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The precise prediction of off-target effects in CRISPR-Cas9 genome editing is critical for ensuring the safety and efficacy of this powerful tool. This study leverages machine learning techniques to predict off-target cleavage sites and investigate the underlying mechanisms that affect cleavage efficiencies. By integrating data from Tsai et al. and Kleinsteiver et al., who employed the GUIDE-seq method, we aim to enhance our understanding of the factors influencing CRISPR-Cas9 activity. Our research analyzed datasets from Tsai et al. and Kleinsteiver et al., standardizing cleavage efficiencies to align with Tsai et al.'s comprehensive dataset. We identified a range of sequence features, including PAM sequence types, nucleotide composition, GC content, chromatin structure, CpG islands, and gene expression levels. Various machine learning models, including Artificial Neural Networks, Support Vector Machines, Naïve Bayes, k-Nearest Neighbors, Logistic Regression, and Extra Trees Classifiers, were developed and evaluated. The Extra Trees Classifier, particularly with class weighting, exhibited robust performance, achieving high accuracy, precision, recall, and F1 scores. SHAP analysis provided insights into feature importance, highlighting the significant factors contributing to model predictions. The application of machine learning to predict CRISPR-Cas9 off-target effects demonstrates significant potential in enhancing the precision of genome editing. Our findings underscore the importance of considering a diverse range of sequence and genomic features to improve prediction models. The insights gained from this study can inform the development of safer and more effective CRISPR-based applications in medicine, agriculture, and biotechnology. Future work will focus on further refining these models and exploring their applicability across different genomic contexts. [ABSTRACT FROM AUTHOR]

RNA interference (RNAi) is a form of gene silencing triggered by double‐stranded RNA (dsRNA) that operates in all eukaryotic cells. RNAi has been widely investigated in insects to determine the underlying molecular mechanism, to investigate its role in systemic antiviral defense, and to develop strategies for pest control. When insect cells are infected by viruses, viral dsRNA signatures trigger a local RNAi response to block viral replication and generate virus‐derived DNA that confers systemic immunity. RNAi‐based insect pest control involves the application of exogenous dsRNA targeting genes essential for insect development or survival, but the efficacy of this approach has limited potency in many pests through a combination of rapid dsRNA degradation, inefficient dsRNA uptake/processing, and ineffective RNAi machinery. This could be addressed by dsRNA screening and evaluation, focusing on dsRNA design and off‐target management, as well as dsRNA production and delivery. This review summarizes recent progress to determine the role of RNAi in antiviral defense and as a pest control strategy in insects, addressing gaps between our fundamental understanding of the RNAi mechanism and the exploitation of RNAi‐based pest control strategies. [ABSTRACT FROM AUTHOR]

Acinetobacter baumannii is widely recognized as a human opportunistic pathogen in nosocomial infections. The proliferation of multidrug-resistant strains of A. baumannii has presented an array of difficulties for clinical anti-infective therapies and diagnostic procedures, owing to the existence of numerous variations. The development of therapy utilizing CRISPR/Cas9 for treatment and diagnosis necessitates an in-depth study of potential off-target consequences. The objective of this work is to assess potential off-target effects associated with a single guide RNA (sgRNA) designed to identify several variants present in A. baumannii. The current investigation involved the identification of Cas12 nuclease-specific protospacer adjacent motif (PAM) and downstream target sequences. This was achieved by utilizing computational tools and software to analyze conserved sections of the A. baumannii siderophore protein gene. Further, the in-silico expression vector was created with the SnapGene software. A total of 24 potential off-target sequences were identified in these sequences with 100% query identity with 96 different A. baumannii strains. In addition, a target-specific oligonucleotide single-guide RNA (sgRNA) template was synthesized by appending an additional nucleotide 'G' to the 5′ end. This research uses A. baumannii as an example of a problem that affects all treatments and diagnosis procedures to illustrate the significance of screening off-targets in different variants of a pathogen. Our findings may impact the safety and effectiveness of CRISPR/Cas9, which may have wider implications for additional targets that are currently being used therapeutically.

Simple Summary: Cancer mortalities are growing at an alarming pace around the globe, a fact related to challenges, such as side effects, selectivity, and resistance, accompanied by cancer therapy. Continuous development of preventive and therapeutic agents is urgently needed. Drug repurposing is a favored drug discovery strategy resulting in faster, safer, easier, and cheaper repurposed drugs. This review shows the clinical benefits of chronically used anti-diabetic and anti-hypertensive drugs in preventing and treating different human malignancies through a repurposing strategy. The safety, tolerability, effectiveness at low doses, and suitability for long-term use of chronically used medications offer exceptional benefits over other drug classes. Promising clinical evidence exists for anti-diabetic and anti-hypertensive agents for treating different human malignancies. As some have reached Phase IV evaluations, this review offers new insights for clinicians in managing cancer, diabetes, and hypertension. Despite the advancement in drug discovery for cancer therapy, drug repurposing remains an exceptional opportunistic strategy. This approach offers many advantages (faster, safer, and cheaper drugs) typically needed to overcome increased challenges, i.e., side effects, resistance, and costs associated with cancer therapy. However, not all drug classes suit a patient's condition or long-time use. For that, repurposing chronically used medications is more appealing. This review highlights the importance of repurposing anti-diabetic and anti-hypertensive drugs in the global fight against human malignancies. Extensive searches of all available evidence (up to 30 March 2023) on the anti-cancer activities of anti-diabetic and anti-hypertensive agents are obtained from multiple resources (PubMed, Google Scholar, ClinicalTrials.gov, Drug Bank database, ReDo database, and the National Institutes of Health). Interestingly, more than 92 clinical trials are evaluating the anti-cancer activity of 14 anti-diabetic and anti-hypertensive drugs against more than 15 cancer types. Moreover, some of these agents have reached Phase IV evaluations, suggesting promising official release as anti-cancer medications. This comprehensive review provides current updates on different anti-diabetic and anti-hypertensive classes possessing anti-cancer activities with the available evidence about their mechanism(s) and stage of development and evaluation. Hence, it serves researchers and clinicians interested in anti-cancer drug discovery and cancer management. [ABSTRACT FROM AUTHOR]

CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein 9) is a popular and effective two-component technology used for targeted genetic manipulation. It is currently the most versatile and accurate method of gene and genome editing, which benefits from a large variety of practical applications. For example, in biomedicine, it has been used in research related to cancer, virus infections, pathogen detection, and genetic diseases. Current CRISPR/Cas9 research is based on data-driven models for on- and off-target prediction as a cleavage may occur at non-target sequence locations. Nowadays, conventional machine learning and deep learning methods are applied on a regular basis to accurately predict on-target knockout efficacy and off-target profile of given single-guide RNAs (sgRNAs). In this paper, we present an overview and a comparative analysis of traditional machine learning and deep learning models used in CRISPR/Cas9. We highlight the key research challenges and directions associated with target activity prediction. We discuss recent advances in the sgRNA–DNA sequence encoding used in state-of-the-art on- and off-target prediction models. Furthermore, we present the most popular deep learning neural network architectures used in CRISPR/Cas9 prediction models. Finally, we summarize the existing challenges and discuss possible future investigations in the field of on- and off-target prediction. Our paper provides valuable support for academic and industrial researchers interested in the application of machine learning methods in the field of CRISPR/Cas9 genome editing. [ABSTRACT FROM AUTHOR]

The 10X Genomics spatial transcriptomics platform enables profiling of fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) tissues. FF analysis relies on PolyA capture of RNAs, whereas FFPE analysis uses predesigned probes to capture coding transcripts. While using FFPE spatial data as a 'negative control' for identifying novel noncoding RNAs in FF data, we found that some probes captured spurious signal from both the target gene and potential lncRNAs. The finding was validated across seven published datasets from 10X Genomics and an in-house dataset. We recommend excluding genes captured by the identified 'spurious' probes from further analyses since the off-target signals account for the reported counts, where some of those genes are known to play role in cancer. We further propose to use existing annotations of lncRNA to redesign FFPE capture probes with better specificity. [ABSTRACT FROM AUTHOR]

Genodermatoses encompass a wide range of inherited skin diseases, many of which are monogenic. Genodermatoses range in severity and result in early-onset cancers or life-threatening damage to the skin, and there are few curative options. As such, there is a clinical need for single-intervention treatments with curative potential. Here, we discuss the nascent field of gene editing for the treatment of genodermatoses, exploring CRISPR–Cas9 and homology-directed repair, base editing, and prime editing tools for correcting pathogenic mutations. We specifically focus on the optimisation of editing efficiency, the minimisation off-targets edits, and the tools for delivery for potential future therapies. Honing each of these factors is essential for translating gene editing therapies into the clinical setting. Therefore, the aim of this review article is to raise important considerations for investigators aiming to develop gene editing approaches for genodermatoses. [ABSTRACT FROM AUTHOR]

Site-specific eukaryotic genome editing with CRISPR (clustered regularly interspaced short palindromic repeats)-Cas (CRISPR-associated) systems has quickly become a commonplace amongst researchers pursuing a wide variety of biological questions. Users most often employ the Cas9 protein derived from Streptococcus pyogenes in a complex with an easily reprogrammed guide RNA (gRNA). These components are introduced into cells, and through a base pairing with a complementary region of the double-stranded DNA (dsDNA) genome, the enzyme cleaves both strands to generate a double-strand break (DSB). Subsequent repair leads to either random insertion or deletion events (indels) or the incorporation of experimenter-provided DNA at the site of the break. The use of a purified single-guide RNA and Cas9 protein, preassembled to form an RNP and delivered directly to cells, is a potent approach for achieving highly efficient gene editing. RNP editing particularly enhances the rate of gene insertion, an outcome that is often challenging to achieve. Compared to the delivery via a plasmid, the shorter persistence of the Cas9 RNP within the cell leads to fewer off-target events. Despite its advantages, many casual users of CRISPR gene editing are less familiar with this technique. To lower the barrier to entry, we outline detailed protocols for implementing the RNP strategy in a range of contexts, highlighting its distinct benefits and diverse applications. We cover editing in two types of primary human cells, T cells and hematopoietic stem/progenitor cells (HSPCs). We also show how Cas9 RNP editing enables the facile genetic manipulation of entire organisms, including the classic model roundworm Caenorhabditis elegans and the more recently introduced model crustacean, Parhyale hawaiensis.

Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 gene editing technology has been widely used to facilitate efficient genome editing. Current popular sgRNA design tools only consider the sgRNA perfectly matched to the target site and provide the results without any on-target mismatch. We suppose taking on-target gRNA-DNA mismatches into consideration might provide better sgRNA with similar binding activity and reduced off-target sites. Here, we trained a seq2seq-attention model with feedback-loop architecture, to automatically generate sgRNAs with on-target mismatches. Dual-luciferase reporter experiment showed that multiple sgRNAs with three mismatches could achieve the 80% of the relative activity of the perfect matched sgRNA. Meanwhile, it could reduce the number of off-target sites using sgRNAs with on-target mismatches. Finally, we provided a freely accessible web server sgRNA design tool named ExsgRNA. Users could submit their target sequence to this server and get optimal sgRNAs with less off-targets and similar on-target activity compared with the perfect-matched sgRNA. [ABSTRACT FROM AUTHOR]

Background: Prostate specific membrane antigen (PSMA)-targeted radioligand therapies represent a highly effective treatment for metastatic prostate cancer. However, high and sustain uptake of PSMA-ligands in the salivary glands led to dose limiting dry mouth (xerostomia), especially with α-emitters. The expression of PSMA and histologic analysis couldn't directly explain the toxicity, suggesting a potential off-target mediator for uptake. In this study, we set out to search for possible off-target non-PSMA protein(s) in the salivary glands. Methods: A machine-learning based quantitative structure activity relationship (QSAR) model was built for seeking the possible off-target(s). The resulting target candidates from the model prediction were subjected to further analysis for salivary protein expression and structural homology at key regions required for PSMA-ligand binding. Furthermore, cellular binding assays were performed utilizing multiple cell lines with high expression of the candidate proteins and low expression of PSMA. Finally, PSMA knockout (PSMA-/-) mice were scanned by small animal PET/MR using [68Ga]Ga-PSMA-11 for in-vivo validation. Results: The screening of the trained QSAR model did not yield a solid off-target protein, which was corroborated in part by cellular binding assays. Imaging using PSMA-/- mice further demonstrated markedly reduced PSMA-radioligand uptake in the salivary glands. Conclusion: Uptake of the PSMA-targeted radioligands in the salivary glands remains primarily PSMA-mediated. Further investigations are needed to illustrate a seemingly different process of uptake and retention in the salivary glands than that in prostate cancer.

Since the introduction of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9), genome editing has been broadly applied in basic research and applied biotechnology, whereas translation into clinical testing has raised safety concerns. Indeed, although frequencies and locations of off-target events have been widely addressed, little is known about their potential biological consequences in large-scale long-term settings. We have developed a long-term adverse treatment effect (LATE) in vitro assay that addresses potential toxicity of designer nucleases by assessing cell transformation events. In small-scale proof-of-principle experiments we reproducibly detected low-frequency (

The reprogrammable CRISPR/Cas9 genome editing tool's growing popularity is hindered by unwanted off-target effects. Efforts have been directed toward designing efficient guide RNAs as well as identifying potential off-target threats, yet factors that determine efficiency and off-target activity remain obscure. Based on sequence features, previous machine learning models performed poorly on new datasets, thus there is a need for the incorporation of novel features. The binding energy estimation of the gRNA-DNA hybrid as well as the Cas9-gRNA-DNA hybrid allowed generating better performing machine learning models for the prediction of Cas9 activity. The analysis of feature contribution towards the model output on a limited dataset indicated that energy features played a determining role along with the sequence features. The binding energy features proved essential for the prediction of on-target activity and off-target sites. The plateau, in the performance on unseen datasets, of current machine learning models could be overcome by incorporating novel features, such as binding energy, among others. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA). [ABSTRACT FROM AUTHOR]

Sdox is a hydrogen sulfide (H2S)-releasing doxorubicin effective in P-glycoprotein-overexpressing/doxorubicin-resistant tumor models and not cytotoxic, as the parental drug, in H9c2 cardiomyocytes. The aim of this study was the assessment of Sdox drug-like features and its absorption, distribution, metabolism, and excretion (ADME)/toxicity properties, by a multi- and transdisciplinary in silico , in vitro , and in vivo approach. Doxorubicin was used as the reference compound. The in silico profiling suggested that Sdox possesses higher lipophilicity and lower solubility compared to doxorubicin, and the off-targets prediction revealed relevant differences between Dox and Sdox towards several cancer targets, suggesting different toxicological profiles. In vitro data showed that Sdox is a substrate with lower affinity for P-glycoprotein, less hepatotoxic, and causes less oxidative damage than doxorubicin. Both anthracyclines inhibited CYP3A4, but not hERG currents. Unlike doxorubicin, the percentage of zebrafish live embryos at 72 hpf was not affected by Sdox treatment. In conclusion, these findings demonstrate that Sdox displays a more favorable drug-like ADME/toxicity profile than doxorubicin, different selectivity towards cancer targets, along with a greater preclinical efficacy in resistant tumors. Therefore, Sdox represents a prototype of innovative anthracyclines, worthy of further investigations in clinical settings. [ABSTRACT FROM AUTHOR]

Sdox is a hydrogen sulfide (H2S)-releasing doxorubicin effective in P-glycoprotein-overexpressing/doxorubicin-resistant tumor models and not cytotoxic, as the parental drug, in H9c2 cardiomyocytes. The aim of this study was the assessment of Sdox drug-like features and its absorption, distribution, metabolism, and excretion (ADME)/toxicity properties, by a multi- and transdisciplinary in silico, in vitro, and in vivo approach. Doxorubicin was used as the reference compound. The in silico profiling suggested that Sdox possesses higher lipophilicity and lower solubility compared to doxorubicin, and the off-targets prediction revealed relevant differences between Dox and Sdox towards several cancer targets, suggesting different toxicological profiles. In vitro data showed that Sdox is a substrate with lower affinity for P-glycoprotein, less hepatotoxic, and causes less oxidative damage than doxorubicin. Both anthracyclines inhibited CYP3A4, but not hERG currents. Unlike doxorubicin, the percentage of zebrafish live embryos at 72 hpf was not affected by Sdox treatment. In conclusion, these findings demonstrate that Sdox displays a more favorable drug-like ADME/toxicity profile than doxorubicin, different selectivity towards cancer targets, along with a greater preclinical efficacy in resistant tumors. Therefore, Sdox represents a prototype of innovative anthracyclines, worthy of further investigations in clinical settings.

Abstract Background Identifying nuclease-induced double-stranded breaks in DNA on a genome-wide scale is critical for assessing the safety and efficacy of genome editing therapies. We previously demonstrated that after administering adeno-associated viral (AAV) vector-mediated genome-editing strategies in vivo, vector sequences integrated into the host organism’s genomic DNA at double-stranded breaks. Thus, identifying the genomic location of inserted AAV sequences would enable us to identify DSB events, mainly derived from the nuclease on- and off-target activity. Results Here, we developed a next-generation sequencing assay that detects insertions of specific AAV vector sequences called inverted terminal repeats (ITRs). This assay, ITR-Seq, enables us to identify off-target nuclease activity in vivo. Using ITR-Seq, we analyzed liver DNA samples of rhesus macaques treated with AAV vectors expressing a meganuclease. We found dose-dependent off-target activity and reductions in off-target events induced by further meganuclease development. In mice, we identified the genomic locations of ITR integration after treatment with Cas9 nucleases and their corresponding single-guide RNAs. Conclusions In sum, ITR-Seq is a powerful method for identifying off-target sequences induced by AAV vector-delivered genome-editing nucleases. ITR-Seq will help us understand the specificity and efficacy of different genome-editing nucleases in animal models and clinical studies. This information can help enhance the safety profile of gene-editing therapies.

The repurposing of the CRISPR/Cas bacterial defense system against bacteriophages as simple and flexible molecular tools has revolutionized the field of gene editing. These tools are now widely used in basic research and clinical trials involving human somatic cells. However, a global moratorium on all clinical uses of human germline editing has been proposed because the technology still lacks the required efficacy and safety. Here we focus on the approaches developed since 2013 to decrease the frequency of unwanted mutations (the off-targets) during CRISPR-based gene editing.

The reprogrammable CRISPR/Cas9 genome editing tool’s growing popularity is hindered by unwanted off-target effects. Efforts have been directed toward designing efficient guide RNAs as well as identifying potential off-target threats, yet factors that determine efficiency and off-target activity remain obscure. Based on sequence features, previous machine learning models performed poorly on new datasets, thus there is a need for the incorporation of novel features. The binding energy estimation of the gRNA-DNA hybrid as well as the Cas9-gRNA-DNA hybrid allowed generating better performing machine learning models for the prediction of Cas9 activity. The analysis of feature contribution towards the model output on a limited dataset indicated that energy features played a determining role along with the sequence features. The binding energy features proved essential for the prediction of on-target activity and off-target sites. The plateau, in the performance on unseen datasets, of current machine learning models could be overcome by incorporating novel features, such as binding energy, among others. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA).

Virus-derived siRNAs (vsiRNAs) generated by the host RNA silencing mechanism are effectors of plant’s defense response and act by targeting the viral RNA and DNA in post-transcriptional gene silencing (PTGS) and transcriptional gene silencing (TGS) pathways, respectively. Contrarily, viral suppressors of RNA silencing (VSRs) compromise the host RNA silencing pathways and also cause disease-associated symptoms. In this backdrop, reports describing the modulation of plant gene(s) expression by vsiRNAs via sequence complementarity between viral small RNAs (sRNAs) and host mRNAs have emerged. In some cases, silencing of host mRNAs by vsiRNAs has been implicated to cause characteristic symptoms of the viral diseases. Similarly, viroid infection results in generation of sRNAs, originating from viroid genomic RNAs, that potentially target host mRNAs causing typical disease-associated symptoms. Pathogen-derived sRNAs have been demonstrated to have the propensity to target wide range of genes including host defense-related genes, genes involved in flowering and reproductive pathways. Recent evidence indicates that vsiRNAs inhibit host RNA silencing to promote viral infection by acting as decoy sRNAs. Nevertheless, it remains unclear if the silencing of host transcripts by viral genome-derived sRNAs are inadvertent effects due to fortuitous pairing between vsiRNA and host mRNA or the result of genuine counter-defense strategy employed by viruses to enhance its survival inside the plant cell. In this review, we analyze the instances of such cross reaction between pathogen-derived vsiRNAs and host mRNAs and discuss the molecular insights regarding the process of pathogenesis.

Lung cancer is a leading cause cancer-related death with diversity. A promising approach to meet the need for improved cancer treatment is drug repurposing. Dasatinib, a second generation of tyrosine kinase inhibitors (TKIs), is a potent treatment agent for chronic myeloid leukemia (CML) approved by FDA, however, its off-targets and the underlying mechanisms in lung cancer have not been elucidated yet. LIM kinase 1 (LIMK1) is a serine/threonine kinase, which is highly upregulated in human cancers. Herein, we demonstrated that dasatinib dose-dependently blocked lung cancer cell proliferation and repressed LIMK1 activities by directly targeting LIMK1. It was confirmed that knockdown of LIMK1 expression suppressed lung cancer cell proliferation. From the in silico screening results, dasatinib may target to LIMK1. Indeed, dasatinib significantly inhibited the LIMK1 activity as evidenced by kinase and binding assay, and computational docking model analysis. Dasatinib inhibited lung cancer cell growth, while induced cell apoptosis as well as cell cycle arrest at the G1 phase. Meanwhile, dasatinib also suppressed the expression of markers relating cell cycle, cyclin D1, D3, and CDK2, and increased the levels of markers involved in cell apoptosis, cleaved caspase-3 and caspase-7 by downregulating phosphorylated LIMK1 (p-LIMK1) and cofilin (p-cofilin). Furthermore, in patient-derived xenografts (PDXs), dasatinib (30 mg/kg) significantly inhibited the growth of tumors in SCID mice which highly expressed LIMK1 without changing the bodyweight. In summary, our results indicate that dasatinib acts as a novel LIMK1 inhibitor to suppress the lung cancer cell proliferation in vitro and tumor growth in vivo, which suggests evidence for the application of dasatinib in lung cancer therapy.

Virus-derived siRNAs (vsiRNAs) generated by the host RNA silencing mechanism are effectors of plant's defense response and act by targeting the viral RNA and DNA in post-transcriptional gene silencing (PTGS) and transcriptional gene silencing (TGS) pathways, respectively. Contrarily, viral suppressors of RNA silencing (VSRs) compromise the host RNA silencing pathways and also cause disease-associated symptoms. In this backdrop, reports describing the modulation of plant gene(s) expression by vsiRNAs via sequence complementarity between viral small RNAs (sRNAs) and host mRNAs have emerged. In some cases, silencing of host mRNAs by vsiRNAs has been implicated to cause characteristic symptoms of the viral diseases. Similarly, viroid infection results in generation of sRNAs, originating from viroid genomic RNAs, that potentially target host mRNAs causing typical disease-associated symptoms. Pathogen-derived sRNAs have been demonstrated to have the propensity to target wide range of genes including host defense-related genes, genes involved in flowering and reproductive pathways. Recent evidence indicates that vsiRNAs inhibit host RNA silencing to promote viral infection by acting as decoy sRNAs. Nevertheless, it remains unclear if the silencing of host transcripts by viral genome-derived sRNAs are inadvertent effects due to fortuitous pairing between vsiRNA and host mRNA or the result of genuine counter-defense strategy employed by viruses to enhance its survival inside the plant cell. In this review, we analyze the instances of such cross reaction between pathogen-derived vsiRNAs and host mRNAs and discuss the molecular insights regarding the process of pathogenesis. [ABSTRACT FROM AUTHOR]

Computer-aided drug design is a mature field by some measures, and it has produced notable successes that underpin the study of interactions between small molecules and living systems. However, unlike a truly mature field, fallacies of logic lie at the heart of the arguments in support of major lines of research on methodology and validation thereof. Two particularly pernicious ones are cum hoc ergo propter hoc (with this, therefore because of this) and confirmation bias (seeking evidence that is confirmatory of the hypothesis at hand). These fallacies will be discussed in the context of off-target predictive modeling, QSAR, molecular similarity computations, and docking. Examples will be shown that avoid these problems.

Abstract Background Recent experimental efforts of CRISPR-Cas9 systems have shown that off-target binding and cleavage are a concern for the system and that this is highly dependent on the selected guide RNA (gRNA) design. Computational predictions of off-targets have been proposed as an attractive and more feasible alternative to tedious experimental efforts. However, accurate scoring of the high number of putative off-targets plays a key role for the success of computational off-targeting assessment. Results We present an approximate binding energy model for the Cas9–gRNA–DNA complex, which systematically combines the energy parameters obtained for RNA–RNA, DNA–DNA, and RNA–DNA duplexes. Based on this model, two novel off-target assessment methods for gRNA selection in CRISPR-Cas9 applications are introduced: CRISPRoff to assign confidence scores to predicted off-targets and CRISPRspec to measure the specificity of the gRNA. We benchmark the methods against current state-of-the-art methods and show that both are in better agreement with experimental results. Furthermore, we show significant evidence supporting the inverse relationship between the on-target cleavage efficiency and specificity of the system, in which introduced binding energies are key components. Conclusions The impact of the binding energies provides a direction for further studies of off-targeting mechanisms. The performance of CRISPRoff and CRISPRspec enables more accurate off-target evaluation for gRNA selections, prior to any CRISPR-Cas9 genome-editing application. For given gRNA sequences or all potential gRNAs in a given target region, CRISPRoff-based off-target predictions and CRISPRspec-based specificity evaluations can be carried out through our webserver at https://rth.dk/resources/crispr/.

Abstract A major challenge for effective application of CRISPR systems is to accurately predict the single guide RNA (sgRNA) on-target knockout efficacy and off-target profile, which would facilitate the optimized design of sgRNAs with high sensitivity and specificity. Here we present DeepCRISPR, a comprehensive computational platform to unify sgRNA on-target and off-target site prediction into one framework with deep learning, surpassing available state-of-the-art in silico tools. In addition, DeepCRISPR fully automates the identification of sequence and epigenetic features that may affect sgRNA knockout efficacy in a data-driven manner. DeepCRISPR is available at http://www.deepcrispr.net/.

Background: Identifying nuclease-induced double-stranded breaks in DNA on a genome-wide scale is critical for assessing the safety and efficacy of genome editing therapies. We previously demonstrated that after administering adeno-associated viral (AAV) vector-mediated genome-editing strategies in vivo, vector sequences integrated into the host organism's genomic DNA at double-stranded breaks. Thus, identifying the genomic location of inserted AAV sequences would enable us to identify DSB events, mainly derived from the nuclease on- and off-target activity. Results: Here, we developed a next-generation sequencing assay that detects insertions of specific AAV vector sequences called inverted terminal repeats (ITRs). This assay, ITR-Seq, enables us to identify off-target nuclease activity in vivo. Using ITR-Seq, we analyzed liver DNA samples of rhesus macaques treated with AAV vectors expressing a meganuclease. We found dose-dependent off-target activity and reductions in off-target events induced by further meganuclease development. In mice, we identified the genomic locations of ITR integration after treatment with Cas9 nucleases and their corresponding single-guide RNAs. Conclusions: In sum, ITR-Seq is a powerful method for identifying off-target sequences induced by AAV vector-delivered genome-editing nucleases. ITR-Seq will help us understand the specificity and efficacy of different genome-editing nucleases in animal models and clinical studies. This information can help enhance the safety profile of gene-editing therapies. [ABSTRACT FROM AUTHOR]

Integration-deficient lentiviral vectors (IDLVs) have recently generated increasing interest, not only as a tool for transient gene delivery, but also as a technique for detecting off-target cleavage in gene-editing methodologies which rely on customized endonucleases (ENs). Despite their broad potential applications, the efficacy of IDLVs has historically been limited by low transgene expression and by the reduced sensitivity to detect low-frequency off-target events. We have previously reported that the incorporation of the chimeric sequence element IS2 into the long terminal repeat (LTR) of IDLVs increases gene expression levels, while also reducing the episome yield inside transduced cells. Our study demonstrates that the effectiveness of IDLVs relies on the balance between two parameters which can be modulated by the inclusion of IS2 sequences. In the present study, we explore new IDLV configurations harboring several elements based on IS2 modifications engineered to mediate more efficient transgene expression without affecting the targeted cell load. Of all the insulators and configurations analysed, the insertion of the IS2 into the 3′LTR produced the best results. After demonstrating a DAPI-low nuclear gene repositioning of IS2-containing episomes, we determined whether, in addition to a positive effect on transcription, the IS2 could improve the capture of IDLVs on double strand breaks (DSBs). Thus, DSBs were randomly generated, using the etoposide or locus-specific CRISPR-Cas9. Our results show that the IS2 element improved the efficacy of IDLV DSB detection. Altogether, our data indicate that the insertion of IS2 into the LTR of IDLVs improved, not only their transgene expression levels, but also their ability to be inserted into existing DSBs. This could have significant implications for the development of an unbiased detection tool for off-target cleavage sites from different specific nucleases.

Huntington’s disease (HD) is an autosomal dominant neurodegenerative disorder resulting from a polyglutamine expansion in the huntingtin (HTT) protein. There is currently no cure for this disease, but recent studies suggest that RNAi to downregulate the expression of both normal and mutant HTT is a promising therapeutic approach. We previously developed a small hairpin RNA (shRNA), vectorized in an HIV-1-derived lentiviral vector (LV), that reduced pathology in an HD rodent model. Here, we modified this vector for preclinical development by using a tat-independent third-generation LV (pCCL) backbone and removing the original reporter genes. We demonstrate that this novel vector efficiently downregulated HTT expression in vitro in striatal neurons derived from induced pluripotent stem cells (iPSCs) of HD patients. It reduced two major pathological HD hallmarks while triggering a minimal inflammatory response, up to 6 weeks after injection, when administered by stereotaxic surgery in the striatum of an in vivo rodent HD model. Further assessment of this shRNA vector in vitro showed proper processing by the endogenous silencing machinery, and we analyzed gene expression changes to identify potential off-targets. These preclinical data suggest that this new shRNA vector fulfills primary biosafety and efficiency requirements for further development in the clinic as a cure for HD.

Studying circular RNAs' function in vivo has been challenging due to the lack of generic tools to manipulate their levels without affecting their linear counterparts. This is particularly challenging as the back-splice junction is the only sequence not shared between the linear and circular version. In this chapter, we describe a method to study circRNA function in vivo targeting shRNAs against the desired back-splice junction to achieve knockdown with tissue-specific resolution in flies., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning. [ABSTRACT FROM AUTHOR]

Safety-related drug failures remain a major challenge for the pharmaceutical industry. One approach to ensuring drug safety involves assessing small molecule drug specificity by examining the ability of a drug candidate to interact with a panel of "off-target" proteins, referred to as secondary pharmacology screening. Information from human genetics and pharmacology can be used to select proteins associated with adverse effects for such screening. In an analysis of marketed drugs, we found a clear relationship between the genetic and pharmacological phenotypes of a drug's off-target proteins and the observed drug side effects. In addition to using this phenotypic information for the selection of secondary pharmacology screens, we also show that it can be used to help identify drug off-target protein interactions responsible for drug-related adverse events. We anticipate that this phenotype-driven approach to secondary pharmacology screening will help to reduce safety-related drug failures due to drug off-target protein interactions. [ABSTRACT FROM AUTHOR]

In the field of kinase inhibitors for applications in cancer research, tubulin is emerging as a targeted cellular protein that can significantly contribute to their activities. However, investigation of kinase inhibitors beyond the kinome is an area often neglected. Herein, we describe the results of pharmacological studies using drugs targeting kinases, tubulin or both. A key finding is that if cells are treated with a kinase inhibitor unintentionally targeting tubulin, their characteristic shape will diminish within a short timeframe. These changes in cell morphology are not seen when cells are treated with bona fide kinase inhibitors that do not directly target tubulin. Thus, early changes in cell morphology upon treatments are a strong indication that the inhibitor is directly targeting tubulin. Recognizing tubulin as a target of kinase inhibitors will build confidence in the future mechanistic studies using kinase inhibitors. [ABSTRACT FROM AUTHOR]

Background: Fusion of DNA methyltransferase domains to the nuclease-deficient clustered regularly interspaced short palindromic repeat (CRISPR) associated protein 9 (dCas9) has been used for epigenome editing, but the specificities of these dCas9 methyltransferases have not been fully investigated. Findings: We generated CRISPR-guided DNA methyltransferases by fusing the catalytic domain of DNMT3A or DNMT3B to the C terminus of the dCas9 protein from Streptococcus pyogenes and validated its on-target and global off-target characteristics. Using targeted quantitative bisulfite pyrosequencing, we prove that dCas9-BFP-DNMT3A and dCas9-BFP-DNMT3B can efficiently methylate the CpG dinucleotides flanking its target sites at different genomic loci (uPA and TGFBR3) in human embryonic kidney cells (HEK293T). Furthermore, we conducted whole genome bisulfite sequencing (WGBS) to address the specificity of our dCas9 methyltransferases. WGBS revealed that although dCas9-BFP-DNMT3A and dCas9-BFP-DNMT3B did not cause global methylation changes, a substantial number (more than 1000) of the off-target differentially methylated regions (DMRs) were identified. The off-target DMRs, which were hypermethylated in cells expressing dCas9 methyltransferase and guide RNAs, were predominantly found in promoter regions, 5΄ untranslated regions, CpG islands, and DNase I hypersensitivity sites, whereas unexpected hypomethylated off-target DMRs were significantly enriched in repeated sequences. Through chromatin immunoprecipitation with massive parallel DNA sequencing analysis, we further revealed that these off-target DMRs were weakly correlated with dCas9 off-target binding sites. Using quantitative polymerase chain reaction, RNA sequencing, and fluorescence reporter cells, we also found that dCas9-BFP-DNMT3A and dCas9-BFP-DNMT3B can mediate transient inhibition of gene expression, which might be caused by dCas9-mediated de novo DNA methylation as well as interference with transcription. Conclusion: Our results prove that dCas9 methyltransferases cause efficient RNA-guided methylation of specific endogenous CpGs. However, there is significant off-target methylation indicating that further improvements of the specificity of CRISPR-dCas9 based DNA methylation modifiers are required. [ABSTRACT FROM AUTHOR]

Dark chemical matter compounds are small molecules that have been recently identified as highly potent and selective hits. For this reason, they constitute a promising class of possible candidates in the process of drug discovery and raise the interest of the scientific community. To this purpose, Wassermann et al. (2015) have described the application of 2D descriptors to characterize dark chemical matter. However, their definition was based on the number of reported positive assays rather than the number of known targets. As there might be multiple assays for one single target, the number of assays does not fully describe target selectivity. Here, we propose an alternative classification of active molecules that is based on the number of known targets. We cluster molecules in four classes: black, gray, and white compounds are active on one, two to four, and more than four targets respectively, whilst inactive compounds are found to be inactive in the considered assays. In this study, black and inactive compounds are found to have not only higher solubility, but also a higher number of chiral centers, sp3 carbon atoms and aliphatic rings. On the contrary, white compounds contain a higher number of double bonds and fused aromatic rings. Therefore, the design of a screening compound library should consider these molecular properties in order to achieve target selectivity or polypharmacology. Furthermore, analysis of four main target classes (GPCRs, kinases, proteases, and ion channels) shows that GPCR ligands are more selective than the other classes, as the number of black compounds is higher in this target superfamily. On the other side, ligands that hit kinases, proteases, and ion channels bind to GPCRs more likely than to other target classes. Consequently, depending on the target protein family, appropriate screening libraries can be designed in order to minimize the likelihood of unwanted side effects early in the drug discovery process. Additionally, synergistic effects may be obtained by library design toward polypharmacology. [ABSTRACT FROM AUTHOR]

Despite the massive global spend on biology-driven drug discovery, tackling the issue of side effects and adverse events resulting from drug promiscuity represents a persistent challenge. Although delivering authentic medical innovations today is more complex than ever, minimization of off-target effects should be a priority. [ABSTRACT FROM AUTHOR]

RNA silencing mechanism functions as a major defense against invading viruses. The caveat in the RNA silencing mechanism is that the effector small interfering RNAs (siRNAs) act on any RNA transcripts with sequence complementarity irrespective of target’s origin. A subset of highly expressed viral small interfering RNAs (vsiRNAs) derived from the tomato spotted wilt virus (TSWV; Tospovirus : Bunyaviridae ) genome was analyzed for their propensity to downregulate the tomato transcriptome. A total of 11898 putative target sites on tomato transcripts were found to exhibit a propensity for down regulation by TSWV-derived vsiRNAs. In total, 2450 unique vsiRNAs were found to have potential cross-reacting capability with the tomato transcriptome. VsiRNAs were found to potentially target a gamut of host genes involved in basal cellular activities including enzymes, transcription factors, membrane transporters, and cytoskeletal proteins. KEGG pathway annotation of targets revealed that the vsiRNAs were mapped to secondary metabolite biosynthesis, amino acids, starch and sucrose metabolism, and carbon and purine metabolism. Transcripts for protein processing, hormone signalling, and plant-pathogen interactions were the most likely targets from the genetic, environmental information processing, and organismal systems, respectively. qRT-PCR validation of target gene expression showed that none of the selected transcripts from tomato cv. Marglobe showed up regulation, and all were down regulated even upto 20 folds (high affinity glucose transporter). However, the expression levels of transcripts from cv. Red Defender revealed differential regulation as three among the target transcripts showed up regulation (Cc-nbs-lrr, resistance protein, AP2-like ethylene-responsive transcription factor, and heat stress transcription factor A3). Accumulation of tomato target mRNAs of corresponding length was proved in both tomato cultivars using 5′ RACE analysis. The TSWV-tomato interaction at the sRNA interface points to the ability of tomato cultivars to overcome vsiRNA-mediated targeting of NBS-LRR class R genes. These results suggest the prevalence of vsiRNA-induced RNA silencing of host transcriptome, and the interactome scenario is the first report on the interaction between tospovirus genome-derived siRNAs and tomato transcripts, and provide a deeper understanding of the role of vsiRNAs in pathogenicity and in perturbing host machinery. [ABSTRACT FROM AUTHOR]

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 ( m ake a nti s ense o ligomers n ow), 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., (© 2023 Jung et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.)