N4BP2L1 interacts with dynactin and contributes to GLUT4 trafficking and glucose uptake in adipocytes

Involvement of N4BP2L1, PLEKHA4, and BEGAIN genes in breast cancer and muscle cell development

Patients with breast cancer show altered expression of genes within the pectoralis major skeletal muscle cells of the breast. Through analyses of The Cancer Genome Atlas (TCGA)-breast cancer (BRCA), we identified three previously uncharacterized putative novel tumor suppressor genes expressed in normal muscle cells, whose expression was downregulated in breast tumors. We found that NEDD4 binding protein 2-like 1 (N4BP2L1), pleckstrin homology domain-containing family A member 4 (PLEKHA4), and brain-enriched guanylate kinase-associated protein (BEGAIN) that are normally highly expressed in breast myoepithelial cells and smooth muscle cells were significantly downregulated in breast tumor tissues of a cohort of 50 patients with this cancer. Our data revealed that the low expression of PLEKHA4 in patients with menopause below 50 years correlated with a higher risk of breast cancer. Moreover, we identified N4BP2L1 and BEGAIN as potential biomarkers of HER2-positive breast cancer. Furthermore, low BEGAIN expression in breast cancer patients with blood fat, heart problems, and diabetes correlated with a higher risk of this cancer. In addition, protein and RNA expression analysis of TCGA-BRCA revealed N4BP2L1 as a promising diagnostic protein biomarker in breast cancer. In addition, the in silico data of scRNA-seq showed high expression of these genes in several cell types of normal breast tissue, including breast myoepithelial cells and smooth muscle cells. Thus, our results suggest their possible tumor-suppressive function in breast cancer and muscle development.

MicroRNA-150-5p-mediated Inhibition of Cell Proliferation, G1/S Transition, and Migration in Bladder Cancer through Targeting NEDD4-binding Protein 2-like 1 Gene

MicroRNA-150-5p (miR-150-5p) has been implicated in the progression of several cancer types, yet its specific functional role and regulatory mechanisms in bladder cancer (BC) remain largely unexplored. Our study revealed significant downregulation of miR-150-5p and upregulation of NEDD4-binding protein 2-like 1 gene (N4BP2L1) in BC tissues compared to controls using quantitative real-time polymerase chain reaction and western blot analysis, respectively. Reduced miR-150-5p expression correlated with advanced tumor stage and lymph node metastasis, while increased N4BP2L1 levels were associated with larger tumor size by the Chi-square test. Functionally, miR-150-5p exerted significant inhibitory effects on BC cell proliferation, migration, inducing G0/G1 phase arrest, and apoptosis. We confirmed N4BP2L1 as a direct target of miR-150-5p in BC cells using luciferase reporter assay. Crucially, N4BP2L1 knockdown mimicked, while overexpression counteracted the inhibitory impacts of miR-150-5p on BC cell proliferation, migration, and invasion. In addition, N4BP2L1 overexpression reversed miR-150-5p-induced alterations in CDK4, Cyclin D1, Bcl-2, PCNA, Ki-67, N-cadherin, Bad, and E-cadherin levels in BC cells. Based on these results, it can be inferred that the miR-150-5p/N4BP2L1 axis might constitute a promising candidate for therapeutic targeting in the treatment of BC.

Molecular dynamics articulated multilevel virtual screening protocol to discover novel dual PPAR α/γ agonists for anti-diabetic and metabolic applications

PPARα and PPARγ are isoforms of the nuclear receptor superfamily which regulate glucose and lipid metabolism. Activation of PPARα and PPARγ receptors by exogenous ligands could transactivate the expression of PPARα and PPARγ-dependent genes, and thereby, metabolic pathways get triggered, which are helpful to ameliorate treatment for the type 2 diabetes mellitus, and related metabolic complications. Herein, by understanding the structural requirements for ligands to activate PPARα and PPARγ proteins, we developed a multilevel in silico-based virtual screening protocol to identify novel chemical scaffolds and further design and synthesize two distinct series of glitazone derivatives with advantages over the classical PPARα and PPARγ agonists. Moreover, the synthesized compounds were biologically evaluated for PPARα and PPARγ transactivation potency from nuclear extracts of 3T3-L1 cell. Furthermore, glucose uptake assay on L6 cells confirmed the potency of the synthesized compounds toward glucose regulation. Percentage lipid-lowering potency was also assessed through triglyceride estimate from 3T3-L1 cell extracts. Results suggested the ligand binding mode was in orthosteric fashion as similar to classical agonists. Thus molecular docking and molecular dynamics (MD) simulation experiments were executed to validate our hypothesis on mode of ligands binding and protein complex stability. Altogether, the present study developed a newer protocol for virtual screening and enables to design of novel glitazones for activation of PPARα and PPARγ-mediated pathways. Accordingly, present approach will offer benefit as a therapeutic strategy against type 2 diabetes mellitus and associated metabolic complications.

Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers

Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC. Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort). A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups. Results: A multi-omics signature including genes, micro RNA's and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (OS) data demonstrated that patients with differential expression levels of 8 genes and 4 micro RNA's showed a statistically significant decrease in OS. We also found an independent prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients. Conclusion: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice.

KBTBD11, encoding a novel PPARγ target gene, is involved in NFATc1 proteolysis by interacting with HSC70 and HSP60

We previously revealed that Kbtbd11 mRNA levels increase during 3T3-L1 differentiation and Kbtbd11 knockdown suppresses whereas its overexpression promotes adipogenesis. However, how Kbtbd11 mRNA is regulated during adipocyte differentiation and how the KBTBD11 protein functions in adipocytes remain elusive. This study aimed to examine the transcriptional regulatory mechanism of Kbtbd11 during adipocyte differentiation, KBTBD11-interacting protein functions, and elucidate the role of KBTBD11 in adipocytes. First, we identified the PPRE consensus sequences in the Kbtbd11 exon 1- and intron 1-containing region and demonstrated that PPARγ acts on this region to regulate Kbtbd11 expression. Next, we purified the KBTBD11 protein complex from 3T3-L1 adipocytes and identified heat shock proteins HSC70 and HSP60 as novel KBTBD11-interacting proteins. HSC70 and HSP60 inhibition increased KBTBD11 protein levels that promoted NFATc1 ubiquitination. These data suggest that HSC70 and HSP60 are involved in KBTBD11 stabilization and are responsible for NFATc1 regulation on the protein level. In summary, this study describes first the protein regulatory mechanism of NFATc1 through the HSC70/HSP60-KBTBD11 interaction that could provide a potential new target for the differentiation and proliferation of various cells, including adipocytes and tumors.

DNA Methylation Mediates the Association Between Individual and Neighborhood Social Disadvantage and Cardiovascular Risk Factors

Low socioeconomic status (SES) and living in a disadvantaged neighborhood are associated with poor cardiovascular health. Multiple lines of evidence have linked DNA methylation to both cardiovascular risk factors and social disadvantage indicators. However, limited research has investigated the role of DNA methylation in mediating the associations of individual- and neighborhood-level disadvantage with multiple cardiovascular risk factors in large, multi-ethnic, population-based cohorts. We examined whether disadvantage at the individual level (childhood and adult SES) and neighborhood level (summary neighborhood SES as assessed by Census data and social environment as assessed by perceptions of aesthetic quality, safety, and social cohesion) were associated with 11 cardiovascular risk factors including measures of obesity, diabetes, lipids, and hypertension in 1,154 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). For significant associations, we conducted epigenome-wide mediation analysis to identify methylation sites mediating the relationship between individual/neighborhood disadvantage and cardiovascular risk factors using the JT-Comp method that assesses sparse mediation effects under a composite null hypothesis. In models adjusting for age, sex, race/ethnicity, smoking, medication use, and genetic principal components of ancestry, epigenetic mediation was detected for the associations of adult SES with body mass index (BMI), insulin, and high-density lipoprotein cholesterol (HDL-C), as well as for the association between neighborhood socioeconomic disadvantage and HDL-C at FDR q < 0.05. The 410 CpG mediators identified for the SES-BMI association were enriched for CpGs associated with gene expression (expression quantitative trait methylation loci, or eQTMs), and corresponding genes were enriched in antigen processing and presentation pathways. For cardiovascular risk factors other than BMI, most of the epigenetic mediators lost significance after controlling for BMI. However, 43 methylation sites showed evidence of mediating the neighborhood socioeconomic disadvantage and HDL-C association after BMI adjustment. The identified mediators were enriched for eQTMs, and corresponding genes were enriched in inflammatory and apoptotic pathways. Our findings support the hypothesis that DNA methylation acts as a mediator between individual- and neighborhood-level disadvantage and cardiovascular risk factors, and shed light on the potential underlying epigenetic pathways. Future studies are needed to fully elucidate the biological mechanisms that link social disadvantage to poor cardiovascular health.