Negative DNA supercoiling induces genome-wide Cas9 off-target activity.

CRISPR-Cas9 is a powerful gene-editing technology; however, off-target activity remains an important consideration for therapeutic applications. We have previously shown that force-stretching DNA induces off-target activity and hypothesized that distortions of the DNA topology in vivo, such as negative DNA supercoiling, could reduce Cas9 specificity. Using single-molecule optical-tweezers, we demonstrate that negative supercoiling λ-DNA induces sequence-specific Cas9 off-target binding at multiple sites, even at low forces. Using an adapted CIRCLE-seq approach, we detect over 10,000 negative-supercoiling-induced Cas9 off-target double-strand breaks genome-wide caused by increased mismatch tolerance. We further demonstrate in vivo that directed local DNA distortion increases off-target activity in cells and that induced off-target events can be detected during Cas9 genome editing. These data demonstrate that Cas9 off-target activity is regulated by DNA topology in vitro and in vivo, suggesting that cellular processes, such as transcription and replication, could induce off-target activity at previously overlooked sites.

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.

Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review.

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.

ExsgRNA: reduce off-target efficiency by on-target mismatched sgRNA.

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.

In vitro safety signals for potential clinical development of the anti-inflammatory pregnane X receptor agonist FKK6.

Based on the mimicry of microbial metabolites, functionalized indoles were demonstrated as the ligands and agonists of the pregnane X receptor (PXR). The lead indole, FKK6, displayed PXR-dependent protective effects in DSS-induced colitis in mice and in vitro cytokine-treated intestinal organoid cultures. Here, we report on the initial in vitro pharmacological profiling of FKK6. FKK6-PXR interactions were characterized by hydrogen-deuterium exchange mass spectrometry. Screening FKK6 against potential cellular off-targets (G protein-coupled receptors, steroid and nuclear receptors, ion channels, and xenobiotic membrane transporters) revealed high PXR selectivity. FKK6 has poor aqueous solubility but was highly soluble in simulated gastric and intestinal fluids. A large fraction of FKK6 was bound to plasma proteins and chemically stable in plasma. The partition coefficient of FKK6 was 2.70, and FKK6 moderately partitioned into red blood cells. In Caco2 cells, FKK6 displayed high permeability (A-B: 22.8 × 10-6 cm.s-1) and no active efflux. These data are indicative of essentially complete in vivo absorption of FKK6. The data from human liver microsomes indicated that FKK6 is rapidly metabolized by cytochromes P450 (t1/2 5 min), notably by CYP3A4. Two oxidized FKK6 derivatives, including DC73 (N6-oxide) and DC97 (C19-phenol), were detected, and these metabolites had 5-7 × lower potency as PXR agonists than FKK6. This implies that despite high intestinal absorption, FKK6 is rapidly eliminated by the liver, and its PXR effects are predicted to be predominantly in the intestines. In conclusion, the PXR ligand and agonist FKK6 has a suitable pharmacological profile supporting its potential preclinical development.

Cleavage-free human genome editing.

Most gene editing technologies introduce breaks or nicks into DNA, leading to the generation of mutagenic insertions and deletions by non-homologous end-joining repair. Here, we report a new, cleavage-free gene editing approach based on replication interrupted template-driven DNA modification (RITDM). The RITDM system makes use of sequence-specific DLR fusion molecules that are specifically designed to enable localized, temporary blockage of DNA replication fork progression, thereby exposing single-stranded DNA that can be bound by DNA sequence modification templates for precise editing. We evaluate the use of zinc-finger arrays for sequence recognition. We demonstrate that RITDM can be used for gene editing at endogenous genomic loci in human cells and highlight its safety profile of low indel frequencies and undetectable off-target side effects in RITDM-edited clones and pools of cells.

Challenges of Gene Editing Therapies for Genodermatoses.

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.

Antibody for Serine 65 Phosphorylated Ubiquitin Identifies PLK1-Mediated Phosphorylation of Mitotic Proteins and APC1.

Deciphering the protein posttranslational modification (PTM) code is one of the greatest biochemical challenges of our time. Phosphorylation and ubiquitylation are key PTMs that dictate protein function, recognition, sub-cellular localization, stability, turnover and fate. Hence, failures in their regulation leads to various disease. Chemical protein synthesis allows preparation of ubiquitinated and phosphorylated proteins to study their biochemical properties in great detail. However, monitoring these modifications in intact cells or in cell extracts mostly depends on antibodies, which often have off-target binding. Here, we report that the most widely used antibody for ubiquitin (Ub) phosphorylated at serine 65 (pUb) has significant off-targets that appear during mitosis. These off-targets are connected to polo-like kinase 1 (PLK1) mediated phosphorylation of cell cycle-related proteins and the anaphase promoting complex subunit 1 (APC1).

The potential application of genome editing by using CRISPR/Cas9, and its engineered and ortholog variants for studying the transcription factors involved in the maintenance of phosphate homeostasis in model plants.

Phosphorus (P), an essential macronutrient, is pivotal for growth and development of plants. Availability of phosphate (Pi), the only assimilable P, is often suboptimal in rhizospheres. Pi deficiency triggers an array of spatiotemporal adaptive responses including the differential regulation of several transcription factors (TFs). Studies on MYB TF PHR1 in Arabidopsis thaliana (Arabidopsis) and its orthologs OsPHRs in Oryza sativa (rice) have provided empirical evidence of their significant roles in the maintenance of Pi homeostasis. Since the functional characterization of PHR1 in 2001, several other TFs have now been identified in these model plants. This raised a pertinent question whether there are any likely interactions across these TFs. Clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system has provided an attractive paradigm for editing genome in plants. Here, we review the applications and challenges of this technique for genome editing of the TFs for deciphering the function and plausible interactions across them. This technology could thus provide a much-needed fillip towards engineering TFs for generating Pi use efficient plants for sustainable agriculture. Furthermore, we contemplate whether this technology could be a viable alternative to the controversial genetically modified (GM) rice or it may also eventually embroil into a limbo.

Binding site matching in rational drug design: algorithms and applications.

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.

Development of an Antigen-Antibody Co-Display System for Detecting Interaction of G-Protein-Coupled Receptors and Single-Chain Variable Fragments.

G-protein-coupled receptors (GPCRs), especially chemokine receptors, are ideal targets for monoclonal antibody drugs. Considering the special multi-pass transmembrane structure of GPCR, it is often a laborious job to obtain antibody information about off-targets and epitopes on antigens. To accelerate the process, a rapid and simple method needs to be developed. The split-ubiquitin-based yeast two hybrid system (YTH) was used as a blue script for a new method. By fusing with transmembrane peptides, scFv antibodies were designed to be anchored on the cytomembrane, where the GPCR was co-displayed as well. The coupled split-ubiquitin system transformed the scFv-GPCR interaction signal into the expression of reporter genes. By optimizing the topological structure of scFv fusion protein and key elements, including signal peptides, transmembrane peptides, and flexible linkers, a system named Antigen-Antibody Co-Display (AACD) was established, which rapidly detected the interactions between antibodies and their target GPCRs, CXCR4 and CXCR5, while also determining the off-target antibodies and antibody-associated epitopes. The AACD system can rapidly determine the association between GPCRs and their candidate antibodies and shorten the research period for off-target detection and epitope identification. This system should improve the process of GPCR antibody development and provide a new strategy for GPCRs antibody screening.

Off-Target-Based Design of Selective HIV-1 PROTEASE Inhibitors.

The approval of the first HIV-1 protease inhibitors (HIV-1 PRIs) marked a fundamental step in the control of AIDS, and this class of agents still represents the mainstay therapy for this illness. Despite the undisputed benefits, the necessary lifelong treatment led to numerous severe side-effects (metabolic syndrome, hepatotoxicity, diabetes, etc.). The HIV-1 PRIs are capable of interacting with "secondary" targets (off-targets) characterized by different biological activities from that of HIV-1 protease. In this scenario, the in-silico techniques undoubtedly contributed to the design of new small molecules with well-fitting selectivity against the main target, analyzing possible undesirable interactions that are already in the early stages of the research process. The present work is focused on a new mixed-hierarchical, ligand-structure-based protocol, which is centered on an on/off-target approach, to identify the new selective inhibitors of HIV-1 PR. The use of the well-established, ligand-based tools available in the DRUDIT web platform, in combination with a conventional, structure-based molecular docking process, permitted to fast screen a large database of active molecules and to select a set of structure with optimal on/off-target profiles. Therefore, the method exposed herein, could represent a reliable help in the research of new selective targeted small molecules, permitting to design new agents without undesirable interactions.

ITR-Seq, a next-generation sequencing assay, identifies genome-wide DNA editing sites in vivo following adeno-associated viral vector-mediated genome editing.

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.

Structure-based prediction of ligand-protein interactions on a genome-wide scale.

We report a template-based method, LT-scanner, which scans the human proteome using protein structural alignment to identify proteins that are likely to bind ligands that are present in experimentally determined complexes. A scoring function that rapidly accounts for binding site similarities between the template and the proteins being scanned is a crucial feature of the method. The overall approach is first tested based on its ability to predict the residues on the surface of a protein that are likely to bind small-molecule ligands. The algorithm that we present, LBias, is shown to compare very favorably to existing algorithms for binding site residue prediction. LT-scanner's performance is evaluated based on its ability to identify known targets of Food and Drug Administration (FDA)-approved drugs and it too proves to be highly effective. The specificity of the scoring function that we use is demonstrated by the ability of LT-scanner to identify the known targets of FDA-approved kinase inhibitors based on templates involving other kinases. Combining sequence with structural information further improves LT-scanner performance. The approach we describe is extendable to the more general problem of identifying binding partners of known ligands even if they do not appear in a structurally determined complex, although this will require the integration of methods that combine protein structure and chemical compound databases.

Changes in cell morphology guide identification of tubulin as the off-target for protein kinase inhibitors.

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.

GUIDEseq: a bioconductor package to analyze GUIDE-Seq datasets for CRISPR-Cas nucleases.

BACKGROUND: Genome editing technologies developed around the CRISPR-Cas9 nuclease system have facilitated the investigation of a broad range of biological questions. These nucleases also hold tremendous promise for treating a variety of genetic disorders. In the context of their therapeutic application, it is important to identify the spectrum of genomic sequences that are cleaved by a candidate nuclease when programmed with a particular guide RNA, as well as the cleavage efficiency of these sites. Powerful new experimental approaches, such as GUIDE-seq, facilitate the sensitive, unbiased genome-wide detection of nuclease cleavage sites within the genome. Flexible bioinformatics analysis tools for processing GUIDE-seq data are needed. RESULTS: Here, we describe an open source, open development software suite, GUIDEseq, for GUIDE-seq data analysis and annotation as a Bioconductor package in R. The GUIDEseq package provides a flexible platform with more than 60 adjustable parameters for the analysis of datasets associated with custom nuclease applications. These parameters allow data analysis to be tailored to different nuclease platforms with different length and complexity in their guide and PAM recognition sequences or their DNA cleavage position. They also enable users to customize sequence aggregation criteria, and vary peak calling thresholds that can influence the number of potential off-target sites recovered. GUIDEseq also annotates potential off-target sites that overlap with genes based on genome annotation information, as these may be the most important off-target sites for further characterization. In addition, GUIDEseq enables the comparison and visualization of off-target site overlap between different datasets for a rapid comparison of different nuclease configurations or experimental conditions. For each identified off-target, the GUIDEseq package outputs mapped GUIDE-Seq read count as well as cleavage score from a user specified off-target cleavage score prediction algorithm permitting the identification of genomic sequences with unexpected cleavage activity. CONCLUSION: The GUIDEseq package enables analysis of GUIDE-data from various nuclease platforms for any species with a defined genomic sequence. This software package has been used successfully to analyze several GUIDE-seq datasets. The software, source code and documentation are freely available at http://www.bioconductor.org/packages/release/bioc/html/GUIDEseq.html .

Finding off-targets, biological pathways, and target diseases for chymase inhibitors via structure-based systems biology approach.

Off-target binding connotes the binding of a small molecule of therapeutic significance to a protein target in addition to the primary target for which it was proposed. Progressively such off-targeting is emerging to be regular practice to reveal side effects. Chymase is an enzyme of hydrolase class that catalyzes hydrolysis of peptide bonds. A link between heart failure and chymase is ascribed, and a chymase inhibitor is in clinical phase II for treatment of heart failure. However, the underlying mechanisms of the off-target effects of human chymase inhibitors are still unclear. Here, we develop a robust computational strategy that is applicable to any enzyme system and that allows the prediction of drug effects on biological processes. Putative off-targets for chymase inhibitors were identified through various structural and functional similarity analyses along with molecular docking studies. Finally, literature survey was performed to incorporate these off-targets into biological pathways and to establish links between pathways and particular adverse effects. Off-targets of chymase inhibitors are linked to various biological pathways such as classical and lectin pathways of complement system, intrinsic and extrinsic pathways of coagulation cascade, and fibrinolytic system. Tissue kallikreins, granzyme M, neutrophil elastase, and mesotrypsin are also identified as off-targets. These off-targets and their associated pathways are elucidated for the effects of inflammation, cancer, hemorrhage, thrombosis, and central nervous system diseases (Alzheimer's disease). Prospectively, our approach is helpful not only to better understand the mechanisms of chymase inhibitors but also for drug repurposing exercises to find novel uses for these inhibitors.

RNA interference in insects: the link between antiviral defense and pest control.

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.

In Vivo Tissue-Specific Knockdown of circRNAs Using shRNAs in Drosophila melanogaster.

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.

Multifaceted computational profiling of thymol and geraniol against the human proteome for bio-repellent alternatives: Toxicity predictions, degradation analysis, and quantum mechanical approaches.

With growing interest in natural compounds as alternative mosquito repellents, assessing the toxicity and structure of potential repellent naturals like thymol (monoterpene phenol) and geraniol (monoterpene alcohol) is vital for understanding their stability and human impact. This study aimed to determine the structural, toxicity, and binding profiles of thymol and geraniol using computational predictions, xTB metadynamics, quantum mechanics, and principal component analysis. Toxicity studies using Protox-II, T.E.S.T, and SwissADME indicated that thymol and geraniol belong to toxicity class 4 and 5, respectively, with low toxicity predictions in other endpoints. Overall pharmacokinetic profile was generated via pkCSM. Off-target predictions via SwissTarget Predictions, LigTMap, Pharmapper, and SuperPred showed that these molecules can bind to 614 human proteins. The degradation of thymol and geraniol were performed using xTB metadynamics and the outcomes showed that the degradants for both compounds were stable and had lower toxicity profile. Nine tautomers were generated via quantum mechanics for thymol and four for geraniol, with RMSD ranging from 3.8 to 6.3 Å for thymol and 3.6 to 4 Å for geraniol after superimpositions. DFT studies found that HOMO-LUMO values and electronegativity parameters of thymol and geraniol did not differ significantly from their isomers. Binding affinity studies against 614 proteins, analysed via PCA and violin plots, highlighted the probable range of binding. These multifaceted in-silico findings corroborate the stability and potential utility of thymol and geraniol as safer alternatives in repellent applications.