Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites

mGWAS: next generation genetic prediction in kidney disease

A recent metabolite genome-wide association study (mGWAS) investigated the relationship between genetic factors and the urine metabolome in kidney disease. The findings demonstrate that mGWAS hold promise for identifying novel genetic factors involved in adsorption, distribution, metabolism and excretion of metabolites and pharmaceuticals, as well as biomarkers for disease progression.

The application of 'omics' to pulmonary arterial hypertension

Recent genome-wide analyses of rare and common sequence variations have brought greater clarity to the genetic architecture of pulmonary arterial hypertension and implicated novel genes in disease development. Transcriptional signatures have been reported in whole lung tissue, pulmonary vascular cells and peripheral circulating cells. High-throughput platforms for plasma proteomics and metabolomics have identified novel biomarkers associated with clinical outcomes and provided molecular instruments for risk assessment. There are methodological challenges to integrating these datasets, coupled to statistical power limitations inherent to the study of a rare disease, but the expectation is that this approach will reveal novel druggable targets and biomarkers that will open the way to personalized medicine. Here, we review the current state-of-the-art and future promise of 'omics' in the field of translational medicine in pulmonary arterial hypertension. LINKED ARTICLES: This article is part of a themed issue on Risk factors, comorbidities, and comedications in cardioprotection. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v178.1/issuetoc.

How Natural Genetic Variation Shapes Behavior

Nervous systems allow animals to acutely respond and behaviorally adapt to changes and recurring patterns in their environment at multiple timescales-from milliseconds to years. Behavior is further shaped at intergenerational timescales by genetic variation, drift, and selection. This sophistication and flexibility of behavior makes it challenging to measure behavior consistently in individual subjects and to compare it across individuals. In spite of these challenges, careful behavioral observations in nature and controlled measurements in the laboratory, combined with modern technologies and powerful genetic approaches, have led to important discoveries about the way genetic variation shapes behavior. A critical mass of genes whose variation is known to modulate behavior in nature is finally accumulating, allowing us to recognize emerging patterns. In this review, we first discuss genetic mapping approaches useful for studying behavior. We then survey how variation acts at different levels-in environmental sensation, in internal neuronal circuits, and outside the nervous system altogether-and then discuss the sources and types of molecular variation linked to behavior and the mechanisms that shape such variation. We end by discussing remaining questions in the field.

Genome-wide scan identifies novel genetic loci regulating salivary metabolite levels

Saliva, as a biofluid, is inexpensive and non-invasive to obtain, and provides a vital tool to investigate oral health and its interaction with systemic health conditions. There is growing interest in salivary biomarkers for systemic diseases, notably cardiovascular disease. Whereas hundreds of genetic loci have been shown to be involved in the regulation of blood metabolites, leading to significant insights into the pathogenesis of complex human diseases, little is known about the impact of host genetics on salivary metabolites. Here we report the first genome-wide association study exploring 476 salivary metabolites in 1419 subjects from the TwinsUK cohort (discovery phase), followed by replication in the Study of Health in Pomerania (SHIP-2) cohort. A total of 14 distinct locus-metabolite associations were identified in the discovery phase, most of which were replicated in SHIP-2. While only a limited number of the loci that are known to regulate blood metabolites were also associated with salivary metabolites in our study, we identified several novel saliva-specific locus-metabolite associations, including associations for the AGMAT (with the metabolites 4-guanidinobutanoate and beta-guanidinopropanoate), ATP13A5 (with the metabolite creatinine) and DPYS (with the metabolites 3-ureidopropionate and 3-ureidoisobutyrate) loci. Our study suggests that there may be regulatory pathways of particular relevance to the salivary metabolome. In addition, some of our findings may have clinical significance, such as the utility of the pyrimidine (uracil) degradation metabolites in predicting 5-fluorouracil toxicity and the role of the agmatine pathway metabolites as biomarkers of oral health.

Traditional Chinese Medicine Formula Kang Shuai Lao Pian Improves Obesity, Gut Dysbiosis, and Fecal Metabolic Disorders in High-Fat Diet-Fed Mice

High-fat diet (HFD)-induced obesity is a risk factor for many metabolic disorders including cardiovascular diseases, diabetes, and fatty liver disease. Although there are accumulating evidences supporting the assumption that regulating gut microbiota as well as its metabolic status is able to mitigate obesity, the inner relationship between the obesity-related gut microbiota and the relevant metabolites are not well defined. In current study, we applied a traditional herbal formula Kang Shuai Lao Pian (KSLP) to HFD-fed mice and evaluated its effect against obesity. Emphases were addressed on identifying profiles of gut microbiota and fecal metabolites with the aid of 16S rRNA gene sequencing and non-target fecal metabolomics techniques. We showed that KSLP could improve HFD-induced obesity, glucose tolerance disorder, as well as gut dysbiosis. In the gut, KSLP corrected the increased abundance of Firmicutes and Proteobacteria, increased ratio of Firmicutes/Bacteroidetes, and decreased abundance of Bacteroidetes caused by HFD. KSLP also reversed HFD-induced significant changes in the abundance of certain genus including Intestinimonas, Oscillibacter, Christensenellaceae_R-7_group, Ruminococcaceae_UCG-010, and Aliihoeflea. Pearson correlation analysis indicated that except for Ruminococcaceae_UCG-010, other four genera had positive correlations with obesity. In addition, 22 key fecal metabolites responding to KSLP treatment were identified. Pearson correlation analysis showed that those metabolites are intimately related to KSLP effective genera of Intestinimonas, Oscillibacter, and Christensenellaceae_R-7_group. Our results indicate that KSLP is a promising traditional Chinese medicine (TCM) applicable for individuals with HFD habit. Intestinimonas, Oscillibacter, and Christensenellaceae_R-7_group might be responsible for the regulatory effect of KSLP. Linking of obesity phenotypes with gut microbiota as well as fecal metabolites is therefore a powerful research strategy to reveal the mechanism of obesity and the targets of intervention.

Systems Biology Analysis Reveals Eight SLC22 Transporter Subgroups, Including OATs, OCTs, and OCTNs

The SLC22 family of OATs, OCTs, and OCTNs is emerging as a central hub of endogenous physiology. Despite often being referred to as “drug” transporters, they facilitate the movement of metabolites and key signaling molecules. An in-depth reanalysis supports a reassignment of these proteins into eight functional subgroups, with four new subgroups arising from the previously defined OAT subclade: OATS1 (SLC22A6, SLC22A8, and SLC22A20), OATS2 (SLC22A7), OATS3 (SLC22A11, SLC22A12, and Slc22a22), and OATS4 (SLC22A9, SLC22A10, SLC22A24, and SLC22A25). We propose merging the OCTN (SLC22A4, SLC22A5, and Slc22a21) and OCT-related (SLC22A15 and SLC22A16) subclades into the OCTN/OCTN-related subgroup. Using data from GWAS, in vivo models, and in vitro assays, we developed an SLC22 transporter-metabolite network and similar subgroup networks, which suggest how multiple SLC22 transporters with mono-, oligo-, and multi-specific substrate specificity interact to regulate metabolites. Subgroup associations include: OATS1 with signaling molecules, uremic toxins, and odorants, OATS2 with cyclic nucleotides, OATS3 with uric acid, OATS4 with conjugated sex hormones, particularly etiocholanolone glucuronide, OCT with neurotransmitters, and OCTN/OCTN-related with ergothioneine and carnitine derivatives. Our data suggest that the SLC22 family can work among itself, as well as with other ADME genes, to optimize levels of numerous metabolites and signaling molecules, involved in organ crosstalk and inter-organismal communication, as proposed by the remote sensing and signaling theory.

Sex and APOE ε4 genotype modify the Alzheimer's disease serum metabolome

Late-onset Alzheimer’s disease (AD) can, in part, be considered a metabolic disease. Besides age, female sex and APOE ε4 genotype represent strong risk factors for AD that also give rise to large metabolic differences. We systematically investigated group-specific metabolic alterations by conducting stratified association analyses of 139 serum metabolites in 1,517 individuals from the AD Neuroimaging Initiative with AD biomarkers. We observed substantial sex differences in effects of 15 metabolites with partially overlapping differences for APOE ε4 status groups. Several group-specific metabolic alterations were not observed in unstratified analyses using sex and APOE ε4 as covariates. Combined stratification revealed further subgroup-specific metabolic effects limited to APOE ε4+ females. The observed metabolic alterations suggest that females experience greater impairment of mitochondrial energy production than males. Dissecting metabolic heterogeneity in AD pathogenesis can therefore enable grading the biomedical relevance for specific pathways within specific subgroups, guiding the way to personalized medicine.

Sex and the APOE ε4 genotype are important risk factors for late-onset Alzheimer’s disease. In the current study, the authors investigate how sex and APOE ε4 genotype modify the association between Alzheimer’s disease biomarkers and metabolites in serum.

Eagle: multi-locus association mapping on a genome-wide scale made routine

MOTIVATION

We present Eagle, a new method for multi-locus association mapping. The motivation for developing Eagle was to make multi-locus association mapping 'easy' and the method-of-choice. Eagle's strengths are that it (i) is considerably more powerful than single-locus association mapping, (ii) does not suffer from multiple testing issues, (iii) gives results that are immediately interpretable and (iv) has a computational footprint comparable to single-locus association mapping.

RESULTS

By conducting a large simulation study, we will show that Eagle finds true and avoids false single-nucleotide polymorphism trait associations better than competing single- and multi-locus methods. We also analyze data from a published mouse study. Eagle found over 50% more validated findings than the state-of-the-art single-locus method.

AVAILABILITY AND IMPLEMENTATION

Eagle has been implemented as an R package, with a browser-based Graphical User Interface for users less familiar with R. It is freely available via the CRAN website at https://cran.r-project.org. Videos, Quick Start guides, FAQs and Demos are available via the Eagle website http://eagle.r-forge.r-project.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine

Quantitative trait loci (QTL) analysis is an important approach to investigate the effects of genetic variants identified through an increasing number of large-scale, multidimensional 'omics data sets. In this 'big data' era, the research community has identified a significant number of molecular QTLs (molQTLs) and increased our understanding of their effects. Herein, we review multiple categories of molQTLs, including those associated with transcriptome, post-transcriptional regulation, epigenetics, proteomics, metabolomics, and the microbiome. We summarize approaches to identify molQTLs and to infer their causal effects. We further discuss the integrative analysis of molQTLs through a multi-omics perspective. Our review highlights future opportunities to better understand the functional significance of genetic variants and to utilize the discovery of molQTLs in precision medicine.

The promise and reality of therapeutic discovery from large cohorts

Technological advances in rapid data acquisition have transformed medical biology into a data mining field, where new data sets are routinely dissected and analyzed by statistical models of ever-increasing complexity. Many hypotheses can be generated and tested within a single large data set, and even small effects can be statistically discriminated from a sea of noise. On the other hand, the development of therapeutic interventions moves at a much slower pace. They are determined from carefully randomized and well-controlled experiments with explicitly stated outcomes as the principal mechanism by which a single hypothesis is tested. In this paradigm, only a small fraction of interventions can be tested, and an even smaller fraction are ultimately deemed therapeutically successful. In this Review, we propose strategies to leverage large-cohort data to inform the selection of targets and the design of randomized trials of novel therapeutics. Ultimately, the incorporation of big data and experimental medicine approaches should aim to reduce the failure rate of clinical trials as well as expedite and lower the cost of drug development.

Genetic drivers of cerebral blood flow dysfunction in TBI: a speculative synthesis

Cerebral autoregulatory dysfunction after traumatic brain injury (TBI) is strongly linked to poor global outcome in patients at 6 months after injury. However, our understanding of the drivers of this dysfunction is limited. Genetic variation among individuals within a population gives rise to single-nucleotide polymorphisms (SNPs) that have the potential to influence a given patient's cerebrovascular response to an injury. Associations have been reported between a variety of genetic polymorphisms and global outcome in patients with TBI, but few studies have explored the association between genetic variants and cerebrovascular function after injury. In this Review, we explore polymorphisms that might play an important part in cerebral autoregulatory capacity after TBI. We outline a variety of SNPs, their biological substrates and their potential role in mediating cerebrovascular reactivity. A number of candidate polymorphisms exist in genes that are involved in myogenic, endothelial, metabolic and neurogenic vascular responses to injury. Furthermore, polymorphisms in genes involved in inflammation, the central autonomic response and cortical spreading depression might drive cerebrovascular reactivity. Identification of candidate genes involved in cerebral autoregulation after TBI provides a platform and rationale for further prospective investigation of the link between genetic polymorphisms and autoregulatory function.

Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts

Understanding the impact of rare variants is essential to understanding human health. We analyze rare (MAF < 0.1%) variants against 4264 phenotypes in 49,960 exome-sequenced individuals from the UK Biobank and 1934 phenotypes (1821 overlapping with UK Biobank) in 21,866 members of the Healthy Nevada Project (HNP) cohort who underwent Exome + sequencing at Helix. After using our rare-variant-tailored methodology to reduce test statistic inflation, we identify 64 statistically significant gene-based associations in our meta-analysis of the two cohorts and 37 for phenotypes available in only one cohort. Singletons make significant contributions to our results, and the vast majority of the associations could not have been identified with a genotyping chip. Our results are available for interactive browsing in a webapp (https://ukb.research.helix.com). This comprehensive analysis illustrates the biological value of large, deeply phenotyped cohorts of unselected populations coupled with NGS data.

Precision medicine integrating whole-genome sequencing, comprehensive metabolomics, and advanced imaging

To understand the value and clinical impact of surveying genome-wide disease-causing genes and variants, we used a prospective cohort study design that enrolled volunteers who agreed to have their whole genome sequenced and to participate in deep phenotyping using clinical laboratory tests, metabolomics technologies, and advanced noninvasive imaging. The genomic results are integrated with the phenotype results. Approximately 1 in 6 adult individuals (17.3%) had genetic findings and, when integrated with deep phenotyping data, including family/medical histories with genetic findings, 1 in 9 (11.5%) had genotype and phenotype associations. Genomics and metabolomics association analysis revealed 5.1% of heterozygotes with phenotype manifestations affecting serum metabolite levels. We report observations from our study in which health outcomes and benefits were not measured.

Genome sequencing has established clinical utility for rare disease diagnosis. While increasing numbers of individuals have undergone elective genome sequencing, a comprehensive study surveying genome-wide disease-associated genes in adults with deep phenotyping has not been reported. Here we report the results of a 3-y precision medicine study with a goal to integrate whole-genome sequencing with deep phenotyping. A cohort of 1,190 adult participants (402 female [33.8%]; mean age, 54 y [range 20 to 89+]; 70.6% European) had whole-genome sequencing, and were deeply phenotyped using metabolomics, advanced imaging, and clinical laboratory tests in addition to family/medical history. Of 1,190 adults, 206 (17.3%) had at least 1 genetic variant with pathogenic (P) or likely pathogenic (LP) assessment that suggests a predisposition of genetic risk. A multidisciplinary clinical team reviewed all reportable findings for the assessment of genotype and phenotype associations, and 137 (11.5%) had genotype and phenotype associations. A high percentage of genotype and phenotype associations (>75%) was observed for dyslipidemia (n = 24), cardiomyopathy, arrhythmia, and other cardiac diseases (n = 42), and diabetes and endocrine diseases (n = 17). A lack of genotype and phenotype associations, a potential burden for patient care, was observed in 69 (5.8%) individuals with P/LP variants. Genomics and metabolomics associations identified 61 (5.1%) heterozygotes with phenotype manifestations affecting serum metabolite levels in amino acid, lipid and cofactor, and vitamin pathways. Our descriptive analysis provides results on the integration of whole-genome sequencing and deep phenotyping for clinical assessments in adults.

Senescence-Associated Metabolomic Phenotype in Primary and iPSC-Derived Mesenchymal Stromal Cells

Long-term culture of primary cells is characterized by functional and secretory changes, which ultimately result in replicative senescence. It is largely unclear how the metabolome of cells changes during replicative senescence and if such changes are consistent across different cell types. We have directly compared culture expansion of primary mesenchymal stromal cells (MSCs) and induced pluripotent stem cell-derived MSCs (iMSCs) until they reached growth arrest. Both cell types acquired similar changes in morphology, in vitro differentiation potential, senescence-associated β-galactosidase, and DNA methylation. Furthermore, MSCs and iMSCs revealed overlapping gene expression changes, particularly in functional categories related to metabolic processes. We subsequently compared the metabolomes of MSCs and iMSCs and observed overlapping senescence-associated changes in both cell types, including downregulation of nicotinamide ribonucleotide and upregulation of orotic acid. Taken together, replicative senescence is associated with a highly reproducible senescence-associated metabolomics phenotype, which may be used to monitor the state of cellular aging.

Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans

The kidneys integrate information from continuous systemic processes related to the absorption, distribution, metabolism and excretion (ADME) of metabolites. To identify underlying molecular mechanisms, we performed genome-wide association studies of the urinary concentrations of 1,172 metabolites among 1,627 patients with reduced kidney function. The 240 unique metabolite-locus associations (metabolite quantitative trait loci, mQTLs) that were identified and replicated highlight novel candidate substrates for transport proteins. The identified genes are enriched in ADME-relevant tissues and cell types, and they reveal novel candidates for biotransformation and detoxification reactions. Fine mapping of mQTLs and integration with single-cell gene expression permitted the prioritization of causal genes, functional variants and target cell types. The combination of mQTLs with genetic and health information from 450,000 UK Biobank participants illuminated metabolic mediators, and hence, novel urinary biomarkers of disease risk. This comprehensive resource of genetic targets and their substrates is informative for ADME processes in humans and is relevant to basic science, clinical medicine and pharmaceutical research.

Dose-escalation trial of budesonide in surfactant for prevention of bronchopulmonary dysplasia in extremely low gestational age high-risk newborns (SASSIE)

BACKGROUND

Initial trials of lung-targeted budesonide (0.25 mg/kg) in surfactant to prevent bronchopulmonary dysplasia (BPD) in premature infants have shown benefit; however, the optimal safe dose is unknown.

METHODS

Dose-escalation study of budesonide (0.025, 0.05, 0.10 mg/kg) in calfactatant in extremely low gestational age neonates (ELGANs) requiring intubation at 3-14 days. Tracheal aspirate (TA) cytokines, blood budesonide concentrations, and untargeted blood metabolomics were measured. Outcomes were compared with matched infants receiving surfactant in the Trial Of Late SURFactant (TOLSURF).

RESULTS

Twenty-four infants with mean gestational age 25.0 weeks and 743 g birth weight requiring mechanical ventilation were enrolled at mean age 6 days. Budesonide was detected in the blood of all infants with a half-life of 3.4 h. Of 11 infants with elevated TA cytokine levels at baseline, treatment was associated with sustained decrease (mean 65%) at all three dosing levels. There were time- and dose-dependent decreases in blood cortisol concentrations and changes in total blood metabolites. Respiratory outcomes did not differ from the historic controls.

CONCLUSIONS

Budesonide/surfactant had no clinical respiratory benefit at any dosing levels for intubated ELGANs. One-tenth the dose used in previous trials had minimal systemic metabolic effects and appeared effective for lung-targeted anti-inflammatory action.

Global metabolic profiling to model biological processes of aging in twins

Aging is intimately linked to system-wide metabolic changes that can be captured in blood. Understanding biological processes of aging in humans could help maintain a healthy aging trajectory and promote longevity. We performed untargeted plasma metabolomics quantifying 770 metabolites on a cross-sectional cohort of 268 healthy individuals including 125 twin pairs covering human lifespan (from 6 months to 82 years). Unsupervised clustering of metabolic profiles revealed 6 main aging trajectories throughout life that were associated with key metabolic pathways such as progestin steroids, xanthine metabolism, and long-chain fatty acids. A random forest (RF) model was successful to predict age in adult subjects (≥16 years) using 52 metabolites (R²  = .97). Another RF model selected 54 metabolites to classify pediatric and adult participants (out-of-bag error = 8.58%). These RF models in combination with correlation network analysis were used to explore biological processes of healthy aging. The models highlighted established metabolites, like steroids, amino acids, and free fatty acids as well as novel metabolites and pathways. Finally, we show that metabolic profiles of twins become more dissimilar with age which provides insights into nongenetic age-related variability in metabolic profiles in response to environmental exposure.

Metabolic GWAS of elite athletes reveals novel genetically-influenced metabolites associated with athletic performance

Genetic research of elite athletic performance has been hindered by the complex phenotype and the relatively small effect size of the identified genetic variants. The aims of this study were to identify genetic predisposition to elite athletic performance by investigating genetically-influenced metabolites that discriminate elite athletes from non-elite athletes and to identify those associated with endurance sports. By conducting a genome wide association study with high-resolution metabolomics profiling in 490 elite athletes, common variant metabolic quantitative trait loci (mQTLs) were identified and compared with previously identified mQTLs in non-elite athletes. Among the identified mQTLs, those associated with endurance metabolites were determined. Two novel genetic loci in FOLH1 and VNN1 are reported in association with N-acetyl-aspartyl-glutamate and Linoleoyl ethanolamide, respectively. When focusing on endurance metabolites, one novel mQTL linking androstenediol (3alpha, 17alpha) monosulfate and SULT2A1 was identified. Potential interactions between the novel identified mQTLs and exercise are highlighted. This is the first report of common variant mQTLs linked to elite athletic performance and endurance sports with potential applications in biomarker discovery in elite athletic candidates, non-conventional anti-doping analytical approaches and therapeutic strategies.

Multivariate Analysis of Plasma Metabolites in Children with Autism Spectrum Disorder and Gastrointestinal Symptoms Before and After Microbiota Transfer Therapy

Current diagnosis of autism spectrum disorder (ASD) is based on assessment of behavioral symptoms, although there is strong evidence that ASD affects multiple organ systems including the gastrointestinal (GI) tract. This study used Fisher discriminant analysis (FDA) to evaluate plasma metabolites from 18 children with ASD and chronic GI problems (ASD + GI cohort) and 20 typically developing (TD) children without GI problems (TD − GI cohort). Using three plasma metabolites that may represent three general groups of metabolic abnormalities, it was possible to distinguish the ASD + GI cohort from the TD − GI cohort with 94% sensitivity and 100% specificity after leave-one-out cross-validation. After the ASD + GI participants underwent Microbiota Transfer Therapy with significant improvement in GI and ASD-related symptoms, their metabolic profiles shifted significantly to become more similar to the TD − GI group, indicating potential utility of this combination of plasma metabolites as a biomarker for treatment efficacy. Two of the metabolites, sarcosine and inosine 5′-monophosphate, improved greatly after treatment. The third metabolite, tyramine O-sulfate, showed no change in median value, suggesting it and correlated metabolites to be a possible target for future therapies. Since it is unclear whether the observed differences are due to metabolic abnormalities associated with ASD or with GI symptoms (or contributions from both), future studies aiming to classify ASD should feature TD participants with GI symptoms and have larger sample sizes to improve confidence in the results.

Metabolomic profiling identifies novel associations with Electrolyte and Acid-Base Homeostatic patterns

Electrolytes have a crucial role in maintaining health and their serum levels are homeostatically maintained within a narrow range by multiple pathways involving the kidneys. Here we use metabolomics profiling (592 fasting serum metabolites) to identify molecular markers and pathways associated with serum electrolyte levels in two independent population-based cohorts. We included 1523 adults from TwinsUK not on blood pressure-lowering therapy and without renal impairment to look for metabolites associated with chloride, sodium, potassium and bicarbonate by running linear mixed models adjusting for covariates and multiple comparisons. For each electrolyte, we further performed pathway enrichment analysis (PAGE algorithm). Results were replicated in an independent cohort. Chloride, potassium, bicarbonate and sodium associated with 10, 58, 36 and 17 metabolites respectively (each P < 2.1 × 10-5), mainly lipids. Of all the electrolytes, serum potassium showed the most significant associations with individual fatty acid metabolites and specific enrichment of fatty acid pathways. In contrast, serum sodium and bicarbonate showed associations predominantly with amino-acid related species. In the first study to examine systematically associations between serum electrolytes and small circulating molecules, we identified novel metabolites and metabolic pathways associated with serum electrolyte levels. The role of these metabolic pathways on electrolyte homeostasis merits further studies.

Phosphorylation-related SNPs influence lipid levels and rheumatoid arthritis risk by altering gene expression and plasma protein levels

Objectives

Phosphorylation-related single-nucleotide polymorphisms (phosSNPs) are missense SNPs that may influence protein phosphorylation. The aim of this study was to evaluate the effect of phosSNPs on lipid levels and RA.

Methods

We examined the association of phosSNPs with lipid levels and RA in large-scale genome-wide association studies (GWAS) and performed random sampling and fgwas analyses to determine whether the phosSNPs associated with lipid levels and RA were significantly enriched. Furthermore, we performed QTL analysis and Mendelian randomization analysis to obtain additional evidence to be associated with the identified phosSNPs and genes.

Results

We found 483 phosSNPs for lipid levels and 243 phosSNPs for RA in the GWAS loci (P &lt; 1.0 × 10−5). SNPs associated with high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, Total cholesterol (TC) and RA were significantly enriched with phosSNPs. Almost all of the identified phosSNPs showed expression quantitative trait loci (eQTL) effects. A total of 48 protein QTLs and 9 metabolite QTLs were found. The phosSNP rs3184504 (p.Trp262Arg) at SH2B3 was significantly associated with RA, SH2B3 expression level, and plasma levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, TC, hypoxanthine and 80 proteins, including beta-2-microglobulin. SH2B3 was differentially expressed between RA cases and controls in peripheral blood mononuclear cells and synovial tissues. Mendelian randomization analysis showed that SH2B3 expression level was significantly associated with TC level and RA. Plasma beta-2-microglobulin level was causally associated with high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, TC levels and RA.

Conclusion

The findings suggested that phosSNPs may play important roles in lipid metabolism and the pathological mechanisms of RA. PhosSNPs may influence lipid levels and RA risk by altering gene expression and plasma protein levels.

Identification of Phosphorylation Associated SNPs for Blood Pressure, Coronary Artery Disease and Stroke from Genome-wide Association Studies

PURPOSE

Phosphorylation-related SNP (phosSNP) is a non-synonymous SNP that might influence protein phosphorylation status. The aim of this study was to assess the effect of phosSNPs on blood pressure (BP), coronary artery disease (CAD) and ischemic stroke (IS).

METHODS

We examined the association of phosSNPs with BP, CAD and IS in shared data from genome-wide association studies (GWAS) and tested if the disease loci were enriched with phosSNPs. Furthermore, we performed quantitative trait locus analysis to find out if the identified phosSNPs have impacts on gene expression, protein and metabolite levels.

RESULTS

We found numerous phosSNPs for systolic BP (count=148), diastolic BP (count=206), CAD (count=20) and IS (count=4). The most significant phosSNPs for SBP, DBP, CAD and IS were rs1801131 in MTHFR, rs3184504 in SH2B3, rs35212307 in WDR12 and rs3184504 in SH2B3, respectively. Our analyses revealed that the associated SNPs identified by the original GWAS were significantly enriched with phosSNPs and many well-known genes predisposing to cardiovascular diseases contain significant phosSNPs. We found that BP, CAD and IS shared for phosSNPs in loci that contain functional genes involve in cardiovascular diseases, e.g., rs11556924 (ZC3HC1), rs1971819 (ICA1L), rs3184504 (SH2B3), rs3739998 (JCAD), rs903160 (SMG6). Four phosSNPs in ADAMTS7 were significantly associated with CAD, including the known functional SNP rs3825807. Moreover, the identified phosSNPs seemed to have the potential to affect transcription regulation and serum levels of numerous cardiovascular diseases-related proteins and metabolites.

CONCLUSION

The findings suggested that phosSNPs may play important roles in BP regulation and the pathological mechanisms of CAD and IS.

Interplay between the human gut microbiome and host metabolism

The human gut is inhabited by a complex and metabolically active microbial ecosystem. While many studies focused on the effect of individual microbial taxa on human health, their overall metabolic potential has been under-explored. Using whole-metagenome shotgun sequencing data in 1,004 twins, we first observed that unrelated subjects share, on average, almost double the number of metabolic pathways (82%) than species (43%). Then, using 673 blood and 713 faecal metabolites, we found metabolic pathways to be associated with 34% of blood and 95% of faecal metabolites, with over 18,000 significant associations, while species showed less than 3,000 associations. Finally, we estimated that the microbiome was involved in a dialogue between 71% of faecal, and 15% of blood, metabolites. This study underlines the importance of studying the microbial metabolic potential rather than focusing purely on taxonomy to find therapeutic and diagnostic targets, and provides a unique resource describing the interplay between the microbiome and the systemic and faecal metabolic environments.

Heritability analysis of nontraditional glycemic biomarkers in the Atherosclerosis Risk in Communities Study

Nontraditional glycemic biomarkers, including fructosamine, glycated albumin, and 1,5-anhydroglucitol (1,5-AG) are potential alternatives or complement to traditional measures of hyperglycemia. Genetic variants are associated with these biomarkers, but the heritability, or extent to which genetics control their variation, is not known. We estimated pedigree-based, SNP-based, and bivariate heritabilities for traditional glycemic biomarkers (fasting glucose, HbA1c), and nontraditional biomarkers (fructosamine, glycated albumin, 1,5-AG) among white participants in the Atherosclerosis Risk in Communities (ARIC) Study (N = 400 first-degree relatives from sibships, N = 5,575 unrelated individuals). Pedigree-based heritabilities (representing heritability from the entire genome) for nontraditional biomarkers were substantial (0.44-0.55) and comparable to HbA1c (0.34); the fasting glucose estimate was nonsignificant. SNP-based heritabilities (representing heritability from common variants) were lower than pedigree-based heritabilities for all biomarkers. Bivariate heritabilities showed shared genetics between fructosamine and glycated albumin (0.46 pedigree-based, 1.00 SNP-based) and glycated albumin and 1,5-AG (0.50 pedigree-based, 0.47 SNP-based). Genetic factors contribute to a considerable proportion of the variance of fructosamine, glycated albumin, and 1,5-AG and a portion of this heritability likely comes from common variants.

Circulating glucuronic acid predicts healthspan and longevity in humans and mice

Glucuronic acid is a metabolite of glucose that is involved in the detoxification of xenobiotic compounds and the structure/remodeling of the extracellular matrix. We report for the first time that circulating glucuronic acid is a robust biomarker of mortality that is conserved across species. We find that glucuronic acid levels are significant predictors of all-cause mortality in three population-based cohorts from different countries with 4-20 years of follow-up (HR=1.44, p=2.9×10^-6 in the discovery cohort; HR=1.13, p=0.032 and HR=1.25, p=0.017, respectively in the replication cohorts), as well as in a longitudinal study of genetically heterogenous mice (HR=1.29, p=0.018). Additionally, we find that glucuronic acid levels increase with age and predict future healthspan-related outcomes. Together, these results demonstrate glucuronic acid as a robust biomarker of longevity and healthspan.

Genetic architecture of human plasma lipidome and its link to cardiovascular disease

Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n = 2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n = 511,700 individuals). We identify 35 lipid-species-associated loci (P <5 ×10-8), 10 of which associate with CVD risk including five new loci-COL5A1, GLTPD2, SPTLC3, MBOAT7 and GALNT16 (false discovery rate<0.05). We identify loci for lipid species that are shown to predict CVD e.g., SPTLC3 for CER(d18:1/24:1). We show that lipoprotein lipase (LPL) may more efficiently hydrolyze medium length triacylglycerides (TAGs) than others. Polyunsaturated lipids have highest heritability and genetic correlations, suggesting considerable genetic regulation at fatty acids levels. We find low genetic correlations between traditional lipids and lipid species. Our results show that lipidomic profiles capture information beyond traditional lipids and identify genetic variants modifying lipid levels and risk of CVD.

TwinsUK: The UK Adult Twin Registry Update

TwinsUK is the largest cohort of community-dwelling adult twins in the UK. The registry comprises over 14,000 volunteer twins (14,838 including mixed, single and triplets); it is predominantly female (82%) and middle-aged (mean age 59). In addition, over 1800 parents and siblings of twins are registered volunteers. During the last 27 years, TwinsUK has collected numerous questionnaire responses, physical/cognitive measures and biological measures on over 8500 subjects. Data were collected alongside four comprehensive phenotyping clinical visits to the Department of Twin Research and Genetic Epidemiology, King’s College London. Such collection methods have resulted in very detailed longitudinal clinical, biochemical, behavioral, dietary and socioeconomic cohort characterization; it provides a multidisciplinary platform for the study of complex disease during the adult life course, including the process of healthy aging. The major strength of TwinsUK is the availability of several ‘omic’ technologies for a range of sample types from participants, which includes genomewide scans of single-nucleotide variants, next-generation sequencing, metabolomic profiles, microbiomics, exome sequencing, epigenetic markers, gene expression arrays, RNA sequencing and telomere length measures. TwinsUK facilitates and actively encourages sharing the ‘TwinsUK’ resource with the scientific community — interested researchers may request data via the TwinsUK website (http://twinsuk.ac.uk/resources-for-researchers/access-our-data/) for their own use or future collaboration with the study team. In addition, further cohort data collection is planned via the Wellcome Open Research gateway (https://wellcomeopenresearch.org/gateways). The current article presents an up-to-date report on the application of technological advances, new study procedures in the cohort and future direction of TwinsUK.

Precision Medicine: Functional Advancements

Precision medicine was conceptualized on the strength of genomic sequence analysis. High-throughput functional metrics have enhanced sequence interpretation and clinical precision. These technologies include metabolomics, magnetic resonance imaging, and I rhythm (cardiac monitoring), among others. These technologies are discussed and placed in clinical context for the medical specialties of internal medicine, pediatrics, obstetrics, and gynecology. Publications in these fields support the concept of a higher level of precision in identifying disease risk. Precise disease risk identification has the potential to enable intervention with greater specificity, resulting in disease prevention-an important goal of precision medicine.

Long-term expression of glomerular genes in diabetic nephropathy

Background

Although diabetic nephropathy (DN) is the most common cause for end-stage renal disease in western societies, its pathogenesis still remains largely unclear. A different gene pattern of diabetic and healthy kidney cells is one of the probable explanations. Numerous signalling pathways have emerged as important pathophysiological mechanisms for diabetes-induced renal injury.

Methods

Glomerular cells, as podocytes or mesangial cells, are predominantly involved in the development of diabetic renal lesions. While many gene assays concerning DN are performed with whole kidney or renal cortex tissue, we isolated glomeruli from black and tan, brachyuric (BTBR) obese/obese (ob/ob) and wildtype mice at four different timepoints (4, 8, 16 and 24 weeks) and performed an mRNA microarray to identify differentially expressed genes (DEGs). In contrast to many other diabetic mouse models, these homozygous ob/ob leptin-deficient mice develop not only a severe type 2 diabetes, but also diabetic kidney injury with all the clinical and especially histologic features defining human DN. By functional enrichment analysis we were able to investigate biological processes and pathways enriched by the DEGs at different disease stages. Altered expression of nine randomly selected genes was confirmed by quantitative polymerase chain reaction from glomerular RNA.

Results

Ob/ob type 2 diabetic mice showed up- and downregulation of genes primarily involved in metabolic processes and pathways, including glucose, lipid, fatty acid, retinol and amino acid metabolism. Members of the CYP4A and ApoB family were found among the top abundant genes. But more interestingly, altered gene loci showed enrichment for processes and pathways linked to angioneogenesis, complement cascades, semaphorin pathways, oxidation and reduction processes and renin secretion.

Conclusion

The gene profile of BTBR ob/ob type 2 diabetic mice we conducted in this study can help to identify new key players in molecular pathogenesis of diabetic kidney injury.

Generating the Blood Exposome Database Using a Comprehensive Text Mining and Database Fusion Approach

BACKGROUND

Blood chemicals are routinely measured in clinical or preclinical research studies to diagnose diseases, assess risks in epidemiological research, or use metabolomic phenotyping in response to treatments. A vast volume of blood-related literature is available via the PubMed database for data mining.

OBJECTIVES

We aimed to generate a comprehensive blood exposome database of endogenous and exogenous chemicals associated with the mammalian circulating system through text mining and database fusion.

METHODS

Using NCBI resources, we retrieved PubMed abstracts, PubChem chemical synonyms, and PMC supplementary tables. We then employed text mining and PubChem crowdsourcing to associate phrases relating to blood with PubChem chemicals. False positives were removed by a phrase pattern and a compound exclusion list.

RESULTS

A query to identify blood-related publications in the PubMed database yielded 1.1 million papers. Matching a total of 15 million synonyms from 6.5 million relevant PubChem chemicals against all blood-related publications yielded 37,514 chemicals and 851,999 publications records. Mapping PubChem compound identifiers to the PubMed database yielded 49,940 unique chemicals linked to 676,643 papers. Analysis of open-access metabolomics papers related to blood phrases in the PMC database yielded 4,039 unique compounds and 204 papers. Consolidating these three approaches summed up to a total of 41,474 achiral structures that were linked to 65,957 PubChem CIDs and to over 878,966 PubMed articles. We mapped these compounds to 50 databases such as those covering metabolites and pathways, governmental and toxicological databases, pharmacology resources, and bioassay repositories. In comparison, HMDB, the Human Metabolome Database, links 1,075 compounds to blood-related primary publications.

CONCLUSION

This new Blood Exposome Database can be used for prioritizing chemicals for systematic reviews, developing target assays in exposome research, identifying compounds in untargeted mass spectrometry, and biological interpretation in metabolomics data. The database is available at http://bloodexposome.org. https://doi.org/10.1289/EHP4713.

Unraveling the functional role of the orphan solute carrier, SLC22A24 in the transport of steroid conjugates through metabolomic and genome-wide association studies

Variation in steroid hormone levels has wide implications for health and disease. The genes encoding the proteins involved in steroid disposition represent key determinants of interindividual variation in steroid levels and ultimately, their effects. Beginning with metabolomic data from genome-wide association studies (GWAS), we observed that genetic variants in the orphan transporter, SLC22A24 were significantly associated with levels of androsterone glucuronide and etiocholanolone glucuronide (sentinel SNPs p-value <1x10-30). In cells over-expressing human or various mammalian orthologs of SLC22A24, we showed that steroid conjugates and bile acids were substrates of the transporter. Phylogenetic, genomic, and transcriptomic analyses suggested that SLC22A24 has a specialized role in the kidney and appears to function in the reabsorption of organic anions, and in particular, anionic steroids. Phenome-wide analysis showed that functional variants of SLC22A24 are associated with human disease such as cardiovascular diseases and acne, which have been linked to dysregulated steroid metabolism. Collectively, these functional genomic studies reveal a previously uncharacterized protein involved in steroid homeostasis, opening up new possibilities for SLC22A24 as a pharmacological target for regulating steroid levels.

Gene-Environment Interactions on Body Fat Distribution

The prevalence of obesity has been increasing markedly in the U.S. and worldwide in the past decades; and notably, the obese populations are signified by not only the overall elevated adiposity but also particularly harmful accumulation of body fat in the central region of the body, namely, abdominal obesity. The profound shift from “traditional” to “obesogenic” environments, principally featured by the abundance of palatable, energy-dense diet, reduced physical activity, and prolonged sedentary time, promotes the obesity epidemics and detrimental body fat distribution. Recent advances in genomics studies shed light on the genetic basis of obesity and body fat distribution. In addition, growing evidence from investigations in large cohorts and clinical trials has lent support to interactions between genetic variations and environmental factors, e.g., diet and lifestyle factors, in relation to obesity and body fat distribution. This review summarizes the recent discoveries from observational studies and randomized clinical trials on the gene–environment interactions on obesity and body fat distribution.

Collagen Formation Assessed by N-Terminal Propeptide of Type 3 Procollagen Is a Heritable Trait and Is Associated With Liver Fibrosis Assessed by Magnetic Resonance Elastography

N-terminal propeptide of type 3 procollagen (PRO-C3) is a biomarker of liver fibrosis in nonalcoholic fatty liver disease (NAFLD). This study examines the association between PRO-C3 concentration and liver fibrosis assessed by magnetic resonance elastography (MRE)-measured stiffness (MRE-stiffness) and the heritability of PRO-C3 concentration in a cohort of twins and families with and without NAFLD. We performed a cross-sectional analysis of a well-characterized prospective cohort of 306 participants, including 44 probands with NAFLD-cirrhosis and their 72 first-degree relatives, 24 probands with NAFLD without advanced fibrosis and their 24 first-degree relatives, and 72 controls without NAFLD and their 72 first-degree relatives. Liver steatosis was assessed by magnetic resonance imaging proton density fat fraction, and liver fibrosis was assessed by MRE-stiffness. Serum PRO-C3 was assessed by competitive, enzyme-linked immunosorbent assay. We assessed the familial correlation of PRO-C3 concentration, the shared gene effects between PRO-C3 concentration and liver steatosis and fibrosis, and the association between PRO-C3 concentration and genetic variants in the patatin-like phospholipase domain-containing 3 (PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2), membrane-bound O-acyltransferase domain-containing (MBOAT), and glucokinase regulator (CGKR) genes. In multivariable-adjusted models including age, sex, body mass index, and ethnicity, serum PRO-C3 correlated strongly with liver fibrosis (r²  = 0.50, P < 0.001) and demonstrated robust heritability (h² , 0.36; 95% confidence interval [CI], 0.07, 0.59; P = 0.016). PRO-C3 concentration and steatosis had a strong genetic correlation (shared genetic determination: 0.62; 95% CI, 0.236, 1.001; P = 0.002), whereas PRO-C3 concentration and fibrosis had a strong environmental correlation (shared environmental determination: 0.55; 95% CI, 0.317, 0.717; P < 0.001). PRO-C3 concentrations were higher in carriers of the TM6SF2 rs58542926-T allele compared with noncarriers: 15.7 (± 10.5) versus 10.8 (± 5.7) ng/L (P = 0.047). Conclusion: Serum PRO-C3 correlates with MRE-assessed fibrosis, is heritable, shares genetic correlation with liver steatosis and shares environmental correlation with liver fibrosis. PRO-C3 concentration appears to be linked to both fibrosis and steatosis and increased in carriers of the TM6SF2 rs58542926 risk allele.

Organic Anion Transporter Polypeptide 1B1 Polymorphism Modulates the Extent of Drug-Drug Interaction and Associated Biomarker Levels in Healthy Volunteers

Understanding transporter-mediated drug-drug interactions is an integral part of risk assessment in drug development. Recent studies support the use of hexadecanedioate (HDA), tetradecanedioate (TDA), coproporphyrin (CP)-I, and CP-III as clinical biomarkers for evaluating organic anion-transporting polypeptide (OATP)1B1 (SLCO1B1) inhibition. The current study investigated the effect of OATP1B1 genotype c.521T>C (OATP1B1-Val174Ala) on the extent of interaction between cyclosporin A (CsA) and pravastatin, and associated endogenous biomarkers of the transporter (HDA, TDA, CP-I, and CP-III), in 20 healthy volunteers. The results show that the levels of each clinical biomarker and pravastatin were significantly increased in plasma samples of the volunteers following administration of pravastatin plus CsA compared with pravastatin plus placebo. The overall fold change in the area under the concentration-time curve (AUC) and maximum plasma concentration (Cmax ) was similar among the four biomarkers (1.8-2.5-fold, paired t-test P value < 0.05) in individuals who were homozygotes or heterozygotes of the major allele, c.521T. However, the fold change in AUC and Cmax for HDA and TDA was significantly abolished in the subjects who were c.521-CC, whereas the respective fold change in AUC and Cmax for pravastatin and CP-I and CP-III were slightly weaker in individuals who were c.521-CC compared with c.521-TT/TC genotypes. In addition, this study provides the first evidence that SLCO1B1 c.521T>C genotype is significantly associated with CP-I but not CP-III levels. Overall, these results suggest that OATP1B1 genotype can modulate the effects of CsA on biomarker levels; the extent of modulation differs among the biomarkers.

Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective

It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

The Consortium of Metabolomics Studies (COMETS): Metabolomics in 47 Prospective Cohort Studies

The Consortium of Metabolomics Studies (COMETS) was established in 2014 to facilitate large-scale collaborative research on the human metabolome and its relationship with disease etiology, diagnosis, and prognosis. COMETS comprises 47 cohorts from Asia, Europe, North America, and South America that together include more than 136,000 participants with blood metabolomics data on samples collected from 1985 to 2017. Metabolomics data were provided by 17 different platforms, with the most frequently used labs being Metabolon, Inc. (14 cohorts), the Broad Institute (15 cohorts), and Nightingale Health (11 cohorts). Participants have been followed for a median of 23 years for health outcomes including death, cancer, cardiovascular disease, diabetes, and others; many of the studies are ongoing. Available exposure-related data include common clinical measurements and behavioral factors, as well as genome-wide genotype data. Two feasibility studies were conducted to evaluate the comparability of metabolomics platforms used by COMETS cohorts. The first study showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories. The second study showed that the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79 (interquartile range, 0.56-0.89).

A Comprehensive Plasma Metabolomics Dataset for a Cohort of Mouse Knockouts within the International Mouse Phenotyping Consortium

Mouse knockouts facilitate the study ofgene functions. Often, multiple abnormal phenotypes are induced when a gene is inactivated. The International Mouse Phenotyping Consortium (IMPC) has generated thousands of mouse knockouts and catalogued their phenotype data. We have acquired metabolomics data from 220 plasma samples from 30 unique mouse gene knockouts and corresponding wildtype mice from the IMPC. To acquire comprehensive metabolomics data, we have used liquid chromatography (LC) combined with mass spectrometry (MS) for detecting polar and lipophilic compounds in an untargeted approach. We have also used targeted methods to measure bile acids, steroids and oxylipins. In addition, we have used gas chromatography GC-TOFMS for measuring primary metabolites. The metabolomics dataset reports 832 unique structurally identified metabolites from 124 chemical classes as determined by ChemRICH software. The GCMS and LCMS raw data files, intermediate and finalized data matrices, R-Scripts, annotation databases, and extracted ion chromatograms are provided in this data descriptor. The dataset can be used for subsequent studies to link genetic variants with molecular mechanisms and phenotypes.

Genetic Determinants of Circulating Glycine Levels and Risk of Coronary Artery Disease

Background Recent studies have revealed sexually dimorphic associations between the carbamoyl-phosphate synthase 1 locus, intermediates of the metabolic pathway leading from choline to urea, and risk of coronary artery disease ( CAD ) in women. Based on evidence from the literature, the atheroprotective association with carbamoyl-phosphate synthase 1 could be mediated by the strong genetic effect of this locus on increased circulating glycine levels. Methods and Results We sought to identify additional genetic determinants of circulating glycine levels by carrying out a meta-analysis of genome-wide association study data in up to 30 118 subjects of European ancestry. Mendelian randomization and other analytical approaches were used to determine whether glycine-associated variants were associated with CAD and traditional risk factors. Twelve loci were significantly associated with circulating glycine levels, 7 of which were not previously known to be involved in glycine metabolism ( ACADM , PHGDH , COX 18- ADAMTS 3, PSPH , TRIB 1, PTPRD , and ABO ). Glycine-raising alleles at several loci individually exhibited directionally consistent associations with decreased risk of CAD . However, these effects could not be attributed directly to glycine because of associations with other CAD -related traits. By comparison, genetic models that only included the 2 variants directly involved in glycine degradation and for which there were no other pleiotropic associations were not associated with risk of CAD or blood pressure, lipid levels, and obesity-related traits. Conclusions These results provide additional insight into the genetic architecture of glycine metabolism, but do not yield conclusive evidence for a causal relationship between circulating levels of this amino acid and risk of CAD in humans.

Integrated analysis of genomics, longitudinal metabolomics, and Alzheimer's risk factors among 1,111 cohort participants

Although Alzheimer's disease (AD) is highly heritable, genetic variants are known to be associated with AD only explain a small proportion of its heritability. Genetic factors may only convey disease risk in individuals with certain environmental exposures, suggesting that a multiomics approach could reveal underlying mechanisms contributing to complex traits, such as AD. We developed an integrated network to investigate relationships between metabolomics, genomics, and AD risk factors using Wisconsin Registry for Alzheimer's Prevention participants. Analyses included 1,111 non-Hispanic Caucasian participants with whole blood expression for 11,376 genes (imputed from dense genome-wide genotyping), 1,097 fasting plasma metabolites, and 17 AD risk factors. A subset of 155 individuals also had 364 fastings cerebral spinal fluid (CSF) metabolites. After adjusting each of these 12,854 variables for potential confounders, we developed an undirected graphical network, representing all significant pairwise correlations upon adjusting for multiple testing. There were many instances of genes being indirectly linked to AD risk factors through metabolites, suggesting that genes may influence AD risk through particular metabolites. Follow-up analyses suggested that glycine mediates the relationship between carbamoyl-phosphate synthase 1 and measures of cardiovascular and diabetes risk, including body mass index, waist-hip ratio, inflammation, and insulin resistance. Further, 38 CSF metabolites explained more than 60% of the variance of CSF levels of tau, a detrimental protein that accumulates in the brain of AD patients and is necessary for its diagnosis. These results further our understanding of underlying mechanisms contributing to AD risk while demonstrating the utility of generating and integrating multiple omics data types.

Circulating levels of the anti-oxidant indoleproprionic acid are associated with higher gut microbiome diversity

The gut microbiome has recently emerged as an important regulator of insulin resistance and abdominal obesity. The tryptophan metabolite generated by the gut microbiome, indoleproprionic acid (IPA) has been shown to predict the onset of type 2 diabetes. IPA is a metabolite produced by gut microbes from dietary tryptophan that exhibits a high degree of inter-individual variation. The microbiome composition parameters that are associated with circulating levels of this potent anti-oxidant have however not been investigated to date in human populations. In 1018 middle-aged women from the TwinsUK cohort, we assessed the relationship between serum IPA levels and gut microbiome composition targeting the 16S rRNA gene. Microbiome alpha-diversity was positively correlated with serum indoleproprionic acid levels (Shannon Diversity: Beta[95%CI] = 0.19[0.13;0.25], P = 6.41 × 10^-10) after adjustment for covariates. Sixteen taxa and 12 operational taxonomic units (OTUs) associated with IPA serum levels. Among these are positive correlations with the butyrate-producing Faecalibacterium prausnitzii, the class Mollicutes and the order RF39 of the Tenericutes, and Coprococcus Negative correlations instead were observed with Eubacterium dolichum previously shown to correlate with visceral fat mass and several genera in the Lachnospiraceae family such as Blautia and Ruminococcus previously shown to correlate with obesity. Microbiome composition parameters explained ~20% of the variation in circulating levels of IPA, whereas nutritional and host genetic parameters explained only ~4%. Our data confirm an association between IPA circulating levels and metabolic syndrome parameters and indicate that gut microbiome composition influences IPA levels.

Metabolism as a key regulator in the pathogenesis of systemic lupus erythematosus

In the middle of the 20th century, biologists focused on investigating the mechanism of gene regulation and signal transduction in cells, which led to the concept that metabolites were products of gene expression and signal transduction pathways. In the 1920s, the importance of cellular metabolism was shown in the Warburg effect, in which cancer cells are characterized by a mitochondrial defect that shifts towards aerobic glycolysis. Recently, it is accepted that each organ and cell subset needs specific metabolic conditions and metabolic regulatory systems. Immunometabolism is a relatively new field of metabolism studies. The immune system consists of various cell subsets that have unique requirements and functions. The metabolic reprogramming in each immune cell causes different effects on different cell subsets. For example, resting lymphocytes generate energy through oxidative phosphorylation (OXPHOS) and fatty acid oxidation (FAO), whereas activated lymphocytes rapidly shift to the glycolytic pathway. A detailed understanding of metabolic regulation has progressed rapidly, especially in T cells during their differentiation from naïve to effector T cells. Metabolism is now considered to play a key role in autoimmune diseases. Metabolic changes in autoimmune diseases might be due to inflammation as well as being involved in autoimmune pathogenesis. Systemic lupus erythematosus (SLE) is an autoimmune disease with heterogenous clinical presentations whose precise pathophysiological mechanism is largely unknown. In this report, we review the altered metabolism in SLE and discuss the potential of metabolomics for accelerating the discovery of novel cellular autoimmune therapies and novel disease biomarkers.

Rare variants in SLC5A10 are associated with serum 1,5-anhydroglucitol (1,5-AG) in the Atherosclerosis Risk in Communities (ARIC) Study

Serum 1,5-anhydroglucitol (1,5-AG) is an emerging biomarker used to monitor glycemic control in persons with diabetes. We performed whole-exome sequencing, examining the association between rare, coding genetic variants and 1,5-AG among European ancestry (N = 6,589) and African ancestry (N = 2,309) participants without diagnosed diabetes in the Atherosclerosis Risk in Communities (ARIC) Study. Five variants representing 3 independent signals on chromosome 17 in SLC5A10, a glucose transporter not previously known to transport 1,5-AG, were associated with 1,5-AG levels up to 10.38 µg/mL lower per allele (1,5-AG range 3.4-32.8 µg/mL) in the European ancestry sample and validated in the African ancestry sample. Together these variants explained 6% of the variance in 1,5-AG. Two of these variants (rs61741107, p = 8.85E-56; rs148178887, p = 1.13E-36) were rare, nonsynonymous, and predicted to be damaging or deleterious by multiple algorithms. Gene-based SKAT-O analysis supported these results (SLC5A10 p = 5.13E-64 in European ancestry, validated in African ancestry, p = 0.006). Interestingly, these novel variants are not associated with other biomarkers of hyperglycemia or diabetes (p > 0.2). The large effect sizes and protein-altering, multiple independent signals suggest SLC5A10 may code for an important transporter of 1,5-AG in the kidney, with a potential nonglucose-related effect on 1,5-AG, impacting its clinical utility as a diabetes biomarker in this subpopulation.

100 Years of evolving gene-disease complexities and scientific debutants

It's been over 100 years since the word `gene' is around and progressively evolving in several scientific directions. Time-to-time technological advancements have heavily revolutionized the field of genomics, especially when it's about, e.g. triple code development, gene number proposition, genetic mapping, data banks, gene-disease maps, catalogs of human genes and genetic disorders, CRISPR/Cas9, big data and next generation sequencing, etc. In this manuscript, we present the progress of genomics from pea plant genetics to the human genome project and highlight the molecular, technical and computational developments. Studying genome and epigenome led to the fundamentals of development and progression of human diseases, which includes chromosomal, monogenic, multifactorial and mitochondrial diseases. World Health Organization has classified, standardized and maintained all human diseases, when many academic and commercial online systems are sharing information about genes and linking to associated diseases. To efficiently fathom the wealth of this biological data, there is a crucial need to generate appropriate gene annotation repositories and resources. Our focus has been how many gene-disease databases are available worldwide and which sources are authentic, timely updated and recommended for research and clinical purposes. In this manuscript, we have discussed and compared 43 such databases and bioinformatics applications, which enable users to connect, explore and, if possible, download gene-disease data.

Effect of glutamate and aspartate on ischemic heart disease, blood pressure, and diabetes: a Mendelian randomization study

BACKGROUND

Evolutionary biology suggests reproduction trades off against longevity. Genetic selection in favor of fertility and ischemic heart disease (IHD) exists in humans. Observationally, soy protects against IHD. Soy amino acids, glutamate and aspartate, may lower androgens. No large randomized controlled trials testing their health effects exist.

OBJECTIVE

Using Mendelian randomization, we assessed how genetically predicted glutamate and aspartate affected IHD, blood pressure, and diabetes.

METHODS

A separate sample instrumental variable analysis with genetic instruments was used to obtain unconfounded estimates using genetic variants strongly (P < 5 × 10(-8)) and solely associated with glutamate or aspartate applied to an IHD case (n ≤76,014)-control (n ≤ 264,785) study (based on a meta-analysis of CARDIoGRAMplusC4D 1000 Genomes, UK Biobank CAD SOFT GWAS and Myocardial Infarction Genetics and CARDIoGRAM Exome), blood pressure from the UK Biobank (n ≤ 361,194), and the DIAbetes Genetics Replication And Meta-analysis diabetes case (n = 26,676)-control (n = 132,532) study. A weighted median and MR-Egger were used for a sensitivity analysis.

RESULTS

Glutamate was not associated with IHD, blood pressure, or diabetes after correction for multiple comparisons. Aspartate was inversely associated with IHD (odds ratio (OR) 0.92 per log-transformed standard deviation (SD); 95% confidence interval (CI) 0.88, 0.96) and diastolic blood pressure (-0.03; 95% CI -0.04, -0.02) using inverse variance weighting, but not diabetes (OR 1.00; 95% CI 0.91, 1.09). Associations were robust to the sensitivity analysis.

CONCLUSIONS

Our findings suggest aspartate may play a role in IHD and blood pressure, potentially underlying cardiovascular benefits of soy. Clarifying the mechanisms would be valuable for IHD prevention and for defining a healthy diet.

Estimating carrier frequencies of newborn screening disorders using a whole-genome reference panel of 3552 Japanese individuals

Incidence rates of Mendelian diseases vary among ethnic groups, and frequencies of variant types of causative genes also vary among human populations. In this study, we examined to what extent we can predict population frequencies of recessive disorders from genomic data, and explored better strategies for variant interpretation and classification. We used a whole-genome reference panel from 3552 general Japanese individuals constructed by the Tohoku Medical Megabank Organization (ToMMo). Focusing on 32 genes for 17 congenital metabolic disorders included in newborn screening (NBS) in Japan, we identified reported and predicted pathogenic variants through variant annotation, interpretation, and multiple ways of classifications. The estimated carrier frequencies were compared with those from the Japanese NBS data based on 1,949,987 newborns from a previous study. The estimated carrier frequency based on genomic data with a recent guideline of variant interpretation for the PAH gene, in which defects cause hyperphenylalaninemia (HPA) and phenylketonuria (PKU), provided a closer estimate to that by the observed incidence than the other methods. In contrast, the estimated carrier frequencies for SLC25A13, which causes citrin deficiency, were much higher compared with the incidence rate. The results varied greatly among the 11 NBS diseases with single responsible genes; the possible reasons for departures from the carrier frequencies by reported incidence rates were discussed. Of note, (1) the number of pathogenic variants increases by including additional lines of evidence, (2) common variants with mild effects also contribute to the actual frequency of patients, and (3) penetrance of each variant remains unclear.

Assessing the causal association of glycine with risk of cardio-metabolic diseases

Circulating levels of glycine have previously been associated with lower incidence of coronary heart disease (CHD) and type 2 diabetes (T2D) but it remains uncertain if glycine plays an aetiological role. We present a meta-analysis of genome-wide association studies for glycine in 80,003 participants and investigate the causality and potential mechanisms of the association between glycine and cardio-metabolic diseases using genetic approaches. We identify 27 genetic loci, of which 22 have not previously been reported for glycine. We show that glycine is genetically associated with lower CHD risk and find that this may be partly driven by blood pressure. Evidence for a genetic association of glycine with T2D is weaker, but we find a strong inverse genetic effect of hyperinsulinaemia on glycine. Our findings strengthen evidence for a protective effect of glycine on CHD and show that the glycine-T2D association may be driven by a glycine-lowering effect of insulin resistance.

Identification of seven novel loci associated with amino acid levels using single-variant and gene-based tests in 8545 Finnish men from the METSIM study

Comprehensive metabolite profiling captures many highly heritable traits, including amino acid levels, which are potentially sensitive biomarkers for disease pathogenesis. To better understand the contribution of genetic variation to amino acid levels, we performed single variant and gene-based tests of association between nine serum amino acids (alanine, glutamine, glycine, histidine, isoleucine, leucine, phenylalanine, tyrosine, and valine) and 16.6 million genotyped and imputed variants in 8545 non-diabetic Finnish men from the METabolic Syndrome In Men (METSIM) study with replication in Northern Finland Birth Cohort (NFBC1966). We identified five novel loci associated with amino acid levels (P = < 5×10-8): LOC157273/PPP1R3B with glycine (rs9987289, P = 2.3×10-26); ZFHX3 (chr16:73326579, minor allele frequency (MAF) = 0.42%, P = 3.6×10-9), LIPC (rs10468017, P = 1.5×10-8), and WWOX (rs9937914, P = 3.8×10-8) with alanine; and TRIB1 with tyrosine (rs28601761, P = 8×10-9). Gene-based tests identified two novel genes harboring missense variants of MAF <1% that show aggregate association with amino acid levels: PYCR1 with glycine (Pgene = 1.5×10-6) and BCAT2 with valine (Pgene = 7.4×10-7); neither gene was implicated by single variant association tests. These findings are among the first applications of gene-based tests to identify new loci for amino acid levels. In addition to the seven novel gene associations, we identified five independent signals at established amino acid loci, including two rare variant signals at GLDC (rs138640017, MAF=0.95%, Pconditional = 5.8×10-40) with glycine levels and HAL (rs141635447, MAF = 0.46%, Pconditional = 9.4×10-11) with histidine levels. Examination of all single variant association results in our data revealed a strong inverse relationship between effect size and MAF (Ptrend<0.001). These novel signals provide further insight into the molecular mechanisms of amino acid metabolism and potentially, their perturbations in disease.

Profound Perturbation of the Metabolome in Obesity Is Associated with Health Risk

Obesity is a heterogeneous phenotype that is crudely measured by body mass index (BMI). There is a need for a more precise yet portable method of phenotyping and categorizing risk in large numbers of people with obesity to advance clinical care and drug development. Here, we used non-targeted metabolomics and whole-genome sequencing to identify metabolic and genetic signatures of obesity. We find that obesity results in profound perturbation of the metabolome; nearly a third of the assayed metabolites associated with changes in BMI. A metabolome signature identifies the healthy obese and lean individuals with abnormal metabolomes-these groups differ in health outcomes and underlying genetic risk. Specifically, an abnormal metabolome associated with a 2- to 5-fold increase in cardiovascular events when comparing individuals who were matched for BMI but had opposing metabolome signatures. Because metabolome profiling identifies clinically meaningful heterogeneity in obesity, this approach could help select patients for clinical trials.