Identification of repurposed drugs targeting significant long non-coding RNAs in the cross-talk between diabetes mellitus and Alzheimer’s disease

Abstract

The relationship between diabetes mellitus (DM) and Alzheimer’s disease (AD) is so strong that scientists called it “brain diabetes”. According to several studies, the critical factor in this relationship is brain insulin resistance. Due to the rapid global spread of both diseases, overcoming this cross-talk has a significant impact on societies. Long non-coding RNAs (lncRNAs), on the other hand, have a substantial impact on complex diseases due to their ability to influence gene expression via a variety of mechanisms. Consequently, the regulation of lncRNA expression in chronic diseases permits the development of innovative therapeutic techniques. However, developing a new drug requires considerable time and money. Recently repurposing existing drugs has gained popularity due to the use of low-risk compounds, which may result in cost and time savings. in this study, we identified drug repurposing candidates capable of controlling the expression of common lncRNAs in the cross-talk between DM and AD. We also utilized drugs that interfered with this cross-talk. To do this, high degree common lncRNAs were extracted from microRNA-lncRNA bipartite network. The drugs that interact with the specified lncRNAs were then collected from multiple data sources. These drugs, referred to as set D, were classified in to positive (D+) and negative (D−) groups based on their effects on the expression of the interacting lncRNAs. A feature selection algorithm was used to select six important features for D. Using a random forest classifier, these features were capable of classifying D+ and D− with an accuracy of 82.5%. Finally, the same six features were extracted for the most recently Food and Drug Administration (FDA) approved drugs in order to identify those with the highest likelihood of belonging to D+ or D−. The most significant FDA-approved positive drugs, chromium nicotinate and tapentadol, were presented as repurposing candidates, while cefepime and dihydro-alpha-ergocryptine were recommended as significant adverse drugs. Moreover, two natural compounds, curcumin and quercetin, were recommended to prevent this cross-talk. According to the previous studies, less attention has been paid to the role of lncRNAs in this cross-talk. Our research not only did identify important lncRNAs, but it also suggested potential repurposed drugs to control them.

Long Non-Coding RNA Levels Are Modulated in Schistosoma mansoni following In Vivo Praziquantel Exposure

“…This shows that there is an extensive reprogramming of the gene expression in S. mansoni worms that goes beyond the changes in protein-coding gene expression. Indeed, this observation is in line with previous studies showing that lncRNA levels can be modulated by drugs in S. mansoni [23] and in other eukaryotes [25,[37][38][39][40].…”

“…Several studies have demonstrated and validated the modulation of lncRNA levels in other eukaryotes as a response to different drugs [25,[37][38][39][40]. According to the recently published "ncRNADrug" database, a manually curated resource containing thousands of ncRNAs in current literature found to be associated with drug response and resistance [37], a set of 67 unique lncRNAs has been validated as DE under the regulation of 74 different molecules, whilst regulating the expression of 127 unique genes.…”

DeepDrug: An Expert-led Domain-specific AI-Driven Drug-Repurposing Mechanism for Selecting the Lead Combination of Drugs for Alzheimer’s Disease

“…Rodriguez et al (2021) developed a machine learning framework to predict a list of genes that associate with different stages of AD, based on gene expression data from multiple datasets, for drug repurposing. Ghiam et al (2022) 38 examined the role of long non-coding RNAs (lncRNAs) in the cross-talk between diabetes and AD using two bipartite networks, including (1) an mRNA-miRNA network with common mRNAs in diabetes and AD and (2) an miRNA-lncRNA network with high-degree miRNAs obtained from the mRNA-miRNA network. They utilized machine learning to predict the effects of drugs on the expression of high-degree lncRNAs selected from the miRNA-lncRNA network.…”