Host Genotype and Gut Microbiome Modulate Insulin Secretion and Diet-Induced Metabolic Phenotypes

Alteration and the Function of Intestinal Microbiota in High-Fat-Diet- or Genetics-Induced Lipid Accumulation

Diet and host genetics influence the composition of intestinal microbiota, yet few studies have compared the function of intestinal microbiota in the diet- or genotype-induced lipid deposition, which limits our understanding of the role of intestinal bacteria in metabolic disorders. The lipid accumulation in wild-type zebrafish fed with control (CON) or high-fat (HF) diet and two gene-knockout zebrafish lines (cpt1b^–/– or pparab^–/–) fed with control diet was measured after a 4-week feeding experiment. The intestinal microbiota composition of these groups was investigated using 16S ribosomal RNA (rRNA) gene sequencing (DNA-based) and 16S rRNA sequencing (RNA-based). The HF diet or deficiency of two genes induced more weight gain and higher triglyceride content in the liver compared with their control group. 16S rRNA gene sequencing (DNA-based) indicated the decreased abundance of Proteobacteria in the HF group compared with CON, but there was no significant difference in bacterial α diversity among treatments. 16S rRNA sequencing (RNA-based) confirmed the decreased abundance of Proteobacteria and the bacterial α diversity in the HF group compared with CON. Deficiency of cpt1b or pparab showed less change in microbiota composition compared with their wild-type group. Intestinal microbiota of each group was transferred to germ-free zebrafish, and the quantification of Nile red staining indicated that the intestinal microbiota of the HF group induced more lipid accumulation compared with CON, whereas intestinal microbiota of cpt1b^–/– and pparab^–/– zebrafish did not. The results showed that RNA-based bacterial sequencing revealed more bacterial alteration than DNA-based bacterial sequencing. HF diet had a more dominant role in shaping gut microbiota composition to induce lipid accumulation compared with the gene-knockout of cpt1b or pparab in zebrafish, and the transplant of intestinal microbiota from HF-fed fish induced more lipid deposition in germ-free zebrafish. Together, these data suggested that a high-fat diet exerted a more dominant role over the deletion of cpt1b or pparab on the intestinal bacterial composition, which corresponded to lipid accumulation.

The role of genotype and diet in shaping gut microbiome in a genetic vitamin A deficient mouse model

Multifactors have been reported to affect the gut microbiome, including genotype, age, diet, and nutrition. However, few reports have investigated the relative capacity of different factors to shape the gut microbiome in a single study. Our design used a genetic vitamin A-deficient mouse model, the Rbp4^-/- mouse, feeding with the low vitamin A diets at different ages of initiation (4 or 7 weeks) for 28 days. Fecal samples were collected for bacterial profiling at seven time points after diet controlling. With RBP4 depletion, Akkermansia decreased and Bacteroides increased, whereas Desulfovibrio, Barnesiella, Clostridium_XlVa, and Lactobacillus fluctuated. The bacterial community swiftly adjusted with the vitamin A-deficient diet administration and gradually changed (e.g., decrease of Barnesiella and increase of Desulfovibrio). Age exerted a relatively weaker but long-last influence. At an earlier age to feed a vitamin A-deficient diet, a higher microbial dysbiosis index will be valued. Of note, the shaping effects of diet and age on the bacterial community varied with the difference of genotype, which might indicate a greater role of genotype than diet and age in shaping the gut microbiome.

Identification of sample mix-ups and mixtures in microbiome data in Diversity Outbred mice

In a Diversity Outbred mouse project with genotype data on 500 mice, including 297 with microbiome data, we identified three sets of sample mix-ups (two pairs and one trio) as well as at least 15 microbiome samples that appear to be mixtures of pairs of mice. The microbiome data consisted of shotgun sequencing reads from fecal DNA, used to characterize the gut microbial communities present in these mice. These sequence reads included sufficient reads derived from the host mouse to identify the individual. A number of microbiome samples appeared to contain a mixture of DNA from two mice. We describe a method for identifying sample mix-ups in such microbiome data, as well as a method for evaluating sample mixtures in this context.

Microbiota and epigenetics: promising therapeutic approaches?

The direct/indirect responsibility of the gut microbiome in disease induction in and outside the digestive tract is well studied. These results are usually from the overpopulation of certain species on the cost of others, interaction with beneficial microflora, interference with normal epigenetic control mechanisms, or suppression of the immune system. Consequently, it is theoretically possible to cure such disorders by rebalancing the microbiome inside our bodies. This can be achieved by changing the lifestyle pattern and diet or by supplementation with beneficial bacteria or their metabolites. Various approaches have been explored to manipulate the normal microbial inhabitants, including nutraceutical, supplementations with prebiotics, probiotics, postbiotics, synbiotics, and antibiotics, or through microbiome transplantation (fecal, skin, or vaginal microbiome transplantation). In the present review, the interaction between the microbiome and epigenetics and their role in disease induction is discussed. Possible future therapeutic approaches via the reestablishment of equilibrium in our internal micro-ecosystem are also highlighted.

The relationship between gut microbiota, short-chain fatty acids and type 2 diabetes mellitus: the possible role of dietary fibre

Gut microbiota and its metabolites have been shown to influence multiple physiological mechanisms related to human health. Among microbial metabolites, short-chain fatty acids (SCFA) are modulators of different metabolic pathways. On the other hand, several studies suggested that diet might influence gut microbiota composition and activity thus modulating the risk of metabolic disease, i.e. obesity, insulin resistance and type 2 diabetes. Among dietary component, dietary fibre may play a pivotal role by virtue of its prebiotic effect on fibre-fermenting bacteria, that may increase SCFA production. The aim of this review was to summarize and discuss current knowledge on the impact of dietary fibre as modulator of the relationship between glucose metabolism and microbiota composition in humans. More specifically, we analysed evidence from observational studies and randomized nutritional intervention investigating the relationship between gut microbiota, short-chain fatty acids and glucose metabolism. The possible mechanisms behind this association were also discussed.

Microbiota's role in health and diseases

The microbiome is a term that usually refers to the community of various microorganisms that inhabit/live inside human/animal bodies or on their skin. It forms a complex ecosystem that includes trillions of commensals, symbiotics, and even pathogenic microorganisms. The external environment, diet, and lifestyle are the major determinants influencing the microbiome's composition and vitality. Recent studies have indicated the tremendous influence of the microbiome on health and disease. Their number, constitution, variation, and viability are dynamic. All these elements are responsible for the induction, development, and treatment of many health disorders. Serious diseases such as cancer, metabolic disorders, cardiovascular diseases, and even psychological disorders such as schizophrenia are influenced directly or indirectly by microbiota. In addition, in the last few weeks, accumulating data about the link between COVID-19 and the microbiota were published. In the present work, the role of the microbiome in health and disease is discussed. A deep understanding of the exact role of microbiota in disease induction enables the prevention of diseases and the development of new therapeutic concepts for most diseases through the correction of diet and lifestyle. The present review brings together evidence from the most recent works and discusses suggested nutraceutical approaches for the management of COVID-19 pandemic.

Shared and distinctive features of the gut microbiome of C57BL/6 mice from different vendors and production sites, and in response to a new vivarium

Animal models play a critical role in establishing causal relationships between gut microbiota and disease. The laboratory mouse is widely used to study the role of microbes in various disorders; however, differences between mouse vendors, genetic lineages and husbandry protocols have been shown to contribute to variation in phenotypes and to non-reproducibility of experimental results. We sought to understand how gut microbiome profiles of mice vary by vendor, vendor production facility and health status upon receipt into an academic facility and how they change over 12 weeks in the new environment. C57BL/6 mice were sourced from two different production sites for each of three different vendors. Mice were shipped to an academic research vivarium, and fresh-catch stool samples were collected from mice immediately from the shipping box upon receipt, and again after 2, 6 and 12 weeks in the new facility. Substantial variation in bacterial proportional abundance was observed among mice from each vendor at the time of receipt, but shared microbes accounted for most sequence reads. Vendor-specific microbes were generally of low abundance. Microbial profiles of mice from all vendors exhibited shifts over time, highlighting the importance of environmental conditions on microbial dynamics. Our results emphasize the need for continued efforts to account for sources of variation in animal models and understand how they contribute to experimental reproducibility.

Hepatic transcriptional profile reveals the role of diet and genetic backgrounds on metabolic traits in female progenitor strains of the Collaborative Cross

Mice have provided critical mechanistic understandings of clinical traits underlying metabolic syndrome (MetSyn) and susceptibility to MetSyn in mice is known to vary among inbred strains. We investigated the diet- and strain-dependent effects on metabolic traits in the eight Collaborative Cross (CC) founder strains (A/J, C57BL/6J, 129S1/SvImJ, NOD/ShiLtJ, NZO/HILtJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ). Liver transcriptomics analysis showed that both atherogenic diet and host genetics have profound effects on the liver transcriptome, which may be related to differences in metabolic traits observed between strains. We found strain differences in circulating trimethylamine N-oxide (TMAO) concentration and liver triglyceride content, both of which are traits associated with metabolic diseases. Using a network approach, we identified a module of transcripts associated with TMAO and liver triglyceride content, which was enriched in functional pathways. Interrogation of the module related to metabolic traits identified NADPH oxidase 4 (Nox4), a gene for a key enzyme in the production of reactive oxygen species, which showed a strong association with plasma TMAO and liver triglyceride. Interestingly, Nox4 was identified as the highest expressed in the C57BL/6J and NZO/HILtJ strains and the lowest expressed in the CAST/EiJ strain. Based on these results, we suggest that there may be genetic variation in the contribution of Nox4 to the regulation of plasma TMAO and liver triglyceride content. In summary, we show that liver transcriptomic analysis identified diet- or strain-specific pathways for metabolic traits in the Collaborative Cross (CC) founder strains.

Global climate change, diet, and the complex relationship between human host and microbiome: Towards an integrated picture

Dietary changes can alter the human microbiome with potential detrimental consequences for health. Given that environment, health, and evolution are interconnected, we ask: Could diet-driven microbiome perturbations have consequences that extend beyond their immediate impact on human health? We address this question in the context of the urgent health challenges posed by global climate change. Drawing on recent studies, we propose that not only can diet-driven microbiome changes lead to dysbiosis, they can also shape life-history traits and fuel human evolution. We posit that dietary shifts prompt mismatched microbiome-host genetics configurations that modulate human longevity and reproductive success. These mismatches can also induce a heritable intra-holobiont stress response, which encourages the holobiont to re-establish equilibrium within the changed nutritional environment. Thus, while mismatches between climate change-related genetic and epigenetic configurations within the holobiont increase the risk and severity of diseases, they may also affect life-history traits and facilitate adaptive responses. These propositions form a framework that can help systematize and address climate-related dietary challenges for policy and health interventions.

The gut mycobiome of healthy mice is shaped by the environment and correlates with metabolic outcomes in response to diet

As an active interface between the host and their diet, the gut microbiota influences host metabolic adaptation; however, the contributions of fungi have been overlooked. Here, we investigate whether variations in gut mycobiome abundance and composition correlate with key features of host metabolism. We obtained animals from four commercial sources in parallel to test if differing starting mycobiomes can shape host adaptation in response to processed diets. We show that the gut mycobiome of healthy mice is shaped by the environment, including diet, and significantly correlates with metabolic outcomes. We demonstrate that exposure to processed diet leads to persistent differences in fungal communities that significantly associate with differential deposition of body mass in male mice compared to mice fed standardized diet. Fat deposition in the liver, transcriptional adaptation of metabolically active tissues and serum metabolic biomarker levels are linked with alterations in fungal community diversity and composition. Specifically, variation in fungi from the genera Thermomyces and Saccharomyces most strongly associate with metabolic disturbance and weight gain. These data suggest that host-microbe metabolic interactions may be influenced by variability in the mycobiome. This work highlights the potential significance of the gut mycobiome in health and has implications for human and experimental metabolic studies.

Age-Related Differences in the Gut Microbiome of Rhesus Macaques

Aging is a multifactorial process characterized by progressive changes in gut physiology and the intestinal mucosal immune system. These changes, along with alterations in lifestyle, diet, nutrition, inflammation and immune function alter both composition and stability of the gut microbiota. Given the impact of environmental influences on the gut microbiota, animal models are particularly useful in this field. To understand the relationship between the gut microbiota and aging in nonhuman primates, we collected fecal samples from 20 male and 20 female rhesus macaques (Macaca mulatta), across the natural macaque age range, for 16S rRNA gene analyses. Operational taxonomic units were then grouped together to summarize taxon abundance at different hierarchical levels of classification and alpha- and beta-diversity were calculated. There were no age or sex differences in alpha diversity. At the phylum level, relative abundance of Proteobacteria and Firmicutes and Firmicutes to Bacteriodetes ratio were different between age groups though significance disappeared after correction for multiple comparisons. At the class level, relative abundance of Firmicutes_Bacilli decreased and Proteobacteria_Alphaproteobacteria and Proteobacteria_Betaproteobacteria increased with each successively older group. Only differences in Firmicutes_Bacilli remained significant after correction for multiple comparisons. No sex differences were identified in relative abundances after correction for multiple comparisons. Our results are not surprising given the known impact of environmental factors on the gut microbiota.

Peripheral and central regulation of insulin by the intestine and microbiome

Blood glucose and insulin homeostasis is disrupted during the progression of type 2 diabetes. Insulin levels and action are regulated by both peripheral and central responses that involve the intestine and microbiome. The intestine and its microbiota process nutrients and generate molecules that influence blood glucose and insulin. Peripheral insulin regulation is regulated by gut-segment-dependent nutrient sensing and microbial factors such as short-chain fatty acids and bile acids that engage G-protein-coupled receptors. Innate immune sensing of gut-derived bacterial cell wall components and lipopolysaccharides also alter insulin homeostasis. These bacterial metabolites and postbiotics influence insulin secretion and insulin clearance in part by altering endocrine responses such as glucagon-like peptide-1. Gut-derived bacterial factors can promote inflammation and insulin resistance, but other postbiotics can be insulin sensitizers. In parallel, activation of small intestinal sirtuin 1 increases insulin sensitivity by reversing high fat-induced hypothalamic insulin resistance through a gut-brain neuronal axis, whereas high fat-feeding alters small intestinal microbiome and increases taurochenodeoxycholic acid in the plasma and the dorsal vagal complex to induce insulin resistance. In summary, emerging evidence indicates that intestinal molecular signaling involving nutrient sensing and the host-microbe symbiosis alters insulin homeostasis and action. Gut-derived host endocrine and paracrine factors as well as microbial metabolites act on the liver, pancreas, and the brain, and in parallel on the gut-brain neuronal axis. Understanding common nodes of peripheral and central insulin homeostasis and action may reveal new ways to target the intestinal host-microbe relationship in obesity, metabolic disease, and type 2 diabetes.

Modulation of Short-Chain Fatty Acids as Potential Therapy Method for Type 2 Diabetes Mellitus

In recent years, the relationship between intestinal microbiota (IM) and the pathogenesis of type 2 diabetes mellitus (T2DM) has attracted much attention. The beneficial effects of IM on the metabolic phenotype of the host are often considered to be mediated by short-chain fatty acids (SCFAs), mainly acetate, butyrate, and propionate, the small-molecule metabolites derived from microbial fermentation of indigestible carbohydrates. SCFAs not only have an essential role in intestinal health but might also enter the systemic circulation as signaling molecules affecting the host's metabolism. In this review, we summarize the effects of SCFAs on glucose homeostasis and energy homeostasis and the mechanism through which SCFAs regulate the function of metabolically active organs (brain, liver, adipose tissue, skeletal muscle, and pancreas) and discuss the potential role of modulation of SCFAs as a therapeutic method for T2DM.

Influence of Lab Adapted Natural Diet and Microbiota on Life History and Metabolic Phenotype of Drosophila melanogaster

Symbiotic microbiota can help its host to overcome nutritional challenges, which is consistent with a holobiont theory of evolution. Our project investigated the effects produced by the microbiota community, acquired from the environment and horizontal transfer, on metabolic traits related to obesity. The study applied a novel approach of raising Drosophila melanogaster, from ten wild-derived genetic lines on naturally fermented peaches, preserving genuine microbial conditions. Larvae raised on the natural and standard lab diets were significantly different in every tested phenotype. Frozen peach food provided nutritional conditions similar to the natural ones and preserved key microbial taxa necessary for survival and development. On the peach diet, the presence of parental microbiota increased the weight and development rate. Larvae raised on each tested diet formed microbial communities distinct from each other. The effect that individual microbial taxa produced on the host varied significantly with changing environmental and genetic conditions, occasionally to the degree of opposite correlations.

Peri-operative antibiotics acutely and significantly impact intestinal microbiota following bariatric surgery

Bariatric surgery is the most effective treatment for weight loss. Vertical sleeve gastrectomy (VSG) involves the resection of ~ 80% of the stomach and was conceived to purely restrict oral intake. However, evidence suggests more complex mechanisms, particularly postoperative changes in gut microbiota, in facilitating weight loss and resolving associated comorbidities. VSG in humans is a complex procedure and includes peri-operative antibiotics and caloric restriction in addition to the altered anatomy. The impact of each of these factors on the intestinal microbiota have not been evaluated. The aim of this study was to determine the relative contributions of each of these factors on intestinal microbiota composition following VSG prior to substantial weight loss. Thirty-two obese patients underwent one of three treatments: (1) VSG plus routine intravenous peri-operative antibiotics (n = 12), (2) VSG with intravenous vancomycin chosen for its low intestinal penetrance (n = 12), and (3) caloric restriction (n = 8). Fecal samples were evaluated for bacterial composition prior to and 7 days following each intervention. Only patients undergoing VSG with routine peri-operative antibiotics showed a significant shift in community composition. Our data support the single dose of routine peri-operative antibiotics as the most influential factor of intestinal microbial composition acutely following VSG.

An update on potential biomarkers for diagnosing diabetic foot ulcer at early stage

As one of major chronic complications of diabetes, diabetic foot ulcer (DFU) is the main cause of disability and death. The clinical diagnosis and prognosis of DFU is inadequate. For clinicians, if the risk stratification of DFU can be obtained earlier in diabetic patients, the hospitalization, disability and mortality rate will be reduced. In addition to the inflammatory biomarkers that have been widely concerned and used, e.g., procalcitonin, pentraxin-3, C-reactive protein (CRP), interleukins (ILs), and tumor necrosis factor-α (TNF-α), etc., a more comprehensive prediction of the risk and severity of DFU is needed to reflect new biomarkers for therapeutic intervention effects. Along with the development of systems biology technology, genomics, proteomics, metabolomics and microbiome have been used in the studies on DFU for better understanding of the disease. In this review, new biomarkers that are expected to assist in the accurate diagnosis and risk stratification of DFU will be discussed and summarized in detail.

Gut microbiome, big data and machine learning to promote precision medicine for cancer

The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The 'omics' technologies used for microbiome analysis continuously evolve and, although much of the research is still at an early stage, large-scale datasets of ever increasing size and complexity are being produced. However, there are varying levels of difficulty in realizing the full potential of these new tools, which limit our ability to critically analyse much of the available data. In this Perspective, we provide a brief overview on the role of gut microbiome in cancer and focus on the need, role and limitations of a machine learning-driven approach to analyse large amounts of complex health-care information in the era of big data. We also discuss the potential application of microbiome-based big data aimed at promoting precision medicine in cancer.

Indole Alleviates Diet-Induced Hepatic Steatosis and Inflammation in a Manner Involving Myeloid Cell 6-Phosphofructo-2-Kinase/Fructose-2,6-Biphosphatase 3

BACKGROUND AND AIMS

Indole is a microbiota metabolite that exerts anti-inflammatory responses. However, the relevance of indole to human non-alcoholic fatty liver disease (NAFLD) is not clear. It also remains largely unknown whether and how indole acts to protect against NAFLD. The present study sought to examine the association between the circulating levels of indole and liver fat content in human subjects and explore the mechanisms underlying indole actions in mice with diet-induced NAFLD.

APPROACH AND RESULTS

In a cohort of 137 subjects, the circulating levels of indole were reversely correlated with body mass index. In addition, the circulating levels of indole in obese subjects were significantly lower than those in lean subjects and were accompanied with increased liver fat content. At the whole-animal level, treatment of high-fat diet (HFD)-fed C57BL/6J mice with indole caused significant decreases in the severity of hepatic steatosis and inflammation. In cultured cells, indole treatment stimulated the expression of 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3), a master regulatory gene of glycolysis, and suppressed macrophage proinflammatory activation in a PFKFB3-dependent manner. Moreover, myeloid cell-specific PFKFB3 disruption exacerbated the severity of HFD-induced hepatic steatosis and inflammation and blunted the effect of indole on alleviating diet-induced NAFLD phenotype.

CONCLUSIONS

Taken together, our results demonstrate that indole is relevant to human NAFLD and capable of alleviating diet-induced NAFLD phenotypes in mice in a myeloid cell PFKFB3-dependent manner. Therefore, indole mimetic and/or macrophage-specific PFKFB3 activation may be the viable preventive and/or therapeutic approaches for inflammation-associated diseases including NAFLD.

Deep longitudinal multiomics profiling reveals two biological seasonal patterns in California

The influence of seasons on biological processes is poorly understood. In order to identify biological seasonal patterns based on diverse molecular data, rather than calendar dates, we performed a deep longitudinal multiomics profiling of 105 individuals over 4 years. Here, we report more than 1000 seasonal variations in omics analytes and clinical measures. The different molecules group into two major seasonal patterns which correlate with peaks in late spring and late fall/early winter in California. The two patterns are enriched for molecules involved in human biological processes such as inflammation, immunity, cardiovascular health, as well as neurological and psychiatric conditions. Lastly, we identify molecules and microbes that demonstrate different seasonal patterns in insulin sensitive and insulin resistant individuals. The results of our study have important implications in healthcare and highlight the value of considering seasonality when assessing population wide health risk and management.

Effects of Smoking and Smoking Cessation on the Intestinal Microbiota

We evaluated associations of smoking heaviness markers and the effects of smoking cessation on the intestinal microbiota and cardiovascular disease risk factors in current smokers undertaking a quit attempt. Participants were current smokers enrolled in a prospective randomized clinical trial of smoking cessation therapies with visits at baseline, 2, and 12 weeks. Genomic DNA was extracted from fecal samples followed by 16S rRNA gene sequencing and analysis using the QIIME2 software workflow. Relative abundances of bacterial taxa and alpha- and beta-diversity measures were used for comparisons. The 36 smokers were (mean (standard deviation)) 51.5 (11.1) years old (42% male) and smoked 15.1 (6.4) cigarettes per day for 22.7 (11.9) pack-years. Relative abundances of the phylum Actinobacteria correlated with pack-years (rho = −0.44, p = 0.008) and Cyanobacteria correlated with CO levels (rho = 0.39, p = 0.021). After 12 weeks, relative abundances of the phylum Bacteroidetes increased (p_ANCOVA = 0.048) and Firmicutes decreased (p_ANCOVA = 0.036) among abstainers compared to continuing smokers. Increases in alpha-diversity were associated with heart rates (rho = −0.59, p = 0.037), systolic blood pressures (rho = −0.58, p = 0.043), and C-reactive protein (rho = −0.60, p = 0.034). Smoking cessation led to minor changes in the intestinal microbiota. It is unclear if the proven health benefits of smoking cessation lead to salutary changes in the intestinal microbiota.

Gut microbiota impairs insulin clearance in obese mice

Objective

Hyperinsulinemia can be both a cause and consequence of obesity and insulin resistance. Hyperinsulinemia can result from increased insulin secretion and/or reduced insulin clearance. While many studies have focused on mechanisms triggering insulin secretion during obesity, the triggers for changes in insulin clearance during obesity are less defined. In this study, we investigated the role of the microbiota in regulating insulin clearance during diet-induced obesity.

Methods

Blood glucose and insulin clearance were tested in conventional male mice treated with antibiotics and germ-free mice colonized with microbes from mice that were fed a control (chow) diet or an obesogenic high-fat diet (HFD). The composition of the fecal microbiota was analyzed using 16S rRNA sequencing.

Results

Short-term HFD feeding and aging did not alter insulin clearance in the mice. Oral antibiotics mitigated impaired blood insulin clearance in the mice fed an HFD for 12 weeks or longer. Germ-free mice colonized with microbes from HFD-fed donor mice had impaired insulin but not C-peptide clearance. Microbe-transmissible insulin clearance impairment was only observed in germ-free mice after more than 6 weeks post-colonization upon HFD feeding. Five bacterial taxa predicted >90% of the variance in insulin clearance. Mechanistically, impaired insulin clearance was associated with lower levels of hepatic Ceacam-1 but increased liver and skeletal muscle insulin-degrading enzyme (IDE) activity.

Conclusions

Gut microbes regulate insulin clearance during diet-induced obesity. A small cluster of microbes or their metabolites may be targeted for mitigating defects in insulin clearance and hyperinsulinemia during the progression of obesity and type 2 diabetes.

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Obesity impairs insulin clearance in mice, which is mitigated by antibiotics.

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The gut microbiota contributes to impaired insulin but not C-peptide clearance.

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The gut microbiota is a stand-alone factor that impairs insulin clearance.

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A cluster of related bacteria predict >90% of the variance in insulin clearance.

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Impaired insulin clearance is associated with lower hepatic Ceacam-1.

High-fat diet negatively impacts both metabolic and behavioral health in outbred heterogeneous stock rats

Obesity is influenced by genetics and diet and has wide ranging comorbidities, including anxiety and depressive disorders. Outbred heterogeneous stock (HS) rats are used for fine-genetic mapping of complex traits and may be useful for understanding gene by diet interactions. In this study, HS rats were fed diets containing 60% kcal from fat (high-fat diet, HFD) or 10% kcal from fat (low-fat diet, LFD) and tested for metabolic (study 1) and behavioral (study 2) outcomes. In study 1, we measured glucose tolerance, fasting glucose and insulin, fat pad weights and despair-like behavior in the forced swim test (FST). In study 2, we assessed anxiety-like (elevated plus maze, EPM; open field test, OFT) and despair-like/coping (splash test, SpT; and FST) behaviors. Body weight and food intake were measured weekly in both studies. We found negative effects of HFD on metabolic outcomes, including increased body weight and fat pad weights, decreased glucose tolerance, and increased fasting insulin. We also found negative effects of HFD on despair-like/coping and anxiety-like behaviors. These include increased immobility in the FST, decreased open arm time in the EPM, and increased movement and rest episodes and decreased rearing in the OFT. The diet-induced changes in EPM and OFT were independent of overall locomotion. Additionally, diet-induced changes in OFT behaviors were independent of adiposity, while adiposity was a confounding factor for EPM and FST behavior. This work establishes the HS as a model to study gene by diet interactions affecting metabolic and behavioral health.

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

Connecting the Dots Between Inflammatory Bowel Disease and Metabolic Syndrome: A Focus on Gut-Derived Metabolites

The role of the microbiome in health and disease has gained considerable attention and shed light on the etiology of complex diseases like inflammatory bowel disease (IBD) and metabolic syndrome (MetS). Since the microorganisms inhabiting the gut can confer either protective or harmful signals, understanding the functional network between the gut microbes and the host provides a comprehensive picture of health and disease status. In IBD, disruption of the gut barrier enhances microbe infiltration into the submucosae, which enhances the probability that gut-derived metabolites are translocated from the gut to the liver and pancreas. Considering inflammation and the gut microbiome can trigger intestinal barrier dysfunction, risk factors of metabolic diseases such as insulin resistance may have common roots with IBD. In this review, we focus on the overlap between IBD and MetS, and we explore the role of common metabolites in each disease in an attempt to connect a common origin, the gut microbiome and derived metabolites that affect the gut, liver and pancreas.

Bound Phenolics Ensure the Antihyperglycemic Effect of Rice Bran Dietary Fiber in db/db Mice via Activating the Insulin Signaling Pathway in Skeletal Muscle and Altering Gut Microbiota

Whole-grain dietary fiber intake is beneficial in the prevention of metabolic syndrome. Considering rich in bound phenolics being a special characteristic of whole-grain dietary fiber, the aim of this study was to evaluate the effects of the presence or absence of bound phenolics in rice bran dietary fiber (RBDF) on regulating glucose metabolism in diabetic db/db mice. In comparison to phenolics-removed RBDF (PR-RBDF) intervention without an antihyperglycemic effect, RBDF and formulated RBDF (F-RBDF, obtained by mixing PR-RBDF and hydrolyzed-bound phenolics) significantly reduced fasting blood glucose levels after 1 and 5 weeks of interventions, respectively. The presence of bound phenolics interventions could activate the IRS1/AKT/GLUT4 insulin signaling pathway in skeletal muscle and alter gut microbiota by modulating gut microbiota dysbiosis and enriching the butyric-acid-producing bacteria genera of the families Lachnospiraceae and Ruminococcaceae, thus leading to the reduction of blood glucose levels. These findings indicate that bound phenolics ensure the antihyperglycemic effect of RBDF.

Potential role of insulin on the pathogenesis of depression

The regulation of insulin on depression and depression-like behaviour has been widely reported. Insulin and activation of its receptor can promote learning and memory, affect the hypothalamic-pituitary-adrenal axis (HPA) balance, regulate the secretion of neurotrophic factors and neurotransmitters, interact with gastrointestinal microbiome, exert neuroprotective effects and have an impact on depression. However, the role of insulin on depression remains largely unclear. Therefore, in this review, we summarized the potential role of insulin on depression. It may provide new insight for clarifying role of insulin on the pathogenesis of depression.

The effect of diet change and insulin dysregulation on the faecal microbiome of ponies

The equine microbiome can change in response to dietary alteration and may play a role in insulin dysregulation. The aim of this study was to determine the effect of adding pasture to a hay diet on the faecal bacterial microbiome of both healthy and insulin-dysregulated ponies. Faecal samples were collected from 16 ponies before and after dietary change to enable bacterial 16S rRNA sequencing of the V3-V4 region. The dominant phyla in all samples were the Firmicutes and Bacteroidetes. The evenness of the bacterial populations decreased after grazing pasture, and when a pony was moderately insulin dysregulated (P=0.001). Evenness scores negatively correlated with post-prandial glucagon-like peptide-1 concentration after a hay-only diet (r²=-0.7, P=0.001). A change in diet explained 3% of faecal microbiome variability. We conclude that metabolically healthy ponies have greater microbial stability when challenged with a subtle dietary change, compared with moderately insulin-dysregulated ponies.

The equine gastrointestinal microbiome: impacts of weight-loss

Background

Obesity is an important equine welfare issue. Whilst dietary restriction is the most effective weight-loss tool, individual animals range in their weight-loss propensity. Gastrointestinal-derived bacteria play a fundamental role in host-health and have been associated with obesity and weight-loss in other species. This study evaluated the faecal microbiome (next-generation sequencing of 16S rRNA genes) of 15 obese Welsh Mountain pony mares, in the same 11-week period across 2 years (n = 8 Year 1; n = 7 Year 2). Following a 4-week acclimation period (pre-diet phase) during which time individuals were fed the same hay to maintenance (2% body mass (BM) as daily dry matter (DM) intake), animals underwent a 7-week period of dietary restriction (1% BM hay as daily DM intake). Faeces were sampled on the final 3 days of the pre-diet phase and the final 3 days of the dietary restriction phase. Bacterial communities were determined using Next Generation Sequencing of amplified V1-V2 hypervariable regions of bacterial 16S rRNA.

Results

Losses in body mass ranged from 7.11 to 11.59%. Changes in the faecal microbiome composition following weight-loss included a reduction in the relative abundance of Firmicutes and Tenericutes and a reduction in indices of bacterial diversity. Pre-diet diversity was negatively associated with weight-loss. Pre-diet faecal acetate concentration was a strong predictor of subsequent weight-loss and negatively associated with Sphaerochaeta (Spirochaetes phylum) abundance. When animals were divided into 3 groups (high, mid, low) based overall weight loss, pre-diet bacterial community structure was found to have the greatest divergence between the high and low weight-loss groups (R = 0.67, p <  0.01), following PERMANOVA and ANOSIM analysis.

Conclusions

Weight-loss in this group of ponies was associated with lower pre-diet faecal bacterial diversity and greater pre-diet acetate concentration. Overall, these data support a role for the faecal microbiome in weight-loss propensity in ponies and provide a baseline for research evaluating elements of the faecal microbiome in predicting weight-loss success in larger cohorts.

Murine Genetic Background Overcomes Gut Microbiota Changes to Explain Metabolic Response to High-Fat Diet

Interactions of diet, gut microbiota, and host genetics play essential roles in the development of metabolic diseases. A/J and C57BL/6J (C57) are two mouse strains known to display different susceptibilities to metabolic disorders. In this context, we analyzed gut microbiota composition in A/J and C57 mice, and assessed its responses to high-fat diet (HFD) and antibiotic (AB) treatment. We also exchanged the gut microbiota between the two strains following AB treatment to evaluate its impact on the metabolism. We showed that A/J and C57 mice have different microbiome structure and composition at baseline. Moreover, A/J and C57 microbiomes responded differently to HFD and AB treatments. Exchange of the gut microbiota between the two strains was successful as recipients' microbiota resembled donor-strain microbiota. Seven weeks after inoculation, the differences between recipients persisted and were still closer from the donor-strain microbiota. Despite effective microbiota transplants, the response to HFD was not markedly modified in C57 and A/J mice. Particularly, body weight gain and glucose intolerance in response to HFD remained different in the two mouse strains whatever the changes in microbiome composition. This indicated that genetic background has a much stronger impact on metabolic responses to HFD than gut microbiome composition.

Ochratoxin A induces liver inflammation: involvement of intestinal microbiota

BACKGROUND

Ochratoxin A (OTA) is a widespread mycotoxin and induces liver inflammation to human and various species of animals. The intestinal microbiota has critical importance in liver inflammation; however, it remains to know whether intestinal microbiota mediates the liver inflammation induced by OTA. Here, we treated ducklings with oral gavage of OTA (235 μg/kg body weight) for 2 weeks. Then, the microbiota in the cecum and liver were analyzed with 16S rRNA sequencing, and the inflammation in the liver was analyzed. To explore the role of intestinal microbiota in OTA-induced liver inflammation, intestinal microbiota was cleared with antibiotics and fecal microbiota transplantation was conducted.

RESULTS

Here, we find that OTA treatment in ducks altered the intestinal microbiota composition and structure [e.g., increasing the relative abundance of lipopolysaccharides (LPS)-producing Bacteroides], and induced the accumulation of LPS and inflammation in the liver. Intriguingly, in antibiotic-treated ducks, OTA failed to induce these alterations in the liver. Notably, with the fecal microbiota transplantation (FMT) program, in which ducks were colonized with intestinal microbiota from control or OTA-treated ducks, we elucidated the involvement of intestinal microbiota, especially Bacteroides, in liver inflammation induced by OTA.

CONCLUSIONS

These results highlight the role of gut microbiota in OTA-induced liver inflammation and open a new window for novel preventative or therapeutic intervention for mycotoxicosis.

Gymnemic Acid Ameliorates Hyperglycemia through PI3K/AKT- and AMPK-Mediated Signaling Pathways in Type 2 Diabetes Mellitus Rats

Gymnemic acid (GA) isolated from Gymnema sylvestre (Retz.) Schult. has been shown to have antihyperglycemic activity; however, the molecular mechanisms governing these effects are unclear. In this study, GA (40 and 80 mg kg^-1 day^-1) was evaluated by type 2 diabetes mellitus (T2DM) rats to explore its hypoglycemic activity and underlying mechanisms of action. The results indicated that GA decreased fasting blood glucose (FBG) concentrations by 26.7% and lowered insulin concentrations by 16.1% after oral administration of GA at a dose of 80 mg kg^-1 day^-1 for 6 weeks in T2DM rats. Our data showed that real-time polymerase chain reaction and western blot indicated that GA upregulated the level of phosphatidylinositol-3-kinase (PI3K) and glycogen synthesis (GS) and promoted the phosphorylation of protein kinase B (Akt) while downregulated the expression of glycogen synthesis kinase-3β (GSK-3β) in T2DM rats. In addition, key proteins involved in adenosine monophosphate (AMP)-activated protein kinase (AMPK)-mediated gluconeogenesis [such as phosphoenolpyruvate carboxy kinase (PEPCK) and glucose-6-phosphatase (G6Pase)] were downregulated in GA-treated T2DM rats. In summary, the hypoglycemic mechanisms of GA may be related to promoting insulin signal transduction and activating PI3K/Akt- and AMPK-mediated signaling pathways in T2DM rats.

The Role of the Gut Microbiome in Predicting Response to Diet and the Development of Precision Nutrition Models. Part II: Results

The gut microbiota is increasingly implicated in the health and metabolism of its human host. The host's diet is a major component influencing the composition and function of the gut microbiota, and mounting evidence suggests that the composition and function of the gut microbiota influence the host's metabolic response to diet. This effect of the gut microbiota on personalized dietary response is a growing focus of precision nutrition research and may inform the effort to tailor dietary advice to the individual. Because the gut microbiota has been shown to be malleable to some extent, it may also allow for therapeutic alterations of the gut microbiota in order to alter response to certain dietary components. This article is the second in a 2-part review of the current research in the field of precision nutrition incorporating the gut microbiota into studies investigating interindividual variability in response to diet. Part I reviews the methods used by researchers to design and carry out such studies as well as analyze the results subsequently obtained. Part II reviews the findings of these studies and discusses the gaps in our current knowledge and directions for future research. The studies reviewed provide the current understanding in this field of research and a foundation from which we may build, utilizing and expanding upon the methods and results they present to inform future studies.

The Role of the Gut Microbiome in Predicting Response to Diet and the Development of Precision Nutrition Models-Part I: Overview of Current Methods

Health care is increasingly focused on health at the individual level. In the rapidly evolving field of precision nutrition, researchers aim to identify how genetics, epigenetics, and the microbiome interact to shape an individual's response to diet. With this understanding, personalized responses can be predicted and dietary advice can be tailored to the individual. With the integration of these complex sources of data, an important aspect of precision nutrition research is the methodology used for studying interindividual variability in response to diet. This article stands as the first in a 2-part review of current research investigating the contribution of the gut microbiota to interindividual variability in response to diet. Part I reviews the methods used by researchers to design and carry out such studies as well as the statistical and bioinformatic methods used to analyze results. Part II reviews the findings of these studies, discusses gaps in our current knowledge, and summarizes directions for future research. Taken together, these reviews summarize the current state of knowledge and provide a foundation for future research on the role of the gut microbiome in precision nutrition.

Faecal microbiome sequences in relation to the egg-laying performance of hens using amplicon-based metagenomic association analysis

Exploring the composition and structure of the faecal microbial community improves the understanding of the role of the gut microbiota in the gastrointestinal function and the egg-laying performance of hens. Therefore, detection of hen-microbial interactions can explore a new breeding marker for the selection of egg production due to the important role of the gut microbiome in the host's metabolism and health. Recently, the gut microbiota has been recognised as a regulator of host performance, which has led to investigations of the productive effects of changes in the faecal microbiome in various animals. In the present study, a metagenomics analysis was applied to characterise the composition and structural diversity of faecal microbial communities under two selections of egg-laying performance, high (H, n = 30) and low (L, n = 30), using 16S rRNA-based metagenomic association analysis. The most abundant bacterial compositions were estimated based on the operational classification units among samples and between the groups from metagenomic data sets. The results indicated that Firmicutes phylum has higher significant (P < 0.01) in the H group than in the L group. In addition, higher relative abundance phyla of Bacteroides and Fusobacteria were estimated in the H group than the L group, contrasting the phyla of Actinobacteria, Cyanobacteria and Proteobacteria were more relative abundance in the L group. The families (Lactobacillus, Bifidobacterium, Acinetobacter, Flavobacteriaceae, Lachnoclostridum and Rhodococcus) were more abundant in the H group based on the comparison between the H and L groups. Meanwhile, three types of phyla (Proteobacteria, Actinobacteria and Cyanobacteria) and six families (Acinetobacter, Avibacterium, Clostridium, Corynebacterium, Helicobacter and Peptoclostridium) were more abundant in the L group (P < 0.01). Overall, the selection of genotypes has enriched a relationship between the gut microbiota and the egg-laying performance. These findings suggest that the faecal microbiomes of chickens with high egg-laying performance have more diverse activities than those of chickens with low egg-laying performance, which may be related to the metabolism and health of the host and egg production variation.

Primate microbiomes over time: Longitudinal answers to standing questions in microbiome research

To date, most insights into the processes shaping vertebrate gut microbiomes have emerged from studies with cross-sectional designs. While this approach has been valuable, emerging time series analyses on vertebrate gut microbiomes show that gut microbial composition can change rapidly from 1 day to the next, with consequences for host physical functioning, health, and fitness. Hence, the next frontier of microbiome research will require longitudinal perspectives. Here we argue that primatologists, with their traditional focus on tracking the lives of individual animals and familiarity with longitudinal fecal sampling, are well positioned to conduct research at the forefront of gut microbiome dynamics. We begin by reviewing some of the most important ecological processes governing microbiome change over time, and briefly summarizing statistical challenges and approaches to microbiome time series analysis. We then introduce five questions of general interest to microbiome science where we think field-based primate studies are especially well positioned to fill major gaps: (a) Do early life events shape gut microbiome composition in adulthood? (b) Do shifting social landscapes cause gut microbial change? (c) Are gut microbiome phenotypes heritable across variable environments? (d) Does the gut microbiome show signs of host aging? And (e) do gut microbiome composition and dynamics predict host health and fitness? For all of these questions, we highlight areas where primatologists are uniquely positioned to make substantial contributions. We review preliminary evidence, discuss possible study designs, and suggest future directions.

Genetic determinants of gut microbiota composition and bile acid profiles in mice

The microbial communities that inhabit the distal gut of humans and other mammals exhibit large inter-individual variation. While host genetics is a known factor that influences gut microbiota composition, the mechanisms underlying this variation remain largely unknown. Bile acids (BAs) are hormones that are produced by the host and chemically modified by gut bacteria. BAs serve as environmental cues and nutrients to microbes, but they can also have antibacterial effects. We hypothesized that host genetic variation in BA metabolism and homeostasis influence gut microbiota composition. To address this, we used the Diversity Outbred (DO) stock, a population of genetically distinct mice derived from eight founder strains. We characterized the fecal microbiota composition and plasma and cecal BA profiles from 400 DO mice maintained on a high-fat high-sucrose diet for ~22 weeks. Using quantitative trait locus (QTL) analysis, we identified several genomic regions associated with variations in both bacterial and BA profiles. Notably, we found overlapping QTL for Turicibacter sp. and plasma cholic acid, which mapped to a locus containing the gene for the ileal bile acid transporter, Slc10a2. Mediation analysis and subsequent follow-up validation experiments suggest that differences in Slc10a2 gene expression associated with the different strains influences levels of both traits and revealed novel interactions between Turicibacter and BAs. This work illustrates how systems genetics can be utilized to generate testable hypotheses and provide insight into host-microbe interactions.

Gut Microbiota, Muscle Mass and Function in Aging: A Focus on Physical Frailty and Sarcopenia

Human gut microbiota is able to influence the host physiology by regulating multiple processes, including nutrient absorption, inflammation, oxidative stress, immune function, and anabolic balance. Aging is associated with reduced microbiota biodiversity, increased inter-individual variability, and over-representation of pathobionts, and these phenomena may have great relevance for skeletal muscle mass and function. For this reason, the presence of a gut-muscle axis regulating the onset and progression of age-related physical frailty and sarcopenia has been recently hypothesized. In this narrative review, we summarize the studies supporting a possible association between gut microbiota-related parameters with measures of muscle mass, muscle function, and physical performance in animal models and humans. Reduced muscle mass has been associated with distinct microbiota composition and reduced fermentative capacity in mice, and the administration of probiotics or butyrate to mouse models of muscle wasting has been associated with improved muscle mass. However, no studies have targeted the human microbiome associated with sarcopenia. Limited evidence from human studies shows an association between microbiota composition, involving key taxa such as Faecalibacterium and Bifidobacterium, and grip strength. Similarly, few studies conducted on patients with parkinsonism showed a trend towards a different microbiota composition in those with reduced gait speed. No studies have assessed the association of fecal microbiota with other measures of physical performance. However, several studies, mainly with a cross-sectional design, suggest an association between microbiota composition and frailty, mostly assessed according to the deficit accumulation model. Namely, frailty was associated with reduced microbiota biodiversity, and lower representation of butyrate-producing bacteria. Therefore, we conclude that the causal link between microbiota and physical fitness is still uncertain due to the lack of targeted studies and the influence of a large number of covariates, including diet, exercise, multimorbidity, and polypharmacy, on both microbiota composition and physical function in older age. However, the relationship between gut microbiota and physical function remains a very promising area of research for the future.

Adaptation to Fasting in Crucian Carp (Carassius auratus): Gut Microbiota and Its Correlative Relationship with Immune Function

Fasting influences the overall physiology of fish, and the knowledge how the gut microbiota, growth performances, and immune function in response to intermittent and long-term fasting is still insufficient. Here, we characterized the effects of fasting on the host-gut microbiota in crucian carp, which would enhance our insight into physiological adaptation to fasting. To achieve this, we investigated the gut microbial communities of crucian carp with different fasting stress, and corresponding immune and growth parameters. The gut microbial communities were structured into four clusters according to different fasting stress, namely one control group (feed regularly), two intermittent fasting groups (fasting period and re-feeding period, respectively), and one long-term fasting group. Intermittent fasting significantly improved the activity of superoxide dismutase (SOD) and lysozyme (LZM) (ANOVA, p < 0.05) and significantly increased alpha diversity and ecosystem stability of gut microbiota (ANOVA, p < 0.05). Gut length (GL) and condition factor (CF) showed no significant difference between the control group (CG) and intermittent fasting group under re-feeding period (RIF) (ANOVA, p = 0.11), but relative gut length (RGL) in group RIF was higher than that in the CG (ANOVA, p = 0.00). The bacterial genera Bacteroides, Akkermansia, and Erysipelotrichaceae were enriched in fishes under intermittent fasting. Two Bacteroides OTUs (OTU50 and OTU1292) correlated positively with immune (SOD, complement, and LZM) and growth (GL and RGL) parameters. These results highlight the possible interplay between growth performances, immune function, and gut microbiota in response to fasting.

A Mechanistic Model of Gut-Brain Axis Perturbation and High-Fat Diet Pathways to Gut Microbiome Homeostatic Disruption, Systemic Inflammation, and Type 2 Diabetes

Type 2 diabetes (T2D) is a highly prevalent metabolic disease, affecting nearly 10% of the American population. Although the etiopathogenesis of T2D remains poorly understood, advances in DNA sequencing technologies have allowed for sophisticated interrogation of the human microbiome, providing insight into the role of the gut microbiome in the development and progression of T2D. An emerging body of research reveals that gut-brain axis (GBA) perturbations and a high-fat diet (HFD), along with other modifiable and nonmodifiable risk factors, contribute to gut microbiome homeostatic imbalance. Homeostatic imbalance or disruption increases gut wall permeability and facilitates translocation of endotoxins (lipopolysaccharides) into the circulation with resultant systemic inflammation. Chronic, low-grade systemic inflammation ensues with pro-inflammatory pathways activated, contributing to obesity, insulin resistance (IR), pancreatic β-cell decline, and, thereby, T2D. While GBA perturbations and HFD are implicated in provoking these conditions, prior mechanistic models have tended to examine HFD and GBA pathways exclusively without considering their shared pathways to T2D. Addressing this gap, this article proposes a mechanistic model informed by animal and human studies to advance scientific understanding of (1) modifiable and nonmodifiable risk factors for gut microbiome homeostatic disruption, (2) HFD and GBA pathways contributing to homeostatic disruption, and (3) shared GBA and HFD pro-inflammatory pathways to obesity, IR, β-cell decline, and T2D. The proposed mechanistic model, based on the extant literature, proposes a framework for studying the complex relationships of the gut microbiome to T2D to advance study in this promising area of research.

Cluster partitions and fitness landscapes of the Drosophila fly microbiome

The concept of genetic epistasis defines an interaction between two genetic loci as the degree of non-additivity in their phenotypes. A fitness landscape describes the phenotypes over many genetic loci, and the shape of this landscape can be used to predict evolutionary trajectories. Epistasis in a fitness landscape makes prediction of evolutionary trajectories more complex because the interactions between loci can produce local fitness peaks or troughs, which changes the likelihood of different paths. While various mathematical frameworks have been proposed to investigate properties of fitness landscapes, Beerenwinkel et al. (Stat Sin 17(4):1317-1342, 2007a) suggested studying regular subdivisions of convex polytopes. In this sense, each locus provides one dimension, so that the genotypes form a cube with the number of dimensions equal to the number of genetic loci considered. The fitness landscape is a height function on the coordinates of the cube. Here, we propose cluster partitions and cluster filtrations of fitness landscapes as a new mathematical tool, which provides a concise combinatorial way of processing metric information from epistatic interactions. Furthermore, we extend the calculation of genetic interactions to consider interactions between microbial taxa in the gut microbiome of Drosophila fruit flies. We demonstrate similarities with and differences to the previous approach. As one outcome we locate interesting epistatic information on the fitness landscape where the previous approach is less conclusive.

Enhancing face validity of mouse models of Alzheimer's disease with natural genetic variation

Classical laboratory strains show limited genetic diversity and do not harness natural genetic variation. Mouse models relevant to Alzheimer's disease (AD) have largely been developed using these classical laboratory strains, such as C57BL/6J (B6), and this has likely contributed to the failure of translation of findings from mice to the clinic. Therefore, here we test the potential for natural genetic variation to enhance the translatability of AD mouse models. Two widely used AD-relevant transgenes, APPswe and PS1de9 (APP/PS1), were backcrossed from B6 to three wild-derived strains CAST/EiJ, WSB/EiJ, PWK/PhJ, representative of three Mus musculus subspecies. These new AD strains were characterized using metabolic, functional, neuropathological and transcriptional assays. Strain-, sex- and genotype-specific differences were observed in cognitive ability, neurodegeneration, plaque load, cerebrovascular health and cerebral amyloid angiopathy. Analyses of brain transcriptional data showed strain was the greatest driver of variation. We identified significant variation in myeloid cell numbers in wild type mice of different strains as well as significant differences in plaque-associated myeloid responses in APP/PS1 mice between the strains. Collectively, these data support the use of wild-derived strains to better model the complexity of human AD.

Genes, geology and germs: gut microbiota across a primate hybrid zone are explained by site soil properties, not host species

Gut microbiota in geographically isolated host populations are often distinct. These differences have been attributed to between-population differences in host behaviours, environments, genetics and geographical distance. However, which factors are most important remains unknown. Here, we fill this gap for baboons by leveraging information on 13 environmental variables from 14 baboon populations spanning a natural hybrid zone. Sampling across a hybrid zone allowed us to additionally test whether phylosymbiosis (codiversification between hosts and their microbiota) is detectable in admixed, closely related primates. We found little evidence of genetic effects: none of host genetic ancestry, host genetic relatedness nor genetic distance between host populations were strong predictors of baboon gut microbiota. Instead, gut microbiota were best explained by the baboons' environments, especially the soil's geologic history and exchangeable sodium. Indeed, soil effects were 15 times stronger than those of host-population FST, perhaps because soil predicts which foods are present, or because baboons are terrestrial and consume soil microbes incidentally with their food. Our results support an emerging picture in which environmental variation is the dominant predictor of host-associated microbiomes. We are the first to show that such effects overshadow host species identity among members of the same primate genus.

Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases

Microbiome-wide association studies on large population cohorts have highlighted associations between the gut microbiome and complex traits, including type 2 diabetes (T2D) and obesity1. However, the causal relationships remain largely unresolved. We leveraged information from 952 normoglycemic individuals for whom genome-wide genotyping, gut metagenomic sequence and fecal short-chain fatty acid (SCFA) levels were available2, then combined this information with genome-wide-association summary statistics for 17 metabolic and anthropometric traits. Using bidirectional Mendelian randomization (MR) analyses to assess causality3, we found that the host-genetic-driven increase in gut production of the SCFA butyrate was associated with improved insulin response after an oral glucose-tolerance test (P = 9.8 × 10-5), whereas abnormalities in the production or absorption of another SCFA, propionate, were causally related to an increased risk of T2D (P = 0.004). These data provide evidence of a causal effect of the gut microbiome on metabolic traits and support the use of MR as a means to elucidate causal relationships from microbiome-wide association findings.

Assessment of a Personalized Approach to Predicting Postprandial Glycemic Responses to Food Among Individuals Without Diabetes

IMPORTANCE

Emerging evidence suggests that postprandial glycemic responses (PPGRs) to food may be influenced by and predicted according to characteristics unique to each individual, including anthropometric and microbiome variables. Interindividual diversity in PPGRs to food requires a personalized approach for the maintenance of healthy glycemic levels.

OBJECTIVES

To describe and predict the glycemic responses of individuals to a diverse array of foods using a model that considers the physiology and microbiome of the individual in addition to the characteristics of the foods consumed.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study using a personalized predictive model enrolled 327 individuals without diabetes from October 11, 2016, to December 13, 2017, in Minnesota and Florida to be part of a study lasting 6 days. The study measured anthropometric variables, described the gut microbial composition, and assessed blood glucose levels every 5 minutes using a continuous glucose monitor. Participants logged their food and activity information for the duration of the study. A predictive model of individualized PPGRs to a diverse array of foods was trained and applied.

MAIN OUTCOMES AND MEASURES

Glycemic responses to food consumed over 6 days for each participant. The predictive model of personalized PPGRs considered individual features, including the microbiome, in addition to the features of the foods consumed.

RESULTS

Postprandial response to the same foods varied across 327 individuals (mean [SD] age, 45 [12] years; 78.0% female). A model predicting each individual's responses to food that considers several individual factors in addition to food features had better overall performance (R = 0.62) than current standard-of-care approaches using nutritional content alone (R = 0.34 for calories and R = 0.40 for carbohydrates) to control postprandial glycemic levels.

CONCLUSIONS AND RELEVANCE

Across the cohort of adults without diabetes who were examined, a personalized predictive model that considers unique features of the individual, such as clinical characteristics, physiological variables, and the microbiome, in addition to nutrient content was more predictive than current dietary approaches that focus only on the calorie or carbohydrate content of foods. Providing individuals with tools to manage their glycemic responses to food based on personalized predictions of their PPGRs may allow them to maintain their blood glucose levels within limits associated with good health.

Host and microbiome multi-omics integration: applications and methodologies

The study of the microbial community-the microbiome-associated with a human host is a maturing research field. It is increasingly clear that the composition of the human's microbiome is associated with various diseases such as gastrointestinal diseases, liver diseases and metabolic diseases. Using high-throughput technologies such as next-generation sequencing and mass spectrometry-based metabolomics, we are able to comprehensively sequence the microbiome-the metagenome-and associate these data with the genomic, epigenomics, transcriptomic and metabolic profile of the host. Our review summarises the application of integrating host omics with microbiome as well as the analytical methods and related tools applied in these studies. In addition, potential future directions are discussed.

The Metabolic Response to a Low Amino Acid Diet is Independent of Diet-Induced Shifts in the Composition of the Gut Microbiome

Obesity and type 2 diabetes are increasing in prevalence around the world, and there is a clear need for new and effective strategies to promote metabolic health. A low protein (LP) diet improves metabolic health in both rodents and humans, but the mechanisms that underlie this effect remain unknown. The gut microbiome has recently emerged as a potent regulator of host metabolism and the response to diet. Here, we demonstrate that a LP diet significantly alters the taxonomic composition of the gut microbiome at the phylum level, altering the relative abundance of Actinobacteria, Bacteroidetes, and Firmicutes. Transcriptional profiling suggested that any impact of the microbiome on liver metabolism was likely independent of the microbiome-farnesoid X receptor (FXR) axis. We therefore tested the ability of a LP diet to improve metabolic health following antibiotic ablation of the gut microbiota. We found that a LP diet promotes leanness, increases energy expenditure, and improves glycemic control equally well in mice treated with antibiotics as in untreated control animals. Our results demonstrate that the beneficial effects of a LP diet on glucose homeostasis, energy balance, and body composition are unlikely to be mediated by diet-induced changes in the taxonomic composition of the gut microbiome.

You are what you eat: diet, health and the gut microbiota

Since the renaissance of microbiome research in the past decade, much insight has accumulated in comprehending forces shaping the architecture and functionality of resident microorganisms in the human gut. Of the multiple host-endogenous and host-exogenous factors involved, diet emerges as a pivotal determinant of gut microbiota community structure and function. By introducing dietary signals into the nexus between the host and its microbiota, nutrition sustains homeostasis or contributes to disease susceptibility. Herein, we summarize major concepts related to the effect of dietary constituents on the gut microbiota, highlighting chief principles in the diet-microbiota crosstalk. We then discuss the health benefits and detrimental consequences that the interactions between dietary and microbial factors elicit in the host. Finally, we present the promises and challenges that arise when seeking to incorporate microbiome data in dietary planning and portray the anticipated revolution that the field of nutrition is facing upon adopting these novel concepts.

Diet, Gut Microbiota, and Obesity: Links with Host Genetics and Epigenetics and Potential Applications

Diverse evidence suggests that the gut microbiota is involved in the development of obesity and associated comorbidities. It has been reported that the composition of the gut microbiota differs in obese and lean subjects, suggesting that microbiota dysbiosis can contribute to changes in body weight. However, the mechanisms by which the gut microbiota participates in energy homeostasis are unclear. Gut microbiota can be modulated positively or negatively by different lifestyle and dietary factors. Interestingly, complex interactions between genetic background, gut microbiota, and diet have also been reported concerning the risk of developing obesity and metabolic syndrome features. Moreover, microbial metabolites can induce epigenetic modifications (i.e., changes in DNA methylation and micro-RNA expression), with potential implications for health status and susceptibility to obesity. Also, microbial products, such as short-chain fatty acids or membrane proteins, may affect host metabolism by regulating appetite, lipogenesis, gluconeogenesis, inflammation, and other functions. Metabolomic approaches are being used to identify new postbiotics with biological activity in the host, allowing discovery of new targets and tools for incorporation into personalized therapies. This review summarizes the current understanding of the relations between the human gut microbiota and the onset and development of obesity. These scientific insights are paving the way to understanding the complex relation between obesity and microbiota. Among novel approaches, prebiotics, probiotics, postbiotics, and fecal microbiome transplantation could be useful to restore gut dysbiosis.

The relationship between vitamin C status, the gut-liver axis, and metabolic syndrome

Metabolic syndrome (MetS) is a constellation of cardiometabolic risk factors, which together predict increased risk of more serious chronic diseases. We propose that one consequence of dietary overnutrition is increased abundance of Gram-negative bacteria in the gut that cause increased inflammation, impaired gut function, and endotoxemia that further dysregulate the already compromised antioxidant vitamin status in MetS. This discussion is timely because "healthy" individuals are no longer the societal norm and specialized dietary requirements are needed for the growing prevalence of MetS. Further, these lines of evidence provide the foundational basis for investigation that poor vitamin C status promotes endotoxemia, leading to metabolic dysfunction that impairs vitamin E trafficking through a mechanism involving the gut-liver axis. This report will establish a critical need for translational research aimed at validating therapeutic approaches to manage endotoxemia-an early, but inflammation-inducing phenomenon, which not only occurs in MetS, but is also prognostic of more advanced metabolic disorders including type 2 diabetes mellitus, as well as the increasing severity of nonalcoholic fatty liver diseases.