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BACKGROUND: Diabetic patients have a greater incidence of adhesive capsulitis (AC) and a more protracted disease course than patients with idiopathic AC. The purpose of this study was to compare gene expression differences between AC with diabetes mellitus and AC without diabetes mellitus. METHODS: Shoulder capsule samples were prospectively obtained from diabetic or nondiabetic patients who presented with shoulder dysfunction and underwent arthroscopy (N = 16). Shoulder samples of AC with and without diabetes (n = 8) were compared with normal shoulder samples with and without diabetes as the control group (n = 8). Shoulder capsule samples were subjected to whole-transcriptome RNA sequencing, and differential expression was analyzed with EdgeR. Only genes with a false discovery rate < 5% were included for further functional enrichment analysis. RESULTS: The sample population had a mean age of 47 years (range, 24–62 years), and the mean hemoglobin A(1c) level for nondiabetic and diabetic patients was 5.18% and 8.71%, respectively. RNA-sequencing analysis revealed that 66 genes were differentially expressed between diabetic patients and nondiabetic patients with AC whereas only 3 genes were differentially expressed when control patients with and without diabetes were compared. Furthermore, 286 genes were differentially expressed in idiopathic AC patients, and 61 genes were differentially expressed in diabetic AC patients. On gene clustering analysis, idiopathic AC was enriched with multiple structural and muscle-related pathways, such as muscle filament sliding, whereas diabetic AC included a greater number of hormonal and inflammatory signaling pathways, such as cellular response to corticotropin-releasing factor. CONCLUSIONS: Whole-transcriptome expression profiles demonstrate a fundamentally different underlying pathophysiology when comparing diabetic AC with idiopathic AC, suggesting that these conditions are distinct clinical entities. The new genes expressed explain the differences in the disease course and suggest new therapeutic targets that may lead to different treatment paradigms in these 2 subsets. LEVEL OF EVIDENCE: Basic Science Study; Molecular Biology

Background New market trends and new challenges in meat quality lead us to improve animal handling. During the animal rearing, many stressful situations could impact on the biochemical process, affecting the transformation of muscle into meat. Dealing with this problem is complicated since stress is a very complex disorder of animals, depending on too many intrinsic and extrinsic factors. Scope and approach To date, the most common stress indicators used were cortisol, creatine kinase and lactate in animal blood. However, currently there is an advanced development of proteomic techniques which enables us to obtain protein stress biomarkers, representing a powerful tool for food industry. In the literature, a large number of biomarkers could be identified in cattle, pigs or chicken linked to stress conditions. An integrative approach, based on functional enrichment analysis of biological processes, showed that the most relevant Gene Ontology term was “muscle filament sliding” for cattle and pig under stress conditions. Key findings and conclusions From a proteomic point of view, a list of biomarkers was provided to control animal welfare at industrial level. Heat stress during the rearing and pre-slaughter management turned out to be one of the most studied factors in pork, beef and chicken. This stressor provoked significant differences in structural contractile skeletal proteins, altering meat quality.

To elucidate the time-resolved molecular events underlying the LV remodeling (LVR) process, we developed a large-scale network model that integrates the 24 molecular variables (plasma proteins and non-coding RNAs) collected in the REVE-2 study at four time points (baseline, 1month, 3months and 1year) after MI. The REVE-2 network model was built by extending the set of REVE-2 variables with their mechanistic context based on known molecular interactions (1310 nodes and 8639 edges). Changes in the molecular variables between the group of patients with high LVR (>20%) and low LVR (