 25 references with the strongest citation bursts, top 25 keywords with the most occurrence frequency, major 15 clusters of keywords, top 25 keywords with the strongest citation bursts, and top 35 disease keywords.

Conclusion

This bibliometric analysis is expected to provide overall perspective for FMT. FMT has gained increasing attention and interest, there are many hotspots in this field, which may help researchers to explore new directions for future research.

Identification of clinical and ecological determinants of strain engraftment after fecal microbiota transplantation using metagenomics

Fecal microbiota transplantation (FMT) is a promising therapeutic approach for microbiota-associated pathologies, but our understanding of the post-FMT microbiome assembly process and its ecological and clinical determinants is incomplete. Here we perform a comprehensive fecal metagenome analysis of 14 FMT trials, involving five pathologies and >250 individuals, and determine the origins of strains in patients after FMT. Independently of the underlying clinical condition, conspecific coexistence of donor and recipient strains after FMT is uncommon and donor strain engraftment is strongly positively correlated with pre-FMT recipient microbiota dysbiosis. Donor strain engraftment was enhanced through antibiotic pretreatment and bowel lavage and dependent on donor and recipient ɑ-diversity; strains from relatively abundant species were more likely and from predicted oral, oxygen-tolerant, and gram-positive species less likely to engraft. We introduce a general mechanistic framework for post-FMT microbiome assembly in alignment with ecological theory, which can guide development of optimized, more targeted, and personalized FMT therapies.

Oral Lactoferrin Supplementation during Induction Chemotherapy Promotes Gut Microbiome Eubiosis in Pediatric Patients with Hematologic Malignancies

Induction chemotherapy is the first-line treatment for pediatric patients with hematologic malignancies. However, several complications may arise, mainly infections and febrile neutropenia, with a strong impact on patient morbidity and mortality. Such complications have been shown to be closely related to alterations of the gut microbiome (GM), making the design of strategies to foster its eubiosis of utmost clinical importance. Here, we evaluated the impact of oral supplementation of lactoferrin (LF), a glycoprotein endowed with anti-inflammatory, immunomodulatory and antimicrobial activities, on GM dynamics in pediatric oncohematologic patients during induction chemotherapy. Specifically, we conducted a double blind, placebo-controlled trial in which GM was profiled through 16S rRNA gene sequencing before and after two weeks of oral supplementation with LF or placebo. LF was safely administered with no adverse effects and promoted GM homeostasis by favoring the maintenance of diversity and preventing the bloom of pathobionts (e.g., Enterococcus). LF could, therefore, be a promising adjunct to current therapeutic strategies in these fragile individuals to reduce the risk of GM-related complications.

Gut microbiota and derived metabolomic profiling in glaucoma with progressive neurodegeneration

Glaucoma is a multifactorial, neurodegenerative disorder characterized by the loss of retinal ganglion cells (RGCs). Crosstalk between the gut microbiota and host is involved in the progression of many neurodegenerative diseases, although little is known about its role in glaucoma. To investigated the alterations of the gut microbiota and derived metabolites in glaucomatous rats, and the interaction with RGCs, we performed 16S rRNA (V1-V9) sequencing and untargeted metabolomic analyses. The microbial composition differed significantly between the two groups, and the diversity of cecal bacteria was dramatically reduced in glaucomatous rats. The Firmicutes/Bacteroidetes (F/B) ratio, Verrucomicrobia phylum, and some bacterial genera (Romboutsia, Akkermansia, and Bacteroides) were dramatically increased in the glaucomatous rat model compared with the control, which showed negative correlation with RGCs. Untargeted metabolomic analysis identified 284 differentially expressed metabolites, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed considerable enrichment mainly in bile secretion pathways. The relationships among the metabolites enriched in the bile secretion pathway, differentially expressed cecal microbiota, and RGCs were investigated, and glutathione (GSH) was found to be negatively correlated with Bacteroides and F/B and positively correlated with RGCs. Reduced GSH level in the blood of glaucoma rats is further established, and was negatively correlated with Romboutsia and the F/B ratio and positively correlated with RGCs. This finding suggests the potential role of the gut microbiota and derived metabolites in glaucoma, and GSH, a major antioxidant metabolite, was related to their effects, indicating the potential for the development of gut microbiota-targeted interventions for glaucoma.

Age-related diseases, therapies and gut microbiome: A new frontier for healthy aging

The gut microbiome is undoubtedly a key modulator of human health, which can promote or impair homeostasis throughout life. This is even more relevant in old age, when there is a gradual loss of function in multiple organ systems, related to growth, metabolism, and immunity. Several studies have described changes in the gut microbiome across age groups up to the extreme limits of lifespan, including maladaptations that occur in the context of age-related conditions, such as frailty, neurodegenerative diseases, and cardiometabolic diseases. The gut microbiome can also interact bi-directionally with anti-age-related disease therapies, being affected and in turn influencing their efficacy. In this framework, the development of integrated microbiome-based intervention strategies, aimed at favoring a eubiotic configuration and trajectory, could therefore represent an innovative approach for the promotion of healthy aging and the achievement of longevity.

Prediction of Smoking Habits From Class-Imbalanced Saliva Microbiome Data Using Data Augmentation and Machine Learning

Human microbiome research is moving from characterization and association studies to translational applications in medical research, clinical diagnostics, and others. One of these applications is the prediction of human traits, where machine learning (ML) methods are often employed, but face practical challenges. Class imbalance in available microbiome data is one of the major problems, which, if unaccounted for, leads to spurious prediction accuracies and limits the classifier's generalization. Here, we investigated the predictability of smoking habits from class-imbalanced saliva microbiome data by combining data augmentation techniques to account for class imbalance with ML methods for prediction. We collected publicly available saliva 16S rRNA gene sequencing data and smoking habit metadata demonstrating a serious class imbalance problem, i.e., 175 current vs. 1,070 non-current smokers. Three data augmentation techniques (synthetic minority over-sampling technique, adaptive synthetic, and tree-based associative data augmentation) were applied together with seven ML methods: logistic regression, k-nearest neighbors, support vector machine with linear and radial kernels, decision trees, random forest, and extreme gradient boosting. K-fold nested cross-validation was used with the different augmented data types and baseline non-augmented data to validate the prediction outcome. Combining data augmentation with ML generally outperformed baseline methods in our dataset. The final prediction model combined tree-based associative data augmentation and support vector machine with linear kernel, and achieved a classification performance expressed as Matthews correlation coefficient of 0.36 and AUC of 0.81. Our method successfully addresses the problem of class imbalance in microbiome data for reliable prediction of smoking habits.

Exploring Gut Microenvironment in Colorectal Patient with Dual-Omics Platform: A Comparison with Adenomatous Polyp or Occult Blood

The gut mucosa is actively absorptive and functions as the physical barrier to separate the gut ecosystem from host. Gut microbiota-utilized or food-derived metabolites are closely relevant to the homeostasis of the gut epithelial cells. Recent studies widely suggested the carcinogenic impact of gut dysbiosis or altered metabolites on the development of colorectal cancer (CRC). In this study, liquid chromatography coupled-mass spectrometry and long-read sequencing was applied to identify gut metabolites and microbiomes with statistically discriminative abundance in CRC patients (n = 20) as compared to those of a healthy group (n = 60) ofenrolled participants diagnosed with adenomatous polyp (n = 67) or occult blood (n = 40). In total, alteration in the relative abundance of 90 operational taxonomic units (OTUs) and 45 metabolites were identified between recruited CRC patients and healthy participants. Among the candidates, the gradual increases in nine OTUs or eight metabolites were identified in healthy participants, patients diagnosed with occult blood and adenomatous polyp, and CRC patients. The random forest regression model constructed with five OTUs or four metabolites achieved a distinct classification potential to differentially discriminate the presence of CRC (area under the ROC curve (AUC) = 0.998 or 0.975) from the diagnosis of adenomatous polyp (AUC = 0.831 or 0.777), respectively. These results provide the validity of CRC-associated markers, including microbial communities and metabolomic profiles across healthy and related populations toward the early screening or diagnosis of CRC.