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

Sphingolipid dysregulation due to lack of functional KDSR impairs proplatelet formation causing thrombocytopenia

Sphingolipids are fundamental to membrane trafficking, apoptosis, and cell differentiation and proliferation. KDSR or 3-keto-dihydrosphingosine reductase is an essential enzyme for de novo sphingolipid synthesis, and pathogenic mutations in KDSR result in the severe skin disorder erythrokeratodermia variabilis et progressiva-4. Four of the eight reported cases also had thrombocytopenia but the underlying mechanism has remained unexplored. Here we expand upon the phenotypic spectrum of KDSR deficiency with studies in two siblings with novel compound heterozygous variants associated with thrombocytopenia, anemia, and minimal skin involvement. We report a novel phenotype of progressive juvenile myelofibrosis in the propositus, with spontaneous recovery of anemia and thrombocytopenia in the first decade of life. Examination of bone marrow biopsies showed megakaryocyte hyperproliferation and dysplasia. Megakaryocytes obtained by culture of CD34⁺ stem cells confirmed hyperproliferation and showed reduced proplatelet formation. The effect of KDSR insufficiency on the sphingolipid profile was unknown, and was explored in vivo and in vitro by a broad metabolomics screen that indicated activation of an in vivo compensatory pathway that leads to normalization of downstream metabolites such as ceramide. Differentiation of propositus-derived induced pluripotent stem cells to megakaryocytes followed by expression of functional KDSR showed correction of the aberrant cellular and biochemical phenotypes, corroborating the critical role of KDSR in proplatelet formation. Finally, Kdsr depletion in zebrafish recapitulated the thrombocytopenia and showed biochemical changes similar to those observed in the affected siblings. These studies support an important role for sphingolipids as regulators of cytoskeletal organization during megakaryopoiesis and proplatelet formation.

Immune diversity sheds light on missing variation in worldwide genetic diversity panels

Defining worldwide human genetic variation is a critical step to reveal how genome plasticity contributes to disease. Yet, there is currently no metric to assess the representativeness and completeness of current and widely used data on genetic variation. We show here that Human Leukocyte Antigen (HLA) genes can serve as such metric as they are both the most polymorphic and the most studied genetic system. As a test case, we investigated the 1,000 Genomes Project panel. Using high-accuracy in silico HLA typing, we find that over 20% of the common HLA variants and over 70% of the rare HLA variants are missing in this reference panel for worldwide genetic variation, due to undersampling and incomplete geographical coverage, in particular in Oceania and West Asia. Because common and rare variants both contribute to disease, this study thus illustrates how HLA diversity can detect and help fix incomplete sampling and hence accelerate efforts to draw a comprehensive overview of the genetic variation that is relevant to health and disease.

A Proteomic Variant Approach (ProVarA) for Personalized Medicine of Inherited and Somatic Disease

The advent of precision medicine for genetic diseases has been hampered by the large number of variants that cause familial and somatic disease, a complexity that is further confounded by the impact of genetic modifiers. To begin to understand differences in onset, progression and therapeutic response that exist among disease-causing variants, we present the proteomic variant approach (ProVarA), a proteomic method that integrates mass spectrometry with genomic tools to dissect the etiology of disease. To illustrate its value, we examined the impact of variation in cystic fibrosis (CF), where 2025 disease-associated mutations in the CF transmembrane conductance regulator (CFTR) gene have been annotated and where individual genotypes exhibit phenotypic heterogeneity and response to therapeutic intervention. A comparative analysis of variant-specific proteomics allows us to identify a number of protein interactions contributing to the basic defects associated with F508del- and G551D-CFTR, two of the most common disease-associated variants in the patient population. We demonstrate that a number of these causal interactions are significantly altered in response to treatment with Vx809 and Vx770, small-molecule therapeutics that respectively target the F508del and G551D variants. ProVarA represents the first comparative proteomic analysis among multiple disease-causing mutations, thereby providing a methodological approach that provides a significant advancement to existing proteomic efforts in understanding the impact of variation in CF disease. We posit that the implementation of ProVarA for any familial or somatic mutation will provide a substantial increase in the knowledge base needed to implement a precision medicine-based approach for clinical management of disease.

Link between gut-microbiome derived metabolite and shared gene-effects with hepatic steatosis and fibrosis in NAFLD

Previous studies have shown that gut-microbiome is associated with nonalcoholic fatty liver disease (NAFLD). We aimed to examine if serum metabolites, especially those derived from the gut-microbiome, have a shared gene-effect with hepatic steatosis and fibrosis. This is a cross-sectional analysis of a prospective discovery cohort including 156 well-characterized twins and families with untargeted metabolome profiling assessment. Hepatic steatosis was assessed using magnetic-resonance-imaging proton-density-fat-fraction (MRI-PDFF) and fibrosis using MR-elastography (MRE). A twin additive genetics and unique environment effects (AE) model was used to estimate the shared gene-effect between metabolites and hepatic steatosis and fibrosis. The findings were validated in an independent prospective validation cohort of 156 participants with biopsy-proven NAFLD including shotgun metagenomics sequencing assessment in a subgroup of the cohort. In the discovery cohort, 56 metabolites including 6 microbial metabolites had a significant shared gene-effect with both hepatic steatosis and fibrosis after adjustment for age, sex and ethnicity. In the validation cohort, 6 metabolites were associated with advanced fibrosis. Among them, only one microbial metabolite, 3-(4-hydroxyphenyl)lactate, remained consistent and statistically significantly associated with liver fibrosis in the discovery and validation cohort (fold-change of higher-MRE versus lower-MRE: 1.78, P < 0.001 and of advanced versus no advanced fibrosis: 1.26, P = 0.037, respectively). The share genetic determination of 3-(4-hydroxyphenyl)lactate with hepatic steatosis was RG :0.57,95%CI:0.27-0.80, P < 0.001 and with fibrosis was RG :0.54,95%CI:0.036-1, P = 0.036. Pathway reconstruction linked 3-(4-hydroxyphenyl)lactate to several human gut-microbiome species. In the validation cohort, 3-(4-hydroxyphenyl)lactate was significantly correlated with the abundance of several gut-microbiome species, belonging only to Firmicutes, Bacteroidetes and Proteobacteria phyla, previously reported as associated with advanced fibrosis. Conclusion: This proof of concept study provides evidence of a link between the gut-microbiome and 3-(4-hydroxyphenyl)lactate that shares gene-effect with hepatic steatosis and fibrosis. (Hepatology 2018).

Fourth European stroke science workshop

Lake Eibsee, Garmisch-Partenkirchen, 16 to 18 November, 2017: The European Stroke Organisation convened >120 stroke experts from 21 countries to discuss latest results and hot topics in clinical, translational and basic stroke research. Since its inception in 2011, the European Stroke Science Workshop has become a cornerstone of European Stroke Organisation's academic activities and a major highlight for researchers in the field. Participants include stroke researchers at all career stages and with different backgrounds, who convene for plenary lectures and discussions. The workshop was organised in seven scientific sessions focusing on the following topics: (1) acute stroke treatment and endovascular therapy; (2) small vessel disease; (3) opportunities for stroke research in the omics era; (4) vascular cognitive impairment; (5) intracerebral and subarachnoid haemorrhage; (6) alternative treatment concepts and (7) neural circuits, recovery and rehabilitation. All sessions started with a keynote lecture providing an overview on current developments, followed by focused talks on a timely topic with the most recent findings, including unpublished data. In the following, we summarise the key contents of the meeting. The program is provided in the online only Data Supplement. The workshop started with a key note lecture on how to improve the efficiency of clinical trial endpoints in stroke, which was delivered by Craig Anderson (Sydney, Australia) and set the scene for the following discussions.

Metabolomic profiling implicates adiponectin as mediator of a favorable lipoprotein profile associated with NT-proBNP

Background

The N-terminal prohormone of brain natriuretic peptide (NT-proBNP) is an important biomarker for the diagnosis of heart failure. Apart from this and only recently recognized, NT-proBNP levels associate with higher HDL- and lower LDL-cholesterol levels comprising a favorable blood lipid profile. To further examine this observation, the lipoprotein profile in relation to NT-proBNP was examined in-depth by proton nuclear magnetic resonance spectroscopy (¹H-NMR). We complemented this investigation with a state-of-the-art untargeted metabolomics approach.

Methods

Lipoprotein particles were determined by ¹H-NMR spectroscopy in 872 subjects without self-reported diabetes from the population-based Study of Health in Pomerania (SHIP)-TREND with available NT-proBNP measurements. Comprehensive metabolomics data for plasma and urine samples were obtained. Linear regression models were performed to assess the associations between serum concentrations of NT-proBNP and the metabolites/lipoprotein particles measured in plasma or urine.

Results

An increase in serum NT-proBNP was associated with a benefical lipoprotein profile, including a decrease in VLDL, IDL and LDL-particles along with an increase in large HDL particles. These findings were replicated in a second independent cohort. Serum concentrations of NT-proBNP showed significant inverse associations with seven plasma metabolites while associations with 39 urinary metabolites, mostly comprising amino acids and related intermediates, were identified. Mediation analyses revealed adiponection as mediating factor for the associations observed with lipoproteins particles.

Conclusions

Most of the metabolic changes associated with NT-proBNP implicate significant influence on the blood lipid profile besides vasodilatory and the diuretic action of BNP signaling. Our data suggest that the more favorable lipoprotein profile as associated with elevated NT-proBNP concentrations in mainly cardiac healthy individuals might relate to adiponectin signaling indicating even indirect cardio-protective effects for NT-proBNP.

Electronic supplementary material

The online version of this article (10.1186/s12933-018-0765-1) contains supplementary material, which is available to authorized users.

Unravelling the Roles of Susceptibility Loci for Autoimmune Diseases in the Post-GWAS Era

Although genome-wide association studies (GWAS) have identified several hundred loci associated with autoimmune diseases, their mechanistic insights are still poorly understood. The human genome is more complex than single nucleotide polymorphisms (SNPs) that are interrogated by GWAS arrays. Apart from SNPs, it also comprises genetic variations such as insertions-deletions, copy number variations, and somatic mosaicism. Although previous studies suggest that common copy number variations do not play a major role in autoimmune disease risk, it is possible that certain rare genetic variations with large effect sizes are relevant to autoimmunity. In addition, other layers of regulations such as gene-gene interactions, epigenetic-determinants, gene and environmental interactions also contribute to the heritability of autoimmune diseases. This review focuses on discussing why studying these elements may allow us to gain a more comprehensive understanding of the aetiology of complex autoimmune traits.

Prioritization and functional assessment of noncoding variants associated with complex diseases

Unraveling functional noncoding variants associated with complex diseases is still a great challenge. We present a novel algorithm, Prioritization And Functional Assessment (PAFA), that prioritizes and assesses the functionality of genetic variants by introducing population differentiation measures and recalibrating training variants. Comprehensive evaluations demonstrate that PAFA exhibits much higher sensitivity and specificity in prioritizing noncoding risk variants than existing methods. PAFA achieves improved performance in distinguishing both common and rare recurrent variants from non-recurrent variants by integrating multiple annotations and metrics. An integrated platform was developed, providing comprehensive functional annotations for noncoding variants by integrating functional genomic data, which can be accessed at http://159.226.67.237:8080/pafa .

Gut microbial diversity is associated with lower arterial stiffness in women

Aims

The gut microbiome influences metabolic syndrome (MetS) and inflammation and is therapeutically modifiable. Arterial stiffness is poorly correlated with most traditional risk factors. Our aim was to examine whether gut microbial composition is associated with arterial stiffness.

Methods and results

We assessed the correlation between carotid-femoral pulse wave velocity (PWV), a measure of arterial stiffness, and gut microbiome composition in 617 middle-aged women from the TwinsUK cohort with concurrent serum metabolomics data. Pulse wave velocity was negatively correlated with gut microbiome alpha diversity (Shannon index, Beta(SE)= -0.25(0.07), P = 1 × 10-4) after adjustment for covariates. We identified seven operational taxonomic units associated with PWV after adjusting for covariates and multiple testing-two belonging to the Ruminococcaceae family. Associations between microbe abundances, microbe diversity, and PWV remained significant after adjustment for levels of gut-derived metabolites (indolepropionate, trimethylamine oxide, and phenylacetylglutamine). We linearly combined the PWV-associated gut microbiome-derived variables and found that microbiome factors explained 8.3% (95% confidence interval 4.3-12.4%) of the variance in PWV. A formal mediation analysis revealed that only a small proportion (5.51%) of the total effect of the gut microbiome on PWV was mediated by insulin resistance and visceral fat, c-reactive protein, and cardiovascular risk factors after adjusting for age, body mass index, and mean arterial pressure.

Conclusions

Gut microbiome diversity is inversely associated with arterial stiffness in women. The effect of gut microbiome composition on PWV is only minimally mediated by MetS. This first human observation linking the gut microbiome to arterial stiffness suggests that targeting the microbiome may be a way to treat arterial ageing.

Standardized Immunomonitoring: Separating the Signals from the Noise

Classical immunoassays are routinely performed in the clinic for disease diagnosis and monitoring. Recent advances in phenotyping technologies offer huge potential in expanding the breadth of immune response monitoring. Challenges remain, however, in translating many of these tools to routine clinical practice. This Opinion focuses on two strategies that may advance the clinical adoption of immune-based biomarkers: protein-based assays employing digital readouts can reduce nonspecific signals that limit more classical assays; and approaches that stimulate immune responses in more standardized ways can help to reveal disease-specific immune response signatures by elevating the signal above the background. The integration of such immune response phenotypes is a critical step for the increased implementation of precision medicine-based strategies.

Contemporary lipidomic analytics: opportunities and pitfalls

Recent advances in analytical techniques have greatly enhanced the depth of coverage, however lipidomic studies are still restricted to analysing only a subset of known lipids. Numerous complementary techniques are used for investigation of cellular lipidomes, including mass spectrometry (MS), nuclear magnetic resonance and vibrational spectroscopy. The development in electrospray ionization (ESI) MS has accelerated lipidomics research in the past two decades and represents one of the most widely used technique. The versatility of ESI-MS systems allows development of methods to detect and quantify a large diversity of lipid species and classes. However, highly targeted and specific approaches can preclude global analysis of many lipid classes. Indeed, experimental procedures are generally optimised for the lipid species, or lipid class of interest. Therefore, careful consideration of experimental procedures is required for characterisation of biological lipidomes. The current review will describe the lipidomic approaches for considering tissue lipid physiology. Discussion of the main sequences in a lipidomics workflow will be presented, including preparation of samples, accurate quantitation of lipid species and statistical modelling.

Genomic insights into the causes of type 2 diabetes

Genome-wide association studies have implicated around 250 genomic regions in predisposition to type 2 diabetes, with evidence for causal variants and genes emerging for several of these regions. Understanding of the underlying mechanisms, including the interplay between β-cell failure, insulin sensitivity, appetite regulation, and adipose storage has been facilitated by the integration of multidimensional data for diabetes-related intermediate phenotypes, detailed genomic annotations, functional experiments, and now multiomic molecular features. Studies in diverse ethnic groups and examples from population isolates have shown the value and need for a broad genomic approach to this global disease. Transethnic discovery efforts and large-scale biobanks in diverse populations and ancestries could help to address some of the Eurocentric bias. Despite rapid progress in the discovery of the highly polygenic architecture of type 2 diabetes, dominated by common alleles with small, cumulative effects on disease risk, these insights have been of little clinical use in terms of disease prediction or prevention, and have made only small contributions to subtype classification or stratified approaches to treatment. Successful development of academia-industry partnerships for exome or genome sequencing in large biobanks could help to deliver economies of scale, with implications for the future of genomics-focused research.

Metabolomic studies identify changes in transmethylation and polyamine metabolism in a brain-specific mouse model of tuberous sclerosis complex

Tuberous sclerosis complex (TSC) is an autosomal dominant neurodevelopmental disorder and the quintessential disorder of mechanistic Target of Rapamycin Complex 1 (mTORC1) dysregulation. Loss of either causative gene, TSC1 or TSC2, leads to constitutive mTORC1 kinase activation and a pathologically anabolic state of macromolecular biosynthesis. Little is known about the organ-specific metabolic reprogramming that occurs in TSC-affected organs. Using a mouse model of TSC in which Tsc2 is disrupted in radial glial precursors and their neuronal and glial descendants, we performed an unbiased metabolomic analysis of hippocampi to identify Tsc2-dependent metabolic changes. Significant metabolic reprogramming was found in well-established pathways associated with mTORC1 activation, including redox homeostasis, glutamine/tricarboxylic acid cycle, pentose and nucleotide metabolism. Changes in two novel pathways were identified: transmethylation and polyamine metabolism. Changes in transmethylation included reduced methionine, cystathionine, S-adenosylmethionine (SAM-the major methyl donor), reduced SAM/S-adenosylhomocysteine ratio (cellular methylation potential), and elevated betaine, an alternative methyl donor. These changes were associated with alterations in SAM-dependent methylation pathways and expression of the enzymes methionine adenosyltransferase 2A and cystathionine beta synthase. We also found increased levels of the polyamine putrescine due to increased activity of ornithine decarboxylase, the rate-determining enzyme in polyamine synthesis. Treatment of Tsc2+/- mice with the ornithine decarboxylase inhibitor α-difluoromethylornithine, to reduce putrescine synthesis dose-dependently reduced hippocampal astrogliosis. These data establish roles for SAM-dependent methylation reactions and polyamine metabolism in TSC neuropathology. Importantly, both pathways are amenable to nutritional or pharmacologic therapy.

The fecal metabolome as a functional readout of the gut microbiome

The human gut microbiome plays a key role in human health 1 , but 16S characterization lacks quantitative functional annotation 2 . The fecal metabolome provides a functional readout of microbial activity and can be used as an intermediate phenotype mediating host-microbiome interactions 3 . In this comprehensive description of the fecal metabolome, examining 1,116 metabolites from 786 individuals from a population-based twin study (TwinsUK), the fecal metabolome was found to be only modestly influenced by host genetics (heritability (H2) = 17.9%). One replicated locus at the NAT2 gene was associated with fecal metabolic traits. The fecal metabolome largely reflects gut microbial composition, explaining on average 67.7% (±18.8%) of its variance. It is strongly associated with visceral-fat mass, thereby illustrating potential mechanisms underlying the well-established microbial influence on abdominal obesity. Fecal metabolic profiling thus is a novel tool to explore links among microbiome composition, host phenotypes, and heritable complex traits.

Identification of urinary metabolites that correlate with clinical improvements in children with autism treated with sulforaphane from broccoli

Background

Children with autism spectrum disorder (ASD) have urinary metabolites suggesting impairments in several pathways, including oxidative stress, inflammation, mitochondrial dysfunction, and gut microbiome alterations. Sulforaphane, a supplement with indirect antioxidant effects that are derived from broccoli sprouts and seeds, was recently shown to lead to improvements in behavior and social responsiveness in children with ASD. We conducted the current open-label study to determine if we could identify changes in urinary metabolites that were associated with clinical improvements with the goal of identifying a potential mechanism of action.

Methods

Children and young adults enrolled in a school for children with ASD and related neurodevelopmental disorders were recruited to participate in a 12-week, open-label study of sulforaphane. Fasting urinary metabolites and measures of behavior (Aberrant Behavior Checklist—ABC) and social responsiveness (Social Responsiveness Scale—SRS) were measured at baseline and at the end of the study. Pearson’s correlation coefficient was calculated for the pre- to post-intervention change in each of the two clinical scales (ABS and SRS) versus the change in each metabolite.

Results

Fifteen children completed the 12-week study. Mean scores on both symptom measures showed improvements (decreases) over the study period, but only the change in the SRS was significant. The ABC improved − 7.1 points (95% CI − 17.4 to 3.2), and the SRS improved − 9.7 points (95% CI − 18.7 to − 0.8). We identified 77 urinary metabolites that were correlated with changes in symptoms, and they clustered into pathways of oxidative stress, amino acid/gut microbiome, neurotransmitters, hormones, and sphingomyelin metabolism.

Conclusions

Urinary metabolomics analysis is a useful tool to identify pathways that may be involved in the mechanism of action of treatments targeting abnormal physiology in ASD.

Trial registration

This study was prospectively registered at clinicaltrials.gov (NCT02654743) on January 11, 2016.

Assessing the Associations of Blood Metabolites With Osteoporosis: A Mendelian Randomization Study

Context

Osteoporosis is a metabolic bone disease. The effect of blood metabolites on the development of osteoporosis remains elusive.

Objective

To explore the relationship between blood metabolites and osteoporosis.

Design and Methods

We used 2286 unrelated white subjects for the discovery samples and 3143 unrelated white subjects from the Framingham Heart Study (FHS) for the replication samples. The bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry. Genome-wide single nucleotide polymorphism (SNP) genotyping was performed using Affymetrix Human SNP Array 6.0 (for discovery samples) and Affymetrix SNP 500K and 50K array (for FHS replication samples). The SNP sets significantly associated with blood metabolites were obtained from a reported whole-genome sequencing study. For each subject, the genetic risk score of the metabolite was calculated from the genotype data of the metabolite-associated SNP sets. Pearson correlation analysis was conducted to evaluate the potential effect of blood metabolites on the variations in bone phenotypes; 10,000 permutations were conducted to calculate the empirical P value and false discovery rate.

Results

We analyzed 481 blood metabolites. We identified multiple blood metabolites associated with hip BMD, such as 1,5-anhydroglucitol (Pdiscovery < 0.0001; Preplication = 0.0361), inosine (Pdiscovery = 0.0018; Preplication = 0.0256), theophylline (Pdiscovery = 0.0048; Preplication = 0.0433, gamma-glutamyl methionine (Pdiscovery = 0.0047; Preplication = 0.0471), 1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6; Pdiscovery = 0.0018; Preplication = 0.0390), and X-12127 (Pdiscovery = 0.0002; Preplication = 0.0249).

Conclusions

Our results suggest a modest effect of blood metabolites on the variations of BMD and identified several candidate blood metabolites for osteoporosis.

The functional genomics laboratory: functional validation of genetic variants

Currently, one of the main challenges in human molecular genetics is the interpretation of rare genetic variants of unknown clinical significance. A conclusive diagnosis is of importance for the patient to obtain certainty about the cause of the disease, for the clinician to be able to provide optimal care to the patient and to predict the disease course, and for the clinical geneticist for genetic counseling of the patient and family members. Conclusive evidence for pathogenicity of genetic variants is therefore crucial. This review gives an introduction to the problem of the interpretation of genetic variants of unknown clinical significance in view of the recent advances in genetic screening, and gives an overview of the possibilities for functional tests that can be performed to answer questions about the function of genes and the functional consequences of genetic variants ("functional genomics") in the field of inborn errors of metabolism (IEM), including several examples of functional genomics studies of mitochondrial disorders and several other IEM.

Next-generation metabolic screening: targeted and untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients

The implementation of whole-exome sequencing in clinical diagnostics has generated a need for functional evaluation of genetic variants. In the field of inborn errors of metabolism (IEM), a diverse spectrum of targeted biochemical assays is employed to analyze a limited amount of metabolites. We now present a single-platform, high-resolution liquid chromatography quadrupole time of flight (LC-QTOF) method that can be applied for holistic metabolic profiling in plasma of individual IEM-suspected patients. This method, which we termed "next-generation metabolic screening" (NGMS), can detect >10,000 features in each sample. In the NGMS workflow, features identified in patient and control samples are aligned using the "various forms of chromatography mass spectrometry (XCMS)" software package. Subsequently, all features are annotated using the Human Metabolome Database, and statistical testing is performed to identify significantly perturbed metabolite concentrations in a patient sample compared with controls. We propose three main modalities to analyze complex, untargeted metabolomics data. First, a targeted evaluation can be done based on identified genetic variants of uncertain significance in metabolic pathways. Second, we developed a panel of IEM-related metabolites to filter untargeted metabolomics data. Based on this IEM-panel approach, we provided the correct diagnosis for 42 of 46 IEMs. As a last modality, metabolomics data can be analyzed in an untargeted setting, which we term "open the metabolome" analysis. This approach identifies potential novel biomarkers in known IEMs and leads to identification of biomarkers for as yet unknown IEMs. We are convinced that NGMS is the way forward in laboratory diagnostics of IEMs.

The role of the clinician in the multi-omics era: are you ready?

Since Garrod's first description of alkaptonuria in 1902, and newborn screening for phenylketonuria introduced in the 1960s, P4 medicine (preventive, predictive, personalized, and participatory) has been a reality for the clinician serving patients with inherited metabolic diseases. The era of high-throughput technologies promises to accelerate its scale dramatically. Genomics, transcriptomics, epigenomics, proteomics, glycomics, metabolomics, and lipidomics offer an amazing opportunity for holistic investigation and contextual pathophysiologic understanding of inherited metabolic diseases for precise diagnosis and tailored treatment. While each of the -omics technologies is important to systems biology, some are more mature than others. Exome sequencing is emerging as a reimbursed test in clinics around the world, and untargeted metabolomics has the potential to serve as a single biochemical testing platform. The challenge lies in the integration and cautious interpretation of these big data, with translation into clinically meaningful information and/or action for our patients. A daunting but exciting task for the clinician; we provide clinical cases to illustrate the importance of his/her role as the connector between physicians, laboratory experts and researchers in the basic, computer, and clinical sciences. Open collaborations, data sharing, functional assays, and model organisms play a key role in the validation of -omics discoveries. Having all the right expertise at the table when discussing the diagnostic approach and individualized management plan according to the information yielded by -omics investigations (e.g., actionable mutations, novel therapeutic interventions), is the stepping stone of P4 medicine. Patient participation and the adjustment of the medical team's plan to his/her and the family's wishes most certainly is the capstone. Are you ready?

Integration of genomics and metabolomics for prioritization of rare disease variants: a 2018 literature review

Many inborn errors of metabolism (IEMs) are amenable to treatment; therefore, early diagnosis and treatment is imperative. Despite recent advances, the genetic basis of many metabolic phenotypes remains unknown. For discovery purposes, whole exome sequencing (WES) variant prioritization coupled with clinical and bioinformatics expertise is the primary method used to identify novel disease-causing variants; however, causation is often difficult to establish due to the number of plausible variants. Integrated analysis of untargeted metabolomics (UM) and WES or whole genome sequencing (WGS) data is a promising systematic approach for identifying disease-causing variants. In this review, we provide a literature-based overview of UM methods utilizing liquid chromatography mass spectrometry (LC-MS), and assess approaches to integrating WES/WGS and LC-MS UM data for the discovery and prioritization of variants causing IEMs. To embed this integrated -omics approach in the clinic, expansion of gene-metabolite annotations and metabolomic feature-to-metabolite mapping methods are needed.

Sequence-Based Analysis of Lipid-Related Metabolites in a Multiethnic Study

Small molecule lipid-related metabolites are important components of fatty acid and steroid metabolism-two important contributors to human health. This study investigated the extent to which rare and common genetic variants spanning the human genome influence the lipid-related metabolome. Sequence data from 1552 European-Americans (EA) and 1872 African-Americans (AA) were analyzed to examine the impact of common and rare variants on the levels of 102 circulating lipid-related metabolites measured by a combination of chromatography and mass spectroscopy. We conducted single variant tests [minor allele frequency (MAF) > 5%, statistical significance P-value ≤ 2.45 × 10^-10] and tests aggregating rare variants (MAF ≤ 5%) across multiple genomic motifs, such as coding regions and regulatory domains, and sliding windows. Multiethnic meta-analyses detected 53 lipid-related metabolites-locus pairs, which were inspected for evidence of consistent signal between the two ethnic groups. Thirty-eight lipid-related metabolite-genomic region associations were consistent across ethnicities, among which seven were novel. The regions contain genes that are related to metabolite transport (SLC10A1) and metabolism (SCD, FDX1, UGT2B15, and FADS2). Six of the seven novel findings lie in expression quantitative trait loci affecting the expression levels of 14 surrounding genes in multiple tissues. Imputed expression levels of 10 of the affected genes were associated with four corresponding lipid-related traits in at least one tissue. Our findings offer valuable insight into circulating lipid-related metabolite regulation in a multiethnic population.

Precision medicine screening using whole-genome sequencing and advanced imaging to identify disease risk in adults

Advances in technology are enabling evaluation for prevention and early detection of age-related chronic diseases associated with premature mortality, such as cancer and cardiovascular diseases. These diseases kill about one-third of men and one-quarter of women between the ages of 50 and 74 years old in the United States. We used whole-genome sequencing, advanced imaging, and other clinical testing to screen 209 active, symptom-free adults. We identified a broad set of complementary age-related chronic disease risks associated with premature mortality.

Reducing premature mortality associated with age-related chronic diseases, such as cancer and cardiovascular disease, is an urgent priority. We report early results using genomics in combination with advanced imaging and other clinical testing to proactively screen for age-related chronic disease risk among adults. We enrolled active, symptom-free adults in a study of screening for age-related chronic diseases associated with premature mortality. In addition to personal and family medical history and other clinical testing, we obtained whole-genome sequencing (WGS), noncontrast whole-body MRI, dual-energy X-ray absorptiometry (DXA), global metabolomics, a new blood test for prediabetes (Quantose IR), echocardiography (ECHO), ECG, and cardiac rhythm monitoring to identify age-related chronic disease risks. Precision medicine screening using WGS and advanced imaging along with other testing among active, symptom-free adults identified a broad set of complementary age-related chronic disease risks associated with premature mortality and strengthened WGS variant interpretation. This and other similarly designed screening approaches anchored by WGS and advanced imaging may have the potential to extend healthy life among active adults through improved prevention and early detection of age-related chronic diseases (and their risk factors) associated with premature mortality.

Genome-Wide Association Studies of Metabolites in Patients with CKD Identify Multiple Loci and Illuminate Tubular Transport Mechanisms

Background The kidneys have a central role in the generation, turnover, transport, and excretion of metabolites, and these functions can be altered in CKD. Genetic studies of metabolite concentrations can identify proteins performing these functions.Methods We conducted genome-wide association studies and aggregate rare variant tests of the concentrations of 139 serum metabolites and 41 urine metabolites, as well as their pairwise ratios and fractional excretions in up to 1168 patients with CKD.Results After correction for multiple testing, genome-wide significant associations were detected for 25 serum metabolites, two urine metabolites, and 259 serum and 14 urinary metabolite ratios. These included associations already known from population-based studies. Additional findings included an association for the uremic toxin putrescine and variants upstream of an enzyme catalyzing the oxidative deamination of polyamines (AOC1, P-min=2.4×10^-12), a relatively high carrier frequency (2%) for rare deleterious missense variants in ACADM that are collectively associated with serum ratios of medium-chain acylcarnitines (P-burden=6.6×10^-16), and associations of a common variant in SLC7A9 with several ratios of lysine to neutral amino acids in urine, including the lysine/glutamine ratio (P=2.2×10^-23). The associations of this SLC7A9 variant with ratios of lysine to specific neutral amino acids were much stronger than the association with lysine concentration alone. This finding is consistent with SLC7A9 functioning as an exchanger of urinary cationic amino acids against specific intracellular neutral amino acids at the apical membrane of proximal tubular cells.Conclusions Metabolomic indices of specific kidney functions in genetic studies may provide insight into human renal physiology.

Metabolomic analysis of 92 pulmonary embolism patients from a nested case-control study identifies metabolites associated with adverse clinical outcomes

Essentials Risk-stratification often fails to predict clinical deterioration in pulmonary embolism (PE). First-ever high-throughput metabolomics analysis of risk-stratified PE patients. Changes in circulating metabolites reflect a compromised energy metabolism in PE. Metabolites play a key role in the pathophysiology and risk stratification of PE.

SUMMARY

Background Patients with acute pulmonary embolism (PE) exhibit wide variation in clinical presentation and outcomes. Our understanding of the pathophysiologic mechanisms differentiating low-risk and high-risk PE is limited, so current risk-stratification efforts often fail to predict clinical deterioration and are insufficient to guide management. Objectives To improve our understanding of the physiology differentiating low-risk from high-risk PE, we conducted the first-ever high-throughput metabolomics analysis (843 named metabolites) comparing PE patients across risk strata within a nested case-control study. Patients/methods We enrolled 92 patients diagnosed with acute PE and collected plasma within 24 h of PE diagnosis. We used linear regression and pathway analysis to identify metabolites and pathways associated with PE risk-strata. Results When we compared 46 low-risk with 46 intermediate/high-risk PEs, 50 metabolites were significantly different after multiple testing correction. These metabolites were enriched in the following pathways: tricarboxylic acid (TCA) cycle, fatty acid metabolism (acyl carnitine) and purine metabolism, (hypo)xanthine/inosine containing. Additionally, energy, nucleotide and amino acid pathways were downregulated in intermediate/high-risk PE patients. When we compared 28 intermediate-risk with 18 high-risk PE patients, 41 metabolites differed at a nominal P-value level. These metabolites were enriched in fatty acid metabolism (acyl cholines), and hemoglobin and porphyrin metabolism. Conclusion Our results suggest that high-throughput metabolomics can provide insight into the pathophysiology of PE. Specifically, changes in circulating metabolites reflect compromised energy metabolism in intermediate/high-risk PE patients. These findings demonstrate the important role metabolites play in the pathophysiology of PE and highlight metabolomics as a potential tool for risk stratification of PE.

Acetaminophen (Paracetamol) Use Modifies the Sulfation of Sex Hormones

Background

Acetaminophen (paracetamol) is one of the most common medications used for management of pain in the world. There is lack of consensus about the mechanism of action, and concern about the possibility of adverse effects on reproductive health.

Methods

We first established the metabolome profile that characterizes use of acetaminophen, and we subsequently trained and tested a model that identified metabolomic differences across samples from 455 individuals with and without acetaminophen use. We validated the findings in a European ancestry adult twin cohort of 1880 individuals (TwinsUK), and in a study of 1235 individuals of African American and Hispanic ancestry. We used genomics to elucidate the mechanisms targeted by acetaminophen.

Findings

We identified a distinctive pattern of depletion of sulfated sex hormones with use of acetaminophen across all populations. We used a Mendelian randomization approach to characterize the role of Sulfotransferase Family 2A Member 1 (SULT2A1) as the site of the interaction. Although CYP3A7-CYP3A51P variants also modified levels of some sulfated sex hormones, only acetaminophen use phenocopied the effect of genetic variants of SULT2A1. Overall, acetaminophen use, age, gender and SULT2A1 and CYP3A7-CYP3A51P genetic variants are key determinants of variation in levels of sulfated sex hormones in blood. The effect of taking acetaminophen on sulfated sex hormones was roughly equivalent to the effect of 35 years of aging.

Interpretation

These findings raise concerns of the impact of acetaminophen use on hormonal homeostasis. In addition, it modifies views on the mechanism of action of acetaminophen in pain management as sulfated sex hormones can function as neurosteroids and modify nociceptive thresholds.

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We use metabolome analysis of 3570 individuals to identify the effect of acetaminophen on metabolic processes.

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Acetaminophen use is associated with decrease sulfation of sexual hormones.

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These findings are relevant in the context of current debate on the use of acetaminophen during pregnancy

Despite decades-long use of acetaminophen, there is an incomplete understanding of the mechanism of action, and of the potential for adverse metabolic effects. Recent epidemiological and animal work supports an effect of acetaminophen on reproductive processes and hormonal homeostasis. We observe a consistent and reproducible effect of acetaminophen use on the levels of sulfated sex hormones. This is relevant to the investigation of hormonal homeostasis during pregnancy – acetaminophen is the most commonly used analgesic by pregnant women. It also opens the door to investigating the role of sulfated hormones in pain management.

Methane utilization in Methylomicrobium alcaliphilum 20ZR: a systems approach

Biological methane utilization, one of the main sinks of the greenhouse gas in nature, represents an attractive platform for production of fuels and value-added chemicals. Despite the progress made in our understanding of the individual parts of methane utilization, our knowledge of how the whole-cell metabolic network is organized and coordinated is limited. Attractive growth and methane-conversion rates, a complete and expert-annotated genome sequence, as well as large enzymatic, ¹³C-labeling, and transcriptomic datasets make Methylomicrobium alcaliphilum 20Z^R an exceptional model system for investigating methane utilization networks. Here we present a comprehensive metabolic framework of methane and methanol utilization in M. alcaliphilum 20Z^R. A set of novel metabolic reactions governing carbon distribution across central pathways in methanotrophic bacteria was predicted by in-silico simulations and confirmed by global non-targeted metabolomics and enzymatic evidences. Our data highlight the importance of substitution of ATP-linked steps with PPi-dependent reactions and support the presence of a carbon shunt from acetyl-CoA to the pentose-phosphate pathway and highly branched TCA cycle. The diverged TCA reactions promote balance between anabolic reactions and redox demands. The computational framework of C₁-metabolism in methanotrophic bacteria can represent an efficient tool for metabolic engineering or ecosystem modeling.

Whole-exome sequencing identifies common and rare variant metabolic QTLs in a Middle Eastern population

Metabolomics-genome-wide association studies (mGWAS) have uncovered many metabolic quantitative trait loci (mQTLs) influencing human metabolic individuality, though predominantly in European cohorts. By combining whole-exome sequencing with a high-resolution metabolomics profiling for a highly consanguineous Middle Eastern population, we discover 21 common variant and 12 functional rare variant mQTLs, of which 45% are novel altogether. We fine-map 10 common variant mQTLs to new metabolite ratio associations, and 11 common variant mQTLs to putative protein-altering variants. This is the first work to report common and rare variant mQTLs linked to diseases and/or pharmacological targets in a consanguineous Arab cohort, with wide implications for precision medicine in the Middle East.

Blood metabolites are influenced by a combination of genetic and environmental factors. Here, Yousri and colleagues perform a whole-exome sequencing study in combination with a metabolomics analysis to identify metabolic quantitative trait loci in a Middle Eastern population.

Reliability of plasma polar metabolite concentrations in a large-scale cohort study using capillary electrophoresis-mass spectrometry

Background

Cohort studies with metabolomics data are becoming more widespread, however, large-scale studies involving 10,000s of participants are still limited, especially in Asian populations. Therefore, we started the Tsuruoka Metabolomics Cohort Study enrolling 11,002 community-dwelling adults in Japan, and using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography–mass spectrometry. The CE-MS method is highly amenable to absolute quantification of polar metabolites, however, its reliability for large-scale measurement is unclear. The aim of this study is to examine reproducibility and validity of large-scale CE-MS measurements. In addition, the study presents absolute concentrations of polar metabolites in human plasma, which can be used in future as reference ranges in a Japanese population.

Methods

Metabolomic profiling of 8,413 fasting plasma samples were completed using CE-MS, and 94 polar metabolites were structurally identified and quantified. Quality control (QC) samples were injected every ten samples and assessed throughout the analysis. Inter- and intra-batch coefficients of variation of QC and participant samples, and technical intraclass correlation coefficients were estimated. Passing-Bablok regression of plasma concentrations by CE-MS on serum concentrations by standard clinical chemistry assays was conducted for creatinine and uric acid.

Results and conclusions

In QC samples, coefficient of variation was less than 20% for 64 metabolites, and less than 30% for 80 metabolites out of the 94 metabolites. Inter-batch coefficient of variation was less than 20% for 81 metabolites. Estimated technical intraclass correlation coefficient was above 0.75 for 67 metabolites. The slope of Passing-Bablok regression was estimated as 0.97 (95% confidence interval: 0.95, 0.98) for creatinine and 0.95 (0.92, 0.96) for uric acid. Compared to published data from other large cohort measurement platforms, reproducibility of metabolites common to the platforms was similar to or better than in the other studies. These results show that our CE-MS platform is suitable for conducting large-scale epidemiological studies.

Whole-genome sequencing study of serum peptide levels: the Atherosclerosis Risk in Communities study

Oligopeptides are important markers of protein metabolism, as they are cleaved from larger polypeptides and proteins. Genetic association studies may help elucidate their origin and function. In 1,552 European Americans and 1,872 African Americans of the Atherosclerosis Risk in Communities study, we performed whole-genome and whole-exome sequencing and measured serum levels of 25 peptides. Common variants (minor allele frequency > 5%) were analysed individually. We grouped low-frequency variants (minor allele frequency ≤ 5%) by a genome-wide sliding window using region-based aggregate tests. Furthermore, low-frequency regulatory variants were grouped by gene, as were functional coding variants. All analyses were performed separately in each ancestry group and then meta-analysed. We identified 22 common variant associations with peptide levels (P-value < 4.2 × 10-10), including 16 novel gene-peptide pairs. Notably, variants in kinin-kallikrein genes KNG1, F12, KLKB1, and ACE were associated with several different peptides. Variants in KLKB1 and ACE were associated with a fragment of complement component 3f. Both common variants and low-frequency coding variants in CPN1 were associated with a fibrinogen cleavage peptide. Four sliding windows were significantly associated with peptide levels (P-value < 4.2 × 10-10). Our results highlight the importance of the kinin-kallikrein system in the regulation of serum peptide levels, strengthen the evidence for a broad link between the kinin-kallikrein and complement systems, and suggest a role of CPN1 in the conversion of fibrinogen to fibrin.

A Decade of Genetic and Metabolomic Contributions to Type 2 Diabetes Risk Prediction

PURPOSE OF REVIEW

The purpose of this review was to summarize and reflect on advances over the past decade in human genetic and metabolomic discovery with particular focus on their contributions to type 2 diabetes (T2D) risk prediction.

RECENT FINDINGS

In the past 10 years, a combination of advances in genotyping efficiency, metabolomic profiling, bioinformatics approaches, and international collaboration have moved T2D genetics and metabolomics from a state of frustration to an abundance of new knowledge. Efforts to control and prevent T2D have failed to stop this global epidemic. New approaches are needed, and although neither genetic nor metabolomic profiling yet have a clear clinical role, the rapid pace of accumulating knowledge offers the possibility for "multi-omic" prediction to improve health.

Precision Nutrition and Omega-3 Polyunsaturated Fatty Acids: A Case for Personalized Supplementation Approaches for the Prevention and Management of Human Diseases

BACKGROUND

Dietary essential omega-6 (n-6) and omega-3 (n-3) 18 carbon (18C-) polyunsaturated fatty acids (PUFA), linoleic acid (LA) and α-linolenic acid (ALA), can be converted (utilizing desaturase and elongase enzymes encoded by FADS and ELOVL genes) to biologically-active long chain (LC; >20)-PUFAs by numerous cells and tissues. These n-6 and n-3 LC-PUFAs and their metabolites (ex, eicosanoids and endocannabinoids) play critical signaling and structural roles in almost all physiologic and pathophysiologic processes.

METHODS

This review summarizes: (1) the biosynthesis, metabolism and roles of LC-PUFAs; (2) the potential impact of rapidly altering the intake of dietary LA and ALA; (3) the genetics and evolution of LC-PUFA biosynthesis; (4) Gene-diet interactions that may lead to excess levels of n-6 LC-PUFAs and deficiencies of n-3 LC-PUFAs; and (5) opportunities for precision nutrition approaches to personalize n-3 LC-PUFA supplementation for individuals and populations.

CONCLUSIONS

The rapid nature of transitions in 18C-PUFA exposure together with the genetic variation in the LC-PUFA biosynthetic pathway found in different populations make mal-adaptations a likely outcome of our current nutritional environment. Understanding this genetic variation in the context of 18C-PUFA dietary exposure should enable the development of individualized n-3 LC-PUFA supplementation regimens to prevent and manage human disease.

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

Endocrinology Meets Metabolomics: Achievements, Pitfalls, and Challenges

The metabolome, although very dynamic, is sufficiently stable to provide specific quantitative traits related to health and disease. Metabolomics requires balanced use of state-of-the-art study design, chemical analytics, biostatistics, and bioinformatics to deliver meaningful answers to contemporary questions in human disease research. The technology is now frequently employed for biomarker discovery and for elucidating the mechanisms underlying endocrine-related diseases. Metabolomics has also enriched genome-wide association studies (GWAS) in this area by providing functional data. The contributions of rare genetic variants to metabolome variance and to the human phenotype have been underestimated until now.

Identification of individuals by trait prediction using whole-genome sequencing data

Prediction of human physical traits and demographic information from genomic data challenges privacy and data deidentification in personalized medicine. To explore the current capabilities of phenotype-based genomic identification, we applied whole-genome sequencing, detailed phenotyping, and statistical modeling to predict biometric traits in a cohort of 1,061 participants of diverse ancestry. Individually, for a large fraction of the traits, their predictive accuracy beyond ancestry and demographic information is limited. However, we have developed a maximum entropy algorithm that integrates multiple predictions to determine which genomic samples and phenotype measurements originate from the same person. Using this algorithm, we have reidentified an average of >8 of 10 held-out individuals in an ethnically mixed cohort and an average of 5 of either 10 African Americans or 10 Europeans. This work challenges current conceptions of personal privacy and may have far-reaching ethical and legal implications.

Genome-wide association study of 1,5-anhydroglucitol identifies novel genetic loci linked to glucose metabolism

1,5-anhydroglucitol (1,5-AG) is a biomarker of hyperglycemic excursions associated with diabetic complications. Because of its structural similarity to glucose, genetic studies of 1,5-AG can deliver complementary insights into glucose metabolism. We conducted genome-wide association studies of serum 1,5-AG concentrations in 7,550 European ancestry (EA) and 2,030 African American participants (AA) free of diagnosed diabetes from the ARIC Study. Seven loci in/near EFNA1/SLC50A1, MCM6/LCT, SI, MGAM, MGAM2, SLC5A10, and SLC5A1 showed genome-wide significant associations (P < 5 × 10^-8) among EA participants, five of which were novel. Six of the seven loci were successfully replicated in 8,790 independent EA individuals, and MCM6/LCT and SLC5A10 were also associated among AA. Most of 1,5-AG-associated index SNPs were not associated with the clinical glycemic markers fasting glucose or the  HbA1c, and vice versa. Only the index variant in SLC5A1 showed a significant association with fasting glucose in the expected opposing direction. Products of genes in all 1,5-AG-associated loci have known roles in carbohydrate digestion and enteral or renal glucose transport, suggesting that genetic variants associated with 1,5-AG influence its concentration via effects on glucose metabolism and handling.