CRISPR-Based Therapies: Revolutionizing Drug Development and Precision Medicine.

With the discovery of CRISPR-Cas9, drug development and precision medicine have undergone a major change. This review article looks at the new ways that CRISPR-based therapies are being used and how they are changing the way medicine is done. CRISPR technology's ability to precisely and flexibly edit genes has opened up new ways to find, validate, and develop drug targets. Also, it has made way for personalized gene therapies, precise gene editing, and advanced screening techniques, all of which hold great promise for treating a wide range of diseases. In this article, we look at the latest research and clinical trials that show how CRISPR could be used to treat genetic diseases, cancer, infectious diseases, and other hard-to-treat conditions. However, ethical issues and problems with regulations are also discussed in relation to CRISPR-based therapies, which shows how important it is to use them safely and responsibly. As CRISPR continues to change how drugs are made and used, this review shines a light on the amazing things that have been done and what the future might hold in this rapidly changing field.

PBITV3 : A robust and comprehensive tool for screening pathogenic proteomes for drug targets and prioritizing vaccine candidates.

Rise of life-threatening superbugs, pandemics and epidemics warrants the need for cost-effective and novel pharmacological interventions. Availability of publicly available proteomes of pathogens supports development of high-throughput discovery platforms to prioritize potential drug-targets and develop testable hypothesis for pharmacological screening. The pipeline builder for identification of target (PBIT) was developed in 2016 and updated in 2021, with the purpose of accelerating the search for drug-targets by integration of methods like comparative and subtractive genomics, essentiality/virulence and druggability analysis. Since then, it has been used for identification of drugs and vaccine targets, safety profiling of multiepitope vaccines and mRNA vaccine construction against a broad-spectrum of pathogens. This tool has now been updated with functionalities related to systems biology and immuno-informatics and validated by analyzing 48 putative antigens of Mycobacterium tuberculosis documented in literature. PBITv3 available as both online and offline tools will enhance drug discovery against emerging drug-resistant infectious agents. PBITv3 can be freely accessed at http://pbit.bicnirrh.res.in/.

A dose-response model for statistical analysis of chemical genetic interactions in CRISPRi screens.

An important application of CRISPR interference (CRISPRi) technology is for identifying chemical-genetic interactions (CGIs). Discovery of genes that interact with exposure to antibiotics can yield insights to drug targets and mechanisms of action or resistance. The objective is to identify CRISPRi mutants whose relative abundance is suppressed (or enriched) in the presence of a drug when the target protein is depleted, reflecting synergistic behavior. Different sgRNAs for a given target can induce a wide range of protein depletion and differential effects on growth rate. The effect of sgRNA strength can be partially predicted based on sequence features. However, the actual growth phenotype depends on the sensitivity of cells to depletion of the target protein. For essential genes, sgRNA efficiency can be empirically measured by quantifying effects on growth rate. We observe that the most efficient sgRNAs are not always optimal for detecting synergies with drugs. sgRNA efficiency interacts in a non-linear way with drug sensitivity, producing an effect where the concentration-dependence is maximized for sgRNAs of intermediate strength (and less so for sgRNAs that induce too much or too little target depletion). To capture this interaction, we propose a novel statistical method called CRISPRi-DR (for Dose-Response model) that incorporates both sgRNA efficiencies and drug concentrations in a modified dose-response equation. We use CRISPRi-DR to re-analyze data from a recent CGI experiment in Mycobacterium tuberculosis to identify genes that interact with antibiotics. This approach can be generalized to non-CGI datasets, which we show via an CRISPRi dataset for E. coli growth on different carbon sources. The performance is competitive with the best of several related analytical methods. However, for noisier datasets, some of these methods generate far more significant interactions, likely including many false positives, whereas CRISPRi-DR maintains higher precision, which we observed in both empirical and simulated data.

Salidroside promotes healthy longevity by interfering with HSP90 activity.

Aging is a risk factor for human health and quality of life. Screening and development of novel supplements and medications to combat aging and delay the incidence of age-related diseases are of great significance. In this study, salidroside (SA), a primary natural small molecule from Rhodiola rosea, was investigated regarding its effects on life and healthspan and the underlying molecular mechanism(s) of anti-aging and antioxidation. Our results showed that SA effectively prolonged lifespan and exhibited anti-aging and antioxidative properties. Computer-assisted methods, label-free interaction analysis, and in vitro assays showed that SA directly bound heat shock protein 90 (HSP90). Furthermore, SA significantly inhibited the ATPase activity of HSP90, affecting the interaction between HSP90 and its interacting proteins and the expression of downstream genes to regulate lifespan and the oxidative stress response. Our findings provided new insights into the pharmacological properties of SA across multiple species and its potential as an anti-aging drug.

Identification of a dihydroorotate dehydrogenase inhibitor that inhibits cancer cell growth by proteomic profiling.

Dihydroorotate dehydrogenase (DHODH) is a central enzyme of the de novo pyrimidine biosynthesis pathway and is a promising drug target for the treatment of cancer and autoimmune diseases. This study presents the identification of a potent DHODH inhibitor by proteomic profiling. Cell-based screening revealed that NPD723, which is reduced to H-006 in cells, strongly induces myeloid differentiation and inhibits cell growth in HL-60 cells. H-006 also suppressed the growth of various cancer cells. Proteomic profiling of NPD723-treated cells in ChemProteoBase showed that NPD723 was clustered with DHODH inhibitors. H-006 potently inhibited human DHODH activity in vitro, whereas NPD723 was approximately 400 times less active than H-006. H-006-induced cell death was rescued by the addition of the DHODH product orotic acid. Moreover, metabolome analysis revealed that H-006 treatment promotes marked accumulation of the DHODH substrate dihydroorotic acid. These results suggest that NPD723 is reduced in cells to its active metabolite H-006, which then targets DHODH and suppresses cancer cell growth. Thus, H-006-related drugs represent a potentially powerful treatment for cancer and other diseases.

Mining gene expression data for rational identification of novel drug targets and vaccine candidates against the cattle tick, Rhipicephalus microplus.

Control of complex parasites via vaccination remains challenging, with the current combination of vaccines and small drugs remaining the choice for an integrated control strategy. Studies conducted to date, are providing evidence that multicomponent vaccines will be needed for the development of protective vaccines against endo- and ectoparasites, though multicomponent vaccines require an in-depth understanding of parasite biology which remains insufficient for ticks. With the rapid development and spread of acaricide resistance in ticks, new targets for acaricide development also remains to be identified, along with novel targets that can be exploited for the design of lead compounds. In this study, we analysed the differential gene expression of Rhipicephalus microplus ticks that were fed on cattle vaccinated with a multi-component vaccine (Bm86 and 3 putative Bm86-binding proteins). The data was scrutinised for the identification of vaccine targets, small drug targets and novel pathways that can be evaluated in future studies. Limitations associated with targeting novel proteins for vaccine and/or drug design is also discussed and placed into the context of challenges arising when targeting large protein families and intracellular localised proteins. Lastly, this study provide insight into how Bm86-based vaccines may reduce successful uptake and digestion of the bloodmeal and overall tick fecundity.

Chemoproteomics and Phosphoproteomics Profiling Reveals Salvianolic Acid A as a Covalent Inhibitor of mTORC1.

Salvianolic acid A (SAA), a major active ingredient of Salvia miltiorrhiza Bunge (Danshen), displays strong antiproliferative activity against cancer cells. However, their protein targets remain unknown. Here, we deconvoluted the protein targets of SAA using chemoproteomics and phosphoproteomics. By using alkynylated SAA as a probe, we discovered that SAA is a covalent ligand that can modify cellular proteins via its electrophilic α,ß-unsaturated ester moiety. The subsequent chemoproteomics profiling revealed that 46 proteins were covalently modified by SAA, including Raptor, a subunit of mTORC1 for recruiting substrates for mTORC1. Although gene ontology enrichment analysis of these proteins suggested that SAA displays a promiscuous protein interaction, phosphoproteomics profiling revealed that the SAA modulated phosphoproteins were mainly enriched in the signaling pathways of PI3K-Akt-mTOR, which is closely related to cell growth and proliferation. This was confirmed by the biochemical assay with purified mTORC1, a Western blot assay with phospho-specific antibodies, and a cellular thermal shift assay. Our work discovered that SAA is a covalent ligand for protein modification and mTORC1 is one of its targets. Moreover, our work demonstrated that the integrative profiling of chemoproteomics and phosphoproteomics can be a powerful tool for target deconvolution for bioactive natural products.

Differential Expression of the ß3 Subunit of Voltage-Gated Ca2+ Channel in Mesial Temporal Lobe Epilepsy.

The purpose of this study was to identify and validate new putative lead drug targets in drug-resistant mesial temporal lobe epilepsy (mTLE) starting from differentially expressed genes (DEGs) previously identified in mTLE in humans by transcriptome analysis. We identified consensus DEGs among two independent mTLE transcriptome datasets and assigned them status as "lead target" if they (1) were involved in neuronal excitability, (2) were new in mTLE, and (3) were druggable. For this, we created a consensus DEG network in STRING and annotated it with information from the DISEASES database and the Target Central Resource Database (TCRD). Next, we attempted to validate lead targets using qPCR, immunohistochemistry, and Western blot on hippocampal and temporal lobe neocortical tissue from mTLE patients and non-epilepsy controls, respectively. Here we created a robust, unbiased list of 113 consensus DEGs starting from two lists of 3040 and 5523 mTLE significant DEGs, respectively, and identified five lead targets. Next, we showed that CACNB3, a voltage-gated Ca2+ channel subunit, was significantly regulated in mTLE at both mRNA and protein level. Considering the key role of Ca2+ currents in regulating neuronal excitability, this suggested a role for CACNB3 in seizure generation. This is the first time changes in CACNB3 expression have been associated with drug-resistant epilepsy in humans, and since efficient therapeutic strategies for the treatment of drug-resistant mTLE are lacking, our finding might represent a step toward designing such new treatment strategies.

Functional characterization of hypothetical proteins from Monkeypox virus.

BACKGROUND: Monkeypox virus is a small, double-stranded DNA virus that causes a zoonotic disease called Monkeypox. The disease has spread from Central and West Africa to Europe and North America and created havoc in some countries all around the world. The complete genome of the Monkeypox virus Zaire-96-I-16 has been sequenced. The viral strain contains 191 protein-coding genes with 30 hypothetical proteins whose structure and function are still unknown. Hence, it is imperative to functionally and structurally annotate the hypothetical proteins to get a clear understanding of novel drug and vaccine targets. The purpose of the study was to characterize the 30 hypothetical proteins through the determination of physicochemical properties, subcellular characterization, function prediction, functional domain prediction, structure prediction, structure validation, structural analysis, and ligand binding sites using Bioinformatics tools. RESULTS: The structural and functional analysis of 30 hypothetical proteins was carried out in this research. Out of these, 3 hypothetical functions (Q8V547, Q8V4S4, Q8V4Q4) could be assigned a structure and function confidently. Q8V547 protein in Monkeypox virus Zaire-96-I-16 is predicted as an apoptosis regulator which promotes viral replication in the infected host cell. Q8V4S4 is predicted as a nuclease responsible for viral evasion in the host. The function of Q8V4Q4 is to prevent host NF-kappa-B activation in response to pro-inflammatory cytokines like TNF alpha or interleukin 1 beta. CONCLUSIONS: Out of the 30 hypothetical proteins of Monkeypox virus Zaire-96-I-16, 3 were annotated using various bioinformatics tools. These proteins function as apoptosis regulators, nuclease, and inhibitors of NF-Kappa-B activator. The functional and structural annotation of the proteins can be used to perform a docking with potential leads to discover novel drugs and vaccines against the Monkeypox. In vivo research can be carried out to identify the complete potential of the annotated proteins.

Target proteins profiling of irreversible kinase inhibitor pelitinib and discovery of degradation of PRDX4 by label free chemoproteomics.

Cell-based methods for profiling the kinase inhibitor selectivity are badly needed, especially for the irreversible kinase inhibitors. Here we reported a chemoproteomics approach for profiling the target proteins of irreversible kinase inhibitor with label free quantitative proteomics by using iodoacetamide alkyne as a chemical probe. In total 41 proteins were identified in high confidence (fold change 3.5, p value < 0.05) including PRDX4, STAT3, E2 conjugating enzymes UBE2L3, UBE2K, UBE2N, UBE2V1 and UBE2Z as well as E3 ligase TRIM 25. We validated the interaction between pelitinib and PRDX4 with a cell-based assay, and discovered that pelitinib can induce the degradation of PRDX4. The discovery was confirmed by biochemical assay, cellular thermal shift assay and miRNA knockdown experiment. Our data suggested that pelitinib can be a covalent molecular glue inducing the degradation of PRDX4. In addition, our work demonstrated that identification of the interactions between ligand and ubiquitylation associated proteins by chemoproteomics profiling can be used as a new strategy for identifying molecular glue degraders.

Identification of PCSK9-like human gene knockouts using metabolomics, proteomics, and whole-genome sequencing in a consanguineous population.

Natural human knockouts of genes associated with desirable outcomes, such as PCSK9 with low levels of LDL-cholesterol, can lead to the discovery of new drug targets and treatments. Rare loss-of-function variants are more likely to be found in the homozygous state in consanguineous populations, and deep molecular phenotyping of blood samples from homozygous carriers can help to discriminate between silent and functional variants. Here, we combined whole-genome sequencing with proteomics and metabolomics for 2,935 individuals from the Qatar Biobank (QBB) to evaluate the power of this approach for finding genes of clinical and pharmaceutical interest. As proof-of-concept, we identified a homozygous carrier of a very rare PCSK9 variant with extremely low circulating PCSK9 levels and low LDL. Our study demonstrates that the chances of finding such variants are about 168 times higher in QBB compared with GnomAD and emphasizes the potential of consanguineous populations for drug discovery.

Solvent-Induced Protein Precipitation for Drug Target Discovery.

Traditional chemical proteomics approaches for screening drug targets usually require the immobilization/modification of the drug molecules to pull down the interacting proteins. The solvent-induced protein precipitation (SIP) approach provides an alternative way to study drug-protein interaction by using complex cell lysate directly without modifying a compound of interest. It relies on the fact that the ligand-bound proteins have higher resistance to solvent-induced precipitation. This chapter describes the protocol for identifying drug-target protein interactions by performing unbiased SIP with total cell lysate using a mass spectrometry-based proteomic strategy.

The druggable genome: Twenty years later.

The concept of the druggable genome has been with us for 20 years. During this time, researchers have developed several methods and resources to help assess a target's druggability. In parallel, evidence for target-disease associations has been collated at scale by Open Targets. More recently, the Protein Data Bank in Europe (PDBe) have built a knowledge base matching per-residue annotations with available protein structure. While each resource is useful in isolation, we believe there is enormous potential in bringing all relevant data into a single knowledge graph, from gene-level to protein residue. Automation is vital for the processing and assessment of all available structures. We have developed scalable, automated workflows that provide hotspot-based druggability assessments for all available structures across large numbers of targets. Ultimately, we will run our method at a proteome scale, an ambition made more realistic by the arrival of AlphaFold 2. Bringing together annotations from the residue up to the gene level and building connections within the graph to represent pathways or protein-protein interactions will create complexity that mirrors the biological systems they represent. Such complexity is difficult for the human mind to utilise effectively, particularly at scale. We believe that graph-based AI methods will be able to expertly navigate such a knowledge graph, selecting the targets of the future.

Non-linearity of Metabolic Pathways Critically Influences the Choice of Machine Learning Model.

The use of machine learning (ML) in life sciences has gained wide interest over the past years, as it speeds up the development of high performing models. Important modeling tools in biology have proven their worth for pathway design, such as mechanistic models and metabolic networks, as they allow better understanding of mechanisms involved in the functioning of organisms. However, little has been done on the use of ML to model metabolic pathways, and the degree of non-linearity associated with them is not clear. Here, we report the construction of different metabolic pathways with several linear and non-linear ML models. Different types of data are used; they lead to the prediction of important biological data, such as pathway flux and final product concentration. A comparison reveals that the data features impact model performance and highlight the effectiveness of non-linear models (e.g., QRF: RMSE = 0.021 nmol·min-1 and R2 = 1 vs. Bayesian GLM: RMSE = 1.379 nmol·min-1 R2 = 0.823). It turns out that the greater the degree of non-linearity of the pathway, the better suited a non-linear model will be. Therefore, a decision-making support for pathway modeling is established. These findings generally support the hypothesis that non-linear aspects predominate within the metabolic pathways. This must be taken into account when devising possible applications of these pathways for the identification of biomarkers of diseases (e.g., infections, cancer, neurodegenerative diseases) or the optimization of industrial production processes.

A Combined Human in Silico and CRISPR/Cas9-Mediated in Vivo Zebrafish Based Approach to Provide Phenotypic Data for Supporting Early Target Validation.

The clinical heterogeneity of heart failure has challenged our understanding of the underlying genetic mechanisms of this disease. In this respect, large-scale patient DNA sequencing studies have become an invaluable strategy for identifying potential genetic contributing factors. The complex aetiology of heart failure, however, also means that in vivo models are vital to understand the links between genetic perturbations and functional impacts as part of the process for validating potential new drug targets. Traditional approaches (e.g., genetically-modified mice) are optimal for assessing small numbers of genes, but less practical when multiple genes are identified. The zebrafish, in contrast, offers great potential for higher throughput in vivo gene functional assessment to aid target prioritisation, by providing more confidence in target relevance and facilitating gene selection for definitive loss of function studies undertaken in mice. Here we used whole-exome sequencing and bioinformatics on human patient data to identify 3 genes (API5, HSPB7, and LMO2) suggestively associated with heart failure that were also predicted to play a broader role in disease aetiology. The role of these genes in cardiovascular system development and function was then further investigated using in vivo CRISPR/Cas9-mediated gene mutation analysis in zebrafish. We observed multiple impacts in F0 knockout zebrafish embryos (crispants) following effective somatic mutation, including changes in ventricle size, pericardial oedema, and chamber malformation. In the case of lmo2, there was also a significant impact on cardiovascular function as well as an expected reduction in erythropoiesis. The data generated from both the human in silico and zebrafish in vivo assessments undertaken supports further investigation of the potential roles of API5, HSPB7, and LMO2 in human cardiovascular disease. The data presented also supports the use of human in silico genetic variant analysis, in combination with zebrafish crispant phenotyping, as a powerful approach for assessing gene function as part of an integrated multi-level drug target validation strategy.

The Ginsenoside Compound K Suppresses Stem-Cell-like Properties and Colorectal Cancer Metastasis by Targeting Hypoxia-Driven Nur77-Akt Feed-Forward Signaling.

Hypoxia reprograms cancer stem cells. Nur77, an orphan nuclear receptor, highly expresses and facilitates colorectal cancer (CRC) stemness and metastasis under a hypoxic microenvironment. However, safe and effective small molecules that target Nur77 for CSC depletion remain unexplored. Here, we report our identification of the ginsenoside compound K (CK) as a new ligand of Nur77. CK strongly inhibits hypoxia-induced CRC sphere formation and CSC phenotypes in a Nur77-dependent manner. Hypoxia induces an intriguing Nur77-Akt feed-forward loop, resulting in reinforced PI3K/Akt signaling that is druggable by targeting Nur77. CK directly binds and modulates Nur77 phosphorylation to block the Nur77-Akt activation loop by disassociating Nur77 from the p63-bound Dicer promoter. The transcription of Dicer that is silenced under a hypoxia microenvironment is thus reactivated by CK. Consequently, the expression and processing capability of microRNA let-7i-5p are significantly increased, which targets PIK3CA mRNA for decay. The in vivo results showed that CK suppresses cancer stemness and metastasis without causing significant adverse effects. Given that the majority of FDA-approved and currently clinically tested PI3K/Akt inhibitors are reversible ATP-competitive kinase antagonists, targeting Nur77 for PI3K/Akt inactivation may provide an alternative strategy to overcoming concerns about drug selectivity and safety. The mechanistic target identification provides a basis for exploring CK as a promising nutraceutical against CRC.

Target Identification of Small Molecules Using Large-Scale CRISPR-Cas Mutagenesis Scanning of Essential Genes.

Target deconvolution of new bioactive agents identified from phenotypic screens remains a challenging task. The discovery of mutations that confer resistance to such agents is regarded as the gold standard proof of target identification. Here, we describe a method that exploits the error-prone repair of CRISPR-induced DNA double-strand breaks to enhance mutagenesis and increase the incidence of drug resistance mutations in essential genes. As each DNA double-strand break is introduced at a targeted genomic site predefined by the presence of a protospacer adjacent motif (PAM) and a particular CRISPR single guide RNA (sgRNA), the genetic location of drug resistance mutations can be easily uncovered through targeted sequencing of CRISPR sgRNAs. Moreover, the method allows for the identification of not only the drug target gene, but also the drug-binding domain within the target gene.

Chemogenomics identifies acetyl-coenzyme A synthetase as a target for malaria treatment and prevention.

We identify the Plasmodium falciparum acetyl-coenzyme A synthetase (PfAcAS) as a druggable target, using genetic and chemical validation. In vitro evolution of resistance with two antiplasmodial drug-like compounds (MMV019721 and MMV084978) selects for mutations in PfAcAS. Metabolic profiling of compound-treated parasites reveals changes in acetyl-CoA levels for both compounds. Genome editing confirms that mutations in PfAcAS are sufficient to confer resistance. Knockdown studies demonstrate that PfAcAS is essential for asexual growth, and partial knockdown induces hypersensitivity to both compounds. In vitro biochemical assays using recombinantly expressed PfAcAS validates that MMV019721 and MMV084978 directly inhibit the enzyme by preventing CoA and acetate binding, respectively. Immunolocalization studies reveal that PfAcAS is primarily localized to the nucleus. Functional studies demonstrate inhibition of histone acetylation in compound-treated wild-type, but not in resistant parasites. Our findings identify and validate PfAcAS as an essential, druggable target involved in the epigenetic regulation of gene expression.

Reverse vaccinology and subtractive genomics approaches for identifying common therapeutics against Mycobacterium leprae and Mycobacterium lepromatosis.

BACKGROUND: Mycobacterium leprae and Mycobacterium lepromatosis are gram-positive bacterial pathogens and the causative agents of leprosy in humans across the world. The elimination of leprosy cannot be achieved by multidrug therapy alone, and highlights the need for new tools and drugs to prevent the emergence of new resistant strains. METHODS: In this study, our contribution includes the prediction of vaccine targets and new putative drugs against leprosy, using reverse vaccinology and subtractive genomics. Six strains of Mycobacterium leprae and Mycobacterium lepromatosis (4 and 2 strains, respectively) were used for comparison taking Mycobacterium leprae strain TN as the reference genome. Briefly, we used a combined reverse vaccinology and subtractive genomics approach. RESULTS: As a result, we identified 12 common putative antigenic proteins as vaccine targets and three common drug targets against Mycobacterium leprae and Mycobacterium lepromatosis. Furthermore, the docking analysis using 28 natural compounds with three drug targets was done. CONCLUSIONS: The bis-naphthoquinone compound Diospyrin (CID 308140) obtained from indigenous plant Diospyros spp. showed the most favored binding affinity against predicted drug targets, which can be a candidate therapeutic target in the future against leprosy.

Reverse vaccinology and subtractive genomics approaches for identifying common therapeutics against Mycobacterium leprae and Mycobacterium lepromatosis

Mycobacterium leprae and Mycobacterium lepromatosis are gram-positive bacterial pathogens and the causative agents of leprosy in humans across the world. The elimination of leprosy cannot be achieved by multidrug therapy alone, and highlights the need for new tools and drugs to prevent the emergence of new resistant strains. Methods In this study, our contribution includes the prediction of vaccine targets and new putative drugs against leprosy, using reverse vaccinology and subtractive genomics. Six strains of Mycobacterium leprae and Mycobacterium lepromatosis (4 and 2 strains, respectively) were used for comparison taking Mycobacterium leprae strain TN as the reference genome. Briefly, we used a combined reverse vaccinology and subtractive genomics approach. Results As a result, we identified 12 common putative antigenic proteins as vaccine targets and three common drug targets against Mycobacterium leprae and Mycobacterium lepromatosis. Furthermore, the docking analysis using 28 natural compounds with three drug targets was done. Conclusions The bis-naphthoquinone compound Diospyrin (CID 308140) obtained from indigenous plant Diospyros spp. showed the most favored binding affinity against predicted drug targets, which can be a candidate therapeutic target in the future against leprosy.(AU)