Central metabolism of functionally heterogeneous mesenchymal stromal cells.

Metabolism and mitochondrial biology have gained a prominent role as determinants of stem cell fate and function. In the context of regenerative medicine, innovative parameters predictive of therapeutic efficacy could be drawn from the association of metabolic or mitochondrial parameters to different degrees of stemness and differentiation potentials. Herein, this possibility was addressed in human mesenchymal stromal/stem cells (hMSC) previously shown to differ in lifespan and telomere length. First, these hMSC were shown to possess significantly distinct proliferation rate, senescence status and differentiation capacity. More potential hMSC were associated to higher mitochondrial (mt) DNA copy number and lower mtDNA methylation. In addition, they showed higher expression levels of oxidative phosphorylation subunits. Consistently, they exhibited higher coupled oxygen consumption rate and lower transcription of glycolysis-related genes, glucose consumption and lactate production. All these data pointed at oxidative phosphorylation-based central metabolism as a feature of higher stemness-associated hMSC phenotypes. Consistently, reduction of mitochondrial activity by complex I and III inhibitors in higher stemness-associated hMSC triggered senescence. Finally, functionally higher stemness-associated hMSC showed metabolic plasticity when challenged by glucose or glutamine shortage, which mimic bioenergetics switches that hMSC must undergo after transplantation or during self-renewal and differentiation. Altogether, these results hint at metabolic and mitochondrial parameters that could be implemented to identify stem cells endowed with superior growth and differentiation potential.

Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: a correlative MILD trial study.

PURPOSE: Recent screening trial results indicate that low-dose computed tomography (LDCT) reduces lung cancer mortality in high-risk patients. However, high false-positive rates, costs, and potential harms highlight the need for complementary biomarkers. The diagnostic performance of a noninvasive plasma microRNA signature classifier (MSC) was retrospectively evaluated in samples prospectively collected from smokers within the randomized Multicenter Italian Lung Detection (MILD) trial. PATIENTS AND METHODS: Plasma samples from 939 participants, including 69 patients with lung cancer and 870 disease-free individuals (n = 652, LDCT arm; n = 287, observation arm) were analyzed by using a quantitative reverse transcriptase polymerase chain reaction-based assay for MSC. Diagnostic performance of MSC was evaluated in a blinded validation study that used prespecified risk groups. RESULTS: The diagnostic performance of MSC for lung cancer detection was 87% for sensitivity and 81% for specificity across both arms, and 88% and 80%, respectively, in the LDCT arm. For all patients, MSC had a negative predictive value of 99% and 99.86% for detection and death as a result of disease, respectively. LDCT had sensitivity of 79% and specificity of 81% with a false-positive rate of 19.4%. Diagnostic performance of MSC was confirmed by time dependency analysis. Combination of both MSC and LDCT resulted in a five-fold reduction of LDCT false-positive rate to 3.7%. MSC risk groups were significantly associated with survival (χ1(2) = 49.53; P < .001). CONCLUSION: This large validation study indicates that MSC has predictive, diagnostic, and prognostic value and could reduce the false-positive rate of LDCT, thus improving the efficacy of lung cancer screening.