A Multimodal Omics Framework to Empower Target Discovery for Cardiovascular Regeneration

Ischaemic heart disease is a global healthcare challenge with high morbidity and mortality. Early revascularisation in acute myocardial infarction has improved survival; however, limited regenerative capacity and microvascular dysfunction often lead to impaired function and the development of heart failure. New mechanistic insights are required to identify robust targets for the development of novel strategies to promote regeneration. Single-cell RNA sequencing (scRNA-seq) has enabled profiling and analysis of the transcriptomes of individual cells at high resolution. Applications of scRNA-seq have generated single-cell atlases for multiple species, revealed distinct cellular compositions for different regions of the heart, and defined multiple mechanisms involved in myocardial injury-induced regeneration. In this review, we summarise findings from studies of healthy and injured hearts in multiple species and spanning different developmental stages. Based on this transformative technology, we propose a multi-species, multi-omics, meta-analysis framework to drive the discovery of new targets to promote cardiovascular regeneration.

Mitigating pathogenesis for target discovery and disease subtyping

Treatments ideally mitigate pathogenesis, or the detrimental effects of the root causes of disease. However, existing definitions of treatment effect fail to account for pathogenic mechanism. We therefore introduce the Treated Root causal Effects (TRE) metric which measures the ability of a treatment to modify root causal effects. We leverage TREs to automatically identify treatment targets and cluster patients who respond similarly to treatment. The proposed algorithm learns a partially linear causal model to extract the root causal effects of each variable and then estimates TREs for target discovery and downstream subtyping. We maintain interpretability even without assuming an invertible structural equation model. Experiments across a range of datasets corroborate the generality of the proposed approach.

Current advances in comprehensive omics data mining for oncology and cancer research

The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research.

Target discovery of bioactive natural products with native-compound-coupled CNBr-activated Sepharose 4B beads (NCCB): Applications, mechanisms and outlooks

Natural products (NPs) represent a treasure trove for drug discovery and development due to their chemical structural diversity and a broad spectrum of biological activities. Uncovering the biological targets and understanding their molecular mechanism of actions are crucial steps in the development of clinical therapeutics. However, the structural complexity of NPs and intricate nature of biological system present formidable challenges in target identification of NPs. Although significant advances have been made in the development of new chemical tools, these methods often require high levels of synthetic skills for preparing chemical probes. This can be costly and time-consuming relaying on operationally complicated procedures and instruments. In recent efforts, we and others have successfully developed an operationally simple and practical chemical tool known as native-compound-coupled CNBr-activated Sepharose 4B beads (NCCB) for NP target identification. In this approach, a native compound readily reacts with commercial CNBr-activated Sepharose 4B beads with a process that is easily performed in any biology laboratory. Based on NCCB, our group has identified the direct targets of more than 60 NPs. In this review, we will elucidate the application scopes, including flavonoids, quinones, terpenoids and others, characteristics, chemical mechanisms, procedures, advantages, disadvantages, and future directions of NCCB in specific target discovery.

Applying Artificial Intelligence to Identify Common Targets for Treatment of Asthma, Eczema, and Food Allergy

INTRODUCTION

Allergic disorders are common diseases marked by the abnormal immune response toward foreign antigens that are not pathogens. Often patients with food allergy also suffer from asthma and eczema. Given the similarities of these diseases and a shortage of effective treatments, developing novel therapeutics against common targets of multiple allergies would offer an efficient and cost-effective treatment for patients.

METHODS

We employed the artificial intelligence-driven target discovery platform, PandaOmics, to identify common targets for treating asthma, eczema, and food allergy. Thirty-two case-control comparisons were generated from 15, 11, and 6 transcriptomics datasets related to asthma (558 cases, 315 controls), eczema (441 cases, 371 controls), and food allergy (208 cases, 106 controls), respectively, and allocated into three meta-analyses for target identification. Top-100 high-confidence targets and Top-100 novel targets were prioritized by PandaOmics for each allergic disease.

RESULTS

Six common high-confidence targets (i.e., IL4R, IL5, JAK1, JAK2, JAK3, and NR3C1) across all three allergic diseases have approved drugs for treating asthma and eczema. Based on the targets' dysregulated expression profiles and their mechanism of action in allergic diseases, three potential therapeutic targets were proposed. IL5 was selected as a high-confidence target due to its strong involvement in allergies. PTAFR was identified for drug repurposing, while RNF19B was selected as a novel target for therapeutic innovation. Analysis of the dysregulated pathways commonly identified across asthma, eczema, and food allergy revealed the well-characterized disease signature and novel biological processes that may underlie the pathophysiology of allergies.

CONCLUSION

Altogether, our study dissects the shared pathophysiology of allergic disorders and reveals the power of artificial intelligence in the exploration of novel therapeutic targets.

CRISPR/Cas9 system: recent applications in immuno-oncology and cancer immunotherapy

Clustered regulatory interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) is essentially an adaptive immunity weapon in prokaryotes against foreign DNA. This system inspires the development of genome-editing technology in eukaryotes. In biomedicine research, CRISPR has offered a powerful platform to establish tumor-bearing models and screen potential targets in the immuno-oncology field, broadening our insights into cancer genomics. In translational medicine, the versatile CRISPR/Cas9 system exhibits immense potential to break the current limitations of cancer immunotherapy, thereby expanding the feasibility of adoptive cell therapy (ACT) in treating solid tumors. Herein, we first explain the principles of CRISPR/Cas9 genome editing technology and introduce CRISPR as a tool in tumor modeling. We next focus on the CRISPR screening for target discovery that reveals tumorigenesis, immune evasion, and drug resistance mechanisms. Moreover, we discuss the recent breakthroughs of genetically modified ACT using CRISPR/Cas9. Finally, we present potential challenges and perspectives in basic research and clinical translation of CRISPR/Cas9. This review provides a comprehensive overview of CRISPR/Cas9 applications that advance our insights into tumor-immune interaction and lay the foundation to optimize cancer immunotherapy.

Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis

Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization and imputation, clustering, identifying differentially expressed genes, and performing gene set enrichment analysis. To make the best choice about which tools to use, objective benchmarks can be developed to compare the quality of different algorithms to extract biological knowledge maximally and accurately from these data. The Dexamethasone Benchmark (Dex-Benchmark) resource aims to fill this need by providing the community with datasets and code templates for benchmarking different gene expression analysis tools and algorithms. The resource provides access to a collection of curated RNA-seq, L1000, and ChIP-seq data from dexamethasone treatment as well as genetic perturbations of its known targets. In addition, the website provides Jupyter Notebooks that use these pre-processed curated datasets to demonstrate how to benchmark the different steps in gene expression analysis. By comparing two independent data sources and data types with some expected concordance, we can assess which tools and algorithms best recover such associations. To demonstrate the usefulness of the resource for discovering novel drug targets, we applied it to optimize data processing strategies for the chemical perturbations and CRISPR single gene knockouts from the L1000 transcriptomics data from the Library of Integrated Network Cellular Signatures (LINCS) program, with a focus on understudied proteins from the Illuminating the Druggable Genome (IDG) program. Overall, the Dex-Benchmark resource can be utilized to assess the quality of transcriptomics and other related bioinformatics data analysis workflows. The resource is available from: https://maayanlab.github.io/dex-benchmark.

Intracellular spatiotemporal metabolism in connection to target engagement

The metabolism in eukaryotic cells is a highly ordered system involving various cellular compartments, which fluctuates based on physiological rhythms. Organelles, as the smallest independent sub-cell unit, are important contributors to cell metabolism and drug metabolism, collectively designated intracellular metabolism. However, disruption of intracellular spatiotemporal metabolism can lead to disease development and progression, as well as drug treatment interference. In this review, we systematically discuss spatiotemporal metabolism in cells and cell subpopulations. In particular, we focused on metabolism compartmentalization and physiological rhythms, including the variation and regulation of metabolic enzymes, metabolic pathways, and metabolites. Additionally, the intricate relationship among intracellular spatiotemporal metabolism, metabolism-related diseases, and drug therapy/toxicity has been discussed. Finally, approaches and strategies for intracellular spatiotemporal metabolism analysis and potential target identification are introduced, along with examples of potential new drug design based on this.

Activity-based protein profiling: A graphical review

Activity-based protein profiling (ABPP) is a chemoproteomic technology that employs small chemical probes to directly interrogate protein function within complex proteomes. Since its initial application almost 25 years ago, ABPP has proven to be a powerful and versatile tool for addressing numerous challenges in drug discovery, including the development of highly selective small-molecule inhibitors, the discovery of new therapeutic targets, and the illumination of target proteins in tissues and organisms. This graphical review provides an overview of the rapid evolution of ABPP strategies, highlighting the versatility of the approach with selected examples of its successful application.

Priority index for asthma (PIA): In silico discovery of shared and distinct drug targets for adult- and childhood-onset disease

Asthma is a chronic disease that is caused by a combination of genetic risks and environmental triggers and can affect both adults and children. Genome-wide association studies have revealed partly distinct genetic architectures for its two age-of-onset subtypes (namely, adult-onset and childhood-onset). We reason that identifying shared and distinct drug targets between these subtypes may inform the development of subtype-specific therapeutic strategies. In attempting this, we here introduce Priority Index for Asthma or PIA, a genetics-led and network-driven drug target prioritisation tool for asthma. We demonstrate the validity of the tool in improving drug target prioritisation for asthma compared to the status quo methods, as well as in capturing the underlying etiology and existing therapeutics for the disease. We also illustrate how PIA can be used to prioritise drug targets for adult- and childhood-onset asthma, as well as to identify shared and distinct pathway crosstalk genes. Shared crosstalk genes are mostly involved in JAK-STAT signaling, with clinical evidence supporting that targeting this pathway may be a promising drug repurposing opportunity for both subtypes. Crosstalk genes specific to childhood-onset asthma are enriched for PI3K-AKT-mTOR signaling, and we identify genes that are already targeted by licensed medications as repurposed drug candidates for this subtype. We make all our results accessible and reproducible at http://www.genetictargets.com/PIA. Collectively, our study has significant implications for asthma computational medicine research and can guide the future development of subtype-specific therapeutic strategies for the disease.

CETSA and thermal proteome profiling strategies for target identification and drug discovery of natural products

BACKGROUND

Monitoring target engagement at various stages of drug development is essential for natural product (NP)-based drug discovery and development. The cellular thermal shift assay (CETSA) developed in 2013 is a novel, broadly applicable, label-free biophysical assay based on the principle of ligand-induced thermal stabilization of target proteins, which enables direct assessment of drug-target engagement in physiologically relevant contexts, including intact cells, cell lysates and tissues. This review aims to provide an overview of the work principles of CETSA and its derivative strategies and their recent progress in protein target validation, target identification and drug lead discovery of NPs.

METHODS

A literature-based survey was conducted using the Web of Science and PubMed databases. The required information was reviewed and discussed to highlight the important role of CETSA-derived strategies in NP studies.

RESULTS

After nearly ten years of upgrading and evolution, CETSA has been mainly developed into three formats: classic Western blotting (WB)-CETSA for target validation, thermal proteome profiling (TPP, also known as MS-CETSA) for unbiased proteome-wide target identification, and high-throughput (HT)-CETSA for drug hit discovery and lead optimization. Importantly, the application possibilities of a variety of TPP approaches for the target discovery of bioactive NPs are highlighted and discussed, including TPP-temperature range (TPP-TR), TPP-compound concentration range (TPP-CCR), two-dimensional TPP (2D-TPP), cell surface-TPP (CS-TPP), simplified TPP (STPP), thermal stability shift-based fluorescence difference in 2D gel electrophoresis (TS-FITGE) and precipitate supported TPP (PSTPP). In addition, the key advantages, limitations and future outlook of CETSA strategies for NP studies are discussed.

CONCLUSION

The accumulation of CETSA-based data can significantly accelerate the elucidation of the mechanism of action and drug lead discovery of NPs, and provide strong evidence for NP treatment against certain diseases. The CETSA strategy will certainly bring a great return far beyond the initial investment and open up more possibilities for future NP-based drug research and development.

Industrializing AI/ML during the end-to-end drug discovery process

Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several artificial intelligence and machine learning (AI/ML)-based drug discovery companies have attempted to build data-driven platforms spanning the end-to-end drug discovery process. The ability to identify elusive targets essentially leads to the diversification of discovery pipelines, thereby increasing the ability to address unmet needs. Modern ML technologies are complementing traditional computer-aided drug discovery by accelerating candidate optimization in innovative ways. This review summarizes recent developments in AI/ML methods from target identification to molecule optimization, and concludes with an overview of current industrial trends in end-to-end AI/ML platforms.

Phenelzine-based probes reveal Secernin-3 is involved in thermal nociception

Chemical platforms that facilitate both the identification and elucidation of new areas for therapeutic development are necessary but lacking. Activity-based protein profiling (ABPP) leverages active site-directed chemical probes as target discovery tools that resolve activity from expression and immediately marry the targets identified with lead compounds for drug design. However, this approach has traditionally focused on predictable and intrinsic enzyme functionality. Here, we applied our activity-based proteomics discovery platform to map non-encoded and post-translationally acquired enzyme functionalities (e.g. cofactors) in vivo using chemical probes that exploit the nucleophilic hydrazine pharmacophores found in a classic antidepressant drug (e.g. phenelzine, Nardil ®). We show the probes are in vivo active and can map proteome-wide tissue-specific target engagement of the drug. In addition to engaging targets (flavoenzymes monoamine oxidase A/B) that are associated with the known therapeutic mechanism as well as several other members of the flavoenzyme family, the probes captured the previously discovered N-terminal glyoxylyl (Glox) group of Secernin-3 (SCRN3) in vivo through a divergent mechanism, indicating this functional feature has biochemical activity in the brain. SCRN3 protein is ubiquitously expressed in the brain, yet gene expression is regulated by inflammatory stimuli. In an inflammatory pain mouse model, behavioral assessment of nociception showed Scrn3 male knockout mice selectively exhibited impaired thermal nociceptive sensitivity. Our study provides a guided workflow to entangle molecular (off)targets and pharmacological mechanisms for therapeutic development.

Deep Mining of Complex Antibody Phage Pools

An important, and rapidly growing class of drugs are antibodies which can be discovered through phage display technology. In this technique, antibodies are typically first enriched through consecutive rounds of selection on a target antigen with amplification in bacteria between each selection round. Thereafter, a subset of random individual clones is analyzed for binding in a screening procedure. This results in discovery of the most abundant antibodies in the pool. However, there are multiple factors affecting the enrichment of antibodies during the selection resulting in a very complex output pool of antibodies. A few antibodies are present in many copies and others only in a few copies, where the most abundant antibodies are not necessarily the functionally best ones. In order to utilize the full potential of the output from a phage display selection, and enable discovery of low abundant, potentially functionally important clones, deep mining technologies are needed. In this chapter, two methods for deep mining of an antibody pool are described, protein depletion and antibody blocking. The methods can be applied both when the target is a single antigen and on complex antigen mixtures such as whole cells and tissues.

Conserved protein targets for developing pan-coronavirus drugs based on sequence and 3D structure similarity analyses

There are 7 known human pathogenic coronaviruses, which are HCoV-229E, HCoV-OC43, HCoV-NL63, HCoV-HKU1, MERS-CoV, SARS-CoV and SARS-CoV-2. While SARS-CoV-2 is currently caused a severe epidemic, experts believe that new pathogenic coronavirus would emerge in the future. Therefore, developing broad-spectrum anti-coronavirus drugs is of great significance. In this study, we performed protein sequence and three-dimensional structure analyses for all the 20 virus-encoded proteins across all the 7 coronaviruses, with the purpose to identify highly conserved proteins and binding sites for developing pan-coronavirus drugs. We found that nsp5, nsp10, nsp12, nsp13, nsp14, and nsp16 are highly conserved both in protein sequences (with average identity percentage higher than 52%, average amino acid conservation scores higher than 5.2) and binding pockets (with average amino acid conservation scores higher than 5.8). We also performed the similarity comparison between these 6 proteins and all the human proteins, and found that all the 6 proteins have similarity less than 25%, indicating that the drugs targeting the 6 proteins should have little interference of human protein function. Accordingly, we suggest that nsp5, nsp10, nsp12, nsp13, nsp14, and nsp16 are potential targets for pan-coronavirus drug development.

Target Discovery for Drug Development Using Mendelian Randomization

Making drug development more efficient by identifying promising drug targets can contribute to resource savings. Identifying promising drug targets using human genetic approaches can remove barriers related to translation. In addition, genetic information can be used to identify potentially causal relationships between a drug target and disease. Mendelian randomization (MR) is a class of approaches used to identify causal associations between pairs of genetically predicted traits using data from human genetic studies. MR can be used to prioritize candidate drug targets by predicting disease outcomes and adverse events that could result from the manipulation of a drug target. The theory behind MR is reviewed, including a discussion of MR assumptions, different MR analytical methods, tests for violations of assumptions, and MR methods that can be robust to some violations of MR assumptions. A protocol to perform two-sample MR (2SMR) with summary genome-wide association study (GWAS) results is described. An example of 2SMR examining the causal relationship between low-density lipoprotein (LDL) and coronary artery disease (CAD) is provided as an illustration of the protocol.

Drug affinity responsive target stability (DARTS) accelerated small molecules target discovery: Principles and application

Drug affinity responsive target stability (DARTS) is a novel target discovery approach and is particularly adept at screening small molecule (SM) targets without requiring any structural modifications. The DARTS method is capable of revealing drug-target interactions from cells or tissues by tracking changes in the stability of proteins acting as receptors of bioactive SMs. Due to its simple operation and high efficiency, the DARTS method has been applied to uncover the drug-action mechanism. This review summarized analytical principles, protocols, validation approaches, applications, and challenges involved in the DARTS method. Due to the innate advantages of the DARTS method, it is expected to be a powerful tool to accelerate SM target discovery, especially for bioactive natural products with unknown mechanisms.

Novel insights on the molecular mechanism of action of the anti-angiogenic pyrazolyl-urea GeGe-3 by functional proteomics

In recent years, 5-pyrazolyl-ureas have mostly been known for their attractive poly-pharmacological outline and, in particular, ethyl 1-(2-hydroxypentyl)-5-(3-(3-(trifluoromethyl) phenyl) ureido)-1H-pyrazole-4-carboxylate (named GeGe-3) has emerged as a capable anti-angiogenic compound. This paper examines its interactome by functional proteomics using a label-free mass spectrometry based platform, coupling Drug Affinity Responsive Target Stability and targeted Limited Proteolysis-Multiple Reaction Monitoring. Calreticulin has been recognized as the GeGe-3 principal target and this evidence has been supported by immunoblotting and in silico molecular docking. Furthermore, cell studies have shown that GeGe-3 lowers cell calcium mobilization, cytoskeleton organization and focal adhesion kinase expression, thus linking its biological potential to calreticulin binding and, ultimately, shedding light on the reasonable action mechanism of this molecule as an anti-angiogenic factor.

Application of omics- and multi-omics-based techniques for natural product target discovery

Natural products continue to be an unparalleled source of pharmacologically active lead compounds because of their unprecedented structures and unique biological activities. Natural product target discovery is a vital component of natural product-based medicine translation and development and is required to understand and potentially reduce mechanisms that may be associated with off-target side effects and toxicity. Omics-based techniques, including genomics, transcriptomics, proteomics, metabolomics, and bioinformatics, have become recognized as effective tools needed to construct innovative strategies to discover natural product targets. Although considerable progress has been made, the successful discovery of natural product targets remains a challenging time-consuming process that has come to increasingly rely on the effective integration of multi-omics-based technologies to create emerging panomics (a.k.a., integrative omics, pan-omics, multiomics)-based strategies. This review summarizes a series of successful studies regarding the application of integrative omics-based methods in natural product target discovery. The advantages and disadvantages of each technique are discussed, with a particular focus on the systematic integration of multi-omics strategies. Further, emerging micro-scale single-cell-based techniques are introduced, especially to deal with minute natural product samples.

Data-driven approaches to advance research and clinical care for pediatric cancer

Pediatric cancer is a rare disease with a distinct etiology and mutational landscape compared with adult cancer. Multi-omics profiling of retrospective and prospective cohorts coupled with innovative computational analysis have been instrumental in uncovering mechanisms of tumorigenesis and drug resistance that are now informing pediatric cancer clinical therapy. In this review we present the major data resources of pediatric cancer and actionable insights into pediatric cancer etiology stemming from the identification of oncogenic gene fusions, mutational signature analysis, systems biology, cancer predisposition and survivorship studies - that have led to improved clinical diagnosis, discovery of new drug-targets, pharmacological therapy, and screening for genetic predisposition. Ultimately, integration of large-scale omics datasets generated through international collaboration is required to maximize the power of data-driven approaches to advance pediatric cancer research informing clinical therapy.

Targeting Cul3-scaffold E3 ligase complex via KLHL substrate adaptors for cancer therapy

Targeted therapy has become increasingly important and indispensable in cancer therapy. Cullin3-RING ligases (CRL3) serve as essential executors for regulating protein homeostasis in cancer development, highlighting that CRL3 might be promising targets in various cancer treatment. However, how to design new targeted therapies by disrupting the function of CRL3 is poorly understood. Here, we focus on the substrate adaptors of CRL3, and carry out a systematical research on the function of Kelch-like (KLHL) family proteins. We have identified twenty-four KLHL proteins with function of tumor promotion and thirteen KLHL proteins with high clinical significance on cancer therapy. Furthermore, we have clarified the novel biological function of KLHL13 as a vital factor that contributes to malignant progression in lung cancer. Taken together, our findings reveal multiple potential therapeutical targets and provide evidence for targeting CRL3 via KLHL substrate adaptors for cancer therapy.

Direct label-free methods for identification of target proteins in agrochemicals

Green agrochemicals are important guarantee for food production and security, and target protein identification is the most important basis for development of novel agrochemicals. Affinity chromatography methods for immobilization of agrochemicals have been widely used to identify and confirm new targets. However, this method often requires modification of the active molecules which can affect or damage its biological activity, and biomacromolecules, particularly most natural products, are hard to be modified either. In order to overcome the shortcomings of molecular modification, label-free technology has been developed based on evaluating responses to thermal or proteolytic treatments. Combined with the chemical biology technology and molecular biology technology, it has been used in the development of drugs and agrochemicals. Herein, common methods of label-free technology for identification of direct target of agrochemicals are reviewed, including the principle, advantages, limitations and applications in the research of agrochemicals in the last decade. And the methods for validation of candidate targets obtained by the label-free methods are also reviewed, which are important to obtain the accurate and reliable targets. Combined application of these methods will greatly reduce the experimental costs and shorten the period for the new target identification and validation by improving its accuracy, which will provide a systematic solution for new ecological agrochemicals research and development.

CRISPR screen in mechanism and target discovery for cancer immunotherapy

CRISPR/Cas-based genetic perturbation screens have emerged as powerful tools for large-scale identification of new targets for cancer immunotherapy. Various strategies of CRISPR screen have been used for immune-oncology (IO) target discovery. The genomic sequences targeted by CRISPR/Cas system range from coding sequences to non-coding RNA/DNA, including miRNAs, LncRNAs, circRNAs, promoters, and enhancers, which may be potential targets for future pharmacological and therapeutic interventions. Rapid progresses have been witnessed in finding novel targets for enhancing tumor antigen presentation, sensitizing of tumor cells to immune-mediated cytotoxicity, and reinvigorating tumor-specific T cells by using CRISPR technologies. In combination with other strategies, the detailed characteristics of the targets for immunotherapy have been obtained by CRISPR screen. In this review, we present an overview of recent progresses in the development of CRISPR-based screens for IO target identification and discuss the challenges and possible solutions in this rapidly growing field.

Explore Protein-Protein Interactions for Cancer Target Discovery Using the OncoPPi Portal

Protein-protein interactions (PPIs) control all functions and physiological states of the cell. Identification and understanding of novel PPIs would facilitate the discovery of new biological models and therapeutic targets for clinical intervention. Numerous resources and PPI databases have been developed to define a global interactome through the PPI data mining, curation, and integration of different types of experimental evidence obtained with various methods in different model systems. On the other hand, the recent advances in cancer genomics and proteomics have revealed a critical role of genomic alterations in acquisition of cancer hallmarks through a dysregulated network of oncogenic PPIs. Deciphering of cancer-specific interactome would uncover new mechanisms of oncogenic signaling for therapeutic interrogation. Toward this goal our team has developed a high-throughput screening platform to detect PPIs between cancer-associated proteins in the context of cancer cells. The established network of oncogenic PPIs, termed the OncoPPi network, is available through the OncoPPi Portal, an interactive web resource that allows to access and interpret a high-quality cancer-focused network of PPIs experimentally detected in cancer cell lines integrated with the analysis of mutual exclusivity of genomic alterations, cellular co-localization of interacting proteins, domain-domain interactions, and therapeutic connectivity. This chapter presents a guide to explore the OncoPPi network using the OncoPPi Portal to facilitate cancer biology.

Integrated chromatin and transcriptomic profiling of patient-derived colon cancer organoids identifies personalized drug targets to overcome oxaliplatin resistance

Colorectal cancer is a leading cause of cancer deaths. Most colorectal cancer patients eventually develop chemoresistance to the current standard-of-care therapies. Here, we used patient-derived colorectal cancer organoids to demonstrate that resistant tumor cells undergo significant chromatin changes in response to oxaliplatin treatment. Integrated transcriptomic and chromatin accessibility analyses using ATAC-Seq and RNA-Seq identified a group of genes associated with significantly increased chromatin accessibility and upregulated gene expression. CRISPR/Cas9 silencing of fibroblast growth factor receptor 1 (FGFR1) and oxytocin receptor (OXTR) helped overcome oxaliplatin resistance. Similarly, treatment with oxaliplatin in combination with an FGFR1 inhibitor (PD166866) or an antagonist of OXTR (L-368,899) suppressed chemoresistant organoids. However, oxaliplatin treatment did not activate either FGFR1 or OXTR expression in another resistant organoid, suggesting that chromatin accessibility changes are patient-specific. The use of patient-derived cancer organoids in combination with transcriptomic and chromatin profiling may lead to precision treatments to overcome chemoresistance in colorectal cancer.

Advances in omics-based methods to identify novel targets for malaria and other parasitic protozoan infections

A major advance in antimalarial drug discovery has been the shift towards cell-based phenotypic screening, with notable progress in the screening of compounds against the asexual blood stage, liver stage, and gametocytes. A primary method for drug target deconvolution in Plasmodium falciparum is in vitro evolution of compound-resistant parasites followed by whole-genome scans. Several of the most promising antimalarial drug targets, such as translation elongation factor 2 (eEF2) and phenylalanine tRNA synthetase (PheRS), have been identified or confirmed using this method. One drawback of this method is that if a mutated gene is uncharacterized, a substantial effort may be required to determine whether it is a drug target, a drug resistance gene, or if the mutation is merely a background mutation. Thus, the availability of high-throughput, functional genomic datasets can greatly assist with target deconvolution. Studies mapping genome-wide essentiality in P. falciparum or performing transcriptional profiling of the host and parasite during liver-stage infection with P. berghei have identified potentially druggable pathways. Advances in mapping the epigenomic regulation of the malaria parasite genome have also enabled the identification of key processes involved in parasite development. In addition, the examination of the host genome during infection has identified novel gene candidates associated with susceptibility to severe malaria. Here, we review recent studies that have used omics-based methods to identify novel targets for interventions against protozoan parasites, focusing on malaria, and we highlight the advantages and limitations of the approaches used. These approaches have also been extended to other protozoan pathogens, including Toxoplasma, Trypanosoma, and Leishmania spp., and these studies highlight how drug discovery efforts against these pathogens benefit from the utilization of diverse omics-based methods to identify promising drug targets.

Integration of RNAi and Small Molecule Screens to Identify Targets for Drug Development

Cellular models for siRNA and small molecule high-throughput screening have been widely used in the last decade to identify targets for drug discovery. As an example, we present a twofold readout approach based on cell viability and multipolar phenotype. To maximize the discovery of potential targets and at the same time reduce the number of false positives in our dataset, we have combined focused and rationally designed custom siRNA libraries with small molecule inhibitor libraries. Here we describe a cellular model for centrosome amplification as an example of how to design and perform a multiple readout/multiple screening strategy.

In Vitro High-Throughput RNAi Screening to Accelerate the Process of Target Identification and Drug Development

High-throughput RNA interference (HT-RNAi) is a powerful tool that can be used to knock down gene expression in order to identify novel genes and pathways involved in many cellular processes. It is a systematic, yet unbiased, approach to identify essential or synthetic lethal genes that promote cell survival in diseased cells as well as genes that confer resistance or sensitivity to drug treatment. This information serves as a foundation for enhancing current treatments for cancer and other diseases by identifying new drug targets, uncovering potential combination therapies, and helping clinicians match patients with the most effective treatment based on genetic information. Here, we describe the method of performing an in vitro HT-RNAi screen using chemically synthesized siRNA.

Analysis of viral diversity for vaccine target discovery

BACKGROUND

Viral vaccine target discovery requires understanding the diversity of both the virus and the human immune system. The readily available and rapidly growing pool of viral sequence data in the public domain enable the identification and characterization of immune targets relevant to adaptive immunity. A systematic bioinformatics approach is necessary to facilitate the analysis of such large datasets for selection of potential candidate vaccine targets.

RESULTS

This work describes a computational methodology to achieve this analysis, with data of dengue, West Nile, hepatitis A, HIV-1, and influenza A viruses as examples. Our methodology has been implemented as an analytical pipeline that brings significant advancement to the field of reverse vaccinology, enabling systematic screening of known sequence data in nature for identification of vaccine targets. This includes key steps (i) comprehensive and extensive collection of sequence data of viral proteomes (the virome), (ii) data cleaning, (iii) large-scale sequence alignments, (iv) peptide entropy analysis, (v) intra- and inter-species variation analysis of conserved sequences, including human homology analysis, and (vi) functional and immunological relevance analysis.

CONCLUSION

These steps are combined into the pipeline ensuring that a more refined process, as compared to a simple evolutionary conservation analysis, will facilitate a better selection of vaccine targets and their prioritization for subsequent experimental validation.

In silico prediction of novel therapeutic targets using gene-disease association data

BACKGROUND

Target identification and validation is a pressing challenge in the pharmaceutical industry, with many of the programmes that fail for efficacy reasons showing poor association between the drug target and the disease. Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space. Here, we explore whether gene-disease association data from the Open Targets platform is sufficient to predict therapeutic targets that are actively being pursued by pharmaceutical companies or are already on the market.

METHODS

To test our hypothesis, we train four different classifiers (a random forest, a support vector machine, a neural network and a gradient boosting machine) on partially labelled data and evaluate their performance using nested cross-validation and testing on an independent set. We then select the best performing model and use it to make predictions on more than 15,000 genes. Finally, we validate our predictions by mining the scientific literature for proposed therapeutic targets.

RESULTS

We observe that the data types with the best predictive power are animal models showing a disease-relevant phenotype, differential expression in diseased tissue and genetic association with the disease under investigation. On a test set, the neural network classifier achieves over 71% accuracy with an AUC of 0.76 when predicting therapeutic targets in a semi-supervised learning setting. We use this model to gain insights into current and failed programmes and to predict 1431 novel targets, of which a highly significant proportion has been independently proposed in the literature.

CONCLUSIONS

Our in silico approach shows that data linking genes and diseases is sufficient to predict novel therapeutic targets effectively and confirms that this type of evidence is essential for formulating or strengthening hypotheses in the target discovery process. Ultimately, more rapid and automated target prioritisation holds the potential to reduce both the costs and the development times associated with bringing new medicines to patients.

Human genetics as a model for target validation: finding new therapies for diabetes

Type 2 diabetes is a global epidemic with major effects on healthcare expenditure and quality of life. Currently available treatments are inadequate for the prevention of comorbidities, yet progress towards new therapies remains slow. A major barrier is the insufficiency of traditional preclinical models for predicting drug efficacy and safety. Human genetics offers a complementary model to assess causal mechanisms for target validation. Genetic perturbations are 'experiments of nature' that provide a uniquely relevant window into the long-term effects of modulating specific targets. Here, we show that genetic discoveries over the past decades have accurately predicted (now known) therapeutic mechanisms for type 2 diabetes. These findings highlight the potential for use of human genetic variation for prospective target validation, and establish a framework for future applications. Studies into rare, monogenic forms of diabetes have also provided proof-of-principle for precision medicine, and the applicability of this paradigm to complex disease is discussed. Finally, we highlight some of the limitations that are relevant to the use of genome-wide association studies (GWAS) in the search for new therapies for diabetes. A key outstanding challenge is the translation of GWAS signals into disease biology and we outline possible solutions for tackling this experimental bottleneck.

Cleavable Linkers in Chemical Proteomics Applications

The discovery of the protein targets of small molecule probes is a crucial aspect of activity-based protein profiling and chemical biology. Mass spectrometry is the primary method for target identification, and in the last decade, cleavable linkers have become a popular strategy to facilitate protein enrichment and identification. In this chapter, we provide an overview of cleavable linkers used in chemical proteomics approaches, discuss their different chemistries, and describe how they aid in protein identification.

Interrogation of individual intratumoral B lymphocytes from lung cancer patients for molecular target discovery

Intratumoral B lymphocytes are an integral part of the lung tumor microenvironment. Interrogation of the antibodies they express may improve our understanding of the host response to cancer and could be useful in elucidating novel molecular targets. We used two strategies to explore the repertoire of intratumoral B cell antibodies. First, we cloned VH and VL genes from single intratumoral B lymphocytes isolated from one lung tumor, expressed the genes as recombinant mAbs, and used the mAbs to identify the cognate tumor antigens. The Igs derived from intratumoral B cells demonstrated class switching, with a mean VH mutation frequency of 4%. Although there was no evidence for clonal expansion, these data are consistent with antigen-driven somatic hypermutation. Individual recombinant antibodies were polyreactive, although one clone demonstrated preferential immunoreactivity with tropomyosin 4 (TPM4). We found that higher levels of TPM4 antibodies were more common in cancer patients, but measurement of TPM4 antibody levels was not a sensitive test for detecting cancer. Second, in an effort to focus our recombinant antibody expression efforts on those B cells that displayed evidence of clonal expansion driven by antigen stimulation, we performed deep sequencing of the Ig genes of B cells collected from seven different tumors. Deep sequencing demonstrated somatic hypermutation but no dominant clones. These strategies may be useful for the study of B cell antibody expression, although identification of a dominant clone and unique therapeutic targets may require extensive investigation.

Trends in Modern Drug Discovery

Drugs discovered by the pharmaceutical industry over the past 100 years have dramatically changed the practice of medicine and impacted on many aspects of our culture. For many years, drug discovery was a target- and mechanism-agnostic approach that was based on ethnobotanical knowledge often fueled by serendipity. With the advent of modern molecular biology methods and based on knowledge of the human genome, drug discovery has now largely changed into a hypothesis-driven target-based approach, a development which was paralleled by significant environmental changes in the pharmaceutical industry. Laboratories became increasingly computerized and automated, and geographically dispersed research sites are now more and more clustered into large centers to capture technological and biological synergies. Today, academia, the regulatory agencies, and the pharmaceutical industry all contribute to drug discovery, and, in order to translate the basic science into new medical treatments for unmet medical needs, pharmaceutical companies have to have a critical mass of excellent scientists working in many therapeutic fields, disciplines, and technologies. The imperative for the pharmaceutical industry to discover breakthrough medicines is matched by the increasing numbers of first-in-class drugs approved in recent years and reflects the impact of modern drug discovery approaches, technologies, and genomics.

HER2/ERBB2 immunoreactivity in human retinoblastoma

Retinoblastoma (RB) is an ocular malignancy of early childhood. Although mutations in the Rb1 gene and expression of stem cell markers have been identified in RB, additional information on RB-specific alterations in signaling pathways and protein expression would be useful for the design of targeted RB therapies. Here we have evaluated the expression of HER2 (ERBB2) in RB. HER2 is a member of the epidermal growth factor family, which is overexpressed in breast, ovarian, gastric, colorectal, pancreatic, and endometrial cancers in a stratified manner. Overexpression and gene amplification of HER2 is associated with aggressive malignancies, accompanied by chemoresistance and poor outcomes. In this study, we present the first evidence of HER2 immunoreactivity in retinoblastoma, as shown by immunocytochemistry, flow cytometry, and western immunoblot, with validation by reverse transcription PCR (RT-PCR) in both RB cell lines and clinical RB tumors. Our results suggest that the HER2 protein expressed in RB is a truncated version that spares the trastuzumab binding site, while HER2 is not detected in normal ocular tissues. Our discovery of HER2 expression in RB may lead to innovative and targeted drug treatment options designed to spare the eye and preserve vision in RB patients.

Virtual screening: an in silico tool for interlacing the chemical universe with the proteome

In silico screening both in the forward (traditional virtual screening) and reverse sense (inverse virtual screening (IVS)) are helpful techniques for interlacing the chemical universe of small molecules with the proteome. The former, which is using a protein structure and a large chemical database, is well-known by the scientific community. We have chosen here to provide an overview on the latter, focusing on validation and target prioritization strategies. By comparing it to complementary or alternative wet-lab approaches, we put IVS in the broader context of chemical genomics, target discovery and drug design. By giving examples from the literature and an own example on how to validate the approach, we provide guidance on the issues related to IVS.