Matrix metalloproteinases, purinergic signaling, and epigenetics, hubs in the spinal neuroglial network following nerve injury.

The neuroglial network characterizes synaptic transmission and accounts for both cellular elements (neurons and glia) and neural extracellular matrix (nECM) roles. Glial cells, neuron, and nECMnetwork is strongly interconnected, in physiological and pathological conditions as shownin several neurodegenerative diseases. Purinergic activation and matrix metalloproteinases (MMPs) remodeling of the spinal cord is pivotal in maladaptive plastic changes following peripheral nerve injury (PNI). To understand how purinergic and MMPs inhibition may modulate and potentially reverse the neuroglial network failure, we used the spared nerve injury (SNI) model of the sciatic nerve. Molecular and morphological analysis of astrocytic and microglial activation, purinergic and neurotrophic receptors, Histone Deacetylase (HDAC)1, HDAC2 were analyzed to define the pathways in response to the purinergic and MMPs inhibition. The data suggest complex protein interconnections, which are not passively influenced by epigenetics but actively contribute to modify the transcriptomics machinery. The present study contributes to unveiling the spinal network consistency and ultimately encourages new paths for targeted treatments in neurological diseases with benefits of neuroprotection, plasticity, and functional recovery.

Prediction of miRNA-disease associations based on Weighted K-Nearest known neighbors and network consistency projection

MicroRNAs (miRNA) are a type of non-coding RNA molecules that are effective on the formation and the progression of many different diseases. Various researches have reported that miRNAs play a major role in the prevention, diagnosis, and treatment of complex human diseases. In recent years, researchers have made a tremendous effort to find the potential relationships between miRNAs and diseases. Since the experimental techniques used to find that new miRNA-disease relationships are time-consuming and expensive, many computational techniques have been developed. In this study, Weighted [Formula: see text]-Nearest Known Neighbors and Network Consistency Projection techniques were suggested to predict new miRNA-disease relationships using various types of knowledge such as known miRNA-disease relationships, functional similarity of miRNA, and disease semantic similarity. An average AUC of 0.9037 and 0.9168 were calculated in our method by 5-fold and leave-one-out cross validation, respectively. Case studies of breast, lung, and colon neoplasms were applied to prove the performance of our proposed technique, and the results confirmed the predictive reliability of this method. Therefore, reported experimental results have shown that our proposed method can be used as a reliable computational model to reveal potential relationships between miRNAs and diseases.