Remote Ischemic Conditioning Influences Mitochondrial Dynamics

Remote ischemic preconditioning (RIPC) has emerged as an attractive strategy to protect the heart against ischemia-reperfusion (I/R) injury. The mechanisms by which remote ischemic conditioning (RIC) is protective are to date unknown, yet a well-accepted theory holds that the mitochondria play a central role. Mitochondria are dynamic organelles that undergo fusion and fission. Interventions that decrease mitochondrial fission or increase mitochondrial fusion have been associated with reduced I/R injury. However, whether RIPC influences mitochondrial dynamics or not has yet to be ascertained.We sought to determine the role played by mitochondrial dynamics in RIPC-induced cardioprotection. Male adult rats exposed in vivo to myocardial I/R were assigned to one of two groups, either undergoing 40 min of myocardial ischemia followed by 120 min of reperfusion (MI group) or four 5-min cycles of limb ischemia interspersed by 5 min of limb reperfusion, immediately prior to myocardial ischemia and 120 min of reperfusion (MI+RIPC group). After reperfusion, infarct size was assessed and myocardial tissue was analyzed by Western blot and electron microscopy. RIPC induced smaller infarct size (-28%), increased mitochondrial fusion protein OPA1, and preserved mitochondrial morphology. These findings suggest that mitochondrial dynamics play a role in the mechanisms of RIPC-induced cardioprotection

Role of hypoxia inducible factor-1α in remote limb ischemic preconditioning.

Remote ischemic preconditioning (RIPC) has emerged as a feasible and attractive therapeutic procedure for heart protection against ischemia/reperfusion (I/R) injury. However, its molecular mechanisms remain poorly understood. Hypoxia inducible factor-1α (HIF-1α) is a transcription factor that plays a key role in the cellular adaptation to hypoxia and ischemia. This study\u27s aim was to test whether RIPC-induced cardioprotection requires HIF-1α upregulation to be effective. In the first study, wild-type mice and mice heterozygous for HIF1a (gene encoding the HIF-1α protein) were subjected to RIPC immediately before myocardial infarction (MI). RIPC resulted in a robust HIF-1α activation in the limb and acute cardioprotection in wild-type mice. RIPC-induced cardioprotection was preserved in heterozygous mice, despite the low HIF-1α expression in their limbs. In the second study, the role of HIF-1α in RIPC was evaluated using cadmium (Cd), a pharmacological HIF-1α inhibitor. Rats were subjected to MI (MI group) or to RIPC immediately prior to MI (R-MI group). Cd was injected 18 0min before RIPC (Cd-R-MI group). RIPC induced robust HIF-1α activation in rat limbs and significantly reduced infarct size (IS). Despite Cd\u27s inhibition of HIF-1α activation, RIPC-induced cardioprotection was preserved in the Cd-R-MI group. RIPC applied immediately prior to MI increased HIF-1α expression and attenuated IS in rats and wild-type mice. However, RIPC-induced cardioprotection was preserved in partially HIF1a-deficient mice and in rats pretreated with Cd. When considered together, these results suggest that HIF-1α upregulation is unnecessary in acute RIPC

Remote ischemic conditioning: from experimental observation to clinical application: report from the 8th Biennial Hatter Cardiovascular Institute Workshop

In 1993, Przyklenk and colleagues made the intriguing experimental observation that 'brief ischemia in one vascular bed also protects remote, virgin myocardium from subsequent sustained coronary artery occlusion' and that this effect '.... may be mediated by factor(s) activated, produced, or transported throughout the heart during brief ischemia/reperfusion'. This seminal study laid the foundation for the discovery of 'remote ischemic conditioning' (RIC), a phenomenon in which the heart is protected from the detrimental effects of acute ischemia/reperfusion injury (IRI), by applying cycles of brief ischemia and reperfusion to an organ or tissue remote from the heart. The concept of RIC quickly evolved to extend beyond the heart, encompassing inter-organ protection against acute IRI. The crucial discovery that the protective RIC stimulus could be applied non-invasively, by simply inflating and deflating a blood pressure cuff placed on the upper arm to induce cycles of brief ischemia and reperfusion, has facilitated the translation of RIC into the clinical setting. Despite intensive investigation over the last 20 years, the underlying mechanisms continue to elude researchers. In the 8th Biennial Hatter Cardiovascular Institute Workshop, recent developments in the field of RIC were discussed with a focus on new insights into the underlying mechanisms, the diversity of non-cardiac protection, new clinical applications, and large outcome studies. The scientific advances made in this field of research highlight the journey that RIC has made from being an intriguing experimental observation to a clinical application with patient benefit

Constraint Score Evaluation for Spectral Feature Selection

International audienceSemi-supervised context characterized by the presence of a few pairs of constraints between learning samples is abundant in many real applications. Analysing these instance constraints by recent spectral scores has shown good performances for semi-supervised feature selection. The performance evaluation of these scores is generally based on classification accuracy and is performed in a ground truth context. However, this supervised context used in the evaluation is inconsistent with the semi-supervised context used in the feature selection. In this paper, we propose a semi-supervised performance evaluation procedure, so that both feature selection and clustering take into account the constraints given by the user. In this way, the selection and the evaluation steps are performed in the same context which is close to real life applications. Extensive experiments on benchmark datasets are carried out in the last section. These experiments are performed using a supervised classical evaluation and the semi-supervised proposed one. They demonstrate the effectiveness of feature selection based on constraint analysis that uses both pairwise constraints and the information brought by the unlabeled data

RISK and SAFE signaling pathway involvement in apolipoprotein A-I-induced cardioprotection.

Recent findings indicate that apolipoprotein A-I (ApoA-I) may be a protective humoral mediator involved in remote ischemic preconditioning (RIPC). This study sought to determine if ApoA-I mediates its protective effects via the RISK and SAFE signaling pathways implicated in RIPC. Wistar rats were allocated to one of the following groups.rats were subjected to myocardial ischemia/reperfusion (I/R) without any further intervention; RIPC: four cycles of limb I/R were applied prior to myocardial ischemia; ApoA-I: 10 mg/Kg of ApoA-I were intravenously injected prior to myocardial ischemia; ApoA-I + inhibitor: pharmacological inhibitors of RISK/SAFE pro-survival kinase (Akt, ERK1/2 and STAT-3) were administered prior to ApoA-I injection. Infarct size was significantly reduced in the RIPC group compared to CONTROL. Similarly, ApoA-I injection efficiently protected the heart, recapitulating RIPC-induced cardioprotection. The ApoA-I protective effect was associated with Akt and GSK-3β phosphorylation and substantially inhibited by pretreatment with Akt and ERK1/2 inhibitors. Pretreatment with ApoA-I in a rat model of I/R recapitulates RIPC-induced cardioprotection and shares some similar molecular mechanisms with those of RIPC-involved protection of the heart

Ensemble constrained Laplacian score for efficient and robust semi-supervised feature selection

International audienceIn this paper, we propose an efficient and robust approach for semi-supervised feature selection, based on the constrainedLaplacian score. Themain drawback of this method is the choice of the scant supervision information, represented by pairwise constraints. In fact, constraints are proven to have some noise which may deteriorate learning performance. In this work, we try to override any negative effects of constraint set by the variation of theirsources. This is achieved by an ensemble technique using both a resampling of data (bagging) and a random subspace strategy. Experiments on high-dimensional datasets are provided for validating the proposed approach and comparing it with other representative feature selection methods