scholarly journals Diabetes Induced Renal Complications by Leukocyte Activation of Nuclear Factor κ-B and its Regulated Genes Expression

2020 ◽  
Author(s):  
Noura Darwish ◽  
Yousif Elnahas ◽  
Fatmah AlQahtany
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Structural Changes ◽  
Kidney Injury ◽  
Leukocyte Activation ◽  
Disease Biomarkers ◽  
Genes Expression ◽  
Nuclear Factor Κ B ◽  
Adhesion Process

Type 2 diabetes mellitus (T2D) is a metabolic disorder characterized by inappropriate insulin function. Despite wide progress in genome studies, defects in gene expression for diabetes prognosis still incompletely identified. Prolonged hyperglycemia activates NF-κB, which is a main player in vascular dysfunctions of diabetes. Activated NF-κB, triggers expression of various genes that promote inflammation and cell adhesion process. Alteration of pro-inflammatory and profibrotic gene expression contribute to the irreversible functional and structural changes in the kidney resulting in diabetic nephropathy (DN). To identify the effect of some important NF-κB related genes on mediation of DN progression, we divided our candidate genes on the basis of their function exerted in bloodstream into three categories (Proinflammatory; NF-κB, IL-1B, IL-6, TNF-α and VEGF); (Profibrotic; FN, ICAM-1, VCAM-1) and (Proliferative; MAPK-1 and EGF). We analyzed their expression profile in leukocytes of patients and explored their correlation to diabetic kidney injury features. Our data revealed the overexpression of both proinflammatory and profibrotic genes in DN group when compared to T2D group and were associated positively with each other in DN group indicating their possible role in DN progression. In DN patients, increased expression of proinflammatory genes correlated positively with glycemic control and inflammatory markers indicating their role in DN progression. Our data revealed that the persistent activation NF-κB and its related genes observed in hyperglycemia might contribute to DN progression and might be a good diagnostic and therapeutic target for DN progression. Large-scale studies are needed to evaluate the potential of these molecules to serve as disease biomarkers.

A gene expression signature for insulin resistance

2011 ◽  
Vol 43 (3) ◽  
pp. 110-120 ◽  
Author(s):  
Nicky Konstantopoulos ◽  
Victoria C. Foletta ◽  
David H. Segal ◽  
Katherine A. Shields ◽  
Andrew Sanigorski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Insulin Resistance ◽  
Type 2 Diabetes ◽  
Large Scale ◽  
Gene Expression Signature ◽  
Channel Blockers ◽  
Expression Signature ◽  
Insulin Resistant ◽  
Novel Strategy

Insulin resistance is a heterogeneous disorder caused by a range of genetic and environmental factors, and we hypothesize that its etiology varies considerably between individuals. This heterogeneity provides significant challenges to the development of effective therapeutic regimes for long-term management of type 2 diabetes. We describe a novel strategy, using large-scale gene expression profiling, to develop a gene expression signature (GES) that reflects the overall state of insulin resistance in cells and patients. The GES was developed from 3T3-L1 adipocytes that were made “insulin resistant” by treatment with tumor necrosis factor-α (TNF-α) and then reversed with aspirin and troglitazone (“resensitized”). The GES consisted of five genes whose expression levels best discriminated between the insulin-resistant and insulin-resensitized states. We then used this GES to screen a compound library for agents that affected the GES genes in 3T3-L1 adipocytes in a way that most closely resembled the changes seen when insulin resistance was successfully reversed with aspirin and troglitazone. This screen identified both known and new insulin-sensitizing compounds including nonsteroidal anti-inflammatory agents, β-adrenergic antagonists, β-lactams, and sodium channel blockers. We tested the biological relevance of this GES in participants in the San Antonio Family Heart Study ( n = 1,240) and showed that patients with the lowest GES scores were more insulin resistant (according to HOMA_IR and fasting plasma insulin levels; P < 0.001). These findings show that GES technology can be used for both the discovery of insulin-sensitizing compounds and the characterization of patients into subtypes of insulin resistance according to GES scores, opening the possibility of developing a personalized medicine approach to type 2 diabetes.

scholarly journals Large-scale gene expression profiling reveals distinct type 2 inflammatory patterns in chronic rhinosinusitis subtypes

2017 ◽  
Vol 139 (3) ◽  
pp. 1061-1064.e4 ◽  
Author(s):  
Matthew A. Tyler ◽  
Chris B. Russell ◽  
Dirk E. Smith ◽  
James B. Rottman ◽  
Caroline J. Padro Dietz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Chronic Rhinosinusitis ◽  
Expression Profiling ◽  
Large Scale ◽  
Distinct Type

scholarly journals Etiology-Specific Analysis of Hepatocellular Carcinoma Transcriptome Reveals Genetic Dysregulation in Pathways Implicated in Immunotherapy Efficacy

Cancers ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1273
Author(s):  
Wei Tse Li ◽  
Angela E. Zou ◽  
Christine O. Honda ◽  
Hao Zheng ◽  
Xiao Qi Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Large Scale ◽  
The Cancer Genome Atlas ◽  
Advanced Hepatocellular Carcinoma ◽  
Sequencing Data ◽  
Genes Expression ◽  
Specific Analysis ◽  
Cancer Genome Atlas

Immunotherapy has emerged in recent years as arguably the most effective treatment for advanced hepatocellular carcinoma (HCC), but the failure of a large percentage of patients to respond to immunotherapy remains as the ultimate obstacle to successful treatment. Etiology-associated dysregulation of immune-associated (IA) genes may be central to the development of this differential clinical response. We identified immune-associated genes potentially dysregulated by alcohol or viral hepatitis B in HCC and validated alcohol-induced dysregulations in vitro while using large-scale RNA-sequencing data from The Cancer Genome Atlas (TCGA). Thirty-four clinically relevant dysregulated IA genes were identified. We profiled the correlation of all genomic alterations in HCC patients to IA gene expression while using the information theory-based algorithm REVEALER to investigate the molecular mechanism for their dysregulation and explore the possibility of genome-based patient stratification. We also studied gene expression regulators and identified multiple microRNAs that were implicated in HCC pathogenesis that can potentially regulate these IA genes’ expression. Our study identified potential key pathways, including the IL-7 signaling pathway and TNFRSF4 (OX40)- NF-κB pathway, to target in immunotherapy treatments and presents microRNAs as promising therapeutic targets for dysregulated IA genes because of their extensive regulatory roles in the cancer immune landscape.

scholarly journals Creation of a gene expression portrait of depression and its application for identifying potential treatments

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Stephen C. Gammie
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Brain Temperature ◽  
Expression Patterns ◽  
Drug Repurposing ◽  
Mental Health Disorder ◽  
Signature Matching ◽  
Genes Expression ◽  
Complex Landscape ◽  
New Treatments

AbstractDepression is a complex mental health disorder and the goal here was to identify a consistent underlying portrait of expression that ranks all genes from most to least dysregulated and indicates direction of change relative to controls. Using large-scale neural gene expression depression datasets, a combined portrait (for men and women) was created along with one for men and one for women only. The depressed brain was characterized by a “hypo” state, that included downregulation of activity-related genes, including EGR1, FOS, and ARC, and indications of a lower brain temperature and sleep-like state. MAP kinase and BDNF pathways were enriched with overlapping genes. Expression patterns suggested decreased signaling for GABA and for neuropeptides, CRH, SST, and CCK. GWAS depression genes were among depression portrait genes and common genes of interest included SPRY2 and PSEN2. The portraits were used with the drug repurposing approach of signature matching to identify treatments that could reverse depression gene expression patterns. Exercise was identified as the top treatment for depression for the combined and male portraits. Other non-traditional treatments that scored well were: curcumin, creatine, and albiflorin. Fluoxetine scored best among typical antidepressants. The creation of the portraits of depression provides new insights into the complex landscape of depression and a novel platform for evaluating and identifying potential new treatments.

Large-Scale Cross-Sectional Study of the Problem Areas in Diabetes Survey among Japanese Patients with Type 2 Diabetes Mellitus

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 2393-PUB
Author(s):  
KENICHIRO TAKAHASHI ◽  
MINORI SHINODA ◽  
RIKA SAKAMOTO ◽  
JUN SUZUKI ◽  
TADASHI YAMAKAWA ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Large Scale ◽  
Japanese Patients ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Diabetes Survey

Recreational Running Training Favourably Affects Antioxidants Gene Expression in Type 2 Diabetes Patients

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 52-LB
Author(s):  
MAYSA SOUSA ◽  
ARITANIA SANTOS ◽  
MARIA ELIZABETH R. SILVA
Keyword(s):  
Gene Expression ◽  
Type 2 Diabetes ◽  
Running Training ◽  
Diabetes Patients

Molecular Epidemiology and Biomarkers in Etiologic Cancer Research: The New in Light of the Old

2020 ◽  
Author(s):  
Lungwani Muungo
Keyword(s):  
Gene Expression ◽  
Cancer Research ◽  
Molecular Epidemiology ◽  
Exposure Assessment ◽  
Large Scale ◽  
First Generation ◽  
Cancer Epidemiology ◽  
Policy Changes ◽  
The Past ◽  
New Generation

The purpose of this review is to evaluate progress inmolecular epidemiology over the past 24 years in canceretiology and prevention to draw lessons for futureresearch incorporating the new generation of biomarkers.Molecular epidemiology was introduced inthe study of cancer in the early 1980s, with theexpectation that it would help overcome some majorlimitations of epidemiology and facilitate cancerprevention. The expectation was that biomarkerswould improve exposure assessment, document earlychanges preceding disease, and identify subgroupsin the population with greater susceptibility to cancer,thereby increasing the ability of epidemiologic studiesto identify causes and elucidate mechanisms incarcinogenesis. The first generation of biomarkers hasindeed contributed to our understanding of riskandsusceptibility related largely to genotoxic carcinogens.Consequently, interventions and policy changes havebeen mounted to reduce riskfrom several importantenvironmental carcinogens. Several new and promisingbiomarkers are now becoming available for epidemiologicstudies, thanks to the development of highthroughputtechnologies and theoretical advances inbiology. These include toxicogenomics, alterations ingene methylation and gene expression, proteomics, andmetabonomics, which allow large-scale studies, includingdiscovery-oriented as well as hypothesis-testinginvestigations. However, most of these newer biomarkershave not been adequately validated, and theirrole in the causal paradigm is not clear. There is a needfor their systematic validation using principles andcriteria established over the past several decades inmolecular cancer epidemiology.

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