Three types of highly promising small RNA therapeutics, namely, small interfering RNAs (siRNAs), microRNAs (miRNAs) and the RNA subtype of antisense oligonucleotides (ASOs), offer advantages over small‐molecule drugs. These small RNAs can target any gene product, opening up new avenues of effective and safe therapeutic approaches for a wide range of diseases. In preclinical research, synthetic small RNAs play an essential role in the investigation of physiological and pathological pathways as silencers of specific genes, facilitating discovery and validation of drug targets in different conditions. Off‐target effects of small RNAs, however, could make it difficult to interpret experimental results in the preclinical phase and may contribute to adverse events of small RNA therapeutics. Out of the two major types of off‐target effects we focused on the hybridization‐dependent, especially on the miRNA‐like off‐target effects. Our main aim was to discuss several approaches, including sequence design, chemical modifications and target prediction, to reduce hybridization‐dependent off‐target effects that should be considered even at the early development phase of small RNA therapy. Because there is no standard way of predicting hybridization‐dependent off‐target effects, this review provides an overview of all major state‐of‐the‐art computational methods and proposes new approaches, such as the possible inclusion of network theory and artificial intelligence (AI) in the prediction workflows. Case studies and a concise survey of experimental methods for validating in silico predictions are also presented. These methods could contribute to interpret experimental results, to minimize off‐target effects and hopefully to avoid off‐target‐related adverse events of small RNA therapeutics.LINKED ARTICLES: This article is part of a themed issue Non‐coding RNA Therapeutics. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v182.2/issuetoc

Background and Purpose: Immune checkpoint inhibitors (ICI), such as anti‐PD‐1 monoclonal antibodies, have revolutionized cancer therapy by enhancing the cytotoxic effects of T‐cells against tumours. However, enhanced T‐cell activity also may cause myocarditis and cardiotoxicity. Our understanding of the mechanisms of ICI‐induced cardiotoxicity is limited. Here, we aimed to investigate the effect of PD‐1 inhibition on cardiac function and explore the molecular mechanisms of ICI‐induced cardiotoxicity.Experimental Approach: C57BL6/J and BALB/c mice were treated with isotype control or anti‐PD‐1 antibody.Echocardiography was used to assess cardiac function. Cardiac transcriptomic changes were investigated by bulk RNA sequencing. Inflammatory changes were assessed by qRT‐PCR and immunohistochemistry in heart, thymus, and spleen of the animals. In follow‐up experiments, anti‐CD4 and anti‐IL‐17A antibodies were used along with PD‐1 blockade in C57BL/6J mice.Key Results: Anti‐PD‐1 treatment led to cardiac dysfunction and left ventricular dilation in C57BL/6J mice, with increased nitrosative stress. Only mild inflammation was observed in the heart. However, PD‐1 inhibition resulted in enhanced thymic inflammatory signalling, whereIl17aincreased most prominently. In BALB/c mice, cardiac dysfunction was not evident, and thymic inflammatory activation was more balanced. Inhibition of IL‐17A prevented anti‐PD‐1‐induced cardiac dysfunction in C57BL6/J mice. Comparing myocardial transcriptomic changes in C57BL/6J and BALB/c mice, differentially regulated genes (Dmd,Ass1,Chrm2,Nfkbia,Stat3,Gsk3b,Cxcl9,Fxyd2, andLdb3) were revealed, related to cardiac structure, signalling, and inflammation.Conclusions: PD‐1 blockade induces cardiac dysfunction in mice with increased IL‐17 signalling in the thymus. Pharmacological inhibition of IL‐17A treatment prevents ICI‐induced cardiac dysfunction.

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

Background: Marfan syndrome (MFS) is a systemic connective tissue disorder with life-threatening manifestations affecting the ascending aorta. MFS is caused by dominant negative (DN) and haploinsufficient (HI) mutations of theFBN1gene. Our aim was to identify mutations of MFS patients with high detection rate and to investigate the use of a gene panel for patients with Marfanoid habitus. We also aimed to examine correlations between genotype and cardiovascular manifestations to predict “malignant” mutations.Methods: 136 individuals were enrolled. In the first phase, next-generation sequencing (NGS) and Sanger sequencing were performed for 57 patients to screen theFBN1gene, followed by multiplex ligation-dependent probe amplification (MLPA) in negative cases. For repeated negative results, NGS gene panel involving 9 genes was used. In the second phase, 79 patients were tested primarily with the same gene panel, negative samples were tested by MLPA.Results: 84 pathogenic mutations were detected, out of which 78 affectedFBN1, 6 non-FBN1mutations (2TGFB2, 1TGFBR2, 2TGFBR1, 1SMAD3) are associated with Loeys-Dietz syndrome (LDS). LDS patients had lower systemic score and they were younger, but their aortic involvement did not differ. MLPA detected 4 multi-exon deletions ofFBN1gene, which could not be identified by our first-step screening method. Aortic involvement (aortic dissection and/or dilation) did not differ significantly among HI and DN mutations (p = 0.061). Combined group of HI and DN mutations eliminating a disulphide-bonding cysteine (DN Cys) had significantly higher aortic involvement rate than DN mutations not eliminating a disulphide-bonding cysteine (DN non-Cys) (p < 0.001). Patients with DN Cys required significantly more aortic surgeries than HI and DN non-Cys mutations (p = 0.042 andp = 0.015, respectively).Conclusions: Due to the relevant number of mutations affecting genes other thanFBN1, preferred approach for testing individuals with Marfanoid habitus is using a gene panel rather than single-gene analysis, followed by MLPA for negative samples. DN Cys and HI mutations should be considered as risk factors for aortic involvement. Genetic testing for patients with Marfanoid features and a systemic score under 7 is recommended, as LDS patients may have lower scores, but they may have severe cardiovascular manifestations.

Although myocardial ischemia-reperfusion injury (I/R) and its pathological consequences are the leading cause of morbidity and mortality worldwide, cardioprotective therapeutics are still not on the market. Oxidative stress, a major contributing factor to myocardial I/R, changes transcription of coding and non-coding RNAs, alters post-transcriptional modulations, and regulate protein function. MicroRNA (miRNA) expression can be altered by oxidative stress and microRNAs may also regulate cytoprotective mechanisms and exert cardioprotection againts I/R. Transcriptomic analysis of I/R and oxidative stress-induced alterations followed by microRNA-mRNA target interaction network analysis may reveal microRNAs and their mRNA targets that may play a role in cardioprotection and serve as microRNA therapeutics or novel molecular targets for further drug development. Here we provide a summary of a systematic literature review and in silico molecular network analysis to reveal important cardioprotective microRNAs and their molecular targets that may provide cardioprotection via regulation of redox signalling.

Background: The investigation of possible interactions between two proteins in intracellular signaling is an expensive and laborious procedure in the wet-lab, therefore, several in silico approaches have been implemented to narrow down the candidates for future experimental validations. Reformulating the problem in the field of network theory, the set of proteins can be represented as the nodes of a network, while the interactions between them as the edges. The resulting protein–protein interaction (PPI) network enables the use of link prediction techniques in order to discover new probable connections. Therefore, here we aimed to offer a novel approach to the link prediction task in PPI networks, utilizing a generative machine learning model. Results: We created a tool that consists of two modules, the data processing framework and the machine learning model. As data processing, we used a modified breadth-first search algorithm to traverse the network and extract induced subgraphs, which served as image-like input data for our model. As machine learning, an image-to-image translation inspired conditional generative adversarial network (cGAN) model utilizing Wasserstein distance-based loss improved with gradient penalty was used, taking the combined representation from the data processing as input, and training the generator to predict the probable unknown edges in the provided induced subgraphs. Our link prediction tool was evaluated on the protein–protein interaction networks of five different species from the STRING database by calculating the area under the receiver operating characteristic, the precision-recall curves and the normalized discounted cumulative gain (AUROC, AUPRC, NDCG, respectively). Test runs yielded the averaged results of AUROC = 0.915, AUPRC = 0.176 and NDCG = 0.763 on all investigated species. Conclusion: We developed a software for the purpose of link prediction in PPI networks utilizing machine learning. The evaluation of our software serves as the first demonstration that a cGAN model, conditioned on raw topological features of the PPI network, is an applicable solution for the PPI prediction problem without requiring often unavailable molecular node attributes. The corresponding scripts are available at https://github.com/semmelweis-pharmacology/ppi_pred.