Microbiome differential abundance methods produce different results across 38 datasets

Genetic and microbiome analysis of feed efficiency in laying hens

Improving feed efficiency is an important target for poultry breeding. Feed efficiency is affected by host genetics and the gut microbiota, but many of the mechanisms remain elusive in laying hens, especially in the late laying period. In this study, we measured feed intake, body weight, and egg mass of 714 hens from a pedigreed line from 69 to 72 wk of age and calculated the residual feed intake (RFI) and feed conversion ratio (FCR). In addition, fecal samples were also collected for 16S ribosomal RNA gene sequencing (V4 region). Genetic analysis was then conducted in DMU packages by using AI-REML with animal model. Moderate heritability estimates for FCR (h² = 0.31) and RFI (h² = 0.52) were observed, suggesting that proper selection programs can directly improve feed efficiency. Genetically, RFI was less correlated with body weight and egg mass than that of FCR. The phenotypic variance explained by gut microbial variance is defined as the microbiability (m²). The microbiability estimates for FCR (m² = 0.03) and RFI (m² = 0.16) suggested the gut microbiota was also involved in the regulation of feed efficiency. In addition, our results showed that the effect of host genetics on fecal microbiota was minor in three aspects: 1) microbial diversity indexes had low heritability estimates, and genera with heritability estimates more than 0.1 accounted for only 1.07% of the tested fecal microbiota; 2) the genetic relationship correlations between host genetics and different microbial distance were very weak, ranging from -0.0057 to -0.0003; 3) the microbial distance between different kinships showed no significant difference. Since the RFI has the highest microbiability, we further screened out three genera, including Anaerosporobacter, Candidatus Stoquefichus, and Fournierella, which were negatively correlated with RFI and played positive roles in improving the feed efficiency. These findings contribute to a great understanding of the genetic background and microbial influences on feed efficiency.

Gut microbiome alterations in preclinical Alzheimer's disease

BACKGROUND

Although some human studies have reported gut microbiome changes in individuals with Alzheimer's disease (AD) dementia or mild cognitive impairment (MCI), gut microbiome alterations in preclinical AD, i.e., cerebral amyloidosis without cognitive impairment, is largely unknown.

OBJECTIVE

We aimed to identify gut microbial alterations associated with preclinical AD by comparing cognitively normal (CN) older adults with cerebral Aβ deposition (Aβ+ CN) and those without cerebral Aβ deposition (Aβ- CN).

METHODS

Seventy-eight CN older participants (18 Aβ+ CN and 60 Aβ- CN) were included, and all participants underwent clinical assessment and Pittsburg compound B-positron emission tomography. The V3-V4 region of the 16S rRNA gene of genomic DNA extracted from feces was amplified and sequenced to establish the microbial community.

RESULTS

Generalized linear model analysis revealed that the genera Megamonas (B = 3.399, q<0.001), Serratia (B = 3.044, q = 0.005), Leptotrichia (B = 5.862, q = 0.024) and Clostridium (family Clostridiaceae) (B = 0.788, q = 0.034) were more abundant in the Aβ+ CN group than the Aβ- CN group. In contrast, genera CF231 (B = -3.237, q< 0.001), Victivallis (B = -3.447, q = 0.004) Enterococcus (B = -2.044, q = 0.042), Mitsuokella (B = -2.119, q = 0.042) and Clostridium (family Erysipelotrichaceae) (B = -2.222, q = 0.043) were decreased in Aβ+ CN compared to Aβ- CN. Notably, the classification model including the differently abundant genera could effectively distinguish Aβ+ CN from Aβ- CN (AUC = 0.823).

CONCLUSION

Our findings suggest that specific alterations of gut bacterial taxa are related to preclinical AD, which means these changes may precede cognitive decline. Therefore, examining changes in the microbiome may be helpful in preclinical AD screening.

Healthy microbiome - a mere idea or a sound concept?

Hundreds of studies in last decades have aimed to compare the microbiome of patients suffering from diverse diseases with that of healthy controls. The microbiome-related component was additionally identified in pathophysiology of many diseases formerly considered to depend only on the host physiology. This, however, opens important questions like: "What is the healthy microbiome?" or "Is it possible to define it unequivocally?". In this review, we describe the main hindrances complicating the definition of "healthy microbiome" in terms of microbiota composition. We discuss the human microbiome from the perspective of classical ecology and we advocate for the shift from the stress on microbiota composition to the functions that microbiome ensures for the host. Finally, we propose to leave the concept of ideal healthy microbiome and replace it by focus on microbiome advantageous for the host, which always depends on the specific context like the age, genetics, dietary habits, body site or physiological state.

Increased abundance of Ruminococcus gnavus in gut microbiota is associated with moyamoya disease and non-moyamoya intracranial large artery disease

Moyamoya disease (MMD) is a rare cerebrovascular disease endemic in East Asia. The p.R4810K mutation in RNF213 gene confers a risk of MMD, but other factors remain largely unknown. We tested the association of gut microbiota with MMD. Fecal samples were collected from 27 patients with MMD, 7 patients with non-moyamoya intracranial large artery disease (ICAD) and 15 control individuals with other disorders, and 16S rRNA were sequenced. Although there was no difference in alpha diversity or beta diversity between patients with MMD and controls, the cladogram showed Streptococcaceae was enriched in patient samples. The relative abundance analysis demonstrated that 23 species were differentially abundant between patients with MMD and controls. Among them, increased abundance of Ruminococcus gnavus > 0.003 and decreased abundance of Roseburia inulinivorans < 0.002 were associated with higher risks of MMD (odds ratio 9.6, P = 0.0024; odds ratio 11.1, P = 0.0051). Also, Ruminococcus gnavus was more abundant and Roseburia inulinivorans was less abundant in patients with ICAD than controls (P = 0.046, P = 0.012). The relative abundance of Ruminococcus gnavus or Roseburia inulinivorans was not different between the p.R4810K mutant and wildtype. Our data demonstrated that gut microbiota was associated with both MMD and ICAD.

Human Gut Microbiota in Coronary Artery Disease: A Systematic Review and Meta-Analysis

In recent years, the importance of the gut microbiome in human health and disease has increased. Growing evidence suggests that gut dysbiosis might be a crucial risk factor for coronary artery disease (CAD). Therefore, we conducted a systematic review and meta-analysis to determine whether or not CAD is associated with specific changes in the gut microbiome. The V3-V4 regions of the 16S rDNA from fecal samples were analyzed to compare the gut microbiome composition between CAD patients and controls. Our search yielded 1181 articles, of which 21 met inclusion criteria for systematic review and 7 for meta-analysis. The alpha-diversity, including observed OTUs, Shannon and Simpson indices, was significantly decreased in CAD, indicating the reduced richness of the gut microbiome. The most consistent results in a systematic review and meta-analysis pointed out the reduced abundance of Bacteroidetes and Lachnospiraceae in CAD patients. Moreover, Enterobacteriaceae, Lactobacillus, and Streptococcus taxa demonstrated an increased trend in CAD patients. The alterations in the gut microbiota composition are associated with qualitative and quantitative changes in bacterial metabolites, many of which have pro-atherogenic effects on endothelial cells, increasing the risk of developing and progressing CAD.

Do we need to change our perspective about gut biomarkers? A public data mining approach to identify differentially abundant bacteria in intestinal inflammatory diseases

Introduction

The gut microbiome is involved in multiple processes that influence host physiology, and therefore, disruptions in microbiome homeostasis have been linked to diseases or secondary infections. Given the importance of the microbiome and the communities of microorganisms that compose it (microbiota), the term biomarkers were coined, which are bacteria correlated with disease states, diets, and the lifestyle of the host. However, a large field in the study of intestinal biomarkers remains unexplored because the bacterial communities associated with a given disease state have not been exactly defined yet.

Methods

Here, we analyzed public data of studies focused on describing the intestinal microbiota of patients with some intestinal inflammatory diseases together with their respective controls. With these analyses, we aimed to identify differentially abundant bacteria between the subjects with the disease and their controls.

Results

We found that frequently reported bacteria such as Fusobacterium, Streptococcus, and Escherichia/Shigella were differentially abundant between the groups, with a higher abundance mostly in patients with the disease in contrast with their controls. On the other hand, we also identified potentially beneficial bacteria such as Faecalibacterium and Phascolarctobacterium, with a higher abundance in control patients.

Discussion

Our results of the differentially abundant bacteria contrast with what was already reported in previous studies on certain inflammatory diseases, but we highlight the importance of considering more comprehensive approaches to redefine or expand the definition of biomarkers. For instance, the intra-taxa diversity within a bacterial community must be considered, as well as environmental and genetic factors of the host, and even consider a functional validation of these biomarkers through in vivo and in vitro approaches. With the above, these key bacterial communities in the intestinal microbiota may have potential as next-generation probiotics or may be functional for the design of specific therapies in certain intestinal diseases.

Metagenomics of Parkinson's disease implicates the gut microbiome in multiple disease mechanisms

Parkinson's disease (PD) may start in the gut and spread to the brain. To investigate the role of gut microbiome, we conducted a large-scale study, at high taxonomic resolution, using uniform standardized methods from start to end. We enrolled 490 PD and 234 control individuals, conducted deep shotgun sequencing of fecal DNA, followed by metagenome-wide association studies requiring significance by two methods (ANCOM-BC and MaAsLin2) to declare disease association, network analysis to identify polymicrobial clusters, and functional profiling. Here we show that over 30% of species, genes and pathways tested have altered abundances in PD, depicting a widespread dysbiosis. PD-associated species form polymicrobial clusters that grow or shrink together, and some compete. PD microbiome is disease permissive, evidenced by overabundance of pathogens and immunogenic components, dysregulated neuroactive signaling, preponderance of molecules that induce alpha-synuclein pathology, and over-production of toxicants; with the reduction in anti-inflammatory and neuroprotective factors limiting the capacity to recover. We validate, in human PD, findings that were observed in experimental models; reconcile and resolve human PD microbiome literature; and provide a broad foundation with a wealth of concrete testable hypotheses to discern the role of the gut microbiome in PD.

Combining p-values from various statistical methods for microbiome data

MOTIVATION

In the field of microbiome analysis, there exist various statistical methods that have been developed for identifying differentially expressed features, that account for the overdispersion and the high sparsity of microbiome data. However, due to the differences in statistical models or test formulations, it is quite often to have inconsistent significance results across statistical methods, that makes it difficult to determine the importance of microbiome taxa. Thus, it is practically important to have the integration of the result from all statistical methods to determine the importance of microbiome taxa. A standard meta-analysis is a powerful tool for integrative analysis and it provides a summary measure by combining p-values from various statistical methods. While there are many meta-analyses available, it is not easy to choose the best meta-analysis that is the most suitable for microbiome data.

RESULTS

In this study, we investigated which meta-analysis method most adequately represents the importance of microbiome taxa. We considered Fisher's method, minimum value of p method, Simes method, Stouffer's method, Kost method, and Cauchy combination test. Through simulation studies, we showed that Cauchy combination test provides the best combined value of p in the sense that it performed the best among the examined methods while controlling the type 1 error rates. Furthermore, it produced high rank similarity with the true ranks. Through the real data application of colorectal cancer microbiome data, we demonstrated that the most highly ranked microbiome taxa by Cauchy combination test have been reported to be associated with colorectal cancer.

C3NA: correlation and consensus-based cross-taxonomy network analysis for compositional microbial data

Background

Studying the co-occurrence network structure of microbial samples is one of the critical approaches to understanding the perplexing and delicate relationship between the microbe, host, and diseases. It is also critical to develop a tool for investigating co-occurrence networks and differential abundance analyses to reveal the disease-related taxa–taxa relationship. In addition, it is also necessary to tighten the co-occurrence network into smaller modules to increase the ability for functional annotation and interpretability of  these taxa-taxa relationships.  Also, it is critical to retain the phylogenetic relationship among the taxa to identify differential abundance patterns, which can be used to resolve contradicting functions reported by different studies.

Results

In this article, we present Correlation and Consensus-based Cross-taxonomy Network Analysis (C3NA), a user-friendly R package for investigating compositional microbial sequencing data to identify and compare co-occurrence patterns across different taxonomic levels. C3NA contains two interactive graphic user interfaces (Shiny applications), one of them dedicated to the comparison between two diagnoses, e.g., disease versus control. We used C3NA to analyze two well-studied diseases, colorectal cancer, and Crohn’s disease. We discovered clusters of study and disease-dependent taxa that overlap with known functional taxa studied by other discovery studies and differential abundance analyses.

Conclusion

C3NA offers a new microbial data analyses pipeline for refined and enriched taxa–taxa co-occurrence network analyses, and the usability was further expanded via the built-in Shiny applications for interactive investigation.

Gut Microbes Are Associated with the Vascular Beneficial Effects of Dietary Strawberry on Metabolic Syndrome-Induced Vascular Inflammation

SCOPE

Metabolic syndrome (MetS) alters the gut microbial ecology and increases the risk of cardiovascular disease. This study investigates whether strawberry consumption reduces vascular complications in an animal model of MetS and identifies whether this effect is associated with changes in the composition of gut microbes.

METHODS AND RESULTS

Seven-week-old male mice consume diets with 10% (C) or 60% kcal from fat (high-fat diet fed mice; HF) for 12 weeks and subgroups are fed a 2.35% freeze-dried strawberry supplemented diet (C+SB or HF+SB). This nutritional dose is equivalent to ≈160 g of strawberry. After 12 weeks treatment, vascular inflammation is enhanced in HF versus C mice as shown by an increased monocyte binding to vasculature, elevated serum chemokines, and increased mRNA expression of inflammatory molecules. However, strawberry supplementation suppresses vascular inflammation in HF+SB versus HF mice. Metabolic variables, blood pressure, and indices of vascular function were similar among the groups. Further, the abundance of opportunistic microbe is decreased in HF+SB. Importantly, circulating chemokines are positively associated with opportunistic microbes and negatively associated with the commensal microbes (Bifidobacterium and Facalibaculum).

CONCLUSION

Dietary strawberry decreases the abundance of opportunistic microbe and this is associated with a decrease in vascular inflammation resulting from MetS.

Uropygial gland microbiota differ between free-living and captive songbirds

Symbiotic microbes can affect host behavior and fitness. Gut microbiota have received the most study, with less attention to other important microbial communities like those of scent-producing glands such as mammalian anal glands and the avian uropygial gland. However, mounting evidence suggests that microbes inhabiting scent-producing glands play an important role in animal behavior by contributing to variation in chemical signals. Free-living and captive conditions typically differ in social environment, food diversity and availability, disease exposure, and other factors-all of which can translate into differences in gut microbiota. However, whether extrinsic factors such as captivity alter microbial communities in scent glands remains an open question. We compared the uropygial gland microbiota of free-living and captive song sparrows (Melospiza melodia) and tested for an effect of dietary manipulations on the gland microbiota of captive birds. As predicted, the uropygial gland microbiota was significantly different between free-living and captive birds. Surprisingly, microbial diversity was higher in captive than free-living birds, and we found no effect of dietary treatments on captive bird microbiota. Identifying the specific factors responsible for microbial differences among groups and determining whether changes in symbiotic microbiota alter behavior and fitness are important next steps in this field.

Microbiota DNA isolation, 16S rRNA amplicon sequencing, and bioinformatic analysis for bacterial microbiome profiling of rodent fecal samples

Fecal samples are frequently used to characterize bacterial populations of the gastrointestinal tract. A protocol is provided to profile gut bacterial populations using rodent fecal samples. We describe the optimal procedures for collecting rodent fecal samples, isolating genomic DNA, 16S rRNA gene V4 region sequencing, and bioinformatic analyses. This protocol includes detailed instructions and example outputs to ensure accurate, reproducible results and data visualization. Comprehensive troubleshooting and limitation sections address technical and statistical issues that may arise when profiling microbiota. For complete details on the use and execution of this protocol, please refer to Gubert et al. (2022).

Recurrent urinary tract infection and estrogen shape the taxonomic ecology and function of the postmenopausal urogenital microbiome

Postmenopausal women are severely affected by recurrent urinary tract infection (rUTI). The urogenital microbiome is a key component of the urinary environment. However, changes in the urogenital microbiome underlying rUTI susceptibility are unknown. Here, we perform shotgun metagenomics and advanced culture on urine from a controlled cohort of postmenopausal women to identify urogenital microbiome compositional and function changes linked to rUTI susceptibility. We identify candidate taxonomic biomarkers of rUTI susceptibility in postmenopausal women and an enrichment of lactobacilli in postmenopausal women taking estrogen hormone therapy. We find robust correlations between Bifidobacterium and Lactobacillus and urinary estrogens in women without urinary tract infection (UTI) history. Functional analyses reveal distinct metabolic and antimicrobial resistance gene (ARG) signatures associated with rUTI. Importantly, we find that ARGs are enriched in the urogenital microbiomes of women with rUTI history independent of current UTI status. Our data suggest that rUTI and estrogen shape the urogenital microbiome in postmenopausal women.

Rural environment reduces allergic inflammation by modulating the gut microbiota

Rural environments and microbiota are linked to a reduction in the prevalence of allergies. However, the mechanism underlying the reduced allergies modulated by rural residency is unclear. Here, we assessed gut bacterial composition and metagenomics in urban and rural children in the EuroPrevall-INCO cohort. Airborne dusts, including mattress and rural henhouse dusts, were profiled for bacterial and fungal composition by amplicon sequencing. Mice were repeatedly exposed to intranasal dust extracts and evaluated for their effects on ovalbumin (OVA)-induced allergic airway inflammation, and gut microbiota restoration was validated by fecal microbiota transplant (FMT) from dust-exposed donor mice. We found that rural children had fewer allergies and unique gut microbiota with fewer Bacteroides and more Prevotella. Indoor dusts in rural environments harbored higher endotoxin level and diversity of bacteria and fungi, whereas indoor urban dusts were enriched with Aspergillus and contained elevated pathogenic bacteria. Intranasal administration of rural dusts before OVA sensitization reduced respiratory eosinophils and blood IgE level in mice and also led to a recovery of gut bacterial diversity and Ruminiclostridium in the mouse model. FMT restored the protective effect by reducing OVA-induced lung eosinophils in recipient mice. Together, these results support a cause-effect relationship between exposure to dust microbiota and allergy susceptibility in children and mice. Specifically, rural environmental exposure modulated the gut microbiota, which was essential in reducing allergy in children from Southern China. Our findings support the notion that the modulation of gut microbiota by exposure to rural indoor dust may improve allergy prevention.

Machine learning and deep learning applications in microbiome research

The many microbial communities around us form interactive and dynamic ecosystems called microbiomes. Though concealed from the naked eye, microbiomes govern and influence macroscopic systems including human health, plant resilience, and biogeochemical cycling. Such feats have attracted interest from the scientific community, which has recently turned to machine learning and deep learning methods to interrogate the microbiome and elucidate the relationships between its composition and function. Here, we provide an overview of how the latest microbiome studies harness the inductive prowess of artificial intelligence methods. We start by highlighting that microbiome data - being compositional, sparse, and high-dimensional - necessitates special treatment. We then introduce traditional and novel methods and discuss their strengths and applications. Finally, we discuss the outlook of machine and deep learning pipelines, focusing on bottlenecks and considerations to address them.

Pathobiology and dysbiosis of the respiratory and intestinal microbiota in 14 months old Golden Syrian hamsters infected with SARS-CoV-2

The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS2) affected the geriatric population. Among research models, Golden Syrian hamsters (GSH) are one of the most representative to study SARS2 pathogenesis and host responses. However, animal studies that recapitulate the effects of SARS2 in the human geriatric population are lacking. To address this gap, we inoculated 14 months old GSH with a prototypic ancestral strain of SARS2 and studied the effects on virus pathogenesis, virus shedding, and respiratory and gastrointestinal microbiome changes. SARS2 infection led to high vRNA loads in the nasal turbinates (NT), lungs, and trachea as well as higher pulmonary lesions scores later in infection. Dysbiosis throughout SARS2 disease progression was observed in the pulmonary microbial dynamics with the enrichment of opportunistic pathogens (Haemophilus, Fusobacterium, Streptococcus, Campylobacter, and Johnsonella) and microbes associated with inflammation (Prevotella). Changes in the gut microbial community also reflected an increase in multiple genera previously associated with intestinal inflammation and disease (Helicobacter, Mucispirillum, Streptococcus, unclassified Erysipelotrichaceae, and Spirochaetaceae). Influenza A virus (FLUAV) pre-exposure resulted in slightly more pronounced pathology in the NT and lungs early on (3 dpc), and more notable changes in lungs compared to the gut microbiome dynamics. Similarities among aged GSH and the microbiome in critically ill COVID-19 patients, particularly in the lower respiratory tract, suggest that GSHs are a representative model to investigate microbial changes during SARS2 infection. The relationship between the residential microbiome and other confounding factors, such as SARS2 infection, in a widely used animal model, contributes to a better understanding of the complexities associated with the host responses during viral infections.

Integrating phylogenetic and functional data in microbiome studies

MOTIVATION

Microbiome functional data are frequently analyzed to identify associations between microbial functions (e.g. genes) and sample groups of interest. However, it is challenging to distinguish between different possible explanations for variation in community-wide functional profiles by considering functions alone. To help address this problem, we have developed POMS, a package that implements multiple phylogeny-aware frameworks to more robustly identify enriched functions.

RESULTS

The key contribution is an extended balance-tree workflow that incorporates functional and taxonomic information to identify functions that are consistently enriched in sample groups across independent taxonomic lineages. Our package also includes a workflow for running phylogenetic regression. Based on simulated data we demonstrate that these approaches more accurately identify gene families that confer a selective advantage compared with commonly used tools. We also show that POMS in particular can identify enriched functions in real-world metagenomics datasets that are potential targets of strong selection on multiple members of the microbiome.

AVAILABILITY AND IMPLEMENTATION

These workflows are freely available in the POMS R package at https://github.com/gavinmdouglas/POMS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Gastrointestinal Microbiome Disruption and Antibiotic-Associated Diarrhea in Children Receiving Antibiotic Therapy for Community-Acquired Pneumonia

Antibiotic-associated diarrhea (AAD) is a common side effect of antibiotics. We examined the gastrointestinal microbiota in children treated with β-lactams for community-acquired pneumonia. Data were from 66 children (n = 198 samples), aged 6-71 months, enrolled in the SCOUT-CAP trial (NCT02891915). AAD was defined as ≥1 day of diarrhea. Stool samples were collected on study days 1, 6-10, and 19-25. Samples were analyzed using 16S ribosomal RNA gene sequencing to identify associations between patient characteristics, microbiota characteristics, and AAD (yes/no). Nineteen (29%) children developed AAD. Microbiota compositional profiles differed between AAD groups (permutational multivariate analysis of variance, P < .03) and across visits (P < .001). Children with higher baseline relative abundances of 2 Bacteroides species were less likely to experience AAD. Higher baseline abundance of Lachnospiraceae and amino acid biosynthesis pathways were associated with AAD. Children in the AAD group experienced prolonged dysbiosis (P < .05). Specific gastrointestinal microbiota profiles are associated with AAD in children.

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.

Neisseria species as pathobionts in bronchiectasis

Neisseria species are frequently identified in the bronchiectasis microbiome, but they are regarded as respiratory commensals. Using a combination of human cohorts, next-generation sequencing, systems biology, and animal models, we show that bronchiectasis bacteriomes defined by the presence of Neisseria spp. associate with poor clinical outcomes, including exacerbations. Neisseria subflava cultivated from bronchiectasis patients promotes the loss of epithelial integrity and inflammation in primary epithelial cells. In vivo animal models of Neisseria subflava infection and metabolipidome analysis highlight immunoinflammatory functional gene clusters and provide evidence for pulmonary inflammation. The murine metabolipidomic data were validated with human Neisseria-dominant bronchiectasis samples and compared with disease in which Pseudomonas-, an established bronchiectasis pathogen, is dominant. Metagenomic surveillance of Neisseria across various respiratory disorders reveals broader importance, and the assessment of the home environment in bronchiectasis implies potential environmental sources of exposure. Thus, we identify Neisseria species as pathobionts in bronchiectasis, allowing for improved risk stratification in this high-risk group.

Machine Learning and Canine Chronic Enteropathies: A New Approach to Investigate FMT Effects

Fecal microbiota transplantation (FMT) represents a very promising approach to decreasing disease activity in canine chronic enteropathies (CE). However, the relationship between remission mechanisms and microbiome changes has not been elucidated yet. The main objective of this study was to report the clinical effects of oral freeze-dried FMT in CE dogs, comparing the fecal microbiomes of three groups: pre-FMT CE-affected dogs, post-FMT dogs, and healthy dogs. Diversity analysis, differential abundance analysis, and machine learning algorithms were applied to investigate the differences in microbiome composition between healthy and pre-FMT samples, while Canine Chronic Enteropathy Clinical Activity Index (CCECAI) changes and microbial diversity metrics were used to evaluate FMT effects. In the healthy/pre-FMT comparison, significant differences were noted in alpha and beta diversity and a list of differentially abundant taxa was identified, while machine learning algorithms predicted sample categories with 0.97 (random forest) and 0.87 (sPLS-DA) accuracy. Clinical signs of improvement were observed in 74% (20/27) of CE-affected dogs, together with a statistically significant decrease in CCECAI (median value from 5 to 2 median). Alpha and beta diversity variations between pre- and post-FMT were observed for each receiver, with a high heterogeneity in the response. This highlighted the necessity for further research on a larger dataset that could identify different healing patterns of microbiome changes.

Characteristics of lower respiratory tract microbiota in the patients with post-hematopoietic stem cell transplantation pneumonia

Background

Pneumonia is a leading cause of non-relapse mortality after hematopoietic stem cell transplantation (HSCT), and the lower respiratory tract (LRT) microbiome has been proven to be associated with various respiratory diseases. However, little is known about the characteristics of the LRT microbiome in patients with post-HSCT compared to healthy controls (HC) and community-acquired pneumonia (CAP).

Methods

Bronchoalveolar lavage samples from 55 patients with post-HSCT pneumonia, 44 patients with CAP, and 30 healthy volunteers were used to detect microbiota using 16S rRNA gene sequencing.

Results

The diversity of the LRT microbiome significantly decreased in patients with post-HSCT pneumonia, and the overall community was different from the CAP and HC groups. At the phylum level, post-HSCT pneumonia samples had a high abundance of Actinobacteria and a relatively low abundance of Bacteroidetes. The same is true for non-survivors compared with survivors in patients with post-HSCT pneumonia. At the genus level, the abundances of Pseudomonas, Acinetobacter, Burkholderia, and Mycobacterium were prominent in the pneumonia group after HSCT. On the other hand, gut-associated bacteria, Enterococcus were more abundant in the non-survivors. Some pathways concerning amino acid and lipid metabolism were predicted to be altered in patients with post-HSCT pneumonia.

Conclusions

Our results reveal that the LRT microbiome in patients with post-HSCT pneumonia differs from CAP patients and healthy controls, which could be associated with the outcome. The LRT microbiota could be a target for intervention during post-HSCT pneumonia.

Chronic dietary exposure to a glyphosate-based herbicide results in total or partial reversibility of plasma oxidative stress, cecal microbiota abundance and short-chain fatty acid composition in broiler hens

Glyphosate-based herbicides (GBHs) are massively used in agriculture. However, few studies have investigated the effects of glyphosate-based herbicides on avian species although they are largely exposed via their food. Here, we investigated the potential reversibility of the effects of chronic dietary exposure to glyphosate-based herbicides in broiler hens. For 42 days, we exposed 32-week-old hens to glyphosate-based herbicides via their food (47 mg/kg/day glyphosate equivalent, glyphosate-based herbicides, n = 75) corresponding to half glyphosate’s no-observed-adverse-effect-level in birds. We compared their performance to that of 75 control animals (CT). Both groups (glyphosate-based herbicides and control animals) were then fed for 28 additional days without glyphosate-based herbicides exposure (Ex-glyphosate-based herbicides and Ex-control animals). Glyphosate-based herbicides temporarily increased the plasma glyphosate and AMPA (aminomethylphosphonic acid) concentrations. Glyphosate and aminomethylphosphonic acid mostly accumulated in the liver and to a lesser extent in the leg muscle and abdominal adipose tissue. Glyphosate-based herbicides also temporarily increased the gizzard weight and plasma oxidative stress monitored by TBARS (thiobarbituric acid reactive substances). Glyphosate-based herbicides temporarily decreased the cecal concentrations of propionate, isobutyrate and propionate but acetate and valerate were durably reduced. The cecal microbiome was also durably affected since glyphosate-based herbicides inhibited Barnesiella and favored Alloprevotella. Body weight, fattening, food intake and feeding behavior as well as plasma lipid and uric acid were unaffected by glyphosate-based herbicides. Taken together, our results show possible disturbances of the cecal microbiota associated with plasma oxidative stress and accumulation of glyphosate in metabolic tissues in response to dietary glyphosate-based herbicides exposure in broiler hens. Luckily, glyphosate-based herbicides at this concentration does not hamper growth and most of the effects on the phenotypes are reversible.

Short- and Long-Term Effects of a Prebiotic Intervention with Polyphenols Extracted from European Black Elderberry—Sustained Expansion of Akkermansia spp

(1) Background: The intestinal microbiome has emerged as a central factor in human physiology and its alteration has been associated with disease. Therefore, great hopes are placed in microbiota-modulating strategies. Among various approaches, prebiotics, substrates with selective metabolization conferring a health benefit to the host, are promising candidates. Herein, we studied the prebiotic properties of a purified extract from European black elderberries, with a high and standardized content of polyphenols and anthocyanins. (2) Methods: The ELDERGUT trial represents a 9-week longitudinal intervention study divided into 3 distinct phases, namely a baseline, an intervention and a washout period, three weeks each. The intervention consisted of capsules containing 300 mg elderberry extract taken twice a day. Patient-reported outcomes and biosamples were collected weekly. Microbiome composition was assessed using 16S amplicon metagenomics. (3) Results: The supplementation was well tolerated. Microbiome trajectories were highly individualized with a profound shift in diversity indices immediately upon initiation and after termination of the compound. This was accompanied by corresponding changes in species abundance over time. Of particular interest, the relative abundance of Akkermansia spp. continued to increase in a subset of participants even beyond the supplementation period. Associations with participant metadata were detected.

The Environmental Microbiome, Allergic Disease, and Asthma

The environmental microbiome represents the entirety of the microbes and their metabolites that we encounter in our environments. A growing body of evidence supports the role of the environmental microbiome in risk for and severity of allergic diseases and asthma. The environmental microbiome represents a ubiquitous, lifelong exposure to non-self antigens. During the critical window between birth and 1 year of life, interactions between our early immune system and the environmental microbiome have 2 consequences: our individual microbiome is populated by environmental microbes, and our immune system is trained regarding which antigens to tolerate. During this time, a diversity of exposures appears largely protective, dramatically decreasing the risk of developing allergic diseases and asthma. As we grow older, our interactions with the environmental microbiome change. While it continues to exert influence over the composition of the human microbiome, the environmental microbiome becomes increasingly a source for antigenic stimulation and infection. The same microbial exposure protective against disease development may exacerbate disease severity. Although much has been learned about the importance of the environmental microbiome in allergic disease, much more remains to be understood about these complicated interactions between our environment, our microbiome, our immune system, and disease.

FXR Signaling-Mediated Bile Acid Metabolism Is Critical for Alleviation of Cholesterol Gallstones by Lactobacillus Strains

Cholesterol gallstone (CGS) disease is characterized by an imbalance in bile acid (BA) metabolism and is closely associated with gut microbiota disorders. However, the role and mechanism by which probiotics targeting the gut microbiota attenuate cholesterol gallstones are still unknown. In this study, Limosilactobacillus reuteri strain CGMCC 17942 and Lactiplantibacillus plantarum strain CGMCC 14407 were individually administered to lithogenic-diet (LD)-fed mice for 8 weeks. Both Lactobacillus strains significantly reduced LD-induced gallstones, hepatic steatosis, and hyperlipidemia. These strains modulated BA profiles in the serum and liver, which may be responsible for the activation of farnesoid X receptor (FXR). At the molecular level, L. reuteri and L. plantarum increased ileal fibroblast growth factor 15 (FGF15) and hepatic fibroblast growth factor receptor 4 (FGFR4) and small heterodimer partner (SHP). Subsequently, hepatic cholesterol 7α-hydroxylase (CYP7A1) and oxysterol 7α-hydroxylase (CYP7B1) were inhibited. Moreover, the two strains enhanced BA transport by increasing the levels of hepatic multidrug resistance-associated protein homologs 3 and 4 (Mrp3/4), hepatic multidrug resistance protein 2 (Mdr2), and the bile salt export pump (BSEP). In addition, both L. reuteri and L. plantarum reduced LD-associated gut microbiota dysbiosis. L. reuteri increased the relative abundance of Muribaculaceae, while L. plantarum increased that of Akkermansia. The changed gut microbiota was significantly negatively correlated with the incidence of cholesterol gallstones and the FXR-antagonistic BAs in the liver and serum and with the FXR signaling pathways. Furthermore, the protective effects of the two strains were abolished by both global and intestine-specific FXR antagonists. These findings suggest that Lactobacillus might relieve CGS through the FXR signaling pathways.

IMPORTANCE Cholesterol gallstone (CGS) disease is prevalent worldwide. None of the medical options for prevention and treatment of CGS disease are recommended, and surgical management has a high rate of recurrence. It has been reported that the factors involved in metabolic syndrome are highly connected with CGS formation. While remodeling of dysbiosis of the gut microbiome during improvement of metabolic syndrome has been well studied, less is known about prevention of CGS formation after regulating the gut microbiome. We used the lithogenic diet (LD) to induce an experimental CGS model in C57BL/6J mice to investigate protection against CGS formation by Limosilactobacillus reuteri strain CGMCC 17942 and Lactiplantibacillus plantarum strain CGMCC 14407. We found that these L. reuteri and L. plantarum strains altered the bile acid composition in mice and improved the dysbiosis of the gut microbiome. These two Lactobacillus strains prevented CGS formation by fully activating the hepatic and ileal FXR signaling pathways. They could be a promising therapeutic strategy for treating CGS or preventing its recurrence.

Machine Learning Data Analysis Highlights the Role of Parasutterella and Alloprevotella in Autism Spectrum Disorders

In recent years, the involvement of the gut microbiota in disease and health has been investigated by sequencing the 16S gene from fecal samples. Dysbiotic gut microbiota was also observed in Autism Spectrum Disorder (ASD), a neurodevelopmental disorder characterized by gastrointestinal symptoms. However, despite the relevant number of studies, it is still difficult to identify a typical dysbiotic profile in ASD patients. The discrepancies among these studies are due to technical factors (i.e., experimental procedures) and external parameters (i.e., dietary habits). In this paper, we collected 959 samples from eight available projects (540 ASD and 419 Healthy Controls, HC) and reduced the observed bias among studies. Then, we applied a Machine Learning (ML) approach to create a predictor able to discriminate between ASD and HC. We tested and optimized three algorithms: Random Forest, Support Vector Machine and Gradient Boosting Machine. All three algorithms confirmed the importance of five different genera, including Parasutterella and Alloprevotella. Furthermore, our results show that ML algorithms could identify common taxonomic features by comparing datasets obtained from countries characterized by latent confounding variables.

A robust knockoff filter for sparse regression analysis of microbiome compositional data

Microbiome data analysis often relies on the identification of a subset of potential biomarkers associated with a clinical outcome of interest. Robust ZeroSum regression, an elastic-net penalized compositional regression built on the least trimmed squares estimator, is a variable selection procedure capable to cope with the high dimensionality of these data, their compositional nature, and, at the same time, it guarantees robustness against the presence of outliers. The necessity of discovering “true” effects and to improve clinical research quality and reproducibility has motivated us to propose a two-step robust compositional knockoff filter procedure, which allows selecting the set of relevant biomarkers, among the many measured features having a nonzero effect on the response, controlling the expected fraction of false positives. We demonstrate the effectiveness of our proposal in an extensive simulation study, and illustrate its usefulness in an application to intestinal microbiome analysis.

Evaluating the clinical relevance of the enterotypes in the Estonian microbiome cohort

Human gut microbiome is subject to high inter-individual and temporal variability, which complicates building microbiome-based applications, including applications that can be used to improve public health. Categorizing the microbiome profiles into a small number of distinct clusters, such as enterotyping, has been proposed as a solution that can ameliorate these shortcomings. However, the clinical relevance of the enterotypes is poorly characterized despite a few studies marking the potential for using the enterotypes for disease diagnostics and personalized nutrition. To gain a further understanding of the clinical relevance of the enterotypes, we used the Estonian microbiome cohort dataset (n = 2,506) supplemented with diagnoses and drug usage information from electronic health records to assess the possibility of using enterotypes for disease diagnostics, detecting disease subtypes, and evaluating the susceptibility for developing a condition. In addition to the previously established 3-cluster enterotype model, we propose a 5-cluster community type model based on our data, which further separates the samples with extremely high Bacteroides and Prevotella abundances. Collectively, our systematic analysis including 231 phenotypic factors, 62 prevalent diseases, and 33 incident diseases greatly expands the knowledge about the enterotype-specific characteristics; however, the evidence suggesting the practical use of enterotypes in clinical practice remains scarce.

Compositional Data Analysis of 16S rRNA Gene Sequencing Results from Hospital Airborne Microbiome Samples

The compositional analysis of 16S rRNA gene sequencing datasets is applied to characterize the bacterial structure of airborne samples collected in different locations of a hospital infection disease department hosting COVID-19 patients, as well as to investigate the relationships among bacterial taxa at the genus and species level. The exploration of the centered log-ratio transformed data by the principal component analysis via the singular value decomposition has shown that the collected samples segregated with an observable separation depending on the monitoring location. More specifically, two main sample clusters were identified with regards to bacterial genera (species), consisting of samples mostly collected in rooms with and without COVID-19 patients, respectively. Human pathogenic genera (species) associated with nosocomial infections were mostly found in samples from areas hosting patients, while non-pathogenic genera (species) mainly isolated from soil were detected in the other samples. Propionibacterium acnes, Staphylococcus pettenkoferi, Corynebacterium tuberculostearicum, and jeikeium were the main pathogenic species detected in COVID-19 patients' rooms. Samples from these locations were on average characterized by smaller richness/evenness and diversity than the other ones, both at the genus and species level. Finally, the ρ metrics revealed that pairwise positive associations occurred either between pathogenic or non-pathogenic taxa.

The many genomes of Parkinson's disease

Genetic component of Parkinson's disease, once firmly believed non-existent, involves the human genome, mitochondrial genome, and the microbiome. Understanding the genomics of PD requires identification of PD-relevant genes and learning how they interact within the hologenome and with their environment. This chapter is an evidence-based perspective of a geneticist on how far we have come in this endeavor. The contemporary scientific society started with a naive and simplistic view of PD, evolved to accept that Parkinson's disease is probably the most complex disease there is, the progress we have made in discovering the genes and elucidating their functions, and now assembling the parts to create the whole.

Microbial risk score for capturing microbial characteristics, integrating multi-omics data, and predicting disease risk

BACKGROUND

With the rapid accumulation of microbiome-wide association studies, a great amount of microbiome data are available to study the microbiome's role in human disease and advance the microbiome's potential use for disease prediction. However, the unique features of microbiome data hinder its utility for disease prediction.

METHODS

Motivated from the polygenic risk score framework, we propose a microbial risk score (MRS) framework to aggregate the complicated microbial profile into a summarized risk score that can be used to measure and predict disease susceptibility. Specifically, the MRS algorithm involves two steps: (1) identifying a sub-community consisting of the signature microbial taxa associated with disease and (2) integrating the identified microbial taxa into a continuous score. The first step is carried out using the existing sophisticated microbial association tests and pruning and thresholding method in the discovery samples. The second step constructs a community-based MRS by calculating alpha diversity on the identified sub-community in the validation samples. Moreover, we propose a multi-omics data integration method by jointly modeling the proposed MRS and other risk scores constructed from other omics data in disease prediction.

RESULTS

Through three comprehensive real-data analyses using the NYU Langone Health COVID-19 cohort, the gut microbiome health index (GMHI) multi-study cohort, and a large type 1 diabetes cohort separately, we exhibit and evaluate the utility of the proposed MRS framework for disease prediction and multi-omics data integration. In addition, the disease-specific MRSs for colorectal adenoma, colorectal cancer, Crohn's disease, and rheumatoid arthritis based on the relative abundances of 5, 6, 12, and 6 microbial taxa, respectively, are created and validated using the GMHI multi-study cohort. Especially, Crohn's disease MRS achieves AUCs of 0.88 (0.85-0.91) and 0.86 (0.78-0.95) in the discovery and validation cohorts, respectively.

CONCLUSIONS

The proposed MRS framework sheds light on the utility of the microbiome data for disease prediction and multi-omics integration and provides a great potential in understanding the microbiome's role in disease diagnosis and prognosis. Video Abstract.

Viral biogeography of the mammalian gut and parenchymal organs

The mammalian virome has been linked to health and disease but our understanding of how it is structured along the longitudinal axis of the mammalian gastrointestinal tract (GIT) and other organs is limited. Here, we report a metagenomic analysis of the prokaryotic and eukaryotic virome occupying luminal and mucosa-associated habitats along the GIT, as well as parenchymal organs (liver, lung and spleen), in two representative mammalian species, the domestic pig and rhesus macaque (six animals per species). Luminal samples from the large intestine of both mammals harboured the highest loads and diversity of bacteriophages (class Caudoviricetes, family Microviridae and others). Mucosal samples contained much lower viral loads but a higher proportion of eukaryotic viruses (families Astroviridae, Caliciviridae, Parvoviridae). Parenchymal organs contained bacteriophages of gut origin, in addition to some eukaryotic viruses. Overall, GIT virome composition was specific to anatomical region and host species. Upper GIT and mucosa-specific viruses were greatly under-represented in distal colon samples (a proxy for faeces). Nonetheless, certain viral and phage species were ubiquitous in all samples from the oral cavity to the distal colon. The dataset and its accompanying methodology may provide an important resource for future work investigating the biogeography of the mammalian gut virome.

Antibiotic and antifungal use in pediatric leukemia and lymphoma patients are associated with increasing opportunistic pathogens and decreasing bacteria responsible for activities that enhance colonic defense

Due to decreased immunity, both antibiotics and antifungals are regularly used in pediatric hematologic-cancer patients as a means to prevent severe infections and febrile neutropenia. The general effect of antibiotics on the human gut microbiome is profound, yielding decreased diversity and changes in community structure. However, the specific effect on pediatric oncology patients is not well-studied. The effect of antifungal use is even less understood, having been studied only in mouse models. Because the composition of the gut microbiome is associated with regulation of hematopoiesis, immune function and gastrointestinal integrity, changes within the patient gut can have implications for the clinical management of hematologic malignancies. The pediatric population is particularly challenging because the composition of the microbiome is age dependent, with some of the most pronounced changes occurring in the first three years of life. We investigated how antibiotic and antifungal use shapes the taxonomic composition of the stool microbiome in pediatric patients with leukemia and lymphoma, as inferred from both 16S rRNA and metagenome data. Associations with age, antibiotic use and antifungal use were investigated using multiple analysis methods. In addition, multivariable differential abundance was used to identify and assess specific taxa that were associated with multiple variables. Both antibiotics and antifungals were linked to a general decline in diversity in stool samples, which included a decrease in relative abundance in butyrate producers that play a critical role in host gut physiology (e.g., Faecalibacterium, Anaerostipes, Dorea, Blautia),. Furthermore, antifungal use was associated with a significant increase in relative abundance of opportunistic pathogens. Collectively, these findings have important implications for the treatment of leukemia and lymphoma patients. Butyrate is important for gastrointestinal integrity; it inhibits inflammation, reinforces colonic defense, mucosal immunity. and decreases oxidative stress. The routine use of broad-spectrum anti-infectives in pediatric oncology patients could simultaneously contribute to a decline in gastrointestinal integrity and colonic defense while promoting increases in opportunistic pathogens within the patient gut. Because the gut microbiome has been linked to both short-term clinical outcomes, and longer-lasting health effects, systematic characterization of the gut microbiome in pediatric patients during, and beyond, treatment is warranted.

Prophylactic Treatment of Undernourished Mice with Cotrimoxazole Induces a Different Profile of Dysbiosis with Functional Metabolic Alterations

Childhood malnutrition affects physiology and development. It increases infection rates, which may not present clinical signs in severe cases. The World Health Organization recommends prophylactic treatment with cotrimoxazole (SXT) and nutritional recovery to overcome this issue. This treatment is controversial, since evidence of a reduction in morbidity and mortality is not a consensus and could induce the development of antibiotic-resistant bacteria. Moreover, the impact of using this wide-spectrum antibiotic on gut microbiota in a critical period of development, and weakness is unknown. To understand how SXT prophylaxis could affect gut microbiota in undernutrition, we induced protein–energy undernutrition (PEU) in weaning C57BL/6 mice for three weeks and treated animals with SXT for two weeks. Using 16S rRNA gene sequencing, we compared the taxonomic composition and metabolic pathways of control mice, animals submitted to undernutrition (UND), treated with SXT, or undernourished and SXT treated (UND + SXT). We identified that UND mice had a significant increase in predicted pathways related to metabolic syndromes later in life. The prophylactic SXT treatment alone resulted in a significant loss in community richness and beta diversity. Furthermore, we identified the reduction of three genera in SXT treated mice, including the butyrate producers Faecalibacterium and Anaerotruncus. Both UND and double challenge (UND + SXT) resulted in a reduction of the amino acid’s biosynthesis pathway related to cell growth. Our results show that the SXT prophylaxis of young mice during an undernourishment period did not re-establish the undernourished microbiota community composition similar to healthy controls but induced a distinct dysbiotic profile with functional metabolic consequences.

Very Preterm Children Gut Microbiota Comparison at the Neonatal Period of 1 Month and 3.5 Years of Life

Prematurity is a risk factor for dysbiosis of the gut microbiota due to particular birth conditions and frequent prolonged hospitalization of neonates. Although gut microbiota colonization after birth and its establishment during the hospitalization period have been studied in preterm infants, data on gut microbiota following discharge, particularly during early childhood, are scarce. The present study investigated the relationship between gut microbiota at 1 month after birth (hospitalization period) and 3.5 years of age in 159 preterm children belonging to the French EPIFLORE prospective observational cohort study. Analysis using bacterial 16S rRNA gene sequencing showed that the gut microbiota of preterm neonates at 1 month was highly variable and characterized by six distinct enterotypes. In contrast, the gut microbiota of the same children at 3.5 years of age showed less variability, with only two discrete enterotypes. An absence of association between enterotypes at 1 month and 3.5 years of age was observed. While the alpha diversity of gut microbiota significantly increased between 1 month and 3.5 years of age, for both alpha and beta diversities, there was no correlation between the 1-month and 3.5-years time points. Comparison at 3.5 years between children born either preterm (n = 159) or full-term (n = 200) showed no differences in terms of enterotypes, but preterm children harbored a lower Shannon diversity index and a different overall composition of microbiota than full-term children. This study suggests that the characteristics of the early gut microbiota of preterm children are not predictive of the microbial community composition at 3.5 years of age. However, the impact of gestational age is still noticeable on the gut microbiota up to 3.5 years of age.

Geographic Dispersal Limitation Dominated Assembly Processes of Bacterial Communities on Microplastics Compared to Water and Sediment

Microplastics provide new microbial niches in aquatic environments. Nevertheless, information on the assembly processes and potential ecological mechanisms of bacterial communities on microplastics from reservoirs is lacking. Here, we investigated the assembly processes and potential ecological mechanisms of bacterial communities on microplastics through full-length 16S rRNA sequencing in the Three Gorges Reservoir area of the Yangtze River, compared to water and sediment. The results showed that the Burkholderiaceae were the dominant composition of bacterial communities in microplastics (9.95%), water (25.14%), and sediment (7.22%). The niche width of the bacterial community on microplastics was lower than those in water and sediment. For the microplastics and sediment, distance-decay relationship results showed that the bacterial community similarity was significantly decreased with increasing geographical distance. In addition, the spatial turnover rate of the bacterial community on microplastics along the ~662-km reaches of the Yangtze River in the Three Gorges Reservoir area was higher than that in sediment. Null model analysis showed that the assembly processes of the bacterial community on microplastics were also different from those in water and sediments. Dispersal limitation (52.4%) was the primary assembly process of the bacterial community on microplastics, but variable selection was the most critical assembly process of the bacterial communities in water (47.6%) and sediment (66.7%). Thus, geographic dispersal limitation dominated the assembly processes of bacterial communities on microplastics. This study can enhance our understanding of the assembly mechanism of bacterial communities caused by the selection preference for microplastics from the surrounding environment. IMPORTANCE In river systems, microplastics create new microbial niches that significantly differ from those of the surrounding environment. However, the potential relationships between the biogeographic distribution and assembly processes of microbial communities on microplastics were still not well understood. This study could help us address the lack of knowledge about the assembly processes of bacterial communities on microplastics caused by selection from the surrounding environment. In this study, strong geographic dispersal limitation dominated assembly processes of bacterial communities on microplastics, compared to water and sediment, which may be responsible for the microplastic bacterial richness, and the niche distance was lower than those in water and sediment. In addition, sediment may be the main potential source of bacterial communities on microplastics in the Three Gorges Reservoir area, which makes higher community similarity between microplastics and sediment than between microplastics and water.

Imbalanced gut microbiota fuels hepatocellular carcinoma development by shaping the hepatic inflammatory microenvironment

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, and therapeutic options for advanced HCC are limited. Here, we observe that intestinal dysbiosis affects antitumor immune surveillance and drives liver disease progression towards cancer. Dysbiotic microbiota, as seen in Nlrp6^−/− mice, induces a Toll-like receptor 4 dependent expansion of hepatic monocytic myeloid-derived suppressor cells (mMDSC) and suppression of T-cell abundance. This phenotype is transmissible via fecal microbiota transfer and reversible upon antibiotic treatment, pointing to the high plasticity of the tumor microenvironment. While loss of Akkermansia muciniphila correlates with mMDSC abundance, its reintroduction restores intestinal barrier function and strongly reduces liver inflammation and fibrosis. Cirrhosis patients display increased bacterial abundance in hepatic tissue, which induces pronounced transcriptional changes, including activation of fibro-inflammatory pathways as well as circuits mediating cancer immunosuppression. This study demonstrates that gut microbiota closely shapes the hepatic inflammatory microenvironment opening approaches for cancer prevention and therapy.

Steatohepatitis is a chronic hepatic inflammation associated with increased risk of hepatocellular carcinoma progression. Here the authors show that intestinal dysbiosis in mice lacking the inflammasome sensor molecule NLRP6 aggravates steatohepatitis and accelerates liver cancer progression, a process that can be delayed by antibiotic treatment.

Temporal Dynamics of the Intestinal Microbiome Following Short-Term Dietary Restriction

Short-term dietary restriction has been proposed as an intriguing pre-operative conditioning strategy designed to attenuate the surgical stress response and improve outcomes. However, it is unclear how this nutritional intervention influences the microbiome, which is known to modulate the systemic condition. Healthy individuals were recruited to participate in a four-day, 70% protein-restricted, 30% calorie-restricted diet, and stool samples were collected at baseline, after the restricted diet, and after resuming normal food intake. Taxonomy and functional pathway analysis was performed via shotgun metagenomic sequencing, prevalence filtering, and differential abundance analysis. High prevalence species were altered by the dietary intervention but quickly returned to baseline after restarting a regular diet. Composition and functional changes after the restricted diet included the decreased relative abundance of commensal bacteria and a catabolic phenotype. Notable species changes included Faecalibacterium prausnitzii and Roseburia intestinalis, which are major butyrate producers within the colon and are characteristically decreased in many disease states. The macronutrient components of the diet might have influenced these changes. We conclude that short-term dietary restriction modulates the ecology of the gut microbiome, with this modulation being characterized by a relative dysbiosis.

Testing Two Somatic Cell Count Cutoff Values for Bovine Subclinical Mastitis Detection Based on Milk Microbiota and Peripheral Blood Leukocyte Transcriptome Profile

Somatic cell count (SCC) is an important indicator of the health state of bovine udders. However, the exact cut-off value used for differentiating the cows with healthy quarters from the cows with subclinical mastitis remains controversial. Here, we collected composite milk (milk from four udder quarters) and peripheral blood samples from individual cows in two different dairy farms and used 16S rRNA gene sequencing combined with RNA-seq to explore the differences in the milk microbial composition and transcriptome of cows with three different SCC levels (LSCC: <100,000 cells/mL, MSCC: 100,000−200,000 cells/mL, HSCC: >200,000 cells/mL). Results showed that the milk microbial profiles and gene expression profiles of samples derived from cows in the MSCC group were indeed relatively easily discriminated from those from cows in the LSCC group. Discriminative analysis also uncovered some differentially abundant microbiota at the genus level, such as Bifidobacterium and Lachnospiraceae_AC2044_group, which were more abundant in milk samples from cows with SCC below 100,000 cells/mL. As for the transcriptome profiling, 79 differentially expressed genes (DEGs) were found to have the same direction of regulation in two sites, and functional analyses also showed that biological processes involved in inflammatory responses were more active in MSCC and HSCC cows. Overall, these results showed a similarity between the milk microbiota and gene expression profiles of MSCC and HSCC cows, which presented further evidence that 100,000 cells/ml is a more optimal cut-off value than 200,000 cells/mL for intramammary infection detection at the cow level.

Microbiome composition of disturbed soils from sandy-gravel mining complexes with different reclamation approaches

Activities connected to mineral mining disrupt the soil layer and bring parent rock material to the surface. It leads to altering the environmental conditions and leaves behind vast areas of disturbed lands. Returning these lands to natural ecosystems is an important contemporary challenge, which can be acquired by reclamation practices. Soil microbiome composition reflects changes happening to disturbed lands; thus, its analysis is a powerful tool for evaluating the disturbance degree and estimating the effect of the implementation of reclamation techniques. Additionally, factors connected to the characteristics of a particular geographical region have a certain impact on the microbiome and should be taken into account. Thereby, studies of soil microbiomes of disturbed soils of different origins are essential in understanding the dynamics of soil restoration. Here, we focus on soil microbiomes from two sandy-gravel mining complexes in mountainous areas with a moderate continental climate of the Central Caucasus. These quarries share the same parent rock material, but differ in benchmark soil type and reclamation approach - one was left for passive recovery and the other was technically reclaimed with overburden material. Comparative analysis of microbiome composition, based on sequencing of 16S rRNA gene libraries, showed that region and disturbance are the key factors explaining microbiome variation, which surpass the influence of local factors. However, the application of reclamation techniques greatly reduces the dissimilarity of soil microbiomes caused by disturbance. Linking of soil chemical parameters to microbiome composition showed that the disturbance factor correlates with a lack of organic carbon. Other chemical parameters, like pH, ammonium, nitrates and total carbon explain microbiome variability on a smaller scale between sampling sites. Thus, while regional and disturbance factors reflected differentiation of soil microbiomes, soil chemical parameters explained local variation of certain groups of microorganisms.

From the bacterial citrus microbiome to the selection of potentially host-beneficial microbes

Citrus is the most cultivated fruit crop worldwide. The modern citrus industry needs new bioproducts to overcome phytopathological threats, tolerate stresses and increase yield and quality. Mutualistic microbes from roots significantly impact host physiology and health and are a potentially beneficial resource. The bacterial microbiome can be surveyed to select potentially host-beneficial microbes. To achieve this goal, a prevalent "core-citrus" bacterial microbiome was obtained by picking those operational taxonomic units (OTUs) shared among samples within and across two Citrus rootstock genotypes grown in the same soil for more than 20 years. A sub-selection of main OTUs from the defined "core-citrus" microbiome was made based on abundance, host-enriched versus bulk soil, and rhizosphere-indicator species. In parallel, an extensive census of the cultivable microbiota was performed to collect a large number of bacterial citrus isolates. Metataxonomic data were linked to cultured microbes, matching 16S rRNA gene sequences from bacterial isolates with those counterpart OTU reference sequences from the selected bacterial "core-citrus" microbiome. This approach allowed selection of potentially host-beneficial bacteria to mine for agricultural probiotics in future biotechnological applications required for the citrus industry.

Microbial Risk Score for Capturing Microbial Characteristics, Integrating Multi-omics Data, and Predicting Disease Risk

Background:

With the rapid accumulation of microbiome-wide association studies, a great amount of microbiome data are available to study the microbiome’s role in human disease and advance the microbiome’s potential use for disease prediction. However, the unique features of microbiome data hinder its utility for disease prediction.

Methods:

Motivated from the polygenic risk score framework, we propose a microbial risk score (MRS) framework to aggregate the complicated microbial profile into a summarized risk score that can be used to measure and predict disease susceptibility. Specifically, the MRS algorithm involves two steps: 1) identifying a sub-community consisting of the signature microbial taxa associated with disease, and 2) integrating the identified microbial taxa into a continuous score. The first step is carried out using the existing sophisticated microbial association tests and pruning and thresholding method in the discovery samples. The second step constructs a community-based MRS by calculating alpha diversity on the identified sub-community in the validation samples. Moreover, we propose a multi-omics data integration method by jointly modeling the proposed MRS and other risk scores constructed from other omics data in disease prediction.

Results:

Through three comprehensive real data analyses using the NYU Langone Health COVID-19 cohort, the gut microbiome health index (GMHI) multi-study cohort, and a large type 1 diabetes cohort separately, we exhibit and evaluate the utility of the proposed MRS framework for disease prediction and multi-omics data integration. In addition, the disease-specific MRSs for colorectal adenoma, colorectal cancer, Crohn’s disease, and rheumatoid arthritis based on the relative abundances of 5, 6, 12, and 6 microbial taxa respectively are created and validated using the GMHI multi-study cohort. Especially, Crohn’s disease MRS achieves AUCs of 0.88 ([0.85–0.91]) and 0.86 ([0.78–0.95]) in the discovery and validation cohorts, respectively.

Conclusions:

The proposed MRS framework sheds light on the utility of the microbiome data for disease prediction and multi-omics integration, and provides great potential in understanding the microbiome’s role in disease diagnosis and prognosis.

Fecal microbiota and bile acids in IBD patients undergoing screening for colorectal cancer

Due to the potential role of the gut microbiota and bile acids in the pathogenesis of both inflammatory bowel disease (IBD) and sporadic colorectal cancer, we aimed to determine whether these factors were associated with colorectal cancer in IBD patients. 215 IBD patients and 51 non-IBD control subjects were enrolled from 10 French IBD centers between September 2011 and July 2018. Fecal samples were processed for bacterial 16S rRNA gene sequencing and bile acid profiling. Demographic, clinical, endoscopic, and histological outcomes were recorded. Characteristics of IBD patients included: median age: 41.6 (IQR 22); disease duration 13.2 (13.1); 47% female; 21.9% primary sclerosing cholangitis; 109 patients with Crohn's disease (CD); 106 patients with ulcerative colitis (UC). The prevalence of cancer was 2.8% (6/215: 1 CD; 5 UC), high-grade dysplasia 3.7% (8/215) and low-grade dysplasia 7.9% (17/215). Lachnospira was decreased in IBD patients with cancer, while Agathobacter was decreased and Escherichia-Shigella increased in UC patients with any neoplasia. Bile acids were not associated with cancer or neoplasia. Unsupervised clustering identified three gut microbiota clusters in IBD patients associated with bile acid composition and clinical features, including a higher risk of neoplasia in UC in two clusters when compared to the third (relative risk (RR) 4.07 (95% CI 1.6-10.3, P < .01) and 3.56 (95% CI 1.4-9.2, P < .01)). In this multicentre observational study, a limited number of taxa were associated with neoplasia and exploratory microbiota clusters co-associated with clinical features, including neoplasia risk in UC. Given the very small number of cancers, the robustness of these findings will require assessment and validation in future studies.

A neural network-based framework to understand the type 2 diabetes-related alteration of the human gut microbiome

The identification of microbial markers adequate to delineate the disease-related microbiome alterations from the complex human gut microbiota is of great interest. Here, we develop a framework combining neural network (NN) and random forest, resulting in 40 marker species and 90 marker genes identified from the metagenomic data set (185 healthy and 183 type 2 diabetes [T2D] samples), respectively. In terms of these markers, the NN model obtained higher accuracy in classifying the T2D-related samples than other methods; the interaction network analyses identified the key species and functional modules; the regression analysis determined that fasting blood glucose is the most significant factor (p < 0.05) in the T2D-related alteration of the human gut microbiome. We also observed that those marker species varied little across the case and control samples greatly shift in the different stages of the T2D development, suggestive of their important roles in the T2D-related microbiome alteration. Our study provides a new way of identifying the disease-related biomarkers and analyzing the role they may play in the development of the disease.

A Guide to Dietary Pattern-Microbiome Data Integration

The human gut microbiome is linked to metabolic and cardiovascular disease risk. Dietary modulation of the human gut microbiome offers an attractive pathway to manipulate the microbiome to prevent microbiome-related disease. However, this promise has not been realized. The complex system of diet and microbiome interactions is poorly understood. Integrating observational human diet and microbiome data can help researchers and clinicians untangle the complex systems of interactions that predict how the microbiome will change in response to foods. The use of dietary patterns to assess diet-microbiome relations holds promise to identify interesting associations and result in findings that can directly translate into actionable dietary intake recommendations and eating plans. In this article, we first highlight the complexity inherent in both dietary and microbiome data and introduce the approaches generally used to explore diet and microbiome simultaneously in observational studies. Second, we review the food group and dietary pattern-microbiome literature focusing on dietary complexity-moving beyond nutrients. Our review identified a substantial and growing body of literature that explores links between the microbiome and dietary patterns. However, there was very little standardization of dietary collection and assessment methods across studies. The 54 studies identified in this review used ≥7 different methods to assess diet. Coupled with the variation in final dietary parameters calculated from dietary data (e.g., dietary indices, dietary patterns, food groups, etc.), few studies with shared methods and assessment techniques were available for comparison. Third, we highlight the similarities between dietary and microbiome data structures and present the possibility that multivariate and compositional methods, developed initially for microbiome data, could have utility when applied to dietary data. Finally, we summarize the current state of the art for diet-microbiome data integration and highlight ways dietary data could be paired with microbiome data in future studies to improve the detection of diet-microbiome signals.

Daily Inclusion of Resistant Starch-Containing Potatoes in a Dietary Guidelines for Americans Dietary Pattern Does Not Adversely Affect Cardiometabolic Risk or Intestinal Permeability in Adults with Metabolic Syndrome: A Randomized Controlled Trial

Poor diet quality influences cardiometabolic risk. Although potatoes are suggested to adversely affect cardiometabolic health, controlled trials that can establish causality are limited. Consistent with potatoes being rich in micronutrients and resistant starch, we hypothesized that their inclusion in a Dietary Guidelines for Americans (DGA)-based dietary pattern would improve cardiometabolic and gut health in metabolic syndrome (MetS) persons. In a randomized cross-over trial, MetS persons (n = 27; 32.5 ± 1.3 year) consumed a DGA-based diet for 2 weeks containing potatoes (DGA + POTATO; 17.5 g/day resistant starch) or bagels (DGA + BAGEL; 0 g/day resistant starch) prior to completing oral glucose and gut permeability tests. Blood pressure, fasting glucose and insulin, and insulin resistance decreased (p < 0.05) from baseline regardless of treatment without any change in body mass. Oral glucose-induced changes in brachial artery flow-mediated dilation, nitric oxide homeostasis, and lipid peroxidation did not differ between treatment arms. Serum endotoxin AUC0−120 min and urinary lactulose/mannitol, but not urinary sucralose/erythritol, were lower in DGA + POTATO. Fecal microbiome showed limited between-treatment differences, but the proportion of acetate was higher in DGA + POTATO. Thus, short-term consumption of a DGA-based diet decreases cardiometabolic risk, and the incorporation of resistant starch-containing potatoes into a healthy diet reduces small intestinal permeability and postprandial endotoxemia.

Examining Relationships between Sleep Physiology and the Gut Microbiome in Pre-Clinical and Translational Research: A Scoping Review Protocol (Preprint)

Background

Objective

Methods

Results

Conclusions

Trial Registration

International Registered Report Identifier (IRRID)