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A Proteomics-Based Approach Reveals Differential Regulation of Urine Proteins between Metabolically Healthy and Unhealthy Obese Patients.

ABSTRACT: Metabolic dysfunction associated with obesity threatens to inundate health care resources by increasing the incidences of obesity-related diseases. The aim of the present study was to investigate the changes in the urinary proteome of 18 individuals classified into metabolically healthy obese (MHO) and metabolically unhealthy obese (MUHO) patients. Proteome analysis was performed using the two-dimensional difference in gel electrophoresis (2D-DIGE) coupled with mass spectrometry (MS). Upon analysis, a total of 54 proteins were found to be affected with ?1.5-fold change (ANOVA, p ? 0.05), of which 44 proteins were upregulated and 10 proteins were downregulated. These differentially abundant proteins were related to nuclear factor ?B (NF-?B) and p38 mitogen-activated protein (MAP) kinase pathways and were involved in cellular compromise, inflammatory response, and cancer. Proteins involved in inflammation (fibrinogen alpha (FIBA), serotransferrin (TRFE, and kininogen-1 (KNG1)) and insulin resistance (ADP-ribosylation factor (ARF)-like protein 15 (ARL15) and retinol-binding protein 4 (RET4)) were found to be significantly increased in the urine samples of MUHO compared to MHO patients. Investigating the effects of obesity on urinary proteins can help in developing efficient diagnostic procedures for early detection and prevention of obesity-related complications.

SUBMITTER: Benabdelkamel H

PROVIDER: S-EPMC6801506 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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A Proteomics-Based Approach Reveals Differential Regulation of Urine Proteins between Metabolically Healthy and Unhealthy Obese Patients.

Benabdelkamel Hicham H Masood Afshan A Okla Meshail M Al-Naami Mohammed Y MY Alfadda Assim A AA

International journal of molecular sciences 20191003 19

Metabolic dysfunction associated with obesity threatens to inundate health care resources by increasing the incidences of obesity-related diseases. The aim of the present study was to investigate the changes in the urinary proteome of 18 individuals classified into metabolically healthy obese (MHO) and metabolically unhealthy obese (MUHO) patients. Proteome analysis was performed using the two-dimensional difference in gel electrophoresis (2D-DIGE) coupled with mass spectrometry (MS). Upon analy ...[more]

PMID: 31623319

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Project description:BackgroundObesity is associated with severe co-morbidities such as type 2 diabetes and nonalcoholic steatohepatitis. However, studies have shown that 10-25 percent of the severely obese individuals are metabolically healthy. To date, the identification of genetic factors underlying the metabolically healthy obese (MHO) state is limited. Systems genetics approaches have led to the identification of genes and pathways in complex diseases. Here, we have used such approaches across tissues to detect genes and pathways involved in obesity-induced disease development.MethodsExpression data of 60 severely obese individuals was accessible, of which 28 individuals were MHO and 32 were metabolically unhealthy obese (MUO). A whole genome expression profile of four tissues was available: liver, muscle, subcutaneous adipose tissue and visceral adipose tissue. Using insulin-related genes, we used the weighted gene co-expression network analysis (WGCNA) method to build within- and inter-tissue gene networks. We identified genes that were differentially connected between MHO and MUO individuals, which were further investigated by homing in on the modules they were active in. To identify potentially causal genes, we integrated genomic and transcriptomic data using an eQTL mapping approach.ResultsBoth IL-6 and IL1B were identified as highly differentially co-expressed genes across tissues between MHO and MUO individuals, showing their potential role in obesity-induced disease development. WGCNA showed that those genes were clustering together within tissues, and further analysis showed different co-expression patterns between MHO and MUO subnetworks. A potential causal role for metabolic differences under similar obesity state was detected for PTPRE, IL-6R and SLC6A5.ConclusionsWe used a novel integrative approach by integration of co-expression networks across tissues to elucidate genetic factors related to obesity-induced metabolic disease development. The identified genes and their interactions give more insight into the genetic architecture of obesity and the association with co-morbidities.

Project description:Background: In pediatric age the prevalence of obesity is high. Obese children who do not have other risk factors than excess weight have been defined as "metabolically healthy obese" (MHO). Aim: The aim of this study is to evaluate, in a population of obese children, the prevalence of the MHO and "metabolically unhealthy obese" (MUO) phenotype. Furthermore, we evaluated the distribution of Uric Acid, HOMA index and Waist-Height ratio (W-Hr) in the MHO and MUO sub-groups and the impact of these non-traditional risk factors on the probability to be MUO. Methods: In 1201 obese children and adolescents [54% males, age (±SD) 11.9 (±3.0) years] weight, height, waist circumference, systolic (SBP) and diastolic (DBP) blood pressure, pubertal status, glucose, insulin, HDL cholesterol, triglycerides and Uric Acid serum values were assessed. MUO phenotype was defined as the presence of at least one of the following risk factors: SBP or DBP ≥ 90th percentile, glycaemia ≥ 100 mg/dl, HDL cholesterol <40 mg/dl, triglycerides ≥100 mg/dl (children <10 years) or ≥130 mg/dl (children ≥10 years). A multivariate logistic regression analysis was used to estimate the association between MUO phenotype and non-traditional cardiovascular risk factors. Results: The prevalence of the MUO status was high (61%). MUO subjects were more often male, older and pubertal (p < 0.001). The levels of the three non-traditional risk factors were significantly higher in MUO children compared to MHO children (p < 0.001) and all of them were independent predictors of the fact of being MUO [OR 1.41 (95% CI 1.24-1.69); 1.15 (95% CI 1.06-1.23) and 1.03 (95% CI1.01-1.05) for Uric Acid, HOMA index and W-Hr, respectively]. About 15% of MHO subjects had serum Uric Acid, HOMA index and W-Hr values within the highest quartile of the study population. Conclusion: The prevalence of MUO subjects in a large pediatric population is high and serum Uric Acid, HOMA index and W-Hr values are independent predictors of the probability of being MUO. A non-negligible percentage of subjects MHO has high values of all three non-traditional risk factors.

Dataset Information

A Proteomics-Based Approach Reveals Differential Regulation of Urine Proteins between Metabolically Healthy and Unhealthy Obese Patients.

Publications

A Proteomics-Based Approach Reveals Differential Regulation of Urine Proteins between Metabolically Healthy and Unhealthy Obese Patients.

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