Proteomic study of skeletal muscle in obesity and type 2 diabetes: progress and potential

ABSTRACT

Introduction: Skeletal muscle is the major site of insulin-stimulated glucose uptake and imparts the beneficial effects of exercise, and hence is an important site of insulin resistance in obesity and type 2 diabetes (T2D). Despite extensive molecular biology-oriented research the molecular mechanisms underlying insulin resistance in skeletal muscle remain to be established.

Areas covered: The proteomic capabilities have greatly improved over the last decades. This review summarizes the technical challenges in skeletal muscle proteomics studies as well as the results of quantitative proteomic studies of skeletal muscle in relation to obesity, T2D, and exercise.

Expert commentary: Current available proteomic studies contribute to the view that insulin resistance in obesity and T2D is associated with increased glycolysis and reduced mitochondrial oxidative metabolism in skeletal muscle, and that the latter can be improved by exercise. Future proteomics studies should be designed to markedly intensify the identification of abnormalities in metabolic and signaling pathways in skeletal muscle of insulin-resistant individuals to increase the understanding of the pathogenesis of T2D, but more importantly to identify multiple novel targets of treatment of which at least some can be safely targeted by novel drugs to treat and prevent T2D and reduce risk of cardiovascular disease.     

Isobaric protein and peptide quantification: perspectives and issues

An important challenge for proteomics is the ability to compare protein levels across biological samples. Since their introduction, isotopic and isobaric peptide labeling have played an important role in relative quantitative comparisons of proteomes. One important drawback of most of the isotopic-labeling techniques is an increase in sample complexity. This problem was successfully addressed with the construction of isobaric labeling strategies, such as isobaric tag for relative and absolute quantification (iTRAQ), tandem mass tagging, the cleavable isobaric affinity tag, dimethylated leucines and isobaric peptide termini labeling. Furthermore, numerous applications for multiplexing using iTRAQ and tandem mass tagging have been reported.     

Deep and quantitative top-down proteomics in clinical and translational research

It has long been understood that it is proteins, expressed and post-translationally modified, that are the primary regulators of both the fate and the function of cells. The ability to measure differences in the expression of the constellation of unique protein forms (proteoforms) with complete molecular specificity has the potential to sharply improve the return on investment for mass spectrometry-based proteomics in translational research and clinical diagnostics.     

Epithelial cell adhesion molecule (EpCAM) is associated with prostate cancer metastasis and chemo/radioresistance via the PI3K/Akt/mTOR signaling pathway

Prostate cancer (CaP) is the second leading malignancy in men. The role of epithelial cell adhesion molecule (EpCAM), also known as CD326, in CaP progression and therapeutic resistance is still uncertain. Here, we aimed to investigate the roles of EpCAM in CaP metastasis and chemo/radioresistance. Expression of EpCAM in CaP cell lines and human CaP tissues was assessed using immunofluorescence and immunohistochemistry, respectively. EpCAM was knocked down (KD) in PC-3, DU145 and LNCaP-C4-2B cells using small interfering RNA (siRNA), and KD results were confirmed by confocal microscope, Western blotting and quantitative real time polymerase chain reaction (qRT-PCR). Cell growth was evaluated by proliferation and colony formation assays. The invasive potential was assessed using a matrigel chamber assay. Tumorigenesis potential was measured by a sphere formation assay. Chemo-/radiosensitivity were measured using a colony formation assay. Over-expression of EpCAM was found in primary CaP tissues and lymph node metastases including cancer cells and surrounding stromal cells. KD of EpCAM suppressed CaP proliferation and invasive ability, reduced sphere formation, enhanced chemo-/radiosensitivity, and down-regulated E-cadherin, p-Akt, p-mTOR, p-4EBP1 and p-S6K expression in CaP cells. Our findings suggest that EpCAM plays an important role in CaP proliferation, invasion, metastasis and chemo-/radioresistance associated with the activation of the PI3K/Akt/mTOR signaling pathway and is a novel therapeutic target to sensitize CaP cells to chemo-/radiotherapy.     

Computational model for autophagic vesicle dynamics in single cells

Macroautophagy (autophagy) is a cellular recycling program essential for homeostasis and survival during cytotoxic stress. This process, which has an emerging role in disease etiology and treatment, is executed in four stages through the coordinated action of more than 30 proteins. An effective strategy for studying complicated cellular processes, such as autophagy, involves the construction and analysis of mathematical or computational models. When developed and refined from experimental knowledge, these models can be used to interrogate signaling pathways, formulate novel hypotheses about systems, and make predictions about cell signaling changes induced by specific interventions. Here, we present the development of a computational model describing autophagic vesicle dynamics in a mammalian system. We used time-resolved, live-cell microscopy to measure the synthesis and turnover of autophagic vesicles in single cells. The stochastically simulated model was consistent with data acquired during conditions of both basal and chemically-induced autophagy. The model was tested by genetic modulation of autophagic machinery and found to accurately predict vesicle dynamics observed experimentally. Furthermore, the model generated an unforeseen prediction about vesicle size that is consistent with both published findings and our experimental observations. Taken together, this model is accurate and useful and can serve as the foundation for future efforts aimed at quantitative characterization of autophagy.     

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.