Genome sequencing reveals loci under artificial selection that underlie disease phenotypes in the laboratory rat.

Large numbers of inbred laboratory rat strains have been developed for a range of complex disease phenotypes. To gain insights into the evolutionary pressures underlying selection for these phenotypes, we sequenced the genomes of 27 rat strains, including 11 models of hypertension, diabetes, and insulin resistance, along with their respective control strains. Altogether, we identified more than 13 million single-nucleotide variants, indels, and structural variants across these rat strains. Analysis of strain-specific selective sweeps and gene clusters implicated genes and pathways involved in cation transport, angiotensin production, and regulators of oxidative stress in the development of cardiovascular disease phenotypes in rats. Many of the rat loci that we identified overlap with previously mapped loci for related traits in humans, indicating the presence of shared pathways underlying these phenotypes in rats and humans. These data represent a step change in resources available for evolutionary analysis of complex traits in disease models.

The impact of forced displacement: trauma, increased levels of inflammation and early presentation of diabetes in women Syrian refugees.

BACKGROUND: Forced displacement and war trauma cause high rates of post-traumatic stress, anxiety disorders and depression in refugee populations. We investigated the impact of forced displacement on mental health status, gender, presentation of type 2 diabetes (T2D) and associated inflammatory markers among Syrian refugees in Lebanon. METHODS: Mental health status was assessed using the Harvard Trauma Questionnaire (HTQ) and the Hopkins Symptom Checklist-25 (HSCL-25). Additional metabolic and inflammatory markers were analyzed. RESULTS: Although symptomatic stress scores were observed in both men and women, women consistently displayed higher symptomatic anxiety/depression scores with the HSCL-25 (2.13 ± 0.58 versus 1.95 ± 0.63). With the HTQ, however, only women aged 35-55 years displayed symptomatic post-traumatic stress disorder (PTSD) scores (2.18 ± 0.43). Furthermore, a significantly higher prevalence of obesity, prediabetes and undiagnosed T2D were observed in women participants (23.43, 14.91 and 15.18%, respectively). Significantly high levels of the inflammatory marker serum amyloid A were observed in women (11.90 ± 11.27 versus 9.28 ± 6.93, P = 0.036). CONCLUSIONS: Symptomatic PTSD, anxiety/depression coupled with higher levels of inflammatory marker and T2D were found in refugee women aged between 35 and 55 years favoring the strong need for psychosocial therapeutic interventions in moderating stress-related immune dysfunction and development of diabetes in this subset of female Syrian refugees.

Human and preclinical studies of the host-gut microbiome co-metabolite hippurate as a marker and mediator of metabolic health.

OBJECTIVE: Gut microbial products are involved in regulation of host metabolism. In human and experimental studies, we explored the potential role of hippurate, a hepatic phase 2 conjugation product of microbial benzoate, as a marker and mediator of metabolic health. DESIGN: In 271 middle-aged non-diabetic Danish individuals, who were stratified on habitual dietary intake, we applied 1H-nuclear magnetic resonance (NMR) spectroscopy of urine samples and shotgun-sequencing-based metagenomics of the gut microbiome to explore links between the urine level of hippurate, measures of the gut microbiome, dietary fat and markers of metabolic health. In mechanistic experiments with chronic subcutaneous infusion of hippurate to high-fat-diet-fed obese mice, we tested for causality between hippurate and metabolic phenotypes. RESULTS: In the human study, we showed that urine hippurate positively associates with microbial gene richness and functional modules for microbial benzoate biosynthetic pathways, one of which is less prevalent in the Bacteroides 2 enterotype compared with Ruminococcaceae or Prevotella enterotypes. Through dietary stratification, we identify a subset of study participants consuming a diet rich in saturated fat in which urine hippurate concentration, independently of gene richness, accounts for links with metabolic health. In the high-fat-fed mice experiments, we demonstrate causality through chronic infusion of hippurate (20 nmol/day) resulting in improved glucose tolerance and enhanced insulin secretion. CONCLUSION: Our human and experimental studies show that a high urine hippurate concentration is a general marker of metabolic health, and in the context of obesity induced by high-fat diets, hippurate contributes to metabolic improvements, highlighting its potential as a mediator of metabolic health.

Dominant gut Prevotella copri in gastrectomised non-obese diabetic Goto-Kakizaki rats improves glucose homeostasis through enhanced FXR signalling.

AIMS/HYPOTHESIS: Drug and surgical-based therapies in type 2 diabetes are associated with altered gut microbiota architecture. Here we investigated the role of the gut microbiome in improved glucose homeostasis following bariatric surgery. METHODS: We carried out gut microbiome analyses in gastrectomised (by vertical sleeve gastrectomy [VSG]) rats of the Goto-Kakizaki (GK) non-obese model of spontaneously occurring type 2 diabetes, followed by physiological studies in the GK rat. RESULTS: VSG in the GK rat led to permanent improvement of glucose tolerance associated with minor changes in the gut microbiome, mostly characterised by significant enrichment of caecal Prevotella copri. Gut microbiota enrichment with P. copri in GK rats through permissive antibiotic treatment, inoculation of gut microbiota isolated from gastrectomised GK rats, and direct inoculation of P. copri, resulted in significant improvement of glucose tolerance, independent of changes in body weight. Plasma bile acids were increased in GK rats following inoculation with P. copri and P. copri-enriched microbiota from VSG-treated rats; the inoculated GK rats then showed increased liver glycogen and upregulated expression of Fxr (also known as Nr1h4), Srebf1c, Chrebp (also known as Mlxipl) and Il10 and downregulated expression of Cyp7a1. CONCLUSIONS: Our data underline the impact of intestinal P. copri on improved glucose homeostasis through enhanced bile acid metabolism and farnesoid X receptor (FXR) signalling, which may represent a promising opportunity for novel type 2 diabetes therapeutics.

pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES 1H NMR spectra.

MOTIVATION: Data processing is a key bottleneck for 1H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1 D 1H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA ("pJRES Binning Algorithm"), which aims to extend the applicability of SRV to pJRES spectra. RESULTS: The performance of JBA is comprehensively evaluated using 617 plasma 1H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building. AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented using the MWASTools R/Bioconductor package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MWASTools: an R/bioconductor package for metabolome-wide association studies.

Summary: MWASTools is an R package designed to provide an integrated pipeline to analyse metabonomic data in large-scale epidemiological studies. Key functionalities of our package include: quality control analysis; metabolome-wide association analysis using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite assignment using statistical total correlation spectroscopy (STOCSY); and biological interpretation of metabolome-wide association studies results. Availability and implementation: The MWASTools R package is implemented in R (version > =3.4) and is available from Bioconductor: https://bioconductor.org/packages/MWASTools/. Contact: m.dumas@imperial.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Implication of gut microbiota metabolites in cardiovascular and metabolic diseases.

Evidence from the literature keeps highlighting the impact of mutualistic bacterial communities of the gut microbiota on human health. The gut microbita is a complex ecosystem of symbiotic bacteria which contributes to mammalian host biology by processing, otherwise, indigestible nutrients, supplying essential metabolites, and contributing to modulate its immune system. Advances in sequencing technologies have enabled structural analysis of the human gut microbiota and allowed detection of changes in gut bacterial composition in several common diseases, including cardiometabolic disorders. Biological signals sent by the gut microbiota to the host, including microbial metabolites and pro-inflammatory molecules, mediate microbiome-host genome cross-talk. This rapidly expanding line of research can identify disease-causing and disease-predictive microbial metabolite biomarkers, which can be translated into novel biodiagnostic tests, dietary supplements, and nutritional interventions for personalized therapeutic developments in common diseases. Here, we review results from the most significant studies dealing with the association of products from the gut microbial metabolism with cardiometabolic disorders. We underline the importance of these postbiotic biomarkers in the diagnosis and treatment of human disorders.

Untargeted Mass Spectrometry Lipidomics identifies correlation between serum sphingomyelins and plasma cholesterol.

BACKGROUND: Lipoproteins are major players in the development and progression of atherosclerotic plaques leading to coronary stenosis and myocardial infarction. Epidemiological, genetic and experimental observations have implicated the association of sphingolipids and intermediates of sphingolipid synthesis in atherosclerosis. We aimed to investigate relationships between quantitative changes in serum sphingolipids, the regulation of the metabolism of lipoproteins (LDL, HDL), and endophenotypes of coronary artery disease (CAD). METHODS: We carried out untargeted liquid chromatography - mass spectrometry (UPLC-MS) lipidomics of serum samples of subjects belonging to a cross-sectional study and recruited on the basis of absence or presence of angiographically-defined CAD, and extensively characterized for clinical and biochemical phenotypes. RESULTS: Among the 2998 spectral features detected in the serum samples, 1328 metabolic features were significantly correlated with at least one of the clinical or biochemical phenotypes measured in the cohort. We found evidence of significant associations between 34 metabolite signals, corresponding to a set of sphingomyelins, and serum HDL cholesterol. Many of these metabolite associations were also observed with serum LDL and total cholesterol levels but not as much with serum triglycerides. CONCLUSION: Among patients with CAD, sphingolipids in the form of sphingomyelins are directly correlated with serum levels of lipoproteins and total cholesterol. Results from this study support the fundamental role of sphingolipids in modulating lipid serum levels, highlighting the importance to identify novel targets in the sphingolipid metabolic pathway for anti-atherogenic therapies.

Molecular genetics of the transcription factor GLIS3 identifies its dual function in beta cells and neurons.

The GLIS family zinc finger 3 isoform (GLIS3) is a risk gene for Type 1 and Type 2 diabetes, glaucoma and Alzheimer's disease endophenotype. We identified GLIS3 binding sites in insulin secreting cells (INS1) (FDR q<0.05; enrichment range 1.40-9.11 fold) sharing the motif wrGTTCCCArTAGs, which were enriched in genes involved in neuronal function and autophagy and in risk genes for metabolic and neuro-behavioural diseases. We confirmed experimentally Glis3-mediated regulation of the expression of genes involved in autophagy and neuron function in INS1 and neuronal PC12 cells. Naturally-occurring coding polymorphisms in Glis3 in the Goto-Kakizaki rat model of type 2 diabetes were associated with increased insulin production in vitro and in vivo, suggestive alteration of autophagy in PC12 and INS1 and abnormal neurogenesis in hippocampus neurons. Our results support biological pleiotropy of GLIS3 in pathologies affecting ß-cells and neurons and underline the existence of trans­nosology pathways in diabetes and its co-morbidities.

Loss of Cardioprotective Effects at the ADAMTS7 Locus as a Result of Gene-Smoking Interactions.

BACKGROUND: Common diseases such as coronary heart disease (CHD) are complex in etiology. The interaction of genetic susceptibility with lifestyle factors may play a prominent role. However, gene-lifestyle interactions for CHD have been difficult to identify. Here, we investigate interaction of smoking behavior, a potent lifestyle factor, with genotypes that have been shown to associate with CHD risk. METHODS: We analyzed data on 60 919 CHD cases and 80 243 controls from 29 studies for gene-smoking interactions for genetic variants at 45 loci previously reported to be associated with CHD risk. We also studied 5 loci associated with smoking behavior. Study-specific gene-smoking interaction effects were calculated and pooled using fixed-effects meta-analyses. Interaction analyses were declared to be significant at a P value of <1.0×10-3 (Bonferroni correction for 50 tests). RESULTS: We identified novel gene-smoking interaction for a variant upstream of the ADAMTS7 gene. Every T allele of rs7178051 was associated with lower CHD risk by 12% in never-smokers (P=1.3×10-16) in comparison with 5% in ever-smokers (P=2.5×10-4), translating to a 60% loss of CHD protection conferred by this allelic variation in people who smoked tobacco (interaction P value=8.7×10-5). The protective T allele at rs7178051 was also associated with reduced ADAMTS7 expression in human aortic endothelial cells and lymphoblastoid cell lines. Exposure of human coronary artery smooth muscle cells to cigarette smoke extract led to induction of ADAMTS7. CONCLUSIONS: Allelic variation at rs7178051 that associates with reduced ADAMTS7 expression confers stronger CHD protection in never-smokers than in ever-smokers. Increased vascular ADAMTS7 expression may contribute to the loss of CHD protection in smokers.

MetaboSignal: a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways.

Summary: MetaboSignal is an R package that allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). It is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes. Availability and Implementation: MetaboSignal is available from Bioconductor: https://bioconductor.org/packages/MetaboSignal/. Contact: m.dumas@imperial.ac.uk . Supplementary information: Supplementary data are available at Bioinformatics online.

Biological roles of microRNAs in the control of insulin secretion and action.

microRNAs (miRNAs) are intracellular and circulating molecular components contributing to genome expression control through binding mRNA targets, which generally results in downregulated mRNA expression. One miRNA can target several mRNAs, and one transcript can be targeted by several miRNAs, resulting in complex fine-tuning of regulation of gene networks and signaling pathways. miRNAs regulate metabolism, adipocyte differentiation, pancreatic development, ß-cell mass, insulin biosynthesis, secretion, and signaling, and their role in diabetes and obesity is emerging. Their pathophysiological effects are essentially dependent on cellular coexpression with their mRNA targets, which can show tissue-specific transcriptional responses to disease conditions and environmental challenges. Current knowledge of miRNA biology and their impact on the pathogenesis of diabetes and obesity is based on experimental data documenting miRNA expression generally in single tissue types that can be correlated with expression of target mRNAs to integrate miRNA in functional pathways and gene networks. Here we present results from the most significant studies dealing with miRNA function in liver, fat, skeletal muscle, and endocrine pancreas and their implication in diabetes and obesity.

J-Resolved 1H NMR 1D-Projections for Large-Scale Metabolic Phenotyping Studies: Application to Blood Plasma Analysis.

1H nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping is now widely used for large-scale epidemiological applications. To minimize signal overlap present in 1D 1H NMR spectra, we have investigated the use of 2D J-resolved (JRES) 1H NMR spectroscopy for large-scale phenotyping studies. In particular, we have evaluated the use of the 1D projections of the 2D JRES spectra (pJRES), which provide single peaks for each of the J-coupled multiplets, using 705 human plasma samples from the FGENTCARD cohort. On the basis of the assessment of several objective analytical criteria (spectral dispersion, attenuation of macromolecular signals, cross-spectral correlation with GC-MS metabolites, analytical reproducibility and biomarker discovery potential), we concluded that the pJRES approach exhibits suitable properties for implementation in large-scale molecular epidemiology workflows.

Application of quantitative metabolomics in systems genetics in rodent models of complex phenotypes.

Genome-wide association studies (GWAS) have provided remarkable advances in our understanding of the etiology of complex diseases in humans and have underlined the need to improve patients' phenotype characterization with intermediate molecular phenotypes. High resolution metabolomics is becoming an increasingly popular and robust strategy for metabolic phenotyping large cohorts of patients and controls in genetic studies, in order to map the genetic control of metabotypes in various biological matrices (organ extracts and biofluids) through Quantitative Trait Locus (mQTL) analysis. This article reviews results from ongoing research in mQTL mapping in rodent models of human complex traits, with a specific focus on the cardiometabolic syndrome, and prospects of applications of untargeted metabolomics to improve knowledge of multilevel genome expression control in health and disease and to detect potential novel biomarkers for complex phenotypes in experimental systems in mice and rats.

A multiplexed targeted assay for high-throughput quantitative analysis of serum methylamines by ultra performance liquid chromatography coupled to high resolution mass spectrometry.

Methylamines are biologically-active metabolites present in serum and urine samples, which play complex roles in metabolic diseases. Methylamines can be detected by proton nuclear magnetic resonance (NMR), but specific methods remain to be developed for their routine assay in human serum in clinical settings. Here we developed and validated a novel reliable "methylamine panel" method for simultaneous quantitative analysis of trimethylamine (TMA), its major detoxification metabolite trimethylamine-N-oxide (TMAO), and precursors choline, betaine and l-carnitine in human serum using Ultra Performance Liquid Chromatography (UPLC) coupled to High Resolution Mass Spectrometry (HRMS). Metabolite separation was carried out on a HILIC stationary phase. For all metabolites, the assay was linear in the range of 0.25-12.5 µmol/L and enabled to reach limit of detection of about 0.10 µmol/L. Relative standard deviations were below 16% for the three levels of concentrations. We demonstrated the strong reliability and robustness of the method, which was applied to serum samples from healthy individuals to establish the range of concentrations of the metabolites and their correlation relationships and detect gender differences. Our data provide original information for implementing in a clinical environment a MS-based diagnostic method with potential for targeted metabolic screening of patients at risk of cardiometabolic diseases.

Caffeine Impact on Metabolic Syndrome Components Is Modulated by a CYP1A2 Variant.

Cultural, dietary, and lifestyle factors are the main modulators of type 2 diabetes mellitus (T2DM) disease risk. Coffee is one of the most popular worldwide beverages, and recent epidemiological studies have showed that coffee consumption is associated with a lower risk of T2DM. This study investigates the impact of coffee intake on T2DM risk and assesses the effect of CYP variants with caffeine exposures on T2DM. Data from 7,607 study subjects were analyzed by logistic regression models, among whom 3,290 GWAS data were available for CYP variants association studies using Plink analysis. These data suggest a protective relationship for women, but not for men; however, the results were not statistically significant in this dataset and there is a significant interaction in favor of women regarding heavy coffee consumption. The interaction between male gender and heavy coffee consumption becomes significant, thereby tending to cancel the protective effect of coffee for males. CYP rs2470890 allele 'C' increases the odds of T2DM by a factor of around 1.2 but decreases the odds of caffeine boosting T2DM of 1.7 by a factor of 0.77. rs2470890 showed an association with T2DM only when the interaction with coffee was considered, thereby setting an example of genetic activation by dietary changes associating with metabolic syndrome.

Genome-wide diversity in the levant reveals recent structuring by culture.

The Levant is a region in the Near East with an impressive record of continuous human existence and major cultural developments since the Paleolithic period. Genetic and archeological studies present solid evidence placing the Middle East and the Arabian Peninsula as the first stepping-stone outside Africa. There is, however, little understanding of demographic changes in the Middle East, particularly the Levant, after the first Out-of-Africa expansion and how the Levantine peoples relate genetically to each other and to their neighbors. In this study we analyze more than 500,000 genome-wide SNPs in 1,341 new samples from the Levant and compare them to samples from 48 populations worldwide. Our results show recent genetic stratifications in the Levant are driven by the religious affiliations of the populations within the region. Cultural changes within the last two millennia appear to have facilitated/maintained admixture between culturally similar populations from the Levant, Arabian Peninsula, and Africa. The same cultural changes seem to have resulted in genetic isolation of other groups by limiting admixture with culturally different neighboring populations. Consequently, Levant populations today fall into two main groups: one sharing more genetic characteristics with modern-day Europeans and Central Asians, and the other with closer genetic affinities to other Middle Easterners and Africans. Finally, we identify a putative Levantine ancestral component that diverged from other Middle Easterners ∼23,700-15,500 years ago during the last glacial period, and diverged from Europeans ∼15,900-9,100 years ago between the last glacial warming and the start of the Neolithic.

Direct quantitative trait locus mapping of mammalian metabolic phenotypes in diabetic and normoglycemic rat models.

Characterizing the relationships between genomic and phenotypic variation is essential to understanding disease etiology. Information-dense data sets derived from pathophysiological, proteomic and transcriptomic profiling have been applied to map quantitative trait loci (QTLs). Metabolic traits, already used in QTL studies in plants, are essential phenotypes in mammalian genetics to define disease biomarkers. Using a complex mammalian system, here we show chromosomal mapping of untargeted plasma metabolic fingerprints derived from NMR spectroscopic analysis in a cross between diabetic and control rats. We propose candidate metabolites for the most significant QTLs. Metabolite profiling in congenic strains provided evidence of QTL replication. Linkage to a gut microbial metabolite (benzoate) can be explained by deletion of a uridine diphosphate glucuronosyltransferase. Mapping metabotypic QTLs provides a practical approach to understanding genome-phenotype relationships in mammals and may uncover deeper biological complexity, as extended genome (microbiome) perturbations that affect disease processes through transgenomic effects may influence QTL detection.

The SAM domain of ANKS6 has different interacting partners and mutations can induce different cystic phenotypes.

The ankyrin repeat and sterile α motif (SAM) domain-containing six gene (Anks6) is a candidate for polycystic kidney disease (PKD). Originally identified in the PKD/Mhm(cy/+) rat model of PKD, the disease is caused by a mutation (R823W) in the SAM domain of the encoded protein. Recent studies support the etiological role of the ANKS6 SAM domain in human cystic diseases, but its function in kidney remains unknown. To investigate the role of ANKS6 in cyst formation, we screened an archive of N-ethyl-N-nitrosourea-treated mice and derived a strain carrying a missense mutation (I747N) within the SAM domain of ANKS6. This mutation is only six amino acids away from the PKD-causing mutation (R823W) in cy/+ rats. Evidence of renal cysts in these mice confirmed the crucial role of the SAM domain of ANKS6 in kidney function. Comparative phenotype analysis in cy/+ rats and our Anks6(I747N) mice further showed that the two models display noticeably different PKD phenotypes and that there is a defective interaction between ANKS6 with ANKS3 in the rat and between ANKS6 and BICC1 (bicaudal C homolog 1) in the mouse. Thus, our data demonstrate the importance of ANKS6 for kidney structure integrity and the essential mediating role of its SAM domain in the formation of protein complexes.

mQTL.NMR: an integrated suite for genetic mapping of quantitative variations of (1)H NMR-based metabolic profiles.

High-throughput (1)H nuclear magnetic resonance (NMR) is an increasingly popular robust approach for qualitative and quantitative metabolic profiling, which can be used in conjunction with genomic techniques to discover novel genetic associations through metabotype quantitative trait locus (mQTL) mapping. There is therefore a crucial necessity to develop specialized tools for an accurate detection and unbiased interpretability of the genetically determined metabolic signals. Here we introduce and implement a combined chemoinformatic approach for objective and systematic analysis of untargeted (1)H NMR-based metabolic profiles in quantitative genetic contexts. The R/Bioconductor mQTL.NMR package was designed to (i) perform a series of preprocessing steps restoring spectral dependency in collinear NMR data sets to reduce the multiple testing burden, (ii) carry out robust and accurate mQTL mapping in human cohorts as well as in rodent models, (iii) statistically enhance structural assignment of genetically determined metabolites, and (iv) illustrate results with a series of visualization tools. Built-in flexibility and implementation in the powerful R/Bioconductor framework allow key preprocessing steps such as peak alignment, normalization, or dimensionality reduction to be tailored to specific problems. The mQTL.NMR package is freely available with its source code through the Comprehensive R/Bioconductor repository and its own website ( http://www.ican-institute.org/tools/ ). It represents a significant advance to facilitate untargeted metabolomic data processing and quantitative analysis and their genetic mapping.