Machine Learning Reinforced Genetic Algorithm for Massive Targeted Discovery of Selectively Cytotoxic Inorganic Nanoparticles

Nanoparticles (NPs) have been employed as drug delivery systems (DDSs) for several decades, primarily as passive carriers, with limited selectivity. However, recent publications have shed light on the emerging phenomenon of NPs exhibiting selective cytotoxicity against cancer cell lines, attributable to distinct metabolic disparities between healthy and pathological cells. This study revisits the concept of NPs selective cytotoxicity, and for the first time proposes a high-throughput in silico screening approach to massive targeted discovery of selectively cytotoxic inorganic NPs. In the first step, this work trains a gradient boosting regression model to predict viability of NP-treated cell lines. The model achieves mean cross-validation (CV) Q2 = 0.80 and root mean square error (RMSE) of 13.6. In the second step, this work develops a machine learning (ML) reinforced genetic algorithm (GA), capable of screening >14 900 candidates/min, to identify the best-performing selectively cytotoxic NPs. As proof-of-concept, DDS candidates for the treatment of liver cancer are screened on HepG2 and hepatocytes cell lines resulting in Ag NPs with selective toxicity score of 42%. This approach opens the door for clinical translation of NPs, expanding their therapeutic application to a wider range of chemical space of NPs and living organisms such as bacteria and fungi.

Fraisinib: a calixpyrrole derivative reducing A549 cell-derived NSCLC tumor in vivo acts as a ligand of the glycine-tRNA synthase, a new molecular target in oncology

Background and purpose: Lung cancer is the leading cause of death in both men and women, constituting a major public health problem worldwide. Non-small-cell lung cancer accounts for 85%-90% of all lung cancers. We propose a compound that successfully fights tumor growth in vivo by targeting the enzyme GARS1. Experimental approach: We present an in-depth investigation of the mechanism through which Fraisinib [meso-(p-acetamidophenyl)-calix(4)pyrrole] affects the human lung adenocarcinoma A549 cell line. In a xenografted model of non-small-cell lung cancer, Fraisinib was found to reduce tumor mass volume without affecting the vital parameters or body weight of mice. Through a computational approach, we uncovered that glycyl-tRNA synthetase is its molecular target. Differential proteomics analysis further confirmed that pathways regulated by Fraisinib are consistent with glycyl-tRNA synthetase inhibition. Key results: Fraisinib displays a strong anti-tumoral potential coupled with limited toxicity in mice. Glycyl-tRNA synthetase has been identified and validated as a protein target of this compound. By inhibiting GARS1, Fraisinib modulates different key biological processes involved in tumoral growth, aggressiveness, and invasiveness. Conclusion and implications: The overall results indicate that Fraisinib is a powerful inhibitor of non-small-cell lung cancer growth by exerting its action on the enzyme GARS1 while displaying marginal toxicity in animal models. Together with the proven ability of this compound to cross the blood-brain barrier, we can assess that Fraisinib can kill two birds with one stone: targeting the primary tumor and its metastases "in one shot." Taken together, we suggest that inhibiting GARS1 expression and/or GARS1 enzymatic activity may be innovative molecular targets for cancer treatment.

A comprehensive AI-driven analysis of large-scale omic datasets reveals novel dual-purpose targets for the treatment of cancer and aging

As aging and tumorigenesis are tightly interconnected biological processes, targeting their common underlying driving pathways may induce dual-purpose anti-aging and anti-cancer effects. Our transcriptomic analyses of 16,740 healthy samples demonstrated tissue-specific age-associated gene expression, with most tumor suppressor genes downregulated during aging. Furthermore, a large-scale pan-cancer analysis of 11 solid tumor types (11,303 cases and 4431 control samples) revealed that many cellular processes, such as protein localization, DNA replication, DNA repair, cell cycle, and RNA metabolism, were upregulated in cancer but downregulated in healthy aging tissues, whereas pathways regulating cellular senescence were upregulated in both aging and cancer. Common cancer targets were identified by the AI-driven target discovery platform-PandaOmics. Age-associated cancer targets were selected and further classified into four groups based on their reported roles in lifespan. Among the 51 identified age-associated cancer targets with anti-aging experimental evidence, 22 were proposed as dual-purpose targets for anti-aging and anti-cancer treatment with the same therapeutic direction. Among age-associated cancer targets without known lifespan-regulating activity, 23 genes were selected based on predicted dual-purpose properties. Knockdown of histone demethylase KDM1A, one of these unexplored candidates, significantly extended lifespan in Caenorhabditis elegans. Given KDM1A's anti-cancer activities reported in both preclinical and clinical studies, our findings propose KDM1A as a promising dual-purpose target. This is the first study utilizing an innovative AI-driven approach to identify dual-purpose target candidates for anti-aging and anti-cancer treatment, supporting the value of AI-assisted target identification for drug discovery.

Comprehensive interrogation of gene lists from genome-scale cancer screens with oncoEnrichR

Genome-scale screening experiments in cancer produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogation of gene lists frequently represents a significant and time-consuming undertaking, in which experimental biologists typically combine results from a variety of bioinformatics resources in an attempt to portray and understand cancer relevance. As a means to simplify and strengthen the support for this endeavor, we have developed oncoEnrichR, a flexible bioinformatics tool that allows cancer researchers to comprehensively interrogate a given gene list along multiple facets of cancer relevance. oncoEnrichR differs from general gene set analysis frameworks through the integration of an extensive set of prior knowledge specifically relevant for cancer, including ranked gene-tumor type associations, literature-supported proto-oncogene and tumor suppressor gene annotations, target druggability data, regulatory interactions, synthetic lethality predictions, as well as prognostic associations, gene aberrations and co-expression patterns across tumor types. The software produces a structured and user-friendly analysis report as its main output, where versions of all underlying data resources are explicitly logged, the latter being a critical component for reproducible science. We demonstrate the usefulness of oncoEnrichR through interrogation of two candidate lists from proteomic and CRISPR screens. oncoEnrichR is freely available as a web-based service hosted by the Galaxy platform (https://oncotools.elixir.no), and can also be accessed as a stand-alone R package (https://github.com/sigven/oncoEnrichR).

Mapping the protein-protein interactome in the tumor immune microenvironment

The cell-to-cell communication primarily occurs through cell-surface and secreted proteins, which form a sophisticated network that coordinates systemic immune function. Uncovering these protein-protein interactions (PPIs) is indispensable for understanding the molecular mechanism and elucidating immune system aberrances under diseases. Traditional biological studies typically focus on a limited number of PPI pairs due to the relative low throughput of commonly used techniques. Encouragingly, classical methods have advanced, and many new systems tailored for large-scale protein-protein screening have been developed and successfully utilized. These high-throughput PPI investigation techniques have already made considerable achievements in mapping the immune cell interactome, enriching PPI databases and analysis tools, and discovering therapeutic targets for cancer and other diseases, which will definitely bring unprecedented insight into this field.

Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs target discovery in ankylosing spondylitis

Ankylosing spondylitis (AS) is a common, highly heritable inflammatory arthritis characterized by enthesitis of the spine and sacroiliac joints. Genome-wide association studies (GWASs) have revealed more than 100 genetic associations whose functional effects remain largely unresolved. Here, we present a comprehensive transcriptomic and epigenomic map of disease-relevant blood immune cell subsets from AS patients and healthy controls. We find that, while CD14⁺ monocytes and CD4⁺ and CD8⁺ T cells show disease-specific differences at the RNA level, epigenomic differences are only apparent upon multi-omics integration. The latter reveals enrichment at disease-associated loci in monocytes. We link putative functional SNPs to genes using high-resolution Capture-C at 10 loci, including PTGER4 and ETS1, and show how disease-specific functional genomic data can be integrated with GWASs to enhance therapeutic target discovery. This study combines epigenetic and transcriptional analysis with GWASs to identify disease-relevant cell types and gene regulation of likely pathogenic relevance and prioritize drug targets.

Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics - an AI-enabled biological target discovery platform

Glioblastoma Multiforme (GBM) is the most aggressive and most common primary malignant brain tumor. The age of GBM patients is considered as one of the disease's negative prognostic factors and the mean age of diagnosis is 62 years. A promising approach to preventing both GBM and aging is to identify new potential therapeutic targets that are associated with both conditions as concurrent drivers. In this work, we present a multi-angled approach of identifying targets, which takes into account not only the disease-related genes but also the ones important in aging. For this purpose, we developed three strategies of target identification using the results of correlation analysis augmented with survival data, differences in expression levels and previously published information of aging-related genes. Several studies have recently validated the robustness and applicability of AI-driven computational methods for target identification in both cancer and aging-related diseases. Therefore, we leveraged the AI predictive power of the PandaOmics TargetID engine in order to rank the resulting target hypotheses and prioritize the most promising therapeutic gene targets. We propose cyclic nucleotide gated channel subunit alpha 3 (CNGA3), glutamate dehydrogenase 1 (GLUD1) and sirtuin 1 (SIRT1) as potential novel dual-purpose therapeutic targets to treat aging and GBM.

Expression of RBMS3 in Breast Cancer Progression

The aim of the study was to evaluate the localization and intensity of RNA-binding motif single-stranded-interacting protein 3 (RBMS3) expression in clinical material using immunohistochemical (IHC) reactions in cases of ductal breast cancer (in vivo), and to determine the level of RBMS3 expression at both the protein and mRNA levels in breast cancer cell lines (in vitro). Moreover, the data obtained in the in vivo and in vitro studies were correlated with the clinicopathological profiles of the patients. Material for the IHC studies comprised 490 invasive ductal carcinoma (IDC) cases and 26 mastopathy tissues. Western blot and RT-qPCR were performed on four breast cancer cell lines (MCF-7, BT-474, SK-BR-3 and MDA-MB-231) and the HME1-hTERT (Me16C) normal immortalized breast epithelial cell line (control). The Kaplan-Meier plotter tool was employed to analyze the predictive value of overall survival of RBMS3 expression at the mRNA level. Cytoplasmatic RBMS3 IHC expression was observed in breast cancer cells and stromal cells. The statistical analysis revealed a significantly decreased RBMS3 expression in the cancer specimens when compared with the mastopathy tissues (p < 0.001). An increased expression of RBMS3 was corelated with HER2(+) cancer specimens (p < 0.05) and ER(-) cancer specimens (p < 0.05). In addition, a statistically significant higher expression of RBMS3 was observed in cancer stromal cells in comparison to the control and cancer cells (p < 0.0001). The statistical analysis demonstrated a significantly higher expression of RBMS3 mRNA in the SK-BR-3 cell line compared with all other cell lines (p < 0.05). A positive correlation was revealed between the expression of RBMS3, at both the mRNA and protein levels, and longer overall survival. The differences in the expression of RBMS3 in cancer cells (both in vivo and in vitro) and the stroma of breast cancer with regard to the molecular status of the tumor may indicate that RBMS3 could be a potential novel target for the development of personalized methods of treatment. RBMS3 can be an indicator of longer overall survival for potential use in breast cancer diagnostic process.

e-TSN: an interactive visual exploration platform for target-disease knowledge mapping from literature

Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target-disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug-target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target-disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.

Emerging target discovery and drug repurposing opportunities in chordoma

The development of effective and personalized treatment options for patients with rare cancers like chordoma is hampered by numerous challenges. Biomarker-guided repurposing of therapies approved in other indications remains the fastest path to redefining the treatment paradigm, but chordoma's low mutation burden limits the impact of genomics in target discovery and precision oncology efforts. As our knowledge of oncogenic mechanisms across various malignancies has matured, it's become increasingly clear that numerous properties of tumors transcend their genomes - leading to new and uncharted frontiers of therapeutic opportunity. In this review, we discuss how the implementation of cutting-edge tools and approaches is opening new windows into chordoma's vulnerabilities. We also note how a convergence of emerging observations in chordoma and other cancers is leading to the identification and evaluation of new therapeutic hypotheses for this rare cancer.

Tyrosine Kinase Inhibitor Independent Gene Expression Signature in CML Offers New Targets for LSPC Eradication Therapy

Tyrosine kinase inhibitors (TKI) have revolutionised the treatment of CML. However, TKI do not eliminate the leukaemia stem cells (LSC), which can re-initiate the disease. Thus, finding new therapeutic targets in CML LSC is key to finding a curative treatment. Using microarray datasets, we defined a list of 227 genes that were differentially expressed in CML LSC compared to the healthy controls but were not affected by TKI in vitro. Two of them, CD33 and PPIF, are targeted by gemtuzumab-ozogamicin and cyclosporin A, respectively. We treated CML and the control CD34+ cells with either drug with or without imatinib to investigate the therapeutic potential of the TKI-independent gene expression programme. Cyclosporine A, in combination with imatinib, reduced the number of CML CFC compared with non-CML controls, but only at supra-therapeutic concentrations. Gemtuzumab-ozogamicin showed an EC50 of 146 ng/mL, below the plasma peak concentration of 630 ng/mL observed in the AML patients and below the EC50 of 3247 ng/mL observed in the non-CML cells. Interestingly, gemtuzumab-ozogamicin seems to promote cell cycle progression in CML CD34+ cells and demonstrated activation of the RUNX1 pathway in an RNAseq experiment. This suggests that targeting the TKI-independent genes in CML LSC could be exploited for the development of new therapies in CML.

Role of RBMS3 Novel Potential Regulator of the EMT Phenomenon in Physiological and Pathological Processes

RNA-binding protein 3 (RBMS3) plays a significant role in embryonic development and the pathogenesis of many diseases, especially cancer initiation and progression. The multiple roles of RBMS3 are conditioned by its numerous alternative expression products. It has been proven that the main form of RBMS3 influences the regulation of microRNA expression or stabilization. The absence of RBMS3 activates the Wnt/β-catenin pathway. The expression of c-Myc, another target of the Wnt/β-catenin pathway, is correlated with the RBMS3 expression. Numerous studies have focused solely on the interaction of RBMS3 with the epithelial-mesenchymal transition (EMT) protein machinery. EMT plays a vital role in cancer progression, in which RBMS3 is a new potential regulator. It is also significant that RBMS3 may act as a prognostic factor of overall survival (OS) in different types of cancer. This review presents the current state of knowledge about the role of RBMS3 in physiological and pathological processes, with particular emphasis on carcinogenesis. The molecular mechanisms underlying the role of RBMS3 are not fully understood; hence, a broader explanation and understanding is still needed.

Endogenous spacing enables co-processing of microRNAs and efficient combinatorial RNAi

We present Multi-miR, a microRNA-embedded shRNA system modeled after endogenous microRNA clusters that enables simultaneous expression of up to three or four short hairpin RNAs (shRNAs) from a single promoter without loss of activity, enabling robust combinatorial RNA interference (RNAi). We further developed complementary all-in-one vectors that are over one log-scale more sensitive to doxycycline-mediated activation in vitro than previous methods and resistant to shRNA inactivation in vivo. We demonstrate the utility of this system for intracranial expression of shRNAs in a glioblastoma model. Additionally, we leverage this platform to target the redundant RAF signaling node in a mouse model of KRAS-mutant cancer and show that robust combinatorial synthetic lethality efficiently abolishes tumor growth.

Computational Discovery of Cancer Immunotherapy Targets by Intercellular CRISPR Screens

Cancer immunotherapy targets the interplay between immune and cancer cells. In particular, interactions between cytotoxic T lymphocytes (CTLs) and cancer cells, such as PD-1 (PDCD1) binding PD-L1 (CD274), are crucial for cancer cell clearance. However, immune checkpoint inhibitors targeting these interactions are effective only in a subset of patients, requiring the identification of novel immunotherapy targets. Genome-wide clustered regularly interspaced short palindromic repeats (CRISPR) screening in either cancer or immune cells has been employed to discover regulators of immune cell function. However, CRISPR screens in a single cell type complicate the identification of essential intercellular interactions. Further, pooled screening is associated with high noise levels. Herein, we propose intercellular CRISPR screens, a computational approach for the analysis of genome-wide CRISPR screens in every interacting cell type for the discovery of intercellular interactions as immunotherapeutic targets. We used two publicly available genome-wide CRISPR screening datasets obtained while triple-negative breast cancer (TNBC) cells and CTLs were interacting. We analyzed 4825 interactions between 1391 ligands and receptors on TNBC cells and CTLs to evaluate their effects on CTL function. Intercellular CRISPR screens discovered targets of approved drugs, a few of which were not identifiable in single datasets. To evaluate the method's performance, we used data for cytokines and costimulatory molecules as they constitute the majority of immunotherapeutic targets. Combining both CRISPR datasets improved the recall of discovering these genes relative to using single CRISPR datasets over two-fold. Our results indicate that intercellular CRISPR screens can suggest novel immunotherapy targets that are not obtained through individual CRISPR screens. The pipeline can be extended to other cancer and immune cell types to discover important intercellular interactions as potential immunotherapeutic targets.

Longitudinal Large-Scale Semiquantitative Proteomic Data Stability Across Multiple Instrument Platforms

With the rapid developments in mass spectrometry (MS)-based proteomics methods, label-free semiquantitative proteomics has become an increasingly popular tool for profiling global protein abundances in an unbiased manner. However, the reproducibility of these data across time and LC-MS platforms is not well characterized. Here, we evaluate the performance of three LC-MS platforms (Orbitrap Elite, Q Exactive HF, and Orbitrap Fusion) in label-free semiquantitative analysis of cell surface proteins over a six-year period. Sucrose gradient ultracentrifugation was used for surfaceome enrichment, following gel separation for in-depth protein identification. With our established workflow, we consistently detected and reproducibly quantified >2300 putative cell surface proteins in a human acute myeloid leukemia (AML) cell line on all three platforms. To our knowledge this is the first study reporting highly reproducible semiquantitative proteomic data collection of biological replicates across multiple years and LC-MS platforms. These data provide experimental justification for semiquantitative proteomic study designs that are executed over multiyear time intervals and on different platforms. Multiyear and multiplatform experimental designs will likely enable larger scale proteomic studies and facilitate longitudinal proteomic studies by investigators lacking access to high throughput MS facilities. Data are available via ProteomeXchange with identifier PXD022721.

Co-Expression Network Analysis of MicroRNAs and Proteins in Severe Traumatic Brain Injury: A Systematic Review

Traumatic brain injury (TBI) represents one of the leading causes of mortality and morbidity worldwide, placing an enormous socioeconomic burden on healthcare services and communities around the world. Survivors of TBI can experience complications ranging from temporary neurological and psychosocial problems to long-term, severe disability and neurodegenerative disease. The current lack of therapeutic agents able to mitigate the effects of secondary brain injury highlights the urgent need for novel target discovery. This study comprises two independent systematic reviews, investigating both microRNA (miRNA) and proteomic expression in rat models of severe TBI (sTBI). The results were combined to perform integrated miRNA-protein co-expression analyses with the aim of uncovering the potential roles of miRNAs in sTBI and to ultimately identify new targets for therapy. Thirty-four studies were included in total. Bioinformatic analysis was performed to identify any miRNA–protein associations. Endocytosis and TNF signalling pathways were highlighted as common pathways involving both miRNAs and proteins found to be differentially expressed in rat brain tissue following sTBI, suggesting efforts to find novel therapeutic targets that should be focused here. Further high-quality investigations are required to ascertain the involvement of these pathways and their miRNAs in the pathogenesis of TBI and other CNS diseases and to therefore uncover those targets with the greatest therapeutic potential.

Uncoupling of gene expression from copy number presents therapeutic opportunities in aneuploid cancers

Uncoupling of mRNA expression from copy number (UECN) might be a strategy for cancer cells to a tolerate high degree of aneuploidy. To test the extent and role of UECN across cancers, we perform integrative multiomic analysis of The Cancer Genome Atlas (TCGA) dataset, encompassing ∼5,000 individual tumors. We find UECN is common in cancers and is associated with increased oncogenic signaling, proliferation, and immune suppression. UECN appears to be orchestrated by complex regulatory changes, with transcription factors (TFs) playing a prominent role. To further dissect the regulatory mechanisms, we develop a systems-biology approach to identify candidate TFs, which could serve as targets to disrupt UECN and reduce tumor fitness. Applying our approach to TCGA data, we identify 21 putative targets, 42.8% of which are validated by independent sources. Together, our study indicates that UECN is likely an important mechanism in development of aneuploid tumors and might be therapeutically targetable.

CRISPR screen in cancer: status quo and future perspectives

Clustered regularly interspaced short palindromic repeats (CRISPR) system offers a powerful platform for genome manipulation, including protein-coding genes, noncoding RNAs and regulatory elements. The development of CRISPR screen enables high-throughput interrogation of gene functions in diverse tumor biologies, such as tumor growth, metastasis, synthetic lethal interactions, therapeutic resistance and immunotherapy response, which are mostly performed in vitro or in transplant models. Recently, direct in vivo CRISPR screens have been developed to identify drivers of tumorigenesis in native microenvironment. Key parameters of CRISPR screen are constantly being optimized to achieve higher targeting efficiency and lower off-target effect. Here, we review the recent advances of CRISPR screen in cancer studies both in vitro and in vivo, with a particular focus on identifying cancer immunotherapy targets, and propose optimizing strategies and future perspectives for CRISPR screen.

In vivo screens using a selective CRISPR antigen removal lentiviral vector system reveal immune dependencies in renal cell carcinoma

CRISPR-Cas9 genome engineering has increased the pace of discovery for immunology and cancer biology, revealing potential therapeutic targets and providing insight into mechanisms underlying resistance to immunotherapy. However, endogenous immune recognition of Cas9 has limited the applicability of CRISPR technologies in vivo. Here, we characterized immune responses against Cas9 and other expressed CRISPR vector components that cause antigen-specific tumor rejection in several mouse cancer models. To avoid unwanted immune recognition, we designed a lentiviral vector system that allowed selective CRISPR antigen removal (SCAR) from tumor cells. The SCAR system reversed immune-mediated rejection of CRISPR-modified tumor cells in vivo and enabled high-throughput genetic screens in previously intractable models. A pooled in vivo screen using SCAR in a CRISPR-antigen-sensitive renal cell carcinoma revealed resistance pathways associated with autophagy and major histocompatibility complex class I (MHC class I) expression. Thus, SCAR presents a resource that enables CRISPR-based studies of tumor-immune interactions and prevents unwanted immune recognition of genetically engineered cells, with implications for clinical applications.

CRISPRing future medicines

Introduction: The ability to engineer mammalian genomes in a quick and cost-effective way has led to rapid adaptation of CRISPR technology in biomedical research. CRISPR-based engineering has the potential to accelerate drug discovery, to support the reduction of high attrition rate in drug development and to enhance development of cell and gene-based therapies.Areas covered: How CRISPR technology is transforming drug discovery is discussed in this review. From target identification to target validation in both in vitro and in vivo models, CRISPR technology is positively impacting the early stages of drug development by providing a straightforward way to genome engineering. This property also attracted attention for CRISPR application in the cell and gene therapy area.Expert opinion: CRISPR technology is rapidly becoming the preferred tool for genome engineering and nowadays it is hard to imagine the drug discovery pipeline without this technology. With the years to come, CRISPR technology will undoubtedly be further refined and will flourish into a mature technology that will play a key role in supporting genome engineering requirements in the drug discovery pipeline as well as in cell and gene therapy development.

Secretome-Based Screening in Target Discovery

Secreted proteins and their cognate plasma membrane receptors regulate human physiology by transducing signals from the extracellular environment into cells resulting in different cellular phenotypes. Systematic use of secretome proteins in assays enables discovery of novel biology and signaling pathways. Several secretome-based phenotypic screening platforms have been described in the literature and shown to facilitate target identification in drug discovery. In this review, we summarize the current status of secretome-based screening. This includes annotation, production, quality control, and sample management of secretome libraries, as well as how secretome libraries have been applied to discover novel target biology using different disease-relevant cell-based assays. A workflow for secretome-based screening is shared based on the AstraZeneca experience. The secretome library offers several advantages compared with other libraries used for target discovery: (1) screening using a secretome library directly identifies the active protein and, in many cases, its cognate receptor, enabling a rapid understanding of the disease pathway and subsequent formation of target hypotheses for drug discovery; (2) the secretome library covers significant areas of biological signaling space, although the size of this library is small; (3) secretome proteins can be added directly to cells without additional manipulation. These factors make the secretome library ideal for testing in physiologically relevant cell types, and therefore it represents an attractive approach to phenotypic target discovery.

A Photo-clickable ATP-Mimetic Reveals Nucleotide Interactors in the Membrane Proteome

ATP is an important energy metabolite and allosteric signal in health and disease. ATP-interacting proteins, such as P2 receptors, control inflammation, cell death, migration, and wound healing. However, identification of allosteric ATP sites remains challenging, and our current inventory of ATP-controlled pathways is likely incomplete. Here, we develop and verify mipATP as a minimally invasive photoaffinity probe for ATP-interacting proteins. Its N⁶ functionalization allows target enrichment by UV crosslinking and conjugation to reporter tags by "click" chemistry. The additions are compact, allowing mipATP to completely retain the calcium signaling responses of native ATP in vitro and in vivo. mipATP specifically enriched for known nucleotide binders in A549 cell lysates and membrane fractions. In addition, it retrieved unannotated ATP interactors, such as the FAS receptor, CD44, and various SLC transporters. Thus, mipATP is a promising tool to identify allosteric ATP sites in the proteome.

Arrayed CRISPR Screening Identifies Novel Targets That Enhance the Productive Delivery of mRNA by MC3-Based Lipid Nanoparticles

Modified messenger RNAs (mRNAs) hold great potential as therapeutics by using the body’s own processes for protein production. However, a key challenge is efficient delivery of therapeutic mRNA to the cell cytosol and productive protein translation. Lipid nanoparticles (LNPs) are the most clinically advanced system for nucleic acid delivery; however, a relatively narrow therapeutic index makes them unsuitable for many therapeutic applications. A key obstacle to the development of more potent LNPs is a limited mechanistic understanding of the interaction of LNPs with cells. To address this gap, we performed an arrayed CRISPR screen to identify novel pathways important for the functional delivery of MC3 lipid-based LNP encapsulated mRNA (LNP-mRNA). Here, we have developed and validated a robust, high-throughput screening–friendly phenotypic assay to identify novel targets that modulate productive LNP-mRNA delivery. We screened the druggable genome (7795 genes) and validated 44 genes that either increased (37 genes) or inhibited (14 genes) the productive delivery of LNP-mRNA. Many of these genes clustered into families involved with host cell transcription, protein ubiquitination, and intracellular trafficking. We show that both UDP-glucose ceramide glucosyltransferase and V-type proton ATPase can significantly modulate the productive delivery of LNP-mRNA, increasing and decreasing, respectively, with both genetic perturbation and by small-molecule inhibition. Taken together, these findings shed new light into the molecular machinery regulating the delivery of LNPs into cells and improve our mechanistic understanding of the cellular processes modulating the interaction of LNPs with cells.

Target Discovery in Ralstonia solanacearum through an Activity-Based Protein Profiling Technique Based on Bioactive Oxadiazole Sulfones

Ralstonia solanacearum is an extremely destructive and rebellious phytopathogen that can cause bacterial wilt diseases in more than 200 plant species. To explore and discover the potential targets in R. solanacearum for the purpose of developing new agrochemicals targeting this infection, here, we exploited a typical activity-based protein profiling technique for target discovery in R. solanacearum based on an activity-based probe 1 derived from bioactive oxadiazole sulfones. A total of 65 specific targets were identified with high confidence through a quantitative chemical proteomic approach. Three representative proteins (glycine cleavage system H protein, thiol peroxidase, and dihydrolipoamide S-succinyltransferase) were validated as the targets by using the immunoblotting analysis with their respective antibodies. Additionally, the in vitro interaction between the recombinant thiol peroxidase and probe 1 further confirmed that this protein was a target of oxadiazole sulfones. We anticipated that these discovered protein targets in R. solanacearum can stimulate the discovery and development of novel agrochemicals targeting bacterial infections caused by R. solanacearum.

Crellastatin A, a PARP-1 Inhibitor Discovered by Complementary Proteomic Approaches

Crellastatin A, a cytotoxic sulfated bis-steroid isolated from the Vanuatu Island marine sponge Crella sp., was selected as an interesting probe for a comprehensive proteomic analysis directed at the characterization of its protein interactors. Given its peculiar structural features, A was submitted to a mass spectrometry-based drug affinity responsive target stability (DARTS) assay combined with (targeted-limited proteolysis-multiple reaction monitoring (t-LiP MRM), rather than a classical affinity purification strategy. Poly-ADP-ribose-polymerase-1 (PARP-1) emerged as the main crellastatin A cellular partner. This result was confirmed by both biochemical and in silico analyses. Further in vitro biological assays highlighted an interesting crellastatin A inhibitory activity on PARP-1.

Applications of Activity-Based Protein Profiling (ABPP) and Bioimaging in Drug Discovery

Activity-based protein profiling (ABPP) and bioimaging have been developed in recent years as powerful technologies in drug discovery. Specifically, both approaches can be applied in critical steps of drug development, such as therapy target discovery, high-throughput drug screening and target identification of bioactive molecules. We have been focused on the development of various strategies that enable simultaneous activity-based protein profiling and bioimaging studies, thus facilitating an understanding of drug actions and potential toxicities. In this Minireview, we summarize these novel strategies and applications, with the aim of promoting these technologies in drug discovery.

Targeting Epithelial Mesenchymal Plasticity in Pancreatic Cancer: A Compendium of Preclinical Discovery in a Heterogeneous Disease

Pancreatic Ductal Adenocarcinoma (PDAC) is a particularly insidious and aggressive disease that causes significant mortality worldwide. The direct correlation between PDAC incidence, disease progression, and mortality highlights the critical need to understand the mechanisms by which PDAC cells rapidly progress to drive metastatic disease in order to identify actionable vulnerabilities. One such proposed vulnerability is epithelial mesenchymal plasticity (EMP), a process whereby neoplastic epithelial cells delaminate from their neighbours, either collectively or individually, allowing for their subsequent invasion into host tissue. This disruption of tissue homeostasis, particularly in PDAC, further promotes cellular transformation by inducing inflammatory interactions with the stromal compartment, which in turn contributes to intratumoural heterogeneity. This review describes the role of EMP in PDAC, and the preclinical target discovery that has been conducted to identify the molecular regulators and effectors of this EMP program. While inhibition of individual targets may provide therapeutic insights, a single ‘master-key’ remains elusive, making their collective interactions of greater importance in controlling the behaviours’ of heterogeneous tumour cell populations. Much work has been undertaken to understand key transcriptional programs that drive EMP in certain contexts, however, a collaborative appreciation for the subtle, context-dependent programs governing EMP regulation is needed in order to design therapeutic strategies to curb PDAC mortality.

Systematic Immunotherapy Target Discovery Using Genome-Scale In Vivo CRISPR Screens in CD8 T Cells

CD8 T cells play essential roles in anti-tumor immune responses. Here, we performed genome-scale CRISPR screens in CD8 T cells directly under cancer immunotherapy settings and identified regulators of tumor infiltration and degranulation. The in vivo screen robustly re-identified canonical immunotherapy targets such as PD-1 and Tim-3, along with genes that have not been characterized in T cells. The infiltration and degranulation screens converged on an RNA helicase Dhx37. Dhx37 knockout enhanced the efficacy of antigen-specific CD8 T cells against triple-negative breast cancer in vivo. Immunological characterization in mouse and human CD8 T cells revealed that DHX37 suppresses effector functions, cytokine production, and T cell activation. Transcriptomic profiling and biochemical interrogation revealed a role for DHX37 in modulating NF-κB. These data demonstrate high-throughput in vivo genetic screens for immunotherapy target discovery and establishes DHX37 as a functional regulator of CD8 T cells.

Strategies to Address Challenges in Neuroscience Drug Discovery and Development

The paucity of novel drugs for neuropsychiatric indications contrasts with the remarkable recent advances in neuroscience research. We have identified 5 challenges the field needs to address and recommend potential solutions. First, we need to drive discovery efforts based on human data. Second, we need to think more carefully about animal models, embracing them as tools to test pathophysiological alterations. Third, we need to develop strategies to select more homogenous groups of patients in our clinical trials. Fourth, we need to develop and validate translational biomarkers, which can be used for pharmacodynamic assessments as well as for patient selection. Fifth, we need to adopt more reliable and objective measures to capture clinical efficacy. The tools that will allow these solutions to be implemented may already be in place but not routinely adopted or are still being developed. Overall, a change in mindset to adopt science- and data-driven paths is needed.

Integration of target discovery, drug discovery and drug delivery: A review on computational strategies

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Vaccine Target Discovery

With the rise in novel infectious agents and disease pandemics, a new era of vaccine discovery is necessary. To address this, the new field of immunomics is described, which is synergistically powered by integrating bioinformatics methodologies with technological advances in biology and high-throughput instrumentation. By incorporating biological data from immunology and molecular biology with current genomics and proteomics, immunomics is geared to deliver an insight into immune function, optimal stimulation of immune responses and precise mapping and rational selection of immune targets that cover antigenic diversity. These efforts are expected to contribute towards the development of new generation of vaccines, tailored to both the genetic make-up of the human population and of the pathogen. Vaccine technologies are also being explored for prevention or control of non-communicable diseases.

Multiomics of azacitidine-treated AML cells reveals variable and convergent targets that remodel the cell-surface proteome

Myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) are diseases of abnormal hematopoietic differentiation with aberrant epigenetic alterations. Azacitidine (AZA) is a DNA methyltransferase inhibitor widely used to treat MDS and AML, yet the impact of AZA on the cell-surface proteome has not been defined. To identify potential therapeutic targets for use in combination with AZA in AML patients, we investigated the effects of AZA treatment on four AML cell lines representing different stages of differentiation. The effect of AZA treatment on these cell lines was characterized at three levels: the DNA methylome, the transcriptome, and the cell-surface proteome. Untreated AML cell lines showed substantial overlap at all three omics levels; however, while AZA treatment globally reduced DNA methylation in all cell lines, changes in the transcriptome and surface proteome were subtle and differed among the cell lines. Transcriptome analysis identified five commonly up-regulated coding genes upon AZA treatment in all four cell lines, TRPM4 being the only gene encoding a surface protein, and surface proteome analysis found no commonly regulated proteins. Gene set enrichment analysis of differentially regulated RNA and surface proteins showed a decrease in metabolic pathways and an increase in immune defense response pathways. As such, AZA treatment led to diverse effects at the individual gene and protein levels but converged to common responses at the pathway level. Given the heterogeneous responses in the four cell lines, we discuss potential therapeutic strategies for AML in combination with AZA.

Structural Remodeling of the Human Colonic Mesenchyme in Inflammatory Bowel Disease

Intestinal mesenchymal cells play essential roles in epithelial homeostasis, matrix remodeling, immunity, and inflammation. But the extent of heterogeneity within the colonic mesenchyme in these processes remains unknown. Using unbiased single-cell profiling of over 16,500 colonic mesenchymal cells, we reveal four subsets of fibroblasts expressing divergent transcriptional regulators and functional pathways, in addition to pericytes and myofibroblasts. We identified a niche population located in proximity to epithelial crypts expressing SOX6, F3 (CD142), and WNT genes essential for colonic epithelial stem cell function. In colitis, we observed dysregulation of this niche and emergence of an activated mesenchymal population. This subset expressed TNF superfamily member 14 (TNFSF14), fibroblastic reticular cell-associated genes, IL-33, and Lysyl oxidases. Further, it induced factors that impaired epithelial proliferation and maturation and contributed to oxidative stress and disease severity in vivo. Our work defines how the colonic mesenchyme remodels to fuel inflammation and barrier dysfunction in IBD.

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Single-cell census of the colonic mesenchyme reveals unexpected heterogeneity

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Identification of the colonic crypt niche mesenchymal cell expressing SOX6 and Wnts

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Definition of fundamental aspects of mesenchymal remodeling in colitis

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Analysis of colitis-associated mesenchymal cells reveals pathogenicity drivers

Aberration hubs in protein interaction networks highlight actionable targets in cancer

Despite efforts for extensive molecular characterization of cancer patients, such as the international cancer genome consortium (ICGC) and the cancer genome atlas (TCGA), the heterogeneous nature of cancer and our limited knowledge of the contextual function of proteins have complicated the identification of targetable genes. Here, we present Aberration Hub Analysis for Cancer (AbHAC) as a novel integrative approach to pinpoint aberration hubs, i.e. individual proteins that interact extensively with genes that show aberrant mutation or expression. Our analysis of the breast cancer data of the TCGA and the renal cancer data from the ICGC shows that aberration hubs are involved in relevant cancer pathways, including factors promoting cell cycle and DNA replication in basal-like breast tumors, and Src kinase and VEGF signaling in renal carcinoma. Moreover, our analysis uncovers novel functionally relevant and actionable targets, among which we have experimentally validated abnormal splicing of spleen tyrosine kinase as a key factor for cell proliferation in renal cancer. Thus, AbHAC provides an effective strategy to uncover novel disease factors that are only identifiable by examining mutational and expression data in the context of biological networks.

Target discovery focused approaches to overcome bottlenecks in the exploitation of antimycobacterial natural products

Tuberculosis is a major global health hazard. The search for new antimycobacterials has focused on such as screening combinational chemistry libraries or designing chemicals to target predefined pockets of essential bacterial proteins. The relative ineffectiveness of these has led to a reappraisal of natural products for new antimycobacterial drug leads. However, progress has been limited, we suggest through a failure in many cases to define the drug target and optimize the hits using this information. We highlight methods of target discovery needed to develop a drug into a candidate for clinical trials. We incorporate these into suggested analysis pipelines which could inform the research strategies to accelerate the development of new drug leads from natural products.

31ièmes Journées Franco-Belges de Pharmacochimie: Meeting Report

The "Journées Franco-Belges de Pharmacochimie" is a recognized two-day annual meeting on Medicinal Chemistry that is renowned for the advanced science presented, conviviality, and outstanding opportunities for senior and young scientists to exchange knowledge. Abstracts of plenary lectures, oral communications, and posters presented during the meeting are collected in this report.

Integrating Proteomics and Transcriptomics for Systematic Combinatorial Chimeric Antigen Receptor Therapy of AML

Chimeric antigen receptor (CAR) therapy targeting CD19 has yielded remarkable outcomes in patients with acute lymphoblastic leukemia. To identify potential CAR targets in acute myeloid leukemia (AML), we probed the AML surfaceome for overexpressed molecules with tolerable systemic expression. We integrated large transcriptomics and proteomics datasets from malignant and normal tissues, and developed an algorithm to identify potential targets expressed in leukemia stem cells, but not in normal CD34⁺CD38^- hematopoietic cells, T cells, or vital tissues. As these investigations did not uncover candidate targets with a profile as favorable as CD19, we developed a generalizable combinatorial targeting strategy fulfilling stringent efficacy and safety criteria. Our findings indicate that several target pairings hold great promise for CAR therapy of AML.

Emerging Opportunities for Target Discovery in Rare Cancers

Rare cancers pose unique challenges to research due to their low incidence. Barriers include a scarcity of tissue and experimental models to enable basic research and insufficient patient accrual for clinical studies. Consequently, an understanding of the genetic and cellular features of many rare cancer types and their associated vulnerabilities has been lacking. However, new opportunities are emerging to facilitate discovery of therapeutic targets in rare cancers. Online platforms are allowing patients with rare cancers to organize on an unprecedented scale, tumor genome sequencing is now routinely performed in research and clinical settings, and the efficiency of patient-derived model generation has improved. New CRISPR/Cas9 and small-molecule libraries permit cancer dependency discovery in a rapid and systematic fashion. In parallel, large-scale studies of common cancers now provide reference datasets to help interpret rare cancer profiling data. Together, these advances motivate consideration of new research frameworks to accelerate rare cancer target discovery.

Data Sharing in the Pharmaceutical Enterprise: The Genie's Out of the Bottle

OBJECTIVE

This Commentary shows that the present emphasis on the sharing of data from clinical trials can be extended to the entire pharmaceutical enterprise.

METHODS

The authors constructed a Data Sharing Dashboard that shows the relationship between all of the life-cycle domains of the pharmaceutical enterprise from discovery to obsolescence and the domain-bridging disciplines, such as target credentialing, structure-activity relationships, and exposure-effect relationships.

FINDINGS

The published literature encompassing the pharmaceutical enterprise is expansive, covering the major domains of discovery, translation, clinical development, and post-marketing outcomes research, all of which have even larger, though generally inaccessible, troves of legacy data bases. Notable exceptions include the fields of genomics and bioinformatics.

IMPLICATIONS

We have the opportunity to broaden the present momentum of interest in data sharing to the entire pharmaceutical enterprise, beginning with discovery and extending into health technology assessment and post-patent expiry generic use with the plan of integrating new levels and disciplines of knowledge and with the ultimate goal of improving the care of our patients.

30ièmes Journées Franco-Belges de Pharmacochimie

The "Journées Franco-Belges de Pharmacochimie" is a recognized annual meeting in organic and medicinal chemistry known for the quality of scientific exchange and conviviality. Young researchers were encouraged to present their work and share ideas with senior scientists. Abstracts of plenary lectures, oral communications, and posters presented during the meeting are collected in this report.

29ièmes Journées Franco-Belges de Pharmacochimie: Meeting Report

The "Journées Franco-Belges de Pharmacochimie" is a recognized two-day annual meeting on Medicinal Chemistry that is renowned for the advanced science presented, conviviality, and outstanding opportunities for senior and young scientists to exchange knowledge. Abstracts of plenary lectures, oral communications, and posters presented during the meeting are collected in this report.

Insights into mechanisms of central nervous system myelination using zebrafish

Myelin is the multi-layered membrane that surrounds most axons and is produced by oligodendrocytes in the central nervous system (CNS). In addition to its important role in enabling rapid nerve conduction, it has become clear in recent years that myelin plays additional vital roles in CNS function. Myelinating oligodendrocytes provide metabolic support to axons and active myelination is even involved in regulating forms of learning and memory formation. However, there are still large gaps in our understanding of how myelination by oligodendrocytes is regulated. The small tropical zebrafish has become an increasingly popular model organism to investigate many aspects of nervous system formation, function, and regeneration. This is mainly due to two approaches for which the zebrafish is an ideally suited vertebrate model--(1) in vivo live cell imaging using vital dyes and genetically encoded reporters, and (2) gene and target discovery using unbiased screens. This review summarizes how the use of zebrafish has helped understand mechanisms of oligodendrocyte behavior and myelination in vivo and discusses the potential use of zebrafish to shed light on important future questions relating to myelination in the context of CNS development, function and repair.

ASCL1 is a lineage oncogene providing therapeutic targets for high-grade neuroendocrine lung cancers

Aggressive neuroendocrine lung cancers, including small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), represent an understudied tumor subset that accounts for approximately 40,000 new lung cancer cases per year in the United States. No targeted therapy exists for these tumors. We determined that achaete-scute homolog 1 (ASCL1), a transcription factor required for proper development of pulmonary neuroendocrine cells, is essential for the survival of a majority of lung cancers (both SCLC and NSCLC) with neuroendocrine features. By combining whole-genome microarray expression analysis performed on lung cancer cell lines with ChIP-Seq data designed to identify conserved transcriptional targets of ASCL1, we discovered an ASCL1 target 72-gene expression signature that (i) identifies neuroendocrine differentiation in NSCLC cell lines, (ii) is predictive of poor prognosis in resected NSCLC specimens from three datasets, and (iii) represents novel "druggable" targets. Among these druggable targets is B-cell CLL/lymphoma 2, which when pharmacologically inhibited stops ASCL1-dependent tumor growth in vitro and in vivo and represents a proof-of-principle ASCL1 downstream target gene. Analysis of downstream targets of ASCL1 represents an important advance in the development of targeted therapy for the neuroendocrine class of lung cancers, providing a significant step forward in the understanding and therapeutic targeting of the molecular vulnerabilities of neuroendocrine lung cancer.

Function-first antibody discovery: Embracing the unpredictable biology of antibodies

Therapeutic antibodies may mediate antineoplastic effects by altering the biological functions of their target, by directly stimulating the demise of cancer cells or by activating antibody-dependent immune effector mechanisms. We have recently provided in vivo proof-of-concept for a "function-first" target and drug discovery platform in which antibodies against a multitude of tumor-associated antigens are screened for biological effects in a target-unbiased manner.