Mitochonic Acid 5 (MA-5) Facilitates ATP Synthase Oligomerization and Cell Survival in Various Mitochondrial Diseases

Mitochondrial dysfunction increases oxidative stress and depletes ATP in a variety of disorders. Several antioxidant therapies and drugs affecting mitochondrial biogenesis are undergoing investigation, although not all of them have demonstrated favorable effects in the clinic. We recently reported a therapeutic mitochondrial drug mitochonic acid MA-5 (Tohoku J. Exp. Med., 2015). MA-5 increased ATP, rescued mitochondrial disease fibroblasts and prolonged the life span of the disease model “Mitomouse” (JASN, 2016). To investigate the potential of MA-5 on various mitochondrial diseases, we collected 25 cases of fibroblasts from various genetic mutations and cell protective effect of MA-5 and the ATP producing mechanism was examined. 24 out of the 25 patient fibroblasts (96%) were responded to MA-5. Under oxidative stress condition, the GDF-15 was increased and this increase was significantly abrogated by MA-5. The serum GDF-15 elevated in Mitomouse was likewise reduced by MA-5. MA-5 facilitates mitochondrial ATP production and reduces ROS independent of ETC by facilitating ATP synthase oligomerization and supercomplex formation with mitofilin/Mic60. MA-5 reduced mitochondria fragmentation, restores crista shape and dynamics. MA-5 has potential as a drug for the treatment of various mitochondrial diseases. The diagnostic use of GDF-15 will be also useful in a forthcoming MA-5 clinical trial

Efficient Network Representation Learning via Cluster Similarity

Abstract Network representation learning is a de facto tool for graph analytics. The mainstream of the previous approaches is to factorize the proximity matrix between nodes. However, if n is the number of nodes, since the size of the proximity matrix is $$n \times n$$ n × n , it needs $$O(n^3)$$ O ( n 3 ) time and $$O(n^2)$$ O ( n 2 ) space to perform network representation learning; they are significantly high for large-scale graphs. This paper introduces the novel idea of using similarities between clusters instead of proximities between nodes; the proposed approach computes the representations of the clusters from similarities between clusters and computes the representations of nodes by referring to them. If l is the number of clusters, since $$l \ll n$$ l ≪ n , we can efficiently obtain the representations of clusters from a small $$l \times l$$ l × l similarity matrix. Furthermore, since nodes in each cluster share similar structural properties, we can effectively compute the representation vectors of nodes. Experiments show that our approach can perform network representation learning more efficiently and effectively than existing approaches