Distinct Metabolomic Profiling of Serum Samples from High-Fat-Diet-Induced Insulin-Resistant Mice.

High-fat-diet (HFD)-induced obesity is associated with an elevated risk of insulin resistance (IR), which may precede the onset of type 2 diabetes mellitus and associated metabolic complications. Being a heterogeneous metabolic condition, it is pertinent to understand the metabolites and metabolic pathways that are altered during the development and progression of IR toward T2DM. Serum samples were collected from C57BL/6J mice fed with HFD or chow diet (CD) for 16 weeks. Collected samples were analyzed by gas chromatography-tandem mass spectrometry (GC-MS/MS). Data on the identified raw metabolites were evaluated using a combination of univariate and multivariate statistical methods. Mice fed with HFD had glucose and insulin intolerance associated with impairment of insulin signaling in key metabolic tissues. From the GC-MS/MS analysis of serum samples, a total of 75 common annotated metabolites were identified between HFD- and CD-fed mice. In the t-test, 22 significantly altered metabolites were identified. Out of these, 16 metabolites were up-accumulated, whereas 6 metabolites were down-accumulated. Pathway analysis identified 4 significantly altered metabolic pathways. In particular, primary bile acid biosynthesis and linoleic acid metabolism were upregulated, whereas the TCA cycle and pentose and glucuronate interconversion were downregulated in HFD-fed mice in comparison to CD-fed mice. These results show the distinct metabolic profiles associated with the onset of IR that could provide promising metabolic biomarkers for diagnostic and clinical applications.

Emerging role of metabolomics for biomarker discovery in obstructive sleep apnea.

Obstructive sleep apnea (OSA) is characterized by the complete or partial blockage of the upper airway passage during sleep which causes repetitive breaks in sleep and may result in excessive daytime sleepiness. OSA has been linked to various metabolic disorders and chronic health conditions, such as obesity, diabetes, hypertension, and depression. Profiling of alterations in metabolites and their regulation in OSA has been hypothesized to be an effective approach for early diagnosis and prognosis of OSA. Several studies have characterized metabolic fingerprints associated with sleep disorders. There is a lack of understanding of metabolite contents and their alterations in OSA that may help to identify specific biomarkers. The information provided in this review will help update new methodologies and interventions of high throughput advanced molecular/metabolomics tools which may clarify the metabolic aspects and mechanisms for improved management and treatment of OSA.

Identification of metabolic fingerprints in severe obstructive sleep apnea using gas chromatography-Mass spectrometry.

Objective: Obstructive sleep apnea (OSA) is considered a major sleep-related breathing problem with an increasing prevalence rate. Retrospective studies have revealed the risk of various comorbidities associated with increased severity of OSA. This study aims to identify novel metabolic biomarkers associated with severe OSA. Methods: In total, 50 cases of OSA patients (49.74 ± 11.87 years) and 30 controls (39.20 ± 3.29 years) were included in the study. According to the polysomnography reports and questionnaire-based assessment, only patients with an apnea-hypopnea index (AHI >30 events/hour) exceeding the threshold representing severe OSA patients were considered for metabolite analysis. Plasma metabolites were analyzed using gas chromatography-mass spectrometry (GC-MS). Results: A total of 92 metabolites were identified in the OSA group compared with the control group after metabolic profiling. Metabolites and their correlated metabolic pathways were significantly altered in OSA patients with respect to controls. The fold-change analysis revealed markers of chronic kidney disease, cardiovascular risk, and oxidative stress-like indoxyl sulfate, 5-hydroxytryptamine, and 5-aminolevulenic acid, respectively, which were significantly upregulated in OSA patients. Conclusion: Identifying these metabolic signatures paves the way to monitor comorbid disease progression due to OSA. Results of this study suggest that blood plasma-based biomarkers may have the potential for disease management.

Metabolic reprograming confers tamoxifen resistance in breast cancer.

Breast cancer is the most common cancer among females and the leading cause of cancer-related deaths. Approximately 70 % of breast cancers are estrogen receptor (ER) positive. An ER antagonist such as tamoxifen is used as adjuvant therapy in ER-positive patients. The major problem with endocrine therapy is the emergence of acquired resistance in approximately 40 % of patients receiving tamoxifen. Metabolic alteration is one of the hallmarks of cancer cells. Rapidly proliferating cancer cells require increased nutritional support to fuel various functions such as proliferation, cell migration, and metastasis. Recent studies have established that the metabolic state of cancer cells influences their susceptibility to chemotherapeutic drugs and that cancer cells reprogram their metabolism to develop into resistant phenotypes. In this review, we discuss the major findings on metabolic pathway alterations in tamoxifen-resistant (TAMR) breast cancer and the molecular mechanisms known to regulate the expression and function of metabolic enzymes and the respective metabolite levels upon tamoxifen treatment. It is anticipated that this in-depth analysis of specific metabolic pathways in TAMR cancer might be exploited therapeutically.

Prognostic significance of autophagy related genes in estrogen receptor positive tamoxifen treated breast cancer.

Resistance to Tamoxifen constitutes a major therapeutic challenge in treating hormone sensitive breast cancer. The induction of autophagy has been shown to be involved as one of the mechanism responsible for Tamoxifen resistance. Autophagy related gene (ATG) members are the regulators and effectors of Macroautophagy process in the cellular systems. In this study, we evaluated the prognostic significance of ATGs in Tamoxifen treated breast cancer. The "Kaplan- Meier plotter" database was utilized to analyze the relevance and significance of ATGs mRNA expression to Relapse Free Survival in breast cancer patients. We used the data of patients who are Estrogen receptor positive and are treated with Tamoxifen. Hazard ratio and log-rank p-value were calculated using KM survival plots for various ATGs. Overexpressed ATG3, ATG 5, ATG 8B and PIK3R4 resulted in a poor prognosis. A gene signature of these ATGs predicts deteriorated RFS (p-value=8.3e-05 and HR=1.84 (1.35-2.51) and Distant Metastasis Free Survival (p value = 0.0027 and HR=2.03 (1.27-3.26). We report the distinct prognostic values of ATGs in patients of breast cancer treated with Tamoxifen. Thus, better understandings of the induction of autophagy pathway may potentially form the basis for use of autophagy inhibitors in the Tamoxifen treated breast cancer.