Quantitative and qualitative detection of tRNAs, tRNA halves and tRFs in human cancer samples: Molecular grounds for biomarker development and clinical perspectives.

Transfer RNAs (tRNAs) are small non-coding RNAs playing a central role during protein synthesis. Besides translation, growing evidence suggests that in many contexts, precursor or mature tRNAs can also be processed into smaller fragments playing many non-canonical regulatory roles in different biological pathways with oncogenic relevance. Depending on the source, these molecules can be classified as tRNA halves (also known as tiRNAs) or tRNA-derived fragments (tRFs), and furtherly divided into 5'-tRNA and 3'-tRNA halves, or tRF-1, tRF-2, tRF-3, tRF-5, and i-tRF, respectively. Unlike DNA and mRNA, high-throughput sequencing of tRNAs is challenging, because of technical limitations of currently developed sequencing methods. In recent years, different sequencing approaches have been proposed allowing the quantification and identification of an increasing number of tRNA fragments with critical functions in distinct physiological and pathophysiological processes. In the present review, we discussed pros and cons of recent advances in different sequencing methods, also introducing the expanding repertoire of bioinformatics tool and resources specifically focused on tRNA research and discussing current issues in the study of these small RNA molecules. Furthermore, we discussed the potential value of tRNA fragments as diagnostic and prognostic biomarkers for different types of cancers.

A History of Repeated Alcohol Intoxication Promotes Cognitive Impairment and Gene Expression Signatures of Disease Progression in the 3xTg Mouse Model of Alzheimer's Disease.

The impact of alcohol abuse on Alzheimer's disease (AD) is poorly understood. Here, we show that the onset of neurocognitive impairment in a mouse model of AD is hastened by repeated alcohol intoxication through exposure to alcohol vapor, and we provide a comprehensive gene expression dataset of the prefrontal cortex by the single-nucleus RNA sequencing of 113,242 cells. We observed a broad dysregulation of gene expression that involves neuronal excitability, neurodegeneration, and inflammation, including interferon genes. Several genes previously associated with AD in humans by genome-wide association studies were differentially regulated in specific neuronal populations. The gene expression signatures of AD mice with a history of alcohol intoxication showed greater similarity to the signatures of older AD mice with advanced disease and cognitive impairment than did the gene expression signatures of AD mice not exposed to alcohol, suggesting that alcohol promotes transcriptional changes consistent with AD progression. Our gene expression dataset at the single-cell level provides a unique resource for investigations of the molecular bases of the detrimental role of excessive alcohol intake in AD.

Detection of pan-cancer surface protein biomarkers via a network-based approach on transcriptomics data.

Cell surface proteins have been used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. Many of these proteins lie at the top of signaling cascades regulating cell responses and gene expression, therefore acting as 'signaling hubs'. It has been previously demonstrated that the integrated network analysis on transcriptomic data is able to infer cell surface protein activity in breast cancer. Such an approach has been implemented in a publicly available method called 'SURFACER'. SURFACER implements a network-based analysis of transcriptomic data focusing on the overall activity of curated surface proteins, with the final aim to identify those proteins driving major phenotypic changes at a network level, named surface signaling hubs. Here, we show the ability of SURFACER to discover relevant knowledge within and across cancer datasets. We also show how different cancers can be stratified in surface-activity-specific groups. Our strategy may identify cancer-wide markers to design targeted therapies and biomarker-based diagnostic approaches.

COVIDrugNet: a network-based web tool to investigate the drugs currently in clinical trial to contrast COVID-19.

The COVID-19 pandemic poses a huge problem of public health that requires the implementation of all available means to contrast it, and drugs are one of them. In this context, we observed an unmet need of depicting the continuously evolving scenario of the ongoing drug clinical trials through an easy-to-use, freely accessible online tool. Starting from this consideration, we developed COVIDrugNet ( http://compmedchem.unibo.it/covidrugnet ), a web application that allows users to capture a holistic view and keep up to date on how the clinical drug research is responding to the SARS-CoV-2 infection. Here, we describe the web app and show through some examples how one can explore the whole landscape of medicines in clinical trial for the treatment of COVID-19 and try to probe the consistency of the current approaches with the available biological and pharmacological evidence. We conclude that careful analyses of the COVID-19 drug-target system based on COVIDrugNet can help to understand the biological implications of the proposed drug options, and eventually improve the search for more effective therapies.

Drug Research Meets Network Science: Where Are We?

Network theory provides one of the most potent analysis tools for the study of complex systems. In this paper, we illustrate the network-based perspective in drug research and how it is coherent with the new paradigm of drug discovery. We first present data sources from which networks are built, then show some examples of how the networks can be used to investigate drug-related systems. A section is devoted to network-based inference applications, i.e., prediction methods based on interactomes, that can be used to identify putative drug-target interactions without resorting to 3D modeling. Finally, we present some aspects of Boolean networks dynamics, anticipating that it might become a very potent modeling framework to develop in silico screening protocols able to simulate phenotypic screening experiments. We conclude that network applications integrated with machine learning and 3D modeling methods will become an indispensable tool for computational drug discovery in the next years.