PFRED: A computational platform for siRNA and antisense oligonucleotides design

Mitigating off-target effects of small RNAs: conventional approaches, network theory and artificial intelligence

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.

ASOptimizer: Optimizing antisense oligonucleotides through deep learning for IDO1 gene regulation

Recent studies have highlighted the effectiveness of using antisense oligonucleotides (ASOs) for cellular RNA regulation, including targets that are considered undruggable; however, manually designing optimal ASO sequences can be labor intensive and time consuming, which potentially limits their broader application. To address this challenge, we introduce a platform, the ASOptimizer, a deep-learning-based framework that efficiently designs ASOs at a low cost. This platform not only selects the most efficient mRNA target sites but also optimizes the chemical modifications for enhanced performance. Indoleamine 2,3-dioxygenase 1 (IDO1) promotes cancer survival by depleting tryptophan and producing kynurenine, leading to immunosuppression through the aryl-hydrocarbon receptor (Ahr) pathway within the tumor microenvironment. We used ASOptimizer to identify ASOs that target IDO1 mRNA as potential cancer therapeutics. Our methodology consists of two stages: sequence engineering and chemical engineering. During the sequence-engineering stage, we optimized and predicted ASO sequences that could target IDO1 mRNA efficiently. In the chemical-engineering stage, we further refined these ASOs to enhance their inhibitory activity while reducing their potential cytotoxicity. In conclusion, our research demonstrates the potential of ASOptimizer for identifying ASOs with improved efficacy and safety.

Bioinformatics Approaches in the Development of Antifungal Therapeutics and Vaccines

Fungal infections are considered a great threat to human life and are associated with high mortality and morbidity, especially in immunocompromised individuals. Fungal pathogens employ various defense mechanisms to evade the host immune system, which causes severe infections. The available repertoire of drugs for the treatment of fungal infections includes azoles, allylamines, polyenes, echinocandins, and antimetabolites. However, the development of multidrug and pandrug resistance to available antimycotic drugs increases the need to develop better treatment approaches. In this new era of -omics, bioinformatics has expanded options for treating fungal infections. This review emphasizes how bioinformatics complements the emerging strategies, including advancements in drug delivery systems, combination therapies, drug repurposing, epitope-based vaccine design, RNA-based therapeutics, and the role of gut-microbiome interactions to combat anti-fungal resistance. In particular, we focused on computational methods that can be useful to obtain potent hits, and that too in a short period.

Tribulations and future opportunities for artificial intelligence in precision medicine

Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.

Application of data science and bioinformatics in RNA therapeutics

Nowadays, information technology (IT) has been holding a significant role in daily life worldwide. The trajectory of data science and bioinformatics promises pioneering personalized therapies, reshaping medical landscapes and patient care. For RNA therapy to reach more patients, a comprehensive understanding of the application of data science and bioinformatics to this therapy is essential. Thus, this chapter has summarized the application of data science and bioinformatics in RNA therapeutics. Data science applications in RNA therapy, such as data integration and analytics, machine learning, and drug development, have been discussed. In addition, aspects of bioinformatics such as RNA design and evaluation, drug delivery system simulation, and databases for personalized medicine have also been covered in this chapter. These insights have shed light on existing evidence and opened potential future directions. From there, scientists can elevate RNA-based therapeutics into an era of tailored treatments and revolutionary healthcare.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

Effective Antiviral Application of Antisense in Plants by Exploiting Accessible Sites in the Target RNA

Antisense oligodeoxynucleotides (ASOs) have long been used to selectively inhibit or modulate gene expression at the RNA level, and some ASOs are approved for clinical use. However, the practicability of antisense technologies remains limited by the difficulty of reliably predicting the sites accessible to ASOs in complex folded RNAs. Recently, we applied a plant-based method that reproduces RNA-induced RNA silencing in vitro to reliably identify sites in target RNAs that are accessible to small interfering RNA (siRNA)-guided Argonaute endonucleases. Here, we show that this method is also suitable for identifying ASOs that are effective in DNA-induced RNA silencing by RNases H. We show that ASOs identified in this way that target a viral genome are comparably effective in protecting plants from infection as siRNAs with the corresponding sequence. The antiviral activity of the ASOs could be further enhanced by chemical modification. This led to two important conclusions: siRNAs and ASOs that can effectively knock down complex RNA molecules can be identified using the same approach, and ASOs optimized in this way could find application in crop protection. The technology developed here could be useful not only for effective RNA silencing in plants but also in other organisms.

An enhanced biophysical screening strategy to investigate the affinity of ASOs for their target RNA

The recent and rapid increase in the discovery of new RNA therapeutics has created the perfect terrain to explore an increasing number of novel targets. In particular, antisense oligonucleotides (ASOs) have long held the promise of an accelerated and effective drug design compared to other RNA-based therapeutics. Although ASOs in silico design has advanced distinctively in the past years, especially thanks to the several predictive frameworks for RNA folding, it is somehow limited by the wide approximation of calculating sequence affinity based on RNA-RNA/DNA sequences. None of the ASO modifications are taken into consideration, losing hybridization information particularly fundamental to ASOs that elicit their function through RNase H1-mediated mechanisms. Here we present an inexpensive and enhanced biophysical screening strategy to investigate the affinity of ASOs for their target RNA using several biophysical techniques such as high throughput differential scanning fluorimetry (DSF), circular dichroism (CD), isothermal calorimetry (ITC), surface plasmon resonance (SPR) and small-angle X-ray scattering (SAXS).

RNA-Based Strategies for Cancer Therapy: In Silico Design and Evaluation of ASOs for Targeted Exon Skipping

Precision medicine in oncology has made significant progress in recent years by approving drugs that target specific genetic mutations. However, many cancer driver genes remain challenging to pharmacologically target ("undruggable"). To tackle this issue, RNA-based methods like antisense oligonucleotides (ASOs) that induce targeted exon skipping (ES) could provide a promising alternative. In this work, a comprehensive computational procedure is presented, focused on the development of ES-based cancer treatments. The procedure aims to produce specific protein variants, including inactive oncogenes and partially restored tumor suppressors. This novel computational procedure encompasses target-exon selection, in silico prediction of ES products, and identification of the best candidate ASOs for further experimental validation. The method was effectively employed on extensively mutated cancer genes, prioritized according to their suitability for ES-based interventions. Notable genes, such as NRAS and VHL, exhibited potential for this therapeutic approach, as specific target exons were identified and optimal ASO sequences were devised to induce their skipping. To the best of our knowledge, this is the first computational procedure that encompasses all necessary steps for designing ASO sequences tailored for targeted ES, contributing with a versatile and innovative approach to addressing the challenges posed by undruggable cancer driver genes and beyond.

Ensemble-Learning and Feature Selection Techniques for Enhanced Antisense Oligonucleotide Efficacy Prediction in Exon Skipping

Antisense oligonucleotide (ASO)-mediated exon skipping has become a valuable tool for investigating gene function and developing gene therapy. Machine-learning-based computational methods, such as eSkip-Finder, have been developed to predict the efficacy of ASOs via exon skipping. However, these methods are computationally demanding, and the accuracy of predictions remains suboptimal. In this study, we propose a new approach to reduce the computational burden and improve the prediction performance by using feature selection within machine-learning algorithms and ensemble-learning techniques. We evaluated our approach using a dataset of experimentally validated exon-skipping events, dividing it into training and testing sets. Our results demonstrate that using a three-way-voting approach with random forest, gradient boosting, and XGBoost can significantly reduce the computation time to under ten seconds while improving prediction performance, as measured by R² for both 2'-O-methyl nucleotides (2OMe) and phosphorodiamidate morpholino oligomers (PMOs). Additionally, the feature importance ranking derived from our approach is in good agreement with previously published results. Our findings suggest that our approach has the potential to enhance the accuracy and efficiency of predicting ASO efficacy via exon skipping. It could also facilitate the development of novel therapeutic strategies. This study could contribute to the ongoing efforts to improve ASO design and optimize gene therapy approaches.

Design and off-target prediction for antisense oligomers targeting bacterial mRNAs with the MASON web server

Antisense oligomers (ASOs), such as peptide nucleic acids (PNAs), designed to inhibit the translation of essential bacterial genes, have emerged as attractive sequence- and species-specific programmable RNA antibiotics. Yet, potential drawbacks include unwanted side effects caused by their binding to transcripts other than the intended target. To facilitate the design of PNAs with minimal off-target effects, we developed MASON (make antisense oligomers now), a web server for the design of PNAs that target bacterial mRNAs. MASON generates PNA sequences complementary to the translational start site of a bacterial gene of interest and reports critical sequence attributes and potential off-target sites. We based MASON's off-target predictions on experiments in which we treated Salmonella enterica serovar Typhimurium with a series of 10-mer PNAs derived from a PNA targeting the essential gene acpP but carrying two serial mismatches. Growth inhibition and RNA-sequencing (RNA-seq) data revealed that PNAs with terminal mismatches are still able to target acpP, suggesting wider off-target effects than anticipated. Comparison of these results to an RNA-seq data set from uropathogenic Escherichia coli (UPEC) treated with eleven different PNAs confirmed that our findings are not unique to Salmonella We believe that MASON's off-target assessment will improve the design of specific PNAs and other ASOs.

Alterations in oligodendrocyte transcriptional networks reveal region-specific vulnerabilities to neurological disease

Neurological disease is characterized the by dysfunction of specific neuroanatomical regions. To determine whether region-specific vulnerabilities have a transcriptional basis at cell-type-specific resolution, we analyzed gene expression in mouse oligodendrocytes across various brain regions. Oligodendrocyte transcriptomes cluster in an anatomical arrangement along the rostrocaudal axis. Moreover, regional oligodendrocyte populations preferentially regulate genes implicated in diseases that target their region of origin. Systems-level analyses identify five region-specific co-expression networks representing distinct molecular pathways in oligodendrocytes. The cortical network exhibits alterations in mouse models of intellectual disability and epilepsy, the cerebellar network in ataxia, and the spinal network in multiple sclerosis. Bioinformatic analyses reveal potential molecular regulators of these networks, which were confirmed to modulate network expression in vitro in human oligodendroglioma cells, including reversal of the disease-associated transcriptional effects of a pathogenic Spinocerebellar ataxia type 1 allele. These findings identify targetable region-specific vulnerabilities to neurological disease mediated by oligodendrocytes.

Recent Progress on Nanocarriers for Topical-Mediated RNAi Strategies for Crop Protection-A Review

To fulfil the growing needs of the global population, sustainability in food production must be ensured. Insect pests and pathogens are primarily responsible for one-third of food losses and harmful synthetic pesticides have been applied to protect crops from these pests and other pathogens such as viruses and fungi. An alternative pathogen control mechanism that is more "friendly" to the environment can be developed by externally applying double-stranded RNAs (dsRNAs) to suppress gene expression. However, the use of dsRNA sprays in open fields is complicated with respect to variable efficiencies in the dsRNA delivery, and the stability of the dsRNA on and in the plants, and because the mechanisms of gene silencing may differ between plants and between different pathogen targets. Thus, nanocarrier delivery systems have been especially used with the goal of improving the efficacy of dsRNAs. Here, we highlight recent developments in nanoparticle-mediated nanocarriers to deliver dsRNA, including layered double hydroxide, carbon dots, carbon nanotubes, gold nanoparticles, chitosan nanoparticles, silica nanoparticles, liposomes, and cell-penetrating peptides, by review of the literature and patent landscape. The effects of nanoparticle size and surface modification on the dsRNA uptake efficiency in plants are also discussed. Finally, we emphasize the overall limitation of dsRNA sprays, the risks associated, and the potential safety concerns for spraying dsRNAs on crops.

Current Scenario of Exogenously Induced RNAi for Lepidopteran Agricultural Pest Control: From dsRNA Design to Topical Application

Invasive insects cost the global economy around USD 70 billion per year. Moreover, increasing agricultural insect pests raise concerns about global food security constraining and infestation rising after climate changes. Current agricultural pest management largely relies on plant breeding-with or without transgenes-and chemical pesticides. Both approaches face serious technological obsolescence in the field due to plant resistance breakdown or development of insecticide resistance. The need for new modes of action (MoA) for managing crop health is growing each year, driven by market demands to reduce economic losses and by consumer demand for phytosanitary measures. The disabling of pest genes through sequence-specific expression silencing is a promising tool in the development of environmentally-friendly and safe biopesticides. The specificity conferred by long dsRNA-base solutions helps minimize effects on off-target genes in the insect pest genome and the target gene in non-target organisms (NTOs). In this review, we summarize the status of gene silencing by RNA interference (RNAi) for agricultural control. More specifically, we focus on the engineering, development and application of gene silencing to control Lepidoptera through non-transforming dsRNA technologies. Despite some delivery and stability drawbacks of topical applications, we reviewed works showing convincing proof-of-concept results that point to innovative solutions. Considerations about the regulation of the ongoing research on dsRNA-based pesticides to produce commercialized products for exogenous application are discussed. Academic and industry initiatives have revealed a worthy effort to control Lepidoptera pests with this new mode of action, which provides more sustainable and reliable technologies for field management. New data on the genomics of this taxon may contribute to a future customized target gene portfolio. As a case study, we illustrate how dsRNA and associated methodologies could be applied to control an important lepidopteran coffee pest.

Recent Progress and Challenges in the Development of Antisense Therapies for Myotonic Dystrophy Type 1

Myotonic dystrophy type 1 (DM1) is a dominant genetic disease in which the expansion of long CTG trinucleotides in the 3' UTR of the myotonic dystrophy protein kinase (DMPK) gene results in toxic RNA gain-of-function and gene mis-splicing affecting mainly the muscles, the heart, and the brain. The CUG-expanded transcripts are a suitable target for the development of antisense oligonucleotide (ASO) therapies. Various chemical modifications of the sugar-phosphate backbone have been reported to significantly enhance the affinity of ASOs for RNA and their resistance to nucleases, making it possible to reverse DM1-like symptoms following systemic administration in different transgenic mouse models. However, specific tissue delivery remains to be improved to achieve significant clinical outcomes in humans. Several strategies, including ASO conjugation to cell-penetrating peptides, fatty acids, or monoclonal antibodies, have recently been shown to improve potency in muscle and cardiac tissues in mice. Moreover, intrathecal administration of ASOs may be an advantageous complementary administration route to bypass the blood-brain barrier and correct defects of the central nervous system in DM1. This review describes the evolution of the chemical design of antisense oligonucleotides targeting CUG-expanded mRNAs and how recent advances in the field may be game-changing by forwarding laboratory findings into clinical research and treatments for DM1 and other microsatellite diseases.

Transgene-free genome editing and RNAi ectopic application in fruit trees: Potential and limitations

For the past fifteen years, significant research advances in sequencing technology have led to a substantial increase in fruit tree genomic resources and databases with a massive number of OMICS datasets (transcriptomic, proteomics, metabolomics), helping to find associations between gene(s) and performance traits. Meanwhile, new technology tools have emerged for gain- and loss-of-function studies, specifically in gene silencing and developing tractable plant models for genetic transformation. Additionally, innovative and adapted transformation protocols have optimized genetic engineering in most fruit trees. The recent explosion of new gene-editing tools allows for broadening opportunities for functional studies in fruit trees. Yet, the fruit tree research community has not fully embraced these new technologies to provide large-scale genome characterizations as in cereals and other staple food crops. Instead, recent research efforts in the fruit trees appear to focus on two primary translational tools: transgene-free gene editing via Ribonucleoprotein (RNP) delivery and the ectopic application of RNA-based products in the field for crop protection. The inherent nature of the propagation system and the long juvenile phase of most fruit trees are significant justifications for the first technology. The second approach might have the public favor regarding sustainability and an eco-friendlier environment for a crop production system that could potentially replace the use of chemicals. Regardless of their potential, both technologies still depend on the foundational knowledge of gene-to-trait relationships generated from basic genetic studies. Therefore, we will discuss the status of gene silencing and DNA-based gene editing techniques for functional studies in fruit trees followed by the potential and limitations of their translational tools (RNP delivery and RNA-based products) in the context of crop production.

Delivering the Promise of Gene Therapy with Nanomedicines in Treating Central Nervous System Diseases

Central Nervous System (CNS) diseases, such as Alzheimer's diseases (AD), Parkinson's Diseases (PD), brain tumors, Huntington's disease (HD), and stroke, still remain difficult to treat by the conventional molecular drugs. In recent years, various gene therapies have come into the spotlight as versatile therapeutics providing the potential to prevent and treat these diseases. Despite the significant progress that has undoubtedly been achieved in terms of the design and modification of genetic modulators with desired potency and minimized unwanted immune responses, the efficient and safe in vivo delivery of gene therapies still poses major translational challenges. Various non-viral nanomedicines have been recently explored to circumvent this limitation. In this review, an overview of gene therapies for CNS diseases is provided and describes recent advances in the development of nanomedicines, including their unique characteristics, chemical modifications, bioconjugations, and the specific applications that those nanomedicines are harnessed to deliver gene therapies.

Evitar: designing anti-viral RNA therapies against future RNA viruses

MOTIVATION

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the threat of emerging respiratory viruses and has exposed the lack of availability of off-the-shelf therapeutics against new RNA viruses. Previous research has established the potential that siRNAs and RNA-targeting CRISPR have in combating known RNA viruses. However, the feasibility and tools for designing anti-viral RNA therapeutics against future RNA viruses have not yet been established.

RESULTS

We develop the Emerging-Virus-Targeting RNA (Evitar) pipeline for designing anti-viral siRNAs and CRISPR Cas13a guide RNA (gRNA) sequences. Within Evitar, we develop Greedy Algorithm with Redundancy and Similarity-weighted Greedy Algorithm with Redundancy to enhance the performance. Time simulations using known coronavirus genomes deposited as early as 10 years prior to the COVID-19 outbreak show that at least three SARS-CoV-2-targeting siRNAs are among the top 30 pre-designed siRNAs. In addition, among the top 19 pre-designed gRNAs, there are three SARS-CoV-2-targeting Cas13a gRNAs that could be predicted using information from 2011. Before-the-outbreak design is also possible against the MERS-CoV virus and the 2009-H1N1 swine flu virus. Designed siRNAs are further shown to suppress SARS-CoV-2 viral sequences using in vitro reporter assays. Our results support the utility of Evitar to pre-design anti-viral siRNAs/gRNAs against future viruses. Therefore, we propose the development of a collection consisting of roughly 30 pre-designed, safety-tested and off-the-shelf siRNA/CRISPR therapeutics that could accelerate responses to future RNA virus outbreaks.

AVAILABILITY AND IMPLEMENTATION

Codes are available at GitHub (https://github.com/dingyaozhang/Evitar).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Challenges and Strategies of Antisense Oligonucleotide Drug Delivery

Antisense oligonucleotides (ASOs) are used to selectively inhibit the translation of disease-associated genes via Ribonuclease H (RNaseH)-mediated cleavage or steric hindrance. They are being developed as a novel and promising class of drugs targeting a wide range of diseases. Despite the great potential and numerous ASO drugs in preclinical research and clinical trials, there are many limitations to this technology. In this review we will focus on the challenges of ASO delivery and the strategies adopted to improve their stability in the bloodstream, delivery to target sites, and cellular uptake. Focusing on liposomal delivery, we will specifically describe liposome-incorporated growth factor receptor-bound protein-2 (Grb2) antisense oligodeoxynucleotide BP1001. BP1001 is unique because it is uncharged and is essentially non-toxic, as demonstrated in preclinical and clinical studies. Additionally, its enhanced biodistribution makes it an attractive therapeutic modality for hematologic malignancies as well as solid tumors. A detailed understanding of the obstacles that ASOs face prior to reaching their targets and continued advances in methods to overcome them will allow us to harness ASOs’ full potential in precision medicine.