Diversity-disease relationships and shared species analyses for human microbiome-associated diseases

Metagenome comparison (MC): A new framework for detecting unique/enriched OMUs (operational metagenomic units) derived from whole-genome sequencing reads

BACKGROUND

Current methods for comparing metagenomes, derived from whole-genome sequencing reads, include top-down metrics or parametric models such as metagenome-diversity, and bottom-up, non-parametric, model-free machine learning approaches like Naïve Bayes for k-mer-profiling. However, both types are limited in their ability to effectively and comprehensively identify and catalogue unique or enriched metagenomic genes, a critical task in comparative metagenomics. This challenge is significant and complex due to its NP-hard nature, which means computational time grows exponentially, or even faster, with the problem size, rendering it impractical for even the fastest supercomputers without heuristic approximation algorithms.

METHOD

In this study, we introduce a new framework, MC (Metagenome-Comparison), designed to exhaustively detect and catalogue unique or enriched metagenomic genes (MGs) and their derivatives, including metagenome functional gene clusters (MFGC), or more generally, the operational metagenomic unit (OMU) that can be considered the counterpart of the OTU (operational taxonomic unit) from amplicon sequencing reads. The MC is essentially a heuristic search algorithm guided by pairs of new metrics (termed MG-specificity or OMU-specificity, MG-specificity diversity or OMU-specificity diversity). It is further constrained by statistical significance (P-value) implemented as a pair of statistical tests.

RESULTS

We evaluated the MC using large metagenomic datasets related to obesity, diabetes, and IBD, and found that the proportions of unique and enriched metagenomic genes ranged from 0.001% to 0.08 % and 0.08%-0.82 % respectively, and less than 10 % for the MFGC.

CONCLUSION

The MC provides a robust method for comparing metagenomes at various scales, from baseline MGs to various function/pathway clusters of metagenomes, collectively termed OMUs.

Multivariate Analysis and Correlation Study Shows the Impact of Anthropometric and Demographic Variables on Gut Microbiota in Obese Egyptian Children

Deciphering the gut microbiome's link to obesity is crucial. Our study characterized the gut microbial community in Egyptian children and investigated the effect of covariates on the gut microbiome, body mass index (BMI), geographical location, gender, and age. We used 16S rRNA sequencing to characterize the gut microbial communities of 49 children. We then evaluated these communities for diversity, potential biomarkers, and functional capacity. Alpha diversity of the non-obese group was higher than that of the obese group (Chao1, P = 0.006 and observed species, P = 0.003). Beta diversity analysis revealed significant variations in the gut microbiome between the two geographical locations, Cairo and Ismailia (unweighted UniFrac, P = 0.03) and between obesity statuses, obese and non-obese (weighted UniFrac, P = 0.034; unweighted UniFrac, P = 0.015). We observed a significantly higher Firmicutes/Bacteroidetes ratio in obese males than in non-obese males (P = 0.004). Interestingly, this difference was not seen in females (P = 0.77). Multivariable association with linear models (MaAsLin2) identified 8 microbial features associated with obesity, 12 associated with non-obesity, and found 29 and 13 features specific to Cairo and Ismailia patients, respectively. It has also shown one microbial feature associated with patients under five years old. MaAsLin2, however, failed to recognize any association between gender and the gut microbiome. Moreover, it could find the most predominant features in groups 2-9 but not in group 1. Another method used in the analysis is the Linear discriminant analysis Effect Size (LEfSe) approach, which effectively identified 19 biomarkers linked to obesity, 9 linked non-obesity, 20 linked to patients residing in Cairo, 14 linked to patients in Ismailia, one linked to males, and 12 linked to females. LEfSe could not, however, detect any prevalent bacteria among children younger or older than five. Future studies should take advantage of such correlations, specifically BMI, to determine the interventions needed for obesity management.

Signs of aging in midlife: physical function and sex differences in microbiota

Microbiota composition has been linked to physical activity, health measures, and biological age, but a shared profile has yet to be shown. The aim of this study was to examine the associations between microbiota composition and measures of function, such as a composite measure of physical capacity, and biological age in midlife, prior to onset of age-related diseases. Seventy healthy midlife individuals (age 44.58 ± 0.18) were examined cross-sectionally, and their gut-microbiota profile was characterized from stool samples using 16SrRNA gene sequencing. Biological age was measured using the Klemera-Doubal method and a composition of blood and physiological biomarkers. Physical capacity was calculated based on sex-standardized functional tests. We demonstrate that the women had significantly richer microbiota, p = 0.025; however, microbiota diversity was not linked with chronological age, biological age, or physical capacity for either women or men. Men had slightly greater β-diversity; however, β-diversity was positively associated with biological age and with physical capacity for women only (p = 0.01 and p = 0.04; respectively). For women, an increase in abundance of Roseburia faecis and Collinsella aerofaciens, as well as genus Ruminococcus and Dorea, was significantly associated with higher biological age and lower physical capacity; an increase in abundance of Akkermansia muciniphila and genera Bacteroides and Alistipes was associated with younger biological age and increased physical capacity. Differentially abundant taxa were also associated with non-communicable diseases. These findings suggest that microbiota composition is a potential mechanism linking physical capacity and health status; personalized probiotics may serve as a new means to support health-promoting interventions in midlife. Investigating additional factors underlying this link may facilitate the development of a more accurate method to estimate the rate of aging.

Towards a unified medical microbiome ecology of the OMU for metagenomes and the OTU for microbes

BACKGROUND

Metagenomic sequencing technologies offered unprecedented opportunities and also challenges to microbiology and microbial ecology particularly. The technology has revolutionized the studies of microbes and enabled the high-profile human microbiome and earth microbiome projects. The terminology-change from microbes to microbiomes signals that our capability to count and classify microbes (microbiomes) has achieved the same or similar level as we can for the biomes (macrobiomes) of plants and animals (macrobes). While the traditional investigations of macrobiomes have usually been conducted through naturalists' (Linnaeus & Darwin) naked eyes, and aerial and satellite images (remote-sensing), the large-scale investigations of microbiomes have been made possible by DNA-sequencing-based metagenomic technologies. Two major types of metagenomic sequencing technologies-amplicon sequencing and whole-genome (shotgun sequencing)-respectively generate two contrastingly different categories of metagenomic reads (data)-OTU (operational taxonomic unit) tables representing microorganisms and OMU (operational metagenomic unit), a new term coined in this article to represent various cluster units of metagenomic genes.

RESULTS

The ecological science of microbiomes based on the OTU representing microbes has been unified with the classic ecology of macrobes (macrobiomes), but the unification based on OMU representing metagenomes has been rather limited. In a previous series of studies, we have demonstrated the applications of several classic ecological theories (diversity, composition, heterogeneity, and biogeography) to the studies of metagenomes. Here I push the envelope for the unification of OTU and OMU again by demonstrating the applications of metacommunity assembly and ecological networks to the metagenomes of human gut microbiomes. Specifically, the neutral theory of biodiversity (Sloan's near neutral model), Ning et al.stochasticity framework, core-periphery network, high-salience skeleton network, special trio-motif, and positive-to-negative ratio are applied to analyze the OMU tables from whole-genome sequencing technologies, and demonstrated with seven human gut metagenome datasets from the human microbiome project.

CONCLUSIONS

All of the ecological theories demonstrated previously and in this article, including diversity, composition, heterogeneity, stochasticity, and complex network analyses, are equally applicable to OMU metagenomic analyses, just as to OTU analyses. Consequently, I strongly advocate the unification of OTU/OMU (microbiomes) with classic ecology of plants and animals (macrobiomes) in the context of medical ecology.

A comprehensive diversity analysis on the gut microbiomes of ASD patients: from alpha, beta to gamma diversities

Autism spectrum disorder (ASD) is estimated to influence as many as 1% children worldwide, but its etiology is still unclear. It has been suggested that gut microbiomes play an important role in regulating abnormal behaviors associated with ASD. A de facto standard analysis on the microbiome-associated diseases has been diversity analysis, and nevertheless, existing studies on ASD-microbiome relationship have not produced a consensus. Here, we perform a comprehensive analysis of the diversity changes associated with ASD involving alpha-, beta-, and gamma-diversity metrics, based on 8 published data sets consisting of 898 ASD samples and 467 healthy controls (HC) from 16S-rRNA sequencing. Our findings include: (i) In terms of alpha-diversity, in approximately 1/3 of the studies cases, ASD patients exhibited significantly higher alpha-diversity than the HC, which seems to be consistent with the "1/3 conjecture" of diversity-disease relationship (DDR). (ii) In terms of beta-diversity, the AKP (Anna Karenina principle) that predict all healthy microbiomes should be similar, and every diseased microbiome should be dissimilar in its own way seems to be true in approximately 1/2 to 3/4 studies cases. (iii) In terms of gamma-diversity, the DAR (diversity-area relationship) modeling suggests that ASD patients seem to have large diversity-area scaling parameter than the HC, which is consistent with the AKP results. However, the MAD (maximum accrual diversity) and RIP (ratio of individual to population diversity) parameters did not suggest significant differences between ASD patients and HC. Throughout the study, we adopted Hill numbers to measure diversity, which stratified the diversity measures in terms of the rarity-commonness-dominance spectrum. It appears that the differences between ASD patients and HC are more propounding on rare-species side than on dominant-species side. Finally, we discuss the apparent inconsistent diversity-ASD relationships among different case studies and postulate that the relationships are not monotonic.

Precision Glycan Supplementation Improves Gut Microbiota Diversity, Performance, and Disease Outbreak Resistance in Broiler Chickens

The poultry industry contributes significantly to the global meat industry but faces many production challenges like high-density housing, welfare issues, and pathogenic infections. While antibiotics have commonly been used to treat many of these issues, they are being removed from poultry production globally due to increased microbial resistance. Precision glycans offer a viable alternative to antibiotics by modulating microbial metabolic pathways. In this study, we investigated the effects of precision glycan supplementation on productivity and gut microbiota in broilers. The experiment was conducted in a commercial setting using 32,400 male Ross chickens randomly divided into three sheds with 10,800 birds each. One shed with 12 pen replicates of 900 birds was used as control, while the other two with an equal number of replicates and birds were assigned to precision glycan supplementation. The treatment significantly improved the average daily weight gain and feed conversion ratio, with a significant modification in the abundance of several bacterial taxa in the caecum, ileum, and ileum mucosa microbial communities. There was increased richness and diversity in the caecum, with a reduction in Proteobacteria and an increase in Firmicutes. Richness remained unchanged in the ileum, with an increase in diversity and reduction in pathogenic genera like Clostridium and Escherichia-Shigella. Ileum mucosa showed a lower abundance of mucin degraders and an increased presence of next-generation probiotics. Supplemented birds showed a high level of disease resistance when the farm experienced an outbreak of infectious bronchitis, evidenced by lower mortality. Histological analysis confirmed improvements in the ileum and liver health, where the precision glycan supplementation reduced the area of congested sinusoids compared to the control group in the liver and significantly improved ileum intestinal morphology by increasing crypt depth and surface area. These results collectively suggest that precision glycans offer substantial benefits in poultry production by improving productivity, gut health, and disease resistance.

Occlusive membranes for guided regeneration of inflamed tissue defects

Guided bone regeneration aided by the application of occlusive membranes is a promising therapy for diverse inflammatory periodontal diseases. Symbiosis, homeostasis between the host microbiome and cells, occurs in the oral environment under normal, but not pathologic, conditions. Here, we develop a symbiotically integrating occlusive membrane by mimicking the tooth enamel growth or multiple nucleation biomineralization processes. We perform human saliva and in vivo canine experiments to confirm that the symbiotically integrating occlusive membrane induces a symbiotic healing environment. Moreover, we show that the membrane exhibits tractability and enzymatic stability, maintaining the healing space during the entire guided bone regeneration therapy period. We apply the symbiotically integrating occlusive membrane to treat inflammatory-challenged cases in vivo, namely, the open and closed healing of canine premolars with severe periodontitis. We find that the membrane promotes symbiosis, prevents negative inflammatory responses, and improves cellular integration. Finally, we show that guided bone regeneration therapy with the symbiotically integrating occlusive membrane achieves fast healing of gingival soft tissue and alveolar bone.

Effects of tris (2-chloroethyl) phosphate exposure on gut microbiome using the simulator of the human intestinal microbial ecosystem (SHIME)

Tris (2-chloroethyl) phosphate (TCEP) has been widely used, and its health risk has received increasing attention. However, the rare research has been conducted on the effects of TCEP exposure on changes in the structure of the human gut microbiome and metabolic functions. In this experiment, Simulator of the human intestinal microbial ecosystem (SHIME) was applied to explore the influences of TCEP on the human gut bacteria community and structure. The results obtained from high-throughput sequencing of 16S rRNA gene have clearly revealed differences among control and exposure groups. High-dose TCEP exposure increased the Shannon and Simpson indexes in the results of α-diversity of the gut microbiome. At phylum level, Firmicutes occupied a higher proportion of gut microbiota, while the proportion of Bacteroidetes decreased. In the genus-level analysis, the relative abundance of Bacteroides descended with the TCEP exposure dose increased in the ascending colon, while the abundances of Roseburia, Lachnospira, Coprococcus and Lachnoclostridium were obviously correlated with exposure dose in each colon. The results of short chain fatty acids (SCFAs) showed a remarkable effect on the distribution after TCEP exposure. In the ascending colon, the control group had the highest acetate concentration (1.666 ± 0.085 mg⋅mL-1), while acetate concentrations in lose-dose medium-dose and high-doseTCEP exposure groups were 1.119 ± 0.084 mg⋅mL-1, 0.437 ± 0.053 mg⋅mL-1 and 0.548 ± 0.106 mg⋅mL-1, respectively. TCEP exposure resulted in a decrease in acetate and propionate concentrations, while increasing butyrate concentrations in each colon. Dorea, Fusicatenibacter, Kineothrix, Lachnospira, and Roseburia showed an increasing tendency in abundance under TCEP exposure, while they had a negatively correlation with acetate and propionate concentrations and positively related with butyrate concentrations. Overall, this study confirms that TCEP exposure alters both the composition and metabolic function of intestinal microbial communities, to arouse public concern about its negative health effects.

A new hypothesis on BV etiology: dichotomous and crisscrossing categorization of complex versus simple on healthy versus BV vaginal microbiomes

IMPORTANCE

BV may influence as many as one-third of women, but its etiology remains unclear. A traditional view is that dominance by Lactobacillus is the hallmark of a healthy vaginal microbiome and lack of dominance may make women BV-prone. Recent studies show that the human VMs can be classified into five major types, four of which possess type-specific dominant species of Lactobacillus. The remaining one (type IV) is not dominated by Lactobacillus and contains a handful of strictly anaerobic bacteria. Nevertheless, exceptions to the first hypothesis have been noticed from the very beginning, and there is not a definite relationship, suggested yet, between the five VM types and BV status. Here, we propose and test a novel hypothesis that assumes the existence of four VM types from dichotomous crisscrossing of "complex versus simple (high diversity or low dominance versus low diversity or high dominance)" on "healthy versus BV." Consequently, there are simple BV versus complex BV.

Tomato Plant Microbiota under Conventional and Organic Fertilization Regimes in a Soilless Culture System

Tomato is the main vegetable cultivated under soilless culture systems (SCSs); production of organic tomato under SCSs has increased due to consumer demands for healthier and environmentally friendly vegetables. However, organic tomato production under SCSs has been associated with low crop performance and fruit quality defects. These agricultural deficiencies could be linked to alterations in tomato plant microbiota; nonetheless, this issue has not been sufficiently addressed. Thus, the main goal of the present study was to characterize the rhizosphere and phyllosphere of tomato plants cultivated under conventional and organic SCSs. To accomplish this goal, tomato plants grown in commercial greenhouses under conventional or organic SCSs were tested at 8, 26, and 44 weeks after seedling transplantation. Substrate (n = 24), root (n = 24), and fruit (n = 24) composite samples were subjected to DNA extraction and high-throughput 16S rRNA gene sequencing. The present study revealed that the tomato core microbiota was predominantly constituted by Proteobacteria, Actinobacteria, and Firmicutes. Remarkably, six bacterial families, Bacillaceae, Microbacteriaceae, Nocardioidaceae, Pseudomonadaceae, Rhodobacteraceae, and Sphingomonadaceae, were shared among all substrate, rhizosphere, and fruit samples. Importantly, it was shown that plants under organic SCSs undergo a dysbiosis characterized by significant changes in the relative abundance of Bradyrhizobiaceae, Caulobacteraceae, Chitinophagaceae, Enterobacteriaceae, Erythrobacteraceae, Flavobacteriaceae, Nocardioidaceae, Rhodobacteraceae, and Streptomycetaceae. These results suggest that microbial alterations in substrates, roots, and fruits could be potential factors in contributing to the crop performance and fruit quality deficiencies observed in organic SCSs.

The oral bacteriomes of patients with allergic rhinitis and asthma differ from that of healthy controls

Allergic rhinitis and asthma are two of the most common chronic respiratory diseases in developed countries and have become a major public health concern. Substantial evidence has suggested a strong link between respiratory allergy and upper airway dysbacteriosis, but the role of the oral bacteriota is still poorly understood. Here we used 16S rRNA massive parallel sequencing to characterize the oral bacteriome of 344 individuals with allergic rhinitis (AR), allergic rhinitis with asthma (ARAS), asthma (AS) and healthy controls (CT). Four of the most abundant (>2%) phyla (Actinobacteriota, Firmicutes, Fusobacteriota, and Proteobacteria) and 10 of the dominant genera (Actinomyces, Fusobacterium, Gemella, Haemophilus, Leptotrichia, Neisseria, Porphyromonas, Prevotella, Streptococcus, and Veillonella) in the oral cavity differed significantly (p ≤ 0.03) between AR, ARAS or AS and CT groups. The oral bacteriome of ARAS patients showed the highest intra-group diversity, while CT showed the lowest. All alpha-diversity indices of microbial richness and evenness varied significantly (p ≤ 0.022) in ARAS vs. CT and ARAS vs. AR, but they were not significantly different in AR vs. CT. All beta-diversity indices of microbial structure (Unifrac, Bray-Curtis, and Jaccard distances) differed significantly (p ≤ 0.049) between each respiratory disease group and controls. Bacteriomes of AR and ARAS patients showed 15 and 28 upregulated metabolic pathways (PICRUSt2) mainly related to degradation and biosynthesis (p < 0.05). A network analysis (SPIEC-EASI) of AR and ARAS bacteriomes depicted simpler webs of interactions among their members than those observed in the bacteriome of CT, suggesting chronic respiratory allergic diseases may disrupt bacterial connectivity in the oral cavity. This study, therefore, expands our understanding of the relationships between the oral bacteriome and allergy-related conditions. It demonstrates for the first time that the mouth harbors distinct bacteriotas during health and allergic rhinitis (with and without comorbid asthma) and identifies potential taxonomic and functional microbial biomarkers of chronic airway disease.

Smoking-induced subgingival dysbiosis precedes clinical signs of periodontal disease

Smoking accelerates periodontal disease and alters the subgingival microbiome. However, the relationship between smoking-associated subgingival dysbiosis and progression of periodontal disease is not well understood. Here, we sampled 233 subgingival sites longitudinally from 8 smokers and 9 non-smokers over 6-12 months, analyzing 804 subgingival plaque samples using 16 rRNA sequencing. At equal probing depths, the microbial richness and diversity of the subgingival microbiome was higher in smokers compared to non-smokers, but these differences decreased as probing depths increased. The overall subgingival microbiome of smokers differed significantly from non-smokers at equal probing depths, which was characterized by colonization of novel minority microbes and a shift in abundant members of the microbiome to resemble periodontally diseased communities enriched with pathogenic bacteria. Temporal analysis showed that microbiome in shallow sites were less stable than deeper sites, but temporal stability of the microbiome was not significantly affected by smoking status or scaling and root planing. We identified 7 taxa-Olsenella sp., Streptococcus cristatus, Streptococcus pneumoniae, Streptococcus parasanguinis, Prevotella sp., Alloprevotella sp., and a Bacteroidales sp. that were significantly associated with progression of periodontal disease. Taken together, these results suggest that subgingival dysbiosis in smokers precedes clinical signs of periodontal disease, and support the hypothesis that smoking accelerates subgingival dysbiosis to facilitate periodontal disease progression.

Nasal Bacteriomes of Patients with Asthma and Allergic Rhinitis Show Unique Composition, Structure, Function and Interactions

Allergic rhinitis and asthma are major public health concerns and economic burdens worldwide. However, little is known about nasal bacteriome dysbiosis during allergic rhinitis, alone or associated with asthma comorbidity. To address this knowledge gap we applied 16S rRNA high-throughput sequencing to 347 nasal samples from participants with asthma (AS = 12), allergic rhinitis (AR = 53), allergic rhinitis with asthma (ARAS = 183) and healthy controls (CT = 99). One to three of the most abundant phyla, and five to seven of the dominant genera differed significantly (p < 0.021) between AS, AR or ARAS and CT groups. All alpha-diversity indices of microbial richness and evenness changed significantly (p < 0.01) between AR or ARAS and CT, while all beta-diversity indices of microbial structure differed significantly (p < 0.011) between each of the respiratory disease groups and controls. Bacteriomes of rhinitic and healthy participants showed 72 differentially expressed (p < 0.05) metabolic pathways each related mainly to degradation and biosynthesis processes. A network analysis of the AR and ARAS bacteriomes depicted more complex webs of interactions among their members than among those of healthy controls. This study demonstrates that the nose harbors distinct bacteriotas during health and respiratory disease and identifies potential taxonomic and functional biomarkers for diagnostics and therapeutics in asthma and rhinitis.

Associations of disordered eating with the intestinal microbiota and short-chain fatty acids among young adults with type 1 diabetes

BACKGROUND AND AIMS

Disordered eating (DE) in type 1 diabetes (T1D) includes insulin restriction for weight loss with serious complications. Gut microbiota-derived short chain fatty acids (SCFA) may benefit host metabolism but are reduced in T1D. We evaluated the hypothesis that DE and insulin restriction were associated with reduced SCFA-producing gut microbes, SCFA, and intestinal microbial diversity in adults with T1D.

METHODS AND RESULTS

We collected stool samples at four timepoints in a hypothesis-generating gut microbiome pilot study ancillary to a weight management pilot in young adults with T1D. 16S ribosomal RNA gene sequencing measured the normalized abundance of SCFA-producing intestinal microbes. Gas-chromatography mass-spectrometry measured SCFA (total, acetate, butyrate, and propionate). The Diabetes Eating Problem Survey-Revised (DEPS-R) assessed DE and insulin restriction. Covariate-adjusted and Bonferroni-corrected generalized estimating equations modeled the associations. COVID-19 interrupted data collection, so models were repeated restricted to pre-COVID-19 data. Data were available for 45 participants at 109 visits, which included 42 participants at 65 visits pre-COVID-19. Participants reported restricting insulin "At least sometimes" at 53.3% of visits. Pre-COVID-19, each 5-point DEPS-R increase was associated with a -0.34 (95% CI -0.56, -0.13, p = 0.07) lower normalized abundance of genus Anaerostipes; and the normalized abundance of Lachnospira genus was -0.94 (95% CI -1.5, -0.42), p = 0.02 lower when insulin restriction was reported "At least sometimes" compared to "Rarely or Never".

CONCLUSION

DE and insulin restriction were associated with a reduced abundance of SCFA-producing gut microbes pre-COVID-19. Additional studies are needed to confirm these associations to inform microbiota-based therapies in T1D.

The Intestinal Microbiota and Short-Chain Fatty Acids in Association with Advanced Metrics of Glycemia and Adiposity Among Young Adults with Type 1 Diabetes and Overweight or Obesity

Background

Comanagement of glycemia and adiposity is the cornerstone of cardiometabolic risk reduction in type 1 diabetes (T1D), but targets are often not met. The intestinal microbiota and microbiota-derived short-chain fatty acids (SCFAs) influence glycemia and adiposity but have not been sufficiently investigated in longstanding T1D.

Objectives

We evaluated the hypothesis that an increased abundance of SCFA-producing gut microbes, fecal SCFAs, and intestinal microbial diversity were associated with improved glycemia but increased adiposity in young adults with longstanding T1D.

Methods

Participants provided stool samples at ≤4 time points (NCT03651622: https://clinicaltrials.gov/ct2/show/NCT03651622). Sequencing of the 16S ribosomal RNA gene measured abundances of SCFA-producing intestinal microbes. GC-MS measured total and specific SCFAs (acetate, butyrate, propionate). DXA (body fat percentage and percentage lean mass) and anthropometrics (BMI) measured adiposity. Continuous glucose monitoring [percentage of time in range (70-180 mg/dL), above range (>180 mg/dL), and below range (54-69 mg/dL)] and glycated hemoglobin (i.e., HbA1c) assessed glycemia. Adjusted and Bonferroni-corrected generalized estimating equations modeled the associations of SCFA-producing gut microbes, fecal SCFAs, and intestinal microbial diversity with glycemia and adiposity. COVID-19 interrupted data collection, so models were repeated restricted to pre-COVID-19 visits.

Results

Data were available for ≤45 participants at 101 visits (including 40 participants at 54 visits pre-COVID-19). Abundance of Eubacterium hallii was associated inversely with BMI (all data). Pre-COVID-19, increased fecal propionate was associated with increased percentage of time above range and reduced percentage of time in target and below range; and abundances of 3 SCFA-producing taxa (Ruminococcus gnavus, Eubacterium ventriosum, and Lachnospira) were associated inversely with body fat percentage, of which two microbes were positively associated with percentage lean mass. Abundance of Anaerostipes was associated with reduced percentage of time in range (all data) and with increased body fat percentage and reduced percentage lean mass (pre-COVID-19).

Conclusions

Unexpectedly, fecal propionate was associated with detriment to glycemia, whereas most SCFA-producing intestinal microbes were associated with benefit to adiposity. Future studies should confirm these associations and determine their potential causal linkages in T1D.This study is registered at clinical.trials.gov (NCT03651622; https://clinicaltrials.gov/ct2/show/NCT03651622).

Structural and functional dysbiosis of gut microbiota in Tibetan subjects with coronary heart disease

The gut microbiota plays a crucial role in coronary heart disease (CHD). However, only a few studies focusing on the relationship between gut microbiota and CHD in ethnic populations are available. Here, we employed shotgun sequencing of the gut metagenome to analyze the taxonomic composition and functional annotation of the gut microbiota of 14 CHD patients, 13 patients with non-stenosis coronary heart disease (NCHD), and 18 healthy controls (HT) in Tibetan subjects. We found that the α-diversity of the gut microbiota was not significantly different among the three groups., whereas β-diversity was significantly altered in the CHD group compared with HT. Based on the receiver operating characteristic curve (ROC) analysis, the relative abundance of Proteobacteria species effectively distinguished patients with CHD from the control group. Most of the enriched species belonged to Proteobacteria. The pathways that contributed the most to the differences between groups were amino acid metabolism-related pathways, especially lysine biosynthesis. The enzymes of the lysine biosynthesis pathway, including K01714 and K00821, were significantly decreased in the CHD group. Our findings increase the understanding of the association between CHD pathogenesis and gut microbiota in the Tibetan population, thus paving the way for the development of improved diagnostic methods and treatments for Tibetan patients with CHD.

Metagenomic features of bioburden serve as outcome indicators in combat extremity wounds

Battlefield injury management requires specialized care, and wound infection is a frequent complication. Challenges related to characterizing relevant pathogens further complicates treatment. Applying metagenomics to wounds offers a comprehensive path toward assessing microbial genomic fingerprints and could indicate prognostic variables for future decision support tools. Wound specimens from combat-injured U.S. service members, obtained during surgical debridements before delayed wound closure, were subjected to whole metagenome analysis and targeted enrichment of antimicrobial resistance genes. Results did not indicate a singular, common microbial metagenomic profile for wound failure, instead reflecting a complex microenvironment with varying bioburden diversity across outcomes. Genus-level Pseudomonas detection was associated with wound failure at all surgeries. A logistic regression model was fit to the presence and absence of antimicrobial resistance classes to assess associations with nosocomial pathogens. A. baumannii detection was associated with detection of genomic signatures for resistance to trimethoprim, aminoglycosides, bacitracin, and polymyxin. Machine learning classifiers were applied to identify wound and microbial variables associated with outcome. Feature importance rankings averaged across models indicated the variables with the largest effects on predicting wound outcome, including an increase in P. putida sequence reads. These results describe the microbial genomic determinants in combat wound bioburden and demonstrate metagenomic investigation as a comprehensive tool for providing information toward aiding treatment of combat-related injuries.

Influences of Helicobacter pylori infection on diversity, heterogeneity, and composition of human gastric microbiomes across stages of gastric cancer development

BACKGROUND

About a half of the world's population is infected with Helicobacter pylori (H. pylori), but only 1%-3% of them develop gastric cancer. As a primary risk factor for gastric cancer, the relationship between H. pylori infection and gastric microbiome has been a focus in recent years.

MATERIALS AND METHODS

We reanalyze 11 human gastric microbiome datasets with or without H. pylori, covering the healthy control (HC) and four disease stages (chronic gastritis (CG), atrophic gastritis (AG), intestinal metaplasia (IM), and gastric cancer (GC)) of gastric cancer development to quantitatively compare the influences of the H. pylori infection and disease stages on the diversity, heterogeneity, and composition of gastric microbiome. Four medical ecology approaches including (i) diversity analysis with Hill numbers, (ii) heterogeneity analysis with Taylor's power law extensions (TPLE), (iii) diversity scaling analysis with diversity-area relationship (DAR) model, and (iv) shared species analysis were applied to fulfill the data reanalysis.

RESULTS

(i) The influences of H. pylori infection on the species diversity, spatial heterogeneity, and potential diversity of gastric microbiome seem to be more prevalent than the influences of disease stages during gastric cancer development. (ii) The influences of H. pyloriinfection on diversity, heterogeneity, and composition of gastric microbiomes in HC, CG, IM, and GC stages appear more prevalent than those in AG stage.

CONCLUSION

Our study confirmed the impact of H. pylori infection on human gastric microbiomes: The influences of H. pylori infection on the diversity, heterogeneity, and composition of gastric microbiomes appear to be disease-stage dependent.

Niche-Neutral Continuum Seems to Explain the Global Niche Differentiation and Local Drift of the Human Digestive Tract Microbiome

The human digestive tract (DT) is differentiated into diverse niches and harbors the greatest microbiome diversity of our bodies. Segata et al. (2012) found that the microbiome of diverse habitats along the DT may be classified as four categories or niches with different microbial compositions and metabolic potentials. Nonetheless, few studies have offered theoretical interpretations of the observed patterns, not to mention quantitative mechanistic parameters. Such parameters should capture the essence of the fundamental processes that shape the microbiome distribution, beyond simple ecological metrics such as diversity or composition descriptors, which only capture the manifestations of the mechanisms. Here, we aim to get educated guesses for such parameters by adopting an integrated approach with multisite neutral (MSN) and niche-neutral hybrid (NNH) modeling, via reanalyzing Segata’s 16s-rRNA samples covering 10 DT-sites from over 200 healthy individuals. We evaluate the relative importance of the four essential processes (drift, dispersal, speciation, and selection) in shaping the microbiome distribution and dynamics along DT, which are assumed to form a niche-neutral continuum. Furthermore, the continuum seems to be hierarchical: the selection or niche differentiations seem to play a predominant role (> 90% based on NNH) at the global (the DT metacommunity) level, but the neutral drifts seem to be prevalent (> 90% based on MSN/NNH) at the local sites except for the gut site. An additional finding is that the DT appears to have a fifth niche for the DT microbiome, namely, Keratinized gingival (KG), while in Segata’s original study, only four niches were identified. Specifically, in Segata’s study, KG was classified into the same niche type including buccal mucosa (BM), hard palate (HP), and KG. However, it should be emphasized that the proposal of the fifth niche of KG requires additional verification in the future studies.

Shared Species Analysis, Augmented by Stochasticity Analysis, Is More Effective Than Diversity Analysis in Detecting Variations in the Gut Microbiomes

Diversity analysis is a de facto standard procedure for most existing microbiome studies. Nevertheless, diversity metrics can be insensitive to changes in community composition (identities). For example, if species A (e.g., a beneficial microbe) is replaced by equal number of species B (e.g., an opportunistic pathogen), the diversity metric may not change, but the community composition has changed. The shared species analysis (SSA) is a computational technique that can discern changes of community composition by detecting the increase/decrease of shared species between two sets of microbiome samples, and it should be more sensitive than standard diversity analysis in discerning changes in microbiome structures. Here, we investigated the effects of ethnicity and lifestyles in China on the structure of Chinese gut microbiomes by reanalyzing the datasets of a large Chinese cohort with 300+ individuals covering 7 biggest Chinese ethnic groups (&gt;95% Chinese population). We found: (i) Regarding lifestyles, SSA revealed significant differences between 100% of pair-wise comparisons in community compositions across all but phylum taxon levels (phylum level = 29%), but diversity analysis only revealed 14–29% pair-wise differences in community diversity across all four taxon levels. (ii) Regarding ethnicities, SSA revealed 100% pair-wise differences in community compositions across all but phylum (phylum level = 48–62%) levels, but diversity analysis only revealed 5–57% differences in community diversity across all four taxon levels. (iii) Ethnicity seems to have more prevalent effects on community structures than lifestyle does (iv) Community structures of the gut microbiomes are more stable at the phylum level than at the other three levels. (v) SSA is more powerful than diversity analysis in detecting the changes of community structures; furthermore, SSA can produce lists of unique and shared OTUs. (vi) Finally, we performed stochasticity analysis to mechanistically interpret the observed differences revealed by the SSA and diversity analyses.

Review article: the future of microbiome-based therapeutics

BACKGROUND

From consumption of fermented foods and probiotics to emerging applications of faecal microbiota transplantation, the health benefit of manipulating the human microbiota has been exploited for millennia. Despite this history, recent technological advances are unlocking the capacity for targeted microbial manipulation as a novel therapeutic.

AIM

This review summarises the current developments in microbiome-based medicines and provides insight into the next steps required for therapeutic development.

METHODS

Here we review current and emerging approaches and assess the capabilities and weaknesses of these technologies to provide safe and effective clinical interventions. Key literature was identified through Pubmed searches with the following key words, 'microbiome', 'microbiome biomarkers', 'probiotics', 'prebiotics', 'synbiotics', 'faecal microbiota transplant', 'live biotherapeutics', 'microbiome mimetics' and 'postbiotics'.

RESULTS

Improved understanding of the human microbiome and recent technological advances provide an opportunity to develop a new generation of therapies. These therapies will range from dietary interventions, prebiotic supplementations, single probiotic bacterial strains, human donor-derived faecal microbiota transplants, rationally selected combinations of bacterial strains as live biotherapeutics, and the beneficial products or effects produced by bacterial strains, termed microbiome mimetics.

CONCLUSIONS

Although methods to identify and refine these therapeutics are continually advancing, the rapid emergence of these new approaches necessitates accepted technological and ethical frameworks for measurement, testing, laboratory practices and clinical translation.

Ecology of Human Medical Enterprises: From Disease Ecology of Zoonoses, Cancer Ecology Through to Medical Ecology of Human Microbiomes

In nature, the interaction between pathogens and their hosts is only one of a handful of interaction relationships between species, including parasitism, predation, competition, symbiosis, commensalism, and among others. From a non-anthropocentric view, parasitism has relatively fewer essential differences from the other relationships; but from an anthropocentric view, parasitism and predation against humans and their well-beings and belongings are frequently related to heinous diseases. Specifically, treating (managing) diseases of humans, crops and forests, pets, livestock, and wildlife constitute the so-termed medical enterprises (sciences and technologies) humans endeavor in biomedicine and clinical medicine, veterinary, plant protection, and wildlife conservation. In recent years, the significance of ecological science to medicines has received rising attentions, and the emergence and pandemic of COVID-19 appear accelerating the trend. The facts that diseases are simply one of the fundamental ecological relationships in nature, and the study of the relationships between species and their environment is a core mission of ecology highlight the critical importance of ecological science. Nevertheless, current studies on the ecology of medical enterprises are highly fragmented. Here, we (i) conceptually overview the fields of disease ecology of wildlife, cancer ecology and evolution, medical ecology of human microbiome-associated diseases and infectious diseases, and integrated pest management of crops and forests, across major medical enterprises. (ii) Explore the necessity and feasibility for a unified medical ecology that spans biomedicine, clinical medicine, veterinary, crop (forest and wildlife) protection, and biodiversity conservation. (iii) Suggest that a unified medical ecology of human diseases is both necessary and feasible, but laissez-faire terminologies in other human medical enterprises may be preferred. (iv) Suggest that the evo-eco paradigm for cancer research can play a similar role of evo-devo in evolutionary developmental biology. (v) Summarized 40 key ecological principles/theories in current disease-, cancer-, and medical-ecology literatures. (vi) Identified key cross-disciplinary discovery fields for medical/disease ecology in coming decade including bioinformatics and computational ecology, single cell ecology, theoretical ecology, complexity science, and the integrated studies of ecology and evolution. Finally, deep understanding of medical ecology is of obvious importance for the safety of human beings and perhaps for all living things on the planet.

Contrasting patterns of bacterial communities in the rearing water and gut of Penaeus vannamei in response to exogenous glucose addition

Supplementing exogenous carbon sources is a practical approach to improving shrimp health by manipulating the microbial communities of aquaculture systems. However, little is known about the microbiological processes and mechanisms of these systems. Here, the effects of glucose addition on shrimp growth performance and bacterial communities of the rearing water and the shrimp gut were investigated to address this knowledge gap. The results showed that glucose addition significantly improved the growth and survival of shrimp. Although the α-diversity indices of both bacterioplankton communities and gut microbiota were significantly decreased by adding glucose, both bacterial communities exhibited divergent response patterns to glucose addition. Glucose addition induced a dispersive bacterioplankton community but a more stable gut bacterial community. Bacterial taxa belonging to Ruegeria were significantly enriched by glucose in the guts, especially the operational taxonomic unit 2575 (OTU2575), which showed the highest relative importance to the survival rate and individual weight of shrimp, with the values of 43.8 and 40.6%, respectively. In addition, glucose addition increased the complexity of interspecies interactions within gut bacterial communities and the network nodes from Rhodobacteraceae accounted for higher proportions and linked more with the nodes from other taxa in the glucose addition group than that in control. These findings suggest that glucose addition may provide a more stable gut microbiota for shrimp by increasing the abundance of certain bacterial taxa, such as Ruegeria.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-021-00124-9.

Metagenomic Analyses of the Soybean Root Mycobiome and Microbiome Reveal Signatures of the Healthy and Diseased Plants Affected by Taproot Decline

Invading pathogens interact with plant-associated microbial communities, which can be altered under the pressure of pathogen infection. Limited information exists on plant-microbe interactions occurring during natural outbreaks in agricultural fields. Taproot decline (TRD) of soybean is an emerging disease caused by Xylaria necrophora. TRD disease occurrence and yield loss associated with TRD are outstanding issues in soybean production. We applied nuclear ribosomal DNA Internal Transcribed Spacers and 16S rRNA gene taxonomic marker sequencing to define the composition of the fungal and bacterial communities associated with healthy and diseased soybean roots collected from the Mississippi Delta. The plant compartment was a significant factor regulating taxonomic diversity, followed by the disease status of the plant. TRD impacted the root endophytes, causing imbalances; at the intermediate and advanced stages of TRD, X. necrophora decreased mycobiome diversity, whereas it increased microbiome richness. Networks of significant co-occurrence and co-exclusion relationships revealed direct and indirect associations among taxa and identified hubs with potential roles in assembling healthy and TRD-affected soybean biomes. These studies advance the understanding of host-microbe interactions in TRD and the part of biomes in plant health and disease.

Role of the Microbiome in the Pathogenesis of COVID-19

The ongoing pandemic coronavirus disease COVID-19 is caused by the highly contagious single-stranded RNA virus, SARS-coronavirus 2 (SARS-CoV-2), which has a high rate of evolution like other RNA viruses. The first genome sequences of SARS-CoV-2 were available in early 2020. Subsequent whole-genome sequencing revealed that the virus had accumulated several mutations in genes associated with viral replication and pathogenesis. These variants showed enhanced transmissibility and infectivity. Soon after the first outbreak due to the wild-type strain in December 2019, a genetic variant D614G emerged in late January to early February 2020 and became the dominant genotype worldwide. Thereafter, several variants emerged, which were found to harbor mutations in essential viral genes encoding proteins that could act as drug and vaccine targets. Numerous vaccines have been successfully developed to assuage the burden of COVID-19. These have different rates of efficacy, including, although rarely, a number of vaccinated individuals exhibiting side effects like thrombosis. However, the recent emergence of the Britain strain with 70% more transmissibility and South African variants with higher resistance to vaccines at a time when several countries have approved these for mass immunization has raised tremendous concern regarding the long-lasting impact of currently available prophylaxis. Apart from studies addressing the pathophysiology, pathogenesis, and therapeutic targets of SARS-CoV-2, analysis of the gut, oral, nasopharyngeal, and lung microbiome dysbiosis has also been undertaken to find a link between the microbiome and the pathogenesis of COVID-19. Therefore, in the current scenario of skepticism regarding vaccine efficacy and challenges over the direct effects of currently available drugs looming large, investigation of alternative therapeutic avenues based on the microbiome can be a rewarding finding. This review presents the currently available understanding of microbiome dysbiosis and its association with cause and consequence of COVID-19. Taking cues from other inflammatory diseases, we propose a hypothesis of how the microbiome may be influencing homeostasis, pro-inflammatory condition, and the onset of inflammation. This accentuates the importance of a healthy microbiome as a protective element to prevent the onset of COVID-19. Finally, the review attempts to identify areas where the application of microbiome research can help in reducing the burden of the disease.

Computational analyses of bacterial strains from shotgun reads

Shotgun sequencing is routinely employed to study bacteria in microbial communities. With the vast amount of shotgun sequencing reads generated in a metagenomic project, it is crucial to determine the microbial composition at the strain level. This study investigated 20 computational tools that attempt to infer bacterial strain genomes from shotgun reads. For the first time, we discussed the methodology behind these tools. We also systematically evaluated six novel-strain-targeting tools on the same datasets and found that BHap, mixtureS and StrainFinder performed better than other tools. Because the performance of the best tools is still suboptimal, we discussed future directions that may address the limitations.

Incorporating genome-based phylogeny and functional similarity into diversity assessments helps to resolve a global collection of human gut metagenomes

Tree-based diversity measures incorporate phylogenetic or functional relatedness into comparisons of microbial communities. This can improve the identification of explanatory factors compared to tree-agnostic diversity measures. However, applying tree-based diversity measures to metagenome data is more challenging than for single-locus sequencing (e.g., 16S rRNA gene). Utilizing the Genome Taxonomy Database (GTDB) for species-level metagenome profiling allows for functional diversity measures based on genomic content or traits inferred from it. Still, it is unclear how metagenome-based assessments of microbiome diversity benefit from incorporating phylogeny or function into measures of diversity. We assessed this by measuring phylogeny-based, function-based, and tree-agnostic diversity measures from a large, global collection of human gut metagenomes composed of 30 studies and 2943 samples. We found tree-based measures to explain phenotypic variation (e.g., westernization, disease status, and gender) better or equivalent to tree-agnostic measures. Ecophylogenetic and functional diversity measures provided unique insight into how microbiome diversity was partitioned by phenotype. Tree-based measures greatly improved machine learning model performance for predicting westernization, disease status, and gender, relative to models trained solely on tree-agnostic measures. Our findings illustrate the usefulness of tree- and function-based measures for metagenomic assessments of microbial diversity, which is a fundamental component of microbiome science. This article is protected by copyright. All rights reserved.

CACONET: a novel classification framework for microbial correlation networks

MOTIVATION

Existing microbiome-based disease prediction relies on the ability of machine learning methods to differentiate disease from healthy subjects based on the observed taxa abundance across samples. Despite numerous microbes have been implicated as potential biomarkers, challenges remain due to not only the statistical nature of microbiome data but also the lack of understanding of microbial interactions which can be indicative of the disease.

RESULTS

We propose CACONET (classification of Compositional-Aware COrrelation NETworks), a computational framework that learns to classify microbial correlation networks and extracts potential signature interactions, taking as input taxa relative abundance across samples and their health status. By using Bayesian compositional-aware correlation inference, a collection of posterior correlation networks can be drawn and used for graph-level classification, thus incorporating uncertainty in the estimates. CACONET then employs a deep learning approach for graph classification, achieving excellent performance metrics by exploiting the correlation structure. We test the framework on both simulated data and a large real-world dataset pertaining to microbiome samples of colorectal cancer (CRC) and healthy subjects, and identify potential network substructure characteristic of CRC microbiota. CACONET is customizable and can be adapted to further improve its utility.

AVAILABILITY AND IMPLEMENTATION

CACONET is available at https://github.com/yuanwxu/corr-net-classify.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Diverse methanogens, bacteria and tannase genes in the feces of the endangered volcano rabbit (Romerolagus diazi)

Background

The volcano rabbit is the smallest lagomorph in Mexico, it is monotypic and endemic to the Trans-Mexican Volcanic Belt. It is classified as endangered by Mexican legislation and as critically endangered by the IUCN, in the Red List. Romerolagus diazi consumes large amounts of grasses, seedlings, shrubs, and trees. Pines and oaks contain tannins that can be toxic to the organisms which consume them. The volcano rabbit microbiota may be rich in bacteria capable of degrading fiber and phenolic compounds.

Methods

We obtained the fecal microbiome of three adults and one young rabbit collected in Coajomulco, Morelos, Mexico. Taxonomic assignments and gene annotation revealed the possible roles of different bacteria in the rabbit gut. We searched for sequences encoding tannase enzymes and enzymes associated with digestion of plant fibers such as cellulose and hemicellulose.

Results

The most representative phyla within the Bacteria domain were: Proteobacteria, Firmicutes and Actinobacteria for the young rabbit sample (S1) and adult rabbit sample (S2), which was the only sample not confirmed by sequencing to correspond to the volcano rabbit. Firmicutes, Actinobacteria and Cyanobacteria were found in adult rabbit samples S3 and S4. The most abundant phylum within the Archaea domain was Euryarchaeota. The most abundant genera of the Bacteria domain were Lachnoclostridium (Firmicutes) and Acinetobacter (Proteobacteria), while Methanosarcina predominated from the Archaea. In addition, the potential functions of metagenomic sequences were identified, which include carbohydrate and amino acid metabolism. We obtained genes encoding enzymes for plant fiber degradation such as endo 1,4 β-xylanases, arabinofuranosidases, endoglucanases and β-glucosidases. We also found 18 bacterial tannase sequences.

The rice foot rot pathogen Dickeya zeae alters the in-field plant microbiome

Studies on bacterial plant diseases have thus far been focused on the single bacterial species causing the disease, with very little attention given to the many other microorganisms present in the microbiome. This study intends to use pathobiome analysis of the rice foot rot disease, caused by Dickeya zeae, as a case study to investigate the effects of this bacterial pathogen to the total resident microbiome and to highlight possible interactions between the pathogen and the members of the community involved in the disease process. The microbiome of asymptomatic and the pathobiome of foot‐rot symptomatic field‐grown rice plants over two growing periods and belonging to two rice cultivars were determined via 16S rRNA gene amplicon sequencing. Results showed that the presence of D. zeae is associated with an alteration of the resident bacterial community in terms of species composition, abundance and richness, leading to the formation of microbial consortia linked to the disease state. Several bacterial species were significantly co‐presented with the pathogen in the two growing periods suggesting that they could be involved in the disease process. Besides, culture‐dependent isolation and in planta inoculation studies of a bacterial member of the pathobiome, identified as positive correlated with the pathogen in our in silico analysis, indicated that it benefits from the presence of D. zeae. A similar microbiome/pathobiome experiment was also performed in a symptomatically different rice disease evidencing that not all plant diseases have the same consequence/relationship with the plant microbiome. This study moves away from a pathogen‐focused stance and goes towards a more ecological perception considering the effect of the entire microbial community which could be involved in the pathogenesis, persistence, transmission and evolution of plant pathogens.

Integrated diversity and shared species analyses of human viromes

Diversity analysis has been performed routinely on microbiomes, including human viromes. Shared species analysis has been conducted only rarely, but it can be a powerful supplement to diversity analysis. In the present study, we conducted integrated diversity and shared species analyses of human viromes by reanalyzing three published datasets of human viromes with more than 250 samples from healthy vs. diseased individuals and/or rural vs. urban individuals. We found significant differences in the virome diversity measured in the Hill numbers between the healthy and diseased individuals, with diseased individuals exhibiting higher virome diversity than healthy individuals, and rural individual exhibiting higher virome diversity than urban individuals. We applied both "read randomization" and "sample randomization" algorithms to perform shared species analysis. With the more conservative sample randomization algorithm, the observed number of shared species was significantly smaller than the expected shared species in 50% (8 of 16) of the comparisons. These results suggest that integrated diversity and shared species analysis can offer more comprehensive insights in comparing human virome samples than standard diversity analysis alone with potentially powerful applications in differentiating the effects of diseases or other meta-factors.

Gut microbiota in obesity

Obesity is a major global health problem determined by heredity and environment, and its incidence is increasing yearly. In recent years, increasing evidence linking obesity to the gut microbiota has been reported. Gut microbiota management has become a new method of obesity treatment. However, the complex interactions among genetics, environment, the gut microbiota, and obesity remain poorly understood. In this review, we summarize the characteristics of the gut microbiota in obesity, the mechanism of obesity induced by the gut microbiota, and the influence of genetic and environmental factors on the gut microbiota and obesity to provide support for understanding the complex relationship between obesity and microbiota. At the same time, the prospect of obesity research related to the gut microbiota is proposed.

Cross-Scale Analyses of Animal and Human Gut Microbiome Assemblies from Metacommunity to Global Landscape

Animal (human) gut microbiomes have been coevolving with their hosts for many millions of years. Understanding how the coevolution shapes the processes of microbiome assembly and diversity maintenance is important but rather challenging. An effort may start with the understanding of how and why animals and humans may differ in their microbiome neutrality (stochasticity) levels. Here, we attempted to perform layered comparative stochasticity analyses across animal species (including humans), class, and kingdom scales, corresponding to microbial metacommunity, landscape, and global-landscape scales. By analyzing 4,903 microbiome samples from 274 animal species covering 4 major invertebrate classes and all 6 vertebrate classes and including 1,787 human gut microbiome samples, we discovered the following: (i) at the microbial metacommunity (animal species) scale, although the general trend of stochasticity (measured in the relationships between fundamental biodiversity/dispersal numbers of Hubbell’s neutral theory and host species phylogenetic timeline) seems continuous, there seems to be a turning point from animals to humans in the passing rate of neutrality tests (12% to 45% versus 100%). We postulate that it should be the human experiences from agricultural/industrial activities (e.g., diet effects) and frequent social/familial contacts that are responsible for the dramatically rising stochastic neutrality in human gut microbiomes. (ii) At the microbial landscape (animal class) and global landscape (animal kingdom) scales, neutrality is not detectable, suggesting that the landscape is niche differentiated—animal species may possess “home niches” for their coadapted microbiomes. We further analyze the reliabilities of our findings by using variable P value thresholds (type I error) and performing power analysis (type II error) of neutrality tests.

IMPORTANCE Understanding how the coevolution (evolutionary time scale) and/or the interactions (ecological time scale) between animal (human) gut microbiomes and their hosts shape the processes of the microbiome assembly and diversity maintenance is important but rather challenging. An effort may start with the understanding of how and why animals and humans may differ in their microbiome neutrality (stochasticity) levels. Here, we attempted to perform layered comparative stochasticity analyses across animal species (including humans), class, and kingdom scales, corresponding to microbial metacommunity, landscape, and global-landscape scales by analyzing 4,903 microbiome samples from 274 animal species covering 4 major invertebrate classes and all 6 vertebrate classes, and including 1,787 human gut microbiome samples. The analyses were implemented by fitting the multisite neutral model and further augmented by checking false-positive and false-negative errors, respectively. It appears that there is a turning (tipping) point in the neutrality level from animal to human microbiomes.

The Interplay between Androgen and Gut Microbiota: Is There a Microbiota-Gut-Testis Axis

The gut microbiota, a large ecosystem interacting with the host, has been shown to affect the health and fitness of the host-microbial superorganism. Increasing evidence suggests that the gut microbiota communicates with distal organs of the host including the brain, liver, and muscle, as well as testis, through various complex mechanisms. So far, we know that the androgen can markedly remodel the gut microbiota and has initiated an interdisciplinary field termed "microgenderome." More recently, the gut microbiota has been found as a major regulator of androgen production and metabolism in turn and even could trespass the blood-testis barrier (BTB) to regulate spermatogenesis, which largely updates the current knowledge on male reproduction. In this review, we provided a brief overview of the context of the gender bias of diseases related to gut microbiota, the sex dimorphism of gut microbiota, and their relationships with androgen. We also summarized the known interaction between the testis and gut microbiota based on published animal studies and tentatively discussed the hypothesis of microbiota-gut-testis axis. Finally, we highlighted the opportunities and challenges underlying the ongoing research. This knowledge may extend our understanding of the role of gut microbiota in male health and microbiota-related diseases.

In silico trio-biomarkers for bacterial vaginosis revealed by species dominance network analysis

BV (bacterial vaginosis) influences 20%–40% of women but its etiology is still poorly understood. An open question about the BV is which of the hundreds of bacteria found in the human vaginal microbiome (HVM) are the major force driving the vaginal microbiota dysbiosis. Here, we recast the question of microbial causality of BV by asking if there are any prevalent ‘signatures’ (network motifs) in the vaginal microbiome networks associated with it? We apply a new framework [species dominance network analysis by Ma & Ellison (2019): Ecological Monographs) to detect critical structures in HVM networks associated with BV risks and etiology. We reanalyzed the 16 s-rRNA gene sequencing datasets of a mixed-cohort of 25 BV patients and healthy women. In these datasets, we detected 15 trio-motifs that occurred exclusively in BV patients. We failed to find any of these 15 trio-motifs in three additional cohorts of 1535 healthy women. Most member-species of the 15 trio motifs are BV-associated anaerobic bacteria (BVAB), Ravel’s community-state type indicators, or the most dominant species; virtually all species interactions in these trios are high-salience skeletons, suggesting that those trios are strongly connected ‘cults’ associated with the occurrence of BV. The presence of the trio motifs unique to BV may act as indicators for its personalized diagnosis and could help elucidate a more mechanistic interpretation of its risks and etiology. We caution that scarcity of large longitudinal datasets of HVM also limited further verifications of our findings, and these findings require further clinical tests to launch their applications.

Ecological and network analyses identify four microbial species with potential significance for the diagnosis/treatment of ulcerative colitis (UC)

BACKGROUND

Ulcerative colitis (UC) is one of the primary types of inflammatory bowel disease (IBD), the occurrence of which has been increasing worldwide. Although IBD is an intensively studied human microbiome-associated disease, research on Chinese populations remains relatively limited, particularly on the mucosal microbiome. The present study aimed to analyze the changes in the mucosal microbiome associated with UC from the perspectives of medical ecology and complex network analysis.

RESULTS

In total, 56 mucosal microbiome samples were collected from 28 Chinese UC patients and their healthy family partners, followed by amplicon sequencing. Based on sequencing data, we analyzed species diversity, shared species, and inter-species interactions at the whole community, main phyla, and core/periphery species levels. We identified four opportunistic "pathogens" (i.e., Clostridium tertium, Odoribacter splanchnicus, Ruminococcus gnavus, and Flavonifractor plautii) with potential significance for the diagnosis and treatment of UC, which were inhibited in healthy individuals, but unrestricted in the UC patients. In addition, we also discovered in this study: (i) The positive-to-negative links (P/N) ratio, which measures the balance of species interactions or inhibition effects in microbiome networks, was significantly higher in UC patients, indicating loss of inhibition against potentially opportunistic "pathogens" associated with dysbiosis. (ii) Previous studies have reported conflicting evidence regarding species diversity and composition between UC patients and healthy controls. Here, significant differences were found at the major phylum and core/periphery scales, but not at the whole community level. Thus, we argue that the paradoxical results found in existing studies are due to the scale effect.

CONCLUSIONS

Our results reveal changes in the ecology and network structure of the gut mucosal microbiome that might be associated with UC, and these changes might provide potential therapeutic mechanisms of UC. The four opportunistic pathogens that were identified in the present study deserve further investigation in future studies.

Host microbiota can facilitate pathogen infection

Animals live in symbiosis with numerous microbe species. While some can protect hosts from infection and benefit host health, components of the microbiota or changes to the microbial landscape have the potential to facilitate infections and worsen disease severity. Pathogens and pathobionts can exploit microbiota metabolites, or can take advantage of a depletion in host defences and changing conditions within a host, to cause opportunistic infection. The microbiota might also favour a more virulent evolutionary trajectory for invading pathogens. In this review, we consider the ways in which a host microbiota contributes to infectious disease throughout the host's life and potentially across evolutionary time. We further discuss the implications of these negative outcomes for microbiota manipulation and engineering in disease management.

Population-level diversity-disease relationship (p-DDR) in the human microbiome associated diseases

Diversity-disease relationship (DDR) is a de facto standard analysis in the studies of human microbiome associated diseases (MADs). For example, the species richness or Shannon entropy are routinely compared between the healthy and diseased groups. Nevertheless, the basic scale of the standard diversity analysis is individual subject rather than a cohort or population because the diversity is computed for individual samples, not for the group. Here we aim to expand the current DDR study from individual focus to population level, which can offer important insights for understanding the epidemiology of MADs. We analyzed the diversity-disease relationship at cohort scale based on a collection of 23 datasets covering the major human MADs. Methodologically, we harness the power of a recent extension to the classic species-area relationship (SAR), i.e., the diversity-area relationship (DAR), to achieve the expansion from individual DDR to inter-subject diversity scaling analysis. Specifically, we apply the DAR analysis to estimate and compare the potentially maximal accrual diversities of the healthy and diseases groups, as well as the inter-subject diversity scaling parameters and the individual-to-population diversity ratios. It was shown that, except for the potential diversity (D_max) at the cohort level in approximately 5.4% cases of MADs, DAR parameters displayed no significant differences between healthy and diseased treatments. That is, the DAR parameters are rather resilient against MADs, except for the potential diversity in some diseases. We compared our population-level DDR with the existing individual-level DDR patterns and proposed a hypothesis to interpret their differences.

The Microbiota-Gut-Brain Axis in Mental Health and Medication Response: Parsing Directionality and Causality

There is increasing evidence for the role of the microbiome in various mental health disorders. Moreover, there has been a growing understanding of the importance of the microbiome in mediating both the efficacy and side effects of various medications, including psychotropics. In this issue, Tomizawa and colleagues report on the effect of psychotropic drugs on the gut microbiome of 40 patients with depression and/or anxiety disorders. In their longitudinal cohort, the authors find that antipsychotics, but not anxiolytics, decrease microbiome alpha diversity. They further find that antipsychotics dosage was negatively correlated with alpha diversity in these patients. The health consequences of these microbiome alterations remain to be fully understood. In this commentary, we will discuss such findings through the lens of several recent studies on the microbiota-gut-brain axis. We also use the paper as a backdrop to discuss directionality and, by extension, causality in relation to microbiota-gut-brain-brain signaling.

Mining microbes for mental health: Determining the role of microbial metabolic pathways in human brain health and disease

There is increasing knowledge regarding the role of the microbiome in modulating the brain and behaviour. Indeed, the actions of microbial metabolites are key for appropriate gut-brain communication in humans. Among these metabolites, short-chain fatty acids, tryptophan, and bile acid metabolites/pathways show strong preclinical evidence for involvement in various aspects of brain function and behaviour. With the identification of neuroactive gut-brain modules, new predictive tools can be applied to existing datasets. We identified 278 studies relating to the human microbiota-gut-brain axis which included sequencing data. This spanned across psychiatric and neurological disorders with a small number also focused on normal behavioural development. With a consistent bioinformatics pipeline, thirty-five of these datasets were reanalysed from publicly available raw sequencing files and the remainder summarised and collated. Among the reanalysed studies, we uncovered evidence of disease-related alterations in microbial metabolic pathways in Alzheimer's Disease, schizophrenia, anxiety and depression. Amongst studies that could not be reanalysed, many sequencing and technical limitations hindered the discovery of specific biomarkers of microbes or metabolites conserved across studies. Future studies are warranted to confirm our findings. We also propose guidelines for future human microbiome analysis to increase reproducibility and consistency within the field.

Effects of Psychotropics on the Microbiome in Patients With Depression and Anxiety: Considerations in a Naturalistic Clinical Setting

BACKGROUND

The antibacterial effects of psychotropics may be part of their pharmacological effects when treating depression. However, limited studies have focused on gut microbiota in relation to prescribed medication.

METHOD

We longitudinally investigated the relationship between patients' prescribed medications and intestinal bacterial diversity in a naturalistic treatment course for patients with major depressive disorders and anxiety disorders. Patients were recruited and their stool was collected at 3 time points during their usual psychiatric treatments. Gut microbiota were analyzed using 16S rRNA gene sequencing. We examined the impact of psychotropics (i.e., antidepressants, anxiolytics, antipsychotics) on their gut microbial diversity and functions.

RESULTS

We collected 246 stool samples from 40 patients. Despite no differences in microbial diversity between medication groups at the baseline, over the course of treatment, phylogenic diversity whole-tree diversity decreased in patients on antipsychotics compared with patients without (P = .027), and beta diversity followed this trend. Based on a fixed-effect model, antipsychotics predicted microbial diversity; the higher doses correlated with less diversity based on the Shannon index and phylogenic diversity whole tree (estimate = -0.00254, SE = 0.000595, P < .0001; estimate = -0.02644, SE = 0.00833, P = .002, respectively).

CONCLUSION

Antipsychotics may play a role in decreasing the alpha diversity of the gut microbiome among patients with depression and anxiety, and our results indicate a relationship with medication dosage. Future studies are warranted and should consider patients' types and doses of antipsychotics in order to further elucidate the mechanisms of gut-brain interactions in psychiatric disorders.

A Metagenome-Wide Association Study of Gut Microbiome in Patients With Multiple Sclerosis Revealed Novel Disease Pathology

While microbiome plays key roles in the etiology of multiple sclerosis (MS), its mechanism remains elusive. Here, we conducted a comprehensive metagenome-wide association study (MWAS) of the relapsing-remitting MS gut microbiome (n_case = 26, n_control = 77) in the Japanese population, by using whole-genome shotgun sequencing. Our MWAS consisted of three major bioinformatic analytic pipelines (phylogenetic analysis, functional gene analysis, and pathway analysis). Phylogenetic case-control association tests showed discrepancies of eight clades, most of which were related to the immune system (false discovery rate [FDR] < 0.10; e.g., Erysipelatoclostridium_sp. and Gemella morbillorum). Gene association tests found an increased abundance of one putative dehydrogenase gene (Clo1100_2356) and one ABC transporter related gene (Mahau_1952) in the MS metagenome compared with controls (FDR < 0.1). Molecular pathway analysis of the microbiome gene case-control comparisons identified enrichment of multiple Gene Ontology terms, with the most significant enrichment on cell outer membrane (P = 1.5 × 10⁻⁷). Interaction between the metagenome and host genome was identified by comparing biological pathway enrichment between the MS MWAS and the MS genome-wide association study (GWAS) results (i.e., MWAS-GWAS interaction). No apparent discrepancies in alpha or beta diversities of metagenome were found between MS cases and controls. Our shotgun sequencing-based MWAS highlights novel characteristics of the MS gut microbiome and its interaction with host genome, which contributes to our understanding of the microbiome’s role in MS pathophysiology.

Heterogeneity-disease relationship in the human microbiome-associated diseases

Space is a critical and also challenging frontier in human microbiome research. It has been found that lack of consideration of scales beyond individual and ignoring of microbe dispersal are two crucial roadblocks in preventing deep understanding of the spatial heterogeneity of human microbiome. Assessing and interpreting the heterogeneity and dispersal in microbiomes explicitly are particularly challenging, but implicit approaches such as Taylor's power law (TPL) can be rather effective. Based on TPL, which achieved a rare status of ecological laws, we introduce a general methodology for characterizing the spatial heterogeneity of microbiome (i.e. characterization of microbial spatial distribution) and further apply it for investigating the heterogeneity-disease relationship (HDR) via analyzing a big dataset of 26 MAD (microbiome-associated disease) studies covering nearly all high-profile MADs including obesity, diabetes and gout. It was found that in majority of the MAD cases, the microbiome was sufficiently resilient to endure the disease disturbances. Specifically, in ∼10-16% cases, disease effects were significant-the healthy and diseased cohorts exhibited statistically significant differences in the TPL heterogeneity parameters. We further compared HDR with classic diversity-disease relationship (DDR) and explained their mechanistic differences. Both HDR and DDR cross-verified remarkable resilience of the human microbiomes against MADs.

Microbiota profile and efficacy of probiotic supplementation on laxation in adults affected by Prader-Willi Syndrome: A randomized, double-blind, crossover trial

Background

Probiotics may provide a benefit for adults with Prader‐Willi syndrome (PWS) experiencing constipation. The primary aim was to determine if Bifidobacterium animalis ssp. lactis B94 (B. lactis B94) improves stool frequency, with secondary aims of stool form and gastrointestinal symptoms. Exploratory aims included diet quality and fecal microbiota composition.

Methods

Following a 4‐week baseline, 25 adults with PWS were randomized to consume B. lactis B94 by capsule (15 billion) or placebo for 4 weeks, followed by 4‐week washout in a double‐blind, crossover design. Stool frequency and Bristol Stool Form (BSF) were assessed daily, and Gastrointestinal Symptom Rating Scale (GSRS) and dietary intake (7‐days food records), per period. Fecal microbiota per period was analyzed using 16S rRNA gene amplicon sequencing and taxa of interest by qPCR (n = 24).

Results

No adverse events were reported. Stool frequency at baseline (n = 25; 2.0 ± 0.1 stools/day), GSRS syndromes, and microbiota composition did not differ with the probiotic intervention overall; however, a delayed, carry‐over effect on BSF types 6 and 7 was seen. Diet quality by HEI‐2015 was 65.4 ± 8.5.

Conclusion

In adults with PWS, B. lactis B94 exhibited little effect on laxation over 4 weeks; however, further research is needed.

Comparison of Gut Microbiota Diversity and Predicted Functions Between Healthy and Diseased Captive Rana dybowskii

The gut microbiota plays a key role in host health, and disruptions to gut bacterial homeostasis can cause disease. However, the effect of disease on gut microbiota assembly remains unclear and gut microbiota-based predictions of health status is a promising yet poorly established field. Using Illumina high-throughput sequencing technology, we compared the gut microbiota between healthy (HA and HB) and diarrhoeic (DS) Rana dybowskii groups and analyzed the functional profiles through a phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) analysis. In addition, we estimated the correlation between gut microbiota structures and predicted the functional compositions. The results showed significant differences in the phylogenetic diversity (Pd), Shannon, and observed richness (Sobs) indices between the DS and HB groups, with significant differences observed in the gut microbiota composition between the DS group and the HA and HB groups. Linear discriminant analysis (LDA) effect size (LEfSe) results revealed that Proteobacteria were significantly enriched in the DS group; Bacteroidetes were significantly enriched in the HA and HB groups; and Aeromonas, Citrobacter, Enterococcus, Hafnia-Obesumbacterium, Morganella, Lactococcus, Providencia, Vagococcus, and Staphylococcus were significantly enriched in the DS group. Venn diagrams revealed that there were many more unique genera in the DS group than the HA and HB groups. Among 102 sensitive species selected using the indicator method, 33 indicated a healthy status and 69 (e.g., Acinetobacter, Aeromonas, Legionella, Morganella, Proteus, Providencia, Staphylococcus, and Vagococcus) indicated a diseased status. There was a significant and positive association between the composition and functional composition of the gut microbiota, thus indicating low functional redundancy of the frog gut bacterial community. Rana dybowskii disease was associated with changes in the gut microbiota, which subsequently disrupted bacterial-mediated functions. The results of this study can aid in revealing the effect of the R. dybowskii gut microbiota on host health and provide a basis for elucidating the mechanism of the occurrence of R. dybowskii disease.

Critical Network Structures and Medical Ecology Mechanisms Underlying Human Microbiome-Associated Diseases

A fundamental problem in studies on human microbiome-associated diseases (MADs) is to understand the relationships between microbiome structures and health status of hosts. For example, species diversity metrics have been routinely evaluated in virtually all studies on MADs, yet a recent meta-analysis revealed that, in only approximately one-third of the cases, diversity and diseases were related. In this study, we ask whether Hubbell's neutral theory (supplemented with the normalized stochasticity ratio [NSR]) or critical microbiome network structures may offer better alternatives. Whereas neutral theory and NSR focus on stochastic processes, we use core/periphery and high-salience skeleton networks to evaluate deterministic, asymmetrical niche effects, assuming that all species or their interactions were not “born” equal and focusing on non-neutral, critical network structures. We found that properties of critical network structures are more indicative of disease effects. Finally, seven findings (mechanisms, interpretations, and postulations) regarding medical ecology mechanisms underlying MADs were summarized.

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Seven findings (mechanisms/interpretations/postulations) of medical ecology proposed

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Critical network structures more indicative of disease effects than ecology metrics

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One-third seems ceiling of diversity-disease relations, half to two-thirds of network structures

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Super resilience (unexplained one-third to half gap) is likely attributed to host genome