Comparative Proteomic Analysis of Visceral Adipose Tissue in Morbidly Obese and Normal Weight Chinese Women

Tissue-specific roles of mitochondrial unfolded protein response during obesity

Obesity is a worldwide multifactorial disease caused by an imbalance in energy metabolism, increasing adiposity, weight gain, and promoting related diseases such as diabetes, cardiovascular diseases, neurodegeneration, and cancer. Recent findings have reported that metabolic stress related to obesity induces a mitochondrial stress response called mitochondrial unfolded protein response (UPRmt), a quality control pathway that occurs in a nuclear DNA-mitochondria crosstalk, causing transduction of chaperones and proteases under stress conditions. The duality of UPRmt signaling, with both beneficial and detrimental effects, acts in different contexts depending on the tissue, cell type, and physiological states, affecting the mitochondrial function and efficiency and the metabolism homeostasis during obesity, which remains not fully clarified. Therefore, this review discusses the most recent findings regarding UPRmt signaling during obesity, bringing an overview of UPRmt across different metabolic tissues.

A Systematic Review of Proteomics in Obesity: Unpacking the Molecular Puzzle

PURPOSE OF REVIEW

The present study aims to review the existing literature to identify pathophysiological proteins in obesity by conducting a systematic review of proteomics studies. Proteomics may reveal the mechanisms of obesity development and clarify the links between obesity and related diseases, improving our comprehension of obesity and its clinical implications.

RECENT FINDINGS

Most of the molecular events implicated in obesity development remain incomplete. Proteomics stands as a powerful tool for elucidating the intricate interactions among proteins in the context of obesity. This methodology has the potential to identify proteins involved in pathological processes and to evaluate changes in protein abundance during obesity development, contributing to the identification of early disease predisposition, monitoring the effectiveness of interventions and improving disease management overall. Despite many non-targeted proteomic studies exploring obesity, a comprehensive and up-to-date systematic review of the molecular events implicated in obesity development is lacking. The lack of such a review presents a significant challenge for researchers trying to interpret the existing literature. This systematic review was conducted following the PRISMA guidelines and included sixteen human proteomic studies, each of which delineated proteins exhibiting significant alterations in obesity. A total of 41 proteins were reported to be altered in obesity by at least two or more studies. These proteins were involved in metabolic pathways, oxidative stress responses, inflammatory processes, protein folding, coagulation, as well as structure/cytoskeleton. Many of the identified proteomic biomarkers of obesity have also been reported to be dysregulated in obesity-related disease. Among them, seven proteins, which belong to metabolic pathways (aldehyde dehydrogenase and apolipoprotein A1), the chaperone family (albumin, heat shock protein beta 1, protein disulfide-isomerase A3) and oxidative stress and inflammation proteins (catalase and complement C3), could potentially serve as biomarkers for the progression of obesity and the development of comorbidities, contributing to personalized medicine in the field of obesity. Our systematic review in proteomics represents a substantial step forward in unravelling the complexities of protein alterations associated with obesity. It provides valuable insights into the pathophysiological mechanisms underlying obesity, thereby opening avenues for the discovery of potential biomarkers and the development of personalized medicine in obesity.

Investigating potential biomarkers of acute pancreatitis in patients with a BMI>30 using Mendelian randomization and transcriptomic analysis

BACKGROUND

Acute pancreatitis (AP) has become a significant global health concern, and a high body mass index (BMI) has been identified as a key risk factor exacerbating this condition. Within this context, lipid metabolism assumes a critical role. The complex relationship between elevated BMI and AP, mediated by lipid metabolism, markedly increases the risk of complications and mortality. This study aimed to accurately define the correlation between BMI and AP, incorporating a comprehensive analysis of the interactions between individuals with high BMI and AP.

METHODS

Mendelian randomization (MR) analysis was first applied to determine the causal relationship between BMI and the risk of AP. Subsequently, three microarray datasets were obtained from the GEO database. This was followed by an analysis of differentially expressed genes and the application of weighted gene coexpression network analysis (WGCNA) to identify key modular genes associated with AP and elevated BMI. Functional enrichment analysis was then performed to shed light on disease pathogenesis. To identify the most informative genes, machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were employed. Subsequent analysis focused on the colocalization of the Quantitative Trait Loci (eQTL) data associated with the selected genes and Genome-Wide Association Studies (GWAS) data related to the disease. Preliminary verification of gene expression trends was conducted using external GEO datasets. Ultimately, the diagnostic potential of these genes was further confirmed through the development of an AP model in mice with a high BMI.

RESULTS

A total of 21 intersecting genes related to BMI>30, AP, and lipid metabolism were identified from the datasets. These genes were primarily enriched in pathways related to cytosolic DNA sensing, cytokine‒cytokine receptor interactions, and various immune and inflammatory responses. Next, three machine learning techniques were utilized to identify HADH as the most prevalent diagnostic gene. Colocalization analysis revealed that HADH significantly influenced the risk factors associated with BMI and AP. Furthermore, the trend in HADH expression within the external validation dataset aligned with the trend in the experimental data, thus providing a preliminary validation of the experimental findings.The changes in its expression were further validated using external datasets and quantitative real-time polymerase chain reaction (qPCR).

CONCLUSION

This study systematically identified HADH as a potential lipid metabolism-grounded biomarker for AP in patients with a BMI>30.

MicroRNA-129-5p inhibits C2C12 myogenesis and represses slow fiber gene expression in vitro

The miR-129 family is widely reported as tumor repressors, although their roles in skeletal muscle have not been fully investigated. Here, the function and mechanism of miR-129-5p in skeletal muscle, a member of the miR-129 family, were explored using C2C12 cell line. Our study showed that miR-129-5p was widely detected in mouse tissues, with the highest expression in skeletal muscle. Gain- and loss-of-function study showed that miR-129-5p could negatively regulate myogenic differentiation, indicated by reduced ratio of MyHC-positive myofibers and repressed expression of myogenic genes, such as MyoD, MyoG, and MyHC. Furthermore, miR-129-5p was more enriched in fast extensor digitorum longus (EDL) than in slow soleus (SOL). Enhanced miR-129-5p could significantly reduce the expression of mitochondrial cox family, together with that of MyHC I, and knockdown of miR-129-5p conversely increased the expression of cox genes and MyHC I. Mechanistically, miR-129-5p directly targeted the 3'-UTR of Mef2a, which was suppressed by miR-129-5p agomir at both mRNA and protein levels in C2C12 cells. Moreover, overexpression of Mef2a could rescue the inhibitory effects of miR-129-5p on the expression of myogenic factors and MyHC I. Collectively, our data revealed that miR-129-5p is a negative regulator of myogenic differentiation and slow fiber gene expression, thus affecting body metabolic homeostasis.

Common and Novel Markers for Measuring Inflammation and Oxidative Stress Ex Vivo in Research and Clinical Practice-Which to Use Regarding Disease Outcomes?

Many chronic conditions such as cancer, chronic obstructive pulmonary disease, type-2 diabetes, obesity, peripheral/coronary artery disease and auto-immune diseases are associated with low-grade inflammation. Closely related to inflammation is oxidative stress (OS), which can be either causal or secondary to inflammation. While a low level of OS is physiological, chronically increased OS is deleterious. Therefore, valid biomarkers of these signalling pathways may enable detection and following progression of OS/inflammation as well as to evaluate treatment efficacy. Such biomarkers should be stable and obtainable through non-invasive methods and their determination should be affordable and easy. The most frequently used inflammatory markers include acute-phase proteins, essentially CRP, serum amyloid A, fibrinogen and procalcitonin, and cytokines, predominantly TNFα, interleukins 1β, 6, 8, 10 and 12 and their receptors and IFNγ. Some cytokines appear to be disease-specific. Conversely, OS-being ubiquitous-and its biomarkers appear less disease or tissue-specific. These include lipid peroxidation products, e.g., F2-isoprostanes and malondialdehyde, DNA breakdown products (e.g., 8-OH-dG), protein adducts (e.g., carbonylated proteins), or antioxidant status. More novel markers include also -omics related ones, as well as non-invasive, questionnaire-based measures, such as the dietary inflammatory-index (DII), but their link to biological responses may be variable. Nevertheless, many of these markers have been clearly related to a number of diseases. However, their use in clinical practice is often limited, due to lacking analytical or clinical validation, or technical challenges. In this review, we strive to highlight frequently employed and useful markers of inflammation-related OS, including novel promising markers.