AQR is a novel type 2 diabetes-associated gene that regulates signaling pathways critical for glucose metabolism

Cardiomyocyte intercellular signalling increases oxidative stress and reprograms the global- and phospho-proteome of cardiac fibroblasts

Pathological reprogramming of cardiomyocyte and fibroblast proteome landscapes drive the initiation and progression of cardiac fibrosis. Although the secretome of dysfunctional cardiomyocytes is emerging as an important driver of pathological fibroblast reprogramming, our understanding of the downstream molecular players remains limited. Here, we show that cardiac fibroblast activation (αSMA+) and oxidative stress mediated by the secretome of TGFβ-stimulated cardiomyocytes is associated with a profound reprogramming of their proteome and phosphoproteome landscape. Within the fibroblast global proteome there was a striking dysregulation of proteins implicated in extracellular matrix, protein localisation/metabolism, KEAP1-NFE2L2 pathway, lysosomes, carbohydrate metabolism, and transcriptional regulation. Kinase substrate enrichment analysis of phosphopeptides revealed potential role of kinases (CK2, CDK2, PKC, GSK3B) during this remodelling. We verified upregulated activity of casein kinase 2 (CK2) in secretome-treated fibroblasts, and pharmacological CK2 inhibitor TBB (4,5,6,7-Tetrabromobenzotriazole) significantly abrogated fibroblast activation and oxidative stress. Our data provides molecular insights into cardiomyocyte to cardiac fibroblast crosstalk, and the potential role of CK2 in regulating cardiac fibroblast activation and oxidative stress.

Exploring the hub mechanisms of ischemic stroke based on protein-protein interaction networks related to ischemic stroke and inflammatory bowel disease

Ischemic stroke is highly concerning because it often leads to severe long-term neurological disability. Among clinical trials, ischemic stroke and inflammatory bowel disease interactions have been increasingly reported in recent years. Therefore, using bioinformatics approaches to explore novel protein interactions between them is of interest. We performed this exploratory analysis by using bioinformatics tools such as string to analyze gene data downloaded from NHGRI-GWAS data related to ischemic stroke and inflammatory bowel disease. We constructed a prospective protein interaction network for ischemic stroke and inflammatory bowel disease, identifying cytokine and interleukin-related signaling pathways, Spliceosome, Ubiquitin-Proteasome System (UPS), Thrombus, and Anticoagulation pathways as the crucial biological mechanisms of the network. Furthermore, we also used data-independent acquisition mass spectrometry (DIA-MS) to detect differential protein expression in eight samples, which also suggested that immune system, signal transduction, and hemostasis-related pathways are key signaling pathways. These findings may provide a basis for understanding the interaction between these two states and exploring possible molecular and therapeutic studies in the future.

Decoding Diabetes Biomarkers and Related Molecular Mechanisms by Using Machine Learning, Text Mining, and Gene Expression Analysis

The molecular basis of diabetes mellitus is yet to be fully elucidated. We aimed to identify the most frequently reported and differential expressed genes (DEGs) in diabetes by using bioinformatics approaches. Text mining was used to screen 40,225 article abstracts from diabetes literature. These studies highlighted 5939 diabetes-related genes spread across 22 human chromosomes, with 112 genes mentioned in more than 50 studies. Among these genes, HNF4A, PPARA, VEGFA, TCF7L2, HLA-DRB1, PPARG, NOS3, KCNJ11, PRKAA2, and HNF1A were mentioned in more than 200 articles. These genes are correlated with the regulation of glycogen and polysaccharide, adipogenesis, AGE/RAGE, and macrophage differentiation. Three datasets (44 patients and 57 controls) were subjected to gene expression analysis. The analysis revealed 135 significant DEGs, of which CEACAM6, ENPP4, HDAC5, HPCAL1, PARVG, STYXL1, VPS28, ZBTB33, ZFP37 and CCDC58 were the top 10 DEGs. These genes were enriched in aerobic respiration, T-cell antigen receptor pathway, tricarboxylic acid metabolic process, vitamin D receptor pathway, toll-like receptor signaling, and endoplasmic reticulum (ER) unfolded protein response. The results of text mining and gene expression analyses used as attribute values for machine learning (ML) analysis. The decision tree, extra-tree regressor and random forest algorithms were used in ML analysis to identify unique markers that could be used as diabetes diagnosis tools. These algorithms produced prediction models with accuracy ranges from 0.6364 to 0.88 and overall confidence interval (CI) of 95%. There were 39 biomarkers that could distinguish diabetic and non-diabetic patients, 12 of which were repeated multiple times. The majority of these genes are associated with stress response, signalling regulation, locomotion, cell motility, growth, and muscle adaptation. Machine learning algorithms highlighted the use of the HLA-DQB1 gene as a biomarker for diabetes early detection. Our data mining and gene expression analysis have provided useful information about potential biomarkers in diabetes.

Hyperglycemia Promotes Endothelial Cell Senescence through AQR/PLAU Signaling Axis

Hyperglycemia is reported to accelerate endothelial cell senescence that contributes to diabetic complications. The underlying mechanism, however, remains elusive. We previously demonstrated AQR as a susceptibility gene for type 2 diabetes mellitus (T2DM) and showed that it was increased in multiple tissues in models with T2DM or metabolic syndrome. This study aimed to investigate the role of AQR in hyperglycemia-induced senescence and its underlying mechanism. Here, we retrieved several datasets of the aging models and found the expression of AQR was increased by high glucose and by aging across species, including C. elegans (whole-body), rat (cardiac tissues), and monkey (blood). we validated the increased AQR expression in senescent human umbilical vein endothelial cells (HUVECs). When overexpressed, AQR promoted the endothelial cell senescence, confirmed by an increased number of cells stained with senescence-associated beta-galactosidase and upregulation of CDKN1A (P21) as well as the prohibited cellular colony formation and G2/M phase arrest. To explore the mechanism by which AQR regulated the cellular senescence, transcriptomic analyses of HUVECs with the overexpression and knockdown of the AQR were performed. We identified 52 co-expressed genes that were enriched, in the terms of plasminogen activation, innate immunity, immunity, and antiviral defense. Among co-expressed genes, PLAU was selected to evaluate its contribution to senescence for its highest strength in the enrichment of the biological process. We demonstrated that the knockdown of PLAU rescued senescence-related phenotypes, endothelial cell activation, and inflammation in models induced by AQR or TNF-α. These findings, for the first time, indicate that AQR/PLAU is a critical signaling axis in the modulation of endothelial cell senescence, revealing a novel link between hyperglycemia and vascular dysfunction. The study may have implications in the prevention of premature vascular aging associated with T2DM.

m6A-seq analysis of microRNAs reveals that the N6-methyladenosine modification of miR-21-5p affects its target expression

In eukaryotes, N6-methyladenosine (m⁶A) is one of the most abundant modifications on RNAs, and it plays important roles in many biological processes and diseases such as cancer. While most m⁶A researches focus on message RNAs and long non-coding RNAs, recent studies have reported the presence of m⁶A in small RNAs. Nevertheless, current knowledge about m⁶A prevalence in mature microRNAs (miRNA) is extremely limited and the functional significance of m⁶A methylation in miRNAs remains to be elucidated. Here, we demonstrated cell-specific m⁶A profiles of miRNAs in A549 human non-small cell lung cancer (NSCLC) cells and HEK293A cells by using miRNA m⁶A immunoprecipitation sequencing and constructed the consensus motif in m⁶A-enriched miRNAs de novo. We found that miR-21-5p, an oncogenic miRNA, showed the highest m⁶A enrichment in NSCLC cells. Depletion of the demethylase ALKBH5 did not change the expression level of miR-21-5p, but altered the m⁶A abundance of miR-21-5p, thereby changing the expression levels of its target gene. We further synthesized m⁶A modified miR-21-5p mimics in vitro and demonstrated that in NSCLC cells, m⁶A marks in mature miR-21-5p could directly affect its silencing potency towards target genes, which finally impaired its promotion to proliferation and motility. Together, our findings reveal the landscape of m⁶A modification in mature miRNAs, and provide the first evidence that it may contribute to the mRNA responses to cancer-related miRNAs.

Methylome-wide change associated with response to electroconvulsive therapy in depressed patients

Electroconvulsive therapy (ECT) is a quick-acting and powerful antidepressant treatment considered to be effective in treating severe and pharmacotherapy-resistant forms of depression. Recent studies have suggested that epigenetic mechanisms can mediate treatment response and investigations about the relationship between the effects of ECT and DNA methylation have so far largely taken candidate approaches. In the present study, we examined the effects of ECT on the methylome associated with response in depressed patients (n = 34), testing for differentially methylated CpG sites before the first and after the last ECT treatment. We identified one differentially methylated CpG site associated with the effect of ECT response (defined as >50% decrease in Hamilton Depression Rating Scale score, HDRS), TNKS (q < 0.05; p = 7.15 × 10^-8). When defining response continuously (ΔHDRS), the top suggestive differentially methylated CpG site was in FKBP5 (p = 3.94 × 10^-7). Regional analyses identified two differentially methylated regions on chromosomes 8 (Šídák's p = 0.0031) and 20 (Šídák's p = 4.2 × 10^-5) associated with ΔHDRS. Functional pathway analysis did not identify any significant pathways. A confirmatory look at candidates previously proposed to be involved in ECT mechanisms found CpG sites associated with response only at the nominally significant level (p < 0.05). Despite the limited sample size, the present study was able to identify epigenetic change associated with ECT response suggesting that this approach, especially when involving larger samples, has the potential to inform the study of mechanisms involved in ECT and severe and treatment-resistant depression.

Identification of Diagnostic CpG Signatures in Patients with Gestational Diabetes Mellitus via Epigenome-Wide Association Study Integrated with Machine Learning

Background

Gestational diabetes mellitus (GDM) is the most prevalent metabolic disease during pregnancy, but the diagnosis is controversial and lagging partly due to the lack of useful biomarkers. CpG methylation is involved in the development of GDM. However, the specific CpG methylation sites serving as diagnostic biomarkers of GDM remain unclear. Here, we aimed to explore CpG signatures and establish the predicting model for the GDM diagnosis.

Methods

DNA methylation data of GSE88929 and GSE102177 were obtained from the GEO database, followed by the epigenome-wide association study (EWAS). GO and KEGG pathway analyses were performed by using the clusterProfiler package of R. The PPI network was constructed in the STRING database and Cytoscape software. The SVM model was established, in which the β-values of selected CpG sites were the predictor variable and the occurrence of GDM was the outcome variable.

Results

We identified 62 significant CpG methylation sites in the GDM samples compared with the control samples. GO and KEGG analyses based on the 62 CpG sites demonstrated that several essential cellular processes and signaling pathways were enriched in the system. A total of 12 hub genes related to the identified CpG sites were found in the PPI network. The SVM model based on the selected CpGs within the promoter region, including cg00922748, cg05216211, cg05376185, cg06617468, cg17097119, and cg22385669, was established, and the AUC values of the training set and testing set in the model were 0.8138 and 0.7576. The AUC value of the independent validation set of GSE102177 was 0.6667.

Conclusion

We identified potential diagnostic CpG signatures by EWAS integrated with the SVM model. The SVM model based on the identified 6 CpG sites reliably predicted the GDM occurrence, contributing to the diagnosis of GDM. Our finding provides new insights into the cross-application of EWAS and machine learning in GDM investigation.

Overview of key molecular and pharmacological targets for diabetes and associated diseases

Diabetes epidemiological quantities are demonstrating one of the most important communities' health worries. The essential diabetic difficulties are including cardiomyopathy, nephropathy, inflammation, and retinopathy. Despite developments in glucose decreasing treatments and drugs, these diabetic complications are still ineffectively reversed or prohibited. Several signaling and molecular pathways are vital targets in the new therapies of diabetes. This review assesses the newest researches about the key molecules and signaling pathways as targets of molecular pharmacology in diabetes and diseases related to it for better treatment based on molecular sciences. The disease is not cured by current pharmacological strategies for type 2 diabetes. While several drug combinations are accessible that can efficiently modulate glycemia and mitigate long-term complications, these agents do not reverse pathogenesis, and in practice, they are not established to modify the patient's specific molecular profiling. Therapeutic companies have benefited from human genetics. Genome exploration, which is agnostic to the information that exists, has revealed tens of loci that impact glycemic modulation. The physiological report has begun to examine subtypes of diseases, illustrate heterogeneity and propose biochemical therapeutic pathways.

Integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes

Pancreatic islet β-cell failure is key to the onset and progression of type 2 diabetes (T2D). The advent of single-cell RNA sequencing (scRNA-seq) has opened the possibility to determine transcriptional signatures specifically relevant for T2D at the β-cell level. Yet, applications of this technique have been underwhelming, as three independent studies failed to show shared differentially expressed genes in T2D β-cells. We performed an integrative analysis of the available datasets from these studies to overcome confounding sources of variability and better highlight common T2D β-cell transcriptomic signatures. After removing low-quality transcriptomes, we retained 3046 single cells expressing 27 931 genes. Cells were integrated to attenuate dataset-specific biases, and clustered into cell type groups. In T2D β-cells (n = 801), we found 210 upregulated and 16 downregulated genes, identifying key pathways for T2D pathogenesis, including defective insulin secretion, SREBP signaling and oxidative stress. We also compared these results with previous data of human T2D β-cells from laser capture microdissection and diabetic rat islets, revealing shared β-cell genes. Overall, the present study encourages the pursuit of single β-cell RNA-seq analysis, preventing presently identified sources of variability, to identify transcriptomic changes associated with human T2D and underscores specific traits of dysfunctional β-cells across different models and techniques.

Genome-Wide Association of Kidney Traits in Hispanics/Latinos Using Dense Imputed Whole-Genome Sequencing Data: The Hispanic Community Health Study/Study of Latinos

BACKGROUND

Genetic factors that influence kidney traits have been understudied for low-frequency and ancestry-specific variants.

METHODS

This study used imputed whole-genome sequencing from the Trans-Omics for Precision Medicine project to identify novel loci for estimated glomerular filtration rate and urine albumin-to-creatinine ratio in up to 12 207 Hispanics/Latinos. Replication was performed in the Women's Health Initiative and the UK Biobank when variants were available.

RESULTS

Two low-frequency intronic variants were associated with estimated glomerular filtration rate (rs58720902 at AQR, minor allele frequency=0.01, P=1.6×10-8) or urine albumin-to-creatinine ratio (rs527493184 at ZBTB16, minor allele frequency=0.002, P=1.1×10-8). An additional variant at PRNT (rs2422935, minor allele frequency=0.54, P=2.89×10-8) was significantly associated with estimated glomerular filtration rate in meta-analysis with replication samples. We also identified 2 known loci for urine albumin-to-creatinine ratio (BCL2L11 rs116907128, P=5.6×10-8 and HBB rs344, P=9.3×10-11) and validated 8 loci for urine albumin-to-creatinine ratio previously identified in the UK Biobank.

CONCLUSIONS

Our study shows gains in gene discovery when using dense imputation from multi-ethnic whole-genome sequencing data in admixed Hispanics/Latinos. It also highlights limitations in genetic research of kidney traits, including the lack of suitable replication samples for variants that are more common in non-European ancestry and those at low frequency in populations.

Co-expression network analysis identified hub genes critical to triglyceride and free fatty acid metabolism as key regulators of age-related vascular dysfunction in mice

Background: Aging has often been linked to age-related vascular disorders. The elucidation of the putative genes and pathways underlying vascular aging likely provides useful insights into vascular diseases at advanced ages. Transcriptional regulatory network analysis is the key to describing genetic interactions between molecular regulators and their target gene transcriptionally changed during vascular aging.

Results: A total of 469 differentially expressed genes were parsed into 6 modules. Among the incorporated sample traits, the most significant module related to vascular aging was associated with triglyceride and enriched with biological terms like proteolysis, blood circulation, and circulatory system process. The module associated with triglyceride was preserved in an independent microarray dataset, indicating the robustness of the identified vascular aging-related subnetwork. Additionally, Enpp5, Fez1, Kif1a, F3, H2-Q7, and their interacting miRNAs mmu-miR-449a, mmu-miR-449c, mmu-miR-34c, mmu-miR-34b-5p, mmu-miR-15a, and mmu-let-7, exhibited the most connectivity with external lipid-related traits. Transcriptional alterations of the hub genes Enpp5, Fez1, Kif1a, and F3, and the interacting microRNAs mmu-miR-34c, mmu-miR-34b-5p, mmu-let-7, mmu-miR-449a, and mmu-miR-449c were confirmed.

Conclusion: Our findings demonstrate that triglyceride and free fatty acid-related genes are key regulators of age-related vascular dysfunction in mice and show that the hub genes for Enpp5, Fez1, Kif1a, and F3 as well as their interacting miRNAs mmu-miR-34c, mmu-miR-34b-5p, mmu-let-7, mmu-miR-449a, and mmu-miR-449c, could serve as potential biomarkers in vascular aging.

Methods: The microarray gene expression profiles of aorta samples from 6-month old mice (n=6) and 20-month old mice (n=6) were processed to identify nominal differentially expressed genes. These nominal differentially expressed genes were subjected to a weighted gene co-expression network analysis. A network-driven integrative analysis with microRNAs and transcription factors was performed to define significant modules and underlying regulatory pathways associated with vascular aging, and module preservation test was conducted to validate the age-related modules based on an independent microarray gene expression dataset in mice aorta samples including three 32-week old wild-type mice (around 6-month old) and three 78-week old wild-type mice (around 20-month old). Gene ontology and protein-protein interaction analyses were conducted to determine the hub genes as potential biomarkers in the progress of vascular aging. The hub genes were further validated with quantitative real-time polymerase chain reaction in aorta samples from 20 young (6-month old) mice and 20 old (20-month old) mice.

Potentially repurposable drugs for schizophrenia identified from its interactome

We previously presented the protein-protein interaction network of schizophrenia associated genes, and from it, the drug-protein interactome which showed the drugs that target any of the proteins in the interactome. Here, we studied these drugs further to identify whether any of them may potentially be repurposable for schizophrenia. In schizophrenia, gene expression has been described as a measurable aspect of the disease reflecting the action of risk genes. We studied each of the drugs from the interactome using the BaseSpace Correlation Engine, and shortlisted those that had a negative correlation with differential gene expression of schizophrenia. This analysis resulted in 12 drugs whose differential gene expression (drug versus normal) had an anti-correlation with differential expression for schizophrenia (disorder versus normal). Some of these drugs were already being tested for their clinical activity in schizophrenia and other neuropsychiatric disorders. Several proteins in the protein interactome of the targets of several of these drugs were associated with various neuropsychiatric disorders. The network of genes with opposite drug-induced versus schizophrenia-associated expression profiles were significantly enriched in pathways relevant to schizophrenia etiology and GWAS genes associated with traits or diseases that had a pathophysiological overlap with schizophrenia. Drugs that targeted the same genes as the shortlisted drugs, have also demonstrated clinical activity in schizophrenia and other related disorders. This integrated computational analysis will help translate insights from the schizophrenia drug-protein interactome to clinical research - an important step, especially in the field of psychiatric drug development which faces a high failure rate.

Re-exploring the core genes and modules in the human frontal cortex during chronological aging: insights from network-based analysis of transcriptomic studies

Frontal cortical dysfunction is a fundamental pathology contributing to age-associated behavioral and cognitive deficits that predispose older adults to neurodegenerative diseases. It is established that aging increases the risk of frontal cortical dysfunction; however, the underlying molecular mechanism remains elusive. Here, we used an integrative meta-analysis to combine five frontal cortex microarray studies with a combined sample population of 161 younger and 155 older individuals. A network-based analysis was used to describe an outline of human frontal cortical aging to identify core genes whose expression changes with age and to reveal the interrelationships among these genes. We found that histone deacetylase 1 (HDAC1) and YES proto-oncogene 1 (YES1) are the two most upregulated genes, while cell division cycle 42 (CDC42) is the central regulatory gene decreased in the aged human frontal cortex. Quantitative PCR assays revealed corresponding changes in frontal cortical Hdac1, Yes1 and Cdc42 mRNA levels in an established aging mouse model. Moreover, analysis of the GSE48350 dataset confirmed similar changes in HDAC1, CDC42 and YES1 expression in Alzheimer's disease, thereby providing a molecular connection between aging and Alzheimer's disease (AD). This framework of network-based analysis could provide novel strategies for detecting and monitoring aging in the brain.