An integrative multiomic network model links lipid metabolism to glucose regulation in coronary artery disease

Sanghuangporus vaninii extract ameliorates hyperlipidemia in rats by mechanisms identified with transcriptome analysis

The increasing incidence of hyperlipidemia is a serious threat to public health. The development of effective and safe lipid-lowering drugs with few side effects is necessary. The purpose of this study was to assess the lipid-lowering activity of Sanghuangporus vaninii extract (SVE) in rat experiments and reveal the molecular mechanism by transcriptome analysis. Hyperlipidemia was induced in the animals using a high-fat diet for 4 weeks. At the end of the 4th week, hyperlipidemic rats were assigned into two control groups (model and positive simvastatin control) and three treatment groups that received SVE at 200, 400, or 800 mg kg-1 day-1 for another 4 weeks. A last control group comprised normal chow-fed rats. At the end of the 8th week, rats were sacrificed and lipid serum levels, histopathology, and liver transcriptome profiles were determined. SVE was demonstrated to relieve the lipid disorder and improve histopathological liver changes in a dose-dependent manner. The transcriptomic analysis identified changes in hepatocyte gene activity for major pathways including steroid biosynthesis, bile secretion, cholesterol metabolism, AMPK signaling, thyroid hormone signaling, and glucagon signaling. The changed expression of crucial genes in the different groups was confirmed by qPCR. Collectively, this study revealed that SVE could relieve hyperlipidemia in rats, the molecular mechanism might be to promote the metabolism of lipids and the excretion of cholesterol, inhibit the biosynthesis of cholesterol by activating the AMPK signaling pathway, the thyroid hormone signaling pathway, and the glucagon signaling pathway.

Mining key circRNA-associated-ceRNA networks for milk fat metabolism in cows with varying milk fat percentages

BACKGROUND

Cow milk fat is an essential indicator for evaluating and measuring milk quality and cow performance. Growing research has identified the molecular functions of circular RNAs (circRNAs) necessary for mammary gland development and lactation in mammals.

METHOD

The present study analyzed circRNA expression profiling data in mammary epithelial cells (MECs) from cows with highly variable milk fat percentage (MFP) using differential expression analysis and weighted gene co-expression network analysis (WGCNA).

RESULTS

A total of 309 differentially expressed circRNAs (DE-circRNAs) were identified in the high and low MFP groups. WGCNA analysis revealed that the pink module was significantly associated with MFP (r = - 0.85, P = 0.007). Parental genes of circRNAs in this module were enriched mainly in lipid metabolism-related signaling pathways, such as focal adhesion, ECM-receptor interaction, adherens junction and AMPK. Finally, six DE-circRNAs were screened from the pink module: circ_0010571, circ_0007797, circ_0002746, circ_0003052, circ_0004319, and circ_0012840. Among them, circ_0002746, circ_0003052, circ_0004319, and circ_0012840 had circular structures and were highly expressed in mammary tissues. Subcellular localization revealed that these four DE-circRNAs may play a regulatory role in the mammary glands of dairy cows, mainly as competitive endogenous RNAs (ceRNAs). Seven hub target genes (GNB1, GNG2, PLCB1, PLCG1, ATP6V0C, NDUFS4, and PIGH) were obtained by constructing the regulatory network of their ceRNAs and then analyzed by CytoHubba and MCODE plugins in Cytoscape. Functional enrichment analysis revealed that these genes are crucial and most probable ceRNA regulators in milk fat metabolism.

CONCLUSIONS

Our study identified several vital circRNAs and ceRNAs affecting milk fat synthesis, providing new research ideas and a theoretical basis for cow lactation, milk quality, and breed improvement.

Cardiac Metabolism, Reprogramming, and Diseases

Cardiovascular diseases (CVD) account for the largest bulk of deaths worldwide, posing a massive burden on societies and the global healthcare system. Besides, the incidence and prevalence of these diseases are on the rise, demanding imminent action to revert this trend. Cardiovascular pathogenesis harbors a variety of molecular and cellular mechanisms among which dysregulated metabolism is of significant importance and may even proceed other mechanisms. The healthy heart metabolism primarily relies on fatty acids for the ultimate production of energy through oxidative phosphorylation in mitochondria. Other metabolites such as glucose, amino acids, and ketone bodies come next. Under pathological conditions, there is a shift in metabolic pathways and the preference of metabolites, termed metabolic remodeling or reprogramming. In this review, we aim to summarize cardiovascular metabolism and remodeling in different subsets of CVD to come up with a new paradigm for understanding and treatment of these diseases.

Idiopathic pulmonary fibrosis-specific Bayesian network integrating extracellular vesicle proteome and clinical information

Idiopathic pulmonary fibrosis (IPF) is a progressive disease characterized by severe lung fibrosis and a poor prognosis. Although the biomolecules related to IPF have been extensively studied, molecular mechanisms of the pathogenesis and their association with serum biomarkers and clinical findings have not been fully elucidated. We constructed a Bayesian network using multimodal data consisting of a proteome dataset from serum extracellular vesicles, laboratory examinations, and clinical findings from 206 patients with IPF and 36 controls. Differential protein expression analysis was also performed by edgeR and incorporated into the constructed network. We have successfully visualized the relationship between biomolecules and clinical findings with this approach. The IPF-specific network included modules associated with TGF-β signaling (TGFB1 and LRC32), fibrosis-related (A2MG and PZP), myofibroblast and inflammation (LRP1 and ITIH4), complement-related (SAA1 and SAA2), as well as serum markers, and clinical symptoms (KL-6, SP-D and fine crackles). Notably, it identified SAA2 associated with lymphocyte counts and PSPB connected with the serum markers KL-6 and SP-D, along with fine crackles as clinical manifestations. These results contribute to the elucidation of the pathogenesis of IPF and potential therapeutic targets.

Integration of transcriptomes of senescent cell models with multi-tissue patient samples reveals reduced COL6A3 as an inducer of senescence

Senescent cells are a major contributor to age-dependent cardiovascular tissue dysfunction, but knowledge of their in vivo cell markers and tissue context is lacking. To reveal tissue-relevant senescence biology, we integrate the transcriptomes of 10 experimental senescence cell models with a 224 multi-tissue gene co-expression network based on RNA-seq data of seven tissues biopsies from ∼600 coronary artery disease (CAD) patients. We identify 56 senescence-associated modules, many enriched in CAD GWAS genes and correlated with cardiometabolic traits-which supports universality of senescence gene programs across tissues and in CAD. Cross-tissue network analyses reveal 86 candidate senescence-associated secretory phenotype (SASP) factors, including COL6A3. Experimental knockdown of COL6A3 induces transcriptional changes that overlap the majority of the experimental senescence models, with cell-cycle arrest linked to modulation of DREAM complex-targeted genes. We provide a transcriptomic resource for cellular senescence and identify candidate biomarkers, SASP factors, and potential drivers of senescence in human tissues.

Decoupling of the Confused Complex in Oxidation of 3,3',5,5'-Tetramethylbenzidine for the Reliable Chromogenic Bioassay

Regulation of the reaction pathways is a perennial theme in the field of chemistry. As a typical chromogenic substrate, 3,3',5,5'-tetramethylbenzidine (TMB) generally undertakes one-electron oxidation, but the product (TMBox1) is essentially a confused complex and is unstable, which significantly hampers the clinic chromogenic bioassays for more than 50 years. Herein, we report that sodium dodecyl sulfate (SDS)-based micelles could drive the direct two-electron oxidation of TMB to the final stable TMBox2. Rather than activation of H2O2 oxidant in the one-electron TMB oxidation by common natural peroxidase, activation of the TMB substrate by SDS micelles decoupled the thermodynamically favorable complex between TMBox2 with unreacted TMB, leading to an unusual direct two-electron oxidation pathway. Mechanism studies demonstrated that the complementary spatial and electrostatic isolation effects, caused by the confined hydrophobic cavities and negatively charged outer surfaces of SDS micelles, were crucial. Further cascading with glucose oxidase, as a proof-of-concept application, allowed glucose to be more reliably measured, even in a broader range of concentrations without any conventional strong acid termination.

Evolution of the search for a common mechanism of congenital risk of coronary heart disease and type 2 diabetes mellitus in the chromosomal locus 9p21.3

9.21.3 chromosomal locus predisposes to coronary heart disease (CHD) and type 2 diabetes mellitus (DM2), but their overall pathological mechanism and clinical applicability remain unclear. The review uses publications of the study results of 9.21.3 chromosomal locus in association with CHD and DM2, which are important for changing the focus of clinical practice. The eligibility criteria are full-text articles published in the PubMed database (MEDLINE) up to December 31, 2022. A total of 56 publications were found that met the inclusion criteria. Using the examples of the progressive stages in understanding the role of the chromosomal locus 9p.21.3, scientific ideas were grouped, from a fragmentary study of independent pathological processes to a systematic study of the overall development of CHD and DM2. The presented review can become a source of new scientific hypotheses for further studies, the results of which can determine the general mechanism of the congenital risk of CHD and DM2 and change the focus of clinical practice.

Impact of feeding dried distillers' grains with solubles diet on microbiome and metabolome of ruminal and cecal contents in Guanling yellow cattle

Dried distillers' grains with solubles (DDGS) are rich in nutrients, and partially alternative feeding of DDGS effectively reduces cost of feed and improves animals' growth. We used 16S rDNA gene sequencing and LC/MS-based metabolomics to explore the effect of feeding cattle with a basal diet (BD) and a Jiang-flavor DDGS diet (replaces 25% concentrate of the diet) on microbiome and metabolome of ruminal and cecal contents in Guanling yellow cattle. The results showed that the ruminal and cecal contents shared the same dominance of Bacteroidetes, Firmicutes and Proteobacteria in two groups. The ruminal dominant genera were Prevotella_1, Rikenellaceae_RC9_gut_group, and Ruminococcaceae_UCG-010; and the cecal dominant genera were Ruminococcaceae_UCG-005, Ruminococcaceae_UCG-010, and Rikenellaceae_RC9_gut_group. Linear discriminant analysis effect size analysis (LDA > 2, P < 0.05) revealed the significantly differential bacteria enriched in the DDGS group, including Ruminococcaceae_UCG_012, Prevotellaceae_UCG_004 and Anaerococcus in the ruminal contents, which was associated with degradation of plant polysaccharides. Besides, Anaerosporobacter, Anaerovibrio, and Caproiciproducens in the cecal contents were involved in fatty acid metabolism. Compared with the BD group, 20 significantly different metabolites obtained in the ruminal contents of DDGS group were down-regulated (P < 0.05), and based on them, 4 significantly different metabolic pathways (P < 0.05) were enriched including "Linoleic acid metabolism," "Biosynthesis of unsaturated fatty acids," "Taste transduction," and "Carbohydrate digestion and absorption." There were 65 significantly different metabolites (47 were upregulated, 18 were downregulated) in the cecal contents of DDGS group when compared with the BD group, and 4 significantly different metabolic pathways (P < 0.05) were enriched including "Longevity regulating pathway," "Bile secretion," "Choline metabolism in cancer," and "HIF-1 signaling pathway." Spearman analysis revealed close negative relationships between the top 20 significantly differential metabolites and Anaerococcus in the ruminal contents. Bacteria with high relevance to cecal differential metabolites were Erysipelotrichaceae_UCG-003, Dielma, and Solobacterium that affect specific metabolic pathways in cattle. Collectively, our results suggest that feeding cattle with a DDGS diet improves the microbial structure and the metabolic patterns of lipids and carbohydrates, thus contributing to the utilization efficiency of nutrients and physical health to some extent. Our findings will provide scientific reference for the utilization of DDGS as feed in cattle industry.

Cholesterol reprograms glucose and lipid metabolism to promote proliferation in colon cancer cells

Hypercholesterolemia is often correlated with obesity which is considered a risk factor for various cancers. With the growing population of hypercholesterolemic individuals, there is a need to understand the role of increased circulatory cholesterol or dietary cholesterol intake towards cancer etiology and pathology. Recently, abnormality in the blood cholesterol level of colon cancer patients has been reported. In the present study, we demonstrate that alteration in cholesterol levels (through a high-cholesterol or high-fat diet) increases the incidence of chemical carcinogen-induced colon polyp occurrence and tumor progression in mice. At the cellular level, low-density lipoprotein cholesterol (LDLc) and high-density lipoprotein cholesterol (HDLc) promote colon cancer cell proliferation by tuning the cellular glucose and lipid metabolism. Mechanistically, supplementation of LDLc or HDLc promotes cellular glucose uptake, and utilization, thereby, causing an increase in lactate production by colon cancer cells. Moreover, LDLc or HDLc upregulates aerobic glycolysis, causing an increase in total ATP production through glycolysis, and a decrease in ATP generation by OXPHOS. Interestingly, the shift in the metabolic status towards a more glycolytic phenotype upon the availability of cholesterol supports rapid cell proliferation. Additionally, an alteration in the expression of the molecules involved in cholesterol uptake along with the increase in lipid and cholesterol accumulation was observed in cells supplemented with LDLc or HDLc. These results indicate that colon cancer cells directly utilize the cholesterol associated with LDLc or HDLc. Moreover, targeting glucose metabolism through LDH inhibitor (oxamate) drastically abrogates the cellular proliferation induced by LDLc or HDLc. Collectively, we illustrate the vital role of cholesterol in regulating the cellular glucose and lipid metabolism of cancer cells and its direct effect on the colon tumorigenesis.

A study of gene expression in the living human brain

A goal of medical research is to determine the molecular basis of human brain health and illness. One way to achieve this goal is through observational studies of gene expression in human brain tissue. Due to the unavailability of brain tissue from living people, most such studies are performed using tissue from postmortem brain donors. An assumption underlying this practice is that gene expression in the postmortem human brain is an accurate representation of gene expression in the living human brain. Here, this assumption - which, until now, had not been adequately tested - is tested by comparing human prefrontal cortex gene expression between 275 living samples and 243 postmortem samples. Expression levels differed significantly for nearly 80% of genes, and a systematic examination of alternative explanations for this observation determined that these differences are not a consequence of cell type composition, RNA quality, postmortem interval, age, medication, morbidity, symptom severity, tissue pathology, sample handling, batch effects, or computational methods utilized. Analyses integrating the data generated for this study with data from earlier landmark studies that used tissue from postmortem brain donors showed that postmortem brain gene expression signatures of neurological and mental illnesses, as well as of normal traits such as aging, may not be accurate representations of these gene expression signatures in the living brain. By using tissue from large cohorts living people, future observational studies of human brain biology have the potential to (1) determine the medical research questions that can be addressed using postmortem tissue as a proxy for living tissue and (2) expand the scope of medical research to include questions about the molecular basis of human brain health and illness that can only be addressed in living people (e.g., "What happens at the molecular level in the brain as a person experiences an emotion?").

Identification of 5 hub genes for diagnosis of coronary artery disease

Background

Coronary artery disease (CAD) is a main cause leading to increasing mortality of cardiovascular disease (CVD) worldwide. We aimed to discover marker genes and develop a diagnostic model for CAD.

Methods

CAD-related target genes were searched from DisGeNET. Count expression data and clinical information were screened from the GSE202626 dataset. edgeR package identified differentially expressed genes (DEGs). Using online STRING tool and Cytoscape, protein-protein reactions (PPI) were predicted. WebGestaltR package was employed to functional enrichment analysis. We used Metascape to conduct module-based network analysis. VarElect algorithm provided genes-phenotype correlation analysis. Immune infiltration was assessed by ESTIMATE package and ssGSEA analysis. mRNAsi was determined by one class logistic regression (OCLR). A diagnostic model was constructed by SVM algorithm.

Results

162 target genes were screened by intersection 1,714 DEGs and 1,708 CAD related target genes. 137 target genes of the 162 target genes were obtained using PPI analysis, in which those targets were enriched in inflammatory cytokine pathways, such as chemokine signaling pathway, and IL-17 signaling pathway. From the above 137 target genes, four functional modules (MCODE1-4) were extracted. From the 162 potential targets, CAD phenotype were directly and indirectly associated with 161 genes and 22 genes, respectively. Finally, 5 hub genes (CCL2, PTGS2, NLRP3, VEGFA, LTA) were screened by intersections with the top 20, directly and indirectly, and genes in MCODE1. PTGS2, NLRP3 and VEGFA were positively, while LTA was negatively correlated with immune cells scores. PTGS2, NLRP3 and VEGFA were negatively, while LTA was positively correlated with mRNAsi. A diagnostic model was successfully established, evidenced by 92.59% sensitivity and AUC was 0.9230 in the GSE202625 dataset and 94.11% sensitivity and AUC was 0.9706 in GSE120774 dataset.

Conclusion

In this work, we identified 5 hub genes, which may be associated with CAD development.

Effects of a genetic variant rs13266634 in the zinc transporter 8 gene (SLC30A8) on insulin and lipid levels before and after a high-fat mixed macronutrient tolerance test in U.S. adults

BACKGROUND

The common C-allele of rs13266634 (c.973C>T or p.Arg325Trp) in SLC30A8 (ZNT8) is associated with increased risk of type 2 diabetes. While previous studies have examined the correlation of the variant with insulin and glucose metabolism, the effects of this variant on insulin and lipid responses after a lipid challenge in humans remain elusive. The goal of this study was to determine whether the C-allele had an impact on an individual's risk to metabolic syndromes in U.S. adults.

METHOD

We studied the genotypes of rs13266634 in 349 individuals aged between 18 and 65 y with BMI ranging from 18.5 to 45 kg/m². The subjects were evaluated for insulin, glucose, HbA1c, ghrelin, and lipid profiles before and after a high-fat mixed macronutrient tolerance test (MMTT).

RESULTS

We found that the effects of variants rs13266634 on glucose and lipid metabolism were sex-dimorphic, greater impact on males than on females. Insulin incremental area under the curve (AUC) after MMTT was significantly decreased in men with the CC genotype (p < 0.05). Men with the CC genotype also had the lowest fasting non-esterified fatty acid (NEFA) concentrations. On the other hand, the TT genotype was associated with a slower triglyceride removal from the circulation in men after MMTT. The reduced triglyceride removal was also observed in subjects with BMI ≥ 30 carrying either the heterozygous or homozygous T-allele. Nevertheless, the SNP had little effect on fasting or postprandial blood glucose and cholesterol concentrations.

CONCLUSION

We conclude that the CC genotype negatively affects insulin response after MMTT while the T-allele may negatively influence lipolysis during fasting and postprandial blood triglyceride removal in men and obese subjects, a novel finding in this study.

Urban particulate air pollution linked to dyslipidemia by modification innate immune cells

Air particulate matter (PM) is an essential risk factor for lipid metabolism disorders. However, the underlying mechanism remains unclear. In this cross-sectional study, 216 healthcare workers were recruited to estimate the associations among the daily exposure dose (DED) of air PM, innate immune cells, and plasma lipid levels. All participants were divided into two groups according to the air particulate combined DED (DED-PMC). The peripheral white blood cell counts, lymphocyte counts, and monocyte counts and percentages were higher in the higher-exposure group (HEG) than in the lower-exposure group (LEG), whereas the percentage of natural-killer cells was lower in the HEG than in the LEG. The plasma concentrations of the total cholesterol, triglycerides, LDL-C, and apolipoprotein B were higher in the HEG than in the LEG, whereas the HDL-C and apolipoprotein A1 were lower in the HEG than in the LEG. A dose-effect analysis indicated that when the DED of the air PM increased, there were increased peripheral monocyte counts and percentages, a decreased NK cell percentage, elevated plasma concentrations of total cholesterol, triglycerides, LDL-C, and apolipoprotein B, and reduced plasma levels of HDL-C and apolipoprotein A1. In addition, the modification of the innate immune cells was accompanied by alterations in the plasma lipid levels in a dose-dependent manner. Mediation effect analysis suggested innate immune cells were the potential mediators for the associations among air PM exposure on abnormal lipid metabolism. These results indicated that chronic exposure to air PM may disturb lipid metabolism by altering the distribution of innate immune cells in the peripheral blood, ultimately advancing cardiovascular disease risk.

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.

Neutral effect of SGLT2 inhibitors on lipoprotein metabolism: From clinical evidence to molecular mechanisms

Sodium-glucose cotransporter-2 inhibitors (SGLT2i) are effective, well-tolerated, and safe glucose-lowering compounds for patients with type 2 diabetes mellitus (T2DM). SGLT2i benefit encompasses protection from heart and kidney failure, independently of the presence of diabetes. In addition, SGLT2i consistently reduce the risk of hospitalization for heart failure and, although with some heterogeneity between specific members of the class, favourably affect the risk of cardiovascular outcomes. The molecular mechanisms underlying the cardiovascular favourable effect are not fully clarified. Studies testing the efficacy of SGLT2i in human cohorts and experimental models of atherosclerotic cardiovascular disease (ASCVD) have reported significant differences in circulating levels and composition of lipoprotein classes. In randomized clinical trials, small but significant increases in low-density lipoprotein cholesterol (LDL-C) levels have been observed, with a still undefined clinical significance; on the other hand, favourable (although modest) effects on high-density lipoprotein cholesterol (HDL-C) and triglycerides have been reported. At the molecular level, glycosuria may promote a starving-like state that ultimately leads to a metabolic improvement through the mobilization of fatty acids from the adipose tissue and their oxidation for the production of ketone bodies. This, however, may also fuel hepatic cholesterol synthesis, thus inhibiting atherogenic lipoprotein uptake from the liver. Long-term studies collecting detailed information on lipid-lowering therapies at baseline and during the trials with SGLT2i, as well as regularly monitoring lipid profiles are warranted to disentangle the potential implications of SGLT2i in modulating lipoprotein-mediated atherosclerotic cardiovascular risk.

Big data and machine learning to tackle diabetes management

BACKGROUND

Type 2 Diabetes (T2D) diagnosis is based solely on glycaemia, even though it is an endpoint of numerous dysmetabolic pathways. Type 2 Diabetes complexity is challenging in a real-world scenario; thus, dissecting T2D heterogeneity is a priority. Cluster analysis, which identifies natural clusters within multidimensional data based on similarity measures, poses a promising tool to unravel Diabetes complexity.

METHODS

In this review, we scrutinize and integrate the results obtained in most of the works up to date on cluster analysis and T2D.

RESULTS

To correctly stratify subjects and to differentiate and individualize a preventive or therapeutic approach to Diabetes management, cluster analysis should be informed with more parameters than the traditional ones, such as etiological factors, pathophysiological mechanisms, other dysmetabolic co-morbidities, and biochemical factors, that is the millieu. Ultimately, the above-mentioned factors may impact on Diabetes and its complications. Lastly, we propose another theoretical model, which we named the Integrative Model. We differentiate three types of components: etiological factors, mechanisms and millieu. Each component encompasses several factors to be projected in separate 2D planes allowing an holistic interpretation of the individual pathology.

CONCLUSION

Fully profiling the individuals, considering genomic and environmental factors, and exposure time, will allow the drive to precision medicine and prevention of complications.

The interaction between polyphyllin I and SQLE protein induces hepatotoxicity through SREBP-2/HMGCR/SQLE/LSS pathway

Polyphyllin I (PPI) and polyphyllin II (PII) are the main active substances in the Paris polyphylla. However, liver toxicity of these compounds has impeded their clinical application and the potential hepatotoxicity mechanisms remain to be elucidated. In this work, we found that PPI and PII exposure could induce significant hepatotoxicity in human liver cell line L-02 and zebrafish in a dose-dependent manner. The results of the proteomic analysis in L-02 cells and transcriptome in zebrafish indicated that the hepatotoxicity of PPI and PII was associated with the cholesterol biosynthetic pathway disorders, which were alleviated by the cholesterol biosynthesis inhibitor lovastatin. Additionally, 3-hydroxy-3-methy-lglutaryl CoA reductase (HMGCR) and squalene epoxidase (SQLE), the two rate-limiting enzymes in the cholesterol synthesis, selected as the potential targets, were confirmed by the molecular docking, the overexpression, and knockdown of HMGCR or SQLE with siRNA. Finally, the pull-down and surface plasmon resonance technology revealed that PPI could directly bind with SQLE but not with HMGCR. Collectively, these data demonstrated that PPI-induced hepatotoxicity resulted from the direct binding with SQLE protein and impaired the sterol-regulatory element binding protein 2/HMGCR/SQLE/lanosterol synthase pathways, thus disturbing the cholesterol biosynthesis pathway. The findings of this research can contribute to a better understanding of the key role of SQLE as a potential target in drug-induced hepatotoxicity and provide a therapeutic strategy for the prevention of drug toxic effects with similar structures in the future.

Diversified Shifts in the Cross Talk between Members of the Gut Microbiota and Development of Coronary Artery Diseases

Coronary artery disease (CAD) is one of leading causes of mortality worldwide. Studies on roles that the gut microbiota plays in development of atherosclerosis or acute myocardial infarction (AMI) have been widely reported. However, the gut microbiota is affected by many factors, including age, body mass index (BMI), and hypertension, that lead to high CAD risk. However, the associations between gut microbiota and CAD development or other CAD risk factors remain unexplored. Here, we performed a 16S RNA gene sequencing analysis of 306 fecal samples collected from patients with mild coronary stenosis (MCS; n = 36), stable angina (SA; n = 91), unstable angina (UA; n = 48), and acute myocardial infarction (AMI; n = 66) and 65 non-CAD controls. Using a noise-corrected method based on principal-component analysis (PCA) and the random forest algorithm, we identified the interference with gut microbial profiling of multiple factors (including age, gender, BMI, and hypertension) that potentially contributed significantly to the development of CAD. After correction of noise interference from certain interfering factors, we found consistent indicator microbiota organisms (such as Vampirovibrio, Ruminococcus, and Eisenbergiella) associated with the presence of MCS, SA, and AMI. Establishment of a diagnostic model revealed better performance in early CAD than clinical indexes with indicator microbes. Furthermore, indicator microbes can improve the accuracy of clinical indexes for the diagnosis of AMI. Additionally, we found that the microbial indicators of AMI Sporobacter and Eisenbergiella showed consistent positive and negative correlations to the clinical indexes creatine kinase (CK) and hemoglobin (Hb), respectively. As a control indicator of AMI, Dorea was negatively correlated with CK but positively correlated with Hb. IMPORTANCE Our study discovered the effect of confounding factors on gut microbial variations and identified gut microbial indicators possibly associated with the CAD development after noise correction. Our discovered indicator microbes may have potential for diagnosis or therapy of cardiovascular disorders.

Drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization

Coronavirus disease 2019 (COVID-19), a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently spreading rapidly around the world. Since SARS-CoV-2 seriously threatens human life and health as well as the development of the world economy, it is very urgent to identify effective drugs against this virus. However, traditional methods to develop new drugs are costly and time-consuming, which makes drug repositioning a promising exploration direction for this purpose. In this study, we collected known antiviral drugs to form five virus-drug association datasets, and then explored drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization (VDA-GKSBMF). By the 5-fold cross-validation, we found that VDA-GKSBMF has an area under curve (AUC) value of 0.8851, 0.8594, 0.8807, 0.8824, and 0.8804, respectively, on the five datasets, which are higher than those of other state-of-art algorithms in four datasets. Based on known virus-drug association data, we used VDA-GKSBMF to prioritize the top-k candidate antiviral drugs that are most likely to be effective against SARS-CoV-2. We confirmed that the top-10 drugs can be molecularly docked with virus spikes protein/human ACE2 by AutoDock on five datasets. Among them, four antiviral drugs ribavirin, remdesivir, oseltamivir, and zidovudine have been under clinical trials or supported in recent literatures. The results suggest that VDA-GKSBMF is an effective algorithm for identifying potential antiviral drugs against SARS-CoV-2.

N6-methyladenosine (m6A) RNA modification in the pathophysiology of heart failure: a narrative review

Background and Objective

Heart failure is the end-stage of various cardiovascular diseases. Recent progress in molecular biology has facilitated the understanding of the mechanisms of heart failure development at the molecular level. N6-adenosine methylation (m6A) is a post-transcriptional modification of RNA. Recent research work reported that m6A regulates gene expression and subsequently affects the activation of cell signaling pathways related to heart failure. Moreover, m6A regulators like methyltransferase-like 3 (METTL3) were reported to participate in myocardium hypertrophy. However, the current research work related to the role of m6A participating in the occurrence of heart failure is rare in some aspects like immune cell infiltration and diabetic heart diseases. Thus, it is reasonable to review the current achievements and provide further study orientation.

Methods

We searched related literature using the keywords: m6A AND heart failure in PubMed, Web of Science and Medline. The language was confined to English. The published year of searched literature ranged from 2012 to 2022. The searched results were put into Endnote software for management. Two authors investigated the searching terms and reviewed the full text of selected terms.

Key Content and Findings

m6A and its regulators are involved in the metabolism of various types of RNAs. m6A modification can regulate various types of cell signaling pathways related to the heart failure via interaction with m6A regulators. m6A and its regulators broadly participate in the myocardium fibrosis, myocardium hypertrophy, myocardial cell apoptosis, and ischemic reperfusion injury. Specifically, m6A participates in the cell apoptosis via regulation of autophagy flux. However, the current research work does not have enough evidence to prove that m6A regulator played its specific effect on the target transcript via regulating the m6A level.

Conclusions

m6A and its regulators participates in the progression of heart failure via modifying the RNA level. Future investigation of m6A should focus on the interaction between the m6A regulators and targeted transcript. Besides, the regulation role of m6A in immune cell infiltration and diabetic heart diseases should also be focused.

Noninvasive Plaque Imaging to Accelerate Coronary Artery Disease Drug Development

Coronary artery disease (CAD) remains the leading cause of adult mortality globally. Targeting known modifiable risk factors has had substantial benefit, but there remains a need for new approaches. Improvements in invasive and noninvasive imaging techniques have enabled an increasing recognition of distinct quantitative phenotypes of coronary atherosclerosis that are prognostically relevant. There are marked differences in plaque phenotype, from the high-risk, lipid-rich, thin-capped atheroma to the low-risk, quiescent, eccentric, nonobstructive calcified plaque. Such distinct phenotypes reflect different pathophysiologic pathways and are associated with different risks for acute ischemic events. Noninvasive coronary imaging techniques, such as computed tomography, positron emission tomography, and coronary magnetic resonance imaging, have major potential to accelerate cardiovascular drug development, which has been affected by the high costs and protracted timelines of cardiovascular outcome trials. This may be achieved through enrichment of high-risk phenotypes with higher event rates or as primary end points of drug efficacy, at least in phase 2 trials, in a manner historically performed through intravascular coronary imaging studies. Herein, we provide a comprehensive review of the current technology available and its application in clinical trials, including implications for sample size requirements, as well as potential limitations. In its effort to accelerate drug development, the US Food and Drug Administration has approved surrogate end points for 120 conditions, but not for CAD. There are robust data showing the beneficial effects of drugs, including statins, on CAD progression and plaque stabilization in a manner that correlates with established clinical end points of mortality and major adverse cardiovascular events. This, together with a clear mechanistic rationale for using imaging as a surrogate CAD end point, makes it timely for CAD imaging end points to be considered. We discuss the importance of global consensus on these imaging end points and protocols and partnership with regulatory bodies to build a more informed, sustainable staged pathway for novel therapies.

APOE alleles modulate associations of plasma metabolites with variants from multiple genes on chromosome 19q13.3

The APOE ε2, ε3, and ε4 alleles differentially impact various complex diseases and traits. We examined whether these alleles modulated associations of 94 single-nucleotide polymorphisms (SNPs) harbored by 26 genes in 19q13.3 region with 217 plasma metabolites using Framingham Heart Study data. The analyses were performed in the E2 (ε2ε2 or ε2ε3 genotype), E3 (ε3ε3 genotype), and E4 (ε3ε4 or ε4ε4 genotype) groups separately. We identified 31, 17, and 22 polymorphism-metabolite associations in the E2, E3, and E4 groups, respectively, at a false discovery rate P FDR < 0.05. These entailed 51 and 19 associations with 20 lipid and 12 polar analytes. Contrasting the effect sizes between the analyzed groups showed 20 associations with group-specific effects at Bonferroni-adjusted P < 7.14E-04. Three associations with glutamic acid or dimethylglycine had significantly larger effects in the E2 than E3 group and 12 associations with triacylglycerol 56:5, lysophosphatidylethanolamines 16:0, 18:0, 20:4, or phosphatidylcholine 38:6 had significantly larger effects in the E2 than E4 group. Two associations with isocitrate or propionate and three associations with phosphatidylcholines 32:0, 32:1, or 34:0 had significantly larger effects in the E4 than E3 group. Nine of 70 SNP-metabolite associations identified in either E2, E3, or E4 groups attained P FDR < 0.05 in the pooled sample of these groups. However, none of them were among the 20 group-specific associations. Consistent with the evolutionary history of the APOE alleles, plasma metabolites showed higher APOE-cluster-related variations in the E4 than E2 and E3 groups. Pathway enrichment mainly highlighted lipids and amino acids metabolism and citrate cycle, which can be differentially impacted by the APOE alleles. These novel findings expand insights into the genetic heterogeneity of plasma metabolites and highlight the importance of the APOE-allele-stratified genetic analyses of the APOE-related diseases and traits.

Aberrant branched-chain amino acid catabolism in cardiovascular diseases

Globally, cardiovascular diseases are the leading cause of death. Research has focused on the metabolism of carbohydrates, fatty acids, and amino acids to improve the prognosis of cardiovascular diseases. There are three types of branched-chain amino acids (BCAAs; valine, leucine, and isoleucine) required for protein homeostasis, energy balance, and signaling pathways. Increasing evidence has implicated BCAAs in the pathogenesis of multiple cardiovascular diseases. This review summarizes the biological origin, signal transduction pathways and function of BCAAs as well as their significance in cardiovascular diseases, including myocardial hypertrophy, heart failure, coronary artery disease, diabetic cardiomyopathy, dilated cardiomyopathy, arrhythmia and hypertension.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.

Metabonomic Characteristics of Myocardial Diastolic Dysfunction in Type 2 Diabetic Cardiomyopathy Patients

Diabetic cardiomyopathy (DCM) is one of the most essential cardiovascular complications in diabetic patients associated with glucose and lipid metabolism disorder, fibrosis, oxidative stress, and inflammation in cardiomyocytes. Despite increasing research on the molecular pathogenesis of DCM, it is still unclear whether metabolic pathways and alterations are probably involved in the development of DCM. This study aims to characterize the metabolites of DCM and to identify the relationship between metabolites and their biological processes or biological states through untargeted metabolic profiling. UPLC-MS/MS was applied to profile plasma metabolites from 78 patients with diabetes (39 diabetes with DCM and 39 diabetes without DCM as controls). A total of 2,806 biochemical were detected. Compared to those of DM patients, 78 differential metabolites in the positive-ion mode were identified in DCM patients, including 33 up-regulated and 45 down-regulated metabolites; however, there were only six differential metabolites identified in the negative mode including four up-regulated and two down-regulated metabolites. Alterations of several serum metabolites, including lipids and lipid-like molecules, organic acids and derivatives, organic oxygen compounds, benzenoids, phenylpropanoids and polyketides, and organoheterocyclic compounds, were associated with the development of DCM. KEGG enrichment analysis showed that there were three signaling pathways (metabolic pathways, porphyrin, chlorophyll metabolism, and lysine degradation) that were changed in both negative- and positive-ion modes. Our results demonstrated that differential metabolites and lipids have specific effects on DCM. These results expanded our understanding of the metabolic characteristics of DCM and may provide a clue in the future investigation of reducing the incidence of DCM. Furthermore, the metabolites identified here may provide clues for clinical management and the development of effective drugs.

Integrative Prioritization of Causal Genes for Coronary Artery Disease

BACKGROUND

Hundreds of candidate genes have been associated with coronary artery disease (CAD) through genome-wide association studies. However, a systematic way to understand the causal mechanism(s) of these genes, and a means to prioritize them for further study, has been lacking. This represents a major roadblock for developing novel disease- and gene-specific therapies for patients with CAD. Recently, powerful integrative genomics analyses pipelines have emerged to identify and prioritize candidate causal genes by integrating tissue/cell-specific gene expression data with genome-wide association study data sets.

METHODS

We aimed to develop a comprehensive integrative genomics analyses pipeline for CAD and to provide a prioritized list of causal CAD genes. To this end, we leveraged several complimentary informatics approaches to integrate summary statistics from CAD genome-wide association studies (from UK Biobank and CARDIoGRAMplusC4D) with transcriptomic and expression quantitative trait loci data from 9 cardiometabolic tissue/cell types in the STARNET study (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task).

RESULTS

We identified 162 unique candidate causal CAD genes, which exerted their effect from between one and up to 7 disease-relevant tissues/cell types, including the arterial wall, blood, liver, skeletal muscle, adipose, foam cells, and macrophages. When their causal effect was ranked, the top candidate causal CAD genes were CDKN2B (associated with the 9p21.3 risk locus) and PHACTR1; both exerting their causal effect in the arterial wall. A majority of candidate causal genes were represented in cross-tissue gene regulatory co-expression networks that are involved with CAD, with 22/162 being key drivers in those networks.

CONCLUSIONS

We identified and prioritized candidate causal CAD genes, also localizing their tissue(s) of causal effect. These results should serve as a resource and facilitate targeted studies to identify the functional impact of top causal CAD genes.

A mechanistic framework for cardiometabolic and coronary artery diseases

Coronary atherosclerosis results from the delicate interplay of genetic and exogenous risk factors, principally taking place in metabolic organs and the arterial wall. Here we show that 224 gene-regulatory coexpression networks (GRNs) identified by integrating genetic and clinical data from patients with (n = 600) and without (n = 250) coronary artery disease (CAD) with RNA-seq data from seven disease-relevant tissues in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study largely capture this delicate interplay, explaining >54% of CAD heritability. Within 89 cross-tissue GRNs associated with clinical severity of CAD, 374 endocrine factors facilitated inter-organ interactions, primarily along an axis from adipose tissue to the liver (n = 152). This axis was independently replicated in genetically diverse mouse strains and by injection of recombinant forms of adipose endocrine factors (EPDR1, FCN2, FSTL3 and LBP) that markedly altered blood lipid and glucose levels in mice. Altogether, the STARNET database and the associated GRN browser (http://starnet.mssm.edu) provide a multiorgan framework for exploration of the molecular interplay between cardiometabolic disorders and CAD.

Obesity Partially Mediates the Diabetogenic Effect of Lowering LDL Cholesterol

OBJECTIVE

LDL cholesterol (LDLc)-lowering drugs modestly increase body weight and type 2 diabetes risk, but the extent to which the diabetogenic effect of lowering LDLc is mediated through increased BMI is unknown.

RESEARCH DESIGN AND METHODS

We conducted summary-level univariable and multivariable Mendelian randomization (MR) analyses in 921,908 participants to investigate the effect of lowering LDLc on type 2 diabetes risk and the proportion of this effect mediated through BMI. We used data from 92,532 participants from 14 observational studies to replicate findings in individual-level MR analyses.

RESULTS

A 1-SD decrease in genetically predicted LDLc was associated with increased type 2 diabetes odds (odds ratio [OR] 1.12 [95% CI 1.01, 1.24]) and BMI (β = 0.07 SD units [95% CI 0.02, 0.12]) in univariable MR analyses. The multivariable MR analysis showed evidence of an indirect effect of lowering LDLc on type 2 diabetes through BMI (OR 1.04 [95% CI 1.01, 1.08]) with a proportion mediated of 38% of the total effect (P = 0.03). Total and indirect effect estimates were similar across a number of sensitivity analyses. Individual-level MR analyses confirmed the indirect effect of lowering LDLc on type 2 diabetes through BMI with an estimated proportion mediated of 8% (P = 0.04).

CONCLUSIONS

These findings suggest that the diabetogenic effect attributed to lowering LDLc is partially mediated through increased BMI. Our results could help advance understanding of adipose tissue and lipids in type 2 diabetes pathophysiology and inform strategies to reduce diabetes risk among individuals taking LDLc-lowering medications.

Genetic Determinants of Plasma Low-Density Lipoprotein Cholesterol Levels: Monogenicity, Polygenicity, and "Missing" Heritability

Changes in plasma low-density lipoprotein cholesterol (LDL-c) levels relate to a high risk of developing some common and complex diseases. LDL-c, as a quantitative trait, is multifactorial and depends on both genetic and environmental factors. In the pregenomic age, targeted genes were used to detect genetic factors in both hyper- and hypolipidemias, but this approach only explained extreme cases in the population distribution. Subsequently, the genetic basis of the less severe and most common dyslipidemias remained unknown. In the genomic age, performing whole-exome sequencing in families with extreme plasma LDL-c values identified some new candidate genes, but it is unlikely that such genes can explain the majority of inexplicable cases. Genome-wide association studies (GWASs) have identified several single-nucleotide variants (SNVs) associated with plasma LDL-c, introducing the idea of a polygenic origin. Polygenic risk scores (PRSs), including LDL-c-raising alleles, were developed to measure the contribution of the accumulation of small-effect variants to plasma LDL-c. This paper discusses other possibilities for unexplained dyslipidemias associated with LDL-c, such as mosaicism, maternal effect, and induced epigenetic changes. Future studies should consider gene-gene and gene-environment interactions and the development of integrated information about disease-driving networks, including phenotypes, genotypes, transcription, proteins, metabolites, and epigenetics.

Detection of an anti-angina therapeutic module in the effective population treated by a multi-target drug Danhong injection: a randomized trial

It’s a challenge for detecting the therapeutic targets of a polypharmacological drug from variations in the responsed networks in the differentiated populations with complex diseases, as stable coronary heart disease. Here, in an adaptive, 31-center, randomized, double-blind trial involving 920 patients with moderate symptomatic stable angina treated by 14-day Danhong injection(DHI), a kind of polypharmacological drug with high quality control, or placebo (0.9% saline), with 76-day following-up, we firstly confirmed that DHI could increase the proportion of patients with clinically significant changes on angina-frequency assessed by Seattle Angina Questionnaire (ΔSAQ-AF ≥ 20) (12.78% at Day 30, 95% confidence interval [CI] 5.86–19.71%, P = 0.0003, 13.82% at Day 60, 95% CI 6.82–20.82%, P = 0.0001 and 8.95% at Day 90, 95% CI 2.06–15.85%, P = 0.01). We also found that there were no significant differences in new-onset major vascular events (P = 0.8502) and serious adverse events (P = 0.9105) between DHI and placebo. After performing the RNA sequencing in 62 selected patients, we developed a systemic modular approach to identify differentially expressed modules (DEMs) of DHI with the Z_summary value less than 0 compared with the control group, calculated by weighted gene co-expression network analysis (WGCNA), and sketched out the basic framework on a modular map with 25 functional modules targeted by DHI. Furthermore, the effective therapeutic module (ETM), defined as the highest correlation value with the phenotype alteration (ΔSAQ-AF, the change in SAQ-AF at Day 30 from baseline) calculated by WGCNA, was identified in the population with the best effect (ΔSAQ-AF ≥ 40), which is related to anticoagulation and regulation of cholesterol metabolism. We assessed the modular flexibility of this ETM using the global topological D value based on Euclidean distance, which is correlated with phenotype alteration (r²: 0.8204, P = 0.019) by linear regression. Our study identified the anti-angina therapeutic module in the effective population treated by the multi-target drug. Modular methods facilitate the discovery of network pharmacological mechanisms and the advancement of precision medicine. (ClinicalTrials.gov identifier: NCT01681316).

Fe-Coordinated Carbon Nanozyme Dots as Peroxidase-Like Nanozymes and Magnetic Resonance Imaging Contrast Agents

The catalytic activities of currently developed peroxidase-mimic nanozymes are generally limited. Therefore, further efforts are still needed to improve the catalytic performance of peroxidase nanozymes. Herein, we synthesized Fe-coordinated carbon nanozyme dots (Fe-CDs) that can serve as both efficient peroxidase nanozymes and T₂-magnetic resonance imaging (MRI) contrast agents. The intrinsic peroxidase-like activity of the Fe-CDs was explored by catalytic oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) with hydrogen peroxide (H₂O₂). The product showed better performance over natural horseradish peroxidase (HRP) and other mimetic peroxidases. Quantification of glucose and ascorbic acid detection showed that this nanozyme could be used to detect a minimum limit as low as 5 μM glucose. Moreover, the colorimetric detection technique was used to detect serum glucose in mice, and the detection result was comparable with autobiochemistry analyzer results using a glucose assay kit. Furthermore, the Fe-CDs showed good magnetism properties and provided promising MR imaging of tumors with excellent biocompatibility.

TRPM2, PDLIM5, BCL3, CD14, GBA Genes as Feasible Markers for Premature Coronary Heart Disease Risk

Background: Beyond non-genetic risk factors, familial hypercholesterolemia (FH) plays a major role in the development of CHD. FH is a genetic disorder characterized by heritable and severely elevated levels of low-density lipoprotein (LDL) cholesterol, which can lead to premature cardiovascular disease, particularly familial coronary heart disease (FH-CHD). Method: To explore genes indicating a risk of familial (premature) coronary heart disease (FH-CHD) development in FH, 30 Thai male volunteers were enrolled: 7 healthy controls (N), 6 patients with hypercholesterolemia (H), 4 with FH, 10 with CHD, and 3 with FH-CHD. Transcriptome data were investigated using next-generation sequencing analysis in whole blood (n = 3). Genes that were significantly expressed in both FH and FH-CHD, but not in N, H, and CHD groups, were selected and functionally analyzed. Results: The findings revealed that 55 intersecting genes were differentially expressed between FH and FH-CHD groups. Ten of the 55 genes (MAPK14, TRPM2, STARD8, PDLIM5, BCL3, BLOC1S5, GBA, RBMS1, CD14, and CD36 were selected for validation. These 10 genes play potential roles in chronic inflammation and are involved in pathways related to pathogenesis of CHD. Using quantitative real-time PCR, we evaluated the mRNA expression of the selected genes in all 30 volunteers. TRPM2, PDLIM5, BCL3 were significantly upregulated and GBA was significantly downregulated in both FH and FH-CHD compared with the N, H, and CHD groups. Conclusion: our preliminary investigation reveals that the TRPM2, PDLIM5, BCL3, and GBA genes may have potential for further development as predictive markers for FH-CHD.

Precision Medicine Approaches to Vascular Disease: JACC Focus Seminar 2/5

In this second of a 5-part Focus Seminar series, we focus on precision medicine in the context of vascular disease. The most common vascular disease worldwide is atherosclerosis, which is the primary cause of coronary artery disease, peripheral vascular disease, and a large proportion of strokes and other disorders. Atherosclerosis is a complex genetic disease that likely involves many hundreds to thousands of single nucleotide polymorphisms, each with a relatively modest effect for causing disease. Conversely, although less prevalent, there are many vascular disorders that typically involve only a single genetic change, but these changes can often have a profound effect that is sufficient to cause disease. These are termed "Mendelian vascular diseases," which include Marfan and Loeys-Dietz syndromes. Given the very different genetic basis of atherosclerosis versus Mendelian vascular diseases, this article was divided into 2 parts to cover the most promising precision medicine approaches for these disease types.

A Molecular network approach reveals shared cellular and molecular signatures between chronic fatigue syndrome and other fatiguing illnesses

The molecular mechanisms of chronic fatigue syndrome (CFS, or Myalgic encephalomyelitis), a disease defined by extreme, long-term fatigue, remain largely uncharacterized, and presently no molecular diagnostic test and no specific treatments exist to diagnose and treat CFS patients. While CFS has historically had an estimated prevalence of 0.1-0.5% [1], concerns of a “long hauler” version of Coronavirus disease 2019 (COVID-19) that symptomatically overlaps CFS to a significant degree(Supplemental Table-1)and appears to occur in 10% of COVID-19 patients[2], has raised concerns of a larger spike in CFS [3]. Here, we established molecular signatures of CFS and a corresponding network-based disease context from RNA-sequencing data generated on whole blood and FACs sorted specific peripheral blood mononuclear cells (PBMCs) isolated from CFS cases and non-CFS controls. The immune cell type specific molecular signatures of CFS we identified, overlapped molecular signatures from other fatiguing illnesses, demonstrating a common molecular etiology. Further, after constructing a probabilistic causal model of the CFS gene expression data, we identified master regulator genes modulating network states associated with CFS, suggesting potential therapeutic targets for CFS.

Hepatocellular Carcinoma and Obesity, Type 2 Diabetes Mellitus, Cardiovascular Disease: Causing Factors, Molecular Links, and Treatment Options

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, which will affect more than a million people by the year 2025. However, current treatment options have limited benefits. Nonalcoholic fatty liver disease (NAFLD) is the fastest growing factor that causes HCC in western countries, including the United States. In addition, NAFLD co-morbidities including obesity, type 2 diabetes mellitus (T2DM), and cardiovascular diseases (CVDs) promote HCC development. Alteration of metabolites and inflammation in the tumor microenvironment plays a pivotal role in HCC progression. However, the underlying molecular mechanisms are still not totally clear. Herein, in this review, we explored the latest molecules that are involved in obesity, T2DM, and CVDs-mediated progression of HCC, as they share some common pathologic features. Meanwhile, several therapeutic options by targeting these key factors and molecules were discussed for HCC treatment. Overall, obesity, T2DM, and CVDs as chronic metabolic disease factors are tightly implicated in the development of HCC and its progression. Molecules and factors involved in these NAFLD comorbidities are potential therapeutic targets for HCC treatment.