Whole-exome sequencing identifies rare genetic variants associated with human plasma metabolites

Effect of the PCSK9 R46L genetic variant on plasma insulin and glucose levels, risk of diabetes mellitus and cardiovascular disease: A meta-analysis

BACKGROUND AND AIM

The impact of the loss-of-function (LOF) genetic variant PCSK9 R46L on glucose homeostasis and cardiovascular disease (CVD) remains uncertain, despite its established correlation with diminished blood cholesterol levels. This meta-analysis aimed at exploring the effect of the PCSK9 R46L genetic variant on plasma insulin and glucose levels, risk of diabetes mellitus and CVD.

METHODS AND RESULTS

PubMed, Embase, and the Cochrane Library were searched for cohort and case-control studies published until October 1, 2023. The studies should report the association of the PCSK9 R46L genetic variant with one of the following: fasting plasma insulin, blood glucose levels, diabetes mellitus, and CVD risk. A dominant model of the PCSK9 R46L genetic variant was employed to statistical analysis. The meta-analyses were performed for continuous variables with standard mean difference (SMD), categorical variables with odds ratio (OR) using a random-effects model. A total of 17 articles with 20 studies engaging 1,186,861 population were identified and mobilized for these analyses. The overall results indicated that, compared with non-carriers of the PCSK9 R46L genetic variant, carriers of the PCSK9 R46L genetic variant did not increase or decrease the levels of fasting plasma insulin (3 studies with 7277 population; SMD, 0.08; 95% CI, -0.04 to 0.19; P = 0.270), and the levels of fasting plasma glucose (7 studies with 9331 population; SMD, 0.03; 95% CI, -0.08 to 0.13; P = 0.610). However, carriers of the PCSK9 R46L genetic variant indeed had 17% reduction in the risk of CVD (11 studies with 558,263 population; OR, 0.83; 95% CI, 0.71 to 0.98; P = 0.030), and 9% increase in the risk of diabetes mellitus (10 studies with 744,466 population; OR, 1.09; 95% CI, 1.04 to 1.14; P < 0.01). Meta-regression analyses indicated that the increased risk of diabetes mellitus and the reduced risk of CVD were positively correlated with reduction in LDL-C (P = 0.004 and 0.033, respectively).

CONCLUSIONS

PCSK9 R46L genetic variant exhibited an elevated susceptibility to diabetes mellitus alongside a reduced vulnerability to CVD.

Association between human blood metabolome and the risk of gastrointestinal tumors

BACKGROUND

The prevalence of gastrointestinal tumors continues to be significant. To uncover promising therapeutic targets for these tumors, we rigorously executed a Mendelian randomization (MR) study to comprehensively screen the blood metabolomes for potential causal mediators of five frequently encountered gastrointestinal tumors (Liver Cancer, Colorectal Cancer, Esophageal Cancer, Gastric Cancer and Pancreatic Cancer).

METHODS

We selected a comprehensive set of 137 distinct blood metabolites derived from three large-scale genome-wide association studies (GWASs) involving a total of 147827 participants of European ancestry. The gastrointestinal tumors-related data were obtained from a GWAS conducted within the Finnish study. Through meticulous MR analyses, we thoroughly assessed the associations between blood metabolites and gastrointestinal tumors. Additionally, a phenome-wide MR (Phe-MR) analysis was employed to investigate the potential on-target side effects of metabolite interventions.

RESULTS

We have identified 1 blood metabolites, namely isovalerylcarnitine (ORlog10: 1.01; 95%CI, 1.01-1.02; P = 1.81×10-7), as the potential causal mediators for liver cancer. However, no potential pathogenic mediators were detected for the other four tumors.

CONCLUSIONS

The current systematic MR analysis elucidated the potential role of isovalerylcarnitine as a causal mediator in the development of liver cancer. Leveraging the power of Phe-MR study facilitated the identification of potential adverse effects associated with drug targets for liver cancer prevention. Considering the weighing of pros and cons, isovalerylcarnitine emerges as a promising candidate for targeted drug interventions in the realm of liver cancer prevention.

Phosphoserine aminotransferase deficiency diagnosed by whole-exome sequencing and LC-MS/MS reanalysis: A case report and review of literature

BACKGROUND

Phosphoserine aminotransferase deficiency (PSATD) is an autosomal recessive disorder associated with hypertonia, psychomotor retardation, and acquired microcephaly. Patients with PSATD have low concentrations of serine in plasma and cerebrospinal fluid.

METHODS

We reported a 2-year-old female child with developmental delay, dyskinesia, and microcephaly. LC-MS/MS was used to detect amino acid concentration in the blood and whole-exome sequencing (WES) was used to identify the variants. PolyPhen-2 web server and PyMol were used to predict the pathogenicity and changes in the 3D model molecular structure of protein caused by variants.

RESULTS

WES demonstrated compound heterozygous variants in PSAT1, which is associated with PSATD, with a paternal likely pathogenic variant (c.235G>A, Gly79Arg) and a maternal likely pathogenic variant (c.43G>C, Ala15Pro). Reduced serine concentration in LC-MS/MS further confirmed the diagnosis of PSATD in this patient.

CONCLUSIONS

Our findings demonstrate the importance of WES combined with LC-MS/MS reanalysis in the diagnosis of genetic diseases and expand the PSAT1 variant spectrum in PSATD. Moreover, we summarize all the cases caused by PSAT1 variants in the literature. This case provides a vital reference for the diagnosis of future cases.

Rare variants in pharmacogenes influence clozapine metabolism in individuals with schizophrenia

Clozapine is the only licensed medication for treatment-resistant schizophrenia (TRS). Few predictors for variation in response to clozapine have been identified, but clozapine metabolism is known to influence therapeutic response and adverse side effects. Here, we expand on genome-wide studies of clozapine metabolism, previously focused on common genetic variation, by analysing whole-exome sequencing data from 2062 individuals with schizophrenia taking clozapine in the UK. We investigated whether rare genomic variation in genes and gene sets involved in the clozapine metabolism pathway influences plasma concentrations of clozapine metabolites, assessed through the longitudinal analysis of 6585 pharmacokinetic assays. We observed a statistically significant association between the burden of rare damaging coding variants (MAF ≤ 1 %) in gene sets broadly related to drug pharmacokinetics and lower clozapine (β = -0.054, SE = 0.019, P-value = 0.005) concentrations in plasma. We estimate that the effects in clozapine plasma concentrations of a single damaging allele in this gene set are akin to reducing the clozapine dose by about 35 mg/day. The gene-based analysis identified rare variants in CYP1A2, which encodes the enzyme responsible for converting clozapine to norclozapine, as having the strongest effects of any gene on clozapine metabolism (β = 0.324, SE = 0.124, P = 0.009). Our findings support the hypothesis that rare genetic variants in known drug-metabolising enzymes and transporters can markedly influence clozapine plasma concentrations; these results suggest that pharmacogenomic efforts trying to predict clozapine metabolism and personalise drug therapy could benefit from the inclusion of rare damaging variants in pharmacogenes beyond those already identified and catalogued as PGx star alleles.

Lessons and Applications of Omics Research in Diabetes Epidemiology

PURPOSE OF REVIEW

Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies.

RECENT FINDINGS

We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.

Genetic influences on circulating retinol and its relationship to human health

Retinol is a fat-soluble vitamin that plays an essential role in many biological processes throughout the human lifespan. Here, we perform the largest genome-wide association study (GWAS) of retinol to date in up to 22,274 participants. We identify eight common variant loci associated with retinol, as well as a rare-variant signal. An integrative gene prioritisation pipeline supports novel retinol-associated genes outside of the main retinol transport complex (RBP4:TTR) related to lipid biology, energy homoeostasis, and endocrine signalling. Genetic proxies of circulating retinol were then used to estimate causal relationships with almost 20,000 clinical phenotypes via a phenome-wide Mendelian randomisation study (MR-pheWAS). The MR-pheWAS suggests that retinol may exert causal effects on inflammation, adiposity, ocular measures, the microbiome, and MRI-derived brain phenotypes, amongst several others. Conversely, circulating retinol may be causally influenced by factors including lipids and serum creatinine. Finally, we demonstrate how a retinol polygenic score could identify individuals more likely to fall outside of the normative range of circulating retinol for a given age. In summary, this study provides a comprehensive evaluation of the genetics of circulating retinol, as well as revealing traits which should be prioritised for further investigation with respect to retinol related therapies or nutritional intervention.

Genetics of congenital heart disease

During embryonic development, the cardiovascular system and the central nervous system exhibit a coordinated developmental process through intricate interactions. Congenital heart disease (CHD) refers to structural or functional abnormalities that occur during embryonic or prenatal heart development and is the most common congenital disorder. One of the most common complications in CHD patients is neurodevelopmental disorders (NDD). However, the specific mechanisms, connections, and precise ways in which CHD co-occurs with NDD remain unclear. According to relevant research, both genetic and non-genetic factors are significant contributors to the co-occurrence of sporadic CHD and NDD. Genetic variations, such as chromosomal abnormalities and gene mutations, play a role in the susceptibility to both CHD and NDD. Further research should aim to identify common molecular mechanisms that underlie the co-occurrence of CHD and NDD, possibly originating from shared genetic mutations or shared gene regulation. Therefore, this review article summarizes the current advances in the genetics of CHD co-occurring with NDD, elucidating the application of relevant gene detection techniques. This is done with the aim of exploring the genetic regulatory mechanisms of CHD co-occurring with NDD at the gene level and promoting research and treatment of developmental disorders related to the cardiovascular and central nervous systems.

Ancestry-driven metabolite variation provides insights into disease states in admixed populations

BACKGROUND

Metabolic pathways are related to physiological functions and disease states and are influenced by genetic variation and environmental factors. Hispanics/Latino individuals have ancestry-derived genomic regions (local ancestry) from their recent admixture that have been less characterized for associations with metabolite abundance and disease risk.

METHODS

We performed admixture mapping of 640 circulating metabolites in 3887 Hispanic/Latino individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Metabolites were quantified in fasting serum through non-targeted mass spectrometry (MS) analysis using ultra-performance liquid chromatography-MS/MS. Replication was performed in 1856 nonoverlapping HCHS/SOL participants with metabolomic data.

RESULTS

By leveraging local ancestry, this study identified significant ancestry-enriched associations for 78 circulating metabolites at 484 independent regions, including 116 novel metabolite-genomic region associations that replicated in an independent sample. Among the main findings, we identified Native American enriched genomic regions at chromosomes 11 and 15, mapping to FADS1/FADS2 and LIPC, respectively, associated with reduced long-chain polyunsaturated fatty acid metabolites implicated in metabolic and inflammatory pathways. An African-derived genomic region at chromosome 2 was associated with N-acetylated amino acid metabolites. This region, mapped to ALMS1, is associated with chronic kidney disease, a disease that disproportionately burdens individuals of African descent.

CONCLUSIONS

Our findings provide important insights into differences in metabolite quantities related to ancestry in admixed populations including metabolites related to regulation of lipid polyunsaturated fatty acids and N-acetylated amino acids, which may have implications for common diseases in populations.

Genetic studies of paired metabolomes reveal enzymatic and transport processes at the interface of plasma and urine

The kidneys operate at the interface of plasma and urine by clearing molecular waste products while retaining valuable solutes. Genetic studies of paired plasma and urine metabolomes may identify underlying processes. We conducted genome-wide studies of 1,916 plasma and urine metabolites and detected 1,299 significant associations. Associations with 40% of implicated metabolites would have been missed by studying plasma alone. We detected urine-specific findings that provide information about metabolite reabsorption in the kidney, such as aquaporin (AQP)-7-mediated glycerol transport, and different metabolomic footprints of kidney-expressed proteins in plasma and urine that are consistent with their localization and function, including the transporters NaDC3 (SLC13A3) and ASBT (SLC10A2). Shared genetic determinants of 7,073 metabolite-disease combinations represent a resource to better understand metabolic diseases and revealed connections of dipeptidase 1 with circulating digestive enzymes and with hypertension. Extending genetic studies of the metabolome beyond plasma yields unique insights into processes at the interface of body compartments.

Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank

Genome-wide association studies (GWASs) have established the contribution of common and low-frequency variants to metabolic blood measurements in the UK Biobank (UKB). To complement existing GWAS findings, we assessed the contribution of rare protein-coding variants in relation to 355 metabolic blood measurements-including 325 predominantly lipid-related nuclear magnetic resonance (NMR)-derived blood metabolite measurements (Nightingale Health Plc) and 30 clinical blood biomarkers-using 412,393 exome sequences from four genetically diverse ancestries in the UKB. Gene-level collapsing analyses were conducted to evaluate a diverse range of rare-variant architectures for the metabolic blood measurements. Altogether, we identified significant associations (p < 1 × 10^-8) for 205 distinct genes that involved 1,968 significant relationships for the Nightingale blood metabolite measurements and 331 for the clinical blood biomarkers. These include associations for rare non-synonymous variants in PLIN1 and CREB3L3 with lipid metabolite measurements and SYT7 with creatinine, among others, which may not only provide insights into novel biology but also deepen our understanding of established disease mechanisms. Of the study-wide significant clinical biomarker associations, 40% were not previously detected on analyzing coding variants in a GWAS in the same cohort, reinforcing the importance of studying rare variation to fully understand the genetic architecture of metabolic blood measurements.

Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases

Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets.

Rare and common genetic determinants of metabolic individuality and their effects on human health

Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P < 1.25 × 10^-11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships.

Precision therapy for three Chinese families with maturity-onset diabetes of the young (MODY12)

Maturity-onset diabetes of the young (MODY) is rare monogenic diabetes. However, MODY is often undiagnosed or misdiagnosed. In this study, we aimed to investigate the pathogenic gene for diabetes and provide precise treatment for diabetes patients in three families. Three families with suspected MODY were enrolled and screened for germline mutations using Whole exome sequencing (WES). Candidate pathogenic variants were validated in other family members and non-related healthy controls. Three heterozygous missense mutations in the ABCC8 gene (NM_001287174), c.1555 C>T (p.R519C), c.3706 A>G (p.I1236V), and c.2885 C>T (p.S962L) were found in families A, B, and C, respectively. All mutation sites cosegregated with diabetes, were predicted to be harmful by bioinformatics and were not found in non-related healthy controls. Two probands (onset ages, 8 and 12 years) were sensitive to glimepiride. However, an insufficient dose (2 mg/day) led to ketoacidosis. When the dosage of glimepiride was increased to 4 mg/day, blood sugar remained under control. A dose of 4 mg glimepiride daily also effectively controlled blood sugar in an adult patient 25-year-old. In addition, all patients were sensitive to liraglutide, which could control blood sugar better. These data suggest that ABCC8 was the pathogenic gene in three families with diabetes. Glimepiride (2 mg/day) was not effective in controlling blood sugar in children with ABCC8 mutations, however, 4 mg/daily glimepiride was effective in both adults and children. Moreover, liraglutide was effective in controlling blood sugar in both adults and children with ABCC8 mutations.