Integrating Mouse and Human Genetic Data to Move beyond GWAS and Identify Causal Genes in Cholesterol Metabolism

Balb/c mice are protected from glucose and acute cold intolerance

The brown adipose tissue is a potential target for interventions aimed at treating obesity and other metabolic disorders. Both genetic and environmental factors are known to regulate brown adipose tissue function and exploring the interaction between these factors could unveil new mechanisms involved in the regulation of thermogenesis. In this study, we evaluated three genetically distinct mice strains submitted to two environmental factors known to modulate brown adipose tissue function, namely, cold exposure and the consumption of a high-fat diet. The comparison of Balb/c, C57BL/6, and Swiss mice revealed that Balb/c mice were the most glucose-tolerant and the most cold-tolerant. In addition, Balb/c presented the greatest brown adipose tissue oxygen consumption, which was independent of differences in uncoupling protein 1 expression and function. The search for uncoupling protein 1-independent mechanisms that could explain the greatest cold tolerance of Balb/c mice resulted in the identification of the N-acyl amino acid regulator, PM20D1, which had a greater gene expression in the brown adipose tissue of Balb/c mice as compared to the other two strains. The immunoneutralization of PM20D1 in Balb/c mice, resulted in increased blood glucose levels and worsening of cold tolerance. In addition, the in silico knockout of Pm20d1 impacted several metabolic processes, including thermogenesis, glucose tolerance, and insulin sensitivity. In conclusion, Balb/c mice are protected from glucose and acute cold intolerance, independently of the diet. We propose that PM20D1, in an uncoupling protein 1-independent fashion, can have an important role in this protection.

The GWAS candidate far upstream element binding protein 3 (FUBP3) is required for normal skeletal growth, and adult bone mass and strength in mice

Bone mineral density (BMD) and height are highly heritable traits for which hundreds of genetic loci have been linked through genome wide association studies (GWAS). FUBP3 is a DNA and RNA binding protein best characterised as a transcriptional regulator of c-Myc, but little is known about its role in vivo. Single nucleotide polymorphisms in FUBP3 at the 9q34.11 locus have been associated with BMD, fracture and height in multiple GWAS, but FUBP3 has no previously established role in the skeleton. We analysed Fubp3-deficient mice to determine the consequence of FUBP3 deficiency in vivo. Mice lacking Fubp3 had reduced survival to adulthood and impaired skeletal growth. Bone mass was decreased, most strikingly in the vertebrae, with altered trabecular micro-architecture. Fubp3 deficient bones were also weak. These data provide the first functional demonstration that Fubp3 is required for normal skeletal growth and development and maintenance of adult bone structure and strength, indicating that FUBP3 contributes to the GWAS association of 9q34.11 with variation in height, BMD and fracture.

Genetic Regulation of Alternative Polyadenylation Provides Novel Insights into Molecular Mechanisms Underlying Non-small Cell Lung Cancer

Emerging evidence emphasizes the critical role of alternative polyadenylation (APA) in posttranscriptional regulation of genes, and APA-associated genetic variants (apaQTLs) show particular relevance for multiple disease. However, genetic regulation of APA and its role in non-small cell lung cancer (NSCLC) risk have not been thoroughly studied. Here, by leveraging genotype and APA data from The Cancer Genome Atlas, the association between genetic variation and APA is determined in NSCLC samples. The identified apaQTLs are distinct from eQTLs and are preferentially enriched in functionally relevant characteristics, including poly(A) motifs, APA-associated RBP binding sites, functional elements, and known NSCLC risk loci. Moreover, genes associated with apaQTLs are broadly involved in cancer-related biological process. Of note, integration of apaQTL variants with traditional GWAS-derived PRS is proved as a potential screening tool for NSCLC. By integrating large-scale population and biological experiments, a functional apaQTL variant rs9606 in LYRM4 is identified. Mechanistically, rs9606 induces aberrant APA process of LYRM4 via allele-specific interacting with NUDT21, which lead to increased expression of oncogene LYRM4 and thus contribute to NSCLC risk. This study demonstrates the distinct contribution of APA-associated genetic variants in NSCLC risk, providing critical clues and potential targets for NSCLC etiology and clinical intervention.

miR-18a-5p/PXR/SREBP2 Was Involved in MAFLD Associated With Methyl Tert-Butyl Ether Among Petrol Station Workers

BACKGROUND

Metabolic associated fatty liver disease (MAFLD), previously defined as non-alcoholic fatty liver disease (NAFLD), has been shown to be closely related to many environmental pollutants. Lately, we found methyl tert-butyl ether (MTBE), a new environmental pollutant, could increase NAFLD risk in American adults, which still needs more population epidemiological studies to verify, and its pathogenic mechanism is not yet clear.

METHODS

We conducted a cross-sectional study among petrol station workers, diagnosed their MAFLD according to internationally recognised diagnostic criteria, assessed the potential association of MTBE exposure with MAFLD risk, and explored the miR-18a-5p/PXR/SREBP2 pathway as possible pathogenic mechanisms in male Wistar rats and HepaRG cells treated with MTBE.

RESULTS

Blood MTBE levels were found to be significantly correlated with an increased risk of MAFLD, and MAFLD risk increased by 24.3% for every 0.1 μg/L increase in blood MTBE. Consistently, we found that MTBE exposure could induce MAFLD in rats and HepaRG cells, and activate pregnane X receptor (PXR) by inhibiting miR-18a-5p to upregulate the expression of sterol regulatory element-binding protein 2 (SREBP2) and its nuclear translocation, thereby upregulating the expression of its downstream target genes.

CONCLUSIONS

Our study demonstrated that MTBE exposure might be a significant risk factor for MAFLD, and MTBE could promote liver cholesterol synthesis and lipid deposition by activating the miRNA-18a-5p/PXR/SREBP2 pathway.

The pleomorphic cholesterol sensing motifs of transmembrane proteins

Millions of years of phylogenetic evolution have shaped the crosstalk between sterols and membrane-embedded proteins. This lengthy process, which began before the appearance of eukaryotic cells, has sculpted the two types of molecules to cover a wide spectrum of structural interconnectedness, ranging from rapid touch-and-go hits of low-affinity between surfaces to stronger lock-and-key type structural contacts. The former usually involve relatively loose contacts between linear amino acid sequences on the membrane-exposed transmembrane domains of the protein, readily accessible to the sterols as they briefly visit clefts between adjacent transmembrane segments while in rapid exchange with the bulk lipid bilayer. This operational mode is probably the most ancestral one, since it was already present in primitive bacteria interacting with hopanoid lipids. At the other end of this spectrum are more complex cholesterol binding sites that have required the acquisition of complex 3D non-sequential segments of the membrane protein to establish stereochemically elaborate 3D designs complementary to the rough and smooth surfaces of the eukaryotic neutral lipid, cholesterol. This short review explores cholesterol-membrane protein interactions using membrane protein paradigms having in common their participation in intercellular communications neurotransmission, hormone signalling, amino acid/neurotransmitter transport- and in cancer.

Evaluating SORT1 and SESN1 genes expression in peripheral blood mononuclear cells and oxidative stress status in patients with coronary artery disease

BACKGROUND

Coronary artery disease (CAD) significantly contributes to global fatalities. Recent studies have demonstrated the crucial roles of sortilin1 (SORT1) and sestrin1 (SESN1) in lipid metabolism, as well as their involvement in the development of CAD. The aberrant expression or activity of SORT1 can consequently lead to metabolic and vascular diseases. Sestrins, including SESN1, play a crucial role in helping cells survive by maintaining metabolic balance while also reducing oxidative stress (OS). OS contributes to the progression of atherosclerosis-related diseases, such as CAD. The study aimed to compare the gene expression of SORT1 and SESN1 in peripheral blood mononuclear cells (PBMCs), alongside serum OS markers, in CAD patients and controls.

MATERIALS

The case-control study included 49 CAD patients and 40 controls. The expression of the SORT1 and SESN1 genes was quantified using qRT-PCR, and the expression of the SORT1 protein was evaluated by western blotting. OS markers, including total oxidation status (TOS), total antioxidant capacity (TAC), and malondialdehyde (MDA), were measured using spectrophotometric and fluorometric methods.

RESULTS

SORT1 gene and protein expressions were similar between groups. CAD patients had a non-significant decrease in SESN1 gene expression. MDA levels were significantly higher in CAD patients, whereas TOS and TAC levels did not differ significantly.

CONCLUSION

For atherosclerosis-related disorders like CAD, MDA shows potential as a non-invasive, easy-to-use, affordable, and stable biomarker. Further research is needed to elucidate the precise roles of SORT1 and SESN1 in CAD pathogenesis.

Investigating the Causal Effects of Exercise-Induced Genes on Sarcopenia

Exercise is increasingly recognized as an effective strategy to counteract skeletal muscle aging and conditions such as sarcopenia. However, the specific exercise-induced genes responsible for these protective effects remain unclear. To address this, we conducted an eight-week aerobic exercise regimen on late-middle-aged mice and developed an integrated approach that combines mouse exercise-induced genes with human GWAS datasets to identify causal genes for sarcopenia. This approach led to significant improvements in the skeletal muscle phenotype of the mice and the identification of exercise-induced genes and miRNAs. By constructing a miRNA regulatory network enriched with transcription factors and GWAS signals related to muscle function and traits, we focused on 896 exercise-induced genes. Using human skeletal muscle cis-eQTLs as instrumental variables, 250 of these exercise-induced genes underwent two-sample Mendelian randomization analysis, identifying 40, 68, and 62 causal genes associated with sarcopenia and its clinical indicators-appendicular lean mass (ALM) and hand grip strength (HGS), respectively. Sensitivity analyses and cross-phenotype validation confirmed the robustness of our findings. Consistently across the three outcomes, RXRA, MDM1, RBL2, KCNJ2, and ADHFE1 were identified as risk factors, while NMB, TECPR2, MGAT3, ECHDC2, and GINM1 were identified as protective factors, all with potential as biomarkers for sarcopenia progression. Biological activity and disease association analyses suggested that exercise exerts its anti-sarcopenia effects primarily through the regulation of fatty acid oxidation. Based on available drug-gene interaction data, 21 of the causal genes are druggable, offering potential therapeutic targets. Our findings highlight key genes and molecular pathways potentially responsible for the anti-sarcopenia benefits of exercise, offering insights into future therapeutic strategies that could mimic the safe and mild protective effects of exercise on age-related skeletal muscle degeneration.

A systems biology-based identification and in vivo functional screening of Alzheimer's disease risk genes reveal modulators of memory function

Genome-wide association studies (GWASs) have uncovered over 75 genomic loci associated with risk for late-onset Alzheimer's disease (LOAD), but identification of the underlying causal genes remains challenging. Studies of induced pluripotent stem cell (iPSC)-derived neurons from LOAD patients have demonstrated the existence of neuronal cell-intrinsic functional defects. Here, we searched for genetic contributions to neuronal dysfunction in LOAD using an integrative systems approach that incorporated multi-evidence-based gene mapping and network-analysis-based prioritization. A systematic perturbation screening of candidate risk genes in Caenorhabditis elegans (C. elegans) revealed that neuronal knockdown of the LOAD risk gene orthologs vha-10 (ATP6V1G2), cmd-1 (CALM3), amph-1 (BIN1), ephx-1 (NGEF), and pho-5 (ACP2) alters short-/intermediate-term memory function, the cognitive domain affected earliest during LOAD progression. These results highlight the impact of LOAD risk genes on evolutionarily conserved memory function, as mediated through neuronal endosomal dysfunction, and identify new targets for further mechanistic interrogation.

Identification of SOCS3 and PTGS2 as new biomarkers for the diagnosis of gout by cross-species comprehensive analysis

BACKGROUND

Gout is the most common inflammatory arthritis in adults. Gout is an arthritic disease caused by the deposition of monosodium urate crystal (MSU) in the joints, which can lead to acute inflammation and damage adjacent tissue. Hyperuricemia is the main risk factor for MSU crystal deposition and gout. With the increasing burden of gout disease, the identification of potential biomarkers and novel targets for diagnosis is urgently needed.

METHODS

For the analysis of this subject paper, we downloaded the human gout data set GSE160170 and the gout mouse model data set GSE190138 from the GEO database. To obtain the differentially expressed genes (DEGs), we intersected the two data sets. Using the cytohubba algorithm, we identified the key genes and enriched them through GO and KEGG. The gene expression trends of three subgroups (normal control group, intermittent gout group and acute gout attack group) were analyzed by Series Test of Cluster (STC) analysis, and the key genes were screened out, and the diagnostic effect was verified by ROC curve. The expression of key genes in dorsal root nerve and spinal cord of gout mice was analyzed. Finally, the clinical samples of normal control group, hyperuricemia group, intermittent gout group and acute gout attack group were collected, and the expression of key genes at protein level was verified by ELISA.

RESULT

We obtained 59 co-upregulated and 28 co-downregulated genes by comparing the DEGs between gout mouse model data set and human gout data set. 7 hub DEGs(IL1B, IL10, NLRP3, SOCS3, PTGS2) were screened out via Cytohubba algorithm. The results of both GO and KEGG enrichment analyses indicate that 7 hub genes play a significant role in regulating the inflammatory response, cytokine production in immune response, and the TNF signaling pathway. The most representative hub genes SOCS3 and PTGS2 were screened out by Series Test of Cluster, and ROC analysis results showed the AUC values were both up to 1.000. In addition, we found that PTGS2 expression was significantly elevated in the dorsal root ganglia and spinal cord in monosodium urate(MSU)-induced gout mouse model. The ELISA results revealed that the expression of SOCS3 and PTGS2 was notably higher in the acute gout attack and intermittent gout groups compared to the normal control group. This difference was statistically significant, indicating a clear distinction between the groups.

CONCLUSION

Through cross-species comprehensive analysis and experimental verification, SOCS3 and PTGS2 were proved to be new biomarkers for diagnosing gout and predicting disease progression.

Mining cholesterol genes from thousands of mouse livers identifies aldolase C as a regulator of cholesterol biosynthesis

The availability of genome-wide transcriptomic and proteomic datasets is ever-increasing and often not used beyond initial publication. Here, we applied module-based coexpression network analysis to a comprehensive catalog of 35 mouse genome-wide liver expression datasets (encompassing more than 3800 mice) with the goal of identifying and validating unknown genes involved in cholesterol metabolism. From these 35 datasets, we identified a conserved module of genes enriched with cholesterol biosynthetic genes. Using a systematic approach across the 35 datasets, we identified three genes (Rdh11, Echdc1, and Aldoc) with no known role in cholesterol metabolism. We then performed functional validation studies and show that each gene is capable of regulating cholesterol metabolism. For the glycolytic gene, Aldoc, we demonstrate that it contributes to de novo cholesterol biosynthesis and regulates cholesterol and triglyceride levels in mice. As Aldoc is located within a genome-wide significant genome-wide association studies locus for human plasma cholesterol levels, our studies establish Aldoc as a causal gene within this locus. Through our work, we develop a framework for leveraging mouse genome-wide liver datasets for identifying and validating genes involved in cholesterol metabolism.

SARS-CoV-2 nucleocapsid protein promotes TMAO-induced NLRP3 inflammasome activation by SCAP-SREBP signaling pathway

The sterol regulatory element-binding protein (SREBP) activation and cytokine level were significantly increased in coronavirus disease-19. The NLRP3 inflammasome is an amplifier for cellular inflammation. This study aimed to elucidate the modulatory effect of SARS-CoV-2 nucleocapsid protein (SARS-CoV-2 NP) on trimethylamine N-oxide (TMAO)-induced lipogenesis and NLRP3 inflammasome activation and the underlying mechanisms in vascular smooth muscle cells (VSMCs). Our data indicated that SARS-CoV-2 NP activates the dissociation of the SREBP cleavage activating protein (SCAP) from the endoplasmic reticulum, resulting in SREBP activation, increased lipogenic gene expression, and NLRP3 inflammasome activation. TMAO was applied to VSMC-induced NLRP3 inflammasome by promoting the SCAP-SREBP complex endoplasmic reticulum-to-Golgi translocation, which facilitates directly binding of SARS-CoV-2 NP to the NLRP3 protein for NLRP3 inflammasome assembly. SARS-CoV-2 NP amplified the TMAO-induced lipogenic gene expression and NLRP3 inflammasome. Knockdown of SCAP-SREBP2 can effectively reduce lipogenic gene expression and alleviate NLRP3 inflammasome-mediated systemic inflammation in VSMCs stimulated with TMAO and SARS-CoV-2 NP. These results reveal that SARS-CoV-2 NP amplified TMAO-induced lipogenesis and NLRP3 inflammasome activation via priming the SCAP-SREBP signaling pathway.

Host genetics and gut microbiota jointly regulate blood biochemical indicators in chickens

Blood biochemical indicators play a crucial role in assessing an individual's overall health status and metabolic function. In this study, we measured five blood biochemical indicators, including total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL-CH), triglycerides (TG), high-density lipoprotein cholesterol (HDL-CH), and blood glucose (BG), as well as 19 growth traits of 206 male chickens. By integrating host whole-genome information and 16S rRNA sequencing of the duodenum, jejunum, ileum, cecum, and feces microbiota, we assessed the contributions of host genetics and gut microbiota to blood biochemical indicators and their interrelationships. Our results demonstrated significant negative phenotypic and genetic correlations (r =  - 0.20 ~  - 0.67) between CHOL and LDL-CH with growth traits such as body weight, abdominal fat content, muscle content, and shin circumference. The results of heritability and microbiability indicated that blood biochemical indicators were jointly regulated by host genetics and gut microbiota. Notably, the heritability of HDL-CH was estimated to be 0.24, while the jejunal microbiability for BG and TG reached 0.45 and 0.23. Furthermore, by conducting genome-wide association study (GWAS) with the single-nucleotide polymorphism (SNPs), insertion/deletion (indels), and structural variation (SV), we identified RAP2C, member of the RAS oncogene family (RAP2C), dedicator of cytokinesis 11 (DOCK11), neurotensin (NTS) and BOP1 ribosomal biogenesis factor (BOP1) as regulators of HDL-CH, and glycerophosphodiester phosphodiesterase domain containing 5 (GDPD5), dihydrodiol dehydrogenase (DHDH), and potassium voltage-gated channel interacting protein 1 (KCNIP1) as candidate genes of BG. Moreover, our findings suggest that cecal RF39 and Clostridia_UCG_014 may be linked to the regulation of CHOL, and jejunal Streptococcaceae may be involved in the regulation of TG. Additionally, microbial GWAS results indicated that the presence of gut microbiota was under host genetic regulation. Our findings provide valuable insights into the complex interaction between host genetics and microbiota in shaping the blood biochemical profile of chickens. KEY POINTS: • Multiple candidate genes were identified for the regulation of CHOL, HDL-CH, and BG. • RF39, Clostridia_UCG_014, and Streptococcaceae were implicated in CHOL and TG modulation. • The composition of gut microbiota is influenced by host genetics.

Systems genetics approaches for understanding complex traits with relevance for human disease

Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.

Genome-wide association studies of human and rat BMI converge on synapse, epigenome, and hormone signaling networks

A vexing observation in genome-wide association studies (GWASs) is that parallel analyses in different species may not identify orthologous genes. Here, we demonstrate that cross-species translation of GWASs can be greatly improved by an analysis of co-localization within molecular networks. Using body mass index (BMI) as an example, we show that the genes associated with BMI in humans lack significant agreement with those identified in rats. However, the networks interconnecting these genes show substantial overlap, highlighting common mechanisms including synaptic signaling, epigenetic modification, and hormonal regulation. Genetic perturbations within these networks cause abnormal BMI phenotypes in mice, too, supporting their broad conservation across mammals. Other mechanisms appear species specific, including carbohydrate biosynthesis (humans) and glycerolipid metabolism (rodents). Finally, network co-localization also identifies cross-species convergence for height/body length. This study advances a general paradigm for determining whether and how phenotypes measured in model species recapitulate human biology.

Lipidomic QTL in Diversity Outbred mice identifies a novel function for α/β hydrolase domain 2 (Abhd2) as an enzyme that metabolizes phosphatidylcholine and cardiolipin

We and others have previously shown that genetic association can be used to make causal connections between gene loci and small molecules measured by mass spectrometry in the bloodstream and in tissues. We identified a locus on mouse chromosome 7 where several phospholipids in liver showed strong genetic association to distinct gene loci. In this study, we integrated gene expression data with genetic association data to identify a single gene at the chromosome 7 locus as the driver of the phospholipid phenotypes. The gene encodes α/β-hydrolase domain 2 (Abhd2), one of 23 members of the ABHD gene family. We validated this observation by measuring lipids in a mouse with a whole-body deletion of Abhd2. The Abhd2KO mice had a significant increase in liver levels of phosphatidylcholine and phosphatidylethanolamine. Unexpectedly, we also found a decrease in two key mitochondrial lipids, cardiolipin and phosphatidylglycerol, in male Abhd2KO mice. These data suggest that Abhd2 plays a role in the synthesis, turnover, or remodeling of liver phospholipids.

Predicting mechanisms of action at genetic loci associated with discordant effects on type 2 diabetes and abdominal fat accumulation

Obesity is a major risk factor for cardiovascular disease, stroke, and type 2 diabetes (T2D). Excessive accumulation of fat in the abdomen further increases T2D risk. Abdominal obesity is measured by calculating the ratio of waist-to-hip circumference adjusted for the body-mass index (WHRadjBMI), a trait with a significant genetic inheritance. Genetic loci associated with WHRadjBMI identified in genome-wide association studies are predicted to act through adipose tissues, but many of the exact molecular mechanisms underlying fat distribution and its consequences for T2D risk are poorly understood. Further, mechanisms that uncouple the genetic inheritance of abdominal obesity from T2D risk have not yet been described. Here we utilize multi-omic data to predict mechanisms of action at loci associated with discordant effects on abdominal obesity and T2D risk. We find six genetic signals in five loci associated with protection from T2D but also with increased abdominal obesity. We predict the tissues of action at these discordant loci and the likely effector Genes (eGenes) at three discordant loci, from which we predict significant involvement of adipose biology. We then evaluate the relationship between adipose gene expression of eGenes with adipogenesis, obesity, and diabetic physiological phenotypes. By integrating these analyses with prior literature, we propose models that resolve the discordant associations at two of the five loci. While experimental validation is required to validate predictions, these hypotheses provide potential mechanisms underlying T2D risk stratification within abdominal obesity.

Network medicine: an approach to complex kidney disease phenotypes

Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.

The potential of integrating human and mouse discovery platforms to advance our understanding of cardiometabolic diseases

Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.

Lipidomic QTL in Diversity Outbred mice identifies a novel function for α/β hydrolase domain 2 ( Abhd2 ) as an enzyme that metabolizes phosphatidylcholine and cardiolipin

We and others have previously shown that genetic association can be used to make causal connections between gene loci and small molecules measured by mass spectrometry in the bloodstream and in tissues. We identified a locus on mouse chromosome 7 where several phospholipids in liver showed strong genetic association to distinct gene loci. In this study, we integrated gene expression data with genetic association data to identify a single gene at the chromosome 7 locus as the driver of the phospholipid phenotypes. The gene encodes α/β-hydrolase domain 2 ( Abhd2 ), one of 23 members of the ABHD gene family. We validated this observation by measuring lipids in a mouse with a whole-body deletion of Abhd2 . The Abhd2 KO mice had a significant increase in liver levels of phosphatidylcholine and phosphatidylethanolamine. Unexpectedly, we also found a decrease in two key mitochondrial lipids, cardiolipin and phosphatidylglycerol, in male Abhd2 KO mice. These data suggest that Abhd2 plays a role in the synthesis, turnover, or remodeling of liver phospholipids.

A Picrocrocin-Enriched Fraction from a Saffron Extract Affects Lipid Homeostasis in HepG2 Cells through a Non-Statin-like Mode

Dyslipidemia is a lipid metabolism disorder associated with the loss of the physiological homeostasis that ensures safe levels of lipids in the organism. This metabolic disorder can trigger pathological conditions such as atherosclerosis and cardiovascular diseases. In this regard, statins currently represent the main pharmacological therapy, but their contraindications and side effects limit their use. This is stimulating the search for new therapeutic strategies. In this work, we investigated in HepG2 cells the hypolipidemic potential of a picrocrocin-enriched fraction, analyzed by high-resolution 1H NMR and obtained from a saffron extract, the stigmas of Crocus sativus L., a precious spice that has already displayed interesting biological properties. Spectrophotometric assays, as well as expression level of the main enzymes involved in lipid metabolism, have highlighted the interesting hypolipidemic effects of this natural compound; they seem to be exerted through a non-statin-like mechanism. Overall, this work provides new insights into the metabolic effects of picrocrocin, thus confirming the biological potential of saffron and paving the way for in vivo studies that could validate this spice or its phytocomplexes as useful adjuvants in balancing blood lipid homeostasis.

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.

The constellation of cholesterol-dependent processes associated with SARS-CoV-2 infection

The role of cholesterol in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other coronavirus-host cell interactions is currently being discussed in the context of two main scenarios: i) the presence of the neutral lipid in cholesterol-rich lipid domains involved in different steps of the viral infection and ii) the alteration of metabolic pathways by the virus over the course of infection. Cholesterol-enriched lipid domains have been reported to occur in the lipid envelope membrane of the virus, in the host-cell plasma membrane, as well as in endosomal and other intracellular membrane cellular compartments. These membrane subdomains, whose chemical and physical properties distinguish them from the bulk lipid bilayer, have been purported to participate in diverse phenomena, from virus-host cell fusion to intracellular trafficking and exit of the virions from the infected cell. SARS-CoV-2 recruits many key proteins that participate under physiological conditions in cholesterol and lipid metabolism in general. This review analyses the status of cholesterol and lipidome proteins in SARS-CoV-2 infection and the new horizons they open for therapeutic intervention.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

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

Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods

In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating reliable large datasets. However, selecting an appropriate data integration and analysis method to improve the efficiency of phenome-phenome and phenome-genome association studies is still a bottleneck. This study proposes a hyperspectral wide association study (HypWAS) approach as a phenome-phenome association analysis through a hierarchical data integration strategy to estimate the prediction power of hyperspectral reflectance bands in predicting soybean seed yield. Using HypWAS, five important hyperspectral reflectance bands in visible, red-edge, and near-infrared regions were identified significantly associated with seed yield. The phenome-genome association analysis of each tested hyperspectral reflectance band was performed using two conventional genome-wide association studies (GWAS) methods and a machine learning mediated GWAS based on the support vector regression (SVR) method. Using SVR-mediated GWAS, more relevant QTL with the physiological background of the tested hyperspectral reflectance bands were detected, supported by the functional annotation of candidate gene analyses. The results of this study have indicated the advantages of using hierarchical data integration strategy and advanced mathematical methods coupled with phenome-phenome and phenome-genome association analyses for a better understanding of the biology and genetic backgrounds of hyperspectral reflectance bands affecting soybean yield formation. The identified yield-related hyperspectral reflectance bands using HypWAS can be used as indirect selection criteria for selecting superior genotypes with improved yield genetic gains in large breeding populations.

Plasma Phospholipids: A Promising Simple Biochemical Parameter to Evaluate COVID-19 Infection Severity

BACKGROUND

Coronavirus-19 (COVID-19) pandemic is a worldwide public health problem that has been known in China since December 25, 2019. Phospholipids are structural components of the mammalian cytoskeleton and cell membranes. They suppress viral attachment to the plasma membrane and subsequent replication in lung cells. In the virus-infected lung, phospholipids are highly prone to oxidation by reactive oxygen species, leading to the production of oxidized phospholipids (OxPLs).

OBJECTIVE

This study was carried out to explain the correlation between the level of plasma phospholipids in patients with COVID-19 infection and the levels of cytokine storms to assess the severity of the disease.

METHODS

Plasma samples from 34 enrolled patients with mild, moderate, and severe COVID-19 infection were collected. Complete blood count (CBC), plasma levels of D-dimer, ferritin, C-reactive protein (CRP), cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), phospholipids, secretory phospholipase A2 (sPLA2)α2, and cytokine storms were estimated, and lung computed tomography (CT) imaging was detected.

RESULTS

The CBC picture showed the presence of leukopenia, lymphopenia, and eosinopenia in patients with COVID-19 infection. Furthermore, a significant increase was found in plasma levels of D-dimer, CRP, ferritin, tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6, and IL-13 as well as sPLA2α2 activity compared to normal persons. However, plasma levels of phospholipids decreased in patients with moderate and severe COVID-19 infection, as well as significantly decreased in levels of triacylglycerols and HDL-C in plasma from patients with severe infection only, compared to normal persons. Furthermore, a lung CT scan showed the presence of inflammation in a patient with mild, moderate, and severe COVID-19 infection.

CONCLUSIONS

This study shows that there is a correlation between plasma phospholipid depletion and elevated cytokine storm in patients with COVID-19 infection. Depletion of plasma phospholipid levels in patients with COVID-19 infection is due to oxidative stress, induction of cytokine storm, and systemic inflammatory response after endothelial cell damage promote coagulation. According to current knowledge, patients with COVID-19 infection may need to administer surfactant replacement therapy and sPLA2 inhibitors to treat respiratory distress syndrome, which helps them to maintain the interconnected surfactant structures.

Could metabolomics drive the fate of COVID-19 pandemic? A narrative review on lights and shadows

Human Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection activates a complex interaction host/virus, leading to the reprogramming of the host metabolism aimed at the energy supply for viral replication. Alterations of the host metabolic homeostasis strongly influence the immune response to SARS-CoV-2, forming the basis of a wide range of outcomes, from the asymptomatic infection to the onset of COVID-19 and up to life-threatening acute respiratory distress syndrome, vascular dysfunction, multiple organ failure, and death. Deciphering the molecular mechanisms associated with the individual susceptibility to SARS-CoV-2 infection calls for a system biology approach; this strategy can address multiple goals, including which patients will respond effectively to the therapeutic treatment. The power of metabolomics lies in the ability to recognize endogenous and exogenous metabolites within a biological sample, measuring their concentration, and identifying perturbations of biochemical pathways associated with qualitative and quantitative metabolic changes. Over the last year, a limited number of metabolomics- and lipidomics-based clinical studies in COVID-19 patients have been published and are discussed in this review. Remarkable alterations in the lipid and amino acid metabolism depict the molecular phenotype of subjects infected by SARS-CoV-2; notably, structural and functional data on the lipids-virus interaction may open new perspectives on targeted therapeutic interventions. Several limitations affect most metabolomics-based studies, slowing the routine application of metabolomics. However, moving metabolomics from bench to bedside cannot imply the mere determination of a given metabolite panel; rather, slotting metabolomics into clinical practice requires the conversion of metabolic patient-specific data into actionable clinical applications.

Cross-species data integration to prioritize causal genes in lipid metabolism

PURPOSE OF REVIEW

More than one hundred loci have been identified from human genome-wide association studies (GWAS) for blood lipids. Despite the success of GWAS in identifying loci, subsequent prioritization of causal genes related to these loci remains a challenge. To address this challenge, recent work suggests that candidate causal genes within loci can be prioritized through cross-species integration using genome-wide data from the mouse.

RECENT FINDINGS

Mouse model systems provide unparalleled access to primary tissues, like the liver, that are not readily available for human studies. Given the key role the liver plays in controlling blood lipid levels and the wealth of liver genome-wide transcript and protein data available in the mouse, these data can be leveraged. Using coexpression network analysis approaches with mouse genome-wide data, coupled with cross-species analysis of human lipid GWAS, causal genes within lipid loci can be prioritized. Prioritization through both mouse and human along with biochemical validation provide a systematic and valuable method to discover lipid metabolism genes.

SUMMARY

The prioritization of causal lipid genes within GWAS loci is a challenging process requiring a multidisciplinary approach. Integration of data types across species, such as the mouse, can aid in causal gene prioritization.

Sestrin2 as a Potential Target for Regulating Metabolic-Related Diseases

Sestrin2 is a highly conserved protein that can be induced under a variety of stress conditions, including DNA damage, oxidative stress, endoplasmic reticulum (ER) stress, and metabolic stress. Numerous studies have shown that the AMP-activated protein kinase (AMPK)/mammalian target of rapamycin (mTOR) signaling pathway has a crucial role in the regulation of metabolism. Sestrin2 regulates metabolism via a number of pathways, including activation of AMPK, inhibition of the mTOR complex 1 (mTORC1), activation of mTOR complex 2 (mTORC2), inhibition of ER stress, and promotion of autophagy. Therefore, modulation of Sestrin2 activity may provide a potential therapeutic target for the prevention of metabolic diseases such as insulin resistance, diabetes, obesity, non-alcoholic fatty liver disease, and myocardial ischemia/reperfusion injury. In this review, we examined the regulatory relationship between Sestrin2 and the AMPK/mTOR signaling pathway and the effects of Sestrin2 on energy metabolism.

Network medicine in Cardiovascular Research

The ability to generate multi-omics data coupled with deeply characterizing the clinical phenotype of individual patients promises to improve understanding of complex cardiovascular pathobiology. There remains an important disconnection between the magnitude and granularity of these data and our ability to improve phenotype-genotype correlations for complex cardiovascular diseases. This shortcoming may be due to limitations associated with traditional reductionist analytical methods, which tend to emphasize a single molecular event in the pathogenesis of diseases more aptly characterized by crosstalk between overlapping molecular pathways. Network medicine is a rapidly growing discipline that considers diseases as the consequences of perturbed interactions between multiple interconnected biological components. This powerful integrative approach has enabled a number of important discoveries in complex disease mechanisms. In this review, we introduce the basic concepts of network medicine and highlight specific examples by which this approach has accelerated cardiovascular research. We also review how network medicine is well-positioned to promote rational drug design for patients with cardiovascular diseases, with particular emphasis on advancing precision medicine.

A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis

Here, we present a joint-tissue imputation (JTI) approach and a Mendelian randomization framework for causal inference, MR-JTI. JTI borrows information across transcriptomes of different tissues, leveraging shared genetic regulation, to improve prediction performance in a tissue-dependent manner. Notably, JTI includes the single-tissue imputation method PrediXcan as a special case and outperforms other single-tissue approaches (the Bayesian sparse linear mixed model and Dirichlet process regression). MR-JTI models variant-level heterogeneity (primarily due to horizontal pleiotropy, addressing a major challenge of transcriptome-wide association study interpretation) and performs causal inference with type I error control. We make explicit the connection between the genetic architecture of gene expression and of complex traits and the suitability of Mendelian randomization as a causal inference strategy for transcriptome-wide association studies. We provide a resource of imputation models generated from GTEx and PsychENCODE panels. Analysis of biobanks and meta-analysis data, and extensive simulations show substantially improved statistical power, replication and causal mapping rate for JTI relative to existing approaches.

Sestrins in Physiological Stress Responses

Sestrins are a family of proteins that respond to a variety of environmental stresses, including genotoxic, oxidative, and nutritional stresses. Sestrins affect multiple signaling pathways: AMP-activated protein kinase, mammalian target of rapamycin complexes, insulin-AKT, and redox signaling pathways. By regulating these pathways, Sestrins are thought to help adapt to stressful environments and subsequently restore cell and tissue homeostasis. In this review, we describe how Sestrins mediate physiological stress responses in the context of nutritional and chemical stresses (liver), physical movement and exercise (skeletal muscle), and chemical, physical, and inflammatory injuries (heart). These findings also support the idea that Sestrins are a molecular mediator of hormesis, a paradoxical beneficial effect of low- or moderate-level stresses in living organisms.

COVID-19-activated SREBP2 disturbs cholesterol biosynthesis and leads to cytokine storm

Sterol regulatory element binding protein-2 (SREBP-2) is activated by cytokines or pathogen, such as virus or bacteria, but its association with diminished cholesterol levels in COVID-19 patients is unknown. Here, we evaluated SREBP-2 activation in peripheral blood mononuclear cells of COVID-19 patients and verified the function of SREBP-2 in COVID-19. Intriguingly, we report the first observation of SREBP-2 C-terminal fragment in COVID-19 patients' blood and propose SREBP-2 C-terminal fragment as an indicator for determining severity. We confirmed that SREBP-2-induced cholesterol biosynthesis was suppressed by Sestrin-1 and PCSK9 expression, while the SREBP-2-induced inflammatory responses was upregulated in COVID-19 ICU patients. Using an infectious disease mouse model, inhibitors of SREBP-2 and NF-κB suppressed cytokine storms caused by viral infection and prevented pulmonary damages. These results collectively suggest that SREBP-2 can serve as an indicator for severity diagnosis and therapeutic target for preventing cytokine storm and lung damage in severe COVID-19 patients.

From GWAS to GEMMs: Sestrin1 and cholesterol biosynthesis

Sestrin1 regulates hepatic cholesterol synthesis and circulating lipid concentrations.

Systematically Sifting Big Data to Identify Novel Causal Genes for Human Traits

Widespread technological advances have propelled human genetics into a "big data" era, in which genome-wide data from extremely large cohorts can be integrated with other "-omics" datasets from humans and model systems. Li et al. (2020) demonstrate the power of applying multiple computational analyses to publicly available data to prioritize the study of genes with novel trait associations.