Guild-based analysis for understanding gut microbiome in human health and diseases

Gut Microbiota and Phenotypic Changes Induced by Ablation of Liver- and Intestinal-Type Fatty Acid-Binding Proteins

Intestinal fatty acid-binding protein (IFABP; FABP2) and liver fatty acid-binding protein (LFABP; FABP1) are small intracellular lipid-binding proteins. Deficiency of either of these proteins in mice leads to differential changes in intestinal lipid transport and metabolism, and to markedly divergent changes in whole-body energy homeostasis. The gut microbiota has been reported to play a pivotal role in metabolic process in the host and can be affected by host genetic factors. Here, we examined the phenotypes of wild-type (WT), LFABP^-/-, and IFABP^-/- mice before and after high-fat diet (HFD) feeding and applied 16S rRNA gene V4 sequencing to explore guild-level changes in the gut microbiota and their associations with the phenotypes. The results show that, compared with WT and IFABP^-/- mice, LFABP^-/- mice gained more weight, had longer intestinal transit time, less fecal output, and more guilds containing bacteria associated with obesity, such as members in family Desulfovibrionaceae. By contrast, IFABP^-/- mice gained the least weight, had the shortest intestinal transit time, the most fecal output, and the highest abundance of potentially beneficial guilds such as those including members from Akkermansia, Lactobacillus, and Bifidobacterium. Twelve out of the eighteen genotype-related bacterial guilds were associated with body weight. Interestingly, compared with WT mice, the levels of short-chain fatty acids in feces were significantly higher in LFABP^-/- and IFABP^-/- mice under both diets. Collectively, these studies show that the ablation of LFABP or IFABP induced marked changes in the gut microbiota, and these were associated with HFD-induced phenotypic changes in these mice.

Daily Exposure to a Cranberry Polyphenol Oral Rinse Alters the Oral Microbiome but Not Taste Perception in PROP Taster Status Classified Individuals

Diet and salivary proteins influence the composition of the oral microbiome, and recent data suggest that TAS2R38 bitter taste genetics may also play a role. We investigated the effects of daily exposure to a cranberry polyphenol oral rinse on taste perception, salivary proteins, and oral microbiota. 6-n-Propylthiouracil (PROP) super-tasters (ST, n = 10) and non-tasters (NT, n = 10) rinsed with 30 mL of 0.75 g/L cranberry polyphenol extract (CPE) in spring water, twice daily for 11 days while consuming their habitual diets. The 16S rRNA gene sequencing showed that the NT oral microbiome composition was different than that of STs at baseline (p = 0.012) but not after the intervention (p = 0.525). Principal coordinates analysis using unweighted UniFrac distance showed that CPE modified microbiome composition in NTs (p = 0.023) but not in STs (p = 0.096). The intervention also altered specific salivary protein levels (α-amylase, MUC-5B, and selected S-type Cystatins) with no changes in sensory perception. Correlation networks between oral microbiota, salivary proteins, and sensory ratings showed that the ST microbiome had a more complex relationship with salivary proteins, particularly proline-rich proteins, than that in NTs. These findings show that CPE modulated the oral microbiome of NTs to be similar to that of STs, which could have implications for oral health.

A transmissible γδ intraepithelial lymphocyte hyperproliferative phenotype is associated with the intestinal microbiota and confers protection against acute infection

Intraepithelial lymphocytes expressing the γδ T cell receptor (γδ IELs) serve as a first line of defense against luminal microbes. Although the presence of an intact microbiota is dispensable for γδ IEL development, several microbial factors contribute to the maintenance of this sentinel population. However, whether specific commensals influence population of the γδ IEL compartment under homeostatic conditions has yet to be determined. We identified a novel γδ IEL hyperproliferative phenotype that arises early in life and is characterized by expansion of multiple Vγ subsets. Horizontal transfer of this hyperproliferative phenotype to mice harboring a phenotypically normal γδ IEL compartment was prevented following antibiotic treatment, thus demonstrating that the microbiota is both necessary and sufficient for the observed increase in γδ IELs. Further, we identified two guilds of small intestinal or fecal bacteria represented by 12 amplicon sequence variants (ASV) that are strongly associated with γδ IEL expansion. Using intravital microscopy, we find that hyperproliferative γδ IELs also exhibit increased migratory behavior leading to enhanced protection against bacterial infection. These findings reveal that transfer of a specific group of commensals can regulate γδ IEL homeostasis and immune surveillance, which may provide a novel means to reinforce the epithelial barrier.

Network-based approaches for the investigation of microbial community structure and function using metagenomics-based data

Network-based approaches offer a powerful framework to evaluate microbial community organization and function as it relates to a variety of environmental processes. Emerging studies are exploring network theory as a method for data integration that is likely to be critical for the integration of 'omics' data using systems biology approaches. Intricacies of network theory and methodological and computational complexities in network construction, however, impede the use of these tools for translational science. We provide a perspective on the methods of network construction, interpretation and emerging uses for these techniques in understanding host-microbiota interactions.

Personal diet-microbiota interactions and weight loss

The aim of this review is to provide an overview of how person-specific interactions between diet and the gut microbiota could play a role in affecting diet-induced weight loss responses. The highly person-specific gut microbiota, which is shaped by our diet, secretes digestive enzymes and molecules that affect digestion in the colon. Therefore, weight loss responses could in part depend on personal colonic fermentation responses, which affect energy extraction of food and production of microbial metabolites, such as short-chain fatty acids (SCFAs), which exert various effects on host metabolism. Colonic fermentation is the net result of the complex interplay between availability of dietary substrates, the functional capacity of the gut microbiome and environmental (abiotic) factors in the gut such as pH and transit time. While animal studies have demonstrated that the gut microbiota can causally affect obesity, causal and mechanistic evidence from human studies is still largely lacking. However, recent human studies have proposed that the baseline gut microbiota composition may predict diet-induced weight loss-responses. In particular, individuals characterised by high relative abundance of Prevotella have been found to lose more weight on diets rich in dietary fibre compared to individuals with low Prevotella abundance. Although harnessing of personal diet-microbiota interactions holds promise for more personalised nutrition and obesity management strategies to improve human health, there is currently insufficient evidence to unequivocally link the gut microbiota and weight loss in human subjects. To move the field forward, a greater understanding of the mechanistic underpinnings of personal diet-microbiota interactions is needed.

Using Community Ecology Theory and Computational Microbiome Methods To Study Human Milk as a Biological System

Human milk is a complex and dynamic biological system that has evolved to optimally nourish and protect human infants. Yet, according to a recent priority-setting review, “our current understanding of human milk composition and its individual components and their functions fails to fully recognize the importance of the chronobiology and systems biology of human milk in the context of milk synthesis, optimal timing and duration of feeding, and period of lactation” (P. Christian et al., Am J Clin Nutr 113:1063–1072, 2021, https://doi.org/10.1093/ajcn/nqab075). We attribute this critical knowledge gap to three major reasons as follows. (i) Studies have typically examined each subsystem of the mother-milk-infant “triad” in isolation and often focus on a single element or component (e.g., maternal lactation physiology or milk microbiome or milk oligosaccharides or infant microbiome or infant gut physiology). This undermines our ability to develop comprehensive representations of the interactions between these elements and study their response to external perturbations. (ii) Multiomics studies are often cross-sectional, presenting a snapshot of milk composition, largely ignoring the temporal variability during lactation. The lack of temporal resolution precludes the characterization and inference of robust interactions between the dynamic subsystems of the triad. (iii) We lack computational methods to represent and decipher the complex ecosystem of the mother-milk-infant triad and its environment. In this review, we advocate for longitudinal multiomics data collection and demonstrate how incorporating knowledge gleaned from microbial community ecology and computational methods developed for microbiome research can serve as an anchor to advance the study of human milk and its many components as a “system within a system.”

Microbiota and Metabolite Profiling as Markers of Mood Disorders: A Cross-Sectional Study in Obese Patients

Obesity is associated with an increased risk of several neurological and psychiatric diseases, but few studies report the contribution of biological features in the occurrence of mood disorders in obese patients. The aim of the study is to evaluate the potential links between serum metabolomics and gut microbiome, and mood disturbances in a cohort of obese patients. Psychological, biological characteristics and nutritional habits were evaluated in 94 obese subjects from the Food4Gut study stratified according to their mood score assessed by the Positive and Negative Affect Schedule (PANAS). The fecal gut microbiota and plasma non-targeted metabolomics were analysed. Obese subjects with increased negative mood display elevated levels of Coprococcus as well as decreased levels of Sutterella and Lactobacillus. Serum metabolite profile analysis reveals in these subjects altered levels of several amino acid-derived metabolites, such as an increased level of L-histidine and a decreased in phenylacetylglutamine, linked to altered gut microbiota composition and function rather than to differences in dietary amino acid intake. Regarding clinical profile, we did not observe any differences between both groups. Our results reveal new microbiota-derived metabolites that characterize the alterations of mood in obese subjects, thereby allowing to propose new targets to tackle mood disturbances in this context. Food4gut, clinicaltrial.gov: NCT03852069.

On the Verge of a Catastrophic Collapse? The Need for a Multi-Ecosystem Approach to Microbiome Studies

While the COVID-19 pandemic has led to increased focus on pathogenic microbes that cross the animal-human species barrier, calls to include non-pathogenic interactions in our perspective on public health are gaining traction in the academic community. Over generations, the diversity of the human gut microbiota is being challenged by external perturbations and reduced acquisition of symbiotic species throughout life. When such reduced diversity concerns not only the microbial species, but also the higher taxonomic levels and even the guild level, adequate compensation for possible losses may be lacking. Shifts from a high-abundance to a low-abundance state, known as a tipping point, may result in simultaneous shifts in covarying taxa and ultimately to a catastrophic collapse in which the ecosystem abruptly and possibly irreversibly shifts to an alternative state. Here, we propose that co-occurrence patterns within and between microbial communities across human, animal, soil, water, and other environmental domains should be studied in light of such critical transitions. Improved mechanistic understanding of factors that shape structure and function is needed to understand whether interventions can sustainably remodel disease-prone microbiota compositions to robust and resilient healthy microbiota. Prerequisites for a rational approach are a better understanding of the microbial interaction network, both within and inter-domain, as well as the identification of early warning signs for a catastrophic collapse, warranting a timely response for intervention. We should not forget that mutualism and pathogenicity are two sides of the same coin. Building upon the planetary health concept, we argue that microbiome research should include system level approaches to conserve ecosystem resilience. HIGHLIGHTS 1. Non-pathogenic interactions between ecosystems play a key role in maintaining health. 2. The human gut microbiome may be on the verge of a catastrophic collapse. 3. Research should identify keystone taxa and guilds that interconnect different domains. 4. We should not forget that mutualism and pathogenicity are two sides of the same coin.

The Effects of Green Tea on Diabetes and Gut Microbiome in db/db Mice: Studies with Tea Extracts vs. Tea Powder

Green tea extracts and tea catechins have been shown to prevent or alleviate diabetes. The present study tests the hypothesis that green tea leaves in powder form (GTP), which also contain fiber and other water non-extractable materials, are more effective than the corresponding green tea extracts (GTE) in impeding the development of diabetes in db/db mice. Female db/db mice were treated with a diet containing 1% of GTE, 2% of GTE, 2% of GTP (with the same catechin content as 1% GTE) or 1% GTP. The 1% GTE group had lower food intake, water consumption, body weight and fasting blood glucose levels than the control group, while 2% GTP did not have any significant effect. Dietary 1% GTE also preserved β-cell insulin secretion. However, 1% GTP increased food intake, water consumption and blood glucose levels. Microbiome analysis with 16S rRNA gene V4 sequencing showed that the gut microbiota was modified by GTE and GTP, and a few bacterial guilds were associated with blood glucose levels. In the Random Forest regression model, the leading predictor of metabolic outcome was food consumption, followed by changes in some bacterial guilds. The results illustrate the importance of food consumption and gut microbiota in affecting the progression of diabetes.

Gut and Vagina Microbiota Associated With Estrus Return of Weaning Sows and Its Correlation With the Changes in Serum Metabolites

More and more studies have indicated that gut microbiota takes part in the biosynthesis and metabolism of sex hormones. Inversely, sex hormones influence the composition of gut microbiota. However, whether microbiota in the gut and vagina is associated with estrus return of weaning sows is largely unknown. Here, using 16S rRNA gene sequencing in 158 fecal and 50 vaginal samples, we reported the shifts in the gut and vaginal microbiota between normal return and non-return sows. In fecal samples, Lactobacillus and S24-7 were enriched in normal return sows, while Streptococcus luteciae, Lachnospiraceae, Clostridium, and Mogibacterium had higher abundance in non-return sows. In vaginal swabs, the operational taxonomic units (OTUs) annotated to Clostridiales, Ruminoccaceae, and Oscillospira were enriched in normal return sows, while those OTUs annotated to Campylobacter, Anaerococcus, Parvimonas, Finegoldia, and Dorea had higher abundances in non-return sows. Co-abundance group (CAG) analysis repeated the identification of the bacterial taxa associated with the estrus return of weaning sows. The predicted functional capacities in both gut and vaginal microbiome were changed between normal return and non-return sows. Serum metabolome profiles were determined by non-targeted metabolome analysis in seven normal return and six non-return sows. The metabolite features having higher abundance in normal return sows were enriched in the pathways Steroid hormone biosynthesis, Starch and sucrose metabolism, Galactose metabolism, and Vitamin B6 metabolism, while the metabolite features belonging to organic acids and derivatives, indoles and derivatives, sulfoxides, and lignans and neolignans had significantly higher abundance in non-return sows. Correlation analysis found that the changes in gut microbiota were associated with the shifts of serum metabolites and suggested that certain bacteria might affect estrus return of weaning sow through serum metabolites. These findings may provide new insights for understanding the role of the gut and vaginal microbiota in sow return to estrus after weaning.

Next Generation Microbiome Research: Identification of Keystone Species in the Metabolic Regulation of Host-Gut Microbiota Interplay

The community of the diverse microorganisms residing in the gastrointestinal tract, known as the gut microbiota, is exceedingly being studied for its impact on health and disease. This community plays a major role in nutrient metabolism, maintenance of the intestinal epithelial barrier but also in local and systemic immunomodulation. A dysbiosis of the gut microbiota, characterized by an unbalanced microbial ecology, often leads to a loss of essential functions that may be associated with proinflammatory conditions. Specifically, some key microbes that are depleted in dysbiotic ecosystems, called keystone species, carry unique functions that are essential for the balance of the microbiota. In this review, we discuss current understanding of reported keystone species and their proposed functions in health. We also elaborate on current and future bioinformatics tools needed to identify missing functions in the gut carried by keystone species. We propose that the identification of such keystone species functions is a major step for the understanding of microbiome dynamics in disease and toward the development of microbiome-based therapeutics.

Elucidating the Beta-Diversity of the Microbiome: from Global Alignment to Local Alignment

Quantitative comparison among microbiomes can link microbial beta-diversity to environmental features, thus enabling prediction of ecosystem properties or dissection of host-microbiome interaction. However, to compute beta-diversity, current methods mainly employ the entire community profiles of taxa or functions, which can miss the subtle differences caused by low-abundance community members that may play crucial roles in the properties of interest. In this work, I review the distance metrics and search engines that we developed to match microbiomes at a large scale based on whole-community-level similarities, as well as their limitations in tackling the microbiome changes caused by less abundant community features. Then I propose the concept of microbiome "local alignment," including an algorithm to measure microbiome similarity on specific fractions of biodiversity and an indexing strategy for rapidly fetching microbiome local-alignment matches from the data repository.

Elucidating the Beta-Diversity of the Microbiome: from Global Alignment to Local Alignment

Quantitative comparison among microbiomes can link microbial beta-diversity to environmental features, thus enabling prediction of ecosystem properties or dissection of host-microbiome interaction. However, to compute beta-diversity, current methods mainly employ the entire community profiles of taxa or functions, which can miss the subtle differences caused by low-abundance community members that may play crucial roles in the properties of interest. In this work, I review the distance metrics and search engines that we developed to match microbiomes at a large scale based on whole-community-level similarities, as well as their limitations in tackling the microbiome changes caused by less abundant community features. Then I propose the concept of microbiome “local alignment,” including an algorithm to measure microbiome similarity on specific fractions of biodiversity and an indexing strategy for rapidly fetching microbiome local-alignment matches from the data repository.

The role of precision nutrition in the modulation of microbial composition and function in people with inflammatory bowel disease

Inflammatory bowel diseases, principally Crohn's disease and ulcerative colitis, are multifactorial chronic conditions. Alterations in gut microbial patterns partly affect disease onset and severity. Moreover, the evolution of dietary patterns, and their effect on gut microbial behaviour, have been shown to play a crucial role in disease processes. This Viewpoint reviews the role of dietary patterns, their influence on the structure and function of the gut microbiome, and their effects on inflammation and immunity in individuals with inflammatory bowel disease. We also discuss innovative dietary intervention strategies, summarise findings that have been used to develop recommendations for clinical practice, and provide suggestions for the design of future studies for development of precision nutrition in patients with inflammatory bowel disease.

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.

Towards multi-label classification: Next step of machine learning for microbiome research

Machine learning (ML) has been widely used in microbiome research for biomarker selection and disease prediction. By training microbial profiles of samples from patients and healthy controls, ML classifiers constructs data models by community features that highly correlated with the target diseases, so as to determine the status of new samples. To clearly understand the host-microbe interaction of specific diseases, previous studies always focused on well-designed cohorts, in which each sample was exactly labeled by a single status type. However, in fact an individual may be associated with multiple diseases simultaneously, which introduce additional variations on microbial patterns that interferes the status detection. More importantly, comorbidities or complications can be missed by regular ML models, limiting the practical application of microbiome techniques. In this review, we summarize the typical ML approaches of single-label classification for microbiome research, and demonstrate their limitations in multi-label disease detection using a real dataset. Then we prospect a further step of ML towards multi-label classification that potentially solves the aforementioned problem, including a series of promising strategies and key technical issues for applying multi-label classification in microbiome-based studies.

High-Fiber Diet or Combined With Acarbose Alleviates Heterogeneous Phenotypes of Polycystic Ovary Syndrome by Regulating Gut Microbiota

OBJECTIVE

Gut microbial dysbiosis is associated with high heterogeneity of polycystic ovary syndrome (PCOS); however, studies about gut microbiota targeted clinical intervention in PCOS are limited. Our study aimed to evaluate the effects of high-fiber diet or combined with acarbose on the clinical phenotypes of PCOS, focusing on the possible influence of gut microbiota in this process.

METHODS

Twenty-five patients with PCOS were recruited and randomly divided into two groups, W group (n = 14) received the WTP diet (a high-fiber diet composed of whole grains, traditional Chinese medicinal foods, and prebiotics), and A group (n = 11) received the WTP diet combined with acarbose. The follow-up time was 12 weeks. The sex hormonal and glycolipid metabolic parameters, inflammatory factors, brain-gut peptides, and alteration of gut microbiota were evaluated.

RESULTS

The PCOS clinical phenotypes, inflammatory state, and brain-gut peptides secretion were all alleviated in both groups, while the hyperandrogenism, insulin resistance, and brain-gut peptides secretion were better improved in the A group. Alpha and beta diversities were altered more significantly in the A group. Amplicon sequence variants (ASVs) were clustered into 14 co-abundant groups (CAGs) as potential functional groups that may respond to the intervention. The CAGs predominantly comprised of Bifidobacterium and Lactobacillus were more enriched, while the CAGs predominantly comprised of Bacteroides vulgatus, Alistipes, Blautia, Lachnospira, and Roseburia were more inhibited in the A group than in W group. Moreover, the CAGs enriched in the A group had a stronger negative correlation with the luteinizing hormone (LH)/follicle-stimulating hormone (FSH) ratio, testosterone, homeostasis model assessment-insulin resistance (HOMA-IR), α-1-acid glycoprotein (α-AGP), and leptin, and positive correlation with adiponectin and spexin, while the CAGs inhibited showed an opposite trend.

CONCLUSIONS

High-fiber diet could alleviate the chronic metabolic inflammation, reproductive function, and brain-gut peptides secretion of patients with PCOS, and high-fiber diet combined with acarbose could better improve the PCOS clinical phenotypes. The remodeling of gut microbiota by our intervention may play an important role in these improvements.

CLINICAL TRIAL REGISTRATION

http://www.chictr.org.cn/showproj.aspx?proj=4500, ChiCTR-TRC-14005075.