Metformin Treatment Modulates Long Non-Coding RNA Isoforms Expression in Human Cells.

Long noncoding RNAs (lncRNAs) undergo splicing and have multiple transcribed isoforms. Nevertheless, for lncRNAs, as well as for mRNA, measurements of expression are routinely performed only at the gene level. Metformin is the first-line oral therapy for type 2 diabetes mellitus and other metabolic diseases. However, its mechanism of action remains not thoroughly explained. Transcriptomic analyses using metformin in different cell types reveal that only protein-coding genes are considered. We aimed to characterize lncRNA isoforms that were differentially affected by metformin treatment on multiple human cell types (three cancer, two non-cancer) and to provide insights into the lncRNA regulation by this drug. We selected six series to perform a differential expression (DE) isoform analysis. We also inferred the biological roles for lncRNA DE isoforms using in silico tools. We found the same isoform of an lncRNA (AC016831.6-205) highly expressed in all six metformin series, which has a second exon putatively coding for a peptide with relevance to the drug action. Moreover, the other two lncRNA isoforms (ZBED5-AS1-207 and AC125807.2-201) may also behave as cis-regulatory elements to the expression of transcripts in their vicinity. Our results strongly reinforce the importance of considering DE isoforms of lncRNA for understanding metformin mechanisms at the molecular level.

The evolution of knowledge on genes associated with human diseases.

Thousands of biomedical scientific articles, including those describing genes associated with human diseases, are published every week. Computational methods such as text mining and machine learning algorithms are now able to automatically detect these associations. In this study, we used a cognitive computing text-mining application to construct a knowledge network comprising 3,723 genes and 99 diseases. We then tracked the yearly changes on these networks to analyze how our knowledge has evolved in the past 30 years. Our systems approach helped to unravel the molecular bases of diseases and detect shared mechanisms between clinically distinct diseases. It also revealed that multi-purpose therapeutic drugs target genes that are commonly associated with several psychiatric, inflammatory, or infectious disorders. By navigating this knowledge tsunami, we were able to extract relevant biological information and insights about human diseases.

Long non-coding RNAs associated with infection and vaccine-induced immunity.

The immune system responds to infection or vaccination through a dynamic and complex process that involves several molecular and cellular factors. Among these factors, long non-coding RNAs (lncRNAs) have emerged as significant players in all areas of biology, particularly in immunology. Most of the mammalian genome is transcribed in a highly regulated manner, generating a diversity of lncRNAs that impact the differentiation and activation of immune cells and affect innate and adaptive immunity. Here, we have reviewed the range of functions and mechanisms of lncRNAs in response to infectious disease, including pathogen recognition, interferon (IFN) response, and inflammation. We describe examples of lncRNAs exploited by pathogenic agents during infection, which indicate that lncRNAs are a fundamental part of the arms race between hosts and pathogens. We also discuss lncRNAs potentially implicated in vaccine-induced immunity and present examples of lncRNAs associated with the antibody response of subjects receiving Influenza or Yellow Fever vaccines. Elucidating the widespread involvement of lncRNAs in the immune system will improve our understanding of the factors affecting immune response to different pathogenic agents, to better prevent and treat disease.

Drug repositioning for psychiatric and neurological disorders through a network medicine approach.

Psychiatric and neurological disorders (PNDs) affect millions worldwide and only a few drugs achieve complete therapeutic success in the treatment of these disorders. Due to the high cost of developing novel drugs, drug repositioning represents a promising alternative method of treatment. In this manuscript, we used a network medicine approach to investigate the molecular characteristics of PNDs and identify novel drug candidates for repositioning. Using IBM Watson for Drug Discovery, a powerful machine learning text-mining application, we built knowledge networks containing connections between PNDs and genes or drugs mentioned in the scientific literature published in the past 50 years. This approach revealed several drugs that target key PND-related genes, which have never been used to treat these disorders to date. We validate our framework by detecting drugs that have been undergoing clinical trial for treating some of the PNDs, but have no published results in their support. Our data provides comprehensive insights into the molecular pathology of PNDs and offers promising drug repositioning candidates for follow-up trials.