Diet-resistant obesity is characterized by a distinct plasma proteomic signature and impaired muscle fiber metabolism

Abstract

Background/Objectives:Inter-individual variability in weight loss during obesity treatment is complex and poorly understood. Here we use whole body and tissue approaches to investigate fuel oxidation characteristics in skeletal muscle fibers, cells and distinct circulating protein biomarkers before and after a high fat meal (HFM) challenge in those who lost the most (obese diet-sensitive; ODS) vs the least (obese diet-resistant; ODR) amount of weight in a highly controlled weight management program.Subjects/Methods:In 20 weight stable-matched ODS and ODR women who previously completed a standardized clinical weight loss program, we analyzed whole-body energetics and metabolic parameters in vastus lateralis biopsies and plasma samples that were obtained in the fasting state and 6 h after a defined HFM, equivalent to 35% of total daily energy requirements.Results:At baseline (fasting) and post-HFM, muscle fatty acid oxidation and maximal oxidative phosphorylation were significantly greater in ODS vs ODR, as was reactive oxygen species emission. Plasma proteomics of 1130 proteins pre and 1, 2, 5 and 6 h after the HFM demonstrated distinct group and interaction differences. Group differences identified S-formyl glutathione hydratase, heat shock 70 kDA protein 1A/B (HSP72), and eukaryotic translation initiation factor 5 (eIF5) to be higher in ODS vs ODR. Group-time differences included aryl hydrocarbon interacting protein (AIP), peptidylpropyl isomerase D (PPID) and tyrosine protein-kinase Fgr, which increased in ODR vs ODS over time. HSP72 levels correlated with muscle oxidation and citrate synthase activity. These proteins circulate in exosomes; exosomes isolated from ODS plasma increased resting, leak and maximal respiration rates in C2C12 myotubes by 58%, 21% and 51%, respectively, vs those isolated from ODR plasma.Conclusions:Findings demonstrate distinct muscle metabolism and plasma proteomics in fasting and post-HFM states corresponding in diet-sensitive vs diet-resistant obese women.

Mitochondrial adaptation in human mesenchymal stem cells following ionizing radiation

“…Following gel electrophoresis, we blotted membranes with antibodies against CI–V subunits. Interestingly, upon qualitative assessment, compared with our previously published BN-PAGE results (13, 37, 38), we found that CI- and CIII-containing subunits were often entirely associated with ETC supercomplexes in these cells ( Fig. 2 A ).…”

“…S3B-D). Similar to some other mammalian cells and tissues (13,22,37,38), MSCs contained very little to no CV dimer under these experimental conditions.…”

Plasma protein patterns as comprehensive indicators of health

“…We therefore hypothesized that protein scanning could deliver comprehensive individualized health assessments-but with single-source convenience and greater usability in typical medical practice. While this approach using modified aptamers has gained provenance for discovering and understanding gene-protein interactions 1 , drug pharmacology 11 , biological control systems 2 , biomarkers in individual diseases and risks [3][4][5][6][7][8] , aging 9 and obesity 10 , it has not been evaluated previously as a potentially holistic, quantitative health assessment for simultaneous evaluation of multiple health issues.…”

Proteomic profiles before and during weight loss: Results from randomized trial of dietary intervention

“…In contrast, the present study is based on a secondary analysis of what was primarily a weight loss diet study involving 609 generally healthy overweight or obese adults without diabetes, randomly assigned to healthy low-fat (HLF) or healthy low-carbohydrate (HLC) diet, in which we analyzed 263 plasma proteins using the Olink Proteomics arrays. Weight loss response in clinical obesity treatment programs is highly variable and we hypothesized that this heterogeneity can be characterized by a distinct plasma proteomic signature 16 . This may open up avenues for incorporation of protein biomarker measurements into weight loss prediction models to help individuals choose a diet for optimal weight loss.…”