A Noncoding Variant Near PPP1R3B Promotes Liver Glycogen Storage and MetS, but Protects Against Myocardial Infarction

Ppp1r3b is a metabolic switch that shifts hepatic energy storage from lipid to glycogen

The PPP1R3B gene, encoding PPP1R3B protein, is critical for liver glycogen synthesis and maintaining blood glucose levels. Genetic variants affecting PPP1R3B expression are associated with several metabolic traits and liver disease, but the precise mechanisms are not fully understood. We studied the effects of both Ppp1r3b overexpression and deletion in mice and cell models and found that both changes in Ppp1r3b expression result in dysregulated metabolism and liver damage, with overexpression increasing liver glycogen stores, while deletion resulted in higher liver lipid accumulation. These patterns were confirmed in humans where variants increasing PPP1R3B expression had lower liver fat and decreased plasma lipids, whereas putative loss-of-function variants were associated with increased liver fat and elevated plasma lipids. These findings support that PPP1R3B is a crucial regulator of hepatic metabolism beyond glycogen synthesis and that genetic variants affecting PPP1R3B expression levels influence if hepatic energy is stored as glycogen or triglycerides.

Human genetics of metabolic dysfunction-associated steatotic liver disease: from variants to cause to precision treatment

Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by increased hepatic steatosis with cardiometabolic disease and is a leading cause of advanced liver disease. We review here the genetic basis of MASLD. The genetic variants most consistently associated with hepatic steatosis implicate genes involved in lipoprotein input or output, glucose metabolism, adiposity/fat distribution, insulin resistance, or mitochondrial/ER biology. The distinct mechanisms by which these variants promote hepatic steatosis result in distinct effects on cardiometabolic disease that may be best suited to precision medicine. Recent work on gene-environment interactions has shown that genetic risk is not fixed and may be exacerbated or attenuated by modifiable (diet, exercise, alcohol intake) and nonmodifiable environmental risk factors. Some steatosis-associated variants, notably those in patatin-like phospholipase domain-containing 3 (PNPLA3) and transmembrane 6 superfamily member 2 (TM6SF2), are associated with risk of developing adverse liver-related outcomes and provide information beyond clinical risk stratification tools, especially in individuals at intermediate to high risk for disease. Future work to better characterize disease heterogeneity by combining genetics with clinical risk factors to holistically predict risk and develop therapies based on genetic risk is required.

Heterogeneous effects of genetic variants and traits associated with fasting insulin on cardiometabolic outcomes

Elevated fasting insulin levels (FI), indicative of altered insulin secretion and sensitivity, may precede type 2 diabetes (T2D) and cardiovascular disease onset. In this study, we group FI-associated genetic variants based on their genetic and phenotypic similarities and identify seven clusters with distinct mechanisms contributing to elevated FI levels. Clusters fall into two types: "non-diabetogenic hyperinsulinemia," where clusters are not associated with increased T2D risk, and "diabetogenic hyperinsulinemia," where T2D associations are driven by body fat distribution, liver function, circulating lipids, or inflammation. In over 1.1 million multi-ancestry individuals, we demonstrated that diabetogenic hyperinsulinemia cluster-specific polygenic scores exhibit varying risks for cardiovascular conditions, including coronary artery disease, myocardial infarction (MI), and stroke. Notably, the visceral adiposity cluster shows sex-specific effects for MI risk in males without T2D. This study underscores processes that decouple elevated FI levels from T2D and cardiovascular risk, offering new avenues for investigating process-specific pathways of disease.

Recent advances in MASLD genetics: Insights into disease mechanisms and the next frontiers in clinical application

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver disease in the world and a growing cause of liver-related morbidity and mortality. Yet, at the same time, our understanding of the pathophysiology and genetic underpinnings of this increasingly common yet heterogeneous disease has increased dramatically over the last 2 decades, with the potential to lead to meaningful clinical interventions for patients. We have now seen the first pharmacologic therapy approved for the treatment of MASLD, and multiple other potential treatments are currently under investigation-including gene-targeted RNA therapies that directly extend from advances in MASLD genetics. Here we review recent advances in MASLD genetics, some of the key pathophysiologic insights that human genetics has provided, and the ways in which human genetics may inform our clinical practice in the field of MASLD in the near future.

Multiple genetic polymorphisms are associated with the risk of metabolic syndrome, fatty liver, and airflow limitation: A Taiwan Biobank study

BACKGROUND

Links have been reported between the airflow limitation and both metabolic syndrome (MetS) and fatty liver (FL). Additionally, associations between genetic factors and risks of MetS, FL, and airflow limitation have been identified separately in different studies. Our study aims to simultaneously explore the association between specific single nucleotide polymorphisms (SNPs) of certain genes and the risk of the three associated diseases.

METHODS

In this retrospective cross-sectional nationwide study, 150,709 participants from the Taiwan Biobank (TWB) were enrolled. We conducted a genotype-phenotype association analysis of nine SNPs on seven genes (ApoE-rs429358, MBOAT7-rs641738, LEPR-rs1805096, APOC3-rs2854116, APOC3-rs2854117, PPP1R3B-rs4240624, PPP1R3B-rs4841132, TM6SF2-rs58542926, and IFNL4-rs368234815) using data from the TWB1.0 and TWB2.0 genotype dataset. Participants underwent a series of assessments including questionnaires, blood examinations, abdominal ultrasounds, and spirometry examinations.

RESULTS

MetS was associated with FL and airflow limitation. ApoE-rs429358, LEPR-rs1805096, APOC3-rs2854116, APOC3-rs2854117, PPP1R3B-rs4240624, PPP1R3B-rs4841132, and TM6SF2-rs58542926 were significantly associated with the risk of MetS. The cumulative impact of T alleles of ApoE-rs429358 and TM6SF2-rs58542926 on the risk of FL was observed (p-value for trend < 0.001). Individuals without MetS and airflow limitation carrying LEPR-rs1805096 G_G genotype exhibited a reduction in the forced expiratory volume in 1 s percentage prediction (Coefficient -35, 95 % confidence interval (CI) -69.7- -0.4), low forced vital capacity percentage prediction (Coefficient -41.6, 95 % CI -82.6- -0.6), and low vital capacity percentage prediction (Coefficient -42.2, 95 % CI -84.2- -0.1).

CONCLUSIONS

MetS significantly correlated with FL and airflow limitation. Multiple SNPs were notably associated with MetS. Specifically, T alleles of ApoE-rs429358 and TM6SF2-rs58542926 cumulatively increased the risk of FL. LEPR-rs1805096 shows a trend-wise association with pulmonary function, which is significant in patients without MetS or airflow limitation.

A common variant that alters SUN1 degradation associates with hepatic steatosis and metabolic traits in multiple cohorts

BACKGROUND & AIMS

Non-alcoholic fatty liver disease (NAFLD), and its progressive form steatohepatitis (NASH), represent a genetically and phenotypically diverse entity for which there is no approved therapy, making it imperative to define the spectrum of pathways contributing to its pathogenesis. Rare variants in genes encoding nuclear envelope proteins cause lipodystrophy with early-onset NAFLD/NASH; we hypothesized that common variants in nuclear envelope-related genes might also contribute to hepatic steatosis and NAFLD.

METHODS

Using hepatic steatosis as the outcome of interest, we performed an association meta-analysis of nuclear envelope-related coding variants in three large discovery cohorts (N >120,000 participants), followed by phenotype association studies in large validation cohorts (N >600,000) and functional testing of the top steatosis-associated variant in cell culture.

RESULTS

A common protein-coding variant, rs6461378 (SUN1 H118Y), was the top steatosis-associated variant in our association meta-analysis (p <0.001). In ancestrally distinct validation cohorts, rs6461378 associated with histologic NAFLD and with NAFLD-related metabolic traits including increased serum fatty acids, type 2 diabetes, hypertension, cardiovascular disease, and decreased HDL. SUN1 H118Y was subject to increased proteasomal degradation relative to wild-type SUN1 in cells, and SUN1 H118Y-expressing cells exhibited insulin resistance and increased lipid accumulation.

CONCLUSIONS

Collectively, these data support a potential causal role for the common SUN1 variant rs6461378 in NAFLD and metabolic disease.

IMPACT AND IMPLICATIONS

Non-alcoholic fatty liver disease (NAFLD), with an estimated global prevalence of nearly 30%, is a growing cause of morbidity and mortality for which there is no approved pharmacologic therapy. Our data provide a rationale for broadening current concepts of NAFLD genetics and pathophysiology to include the nuclear envelope, and particularly Sad1 and UNC84 domain containing 1 (SUN1), as novel contributors to this common liver disease. Furthermore, if future studies confirm causality of the common SUN1 H118Y variant, it has the potential to become a broadly relevant therapeutic target in NAFLD and metabolic disease.

Genome-wide association meta-analysis identifies 17 loci associated with nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) is common and partially heritable and has no effective treatments. We carried out a genome-wide association study (GWAS) meta-analysis of imaging (n = 66,814) and diagnostic code (3,584 cases versus 621,081 controls) measured NAFLD across diverse ancestries. We identified NAFLD-associated variants at torsin family 1 member B (TOR1B), fat mass and obesity associated (FTO), cordon-bleu WH2 repeat protein like 1 (COBLL1)/growth factor receptor-bound protein 14 (GRB14), insulin receptor (INSR), sterol regulatory element-binding transcription factor 1 (SREBF1) and patatin-like phospholipase domain-containing protein 2 (PNPLA2), as well as validated NAFLD-associated variants at patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily 2 (TM6SF2), apolipoprotein E (APOE), glucokinase regulator (GCKR), tribbles homolog 1 (TRIB1), glycerol-3-phosphate acyltransferase (GPAM), mitochondrial amidoxime-reducing component 1 (MARC1), microsomal triglyceride transfer protein large subunit (MTTP), alcohol dehydrogenase 1B (ADH1B), transmembrane channel like 4 (TMC4)/membrane-bound O-acyltransferase domain containing 7 (MBOAT7) and receptor-type tyrosine-protein phosphatase δ (PTPRD). Implicated genes highlight mitochondrial, cholesterol and de novo lipogenesis as causally contributing to NAFLD predisposition. Phenome-wide association study (PheWAS) analyses suggest at least seven subtypes of NAFLD. Individuals in the top 10% and 1% of genetic risk have a 2.5-fold to 6-fold increased risk of NAFLD, cirrhosis and hepatocellular carcinoma. These genetic variants identify subtypes of NAFLD, improve estimates of disease risk and can guide the development of targeted therapeutics.

Heterogeneous effects on type 2 diabetes and cardiovascular outcomes of genetic variants and traits associated with fasting insulin

Hyperinsulinemia is a complex and heterogeneous phenotype that characterizes molecular alterations that precede the development of type 2 diabetes (T2D). It results from a complex combination of molecular processes, including insulin secretion and insulin sensitivity, that differ between individuals. To better understand the physiology of hyperinsulinemia and ultimately T2D, we implemented a genetic approach grouping fasting insulin (FI)-associated genetic variants based on their molecular and phenotypic similarities. We identified seven distinctive genetic clusters representing different physiologic mechanisms leading to rising FI levels, ranging from clusters of variants with effects on increased FI, but without increased risk of T2D (non-diabetogenic hyperinsulinemia), to clusters of variants that increase FI and T2D risk with demonstrated strong effects on body fat distribution, liver, lipid, and inflammatory processes (diabetogenic hyperinsulinemia). We generated cluster-specific polygenic scores in 1,104,258 individuals from five multi-ancestry cohorts to show that the clusters differed in associations with cardiometabolic traits. Among clusters characterized by non-diabetogenic hyperinsulinemia, there was both increased and decreased risk of coronary artery disease despite the non-increased risk of T2D. Similarly, the clusters characterized by diabetogenic hyperinsulinemia were associated with an increased risk of T2D, yet had differing risks of cardiovascular conditions, including coronary artery disease, myocardial infarction, and stroke. The strongest cluster-T2D associations were observed with the same direction of effect in non-Hispanic Black, Hispanic, non-Hispanic White, and non-Hispanic East Asian populations. These genetic clusters provide important insights into granular metabolic processes underlying the physiology of hyperinsulinemia, notably highlighting specific processes that decouple increasing FI levels from T2D and cardiovascular risk. Our findings suggest that increasing FI levels are not invariably associated with adverse cardiometabolic outcomes.

A genetic strategy to measure insulin signaling regulation and physiology in Drosophila

Insulin regulation is a hallmark of health, and impaired insulin signaling promotes metabolic diseases like diabetes mellitus. However, current assays for measuring insulin signaling in all animals remain semi-quantitative and lack the sensitivity, tissue-specificity or temporal resolution needed to quantify in vivo physiological signaling dynamics. Insulin signal transduction is remarkably conserved across metazoans, including insulin-dependent phosphorylation and regulation of Akt/Protein kinase B. Here, we generated transgenic fruit flies permitting tissue-specific expression of an immunoepitope-labelled Akt (AktHF). We developed enzyme-linked immunosorption assays (ELISA) to quantify picomolar levels of phosphorylated (pAktHF) and total AktHF in single flies, revealing dynamic tissue-specific physiological regulation of pAktHF in response to fasting and re-feeding, exogenous insulin, or targeted genetic suppression of established insulin signaling regulators. Genetic screening revealed Pp1-87B as an unrecognized regulator of Akt and insulin signaling. Tools and concepts here provide opportunities to discover tissue-specific regulators of in vivo insulin signaling responses.

Allele-specific expression analysis for complex genetic phenotypes applied to a unique dilated cardiomyopathy cohort

Allele-specific expression (ASE) analysis detects the relative abundance of alleles at heterozygous loci as a proxy for cis-regulatory variation, which affects the personal transcriptome and proteome. This study describes the development and application of an ASE analysis pipeline on a unique cohort of 87 well phenotyped and RNA sequenced patients from the Maastricht Cardiomyopathy Registry with dilated cardiomyopathy (DCM), a complex genetic disorder with a remaining gap in explained heritability. Regulatory processes for which ASE is a proxy might explain this gap. We found an overrepresentation of known DCM-associated genes among the significant results across the cohort. In addition, we were able to find genes of interest that have not been associated with DCM through conventional methods such as genome-wide association or differential gene expression studies. The pipeline offers RNA sequencing data processing, individual and population level ASE analyses as well as group comparisons and several intuitive visualizations such as Manhattan plots and protein-protein interaction networks. With this pipeline, we found evidence supporting the case that cis-regulatory variation contributes to the phenotypic heterogeneity of DCM. Additionally, our results highlight that ASE analysis offers an additional layer to conventional genomic and transcriptomic analyses for candidate gene identification and biological insight.

Identification of biomarkers, pathways, and potential therapeutic targets for heart failure using next-generation sequencing data and bioinformatics analysis

BACKGROUND

Heart failure (HF) is the most common cardiovascular diseases and the leading cause of cardiovascular diseases related deaths. Increasing molecular targets have been discovered for HF prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might aid the diagnosis and treatment of HF.

METHODS

We searched next-generation sequencing (NGS) dataset (GSE161472) and identified differentially expressed genes (DEGs) by comparing 47 HF samples and 37 normal control samples using limma in R package. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g: Profiler database. The protein-protein interaction (PPI) network was plotted with Human Integrated Protein-Protein Interaction rEference (HiPPIE) and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. Then, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed by Cytoscape software. Finally, we performed receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes.

RESULTS

A total of 930 DEGs, 464 upregulated genes and 466 downregulated genes, were identified in HF. GO and REACTOME pathway enrichment results showed that DEGs mainly enriched in localization, small molecule metabolic process, SARS-CoV infections, and the citric acid tricarboxylic acid (TCA) cycle and respiratory electron transport. After combining the results of the PPI network miRNA-hub gene regulatory network and TF-hub gene regulatory network, 10 hub genes were selected, including heat shock protein 90 alpha family class A member 1 (HSP90AA1), arrestin beta 2 (ARRB2), myosin heavy chain 9 (MYH9), heat shock protein 90 alpha family class B member 1 (HSP90AB1), filamin A (FLNA), epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), cullin 4A (CUL4A), YEATS domain containing 4 (YEATS4), and lysine acetyltransferase 2B (KAT2B).

CONCLUSIONS

This discovery-driven study might be useful to provide a novel insight into the diagnosis and treatment of HF. However, more experiments are needed in the future to investigate the functional roles of these genes in HF.

The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases

Background

The remarkable growth of genome-wide association studies (GWAS) has created a critical need to experimentally validate the disease-associated variants, 90% of which involve non-coding variants.

Methods

To determine how the field is addressing this urgent need, we performed a comprehensive literature review identifying 36,676 articles. These were reduced to 1454 articles through a set of filters using natural language processing and ontology-based text-mining. This was followed by manual curation and cross-referencing against the GWAS catalog, yielding a final set of 286 articles.

Results

We identified 309 experimentally validated non-coding GWAS variants, regulating 252 genes across 130 human disease traits. These variants covered a variety of regulatory mechanisms. Interestingly, 70% (215/309) acted through cis-regulatory elements, with the remaining through promoters (22%, 70/309) or non-coding RNAs (8%, 24/309). Several validation approaches were utilized in these studies, including gene expression (n = 272), transcription factor binding (n = 175), reporter assays (n = 171), in vivo models (n = 104), genome editing (n = 96) and chromatin interaction (n = 33).

Conclusions

This review of the literature is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. Our results clearly underscore the multifaceted approach needed for experimental validation, have practical implications on variant prioritization and considerations of target gene nomination. While the field has a long way to go to validate the thousands of GWAS associations, we show that progress is being made and provide exemplars of validation studies covering a wide variety of mechanisms, target genes, and disease areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01216-w.

The β Cell in Diabetes: Integrating Biomarkers With Functional Measures

The pathogenesis of hyperglycemia observed in most forms of diabetes is intimately tied to the islet β cell. Impairments in propeptide processing and secretory function, along with the loss of these vital cells, is demonstrable not only in those in whom the diagnosis is established but typically also in individuals who are at increased risk of developing the disease. Biomarkers are used to inform on the state of a biological process, pathological condition, or response to an intervention and are increasingly being used for predicting, diagnosing, and prognosticating disease. They are also proving to be of use in the different forms of diabetes in both research and clinical settings. This review focuses on the β cell, addressing the potential utility of genetic markers, circulating molecules, immune cell phenotyping, and imaging approaches as biomarkers of cellular function and loss of this critical cell. Further, we consider how these biomarkers complement the more long-established, dynamic, and often complex measurements of β-cell secretory function that themselves could be considered biomarkers.

Genome-wide association study of serum liver enzymes implicates diverse metabolic and liver pathology

Serum liver enzyme concentrations are the most frequently-used laboratory markers of liver disease, a major cause of mortality. We conduct a meta-analysis of genome-wide association studies of liver enzymes from UK BioBank and BioBank Japan. We identified 160 previously-unreported independent alanine aminotransferase, 190 aspartate aminotransferase, and 199 alkaline phosphatase genome-wide significant associations, with some affecting multiple different enzymes. Associated variants implicate genes that demonstrate diverse liver cell type expression and promote a range of metabolic and liver diseases. These findings provide insight into the pathophysiology of liver and other metabolic diseases that are associated with serum liver enzyme concentrations.

Serum liver enzymes are used as markers of liver disease, their concentration influenced in part by genetic factors. Here the authors meta-analyse genome-wide association studies on the UK Biobank and BioBank Japan to evaluate the association of three liver enzymes with liver and other metabolic diseases.