A clinician's guide to microbiome analysis

The researcher's guide to selecting biomarkers in mental health studies

Clinical mental health researchers may understandably struggle with how to incorporate biological assessments in clinical research. The options are numerous and are described in a vast and complex body of literature. Here we provide guidelines to assist mental health researchers seeking to include biological measures in their studies. Apart from a focus on behavioral outcomes as measured via interviews or questionnaires, we advocate for a focus on biological pathways in clinical trials and epidemiological studies that may help clarify pathophysiology and mechanisms of action, delineate biological subgroups of participants, mediate treatment effects, and inform personalized treatment strategies. With this paper we aim to bridge the gap between clinical and biological mental health research by (1) discussing the clinical relevance, measurement reliability, and feasibility of relevant peripheral biomarkers; (2) addressing five types of biological tissues, namely blood, saliva, urine, stool and hair; and (3) providing information on how to control sources of measurement variability.

Advancing microbiome research through standardized data and metadata collection: introducing the Microbiome Research Data Toolkit

Microbiome research has made significant gains with the evolution of sequencing technologies. Ensuring comparability between studies and enhancing the findability, accessibility, interoperability and reproducibility of microbiome data are crucial for maximizing the value of this growing body of research. Addressing the challenges of standardized metadata reporting, collection and curation, the Microbiome Working Group of the Human Hereditary and Health in Africa (H3Africa) consortium aimed to develop a comprehensive solution. In this paper, we present the Microbiome Research Data Toolkit, a versatile tool designed to standardize microbiome research metadata, facilitate MIxS-MIMS and PhenX reporting, standardize prospective collection of participant biological and lifestyle data, and retrospectively harmonize such data. This toolkit enables past, present and future microbiome research endeavors to collaborate effectively, fostering novel collaborations and accelerating knowledge discovery in the field. Database URL: https://doi.org/10.25375/uct.24218999.v2.

Changes in oral, skin, and gut microbiota in children with atopic dermatitis: a case-control study

Introduction

Atopic dermatitis (AD) is a common clinical recurrent atopic disease in dermatology, most seen in children and adolescents. In recent years, AD has been found to be closely associated with microbial communities.

Methods

To explore the synergistic effects between colonizing bacteria from different sites and AD, we comparatively analyzed the skin, oral, and gut microbiota of children with AD (50 individuals) and healthy children (50 individuals) by 16S rRNA gene sequencing. Twenty samples were also randomly selected from both groups for metabolic and macrogenomic sequencing.

Results

The results of our sequencing study showed reduced microbiota diversity in the oral, skin, and gut of children with AD (P < 0.05). Metabolomics analysis showed that serotonergic synapse, arachidonic acid metabolism, and steroid biosynthesis were downregulated at all three loci in the oral, skin, and gut of children with AD (P < 0.05). Macrogenomic sequencing analysis showed that KEGG functional pathways of the three site flora were involved in oxidative phosphorylation, ubiquitin-mediated proteolysis, mRNA surveillance pathway, ribosome biogenesis in eukaryotes, proteasome, basal transcription factors, peroxisome, MAPK signaling pathway, mitophagy, fatty acid elongation, and so on (P < 0.05).

Discussion

The combined microbial, metabolic, and macrogenetic analyses identified key bacteria, metabolites, and pathogenic pathways that may be associated with AD development. We provides a more comprehensive and in-depth understanding of the role of the microbiota at different sites in AD patients, pointing to new directions for future diagnosis, treatment and prognosis.

The effect of probiotic-fortified kefir on cardiovascular risk factors in elderly population: a double-blind, randomized, placebo-controlled clinical trial

INTRODUCTION

The outbreak of cardiovascular disease (CVD) augments with age. Gut dysbiosis can worsen or initiate systemic disorders such as metabolic diseases and CVDs. Therefore, this research aimed to assess the effect of kefir fortified with Lactobacillus helveticus R0052 and Bifidobacterium longum R017 on CVD risk factors in the elderly population. The subjects of this study were selected from the Motahari Clinic in Shiraz, Iran.

METHOD

This study was a double-blind, randomized, and controlled clinical trial that was conducted on 67 elderly people who were randomly divided into two groups: the fortified kefir group (n = 32), which received one bottle of fortified kefir (240 cc), and the placebo group (n = 35), which received one bottle of regular kefir for eight weeks. To analyze the data, SPSS software was applied.

RESULTS

After eight weeks, significant differences were seen in atherogenic and Castell's risk index I between the fortified and regular groups (p = 0.048 and p = 0.048, respectively). No significant differences were found in Castelli's risk index II, high-density lipoprotein cholesterol (HDL-C), total cholesterol, triglycerides (TG), non-HDL-C, TG-cholesterol index, and fasting blood sugar by comparing the two groups.

CONCLUSION

Our investigation demonstrated that fortified kefir with probiotics did not significantly affect lipid profiles. Still, it could significantly affect some indices, including Castelli's risk index I and atherogenic index. More studies are required to confirm the findings and mechanisms of probiotics' effect on CVD risk factors.

TRIAL NUMBER

The present registered at the Iranian Registry of Clinical Trials (IRCT20130227012628N3) at 2023-02-21.

Impact of dietary carbohydrate, fat or protein restriction on the human gut microbiome: a systematic review

Restriction of dietary carbohydrates, fat and/or protein is often used to reduce body weight and/or treat (metabolic) diseases. Since diet is a key modulator of the human gut microbiome, which plays an important role in health and disease, this review aims to provide an overview of current knowledge of the effects of macronutrient-restricted diets on gut microbial composition and metabolites. A structured search strategy was performed in several databases. After screening for inclusion and exclusion criteria, thirty-six articles could be included. Data are included in the results only when supported by at least three independent studies to enhance the reliability of our conclusions. Low-carbohydrate (<30 energy%) diets tended to induce a decrease in the relative abundance of several health-promoting bacteria, including Bifidobacterium, as well as a reduction in short-chain fatty acid (SCFA) levels in faeces. In contrast, low-fat diets (<30 energy%) increased alpha diversity, faecal SCFA levels and abundance of some beneficial bacteria, including Faecalibacterium prausnitzii. There were insufficient data to draw conclusions concerning the effects of low-protein (<10 energy%) diets on gut microbiota. Although the data of included studies unveil possible benefits of low-fat and potential drawbacks of low-carbohydrate diets for human gut microbiota, the diversity in study designs made it difficult to draw firm conclusions. Using a more uniform methodology in design, sample processing and sharing raw sequence data could foster our understanding of the effects of macronutrient restriction on gut microbiota composition and metabolic dynamics relevant to health. This systematic review was registered at https://www.crd.york.ac.uk/prospero as CRD42020156929.

Current progresses and challenges for microbiome research in human health: a perspective

It is becoming increasingly clear that the human microbiota, also known as "the hidden organ", possesses a pivotal role in numerous processes involved in maintaining the physiological functions of the host, such as nutrient extraction, biosynthesis of bioactive molecules, interplay with the immune, endocrine, and nervous systems, as well as resistance to the colonization of potential invading pathogens. In the last decade, the development of metagenomic approaches based on the sequencing of the bacterial 16s rRNA gene via Next Generation Sequencing, followed by whole genome sequencing via third generation sequencing technologies, has been one of the great advances in molecular biology, allowing a better profiling of the human microbiota composition and, hence, a deeper understanding of the importance of microbiota in the etiopathogenesis of different pathologies. In this scenario, it is of the utmost importance to comprehensively characterize the human microbiota in relation to disease pathogenesis, in order to develop novel potential treatment or preventive strategies by manipulating the microbiota. Therefore, this perspective will focus on the progress, challenges, and promises of the current and future technological approaches for microbiome profiling and analysis.

The bidirectional interaction between antidepressants and the gut microbiota: are there implications for treatment response?

This review synthesizes the evidence on associations between antidepressant use and gut microbiota composition and function, exploring the microbiota's possible role in modulating antidepressant treatment outcomes. Antidepressants exert an influence on measures of gut microbial diversity. The most consistently reported differences were in β-diversity between those exposed to antidepressants and those not exposed, with longitudinal studies supporting a potential causal association. Compositional alterations in antidepressant users include an increase in the Bacteroidetes phylum, Christensenellaceae family, and Bacteroides and Clostridium genera, while a decrease was found in the Firmicutes phylum, Ruminococcaceae family, and Ruminococcus genus. In addition, antidepressants attenuate gut microbial differences between depressed and healthy individuals, modulate microbial serotonin transport, and influence microbiota's metabolic functions. These include lyxose degradation, peptidoglycan maturation, membrane transport, and methylerythritol phosphate pathways, alongside gamma-aminobutyric acid metabolism. Importantly, baseline increased α-diversity and abundance of the Roseburia and Faecalibacterium genera, in the Firmicutes phylum, are associated with antidepressant response, emerging as promising biomarkers. This review highlights the potential for gut microbiota as a predictor of treatment response and emphasizes the need for further research to elucidate the mechanisms underlying antidepressant-microbiota interactions. More homogeneous studies and standardized techniques are required to confirm these initial findings.

Ecological niches and assembly dynamics of diverse microbial consortia in the gastrointestine of goat kids

Goats are globally invaluable ruminants that balance food security and environmental impacts, and their commensal microbiome residing in the gastrointestinal tract (GIT) is associated with animal health and productivity. However, the reference genomes and functional repertoires of GIT microbes in goat kids have not been fully elucidated. Herein, we performed a comprehensive landscape survey of the GIT microbiome of goat kids using metagenomic sequencing and binning, spanning a dense sampling regime covering three gastrointestinal compartments spatially and five developmental ages temporally. We recovered 1002 high-quality metagenome-assembled genomes (termed the goat kid GIT microbial catalog [GKGMC]), 618 of which were novel. They encode more than 2.3 million nonredundant proteins, and represent a variety of carbohydrate-degrading enzymes and metabolic gene clusters. The GKGMC-enriched microbial taxa, particularly Sodaliphilus, expanded the microbial tree of life in goat kids. Using this GKGMC, we first deciphered the prevalence of fiber-degrading bacteria for carbohydrate decomposition in the rumen and colon, while the ileal microbiota specialized in the uptake and conversion of simple sugars. Moreover, GIT microorganisms were rapidly assembled after birth, and their carbohydrate metabolic adaptation occurred in three phases of progression. Finally, phytobiotics modified the metabolic cascades of the ileal microbiome, underpinned by the enrichment of Sharpea azabuensis and Olsenella spp. implicated in lactate formation and utilization. This GKGMC reference provides novel insights into the early-life microbial developmental dynamics in distinct compartments, and offers expanded resources for GIT microbiota-related research in goat kids.

Fecal Metagenomics to Identify Biomarkers of Food Intake in Healthy Adults: Findings from Randomized, Controlled, Nutrition Trials

BACKGROUND

Undigested components of the human diet affect the composition and function of the microorganisms present in the gastrointestinal tract. Techniques like metagenomic analyses allow researchers to study functional capacity, thus revealing the potential of using metagenomic data for developing objective biomarkers of food intake.

OBJECTIVES

As a continuation of our previous work using 16S and metabolomic datasets, we aimed to utilize a computationally intensive, multivariate, machine-learning approach to identify fecal KEGG (Kyoto encyclopedia of genes and genomes) Orthology (KO) categories as biomarkers that accurately classify food intake.

METHODS

Data were aggregated from 5 controlled feeding studies that studied the individual impact of almonds, avocados, broccoli, walnuts, barley, and oats on the adult gastrointestinal microbiota. Deoxyribonucleic acid from preintervention and postintervention fecal samples underwent shotgun genomic sequencing. After preprocessing, sequences were aligned and functionally annotated with Double Index AlignMent Of Next-generation sequencing Data v2.0.11.149 and MEtaGenome ANalyzer v6.12.2, respectively. After the count normalization, the log of the fold change ratio for resulting KOs between pre- and postintervention of the treatment group against its corresponding control was utilized to conduct differential abundance analysis. Differentially abundant KOs were used to train machine-learning models examining potential biomarkers in both single-food and multi-food models.

RESULTS

We identified differentially abundant KOs in the almond (n = 54), broccoli (n = 2474), and walnut (n = 732) groups (q < 0.20), which demonstrated classification accuracies of 80%, 87%, and 86% for the almond, broccoli, and walnut groups using a random forest model to classify food intake into each food group's respective treatment and control arms, respectively. The mixed-food random forest achieved 81% accuracy.

CONCLUSIONS

Our findings reveal promise in utilizing fecal metagenomics to objectively complement self-reported measures of food intake. Future research on various foods and dietary patterns will expand these exploratory analyses for eventual use in feeding study compliance and clinical settings.

Effect of potassium-competitive acid blockers on human gut microbiota: a systematic review and meta-analysis

Background: Vonoprazan has been reported to exert more potent and long-lasting gastric acid inhibition than proton pump inhibitors, potentially leading to a greater impact on the gut microbiota. This study aimed to clarify changes in microbial diversity and bacterial composition after VPZ treatments. Methods: We searched from PubMed, Embase, WOS, Scopus, Cochrane Library, and ClinicalTrials.gov (all years up to May 2023). The primary outcomes were alpha and beta diversity, as well as differences in gut microbiota composition between before and after VPZ treatments. We performed a meta-analysis to uncover the potential changes in human gut microbiota among VPZ users by pooled mean difference (MD) with a 95% confidence interval (CI). The risk of bias was assessed using the ROBINS-I tool. Results: A total of 12 studies were included to compare differences before and after VPZ treatments. Compared with baseline, alpha diversity was significantly reduced after VPZ treatments and gradually returned to baseline with longer follow-up. At the phylum level, there was a decrease in the relative abundance of Firmicutes and Actinobacteria, while Bacteroidetes increased compared with baseline. At the genus level, we found a significant decrease in the relative abundance of Coprococcus and Bifidobacterium and a significant increase in the relative abundance of Bacteroides compared with those before treatment. In subgroup analyses according to country and participants, we found differences in microbial changes after VPZ treatments. Conclusion: Vonoprazan can affect the changes of gut microbiota, which may be potentially associated with its strong ability of acid inhibition. However, due to the large heterogeneity, further studies are required to validate these findings. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023412265.

Machine learning approaches in microbiome research: challenges and best practices

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

Compositional and Functional Alterations in Intestinal Microbiota in Patients with Psychosis or Schizophrenia: A Systematic Review and Meta-analysis

BACKGROUND AND HYPOTHESIS

Intestinal microbiota is intrinsically linked to human health. Evidence suggests that the composition and function of the microbiome differs in those with schizophrenia compared with controls. It is not clear how these alterations functionally impact people with schizophrenia. We performed a systematic review and meta-analysis to combine and evaluate data on compositional and functional alterations in microbiota in patients with psychosis or schizophrenia.

STUDY DESIGN

Original studies involving humans and animals were included. The electronic databases PsycINFO, EMBASE, Web of Science, PubMed/MEDLINE, and Cochrane were systematically searched and quantitative analysis performed.

STUDY RESULTS

Sixteen original studies met inclusion criteria (1376 participants: 748 cases and 628 controls). Ten were included in the meta-analysis. Although observed species and Chao 1 show a decrease in diversity in people with schizophrenia compared with controls (SMD = -0.14 and -0.66 respectively), that did not reach statistical significance. We did not find evidence for variations in richness or evenness of microbiota between patients and controls overall. Differences in beta diversity and consistent patterns in microbial taxa were noted across studies. We found increases in Bifidobacterium, Lactobacillus, and Megasphaera in schizophrenia groups. Variations in brain structure, metabolic pathways, and symptom severity may be associated with compositional alterations in the microbiome. The heterogeneous design of studies complicates a similar evaluation of functional readouts.

CONCLUSIONS

The microbiome may play a role in the etiology and symptomatology of schizophrenia. Understanding how the implications of alterations in microbial genes for symptomatic expression and clinical outcomes may contribute to the development of microbiome targeted interventions for psychosis.

A Scoping Review Evaluating the Current State of Gut Microbiota Research in Africa

The gut microbiota has emerged as a key human health and disease determinant. However, there is a significant knowledge gap regarding the composition, diversity, and function of the gut microbiota, specifically in the African population. This scoping review aims to examine the existing literature on gut microbiota research conducted in Africa, providing an overview of the current knowledge and identifying research gaps. A comprehensive search strategy was employed to identify relevant studies. Databases including MEDLINE (PubMed), African Index Medicus (AIM), CINAHL (EBSCOhost), Science Citation index (Web of Science), Embase (Ovid), Scopus (Elsevier), WHO International Clinical Trials Registry Platform (ICTRP), and Google Scholar were searched for relevant articles. Studies investigating the gut microbiota in African populations of all age groups were included. The initial screening included a total of 2136 articles, of which 154 were included in this scoping review. The current scoping review revealed a limited number of studies investigating diseases of public health significance in relation to the gut microbiota. Among these studies, HIV (14.3%), colorectal cancer (5.2%), and diabetes mellitus (3.9%) received the most attention. The top five countries that contributed to gut microbiota research were South Africa (16.2%), Malawi (10.4%), Egypt (9.7%), Kenya (7.1%), and Nigeria (6.5%). The high number (n = 66) of studies that did not study any specific disease in relation to the gut microbiota remains a gap that needs to be filled. This scoping review brings attention to the prevalent utilization of observational study types (38.3%) in the studies analysed and emphasizes the importance of conducting more experimental studies. Furthermore, the findings reflect the need for more disease-focused, comprehensive, and population-specific gut microbiota studies across diverse African regions and ethnic groups to better understand the factors shaping gut microbiota composition and its implications for health and disease. Such knowledge has the potential to inform targeted interventions and personalized approaches for improving health outcomes in African populations.

The Effects of Ionizing Radiation on Gut Microbiota: What Can Animal Models Tell Us?-A Systematic Review

BACKGROUND

The gut microbiota is relatively stable; however, various factors can precipitate an imbalance that is known to be associated with various diseases. We aimed to conduct a systematic literature review of studies reporting the effects of ionizing radiation on the composition, richness, and diversity of the gut microbiota of animals.

METHODS

A systematic literature search was performed in PubMed, EMBASE, and Cochrane library databases. The standard methodologies expected by Cochrane were utilized.

RESULTS

We identified 3531 non-duplicated records and selected twenty-nine studies after considering the defined inclusion criteria. The studies were found to be heterogeneous, with significant differences in the chosen populations, methodologies, and outcomes. Overall, we found evidence of an association between ionizing radiation exposure and dysbiosis, with a reduction of microbiota diversity and richness and alterations in the taxonomic composition. Although differences in taxonomic composition varied across studies, Proteobacteria, Verrucomicrobia, Alistipes, and Akkermancia most consistently reported to be relatively more abundant after ionizing radiation exposure, whereas Bacteroidetes, Firmicutes, and Lactobacillus were relatively reduced.

CONCLUSIONS

This review highlights the effect of ionizing exposure on gut microbiota diversity, richness, and composition. It paves the way for further studies on human subjects regarding gastrointestinal side effects in patients submitted to treatments with ionizing radiation and the development of potential preventive, therapeutic approaches.

Colonoscopy aspiration lavages for mucosal metataxonomic profiling of spondylarthritis-associated gastrointestinal tract alterations

The study of the GI-tract microbiota of spondylarthritis (SpA) patients has focused on the analysis of feces samples, that picture mostly the luminal microbiota. The aim of this study was to determine the contribution of mucosal and luminal microbiome to the gut dysbiosis in SpA, using colonoscopy aspiration lavages (CAL), a recent alternative for regional studies of the GI-tract. We analyzed 59 CAL (from sigmoid colon and distal ileum), and 41 feces samples, from 32 SpA patients and 7 healthy individuals, using 16S rRNA gene-targeted metataxonomic profiling. It was found high prevalence of GI-tract manifestations among SpA patients (65.3%). Metataxonomic profiling, confirmed CAL samples from the lower GI tract (colon or ileum) presented a distinctive and undifferentiated bacteriome and separate from that found in feces' samples or in the beginning of the GI tract (oral cavity (OC)). Lower GI-tract samples and feces of SpA patients exhibited similar behavior to the microbiota of IBD group with reduced microbial richness and diversity, comparing to the healthy controls. Interestingly, it was found increase in proinflammatory taxa in SpA patients, such as Enterobacteriaceae family (mostly in the ileum), Succinivibrio spp. and Prevotella stercorea. Conversely, SpA patients presented significant decrease in the SCFA producers Coprococcus catus and Eubacterium biforme. Our data support the value of CAL samples for the regional study of GI-tract and contribute with information of potential "disruptor taxa" involved in the GI-tract associated disorders observed in SpA patients.

Sex-dependent gut microbiota-brain-cognition associations: a multimodal MRI study

BACKGROUND

There is bidirectional communication between the gut microbiota and the brain. Empirical evidence has demonstrated sex differences in both the gut microbiome and the brain. However, the effects of sex on the gut microbiota-brain associations have yet to be determined. We aim to elucidate the sex-specific effects of gut microbiota on brain and cognition.

METHODS

One hundred fifty-seven healthy young adults underwent brain structural, perfusion, functional and diffusion MRIs to measure gray matter volume (GMV), cerebral blood flow (CBF), functional connectivity strength (FCS) and white matter integrity, respectively. Fecal samples were collected and 16S amplicon sequencing was utilized to assess gut microbial diversity. Correlation analyses were conducted to test for sex-dependent associations between microbial diversity and brain imaging parameters, and mediation analysis was performed to further characterize the gut microbiota-brain-cognition relationship.

RESULTS

We found that higher gut microbial diversity was associated with higher GMV in the right cerebellum VI, higher CBF in the bilateral calcarine sulcus yet lower CBF in the left superior frontal gyrus, higher FCS in the bilateral paracentral lobule, and lower diffusivity in widespread white matter regions in males. However, these associations were absent in females. Of more importance, these neuroimaging biomarkers significantly mediated the association between gut microbial diversity and behavioral inhibition in males.

CONCLUSIONS

These findings highlight sex as a potential influential factor underlying the gut microbiota-brain-cognition relationship, and expose the gut microbiota as a biomarker-driven and sex-sensitive intervention target for mental disorders with abnormal behavioral inhibition.

Pairwise ratio-based differential abundance analysis of infant microbiome 16S sequencing data

Differential abundance analysis of infant 16S microbial sequencing data is complicated by challenging data properties, including high sparsity, extreme dispersion and the relative nature of the information contained within the data. In this study, we propose a pairwise ratio analysis that uses the compositional data analysis principle of subcompositional coherence and merges it with a beta-binomial regression model. The resulting method provides a flexible and easily interpretable approach to infant 16S sequencing data differential abundance analysis that does not require zero imputation. We evaluate the proposed method using infant 16S data from clinical trials and demonstrate that the proposed method has the power to detect differences, and demonstrate how its results can be used to gain insights. We further evaluate the method using data-inspired simulations and compare its power against related methods. Our results indicate that power is high for pairwise differential abundance analysis of taxon pairs that have a large abundance. In contrast, results for sparse taxon pairs show a decrease in power and substantial variability in method performance. While our method shows promising performance on well-measured subcompositions, we advise strong filtering steps in order to avoid excessive numbers of underpowered comparisons in practical applications.

Fecal Metabolites as Biomarkers for Predicting Food Intake by Healthy Adults

BACKGROUND

The fecal metabolome is affected by diet and includes metabolites generated by human and microbial metabolism. Advances in -omics technologies and analytic approaches have allowed researchers to identify metabolites and better utilize large data sets to generate usable information. One promising aspect of these advancements is the ability to determine objective biomarkers of food intake.

OBJECTIVES

We aimed to utilize a multivariate, machine learning approach to identify metabolite biomarkers that accurately predict food intake.

METHODS

Data were aggregated from 5 controlled feeding studies in adults that tested the impact of specific foods (almonds, avocados, broccoli, walnuts, barley, and oats) on the gastrointestinal microbiota. Fecal samples underwent GC-MS metabolomic analysis; 344 metabolites were detected in preintervention samples, whereas 307 metabolites were detected postintervention. After removing metabolites that were only detected in either pre- or postintervention and those undetectable in ≥80% of samples in all study groups, changes in 96 metabolites relative concentrations (treatment postintervention minus preintervention) were utilized in random forest models to 1) examine the relation between food consumption and fecal metabolome changes and 2) rank the fecal metabolites by their predictive power (i.e., feature importance score).

RESULTS

Using the change in relative concentration of 96 fecal metabolites, 6 single-food random forest models for almond, avocado, broccoli, walnuts, whole-grain barley, and whole-grain oats revealed prediction accuracies between 47% and 89%. When comparing foods with one another, almond intake was differentiated from walnut intake with 91% classification accuracy.

CONCLUSIONS

Our findings reveal promise in utilizing fecal metabolites as objective complements to certain self-reported food intake estimates. Future research on other foods at different doses and dietary patterns is needed to identify biomarkers that can be applied in feeding study compliance and clinical settings.

Comparing the efficacy of different methods of faecal microbiota transplantation via oral capsule, oesophagogastroduodenoscopy, colonoscopy, or gastric tube

BACKGROUND

The increasing prevalence of multidrug-resistant organism (MDRO) carriage poses major challenges to medicine as healthcare costs increase. Recently, faecal microbiota transplantation (FMT) has been discussed as a novel and effective method for decolonizing MDRO.

AIM

To compare the efficacy of different FMT methods to optimize the success rate of decolonization in patients with MDRO carriage.

METHODS

This prospective cohort study enrolled patients with MDRO carriages from 2018 to 2021. Patients underwent FMT via one of the following methods: oral capsule, oesophagogastroduodenoscopy (EGD), colonoscopy, or gastric tube.

FINDINGS

A total of 57 patients underwent FMT for MDRO decolonization. The colonoscopy group required the shortest time for decolonization, whereas the EGD group required the longest (24.9 vs 190.4 days, P = 0.022). The decolonization rate in the oral capsule group was comparable to that in the EGD group (84.6% vs 85.7%, P = 0.730). An important clinical factor associated with decolonization failure was antibiotic use after FMT (odds ratio = 6.810, P = 0.008). All four groups showed reduced proportions of MDRO species in microbiome analysis after FMT.

CONCLUSION

Compared to other conventional methods, the oral capsule is an effective FMT method for patients who can tolerate an oral diet. The discontinuation of antibiotics after FMT is a key factor in the success of decolonization.

Effects of topical fluoride application on oral microbiota in young children with severe dental caries

While the effect of fluoride on severe early childhood caries (S-ECC) is clear, knowledge of how it influences the oral microbiota and the consequential effects on oral health is limited. In this cohort study, we investigated the changes introduced in the oral ecosystem before and after using fluoride varnish in 54- to 66-month-old individuals (n=90: 18 children were sampled at 5 different time points). 16S rDNA was amplified from bacterial samples using polymerase chain reaction, and high-throughput sequencing was performed using Illumina MiSeq platforms. Many pronounced microbial changes were related to the effects of fluoride varnishing. The health-associated Bacteroides and Uncultured_bacterium_f_Enterobacteriaceae were enriched in the saliva microbiome following treatment with fluoride varnishing. Co-occurrence network analysis of the dominant genera showed that different groups clearly showed different bacterial correlations. The PICRUSt algorithm was used to predict the function of the microbial communities from saliva samples. The results showed that starch and sucrose metabolism was greater after fluoride use. BugBase was used to determine phenotypes present in microbial community samples. The results showed that Haemophilus and Neisseria (phylum Proteobacteria) was greater before fluoride use. We conclude that the changes in oral microbiology play a role in fluoride prevention of S-ECC.

The diagnostic potential and barriers of microbiome based therapeutics

High throughput technological innovations in the past decade have accelerated research into the trillions of commensal microbes in the gut. The 'omics' technologies used for microbiome analysis are constantly evolving, and large-scale datasets are being produced. Despite of the fact that much of the research is still in its early stages, specific microbial signatures have been associated with the promotion of cancer, as well as other diseases such as inflammatory bowel disease, neurogenerative diareses etc. It has been also reported that the diversity of the gut microbiome influences the safety and efficacy of medicines. The availability and declining sequencing costs has rendered the employment of RNA-based diagnostics more common in the microbiome field necessitating improved data-analytical techniques so as to fully exploit all the resulting rich biological datasets, while accounting for their unique characteristics, such as their compositional nature as well their heterogeneity and sparsity. As a result, the gut microbiome is increasingly being demonstrating as an important component of personalised medicine since it not only plays a role in inter-individual variability in health and disease, but it also represents a potentially modifiable entity or feature that may be addressed by treatments in a personalised way. In this context, machine learning and artificial intelligence-based methods may be able to unveil new insights into biomedical analyses through the generation of models that may be used to predict category labels, and continuous values. Furthermore, diagnostic aspects will add value in the identification of the non invasive markers in the critical diseases like cancer.

An Overview on the Impact of Microbiota on Malaria Transmission and Severity: Plasmodium-Vector-Host Axis

PURPOSE

Malaria, which is a vector-borne disease caused by Plasmodium sp., continue to become a serious threat, causing more than 600,000 deaths annually, especially in developing countries. Due to the lack of a long-term, and effective vaccine, and an increasing resistance to antimalarials, new strategies are needed for prevention and treatment of malaria. Recently, the impact of microbiota on development and transmission of Plasmodium, and the severity of malaria has only begun to emerge, although its contribution to homeostasis and a wide variety of disorders is well-understood. Further evidence has shown that microbiota of both mosquito and human host play important roles in transmission, progression, and clearance of Plasmodium infection. Furthermore, Plasmodium can cause significant alterations in the host and mosquito gut microbiota, affecting the clinical outcome of malaria.

METHODOLOGY

In this review, we attempt to summarize results from published studies on the influence of the host microbiota on the outcome of Plasmodium infections in both arthropods and mammalian hosts.

CONCLUSION

Modifications of microbiota may be an important potential strategy in blocking Plasmodium transmission in vectors and in the diagnosis, treatment, and prevention of malaria in humans in the future.

Machine learning on the road to unlocking microbiota's potential for boosting immune checkpoint therapy

The intestinal microbiota is a complex and diverse ecological community that fulfills multiple functions and substantially impacts human health. Despite its plasticity, unfavorable conditions can cause perturbations leading to so-called dysbiosis, which have been connected to multiple diseases. Unfortunately, understanding the mechanisms underlying the crosstalk between those microorganisms and their host is proving to be difficult. Traditionally used bioinformatic tools have difficulties to fully exploit big data generated for this purpose by modern high throughput screens. Machine Learning (ML) may be a potential means of solving such problems, but it requires diligent application to allow for drawing valid conclusions. This is especially crucial as gaining insight into the mechanistic basis of microbial impact on human health is highly anticipated in numerous fields of study. This includes oncology, where growing amounts of studies implicate the gut ecosystems in both cancerogenesis and antineoplastic treatment outcomes. Based on these reports and first signs of clinical benefits related to microbiota modulation in human trials, hopes are rising for the development of microbiome-derived diagnostics and therapeutics. In this mini-review, we're inspecting analytical approaches used to uncover the role of gut microbiome in immune checkpoint therapy (ICT) with the use of shotgun metagenomic sequencing (SMS) data.

Changes in the Intestinal Microbiota in Patients with Chronic Pancreatitis: Systematizing Literature Data

The purpose of the review. To systematize literature data on changes in the structure of the intestinal microbiota in patients with chronic pancreatitis (CP).Key findings. The human intestinal microbiota is a dynamically changing system that is constantly undergoing qualitative and quantitative changes, especially in several pathological conditions of the digestive system. At present, the differences in the intestinal microbiota in pancreatic diseases are poorly understood. The severe CP is associated with impaired synthesis of antimicrobial peptides, bicarbonates, and digestive enzymes by the pancreas, which is a risk factor for dysbiotic changes in the intestinal microbiota, consisting in the development of small intestinal bacterial overgrowth (SIBO) and gut dysbiosis. The results of two large meta-analyses show that about a third of CP patients have SIBO. The colonic microbiota in patients with CP is also characterized by dysbiotic disorders, primarily in the reduction of alpha-diversity. Some studies have shown that these patients have an increase in Firmicutes, while Bacteroides and Faecalibacterium are reduced. In addition, as a rule, in patients with CP, the growth of Escherichia, Shigella and Streptococcus is recorded.Conclusion. In general, scientific papers have revealed significant heterogeneity in the profiles of the intestinal microbiota in patients with CP. Thus, several questions remain open, prioritizing the further study of the intestinal microbiota in patients with CP for identifying the specifics of its structure that can personalize the selection of enzyme replacement therapy and restrict the unreasonable prescription of additional pharmacotherapy (the use of proton pump inhibitors and / or antibacterial drugs).

Identification of Functional Microbial Modules Through Network-Based Analysis of Meta-Microbial Features Using Matrix Factorization

As the microbiome is composed of a variety of microbial interactions, it is imperative in microbiome research to identify a microbial sub-community that collectively conducts a specific function. However, current methodologies have been highly limited to analyzing conditional abundance changes of individual microorganisms without considering group-wise collective microbial features. To overcome this limitation, we developed a network-based method using nonnegative matrix factorization (NMF) to identify functional meta-microbial features (MMFs) that, as a group, better discriminate specific environmental conditions of samples using microbiome data. As proof of concept, large-scale human microbiome data collected from different body sites were used to identify body site-specific MMFs by applying NMF. The statistical test for MMFs led us to identify highly discriminative MMFs on sample classes, called synergistic MMFs (SYMMFs). Finally, we constructed a SYMMF-based microbial interaction network (SYMMF-net) by integrating all of the SYMMF information. Network analysis revealed core microbial modules closely related to critical sample properties. Similar results were also found when the method was applied to various disease-associated microbiome data. The developed method interprets high-dimensional microbiome data by identifying functional microbial modules on sample properties and intuitively representing their systematic relationships via a microbial network.

MB-SupCon: Microbiome-based Predictive Models via Supervised Contrastive Learning

Human microbiome consists of trillions of microorganisms. Microbiota can modulate the host physiology through molecule and metabolite interactions. Integrating microbiome and metabolomics data have the potential to predict different diseases more accurately. Yet, most datasets only measure microbiome data but without paired metabolome data. Here, we propose a novel integrative modeling framework, Microbiome-based Supervised Contrastive Learning Framework (MB-SupCon). MB-SupCon integrates microbiome and metabolome data to generate microbiome embeddings, which can be used to improve the prediction accuracy in datasets that only measure microbiome data. As a proof of concept, we applied MB-SupCon on 720 samples with paired 16S microbiome data and metabolomics data from patients with type 2 diabetes. MB-SupCon outperformed existing prediction methods and achieved high average prediction accuracies for insulin resistance status (84.62%), sex (78.98%), and race (80.04%). Moreover, the microbiome embeddings form separable clusters for different covariate groups in the lower-dimensional space, which enhances data visualization. We also applied MB-SupCon on a large inflammatory bowel disease study and observed similar advantages. Thus, MB-SupCon could be broadly applicable to improve microbiome prediction models in multi-omics disease studies.

Effect of Helicobacter Pylori Eradication on Human Gastric Microbiota: A Systematic Review and Meta-Analysis

Background

Helicobacter pylori (H. pylori) infection is a major risk factor for gastric cancer and eradication of H. pylori is recommended as an effective gastric cancer prevention strategy. The infected individuals show microbial dysbiosis of gastric microbiota. In recent years, agrowing number of studies have focused on gastric microbiota changes following H. pylori eradication. In the present study, we aim to evaluate the influence of successful H. pylori eradication on the short-term and long-term alterations of human gastric microbiota using a method of systematic review and meta-analysis.

Methods

We did a systematic search based on three databases (PubMed, EMBASE, and Web of Science) in November 2021. Additional articles were also identified by reviewing references cited in the included papers. Human studies that reported changes in gastric microbiota following successful H. pylori eradication were enrolled. PROSPERO registration number: CRD42021293796.

Results

In total, nine studies enrolling 546 participants were included. Regarding quadruple therapy, alpha diversity indexes increased within 1 month after eradication; significant differences in gastric microbial community structure between before and after eradication were also seen within 1 month. The trends of the above-mentioned diversity changes persisted with a follow-up of 6 months. The microbial composition altered significantly after eradication and the relative abundance of H. pylori-related taxa decreased. Accordingly, gastric commonly dominant commensals were enriched. Bioinformatic analyses of microbiota functions showed that bacteria reproduction-related pathways were down-regulated and pathways of gastric acid secretion, etc. were up-regulated. For triple therapy, similar trends of alpha diversity and beta diversity changes were observed in the short-term and long-term follow-up. Also, after eradication, H. pylori was not the gastric dominant bacteria and similar changes in gastric microbial composition were found. For gastric microbial interactions, a decrease in microbial interactions was seen after eradication. Additionally, regarding whether successful H. pylori eradication could restore gastric microbiota to uninfected status, the results remain controversial.

Conclusion

In conclusion, successful H. pylori eradication could reverse the gastric microbiota dysbiosis and show beneficial effects on gastric microbiota. Our findings may provide new insight for exploring the role of H. pylori and the whole gastric microbiota in gastric carcinogenesis.

Enhancing Metabolic Efficiency through Optimizing Metabolizable Protein Profile in a Time Progressive Manner with Weaned Goats as a Model: Involvement of Gut Microbiota

Feeding a growing global population and lowering environmental pollution are the two biggest challenges facing ruminant livestock. Considering the significance of nitrogen metabolism in these challenges, a dietary intervention regarding metabolizable protein profiles with different rumen-undegradable protein (RUP) ratios (high RUP [HRUP] versus low RUP [LRUP]) was conducted in young ruminants with weaned goats as a model. Fecal samples were collected longitudinally for nine consecutive weeks to dissect the timing and duration of intervention, as well as its mechanism of action involving the gut microbiota. Results showed that at least 6 weeks of intervention were needed to distinguish the beneficial effects of HRUP, and HRUP intervention improved the metabolic efficiency of goats as evidenced by enhanced growth performance and nutrient-apparent digestibility at week 6 and week 8 after weaning. Integrated analysis of bacterial diversity, metabolites, and inferred function indicated that HRUP intervention promoted Eubacterium abundance, several pathways related to bacterial chemotaxis pathway, ABC transporters, and butanoate metabolism and thereafter elicited a shift from acetate production toward butyrate and branched-chain amino acid (BCAA) production. Meanwhile, three distinct phases of microbial progression were noted irrespective of dietary treatments, including the enrichment of fiber-degrading Ruminococcus, the enhancement of microbial cell motility, and the shift of fermentation type as weaned goats aged. The current report provides novel insights into early-life diet-microbiota axis triggered by metabolic protein intervention and puts high emphasis on the time window and duration of dietary intervention in modulating lifelong performance of ruminants. IMPORTANCE Precise dietary intervention in early-life gastrointestinal microbiota has significant implications in the long-life productivity and health of young ruminants, as well as in lowering their environmental footprint. Here, using weaned goats as a model, we report that animals adapted to high rumen-undegradable protein diet in a dynamic manner by enriching fecal community that could effectively move toward and scavenge nutrients such as glucose and amino acids and, thereafter, elicit butyrate and BCAA production. Meanwhile, the three dynamic assembly trajectories in fecal microbiota highlight the importance of taking microbiota dynamics into account. Our findings systematically reported when, which, and how the fecal microbiome responded to metabolizable protein profile intervention in young ruminants and laid a foundation for improving the productivity and health of livestock due to the host-microbiota interplay.

Increasing transparency and reproducibility in stroke-microbiota research: A toolbox for microbiota analysis

Homeostasis of gut microbiota is crucial in maintaining human health. Alterations, or "dysbiosis," are increasingly implicated in human diseases, such as cancer, inflammatory bowel diseases, and, more recently, neurological disorders. In ischemic stroke patients, gut microbial profiles are markedly different compared to healthy controls, whereas manipulation of microbiota in animal models of stroke modulates outcome, further implicating microbiota in stroke pathobiology. Despite this, evidence for the involvement of specific microbes or microbial products and microbial signatures have yet to be identified, likely owing to differences in methodology, data analysis, and confounding variables between different studies. Here, we provide a set of guidelines to enable researchers to conduct high-quality, reproducible, and transparent microbiota studies, focusing on 16S rRNA sequencing in the emerging subfield of the stroke-microbiota. In doing so, we aim to facilitate novel and reproducible associations between the microbiota and brain diseases, including stroke, and translation into clinical practice.

Designing highly multiplex PCR primer sets with Simulated Annealing Design using Dimer Likelihood Estimation (SADDLE)

One major challenge in the design of highly multiplexed PCR primer sets is the large number of potential primer dimer species that grows quadratically with the number of primers to be designed. Simultaneously, there are exponentially many choices for multiplex primer sequence selection, resulting in systematic evaluation approaches being computationally intractable. Here, we present and experimentally validate Simulated Annealing Design using Dimer Likelihood Estimation (SADDLE), a stochastic algorithm for design of multiplex PCR primer sets that minimize primer dimer formation. In a 96-plex PCR primer set (192 primers), the fraction of primer dimers decreases from 90.7% in a naively designed primer set to 4.9% in our optimized primer set. Even when scaling to 384-plex (768 primers), the optimized primer set maintains low dimer fraction. In addition to NGS, SADDLE-designed primer sets can also be used in qPCR settings to allow highly multiplexed detection of gene fusions in cDNA, with a single-tube assay comprising 60 primers detecting 56 distinct gene fusions recurrently observed in lung cancer.

The design of highly multiplex PCR primers to amplify and enrich many different DNA sequences is increasing in biomedical importance as new mutations and pathogens are identified. The authors present and experimentally validate Simulated Annealing Design using Dimer Likelihood Estimation (SADDLE), a stochastic algorithm for design of highly multiplex PCR primer sets that minimize primer dimer formation.

Multimodal neuroimaging fusion biomarkers mediate the association between gut microbiota and cognition

Background The field of microbiota-gut-brain research in animals has progressed, while the exact nature of gut microbiota-brain-cognition relationship in humans is not completely elucidated, likely due to small sample sizes and single neuroimaging modality utilized to delineate limited aspects of the brain. We aimed to comprehensively investigate such association in a large sample using multimodal MRI. Methods Fecal samples were collected from 157 healthy young adults and 16S sequencing was used to assess gut microbial diversity and enterotypes. Five brain imaging measures, including regional homogeneity (ReHo) and functional connectivity density (FCD) from resting-state functional MRI, cerebral blood flow (CBF) from arterial spin labeling, gray matter volume (GMV) from structural MRI, and fractional anisotropy (FA) from diffusion tensor imaging, were jointly analyzed with a data-driven multivariate fusion method. Cognition was evaluated by 3-back and digit span tasks. Results We found significant associations of gut microbial diversity with ReHo, FCD, CBF, and GMV within the frontoparietal, default mode and visual networks, as well as with FA in a distributed set of juxtacortical white matter regions. In addition, there were FCD, CBF, GMV, and FA differences between Prevotella- versus Bacteroides-enterotypes in females and between Prevotella- versus Ruminococcaceae-enterotypes in males. Moreover, the identified neuroimaging fusion biomarkers could mediate the associations between microbial diversity and cognition. Conclusions Our findings not only expand existing knowledge of the microbiota-gut-brain axis, but also have potential clinical and translational implications by exposing the gut microbiota as a promising treatment and prevention target for cognitive impairment and related brain disorders.

Tailoring Multi-omics to Inflammatory Bowel Diseases: All for One and One for All

Inflammatory bowel disease [IBD] has a multifactorial origin and originates from a complex interplay of environmental factors with the innate immune system at the intestinal epithelial interface in a genetically susceptible individual. All these factors make its aetiology intricate and largely unknown. Multi-omic datasets obtained from IBD patients are required to gain further insights into IBD biology. We here review the landscape of multi-omic data availability in IBD and identify barriers and gaps for future research. We also outline the various technical and non-technical factors that influence the utility and interpretability of multi-omic datasets and thereby the study design of any research project generating such datasets. Coordinated generation of multi-omic datasets and their systemic integration with clinical phenotypes and environmental exposures will not only enhance understanding of the fundamental mechanisms of IBD but also improve therapeutic strategies. Finally, we provide recommendations to enable and facilitate generation of multi-omic datasets.

EasyMicroPlot: An Efficient and Convenient R Package in Microbiome Downstream Analysis and Visualization for Clinical Study

Advances in next-generation sequencing (NGS) have revolutionized microbial studies in many fields, especially in clinical investigation. As the second human genome, microbiota has been recognized as a new approach and perspective to understand the biological and pathologic basis of various diseases. However, massive amounts of sequencing data remain a huge challenge to researchers, especially those who are unfamiliar with microbial data analysis. The mathematic algorithm and approaches introduced from another scientific field will bring a bewildering array of computational tools and acquire higher quality of script experience. Moreover, a large cohort research together with extensive meta-data including age, body mass index (BMI), gender, medical results, and others related to subjects also aggravate this situation. Thus, it is necessary to develop an efficient and convenient software for clinical microbiome data analysis. EasyMicroPlot (EMP) package aims to provide an easy-to-use microbial analysis tool based on R platform that accomplishes the core tasks of metagenomic downstream analysis, specially designed by incorporation of popular microbial analysis and visualization used in clinical microbial studies. To illustrate how EMP works, 694 bio-samples from Guangdong Gut Microbiome Project (GGMP) were selected and analyzed with EMP package. Our analysis demonstrated the influence of dietary style on gut microbiota and proved EMP package's powerful ability and excellent convenience to address problems for this field.

Transcriptional Heterogeneity and the Microbiome of Cutaneous T-Cell Lymphoma

Cutaneous T-Cell Lymphomas (CTCL) presents with substantial clinical variability and transcriptional heterogeneity. In the recent years, several studies paved the way to elucidate aetiology and pathogenesis of CTCL using sequencing methods. Several T-cell subtypes were suggested as the source of disease thereby explaining clinical and transcriptional heterogeneity of CTCL entities. Several differentially expressed pathways could explain disease progression. However, exogenous triggers in the skin microenvironment also seem to affect CTCL status. Especially Staphylococcus aureus was shown to contribute to disease progression. Only little is known about the complex microbiome patterns involved in CTCL and how microbial shifts might impact this malignancy. Nevertheless, first hints indicate that the microbiome might at least in part explain transcriptional heterogeneity and that microbial approaches could serve in diagnosis and prognosis. Shaping the microbiome could be a treatment option to maintain stable disease. Here, we review current knowledge of transcriptional heterogeneity of and microbial influences on CTCL. We discuss potential benefits of microbial applications and microbial directed therapies to aid patients with CTCL burden.

Effect of treatments on skin microbiota in patients with atopic dermatitis: a protocol for systematic review

INTRODUCTION

Atopic dermatitis (AD) is a chronic inflammatory skin disease and skin microbiota dysbiosis shows an important role in the pathogenesis of AD. Effects of treatment on skin microbiota for patients with AD have been evaluated in recent years; however, the results remained controversial across studies. This systematic review will summarise studies evaluating the effect of treatments on skin microbiota among patients with AD.

METHODS AND ANALYSIS

We will search PubMed, EMBASE, Web of Science, ClinicalTrials.gov and Chinese Clinical Trial Registry in November 2021; other data sources will also be considered, including searching specific authors and screening references cited in the enrolled articles. Interventional studies, which enrolled patients with AD receiving treatments and reported treatment-related skin microbiota changes, will be included. Our primary outcomes include skin microbiota diversity and treatment-related differential microbes; the secondary outcomes include microbiota functions and microbial interactions. Risk of bias assessment will be performed using Cochrane risk-of-bias tool for randomised trials, risk of bias in non-randomised studies of interventions and methodological index for non-randomised studies. Two researchers will independently perform study selection, data extraction and risk of bias assessment, with disagreements resolved by group discussions. Subgroup analyses will be performed according to different types of treatment for AD.

ETHICS AND DISSEMINATION

Ethics approval is not required for this systematic review. Findings will be disseminated via peer-reviewed publication or conference proceedings.

PROSPERO REGISTRATION NUMBER

CRD42021246566.

Effect of Fluoride in Drinking Water on Fecal Microbial Community in Rats

Intestinal nutrition has a close association with the onset and development of fluorosis. Intestinal microbes play a major role in intestinal nutrition. However, the effect of fluoride on intestinal microbes is still not fully understood. This study aimed to evaluate the dose-response of fluoride on fecal microbes as well as the link between fluorosis and fecal microbes. The results showed that fluoride did not significantly alter the diversity of fecal microbiota, but richness estimators (ACE and Chao) increased first, and then decreased with the increase of water fluoride. At the genus level, 150 mg/L fluoride significantly reduced the abundances of Roseburia and Clostridium sensu stricto, and 100 mg/L and 150 mg/L fluoride obviously increased the abundances of Unclassified Ruminococcaceaes and Unclassified Bdellovibrionales, respectively. The correlation analysis showed fluoride exposure had a negative association with Roseburia and Turicibacter and was positively associated with Pelagibacterium, Unclassified Ruminococcaceae, and Unclassified Bdellovibrionales. Dental fluorosis was negatively associated with Clostridium sensu stricto, Roseburia, Turicibacter, and Paenalcaligenes and had a positive association with Pelagibacterium, Unclassified Ruminococcaceae, and Unclassified Bdellovibrionales. In conclusion, this study firstly reports fluoride in drinking water has a remarkable biphasic effect on fecal microbiota in rats, and some bacteria are significantly associated with fluoride exposure and dental fluorosis. These results indicate the gut microbiota may play an important role in fluorosis, and some bacteria are likely to be developed as biomarkers for fluorosis.

Enterococcus faecium are associated with the modification of gut microbiota and shrimp post-larvae survival

BACKGROUND

Probiotics are widely used to promote host health. Compared to mammals and terrestrial invertebrates, little is known the role of probiotics in aquatic invertebrates. In this study, eighteen tanks with eight hundred of shrimp post-larvae individuals each were randomly grouped into three groups, one is shrimps administered with E. faecium as probiotic (Tre) and others are shrimps without probiotic-treatment (CK1: blank control, CK2: medium control). We investigated the correlations between a kind of commercial Enterococcus faecium (E. faecium) powder and microbiota composition with function potentials in shrimp post-larvae gut.

RESULTS

We sequenced the 16S rRNA gene (V4) of gut samples to assess diversity and composition of the shrimp gut microbiome and used differential abundance and Tax4Fun2 analyses to identify the differences of taxonomy and predicted function between different treatment groups. The ingested probiotic bacteria (E. faecium) were tracked in gut microbiota of Tre and the shrimps here showed the best growth performance especially in survival ratio (SR). The distribution of SR across samples was similar to that in PCoA plot based on Bray-Curits and two subgroups generated (SL: SR < 70%, SH: SR ≥ 70%). The gut microbiota structure and predicted function were correlated with both treatment and SR, and SR was a far more important factor driving taxonomic and functional differences than treatment. Both Tre and SH showed a low and uneven community species and shorted phylogenetic distance. We detected a shift in composition profile at phylum and genus level and further identified ten OTUs as relevant taxa that both closely associated with treatment and SR. The partial least squares path model further supported the important role of relevant taxa related to shrimp survival ratio.

CONCLUSIONS

Overall, we found gut microbiota correlated to both shrimp survival and ingested probiotic bacteria (E. faecium). These correlations should not be dismissed without merit and will uncover a promising strategy for developing novel probiotics through certain consortium of gut microbiota.

Mikrobiota jelitowa a leki. Interakcje wpływające na skuteczność i bezpieczeństwo farmakoterapii

Mikrobiota jelitowa stanowi nieodłączny element organizmu umożliwiający jego prawidłowe funkcjonowanie. Dzięki mikroorganizmom jelitowym możliwa jest stymulacja układu odpornościowego, synteza witamin czy poprawa wchłaniania składników odżywczych. Jednak jej aktywność może również niekorzystnie działać na organizm, m.in. z powodu przetwarzania treści jelitowej. Opisywana w artykule interakcja mikrobiota–lek uwzględnia pozytywny i negatywny wpływ mikroorganizmów jelitowych na farmakoterapię poprzez bezpośrednie i pośrednie oddziaływanie na lek w organizmie. Ze względu na to, że mikrobiom stanowi nieodłączny element organizmu, ingerencja nawet w jego niewielką część może doprowadzić do wystąpienia daleko idących, czasami niespodziewanych skutków. Stąd w celu poprawy skuteczności i bezpieczeństwa farmakoterapii konieczne jest wyjaśnienie mechanizmów oddziaływania mikrobioty na lek w organizmie.

W artykule podsumowano obecną wiedzę na temat biologicznej aktywności mikrobioty jelitowej, a zwłaszcza oddziaływań mikrobiota–leki determinujących skuteczność i bezpieczeństwo farmakoterapii. Wyszukiwanie przeprowadzono we wrześniu 2020 r. w bazach danych PubMed, Scopus, Web of Science, Cochrane Library i powszechnie dostępnej literaturze z użyciem terminów: „mikrobiota jelitowa”, „mikrobiom”, „metabolizm leku”, „interakcje mikrobiota–lek”. W artykule omówiono interakcje między mikrobiotą a lekami m.in. z grupy antybiotyków, inhibitorów pompy protonowej, sulfonamidów, pochodnych kwasu 5-aminosalicylowego, niesteroidowych leków przeciwzapalnych, przeciwnowotworowych, statyn czy metforminą.

Is there a dysbiosis in individuals with a neurodevelopmental disorder compared to controls over the course of development? A systematic review

Many scientific papers reported that an unbalanced gut microbiota could lead to or worsen neurodevelopmental disorders (NDD). A dysbiosis may then be observed in the course of development and mark a dysfunction within what is called the gut-brain axis. The aim of this systematic review is to investigate potential evidence of dysbiosis in children and young adults with NDD compared to controls. Using the PRISMA guidelines we systematically reviewed studies that compared the gut microbiota in NDD participants (with an age inferior to thirty) to the gut microbiota of controls, regardless of the data analysis methods used. The MEDLINE, Scopus and PsycINFO databases were searched up to September 2018. 31 studies with a total sample size of 3002 ASD (Autism Spectrum Disorder) and 84 ADHD (Attention Deficit Hyperactivity Disorder) participants were included in this systematic review. Independent data extraction and quality assessment were conducted. The quality of the studies was rated from low to high. Population characterization and experimental methods were highly heterogeneous in terms of the data available, selection of criteria, and dysbiosis measurement. A dysbiosis was reported in 28 studies in terms of either diversity, bacterial composition or metabolome dysfunction. Due to heterogeneity, a quantitative synthesis was not applicable. In this paper, we discuss the different biases to understand the complexity of microbiota and neurodevelopmental disorders to provide leads for future cohort studies looking to answer the questions raised by the trillions of microorganisms that inhabit key body niches.

Paediatric Inflammatory Bowel Disease and its Relationship with the Microbiome

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

Does algae β-glucan affect the fecal bacteriome in dairy calves?

β-glucans has been reported to be associated with many health-promoting and improvements in animal performance, however, information about their effects on the bacterial community remains unknown. This study aimed to investigate how the addition of β-glucans can affect the fecal bacterial community with possible consequences on animal growth and health. For this, newborn Holstein calves (n = 14) were individually housed in tropical shelters and blocked according to sex, date, and weight at birth and randomly assigned to one of the following treatments: (1) Control: milk replacer (14% solids, 24% CP, 18.5% fat); (2) β-glucans: milk replacer supplemented with β-glucans (2 g/d). All calves were bucket fed 6 L/d of milk replacer and received water and starter concentrate ad libitum starting on d 2. To evaluate the bacteriome, fecal samples were collected at weeks 1, 2, 4, and 8. The bacterial community was assessed through sequencing of the V3-V4 region of the 16S rRNA gene on the Illumina MiSeq platform and analyzed using the DADA2 pipeline. No differences for Shannon and Chao1 indexes were observed for treatments, but both indexes increased with age (P < 0.001). There were dissimilarities in the structure of the bacterial community during the pre-weaning period (P = 0.01). In a deeper taxonomic level, Collinsella (Actinobacteriota), Prevotella (Bacteroidota), and Lactobacillus (Firmicutes) were the most abundant genera (9.84, 9.54, and 8.82% of the sequences, respectively). β-glucans promoted a higher abundance of Alloprevotella and Holdemanella, which may indicate a beneficial effect of supplementation on dairy calves. The bacterial community was highly correlated with the fecal score at weeks 1 and 2 and with starter concentrate intake at week 8. In conclusion, algae β-glucan supplementation could be beneficial to fecal bacteriome and consequently to the health and performance of dairy calves.

The Effects of Ionizing Radiation on Gut Microbiota, a Systematic Review

BACKGROUND

The human gut microbiota is defined as the microorganisms that collectively inhabit the intestinal tract. Its composition is relatively stable; however, an imbalance can be precipitated by various factors and is known to be associated with various diseases. Humans are daily exposed to ionizing radiation from ambient and medical procedures, and gastrointestinal side effects are not rare.

METHODS

A systematic search of PubMed, EMBASE, and Cochrane Library databases was conducted. Primary outcomes were changes in composition, richness, and diversity of the gut microbiota after ionizing radiation exposure. Standard methodological procedures expected by Cochrane were used.

RESULTS

A total of 2929 nonduplicated records were identified, and based on the inclusion criteria, 11 studies were considered. Studies were heterogeneous, with differences in population and outcomes. Overall, we found evidence for an association between ionizing radiation exposure and dysbiosis: reduction in microbiota diversity and richness, increase in pathogenic bacteria abundance (Proteobacteria and Fusobacteria), and decrease in beneficial bacteria (Faecalibacterium and Bifidobacterium).

CONCLUSIONS

This review highlights the importance of considering the influence of ionizing radiation exposure on gut microbiota, especially when considering the side effects of abdominal and pelvic radiotherapy. Better knowledge of these effects, with larger population studies, is needed.

Large-scale functional network connectivity mediate the associations of gut microbiota with sleep quality and executive functions

Network neuroscience has broadly conceptualized the functions of the brain as complex communication within and between large-scale neural networks. Nevertheless, whether and how the gut microbiota influence functional network connectivity that in turn impact human behaviors has yet to be determined. We collected fecal samples from 157 healthy young adults and used 16S sequencing to assess gut microbial diversity and enterotypes. Large-scale inter- and intranetwork functional connectivity was measured using a combination of resting-state functional MRI data and independent component analysis. Sleep quality and core executive functions were also evaluated. Then, we tested for potential associations between gut microbiota, functional network connectivity and behaviors. We found significant associations of gut microbial diversity with internetwork functional connectivity between the executive control, default mode and sensorimotor systems, and intranetwork connectivity of the executive control system. Moreover, some internetwork functional connectivity mediated the relations of microbial diversity with sleep quality, working memory, and attention. In addition, there was a significant effect of enterotypes on intranetwork connectivity of the executive control system, which could mediate the link between enterotypes and executive function. Our findings not only may expand existing biological knowledge of the gut microbiota-brain-behavior relationships from the perspective of large-scale functional network organization, but also may ultimately inform a translational conceptualization of how to improve sleep quality and executive functions through the regulation of gut microbiota.

A 16S rRNA gene sequencing based study of oral microbiota in migraine patients in China

Migraine is a primary headache characterized by moderate or severe headache attacks, accompanied with reversible neurological and systemic symptoms. There are rare biomarkers for the disease. While emerging evidence has indicated the connection between gut microbiota and migraine, the relation between oral microbiota and migraine is barely known. Thus, the objective of the current study was to explore a possible correlation between oral microbiota and migraine. We compared the oral microbiota communities of migraine patients (26) with healthy subjects (29) via 16S rRNA gene sequencing. Alpha diversity indices were higher in migraine group compared with control group, whereas beta diversity indices also showed significant difference. A total of 23 genera were found differentially abundant between migraine and control groups. To conclude, there was a significant compositional difference in oral microbiota in migraine patients compared with healthy subjects.

Gut Microbiota Dysbiosis in Human Hypertension: A Systematic Review of Observational Studies

Introduction: Hypertension is one of the major risk factors to human health and human studies on association between gut microbiota and hypertension or blood pressure have received increased attention. In the present study, we aim to evaluate gut microbiota dysbiosis in human hypertension using a method of systematic review. Methods: PubMed, EMBASE, and Web of Science databases were searched until March 2021 to identify eligible articles. Additional articles were also identified by searching specific authors in this field. Inclusion criteria were observational studies based on stool samples with hypertension group and control group. Newcastle-Ottawa quality assessment scale (NOS) was used to assess the quality of the included studies. PROSPERO registration number: CRD42020212219. Results: A total of 17 studies enrolling 9,085 participants were included. Fifteen of the enrolled studies showed good quality and two studies showed fair quality based on NOS. We found alpha diversity in hypertension decreased significantly and microbial structure can be separated compared with control groups. Gut microbiota of hypertension showed depletion of short chain fatty acids (SCFAs) producers and over-growth of some Proteobacteria and Bacteroidetes members. Up-regulation of lipopolysaccharide biosynthesis, phosphotransferase system, ABC transporters, etc. and down-regulation of some amino acid metabolism, etc. in hypertension were reported. Fecal SCFAs levels increased and plasma SCFAs levels decreased in hypertension. Stronger microbial interactions in hypertension were seen. Conclusion: In conclusion, gut microbiota dysbiosis was observed in hypertension, including decreased diversity, altered microbial structure, compositional change of taxa, alterations of microbial function, nutritional and immunological factors, and microbial interactions. Poor absorption and high excretion of SCFAs may play an important role in the pathogenesis of hypertension. These findings may provide insights into etiology study and new microbial-based therapies of hypertension. Systematic Review Registration: PROSPERO database, identifier CRD42020212219.

A guide to human microbiome research: study design, sample collection, and bioinformatics analysis

The purpose of this review is to provide medical researchers, especially those without a bioinformatics background, with an easy-to-understand summary of the concepts and technologies used in microbiome research. First, we define primary concepts such as microbiota, microbiome, and metagenome. Then, we discuss study design schemes, the methods of sample size calculation, and the methods for improving the reliability of research. We emphasize the importance of negative and positive controls in this section. Next, we discuss statistical analysis methods used in microbiome research, focusing on problems with multiple comparisons and ways to compare β-diversity between groups. Finally, we provide step-by-step pipelines for bioinformatics analysis. In summary, the meticulous study design is a key step to obtaining meaningful results, and appropriate statistical methods are important for accurate interpretation of microbiome data. The step-by-step pipelines provide researchers with insights into newly developed bioinformatics analysis methods.

The Liquid Diet Composition Affects the Fecal Bacterial Community in Pre-weaning Dairy Calves

Feeding a liquid diet to the newborn calf has considerable implications for developing the intestinal microbiota, as its composition can shift the population to a highly adapted microbiota. The present work evaluated 15 Holstein calves individually housed and fed one of the three liquid diets: I – whole milk (n = 5), II – milk replacer (22.9% CP; 16.2% fat; diluted to 14% solids; n = 5) and III – acidified whole milk to pH 4.5 with formic acid (n = 5). All animals received 6 L of liquid diet, divided into two meals, being weaned at week 8 of life. Calves also had free access to water and starter concentrate. After weaning, all calves were grouped on pasture, fed with starter concentrate, and hay ad libitum. The fecal samples were collected at birth (0) and at weeks 1, 2, 4, 8, and 10 of life. The bacterial community was assessed the through sequencing of the V3-V4 region of the 16S rRNA gene on the Illumina MiSeq platform and analyzed using the DADA2 pipeline. Diversity indices were not affected by the liquid diets, but by age (P &lt; 0.001) with weeks 1 and 2 presenting lower diversity, evenness, and richness values. The bacterial community structure was affected by diet, age, and the interaction of these factors (P &lt; 0.01). Twenty-eight bacterial phyla were identified in the fecal samples, and the most predominant phyla were Firmicutes (42.35%), Bacteroidota (39.37%), and Proteobacteria (9.36%). The most prevalent genera were Bacteroides (10.71%), Lactobacillus (8.11%), Alloprevotella (6.20%). Over the weeks, different genera were predominant, with some showing significant differences among treatments. The different liquid diets altered the fecal bacterial community during the pre-weaning period. However, differences in the initial colonization due to different liquid diets are alleviated after weaning, when animals share a common environment and solid diet composition.