Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines.

Diabetes and associated comorbidities are a global health threat on the rise. We conducted a six-month dietary intervention in pre-diabetic individuals (NCT03222791), to mitigate the hyperglycemia and enhance metabolic health. The current work explores early diabetes markers in the 200 individuals who completed the trial. We find 166 of 2,803 measured features, including oral and gut microbial species and pathways, serum metabolites and cytokines, show significant change in response to a personalized postprandial glucose-targeting diet or the standard of care Mediterranean diet. These changes include established markers of hyperglycemia as well as novel features that can now be investigated as potential therapeutic targets. Our results indicate the microbiome mediates the effect of diet on glycemic, metabolic and immune measurements, with gut microbiome compositional change explaining 12.25% of serum metabolites variance. Although the gut microbiome displays greater compositional changes compared to the oral microbiome, the oral microbiome demonstrates more changes at the genetic level, with trends dependent on environmental richness and species prevalence in the population. In conclusion, our study shows dietary interventions can affect the microbiome, cardiometabolic profile and immune response of the host, and that these factors are well associated with each other, and can be harnessed for new therapeutic modalities.

Gut microbiome modulates the effects of a personalised postprandial-targeting (PPT) diet on cardiometabolic markers: a diet intervention in pre-diabetes.

OBJECTIVE: To explore the interplay between dietary modifications, microbiome composition and host metabolic responses in a dietary intervention setting of a personalised postprandial-targeting (PPT) diet versus a Mediterranean (MED) diet in pre-diabetes. DESIGN: In a 6-month dietary intervention, adults with pre-diabetes were randomly assigned to follow an MED or PPT diet (based on a machine-learning algorithm for predicting postprandial glucose responses). Data collected at baseline and 6 months from 200 participants who completed the intervention included: dietary data from self-recorded logging using a smartphone application, gut microbiome data from shotgun metagenomics sequencing of faecal samples, and clinical data from continuous glucose monitoring, blood biomarkers and anthropometrics. RESULTS: PPT diet induced more prominent changes to the gut microbiome composition, compared with MED diet, consistent with overall greater dietary modifications observed. Particularly, microbiome alpha-diversity increased significantly in PPT (p=0.007) but not in MED arm (p=0.18). Post hoc analysis of changes in multiple dietary features, including food-categories, nutrients and PPT-adherence score across the cohort, demonstrated significant associations between specific dietary changes and species-level changes in microbiome composition. Furthermore, using causal mediation analysis we detect nine microbial species that partially mediate the association between specific dietary changes and clinical outcomes, including three species (from Bacteroidales, Lachnospiraceae, Oscillospirales orders) that mediate the association between PPT-adherence score and clinical outcomes of hemoglobin A1c (HbA1c), high-density lipoprotein cholesterol (HDL-C) and triglycerides. Finally, using machine-learning models trained on dietary changes and baseline clinical data, we predict personalised metabolic responses to dietary modifications and assess features importance for clinical improvement in cardiometabolic markers of blood lipids, glycaemic control and body weight. CONCLUSIONS: Our findings support the role of gut microbiome in modulating the effects of dietary modifications on cardiometabolic outcomes, and advance the concept of precision nutrition strategies for reducing comorbidities in pre-diabetes. TRIAL REGISTRATION NUMBER: NCT03222791.

BREAst Cancer Personalised NuTrition (BREACPNT): dietary intervention in breast cancer survivors treated with endocrine therapy - a protocol for a randomised clinical trial.

INTRODUCTION: Breast cancer survivors treated with adjuvant endocrine therapy commonly experience weight gain, which has been associated with low adherence to therapy and worse breast cancer prognosis. We aim to assess whether a personalised postprandial glucose targeting diet will be beneficial for weight management as compared with the recommended Mediterranean diet in this patient population METHODS AND ANALYSIS: The BREAst Cancer Personalised NuTrition study is a phase-2 randomised trial in hormone receptor positive patients with breast cancer, treated with adjuvant endocrine therapy. The study objective is to assess whether dietary intervention intended to improve postprandial glycaemic response to meals results in better weight and glycaemic control in this population as compared with the standard recommended Mediterranean diet. Consenting participants will be assigned in a single blinded fashion to either of two dietary arms (Mediterranean diet or an algorithm-based personalised diet). They will be asked to provide a stool sample for microbiome analysis and will undergo continuous glucose monitoring for 2 weeks, at the initiation and termination of the intervention period. Microbiome composition data will be used to tailor personal dietary recommendations. After randomisation and provision of dietary recommendations, participants will be asked to continuously log their diet and lifestyle activities on a designated smartphone application during the 6-month intervention period, during which they will be monthly monitored by a certified dietitian. Participants' clinical records will be followed twice yearly for 5 years for treatment adherence, disease-free survival and recurrence. ETHICS AND DISSEMINATION: The study has been approved by the ethics committee in the Sheba medical centre (file 5725-18-SMC, Ramat Gan, Israel) and the Weizmann Institutional Review Board (file 693-2, Rehovot, Israel). The findings of this study will be published in a peer reviewed publication. TRIAL REGISTRATION NUMBER: NCT04079270.

Effects of personalized diets by prediction of glycemic responses on glycemic control and metabolic health in newly diagnosed T2DM: a randomized dietary intervention pilot trial.

BACKGROUND: Dietary modifications are crucial for managing newly diagnosed type 2 diabetes mellitus (T2DM) and preventing its health complications, but many patients fail to achieve clinical goals with diet alone. We sought to evaluate the clinical effects of a personalized postprandial-targeting (PPT) diet on glycemic control and metabolic health in individuals with newly diagnosed T2DM as compared to the commonly recommended Mediterranean-style (MED) diet. METHODS: We enrolled 23 adults with newly diagnosed T2DM (aged 53.5 ± 8.9 years, 48% males) for a randomized crossover trial of two 2-week-long dietary interventions. Participants were blinded to their assignment to one of the two sequence groups: either PPT-MED or MED-PPT diets. The PPT diet relies on a machine learning algorithm that integrates clinical and microbiome features to predict personal postprandial glucose responses (PPGR). We further evaluated the long-term effects of PPT diet on glycemic control and metabolic health by an additional 6-month PPT intervention (n = 16). Participants were connected to continuous glucose monitoring (CGM) throughout the study and self-recorded dietary intake using a smartphone application. RESULTS: In the crossover intervention, the PPT diet lead to significant lower levels of CGM-based measures as compared to the MED diet, including average PPGR (mean difference between diets, - 19.8 ± 16.3 mg/dl × h, p < 0.001), mean glucose (mean difference between diets, - 7.8 ± 5.5 mg/dl, p < 0.001), and daily time of glucose levels > 140 mg/dl (mean difference between diets, - 2.42 ± 1.7 h/day, p < 0.001). Blood fructosamine also decreased significantly more during PPT compared to MED intervention (mean change difference between diets, - 16.4 ± 37 µmol/dl, p < 0.0001). At the end of 6 months, the PPT intervention leads to significant improvements in multiple metabolic health parameters, among them HbA1c (mean ± SD, - 0.39 ± 0.48%, p < 0.001), fasting glucose (- 16.4 ± 24.2 mg/dl, p = 0.02) and triglycerides (- 49 ± 46 mg/dl, p < 0.001). Importantly, 61% of the participants exhibited diabetes remission, as measured by HbA1c < 6.5%. Finally, some clinical improvements were significantly associated with gut microbiome changes per person. CONCLUSION: In this crossover trial in subjects with newly diagnosed T2DM, a PPT diet improved CGM-based glycemic measures significantly more than a Mediterranean-style MED diet. Additional 6-month PPT intervention further improved glycemic control and metabolic health parameters, supporting the clinical efficacy of this approach. TRIAL REGISTRATION: ClinicalTrials.gov number, NCT01892956.

Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease.

Complex diseases, such as coronary artery disease (CAD), are often multifactorial, caused by multiple underlying pathological mechanisms. Here, to study the multifactorial nature of CAD, we performed comprehensive clinical and multi-omic profiling, including serum metabolomics and gut microbiome data, for 199 patients with acute coronary syndrome (ACS) recruited from two major Israeli hospitals, and validated these results in a geographically distinct cohort. ACS patients had distinct serum metabolome and gut microbial signatures as compared with control individuals, and were depleted in a previously unknown bacterial species of the Clostridiaceae family. This bacterial species was associated with levels of multiple circulating metabolites in control individuals, several of which have previously been linked to an increased risk of CAD. Metabolic deviations in ACS patients were found to be person specific with respect to their potential genetic or environmental origin, and to correlate with clinical parameters and cardiovascular outcomes. Moreover, metabolic aberrations in ACS patients linked to microbiome and diet were also observed to a lesser extent in control individuals with metabolic impairment, suggesting the involvement of these aberrations in earlier dysmetabolic phases preceding clinically overt CAD. Finally, a metabolomics-based model of body mass index (BMI) trained on the non-ACS cohort predicted higher-than-actual BMI when applied to ACS patients, and the excess BMI predictions independently correlated with both diabetes mellitus (DM) and CAD severity, as defined by the number of vessels involved. These results highlight the utility of the serum metabolome in understanding the basis of risk-factor heterogeneity in CAD.

Personalized Postprandial Glucose Response-Targeting Diet Versus Mediterranean Diet for Glycemic Control in Prediabetes.

OBJECTIVE: To compare the clinical effects of a personalized postprandial-targeting (PPT) diet versus a Mediterranean (MED) diet on glycemic control and metabolic health in prediabetes. RESEARCH DESIGN AND METHODS: We randomly assigned adults with prediabetes (n = 225) to follow a MED diet or a PPT diet for a 6-month dietary intervention and additional 6-month follow-up. The PPT diet relies on a machine learning algorithm that integrates clinical and microbiome features to predict personal postprandial glucose responses. During the intervention, all participants were connected to continuous glucose monitoring (CGM) and self-reported dietary intake using a smartphone application. RESULTS: Among 225 participants randomized (58.7% women, mean ± SD age 50 ± 7 years, BMI 31.3 ± 5.8 kg/m2, HbA1c, 5.9 ± 0.2% [41 ± 2.4 mmol/mol], fasting plasma glucose 114 ± 12 mg/dL [6.33 ± 0.67 mmol/L]), 200 (89%) completed the 6-month intervention. A total of 177 participants also contributed 12-month follow-up data. Both interventions reduced the daily time with glucose levels >140 mg/dL (7.8 mmol/L) and HbA1c levels, but reductions were significantly greater in PPT compared with MED. The mean 6-month change in "time above 140" was -0.3 ± 0.8 h/day and -1.3 ± 1.5 h/day for MED and PPT, respectively (95% CI between-group difference -1.29 to -0.66, P < 0.001). The mean 6-month change in HbA1c was -0.08 ± 0.19% (-0.9 ± 2.1 mmol/mol) and -0.16 ± 0.24% (-1.7 ± 2.6 mmol/mol) for MED and PPT, respectively (95% CI between-group difference -0.14 to -0.02, P = 0.007). The significant between-group differences were maintained at 12-month follow-up. No significant differences were noted between the groups in a CGM-measured oral glucose tolerance test. CONCLUSIONS: In this clinical trial in prediabetes, a PPT diet improved glycemic control significantly more than a MED diet as measured by daily time of glucose levels >140 mg/dL (7.8 mmol/L) and HbA1c. These findings may have implications for dietary advice in clinical practice.

10 K: a large-scale prospective longitudinal study in Israel.

The 10 K is a large-scale prospective longitudinal cohort and biobank that was established in Israel. The primary aims of the study include development of prediction models for disease onset and progression and identification of novel molecular markers with a diagnostic, prognostic and therapeutic value. The recruitment was initiated in 2018 and is expected to complete in 2021. Between 28/01/2019 and 13/12/2020, 4,629 from the expected 10,000 participants were recruited (46 %). Follow-up visits are scheduled every year for a total of 25 years. The cohort includes individuals between the ages of 40 and 70 years. Predefined medical conditions were determined as exclusions. Information collected at baseline includes medical history, lifestyle and nutritional habits, vital signs, anthropometrics, blood tests results, Electrocardiography, Ankle-brachial pressure index (ABI), liver US and Dual-energy X-ray absorptiometry (DXA) tests. Molecular profiling includes transcriptome, proteome, gut and oral microbiome, metabolome and immune system profiling. Continuous measurements include glucose levels using a continuous glucose monitoring device for 2 weeks and sleep monitoring by a home sleep apnea test device for 3 nights. Blood and stool samples are collected and stored at - 80 °C in a storage facility for future research. Linkage is being established with national disease registries.

Potential role of indolelactate and butyrate in multiple sclerosis revealed by integrated microbiome-metabolome analysis.

Multiple sclerosis (MS) is an immune-mediated disease whose precise etiology is unknown. Several studies found alterations in the microbiome of individuals with MS, but the mechanism by which it may affect MS is poorly understood. Here we analyze the microbiome of 129 individuals with MS and find that they harbor distinct microbial patterns compared with controls. To study the functional consequences of these differences, we measure levels of 1,251 serum metabolites in a subgroup of subjects and unravel a distinct metabolite signature that separates affected individuals from controls nearly perfectly (AUC = 0.97). Individuals with MS are found to be depleted in butyrate-producing bacteria and in bacteria that produce indolelactate, an intermediate in generation of the potent neuroprotective antioxidant indolepropionate, which we found to be lower in their serum. We identify microbial and metabolite candidates that may contribute to MS and should be explored further for their causal role and therapeutic potential.

Identification of bacteria-derived HLA-bound peptides in melanoma.

A variety of species of bacteria are known to colonize human tumours1-11, proliferate within them and modulate immune function, which ultimately affects the survival of patients with cancer and their responses to treatment12-14. However, it is not known whether antigens derived from intracellular bacteria are presented by the human leukocyte antigen class I and II (HLA-I and HLA-II, respectively) molecules of tumour cells, or whether such antigens elicit a tumour-infiltrating T cell immune response. Here we used 16S rRNA gene sequencing and HLA peptidomics to identify a peptide repertoire derived from intracellular bacteria that was presented on HLA-I and HLA-II molecules in melanoma tumours. Our analysis of 17 melanoma metastases (derived from 9 patients) revealed 248 and 35 unique HLA-I and HLA-II peptides, respectively, that were derived from 41 species of bacteria. We identified recurrent bacterial peptides in tumours from different patients, as well as in different tumours from the same patient. Our study reveals that peptides derived from intracellular bacteria can be presented by tumour cells and elicit immune reactivity, and thus provides insight into a mechanism by which bacteria influence activation of the immune system and responses to therapy.

A reference map of potential determinants for the human serum metabolome.

The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites-in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites.

The human tumor microbiome is composed of tumor type-specific intracellular bacteria.

Bacteria were first detected in human tumors more than 100 years ago, but the characterization of the tumor microbiome has remained challenging because of its low biomass. We undertook a comprehensive analysis of the tumor microbiome, studying 1526 tumors and their adjacent normal tissues across seven cancer types, including breast, lung, ovary, pancreas, melanoma, bone, and brain tumors. We found that each tumor type has a distinct microbiome composition and that breast cancer has a particularly rich and diverse microbiome. The intratumor bacteria are mostly intracellular and are present in both cancer and immune cells. We also noted correlations between intratumor bacteria or their predicted functions with tumor types and subtypes, patients' smoking status, and the response to immunotherapy.

Structural variation in the gut microbiome associates with host health.

Differences in the presence of even a few genes between otherwise identical bacterial strains may result in critical phenotypic differences. Here we systematically identify microbial genomic structural variants (SVs) and find them to be prevalent in the human gut microbiome across phyla and to replicate in different cohorts. SVs are enriched for CRISPR-associated and antibiotic-producing functions and depleted from housekeeping genes, suggesting that they have a role in microbial adaptation. We find multiple associations between SVs and host disease risk factors, many of which replicate in an independent cohort. Exploring genes that are clustered in the same SV, we uncover several possible mechanistic links between the microbiome and its host, including a region in Anaerostipes hadrus that encodes a composite inositol catabolism-butyrate biosynthesis pathway, the presence of which is associated with lower host metabolic disease risk. Overall, our results uncover a nascent layer of variability in the microbiome that is associated with microbial adaptation and host health.

A Systematic p53 Mutation Library Links Differential Functional Impact to Cancer Mutation Pattern and Evolutionary Conservation.

The TP53 gene is frequently mutated in human cancer. Research has focused predominantly on six major "hotspot" codons, which account for only ∼30% of cancer-associated p53 mutations. To comprehensively characterize the consequences of the p53 mutation spectrum, we created a synthetically designed library and measured the functional impact of ∼10,000 DNA-binding domain (DBD) p53 variants in human cells in culture and in vivo. Our results highlight the differential outcome of distinct p53 mutations in human patients and elucidate the selective pressure driving p53 conservation throughout evolution. Furthermore, while loss of anti-proliferative functionality largely correlates with the occurrence of cancer-associated p53 mutations, we observe that selective gain-of-function may further favor particular mutants in vivo. Finally, when combined with additional acquired p53 mutations, seemingly neutral TP53 SNPs may modulate phenotypic outcome and, presumably, tumor progression.

Environment dominates over host genetics in shaping human gut microbiota.

Human gut microbiome composition is shaped by multiple factors but the relative contribution of host genetics remains elusive. Here we examine genotype and microbiome data from 1,046 healthy individuals with several distinct ancestral origins who share a relatively common environment, and demonstrate that the gut microbiome is not significantly associated with genetic ancestry, and that host genetics have a minor role in determining microbiome composition. We show that, by contrast, there are significant similarities in the compositions of the microbiomes of genetically unrelated individuals who share a household, and that over 20% of the inter-person microbiome variability is associated with factors related to diet, drugs and anthropometric measurements. We further demonstrate that microbiome data significantly improve the prediction accuracy for many human traits, such as glucose and obesity measures, compared to models that use only host genetic and environmental data. These results suggest that microbiome alterations aimed at improving clinical outcomes may be carried out across diverse genetic backgrounds.

Bread Affects Clinical Parameters and Induces Gut Microbiome-Associated Personal Glycemic Responses.

Bread is consumed daily by billions of people, yet evidence regarding its clinical effects is contradicting. Here, we performed a randomized crossover trial of two 1-week-long dietary interventions comprising consumption of either traditionally made sourdough-leavened whole-grain bread or industrially made white bread. We found no significant differential effects of bread type on multiple clinical parameters. The gut microbiota composition remained person specific throughout this trial and was generally resilient to the intervention. We demonstrate statistically significant interpersonal variability in the glycemic response to different bread types, suggesting that the lack of phenotypic difference between the bread types stems from a person-specific effect. We further show that the type of bread that induces the lower glycemic response in each person can be predicted based solely on microbiome data prior to the intervention. Together, we present marked personalization in both bread metabolism and the gut microbiome, suggesting that understanding dietary effects requires integration of person-specific factors.

Involvement of a gut-retina axis in protection against dietary glycemia-induced age-related macular degeneration.

Age-related macular degeneration (AMD) is the major cause of blindness in developed nations. AMD is characterized by retinal pigmented epithelial (RPE) cell dysfunction and loss of photoreceptor cells. Epidemiologic studies indicate important contributions of dietary patterns to the risk for AMD, but the mechanisms relating diet to disease remain unclear. Here we investigate the effect on AMD of isocaloric diets that differ only in the type of dietary carbohydrate in a wild-type aged-mouse model. The consumption of a high-glycemia (HG) diet resulted in many AMD features (AMDf), including RPE hypopigmentation and atrophy, lipofuscin accumulation, and photoreceptor degeneration, whereas consumption of the lower-glycemia (LG) diet did not. Critically, switching from the HG to the LG diet late in life arrested or reversed AMDf. LG diets limited the accumulation of advanced glycation end products, long-chain polyunsaturated lipids, and their peroxidation end-products and increased C3-carnitine in retina, plasma, or urine. Untargeted metabolomics revealed microbial cometabolites, particularly serotonin, as protective against AMDf. Gut microbiota were responsive to diet, and we identified microbiota in the Clostridiales order as being associated with AMDf and the HG diet, whereas protection from AMDf was associated with the Bacteroidales order and the LG diet. Network analysis revealed a nexus of metabolites and microbiota that appear to act within a gut-retina axis to protect against diet- and age-induced AMDf. The findings indicate a functional interaction between dietary carbohydrates, the metabolome, including microbial cometabolites, and AMDf. Our studies suggest a simple dietary intervention that may be useful in patients to arrest AMD.

Systematic Investigation of Transcription Factor Activity in the Context of Chromatin Using Massively Parallel Binding and Expression Assays.

Precise gene expression patterns are established by transcription factor (TFs) binding to regulatory sequences. While these events occur in the context of chromatin, our understanding of how TF-nucleosome interplay affects gene expression is highly limited. Here, we present an assay for high-resolution measurements of both DNA occupancy and gene expression on large-scale libraries of systematically designed regulatory sequences. Our assay reveals occupancy patterns at the single-cell level. It provides an accurate quantification of the fraction of the population bound by a nucleosome and captures distinct, even adjacent, TF binding events. By applying this assay to over 1,500 promoter variants in yeast, we reveal pronounced differences in the dependency of TF activity on chromatin and classify TFs by their differential capacity to alter chromatin and promote expression. We further demonstrate how different regulatory sequences give rise to nucleosome-mediated TF collaborations that quantitatively account for the resulting expression.

Large-scale mapping of gene regulatory logic reveals context-dependent repression by transcriptional activators.

Transcription factors (TFs) are key mediators that propagate extracellular and intracellular signals through to changes in gene expression profiles. However, the rules by which promoters decode the amount of active TF into target gene expression are not well understood. To determine the mapping between promoter DNA sequence, TF concentration, and gene expression output, we have conducted in budding yeast a large-scale measurement of the activity of thousands of designed promoters at six different levels of TF. We observe that maximum promoter activity is determined by TF concentration and not by the number of binding sites. Surprisingly, the addition of an activator site often reduces expression. A thermodynamic model that incorporates competition between neighboring binding sites for a local pool of TF molecules explains this behavior and accurately predicts both absolute expression and the amount by which addition of a site increases or reduces expression. Taken together, our findings support a model in which neighboring binding sites interact competitively when TF is limiting but otherwise act additively.