Metabolomic Quantitative Trait Loci (mQTL) Mapping Implicates the Ubiquitin Proteasome System in Cardiovascular Disease Pathogenesis

Metabolic origin and significance of 3-methylglutaryl CoA

3-Methylglutaryl (3MG) CoA is not part of any biochemical pathway, yet its byproducts, 3MG carnitine and 3MG acid, are disease biomarkers. Both compounds are excreted in HMG CoA lyase deficiency, while 3MG aciduria occurs in inborn errors of metabolism (IEM) associated with compromised mitochondrial energy metabolism. In one such disorder (i.e., TMEM70 deficiency), 3MG carnitine is also present. Moreover, in a number of chronic and acute maladies, elevated levels of 3MG carnitine are present. The precursor of 3MG CoA istrans-3-methylglutaconyl (3MGC) CoA. Whentrans-3MGC CoA levels rise, a portion of this metabolite pool is reduced to 3MG CoA, potentially via a side reaction involving glutaryl CoA dehydrogenase (GCDH), which normally catalyzes the oxidative decarboxylation of glutaryl CoA to crotonyl CoA and CO2. This reaction occurs via a two-step process wherein glutaryl CoA is initially oxidized to glutaconyl CoA, coupled to reduction of the enzyme's FAD prosthetic group. Enzyme-bound glutaconyl CoA is then decarboxylated to the reaction product, crotonyl CoA. Before GCDH can accept another glutaryl CoA the flavin prosthetic group must be oxidized to FAD by donating electrons to electron transferring flavoprotein (ETF). However, genetic- or disease-induced defects in electron transport chain function can impede this reaction. We propose thattrans-3MGC CoA is a substrate for reduced GCDH and, when glutaryl CoA andtrans-3MGC CoA are present, GCDH is able to bypass ETF and cycle between oxidized and reduced states, producing crotonyl CoA and CO2from glutaryl CoA, and 3MG CoA fromtrans-3MGC CoA.

Plasma metabolites, systolic blood pressure, lifestyle, and stroke risk: A prospective cohort study

BACKGROUND

To estimate the associations of stroke risk with plasma metabolites, metabolic risk score (MRS), the combinations of MRS with hypertension or lifestyle, and lifestyle-related metabolic signature. To assess the improvement of the stroke risk prediction model through the incorporation of MRS.

METHODS

A total of 77,315 participants from the UK Biobank were included in this study. Xgboost and LASSO-Cox regression were used to select metabolites and construct MRS. Elastic net regression was utilized to construct the lifestyle-related metabolic signature. Multivariate Cox regression was used to estimate the associations between metabolites, MRS, the combinations of MRS with hypertension or lifestyle, lifestyle-related metabolic signature, and stroke risk.

RESULTS

We identified 48, 63, 39, and 4 metabolites associated with the risk of stroke, ischemic stroke (IS), subarachnoid hemorrhage (SAH), and intracerebral hemorrhage (ICH), respectively. High MRS significantly increased the risk of stroke (HR = 2.65 (95% confidence interval (CI): 2.09-3.35)), IS (HR = 2.45 (95% CI: 1.89-3.17)), ICH (HR = 2.74 (95% CI: 1.55-4.85)), and SAH (HR = 4.64 (95% CI: 2.25-9.56)). In the combination analyses, compared with normal systolic blood pressure (SBP) and low MRS, normal/high SBP, and high MRS significantly increased stroke risk (HR = 5.80 (95% CI: 2.75-12.27)/6.37 (95% CI: 3.22-12.62)). A favorable/unfavorable lifestyle and high MRS also significantly increased stroke risk (HR = 2.38 (95% CI: 1.73-3.28)/3.86 (95% CI: 2.63-5.67)) compared with a favorable lifestyle and low MRS. Incorporating MRS into the 15-year stroke and IS risk prediction model increased the areas under the curves (AUCs) from 0.746 to 0.766 and from 0.771 to 0.811, respectively. The metabolic signature was correlated with adherence to a healthy lifestyle (r = 0.414; P = 2.22e-16) and inversely associated with stroke risk (HR = 0.80 (95% CI: 0.73-0.86)).

CONCLUSIONS

Various metabolites and MRS were significantly associated with the risk of stroke, IS, ICH, and SAH. Individuals with a high MRS may face an elevated stroke risk among populations with high SBP or unhealthy lifestyle, even those with normal SBP or healthy lifestyle. MRS provided modest improvement to the stroke risk prediction model. The lifestyle-related metabolic signature could reduce 20% stroke risk.

Quantitative trait loci mapping of circulating metabolites in cerebrospinal fluid to uncover biological mechanisms involved in brain-related phenotypes

Genomic studies of molecular traits have provided mechanistic insights into complex disease, though these lag behind for brain-related traits due to the inaccessibility of brain tissue. We leveraged cerebrospinal fluid (CSF) to study neurobiological mechanisms in vivo, measuring 5543 CSF metabolites, the largest panel in CSF to date, in 977 individuals of European ancestry. Individuals originated from two separate cohorts including cognitively healthy subjects (n = 490) and a well-characterized memory clinic sample, the Amsterdam Dementia Cohort (ADC, n = 487). We performed metabolite quantitative trait loci (mQTL) mapping on CSF metabolomics and found 126 significant mQTLs, representing 65 unique CSF metabolites across 51 independent loci. To better understand the role of CSF mQTLs in brain-related disorders we integrated our CSF mQTL results with pre-existing summary statistics on brain traits, identifying 34 genetic associations between CSF metabolites and brain traits. Over 90% of significant mQTLs demonstrated colocalized associations with brain-specific gene expression, unveiling potential neurobiological pathways.

Multiomics of Aging and Aging-Related Diseases

Despite their astonishing biological diversity, surprisingly few shared traits connect all or nearly all living organisms. Aging, i.e., the progressive and irreversible decline in the function of multiple cells and tissues, is one of these fundamental features of all organisms, ranging from single-cell creatures to complex animals, alongside variability, adaptation, growth, healing, reproducibility, mobility, and, finally, death. Age is a key determinant for many pathologies, shaping the risks of incidence, severity, and treatment outcomes for cancer, neurodegeneration, heart failure, sarcopenia, atherosclerosis, osteoporosis, and many other diseases. In this review, we aim to systematically investigate the age-related features of the development of several diseases through the lens of multiomics: from genome instability and somatic mutations to pathway alterations and dysregulated metabolism.

Pleiotropic influence of DNA methylation QTLs on physiological and ageing traits

DNA methylation is influenced by genetic and non-genetic factors. Here, we chart quantitative trait loci (QTLs) that modulate levels of methylation at highly conserved CpGs using liver methylome data from mouse strains belonging to the BXD family. A regulatory hotspot on chromosome 5 had the highest density of trans-acting methylation QTLs (trans-meQTLs) associated with multiple distant CpGs. We refer to this locus as meQTL.5a. Trans-modulated CpGs showed age-dependent changes and were enriched in developmental genes, including several members of the MODY pathway (maturity onset diabetes of the young). The joint modulation by genotype and ageing resulted in a more 'aged methylome' for BXD strains that inherited the DBA/2J parental allele at meQTL.5a. Further, several gene expression traits, body weight, and lipid levels mapped to meQTL.5a, and there was a modest linkage with lifespan. DNA binding motif and protein-protein interaction enrichment analyses identified the hepatic nuclear factor, Hnf1a (MODY3 gene in humans), as a strong candidate. The pleiotropic effects of meQTL.5a could contribute to variations in body size and metabolic traits, and influence CpG methylation and epigenetic ageing that could have an impact on lifespan.

3-Methylglutarylcarnitine: A biomarker of mitochondrial dysfunction

The acylcarnitines comprise a wide range of acyl groups linked via an ester bond to the hydroxyl group of L-carnitine. Mass spectrometry methods are capable of measuring the relative abundance of hundreds of acylcarnitines in a single drop of blood. As such, acylcarnitines can serve as sensitive biomarkers of disease. For certain acylcarnitines, however, their biochemical origin, and biomedical significance, remain unclear. One such example is 3-methylglutaryl (3MG) carnitine (C5-3M-DC). Whereas 3MG carnitine levels are normally very low, elevated levels are detected in discrete inborn errors of metabolism (IEM) as well as different forms of heart disease. Moreover, acute injury, including γ radiation exposure, paraquat poisoning, and traumatic brain injury manifest elevated levels of 3MG carnitine in blood and/or urine. Recent evidence indicates that two distinct biosynthetic routes to 3MG carnitine exist. The first, caused by an inherited deficiency in the leucine catabolism pathway enzyme, 3-hydroxy-3-methylglutaryl (HMG) CoA lyase, leads to a buildup of trans-3-methylglutaconyl (3MGC) CoA. Reduction of the double bond in trans-3MGC CoA generates 3MG CoA, which is then converted to 3MG carnitine by carnitine acyltransferase. This route, however, cannot explain why 3MG carnitine levels increase in IEMs that do not affect leucine metabolism or various chronic and acute disease states. In these cases, disease-related defects in aerobic energy metabolism result in diversion of acetyl CoA to trans-3MGC CoA. Once formed, trans-3MGC CoA is reduced to 3MG CoA and esterified to form 3MG carnitine. Thus, 3MG carnitine, represents a potential biomarker of disease processes associated with compromised mitochondrial energy metabolism.

Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective

Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.

Quantitative trait loci mapping of circulating metabolites in cerebrospinal fluid to uncover biological mechanisms involved in brain-related phenotypes

Genomic studies of molecular traits have provided mechanistic insights into complex disease, though these lag behind for brain-related traits due to the inaccessibility of brain tissue. We leveraged cerebrospinal fluid (CSF) to study neurobiological mechanisms in vivo , measuring 5,543 CSF metabolites, the largest panel in CSF to date, in 977 individuals of European ancestry. Individuals originated from two separate cohorts including cognitively healthy subjects (n=490) and a well-characterized memory clinic sample, the Amsterdam Dementia Cohort (ADC, n=487). We performed metabolite quantitative trait loci (mQTL) mapping on CSF metabolomics and found 126 significant mQTLs, representing 65 unique CSF metabolites across 51 independent loci. To better understand the role of CSF mQTLs in brain-related disorders, we performed a metabolome-wide association study (MWAS), identifying 40 associations between CSF metabolites and brain traits. Similarly, over 90% of significant mQTLs demonstrated colocalized associations with brain-specific gene expression, unveiling potential neurobiological pathways.

Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma

Although many novel gene-metabolite and gene-protein associations have been identified using high-throughput biochemical profiling, systematic studies that leverage human genetics to illuminate causal relationships between circulating proteins and metabolites are lacking. Here, we performed protein-metabolite association studies in 3,626 plasma samples from three human cohorts. We detected 171,800 significant protein-metabolite pairwise correlations between 1,265 proteins and 365 metabolites, including established relationships in metabolic and signaling pathways such as the protein thyroxine-binding globulin and the metabolite thyroxine, as well as thousands of new findings. In Mendelian randomization (MR) analyses, we identified putative causal protein-to-metabolite associations. We experimentally validated top MR associations in proof-of-concept plasma metabolomics studies in three murine knockout strains of key protein regulators. These analyses identified previously unrecognized associations between bioactive proteins and metabolites in human plasma. We provide publicly available data to be leveraged for studies in human metabolism and disease.

Role of STIM1 in the Regulation of Cardiac Energy Substrate Preference

The heart requires a variety of energy substrates to maintain proper contractile function. Glucose and long-chain fatty acids (FA) are the major cardiac metabolic substrates under physiological conditions. Upon stress, a shift of cardiac substrate preference toward either glucose or FA is associated with cardiac diseases. For example, in pressure-overloaded hypertrophic hearts, there is a long-lasting substrate shift toward glucose, while in hearts with diabetic cardiomyopathy, the fuel is switched toward FA. Stromal interaction molecule 1 (STIM1), a well-established calcium (Ca2+) sensor of endoplasmic reticulum (ER) Ca2+ store, is increasingly recognized as a critical player in mediating both cardiac hypertrophy and diabetic cardiomyopathy. However, the cause-effect relationship between STIM1 and glucose/FA metabolism and the possible mechanisms by which STIM1 is involved in these cardiac metabolic diseases are poorly understood. In this review, we first discussed STIM1-dependent signaling in cardiomyocytes and metabolic changes in cardiac hypertrophy and diabetic cardiomyopathy. Second, we provided examples of the involvement of STIM1 in energy metabolism to discuss the emerging role of STIM1 in the regulation of energy substrate preference in metabolic cardiac diseases and speculated the corresponding underlying molecular mechanisms of the crosstalk between STIM1 and cardiac energy substrate preference. Finally, we briefly discussed and presented future perspectives on the possibility of targeting STIM1 to rescue cardiac metabolic diseases. Taken together, STIM1 emerges as a key player in regulating cardiac energy substrate preference, and revealing the underlying molecular mechanisms by which STIM1 mediates cardiac energy metabolism could be helpful to find novel targets to prevent or treat cardiac metabolic diseases.

Pyruvate-supported flux through medium-chain ketothiolase promotes mitochondrial lipid tolerance in cardiac and skeletal muscles

Even-chain acylcarnitine (AC) metabolites, most of which are generated as byproducts of incomplete fatty acid oxidation (FAO), are viewed as biomarkers of mitochondrial lipid stress attributable to one or more metabolic bottlenecks in the β-oxidation pathway. The origins and functional implications of FAO bottlenecks remain poorly understood. Here, we combined a sophisticated mitochondrial phenotyping platform with state-of-the-art molecular profiling tools and multiple two-state mouse models of respiratory function to uncover a mechanism that connects AC accumulation to lipid intolerance, metabolic inflexibility, and respiratory inefficiency in skeletal muscle mitochondria. These studies also identified a short-chain carbon circuit at the C4 node of FAO wherein reverse flux of glucose-derived acetyl CoA through medium-chain ketothiolase enhances lipid tolerance and redox stability in heart mitochondria by regenerating free CoA and NAD⁺. The findings help to explain why diminished FAO capacity, AC accumulation, and metabolic inflexibility are tightly linked to poor health outcomes.

Regulation of MAPK Signaling Pathways by the Large HERC Ubiquitin Ligases

Protein ubiquitylation acts as a complex cell signaling mechanism since the formation of different mono- and polyubiquitin chains determines the substrate's fate in the cell. E3 ligases define the specificity of this reaction by catalyzing the attachment of ubiquitin to the substrate protein. Thus, they represent an important regulatory component of this process. Large HERC ubiquitin ligases belong to the HECT E3 protein family and comprise HERC1 and HERC2 proteins. The physiological relevance of the Large HERCs is illustrated by their involvement in different pathologies, with a notable implication in cancer and neurological diseases. Understanding how cell signaling is altered in these different pathologies is important for uncovering novel therapeutic targets. To this end, this review summarizes the recent advances in how the Large HERCs regulate the MAPK signaling pathways. In addition, we emphasize the potential therapeutic strategies that could be followed to ameliorate the alterations in MAPK signaling caused by Large HERC deficiencies, focusing on the use of specific inhibitors and proteolysis-targeting chimeras.

Blood cell DNA methylation biomarkers in preclinical malignant pleural mesothelioma: The EPIC prospective cohort

Malignant pleural mesothelioma (MPM) is a rare and aggressive cancer mainly caused by asbestos exposure. Specific and sensitive noninvasive biomarkers may facilitate and enhance screening programs for the early detection of cancer. We investigated DNA methylation (DNAm) profiles in MPM prediagnostic blood samples in a case-control study nested in the European Prospective Investigation into Cancer and nutrition (EPIC) cohort, aiming to characterise DNAm biomarkers associated with MPM. From the EPIC cohort, we included samples from 135 participants who developed MPM during 20 years of follow-up and from 135 matched, cancer-free, controls. For the discovery phase we selected EPIC participants who developed MPM within 5 years from enrolment (n = 36) with matched controls. We identified nine differentially methylated CpGs, selected by 10-fold cross-validation and correlation analyses: cg25755428 (MRI1), cg20389709 (KLF11), cg23870316, cg13862711 (LHX6), cg06417478 (HOOK2), cg00667948, cg01879420 (AMD1), cg25317025 (RPL17) and cg06205333 (RAP1A). Receiver operating characteristic (ROC) analysis showed that the model including baseline characteristics (age, sex and PC1wbc) along with the nine MPM-related CpGs has a better predictive value for MPM occurrence than the baseline model alone, maintaining some performance also at more than 5 years before diagnosis (area under the curve [AUC] < 5 years = 0.89; AUC 5-10 years = 0.80; AUC >10 years = 0.75; baseline AUC range = 0.63-0.67). DNAm changes as noninvasive biomarkers in prediagnostic blood samples of MPM cases were investigated for the first time. Their application can improve the identification of asbestos-exposed individuals at higher MPM risk to possibly adopt more intensive monitoring for early disease identification.

Genome-wide metabolite quantitative trait loci analysis (mQTL) in red blood cells from volunteer blood donors

The red blood cell (RBC)-Omics study, part of the larger NHLBI-funded Recipient Epidemiology and Donor Evaluation Study (REDS-III), aims to understand the genetic contribution to blood donor RBC characteristics. Previous work identified donor demographic, behavioral, genetic, and metabolic underpinnings to blood donation, storage, and (to a lesser extent) transfusion outcomes, but none have yet linked the genetic and metabolic bodies of work. We performed a genome-wide association (GWA) analysis using RBC-Omics study participants with generated untargeted metabolomics data to identify metabolite quantitative trait loci in RBCs. We performed GWA analyses of 382 metabolites in 243 individuals imputed using the 1000 Genomes Project phase 3 all-ancestry reference panel. Analyses were conducted using ProbABEL and adjusted for sex, age, donation center, number of whole blood donations in the past 2 years, and first 10 principal components of ancestry. Our results identified 423 independent genetic loci associated with 132 metabolites (p < 5×10^-8). Potentially novel locus-metabolite associations were identified for the region encoding heme transporter FLVCR1 and choline and for lysophosphatidylcholine acetyltransferase LPCAT3 and lysophosphatidylserine 16.0, 18.0, 18.1, and 18.2; these associations are supported by published rare disease and mouse studies. We also confirmed previous metabolite GWA results for associations, including N(6)-methyl-L-lysine and protein PYROXD2 and various carnitines and transporter SLC22A16. Association between pyruvate levels and G6PD polymorphisms was validated in an independent cohort and novel murine models of G6PD deficiency (African and Mediterranean variants). We demonstrate that it is possible to perform metabolomics-scale GWA analyses with a modest, trans-ancestry sample size.

Further Introduction of DNA Methylation (DNAm) Arrays in Regular Diagnostics

Methylation tests have been used for decades in regular DNA diagnostics focusing primarily on Imprinting disorders or specific loci annotated to specific disease associated gene promotors. With the introduction of DNA methylation (DNAm) arrays such as the Illumina Infinium HumanMethylation450 Beadchip array or the Illumina Infinium Methylation EPIC Beadchip array (850 k), it has become feasible to study the epigenome in a timely and cost-effective way. This has led to new insights regarding the complexity of well-studied imprinting disorders such as the Beckwith Wiedemann syndrome, but it has also led to the introduction of tests such as EpiSign, implemented as a diagnostic test in which a single array experiment can be compared to databases with known episignatures of multiple genetic disorders, especially neurodevelopmental disorders. The successful use of such DNAm tests is rapidly expanding. More and more disorders are found to be associated with discrete episignatures which enables fast and definite diagnoses, as we have shown. The first examples of environmentally induced clinical disorders characterized by discrete aberrant DNAm are discussed underlining the broad application of DNAm testing in regular diagnostics. Here we discuss exemplary findings in our laboratory covering this broad range of applications and we discuss further use of DNAm tests in the near future.

Sex-dimorphic gene effects on survival outcomes in people with coronary artery disease

Background:

Ischemic coronary heart disease (IHD) is the leading cause of death worldwide. Genetic variation is presumed to be a major factor underlying sex differences for IHD events, including mortality. The purpose of this study was to identify sex-specific candidate genes associated with all-cause mortality among people diagnosed with coronary artery disease (CAD).

Methods:

We performed a sex-stratified, exploratory genome-wide association (GWAS) screen using existing data from CAD-diagnosed males (n = 510) and females (n = 174) who reported European ancestry from the Duke Catheterization Genetics biorepository. Extant genotype data for 785,945 autosomal SNPs generated with the Human Omni1-Quad BeadChip (Illumina, CA, USA) were analyzed using an additive inheritance model. We estimated instantaneous risk of all-cause mortality by genotype groups across the 11-year follow-up using Cox multivariate regression, covarying for age and genomic ancestry.

Results:

The top GWAS hits associated with all-cause mortality among people with CAD included 8 SNPs among males and 15 among females (p = 1 × 10⁻⁶ or 10⁻⁷), adjusted for covariates. Cross-sex comparisons revealed distinct candidate genes. Biologically relevant candidates included rs9932462 (EMP2/TEKT5) and rs2835913 (KCNJ6) among males and rs7217169 (RAP1GAP2), rs8021816 (PRKD1), rs8133010 (PDE9A), and rs12145981 (LPGAT1) among females.

Conclusions:

We report 20 sex-specific candidate genes having suggestive association with all-cause mortality among CAD-diagnosed subjects. Findings demonstrate proof of principle for identifying sex-associated genetic factors that may help explain differential mortality risk in people with CAD. Replication and meta-analyses in larger studies with more diverse samples will strengthen future work in this area.

STIM and Orai Mediated Regulation of Calcium Signaling in Age-Related Diseases

Tight spatiotemporal regulation of intracellular Ca²⁺ plays a critical role in regulating diverse cellular functions including cell survival, metabolism, and transcription. As a result, eukaryotic cells have developed a wide variety of mechanisms for controlling Ca²⁺ influx and efflux across the plasma membrane as well as Ca²⁺ release and uptake from intracellular stores. The STIM and Orai protein families comprising of STIM1, STIM2, Orai1, Orai2, and Orai3, are evolutionarily highly conserved proteins that are core components of all mammalian Ca²⁺ signaling systems. STIM1 and Orai1 are considered key players in the regulation of Store Operated Calcium Entry (SOCE), where release of Ca²⁺ from intracellular stores such as the Endoplasmic/Sarcoplasmic reticulum (ER/SR) triggers Ca²⁺ influx across the plasma membrane. SOCE, which has been widely characterized in non-excitable cells, plays a central role in Ca²⁺-dependent transcriptional regulation. In addition to their role in Ca²⁺ signaling, STIM1 and Orai1 have been shown to contribute to the regulation of metabolism and mitochondrial function. STIM and Orai proteins are also subject to redox modifications, which influence their activities. Considering their ubiquitous expression, there has been increasing interest in the roles of STIM and Orai proteins in excitable cells such as neurons and myocytes. While controversy remains as to the importance of SOCE in excitable cells, STIM1 and Orai1 are essential for cellular homeostasis and their disruption is linked to various diseases associated with aging such as cardiovascular disease and neurodegeneration. The recent identification of splice variants for most STIM and Orai isoforms while complicating our understanding of their function, may also provide insight into some of the current contradictions on their roles. Therefore, the goal of this review is to describe our current understanding of the molecular regulation of STIM and Orai proteins and their roles in normal physiology and diseases of aging, with a particular focus on heart disease and neurodegeneration.

The Potential of Metabolomics in Biomedical Applications

The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.

OUP accepted manuscript

AIMS

Gene expression biosignatures may hold promise to individualize antiplatelet therapy in conjunction with current guidelines and risk scores. The Aspirin Response Signature (ARS) score is comprised of a weighted sum of correlated, pro-thrombotic gene transcripts measured in whole blood. In prior work where volunteers were exposed to aspirin 325 mg daily, higher ARS score was associated with lower platelet function; separately, in a clinical cohort of patients, higher ARS scores were associated with increased risk of adverse cardiovascular events. To better understand this apparent paradox, we measured ARS gene expression and score in volunteers to determine aspirin dose-response and ticagrelor relationships with ARS score and separately in patients to assess whether ARS is associated with incident bleeding.

METHODS AND RESULTS

Blood samples were collected from volunteers (N = 188) who were exposed to 4 weeks of daily aspirin 81 mg, daily aspirin 325 mg, and/or twice-daily ticagrelor 90 mg. ARS scores were calculated from whole blood RNA qPCR, and platelet function and protein expression were assessed in platelet-rich plasma. In mixed linear regression models, aspirin 81 mg exposure was not associated with changes in ARS gene expression or score. Aspirin 325 mg exposure resulted in a 6.0% increase in ARS gene expression (P = 7.5 × 10-9 vs. baseline, P = 2.1 × 10-4 vs. aspirin 81 mg) and an increase in expression of platelet proteins corresponding to ARS genes. Ticagrelor exposure resulted in a 30.7% increase in ARS gene expression (P < 1 × 10-10 vs. baseline and each aspirin dose) and ARS score (P = 7.0 × 10-7 vs. baseline, P = 3.6 × 10-6 and 5.59 × 10-4 vs. aspirin 81 and 325 mg, respectively). Increases in ARS gene expression or score were associated with the magnitude of platelet inhibition across agents. To assess the association between ARS scores and incident bleeding, ARS scores were calculated in patients undergoing cardiac catheterization (N = 1421), of whom 25.4% experienced bleeding events over a median 6.2 years of follow-up. In a Cox model adjusting for demographics and baseline antithrombotic medication use, patients with ARS scores above the median had a higher risk of incident bleeding [hazard ratio 1.26 (95% CI 1.01-1.56), P = 0.038].

CONCLUSIONS

The ARS is an Antiplatelet Response Signature that increases in response to greater platelet inhibition due to antiplatelet therapy and may represent a homeostatic mechanism to prevent bleeding. ARS scores could inform future strategies to prevent bleeding while maintaining antiplatelet therapy's benefit of ischaemic cardiovascular event protection.

COMETS Analytics: An Online Tool for Analyzing and Meta-Analyzing Metabolomics Data in Large Research Consortia

Consortium-based research is crucial for producing reliable, high-quality findings, but existing tools for consortium studies have important drawbacks with respect to data protection, ease of deployment, and analytical rigor. To address these concerns, we developed COnsortium of METabolomics Studies (COMETS) Analytics to support and streamline consortium-based analyses of metabolomics and other -omics data. The application requires no specialized expertise and can be run locally to guarantee data protection or through a Web-based server for convenience and speed. Unlike other Web-based tools, COMETS Analytics enables standardized analyses to be run across all cohorts, using an algorithmic, reproducible approach to diagnose, document, and fix model issues. This eliminates the time-consuming and potentially error-prone step of manually customizing models by cohort, helping to accelerate consortium-based projects and enhancing analytical reproducibility. We demonstrated that the application scales well by performing 2 data analyses in 45 cohort studies that together comprised measurements of 4,647 metabolites in up to 134,742 participants. COMETS Analytics performed well in this test, as judged by the minimal errors that analysts had in preparing data inputs and the successful execution of all models attempted. As metabolomics gathers momentum among biomedical and epidemiologic researchers, COMETS Analytics may be a useful tool for facilitating large-scale consortium-based research.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Relevance of stromal interaction molecule 1 (STIM1) in experimental and human stroke

Stroke represents a main cause of death and permanent disability worldwide. In the attempt to develop targeted preventive and therapeutic strategies, several efforts were performed over the last decades to identify the specific molecular abnormalities preceding cerebral ischemia and neuronal death. In this regard, mitochondrial dysfunction, autophagy, and intracellular calcium homeostasis appear important contributors to stroke development, as underscored by recent pre-clinical evidence. Intracellular calcium (Ca²⁺) homeostasis is regulated, among other mechanisms, by the calcium sensor stromal interaction molecule 1 (STIM1) and calcium release-activated calcium modulator (ORAI) members, which mediate the store-operated Ca²⁺ entry (SOCE). The activity of SOCE is deregulated in animal models of ischemic stroke, leading to ischemic injury exacerbation. We found a different pattern of expression of few SOCE components, dependent from a STIM1 mutation, in cerebral endothelial cells isolated from the stroke-prone spontaneously hypertensive rat (SHRSP), compared to the stroke-resistant (SHRSR) strain, suggesting a potential involvement of this mechanism into the stroke predisposition of SHRSP. In this article, we discuss the relevant role of STIM1 in experimental stroke, as highlighted by the current literature and by our recent experimental findings, and the available evidence in the human disease. We also provide a glance on future perspectives and clinical implications of STIM1.

Genome-Wide Variants Associated With Longitudinal Survival Outcomes Among Individuals With Coronary Artery Disease

Objective

Coronary artery disease (CAD) is an age-associated condition that greatly increases the risk of mortality. The purpose of this study was to identify gene variants associated with all-cause mortality among individuals with clinically phenotyped CAD using a genome-wide screening approach.

Approach and Results

We performed discovery (n = 684), replication (n = 1,088), and meta-analyses (N = 1,503) for association of genomic variants with survival outcome using secondary data from White participants with CAD from two GWAS sub-studies of the Duke Catheterization Genetics Biorepository. We modeled time from catheterization to death or last follow-up (median 7.1 years, max 12 years) using Cox multivariable regression analysis. Target statistical screening thresholds were p × 10^–8 for the discovery phase and Bonferroni-calculated p-values for the replication (p < 5.3 × 10^–4) and meta-analysis (p < 1.6 × 10^–3) phases. Genome-wide analysis of 785,945 autosomal SNPs revealed two SNPs (rs13007553 and rs587936) that had the same direction of effect across all three phases of the analysis, with suggestive p-value association in discovery and replication and significant meta-analysis association in models adjusted for clinical covariates. The rs13007553 SNP variant, LINC01250, which resides between MYTIL and EIPR1, conferred increased risk for all-cause mortality even after controlling for clinical covariates [HR 1.47, 95% CI 1.17–1.86, p(adj) = 1.07 × 10^–3 (discovery), p(adj) = 0.03 (replication), p(adj) = 9.53 × 10^–5 (meta-analysis)]. MYT1L is involved in neuronal differentiation. TSSC1 is involved in endosomal recycling and is implicated in breast cancer. The rs587936 variant annotated to DAB2IP was associated with increased survival time [HR 0.65, 95% CI 0.51–0.83, p(adj) = 4.79 × 10^–4 (discovery), p(adj) = 0.02 (replication), p(adj) = 2.25 × 10^–5 (meta-analysis)]. DAB2IP is a ras/GAP tumor suppressor gene which is highly expressed in vascular tissue. DAB2IP has multiple lines of evidence for protection against atherosclerosis.

Conclusion

Replicated findings identified two candidate genes for further study regarding association with survival in high-risk CAD patients: novel loci LINC01250 (rs13007553) and biologically relevant candidate DAB2IP (rs587936). These candidates did not overlap with validated longevity candidate genes. Future research could further define the role of common variants in survival outcomes for people with CAD and, ultimately, improve longitudinal outcomes for these patients.

Genomic-Metabolomic Associations Support the Role of LIPC and Glycerophospholipids in Age-Related Macular Degeneration

Purpose

Large-scale genome-wide association studies (GWAS) have reported important single nucleotide polymorphisms (SNPs) with significant associations with age-related macular degeneration (AMD). However, their role in disease development remains elusive. This study aimed to assess SNP–metabolite associations (i.e., metabolite quantitative trait loci [met-QTL]) and to provide insights into the biological mechanisms of AMD risk SNPs.

Design

Cross-sectional multicenter study (Boston, Massachusetts, and Coimbra, Portugal).

Participants

Patients with AMD (n = 388) and control participants (n = 98) without any vitreoretinal disease (> 50 years).

Methods

Age-related macular degeneration grading was performed using color fundus photographs according to the Age-Related Eye Disease Study classification scheme. Fasting blood samples were collected and evaluated with mass spectrometry for metabolomic profiling and Illumina OmniExpress for SNPs profiling. Analyses of met-QTL of endogenous metabolites were conducted using linear regression models adjusted for age, gender, smoking, 10 metabolite principal components (PCs), and 10 SNP PCs. Additionally, we analyzed the cumulative effect of AMD risk SNPs on plasma metabolites by generating genetic risk scores and assessing their associations with metabolites using linear regression models, accounting for the same covariates. Modeling was performed first for each cohort, and then combined by meta-analysis. Multiple comparisons were accounted for using the false discovery rate (FDR).

Main Outcome Measures

Plasma metabolite levels associated with AMD risk SNPs.

Results

After quality control, data for 544 plasma metabolites were included. Meta-analysis of data from all individuals (AMD patients and control participants) identified 28 significant met-QTL (β = 0.016–0.083; FDR q-value < 1.14 × 10^–2), which corresponded to 5 metabolites and 2 genes: ASPM and LIPC. Polymorphisms in the LIPC gene were associated with phosphatidylethanolamine metabolites, which are glycerophospholipids, and polymorphisms in the ASPM gene with branched-chain amino acids. Similar results were observed when considering only patients with AMD. Genetic risk score–metabolite associations further supported a global impact of AMD risk SNPs on the plasma metabolome.

Conclusions

This study demonstrated that genomic–metabolomic associations can provide insights into the biological relevance of AMD risk SNPs. In particular, our results support that the LIPC gene and the glycerophospholipid metabolic pathway may play an important role in AMD, thus offering new potential therapeutic targets for this disease.

pgainsim: an R-package to assess the mode of inheritance for quantitative trait loci in GWAS

MOTIVATION

When performing genome-wide association studies conventionally the additive genetic model is used to explore whether a single nucleotide polymorphism (SNP) is associated with a quantitative trait. But for variants, which do not follow an intermediate mode of inheritance (MOI), the recessive or the dominant genetic model can have more power to detect associations and furthermore the MOI is important for downstream analyses and clinical interpretation. When multiple MOIs are modelled the question arises, which describes the true underlying MOI best.

RESULTS

We developed an R-package allowing for the first time to determine study specific critical values when one of the three models is more informative than the other ones for a quantitative trait locus. The package allows for user-friendly simulations to determine these critical values with predefined minor allele frequencies and study sizes. For application scenarios with extensive multiple testing we integrated an interpolation functionality to determine critical values already based on a moderate number of random draws.

AVAILABILITY AND IMPLEMENTATION

The R-package pgainsim is freely available for download on Github at https://github.com/genepi-freiburg/pgainsim.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Novel plasma biomarkers improve discrimination of metabolic health independent of weight

We sought to determine if novel plasma biomarkers improve traditionally defined metabolic health (MH) in predicting risk of cardiovascular disease (CVD) events irrespective of weight. Poor MH was defined in CATHGEN biorepository participants (n > 9300), a follow-up cohort (> 5600 days) comprising participants undergoing evaluation for possible ischemic heart disease. Lipoprotein subparticles, lipoprotein-insulin resistance (LP-IR), and GlycA were measured using NMR spectroscopy (n = 8385), while acylcarnitines and amino acids were measured using flow-injection, tandem mass spectrometry (n = 3592). Multivariable Cox proportional hazards models determined association of poor MH and plasma biomarkers with time-to-all-cause mortality or incident myocardial infarction. Low-density lipoprotein particle size and high-density lipoprotein, small and medium particle size (HMSP), GlycA, LP-IR, short-chain dicarboxylacylcarnitines (SCDA), and branched-chain amino acid plasma biomarkers were independently associated with CVD events after adjustment for traditionally defined MH in the overall cohort (p = 3.3 × 10-4-3.6 × 10-123), as well as within most of the individual BMI categories (p = 8.1 × 10-3-1.4 × 10-49). LP-IR, GlycA, HMSP, and SCDA improved metrics of model fit analyses beyond that of traditionally defined MH. We found that LP-IR, GlycA, HMSP, and SCDA improve traditionally defined MH models in prediction of adverse CVD events irrespective of BMI.

Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits

The ability to accurately predict complex trait phenotypes from genetic and genomic data are critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ∼200 inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels (r = 0.28 and 0.38, for females and males, respectively) provided higher prediction accuracy than genotypes (r = 0.07 and 0.15, for females and males, respectively) for starvation resistance, similar prediction accuracy for chill coma recovery (null for both models and sexes), and lower prediction accuracy for startle response (r = 0.15 and 0.14 for female and male genotypes, respectively; and r = 0.12 and 0.11, for females and male transcripts, respectively). Models including both genotype and expression levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included gene ontology (GO) category as an additional layer of information for both genomic variants and transcripts. We found strongly predictive GO terms for each of the three traits, some of which had a clear plausible biological interpretation. For example, for starvation resistance in females, GO:0033500 (r = 0.39 for transcripts) and GO:0032870 (r = 0.40 for transcripts), have been implicated in carbohydrate homeostasis and cellular response to hormone stimulus (including the insulin receptor signaling pathway), respectively. In summary, this study shows that integrating different sources of information improved prediction accuracy and helped elucidate the genetic architecture of three Drosophila complex phenotypes.

Mitochondria orchestrate macrophage effector functions in atherosclerosis

Macrophages are pivotal in the initiation and development of atherosclerotic cardiovascular diseases. Recent studies have reinforced the importance of mitochondria in metabolic and signaling pathways to maintain macrophage effector functions. In this review, we discuss the past and emerging roles of macrophage mitochondria metabolic diversity in atherosclerosis and the potential avenue as biomarker. Beyond metabolic functions, mitochondria are also a signaling platform integrating epigenetic, redox, efferocytic and apoptotic regulations, which are exquisitely linked to their dynamics. Indeed, mitochondria functions depend on their density and shape perpetually controlled by mitochondria fusion/fission and biogenesis/mitophagy balances. Mitochondria can also communicate with other organelles such as the endoplasmic reticulum through mitochondria-associated membrane (MAM) or be secreted for paracrine actions. All these functions are perturbed in macrophages from mouse or human atherosclerotic plaques. A better understanding and integration of how these metabolic and signaling processes are integrated and dictate macrophage effector functions in atherosclerosis may ultimately help the development of novel therapeutic approaches.

Pharmacogenomics meets precision cardio-oncology: is there synergistic potential?

An individual's inherited genetic makeup and acquired genomic variants may account for a significant portion of observable variability in therapy efficacy and toxicity. Pharmacogenomics (PGx) is the concept that treatments can be modified to account for these differences to increase chances of therapeutic efficacy while minimizing risk of adverse effects. This is particularly applicable to oncology in which treatment may be multimodal. Each tumor type has a unique genomic signature that lends to inclusion of targeted therapy but may be associated with cumulative toxicity, such as cardiotoxicity, and can impact quality of life. A greater understanding of therapeutic agents impacted by PGx and subsequent implementation has the potential to improve outcomes and reduce risk of drug-induced adverse effects.

Comprehensive Analysis of Genetic Ancestry and Its Molecular Correlates in Cancer

We evaluated ancestry effects on mutation rates, DNA methylation, and mRNA and miRNA expression among 10,678 patients across 33 cancer types from The Cancer Genome Atlas. We demonstrated that cancer subtypes and ancestry-related technical artifacts are important confounders that have been insufficiently accounted for. Once accounted for, ancestry-associated differences spanned all molecular features and hundreds of genes. Biologically significant differences were usually tissue specific but not specific to cancer. However, admixture and pathway analyses suggested some of these differences are causally related to cancer. Specific findings included increased FBXW7 mutations in patients of African origin, decreased VHL and PBRM1 mutations in renal cancer patients of African origin, and decreased immune activity in bladder cancer patients of East Asian origin.

Multi-omics Data Integration for Identifying Osteoporosis Biomarkers and Their Biological Interaction and Causal Mechanisms

Osteoporosis is characterized by low bone mineral density (BMD). The advancement of high-throughput technologies and integrative approaches provided an opportunity for deciphering the mechanisms underlying osteoporosis. Here, we generated genomic, transcriptomic, methylomic, and metabolomic datasets from 119 subjects with high (n = 61) and low (n = 58) BMDs. By adopting sparse multiple discriminative canonical correlation analysis, we identified an optimal multi-omics biomarker panel with 74 differentially expressed genes (DEGs), 75 differentially methylated CpG sites (DMCs), and 23 differential metabolic products (DMPs). By linking genetic data, we identified 199 targeted BMD-associated expression/methylation/metabolite quantitative trait loci (eQTLs/meQTLs/metaQTLs). The reconstructed networks/pathways showed extensive biomarker interactions, and a substantial proportion of these biomarkers were enriched in RANK/RANKL, MAPK/TGF-β, and WNT/β-catenin pathways and G-protein-coupled receptor, GTP-binding/GTPase, telomere/mitochondrial activities that are essential for bone metabolism. Five biomarkers (FADS2, ADRA2A, FMN1, RABL2A, SPRY1) revealed causal effects on BMD variation. Our study provided an innovative framework and insights into the pathogenesis of osteoporosis.

Advantages and Limitations of the Neonatal Immune System

During early post-natal life, neonates must adjust to the transition from the sheltered intra-uterine environment to the microbe-laden external world, wherein they encounter a constellation of antigens and the colonization by the microbiome. At this vulnerable stage, neonatal immune responses are considered immature and present significant differences to those of adults. Pertinent to innate immunity, functional and quantitative deficiencies in antigen-presenting cells and phagocytes are often documented. Exposure to environmental antigens and microbial colonization is associated with epigenetic immune cell reprogramming and activation of effector and regulatory mechanisms that ensure age-depended immune system maturation and prevention of tissue damage. Moreover, neonatal innate immune memory has emerged as a critical mechanism providing protection against infectious agents. Still, in neonates, inexperience to antigenic exposure, along with enhancement of tissue-protective immunosuppressive mechanisms are often associated with severe immunopathological conditions, including sepsis and neurodevelopmental disorders. Despite significant advances in the field, adequate vaccination in newborns is still in its infancy due to elemental restrictions associated also with defective immune responses. In this review, we provide an overview of neonatal innate immune cells, highlighting phenotypic and functional disparities with their adult counterparts. We also discuss the effects of epigenetic modifications and microbial colonization on the regulation of neonatal immunity. A recent update on mechanisms underlying dysregulated neonatal innate immunity and linked infectious and neurodevelopmental diseases is provided. Understanding of the mechanisms that augment innate immune responsiveness in neonates may facilitate the development of improved vaccination protocols that can protect against pathogens and organ damage.

Analysis of NMR Metabolomics Data

In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.

Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics

How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.

Epigenome-Wide Association Study for All-Cause Mortality in a Cardiovascular Cohort Identifies Differential Methylation in Castor Zinc Finger 1 (CASZ1)

Background DNA methylation is implicated in many chronic diseases and may contribute to mortality. Therefore, we conducted an epigenome-wide association study (EWAS) for all-cause mortality with whole-transcriptome data in a cardiovascular cohort (CATHGEN [Catheterization Genetics]). Methods and Results Cases were participants with mortality≥7 days postcatheterization whereas controls were alive with≥2 years of follow-up. The Illumina Human Methylation 450K and EPIC arrays (Illumina, San Diego, CA) were used for the discovery and validation sets, respectively. A linear model approach with empirical Bayes estimators adjusted for confounders was used to assess difference in methylation (Δβ). In the discovery set (55 cases, 49 controls), 25 629 (6.5%) probes were differently methylated (P<0.05). In the validation set (108 cases, 108 controls), 3 probes were differentially methylated with a false discovery rate-adjusted P<0.10: cg08215811 (SLC4A9; log2 fold change=-0.14); cg17845532 (MATK; fold change=-0.26); and cg17944110 (castor zinc finger 1 [CASZ1]; FC=0.26; P<0.0001; false discovery rate-adjusted P=0.046-0.080). Meta-analysis identified 6 probes (false discovery rate-adjusted P<0.05): the 3 above, cg20428720 (intergenic), cg17647904 (NCOR2), and cg23198793 (CAPN3). Messenger RNA expression of 2 MATK isoforms was lower in cases (fold change=-0.24 [P=0.007] and fold change=-0.61 [P=0.009]). The CASZ1, NCOR2, and CAPN3 transcripts did not show differential expression (P>0.05); the SLC4A9 transcript did not pass quality control. The cg17944110 probe is located within a potential regulatory element; expression of predicted targets (using GeneHancer) of the regulatory element, UBIAD1 (P=0.01) and CLSTN1 (P=0.03), were lower in cases. Conclusions We identified 6 novel methylation sites associated with all-cause mortality. Methylation in CASZ1 may serve as a regulatory element associated with mortality in cardiovascular patients. Larger studies are necessary to confirm these observations.

Heart Failure in Type 2 Diabetes Mellitus

Patients with diabetes mellitus have >2× the risk for developing heart failure (HF; HF with reduced ejection fraction and HF with preserved ejection fraction). Cardiovascular outcomes, hospitalization, and prognosis are worse for patients with diabetes mellitus relative to those without. Beyond the structural and functional changes that characterize diabetic cardiomyopathy, a complex underlying, and interrelated pathophysiology exists. Despite the success of many commonly used antihyperglycemic therapies to lower hyperglycemia in type 2 diabetes mellitus the high prevalence of HF persists. This, therefore, raises the possibility that additional factors beyond glycemia might contribute to the increased HF risk in diabetes mellitus. This review summarizes the state of knowledge about the impact of existing antihyperglycemic therapies on HF and discusses potential mechanisms for beneficial or deleterious effects. Second, we review currently approved pharmacological therapies for HF and review evidence that addresses their efficacy in the context of diabetes mellitus. Dysregulation of many cellular mechanisms in multiple models of diabetic cardiomyopathy and in human hearts have been described. These include oxidative stress, inflammation, endoplasmic reticulum stress, aberrant insulin signaling, accumulation of advanced glycated end-products, altered autophagy, changes in myocardial substrate metabolism and mitochondrial bioenergetics, lipotoxicity, and altered signal transduction such as GRK (g-protein receptor kinase) signaling, renin angiotensin aldosterone signaling and β-2 adrenergic receptor signaling. These pathophysiological pathways might be amenable to pharmacological therapy to reduce the risk of HF in the context of type 2 diabetes mellitus. Successful targeting of these pathways could alter the prognosis and risk of HF beyond what is currently achieved using existing antihyperglycemic and HF therapeutics.

In silico approach to calculate the transcript capacity

We sought the novel concept, transcript capacity (TC) and analyzed TC. Our approach to estimate TC was through an in silico method. TC refers to the capacity that a transcript exerts in a cell as enzyme or protein function after translation. We used the genome-wide association study (GWAS) beta effect and transcription level in RNA-sequencing to estimate TC. The trait was body fat percent and the transcript reads were obtained from the human protein atlas. The assumption was that the GWAS beta effect is the gene’s effect and TC was related to the corresponding gene effect and transcript reads. Further, we surveyed gene ontology (GO) in the highest TC and the lowest TC genes. The most frequent GOs with the highest TC were neuronal-related and cell projection organization related. The most frequent GOs with the lowest TC were wound-healing related and embryo development related. We expect that our analysis contributes to estimating TC in the diverse species and playing a benevolent role to the new bioinformatic analysis.

Metabolic Profiling Associates with Disease Severity in Nonischemic Dilated Cardiomyopathy

BACKGROUND

Metabolomic profiling may have diagnostic and prognostic value in heart failure. This study investigated whether targeted blood and urine metabolomics reflects disease severity in patients with nonischemic dilated cardiomyopathy (DCM) and compared its incremental value on top of N-terminal prohormone of brain natriuretic peptide (NT-proBNP).

METHODS AND RESULTS

A total of 149 metabolites were measured in plasma and urine samples of 273 patients with DCM and with varying stages of disease (patients with DCM and normal left ventricular reverse remodeling, n = 70; asymptomatic DCM, n = 72; and symptomatic DCM, n = 131). Acylcarnitines, sialic acid and glutamic acid are the most distinctive metabolites associated with disease severity, as repeatedly revealed by unibiomarker linear regression, sparse partial least squares discriminant analysis, random forest, and conditional random forest analyses. However, the absolute difference in the metabolic profile among groups was marginal. A decision-tree model based on the top metabolites did not surpass NT-proBNP in classifying stages. However, a combination of NT-proBNP and the top metabolites improved the decision tree to distinguish patients with DCM and left ventricular reverse remodeling from symptomatic DCM (area under the curve 0.813 ± 0.138 vs 0.739 ± 0.114; P = 0.02).

CONCLUSION

Functional cardiac recovery is reflected in metabolomics. These alterations reveal potential alternative treatment targets in advanced symptomatic DCM. The metabolic profile can complement NT-proBNP in determining disease severity in nonischemic DCM.

Metabolomics in genetic testing

Metabolomics is an intriguing field of study providing a new readout of the biochemical activities taking place at the moment of sampling within a subject's biofluid or tissue. Metabolite concentrations are influenced by several factors including disease, environment, drugs, diet and, importantly, genetics. Metabolomics signatures, which describe a subject's phenotype, are useful for disease diagnosis and prognosis, as well as for predicting and monitoring the effectiveness of treatments. Metabolomics is conventionally divided into targeted (i.e., the quantitative analysis of a predetermined group of metabolites) and untargeted studies (i.e., analysis of the complete set of small-molecule metabolites contained in a biofluid without a pre-imposed metabolites-selection). Both approaches have demonstrated high value in the investigation and understanding of several monogenic and multigenic conditions. Due to low costs per sample and relatively short analysis times, metabolomics can be a useful and robust complement to genetic sequencing.

Advanced Statistical Methods for NMR-Based Metabolomics

Despite the increasing popularity and applicability of metabolomics for putative biomarker identification, analysis of the data is challenged by low statistical power resulting from the small sample sizes and large numbers of metabolites and other omics information, as well as confounding demographic and clinical variables. To enhance the statistical power and improve reproducibility of the identified metabolite-based biomarkers, we advocate the use of advanced statistical methods that can simultaneously evaluate the relationship between a group of metabolites and various types of variables including other omics profiles, demographic and clinical data, as well as the complex interactions between them. Accordingly, in this chapter, we describe the method of seemingly unrelated regression that can simultaneously analyze multiple metabolites while controlling the confounding effects of demographic and clinical variables (such as gender, age, BMI, smoking status). We also introduce penalized orthogonal components regression as a screening approach that can handle millions of omics predictors in the model.

Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS)

The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.

Human Plasma Metabolomics in Age-Related Macular Degeneration: Meta-Analysis of Two Cohorts

The pathogenesis of age-related macular degeneration (AMD), a leading cause of blindness worldwide, remains only partially understood. This has led to the current lack of accessible and reliable biofluid biomarkers for diagnosis and prognosis, and absence of treatments for dry AMD. This study aimed to assess the plasma metabolomic profiles of AMD and its severity stages with the ultimate goal of contributing to addressing these needs. We recruited two cohorts: Boston, United States (n = 196) and Coimbra, Portugal (n = 295). Fasting blood samples were analyzed using ultra-high performance liquid chromatography mass spectrometry. For each cohort, we compared plasma metabolites of AMD patients versus controls (logistic regression), and across disease stages (permutation-based cumulative logistic regression considering both eyes). Meta-analyses were then used to combine results from the two cohorts. Our results revealed that 28 metabolites differed significantly between AMD patients versus controls (false discovery rate (FDR) q-value: 4.1 × 10-2-1.8 × 10-5), and 67 across disease stages (FDR q-value: 4.5 × 10-2-1.7 × 10-4). Pathway analysis showed significant enrichment of glycerophospholipid, purine, taurine and hypotaurine, and nitrogen metabolism (p-value ≤ 0.04). In conclusion, our findings support that AMD patients present distinct plasma metabolomic profiles, which vary with disease severity. This work contributes to the understanding of AMD pathophysiology, and can be the basis of future biomarkers and precision medicine for this blinding condition.

Sildenafil Treatment in Heart Failure With Preserved Ejection Fraction: Targeted Metabolomic Profiling in the RELAX Trial

Importance

Phosphodiesterase-5 inhibition with sildenafil compared with a placebo had no effect on the exercise capacity or clinical status of patients with heart failure with preserved ejection fraction (HFpEF) in the PhosphodiesteRasE-5 Inhibition to Improve Clinical Status and Exercise Capacity in Diastolic Heart Failure with Preserved Ejection Fraction (RELAX) clinical trial. Metabolic impairments may explain the neutral results.

Objective

To test the hypothesis that profiling metabolites in the RELAX trial would clarify the mechanisms of sildenafil effects and identify metabolites associated with clinical outcomes in HFpEF.

Design, Setting, and Participants

Paired baseline and 24-week plasma samples of 160 stable outpatient individuals with HFpEF enrolled in the RELAX clinical trial were analyzed using flow injection tandem mass spectrometry (60 metabolites) and conventional assays (5 metabolites).

Interventions

Sildenafil (n = 79) or a placebo (n = 81) administered orally at 20 mg, 3 times daily for 12 weeks, followed by 60 mg, 3 times daily for 12 weeks.

Main Outcomes and Measures

The primary measure was metabolite level changes between baseline and 24 weeks stratified by treatments. Secondary measures included correlations between metabolite level changes and clinical biomarkers and associations between baseline metabolite levels and the composite clinical score.

Results

No metabolites changed between baseline and 24 weeks in the group treated with a placebo; however, 7 metabolites changed in the group treated with sildenafil, including decreased amino acids (alanine and proline; median change [25th-75th], -38.26 [-100.3 to 28.19] and -28.24 [-56.29 to 12.08], respectively; false discovery rate-adjusted P = .01 and .03, respectively), and increased short-chain dicarboxylacylcarnitines glutaryl carnitine, octenedioyl carnitine, and adipoyl carnitine (median change, 6.19 [-3.37 to 14.18], 2.72 [-3 to 12.57], and 10.72 [-11.23 to 29.57], respectively; false discovery rate-adjusted P = .01, .04, and .05, respectively), and 1 long-chain acylcarnitine metabolite (palmitoyl carnitine; median change, 7.83 [-5.64 to 26.99]; false discovery rate-adjusted P = .03). The increases in long-chain acylarnitine metabolites and short-chain dicarboxylacylcarnitines correlated with increases in endothelin-1 and creatinine/cystatin C, respectively. Higher baseline levels of short-chain dicarboxylacylcarnitine metabolite 3-hydroxyisovalerylcarnitine/malonylcarnitine and asparagine/aspartic acid were associated with worse clinical rank scores in both treatment groups (β, -96.60, P = .001 and β, -0.02, P = .01; after renal adjustment, P = .09 and .02, respectively).

Conclusions and Relevance

Our study provides a potential mechanism for the effects of sildenafil that, through adverse effects on mitochondrial function and endoplasmic reticulum stress, could have contributed to the neutral trial results in RELAX. Short-chain dicarboxylacylcarnitine metabolites and asparagine/aspartic acid could serve as biomarkers associated with adverse clinical outcomes in HFpEF.

Disrupted Maturation of the Microbiota and Metabolome among Extremely Preterm Infants with Postnatal Growth Failure

Growth failure during infancy is a major global problem that has adverse effects on long-term health and neurodevelopment. Preterm infants are disproportionately affected by growth failure and its effects. Herein we found that extremely preterm infants with postnatal growth failure have disrupted maturation of the intestinal microbiota, characterized by persistently low diversity, dominance of pathogenic bacteria within the Enterobacteriaceae family, and a paucity of strictly anaerobic taxa including Veillonella relative to infants with appropriate postnatal growth. Metabolomic profiling of infants with growth failure demonstrated elevated serum acylcarnitines, fatty acids, and other byproducts of lipolysis and fatty acid oxidation. Machine learning algorithms for normal maturation of the microbiota and metabolome among infants with appropriate growth revealed a pattern of delayed maturation of the microbiota and metabolome among infants with growth failure. Collectively, we identified novel microbial and metabolic features of growth failure in preterm infants and potentially modifiable targets for intervention.

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).

An 85-Gene Coexpression Module for Progression of Hypertension-Induced Spontaneous Intracerebral Hemorrhage

Intracerebral hemorrhage (ICH) represents the most lethal form of stroke. We sought to identify potential genes that might contribute to progression of hypertension-induced spontaneous ICH (HIS-ICH). RNA-sequencing data set of cerebral vessel samples from HIS-ICH mice and normal mice was obtained from the Gene Expression Omnibus. Differential expression genes in HIS-ICH samples were obtained compared with normal samples followed by functional enrichment analysis. What is more, we explored the potential gene coexpression module (GCM) for HIS-ICH progression by using weighted gene coexpression network analysis. We further conducted protein-protein interaction network analysis for genes contained in GCM that was closely correlated with HIS-ICH to disclose their biological interactions. As a result, 554 genes were found to aberrantly express in HIS-ICH mice compared with normal mice, which were mainly associated with cancer-related pathways in addition to some well-known ICH-related pathways. A total of 28 GCMs were obtained, and darkturquoise module that contained 85 genes, which were closely associated with mitochondrion and hydrolase activity, was significantly correlated with HIS-ICH progression. Besides, we identified dense biological interactions among some genes in darkturquoise, such as Psma gene family and Hsp90a gene family. This study should shed new light on HIS-ICH progression and its treatment.