Unexplored therapeutic opportunities in the human genome

Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease

The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts. Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development. Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists. AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.

Properties of FDA-approved small molecule protein kinase inhibitors: A 2024 update

Owing to the dysregulation of protein kinase activity in many diseases including cancer, this enzyme family has become one of the most important drug targets in the 21st century. There are 80 FDA-approved therapeutic agents that target about two dozen different protein kinases and seven of these drugs were approved in 2023. Of the approved drugs, thirteen target protein-serine/threonine protein kinases, four are directed against dual specificity protein kinases (MEK1/2), twenty block nonreceptor protein-tyrosine kinases, and 43 inhibit receptor protein-tyrosine kinases. The data indicate that 69 of these drugs are prescribed for the treatment of neoplasms. Six drugs (abrocitinib, baricitinib, deucravacitinib, ritlecitinib, tofacitinib, upadacitinib) are used for the treatment of inflammatory diseases (atopic dermatitis, rheumatoid arthritis, psoriasis, alopecia areata, and ulcerative colitis). Of the 80 approved drugs, nearly two dozen are used in the treatment of multiple diseases. The following seven drugs received FDA approval in 2023: capivasertib (HER2-positive breast cancer), fruquintinib (metastatic colorectal cancer), momelotinib (myelofibrosis), pirtobrutinib (mantle cell lymphoma, chronic lymphocytic leukemia, small lymphocytic lymphoma), quizartinib (Flt3-mutant acute myelogenous leukemia), repotrectinib (ROS1-positive lung cancer), and ritlecitinib (alopecia areata). All of the FDA-approved drugs are orally effective with the exception of netarsudil, temsirolimus, and trilaciclib. This review summarizes the physicochemical properties of all 80 FDA-approved small molecule protein kinase inhibitors including the molecular weight, number of hydrogen bond donors/acceptors, polar surface area, potency, solubility, lipophilic efficiency, and ligand efficiency.

A mitochondria-regulated p53-CCF circuit integrates genome integrity with inflammation

Genomic instability and inflammation are distinct hallmarks of aging, but the connection between them is poorly understood. Understanding their interrelationship will help unravel new mechanisms and therapeutic targets of aging and age-associated diseases. Here we report a novel mechanism directly linking genomic instability and inflammation in senescent cells, through a mitochondria-regulated molecular circuit that connects the p53 tumor suppressor and cytoplasmic chromatin fragments (CCF), a driver of inflammation through the cGAS-STING pathway. Activation or inactivation of p53 by genetic and pharmacologic approaches showed that p53 suppresses CCF accumulation and the downstream inflammatory senescence-associated secretory phenotype (SASP), independent of its effects on cell cycle arrest. p53 activation suppressed CCF formation by promoting DNA repair, reflected in maintenance of genomic integrity, particularly in subtelomeric regions, as shown by single cell genome resequencing. Activation of p53 by pharmacological inhibition of MDM2 in old mice decreased features of SASP in liver, indicating a senomorphic role in vivo . Remarkably, mitochondria in senescent cells suppressed p53 activity by promoting CCF formation and thereby restricting ATM-dependent nuclear DNA damage signaling. These data provide evidence for a mitochondria-regulated p53-CCF circuit in senescent cells that controls DNA repair, genome integrity and inflammatory SASP, and is a potential target for senomorphic healthy aging interventions.

Broad functional profiling of fission yeast proteins using phenomics and machine learning

Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches with Schizosaccharomyces pombe for broad cues on protein functions. We assayed colony-growth phenotypes to measure the fitness of deletion mutants for 3509 non-essential genes in 131 conditions with different nutrients, drugs, and stresses. These analyses exposed phenotypes for 3492 mutants, including 124 mutants of 'priority unstudied' proteins conserved in humans, providing varied functional clues. For example, over 900 proteins were newly implicated in the resistance to oxidative stress. Phenotype-correlation networks suggested roles for poorly characterized proteins through 'guilt by association' with known proteins. For complementary functional insights, we predicted Gene Ontology (GO) terms using machine learning methods exploiting protein-network and protein-homology data (NET-FF). We obtained 56,594 high-scoring GO predictions, of which 22,060 also featured high information content. Our phenotype-correlation data and NET-FF predictions showed a strong concordance with existing PomBase GO annotations and protein networks, with integrated analyses revealing 1675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation identified new proteins involved in cellular aging, showing that these predictions and phenomics data provide a rich resource to uncover new protein functions.

The Future of Precision Oncology

Our understanding of the molecular mechanisms underlying cancer development and evolution have evolved rapidly over recent years, and the variation from one patient to another is now widely recognized. Consequently, one-size-fits-all approaches to the treatment of cancer have been superseded by precision medicines that target specific disease characteristics, promising maximum clinical efficacy, minimal safety concerns, and reduced economic burden. While precision oncology has been very successful in the treatment of some tumors with specific characteristics, a large number of patients do not yet have access to precision medicines for their disease. The success of next-generation precision oncology depends on the discovery of new actionable disease characteristics, rapid, accurate, and comprehensive diagnosis of complex phenotypes within each patient, novel clinical trial designs with improved response rates, and worldwide access to novel targeted anticancer therapies for all patients. This review outlines some of the current technological trends, and highlights some of the complex multidisciplinary efforts that are underway to ensure that many more patients with cancer will be able to benefit from precision oncology in the near future.

G protein-coupled receptors as targets for transformative neuropsychiatric therapeutics

G protein-coupled receptors (GPCRs) constitute the largest family of druggable genes in the human genome. Even though perhaps 30% of approved medications target GPCRs, they interact with only a small number of them. Here, we consider whether there might be new opportunities for transformative therapeutics for neuropsychiatric disorders by specifically targeting both known and understudied GPCRs. Using psychedelic drugs that target serotonin receptors as an example, we show how recent insights into the structure, function, signaling, and cell biology of these receptors have led to potentially novel therapeutics. We next focus on the possibility that nonpsychedelic 5-HT2A receptor agonists might prove to be safe and rapidly acting antidepressants. Finally, we examine understudied and orphan GPCRs using the MRGPR family of receptors as an example.

A multi-scale map of protein assemblies in the DNA damage response

The DNA damage response (DDR) ensures error-free DNA replication and transcription and is disrupted in numerous diseases. An ongoing challenge is to determine the proteins orchestrating DDR and their organization into complexes, including constitutive interactions and those responding to genomic insult. Here, we use multi-conditional network analysis to systematically map DDR assemblies at multiple scales. Affinity purifications of 21 DDR proteins, with/without genotoxin exposure, are combined with multi-omics data to reveal a hierarchical organization of 605 proteins into 109 assemblies. The map captures canonical repair mechanisms and proposes new DDR-associated proteins extending to stress, transport, and chromatin functions. We find that protein assemblies closely align with genetic dependencies in processing specific genotoxins and that proteins in multiple assemblies typically act in multiple genotoxin responses. Follow-up by DDR functional readouts newly implicates 12 assembly members in double-strand-break repair. The DNA damage response assemblies map is available for interactive visualization and query (ccmi.org/ddram/).

Rule of five violations among the FDA-approved small molecule protein kinase inhibitors

Because genetic alterations including mutations, overexpression, translocations, and dysregulation of protein kinases are involved in the pathogenesis of many illnesses, this enzyme family is the target of many drug discovery programs in the pharmaceutical industry. Overall, the US FDA has approved 74 small molecule protein kinase inhibitors, nearly all of which are orally effective. Of the 74 approved drugs, forty block receptor protein-tyrosine kinases, eighteen target nonreceptor protein-tyrosine kinases, twelve are directed against protein-serine/threonine protein kinases, and four target dual specificity protein kinases. The data indicate that 63 of these medicinals are approved for the management of neoplasms (51 against solid tumors such as breast, colon, and lung cancers, eight against nonsolid tumors such as leukemia, and four against both types of tumors). Seven of the FDA-approved kinase inhibitors form covalent bonds with their target enzymes and they are accordingly classified as TCIs (targeted covalent inhibitors). Medicinal chemists have examined the physicochemical properties of drugs that are orally effective. Lipinski's rule of five (Ro5) is a computational procedure that is used to estimate solubility, membrane permeability, and pharmacological effectiveness in the drug-discovery setting. It relies on four parameters including molecular weight, number of hydrogen bond donors and acceptors, and the Log of the partition coefficient. Other important descriptors include the lipophilic efficiency, the polar surface area, and the number of rotatable bonds and aromatic rings. We tabulated these and other properties of the FDA-approved kinase inhibitors. Of the 74 approved drugs, 30 fail to comply with the rule of five.

PROTAC'ing oncoproteins: targeted protein degradation for cancer therapy

Molecularly targeted cancer therapies substantially improve patient outcomes, although the durability of their effectiveness can be limited. Resistance to these therapies is often related to adaptive changes in the target oncoprotein which reduce binding affinity. The arsenal of targeted cancer therapies, moreover, lacks coverage of several notorious oncoproteins with challenging features for inhibitor development. Degraders are a relatively new therapeutic modality which deplete the target protein by hijacking the cellular protein destruction machinery. Degraders offer several advantages for cancer therapy including resiliency to acquired mutations in the target protein, enhanced selectivity, lower dosing requirements, and the potential to abrogate oncogenic transcription factors and scaffolding proteins. Herein, we review the development of proteolysis targeting chimeras (PROTACs) for selected cancer therapy targets and their reported biological activities. The medicinal chemistry of PROTAC design has been a challenging area of active research, but the recent advances in the field will usher in an era of rational degrader design.

PrismEXP: gene annotation prediction from stratified gene-gene co-expression matrices

Background

Gene-gene co-expression correlations measured by mRNA-sequencing (RNA-seq) can be used to predict gene annotations based on the co-variance structure within these data. In our prior work, we showed that uniformly aligned RNA-seq co-expression data from thousands of diverse studies is highly predictive of both gene annotations and protein-protein interactions. However, the performance of the predictions varies depending on whether the gene annotations and interactions are cell type and tissue specific or agnostic. Tissue and cell type-specific gene-gene co-expression data can be useful for making more accurate predictions because many genes perform their functions in unique ways in different cellular contexts. However, identifying the optimal tissues and cell types to partition the global gene-gene co-expression matrix is challenging.

Results

Here we introduce and validate an approach called PRediction of gene Insights from Stratified Mammalian gene co-EXPression (PrismEXP) for improved gene annotation predictions based on RNA-seq gene-gene co-expression data. Using uniformly aligned data from ARCHS4, we apply PrismEXP to predict a wide variety of gene annotations including pathway membership, Gene Ontology terms, as well as human and mouse phenotypes. Predictions made with PrismEXP outperform predictions made with the global cross-tissue co-expression correlation matrix approach on all tested domains, and training using one annotation domain can be used to predict annotations in other domains.

Conclusions

By demonstrating the utility of PrismEXP predictions in multiple use cases we show how PrismEXP can be used to enhance unsupervised machine learning methods to better understand the roles of understudied genes and proteins. To make PrismEXP accessible, it is provided via a user-friendly web interface, a Python package, and an Appyter. AVAILABILITY. The PrismEXP web-based application, with pre-computed PrismEXP predictions, is available from: https://maayanlab.cloud/prismexp; PrismEXP is also available as an Appyter: https://appyters.maayanlab.cloud/PrismEXP/; and as Python package: https://github.com/maayanlab/prismexp.

Properties of FDA-approved small molecule protein kinase inhibitors: A 2023 update

Owing to the dysregulation of protein kinase activity in many diseases including cancer, this enzyme family has become one of the most important drug targets in the 21st century. There are 72 FDA-approved therapeutic agents that target about two dozen different protein kinases and three of these drugs were approved in 2022. Of the approved drugs, twelve target protein-serine/threonine protein kinases, four are directed against dual specificity protein kinases (MEK1/2), sixteen block nonreceptor protein-tyrosine kinases, and 40 target receptor protein-tyrosine kinases. The data indicate that 62 of these drugs are prescribed for the treatment of neoplasms (57 against solid tumors including breast, lung, and colon, ten against nonsolid tumors such as leukemia, and four against both solid and nonsolid tumors: acalabrutinib, ibrutinib, imatinib, and midostaurin). Four drugs (abrocitinib, baricitinib, tofacitinib, upadacitinib) are used for the treatment of inflammatory diseases (atopic dermatitis, psoriatic arthritis, rheumatoid arthritis, Crohn disease, and ulcerative colitis). Of the 72 approved drugs, eighteen are used in the treatment of multiple diseases. The following three drugs received FDA approval in 2022 for the treatment of these specified diseases: abrocitinib (atopic dermatitis), futibatinib (cholangiocarcinomas), pacritinib (myelofibrosis). All of the FDA-approved drugs are orally effective with the exception of netarsudil, temsirolimus, and trilaciclib. This review summarizes the physicochemical properties of all 72 FDA-approved small molecule protein kinase inhibitors including lipophilic efficiency and ligand efficiency.

The era of high-quality chemical probes

Small-molecule chemical probes are among the most important tools to study the function of proteins in cells and organisms. Regrettably, the use of weak and non-selective small molecules has generated an abundance of erroneous conclusions in the scientific literature. More recently, minimal criteria have been outlined for investigational compounds, encouraging the selection and use of high-quality chemical probes. Here, we briefly recall the milestones and key initiatives that have paved the way to this new era, illustrate examples of recent high-quality chemical probes and provide our perspective on future challenges and developments.

Perturbation of biological processes with small molecule kinase inhibitors

The reversible phosphorylation of substrates mediated by kinases and phosphatases affects their subcellular localization, catalytic activity, and/or interaction with other molecules. It is essential for signal transduction and the regulation of nearly all cellular processes, such as proliferation, apoptosis, metabolism, motility, and differentiation. Small molecule kinase inhibitors (SMKIs) have served as critical chemical probes to reveal the biological functions and mechanisms of kinases and their potential as therapeutic targets. In this review, we focused on a few novel SMKIs and their recent application in biological and preclinical studies to showcase how highly selective and potent SMKIs can be developed and utilized to propel the investigations on kinases and the biology behind.

Systematic illumination of druggable genes in cancer genomes

By combining 6 druggable genome resources, we identify 6,083 genes as potential druggable genes (PDGs). We characterize their expression, recurrent genomic alterations, cancer dependencies, and therapeutic potentials by integrating genome, functionome, and druggome profiles across cancers. 81.5% of PDGs are reliably expressed in major adult cancers, 46.9% show selective expression patterns, and 39.1% exhibit at least one recurrent genomic alteration. We annotate a total of 784 PDGs as dependent genes for cancer cell growth. We further quantify 16 cancer-related features and estimate a PDG cancer drug target score (PCDT score). PDGs with higher PCDT scores are significantly enriched for genes encoding kinases and histone modification enzymes. Importantly, we find that a considerable portion of high PCDT score PDGs are understudied genes, providing unexplored opportunities for drug development in oncology. By integrating the druggable genome and the cancer genome, our study thus generates a comprehensive blueprint of potential druggable genes across cancers.

Jiang et al. generate a comprehensive blueprint of potential druggable genes (PDGs) across cancers by a systematic integration of the druggable genome and the cancer genome. This resource is publicly available to the cancer research community in The Cancer Druggable Gene Atlas (TCDA) through the Functional Cancer Genome data portal.

Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity

With increased research funding for Alzheimer's disease (AD) and related disorders across the globe, large amounts of data are being generated. Several studies employed machine learning methods to understand the ever-growing omics data to enhance early diagnosis, map complex disease networks, or uncover potential drug targets. We describe results based on a Target Central Resource Database protein knowledge graph and evidence paths transformed into vectors by metapath matching. We extracted features between specific genes and diseases, then trained and optimized our model using XGBoost, termed MPxgb(AD). To determine our MPxgb(AD) prediction performance, we examined the top twenty predicted genes through an experimental screening pipeline. Our analysis identified potential AD risk genes: FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2. FRRS1 and FAM92B are considered dark genes, while CTRAM, SCGB3A1, and TMEFF2 are connected to TREM2-TYROBP, IL-1β-TNFα, and MTOR-APP AD-risk nodes, suggesting relevance to the pathogenesis of AD.

Target 2035 - update on the quest for a probe for every protein

Twenty years after the publication of the first draft of the human genome, our knowledge of the human proteome is still fragmented. The challenge of translating the wealth of new knowledge from genomics into new medicines is that proteins, and not genes, are the primary executers of biological function. Therefore, much of how biology works in health and disease must be understood through the lens of protein function. Accordingly, a subset of human proteins has been at the heart of research interests of scientists over the centuries, and we have accumulated varying degrees of knowledge about approximately 65% of the human proteome. Nevertheless, a large proportion of proteins in the human proteome (∼35%) remains uncharacterized, and less than 5% of the human proteome has been successfully targeted for drug discovery. This highlights the profound disconnect between our abilities to obtain genetic information and subsequent development of effective medicines. Target 2035 is an international federation of biomedical scientists from the public and private sectors, which aims to address this gap by developing and applying new technologies to create by year 2035 chemogenomic libraries, chemical probes, and/or biological probes for the entire human proteome.

Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination

Background

LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches.

Results

Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG’s resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD.

Conclusions

The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04530-9.

Getting Started with the IDG KMC Datasets and Tools

The Illuminating the Druggable Genome (IDG) consortium is a National Institutes of Health (NIH) Common Fund program designed to enhance our knowledge of under-studied proteins, more specifically, proteins unannotated within the three most commonly drug-targeted protein families: G-protein coupled receptors, ion channels, and protein kinases. Since 2014, the IDG Knowledge Management Center (IDG-KMC) has generated several open-access datasets and resources that jointly serve as a highly translational machine-learning-ready knowledgebase focused on human protein-coding genes and their products. The goal of the IDG-KMC is to develop comprehensive integrated knowledge for the druggable genome to illuminate the uncharacterized or poorly annotated portion of the druggable genome. The tools derived from the IDG-KMC provide either user-friendly visualizations or ways to impute the knowledge about potential targets using machine learning strategies. In the following protocols, we describe how to use each web-based tool to accelerate illumination in under-studied proteins. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Interacting with the Pharos user interface Basic Protocol 2: Accessing the data in Harmonizome Basic Protocol 3: The ARCHS4 resource Basic Protocol 4: Making predictions about gene function with PrismExp Basic Protocol 5: Using Geneshot to illuminate knowledge about under-studied targets Basic Protocol 6: Exploring under-studied targets with TIN-X Basic Protocol 7: Interacting with the DrugCentral user interface Basic Protocol 8: Estimating Anti-SARS-CoV-2 activities with DrugCentral REDIAL-2020 Basic Protocol 9: Drug Set Enrichment Analysis using Drugmonizome Basic Protocol 10: The Drugmonizome-ML Appyter Basic Protocol 11: The Harmonizome-ML Appyter Basic Protocol 12: GWAS target illumination with TIGA Basic Protocol 13: Prioritizing kinases for lists of proteins and phosphoproteins with KEA3 Basic Protocol 14: Converting PubMed searches to drug sets with the DrugShot Appyter.

Properties of FDA-approved small molecule protein kinase inhibitors: A 2022 update

Owing to the dysregulation of protein kinase activity in many diseases including cancer, this enzyme family has become one of the most important drug targets in the 21st century. There are 68 FDA-approved therapeutic agents that target about two dozen different protein kinases and six of these drugs were approved in 2021. Of the approved drugs, twelve target protein-serine/threonine protein kinases, four are directed against dual specificity protein kinases (MEK1/2), thirteen block nonreceptor protein-tyrosine kinases, and 39 target receptor protein-tyrosine kinases. The data indicate that 58 of these drugs are prescribed for the treatment of neoplasms (49 against solid tumors including breast, lung, and colon, five against nonsolid tumors such as leukemias, and four against both solid and nonsolid tumors: acalabrutinib, ibrutinib, imatinib, and midostaurin). Three drugs (baricitinib, tofacitinib, upadacitinib) are used for the treatment of inflammatory diseases including rheumatoid arthritis. Of the 68 approved drugs, eighteen are used in the treatment of multiple diseases. The following six drugs received FDA approval in 2021 for the treatment of these specified diseases: belumosudil (graft vs. host disease), infigratinib (cholangiocarcinomas), mobocertinib and tepotinib (specific forms of non-small cell lung cancer), tivozanib (renal cell carcinoma), and trilaciclib (to decrease chemotherapy-induced myelosuppression). All of the FDA-approved drugs are orally effective with the exception of netarsudil, temsirolimus, and the newly approved trilaciclib. This review summarizes the physicochemical properties of all 68 FDA-approved small molecule protein kinase inhibitors including lipophilic efficiency and ligand efficiency.

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.

TIGA: target illumination GWAS analytics

MOTIVATION

Genome-wide association studies can reveal important genotype-phenotype associations; however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study.

RESULTS

Here, we describe rational ranking, filtering and interpretation of inferred gene-trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene-trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene-trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite relative citation ratio, and meanRank scores, to aggregate multivariate evidence.This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists.

AVAILABILITY AND IMPLEMENTATION

Web application, datasets and source code via https://unmtid-shinyapps.net/tiga/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Selectivity aspects of activity-based (chemical) probes

Selective chemical modulators are ideal tools to study the function of a protein. Yet, the poor ligandability of many proteins has hampered the development of specific chemical probes for numerous protein classes. Tools, such as covalent inhibitors and activity-based protein profiling, have enhanced our understanding of thus-far difficult-to-target proteins and have enabled correct assessment of the selectivity of small-molecule modulators. This also requires deeper knowledge of compound and target site reactivity, evaluation of binding to noncovalent targets and protein turnover. The availability of highly selective chemical probes, the evolution of activity-based probes, and the development of profiling methods will open a new era of drugging the undruggable proteome.

Designing small molecules for therapeutic success: A contemporary perspective

Successful small-molecule drug design requires a molecular target with inherent therapeutic potential and a molecule with the right properties to unlock its potential. Present-day drug design strategies have evolved to leave little room for improvement in drug-like properties. As a result, inadequate safety or efficacy associated with molecular targets now constitutes the primary cause of attrition in preclinical development through Phase II. This finding has led to a deeper focus on target selection. In this current reality, design tactics that enable rapid identification of risk-balanced clinical candidates, translation of clinical experience into meaningful differentiation strategies, and expansion of the druggable proteome represent significant levers by which drug designers can accelerate the discovery of the next generation of medicines.

Casein kinase 1 epsilon and circadian misalignment impact affective behaviours in mice

Affective behaviours and mental health are profoundly affected by disturbances in circadian rhythms. Casein kinase 1 epsilon (CSNK1E) is a core component of the circadian clock. Mice with tau or null mutation of this gene have shortened and lengthened circadian period respectively. Here, we examined anxiety-like, fear, and despair behaviours in both male and female mice of these two different mutants. Compared with wild-type mice, we found reductions in fear and anxiety-like behaviours in both mutant lines and in both sexes, with the tau mutants exhibiting the greatest phenotypic changes. However, the behavioural despair had distinct phenotypic patterns, with markedly less behavioural despair in female null mutants, but not in tau mutants of either sex. To determine whether abnormal light entrainment of tau mutants to 24-h light-dark cycles contributes to these phenotypic differences, we also examined these behaviours in tau mutants on a 20-h light-dark cycle close to their endogenous circadian period. The normalized entrainment restored more wild-type-like behaviours for fear and anxiety, but it induced behavioural despair in tau mutant females. These data show that both mutations of Csnk1e broadly affect fear and anxiety-like behaviours, while the effects on behavioural despair vary with genetics, photoperiod, and sex, suggesting that the mechanisms by which Csnk1e affects fear and anxiety-like behaviours may be similar, but distinct from those affecting behavioural despair. Our study also provides experimental evidence in support of the hypothesis of beneficial outcomes from properly entrained circadian rhythms in terms of the anxiety-like and fear behaviours.

Differential expression and regulation of MS4A family members in myeloid cells in physiological and pathological conditions

The MS4A gene family encodes 18 tetraspanin-like proteins, most of which with unknown function. MS4A1 (CD20), MS4A2 (FcεRIβ), MS4A3 (HTm4), and MS4A4A play important roles in immunity, whereas expression and function of other members of the family are unknown. The present investigation was designed to obtain an expression fingerprint of MS4A family members, using bioinformatics analysis of public databases, RT-PCR, and protein analysis when possible. MS4A3, MS4A4A, MS4A4E, MS4A6A, MS4A7, and MS4A14 were expressed by myeloid cells. MS4A6A and MS4A14 were expressed in circulating monocytes and decreased during monocyte-to-Mϕ differentiation in parallel with an increase in MS4A4A expression. Analysis of gene expression regulation revealed a strong induction of MS4A4A, MS4A6A, MS4A7, and MS4A4E by glucocorticoid hormones. Consistently with in vitro findings, MS4A4A and MS4A7 were expressed in tissue Mϕs from COVID-19 and rheumatoid arthritis patients. Interestingly, MS4A3, selectively expressed in myeloid precursors, was found to be a marker of immature circulating neutrophils, a cellular population associated to COVID-19 severe disease. The results reported here show that members of the MS4A family are differentially expressed and regulated during myelomonocytic differentiation, and call for assessment of their functional role and value as therapeutic targets.

Identification of Conserved Proteomic Networks in Neurodegenerative Dementia

Data-driven analyses are increasingly valued in modern medicine. We integrate quantitative proteomics and transcriptomics from over 1,000 post-mortem brains from six cohorts representing Alzheimer’s disease (AD), asymptomatic AD, progressive supranuclear palsy (PSP), and control patients from the Accelerating Medicines Partnership – Alzheimer’s Disease consortium. We define robust co-expression trajectories related to disease progression, including early neuronal, microglial, astrocyte, and immune response modules, and later mRNA splicing and mitochondrial modules. The majority of, but not all, modules are conserved at the transcriptomic level, including module C3, which is only observed in proteome networks and enriched in mitogen-activated protein kinase (MAPK) signaling. Genetic risk enriches in modules changing early in disease and indicates that AD and PSP have distinct causal biological drivers at the pathway level, despite aspects of similar pathology, including synaptic loss and glial inflammatory changes. The conserved, high-confidence proteomic changes enriched in genetic risk represent targets for drug discovery.

Swarup et al. use a multi-omic, multi-cohort approach to identify robust early and late proteomic changes in AD and other neurodegenerative dementias and find that genetic risk is differentially enriched across disorders. Shared co-expression modules showing consistent molecular alterations at multi-omic levels are ripe for future investigation as drug targets.

Soluble ligands as drug targets for treatment of inflammatory bowel disease

Inflammatory bowel disease (IBD), which includes Crohn's disease and ulcerative colitis, is characterized by persistent inflammation in a hereditarily susceptible host. In addition to gastrointestinal symptoms, patients with IBD frequently suffer from extra-intestinal complications such as fibrosis, stenosis or cancer. Mounting evidence supports the targeting of cytokines for effective treatment of IBD. Cytokines can be included in a newly proposed classification "soluble ligands" that has become the third major target of human protein therapeutic drugs after enzymes and receptors. Soluble ligands have potential significance for research and development of anti-IBD drugs. Compared with traditional drug targets for IBD treatment, such as receptors, at least three factors contribute to the increasing importance of soluble ligands as drug targets. Firstly, cytokines are the main soluble ligands and targeting of them has demonstrated efficacy in patients with IBD. Secondly, soluble ligands are more accessible than receptors, which are embedded in the cell membrane and have complex tertiary membrane structures. Lastly, certain potential target proteins that are present in membrane-bound forms can become soluble following cleavage, providing further opportunities for intervention in the treatment of IBD. In this review, 49 drugs targeting 25 distinct ligands have been evaluated, including consideration of the characteristics of the ligands and drugs in respect of IBD treatment. In addition to approved drugs targeting soluble ligands, we have also assessed drugs that are in preclinical research and drugs inhibiting ligand-receptor binding. Some new types of targetable soluble ligands/proteins, such as epoxide hydrolase and p-selectin glycoprotein ligand-1, are also introduced. Targeting soluble ligands not only opens a new field of anti-IBD drug development, but the circulating soluble ligands also provide diagnostic insights for early prediction of treatment response. In conclusion, soluble ligands serve as the third-largest protein target class in medicine, with much potential for the drugs targeting them.

High-Throughput Mass Spectrometry for Hit Identification: Current Landscape and Future Perspectives

For nearly two decades mass spectrometry has been used as a label-free, direct-detection method for both functional and affinity-based screening of a wide range of therapeutically relevant target classes. Here, we present an overview of several established and emerging mass spectrometry platforms and summarize the unique strengths and performance characteristics of each as they apply to high-throughput screening. Multiple examples from the recent literature are highlighted in order to illustrate the power of each individual technique, with special emphasis given to cases where the use of mass spectrometry was found to be differentiating when compared with other detection formats. Indeed, as many of these examples will demonstrate, the inherent strengths of mass spectrometry-sensitivity, specificity, wide dynamic range, and amenability to complex matrices-can be leveraged to enhance the discriminating power and physiological relevance of assays included in screening cascades. It is our hope that this review will serve as a useful guide to readers of all backgrounds and experience levels on the applicability and benefits of mass spectrometry in the search for hits, leads, and, ultimately, drugs.

The Dark Kinase Knowledgebase: an online compendium of knowledge and experimental results of understudied kinases

Kinases form the backbone of numerous cell signaling pathways, with their dysfunction similarly implicated in multiple pathologies. Further facilitated by their druggability, kinases are a major focus of therapeutic development efforts in diseases such as cancer, infectious disease and autoimmune disorders. While their importance is clear, the role or biological function of nearly one-third of kinases is largely unknown. Here, we describe a data resource, the Dark Kinase Knowledgebase (DKK; https://darkkinome.org), that is specifically focused on providing data and reagents for these understudied kinases to the broader research community. Supported through NIH’s Illuminating the Druggable Genome (IDG) Program, the DKK is focused on data and knowledge generation for 162 poorly studied or ‘dark’ kinases. Types of data provided through the DKK include parallel reaction monitoring (PRM) peptides for quantitative proteomics, protein interactions, NanoBRET reagents, and kinase-specific compounds. Higher-level data is similarly being generated and consolidated such as tissue gene expression profiles and, longer-term, functional relationships derived through perturbation studies. Associated web tools that help investigators interrogate both internal and external data are also provided through the site. As an evolving resource, the DKK seeks to continually support and enhance knowledge on these potentially high-impact druggable targets.

The Kinase Chemogenomic Set (KCGS): An Open Science Resource for Kinase Vulnerability Identification

We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS), current Version 1.0, is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.

The Clinical Kinase Index: A Method to Prioritize Understudied Kinases as Drug Targets for the Treatment of Cancer

The approval of the first kinase inhibitor, Gleevec, ushered in a paradigm shift for oncological treatment-the use of genomic data for targeted, efficacious therapies. Since then, over 48 additional small-molecule kinase inhibitors have been approved, solidifying the case for kinases as a highly druggable and attractive target class. Despite the role deregulated kinase activity plays in cancer, only 8% of the kinome has been effectively "drugged." Moreover, 24% of the 634 human kinases are understudied. We have developed a comprehensive scoring system that utilizes differential gene expression, pathological parameters, overall survival, and mutational hotspot analysis to rank and prioritize clinically relevant kinases across 17 solid tumor cancers from The Cancer Genome Atlas. We have developed the clinical kinase index (CKI) app (http://cki.ccs.miami.edu) to facilitate interactive analysis of all kinases in each cancer. Collectively, we report that understudied kinases have potential clinical value as biomarkers or drug targets that warrant further study.

Structure and expression of the long noncoding RNA gene MIR503 in humans and non-human primates

Recent technical and other advances in genomics provide unique opportunities to improve our understanding of human physiology and disease predisposition through a detailed analysis of gene structure and expression by examining data in public genome and gene-expression repositories. Yet, the vast majority of human genes remain understudied. This is particularly true of genes for long noncoding RNAs (lncRNAs). Here, we describe the detailed characterization of MIR503HG, a lncRNA gene found on the X chromosome in humans. Using information extracted from public databases, we show that human MIR503HG is a 5-exon gene, and that it is highly conserved among 5 non-human primates spanning over 85 million years ago of evolutionary diversification. MIR503HG is transcribed and processed into multiple distinct RNAs in each of these species through differential exon use and alternative RNA splicing, with a higher abundance of transcripts being found in reproductive tissues, especially during the early stages of ovary and testis development, indicating a possible role in reproductive biology. Furthermore, in select reproductive system cancers, MIR503HG transcripts are downregulated, with higher levels of RNA expression being associated with clinical outcomes. Collectively, these investigations show how the use of genomic, gene expression, and other genetic resources can lead to new insights about human biology and disease, and argue that MIR503HG is worthy of additional study.

Mitochondria-to-nucleus retrograde signaling drives formation of cytoplasmic chromatin and inflammation in senescence

In this study, Vizioli et al. investigated the upstream signaling events that promote cytoplasmic formation of chromatin fragments (CCFs), which are extruded from the nucleus of senescent cells and trigger the senescence-associated secretory phenotype (SASP). They show that dysfunctional mitochondria, linked to down-regulation of nuclear-encoded mitochondrial oxidative phosphorylation genes, trigger a ROS–JNK retrograde signaling pathway that drives CCF formation and hence the SASP.

Cellular senescence is a potent tumor suppressor mechanism but also contributes to aging and aging-related diseases. Senescence is characterized by a stable cell cycle arrest and a complex proinflammatory secretome, termed the senescence-associated secretory phenotype (SASP). We recently discovered that cytoplasmic chromatin fragments (CCFs), extruded from the nucleus of senescent cells, trigger the SASP through activation of the innate immunity cytosolic DNA sensing cGAS–STING pathway. However, the upstream signaling events that instigate CCF formation remain unknown. Here, we show that dysfunctional mitochondria, linked to down-regulation of nuclear-encoded mitochondrial oxidative phosphorylation genes, trigger a ROS–JNK retrograde signaling pathway that drives CCF formation and hence the SASP. JNK links to 53BP1, a nuclear protein that negatively regulates DNA double-strand break (DSB) end resection and CCF formation. Importantly, we show that low-dose HDAC inhibitors restore expression of most nuclear-encoded mitochondrial oxidative phosphorylation genes, improve mitochondrial function, and suppress CCFs and the SASP in senescent cells. In mouse models, HDAC inhibitors also suppress oxidative stress, CCF, inflammation, and tissue damage caused by senescence-inducing irradiation and/or acetaminophen-induced mitochondria dysfunction. Overall, our findings outline an extended mitochondria-to-nucleus retrograde signaling pathway that initiates formation of CCF during senescence and is a potential target for drug-based interventions to inhibit the proaging SASP.

Genetic Support for Longevity-Enhancing Drug Targets: Issues, Preliminary Data, and Future Directions

Interventions meant to promote longevity and healthy aging have often been designed or observed to modulate very specific gene or protein targets. If there are naturally occurring genetic variants in such a target that affect longevity as well as the molecular function of that target (eg, the variants influence the expression of the target, acting as "expression quantitative trait loci" or "eQTLs"), this could support a causal relationship between the pharmacologic modulation of the target and longevity and thereby validate the target at some level. We considered the gene targets of many pharmacologic interventions hypothesized to enhance human longevity and explored how many variants there are in those targets that affect gene function (eg, as expression quantitative trait loci). We also determined whether variants in genes associated with longevity-related phenotypes affect gene function or are in linkage disequilibrium with variants that do, and whether pharmacologic studies point to compounds exhibiting activity against those genes. Our results are somewhat ambiguous, suggesting that integrating genetic association study results with functional genomic and pharmacologic studies is necessary to shed light on genetically mediated targets for longevity-enhancing drugs. Such integration will require more sophisticated data sets, phenotypic definitions, and bioinformatics approaches to be useful.

Exploring the dark genome: implications for precision medicine

The increase in the number of both patients and healthcare practitioners who grew up using the Internet and computers (so-called "digital natives") is likely to impact the practice of precision medicine, and requires novel platforms for data integration and mining, as well as contextualized information retrieval. The "Illuminating the Druggable Genome Knowledge Management Center" (IDG KMC) quantifies data availability from a wide range of chemical, biological, and clinical resources, and has developed platforms that can be used to navigate understudied proteins (the "dark genome"), and their potential contribution to specific pathologies. Using the "Target Importance and Novelty Explorer" (TIN-X) highlights the role of LRRC10 (a dark gene) in dilated cardiomyopathy. Combining mouse and human phenotype data leads to increased strength of evidence, which is discussed for four additional dark genes: SLX4IP and its role in glucose metabolism, the role of HSF2BP in coronary artery disease, the involvement of ELFN1 in attention-deficit hyperactivity disorder and the role of VPS13D in mouse neural tube development and its confirmed role in childhood onset movement disorders. The workflow and tools described here are aimed at guiding further experimental research, particularly within the context of precision medicine.

Molecular pharmacology of metabotropic receptors targeted by neuropsychiatric drugs

Metabotropic receptors are responsible for so-called 'slow synaptic transmission' and mediate the effects of hundreds of peptide and non-peptide neurotransmitters and neuromodulators. Over the past decade or so, a revolution in membrane-protein structural determination has clarified the molecular determinants responsible for the actions of these receptors. This Review focuses on the G protein-coupled receptors (GPCRs) that are targets of neuropsychiatric drugs and shows how insights into the structure and function of these important synaptic proteins are accelerating understanding of their actions. Notably, elucidating the structure and function of GPCRs should enhance the structure-guided discovery of novel chemical tools with which to manipulate and understand these synaptic proteins.

Predicting human disease mutations and identifying drug targets from mouse gene knockout phenotyping campaigns

Two large-scale mouse gene knockout phenotyping campaigns have provided extensive data on the functions of thousands of mammalian genes. The ongoing International Mouse Phenotyping Consortium (IMPC), with the goal of examining all ∼20,000 mouse genes, has examined 5115 genes since 2011, and phenotypic data from several analyses are available on the IMPC website (www.mousephenotype.org). Mutant mice having at least one human genetic disease-associated phenotype are available for 185 IMPC genes. Lexicon Pharmaceuticals' Genome5000™ campaign performed similar analyses between 2000 and the end of 2008 focusing on the druggable genome, including enzymes, receptors, transporters, channels and secreted proteins. Mutants (4654 genes, with 3762 viable adult homozygous lines) with therapeutically interesting phenotypes were studied extensively. Importantly, phenotypes for 29 Lexicon mouse gene knockouts were published prior to observations of similar phenotypes resulting from homologous mutations in human genetic disorders. Knockout mouse phenotypes for an additional 30 genes mimicked previously published human genetic disorders. Several of these models have helped develop effective treatments for human diseases. For example, studying Tph1 knockout mice (lacking peripheral serotonin) aided the development of telotristat ethyl, an approved treatment for carcinoid syndrome. Sglt1 (also known as Slc5a1) and Sglt2 (also known as Slc5a2) knockout mice were employed to develop sotagliflozin, a dual SGLT1/SGLT2 inhibitor having success in clinical trials for diabetes. Clinical trials evaluating inhibitors of AAK1 (neuropathic pain) and SGLT1 (diabetes) are underway. The research community can take advantage of these unbiased analyses of gene function in mice, including the minimally studied 'ignorome' genes.

Far away from the lamppost

This Formal Comment responds to a recent Meta-Research Article by identifying initiatives that are already in place for funding risky exploratory research that illuminate mysteries of the dark genome.

Ion channels as therapeutic antibody targets

It is now well established that antibodies have numerous potential benefits when developed as therapeutics. Here, we evaluate the technical challenges of raising antibodies to membrane-spanning proteins together with enabling technologies that may facilitate the discovery of antibody therapeutics to ion channels. Additionally, we discuss the potential targeting opportunities in the anti-ion channel antibody landscape, along with a number of case studies where functional antibodies that target ion channels have been reported. Antibodies currently in development and progressing towards the clinic are highlighted.

Large-scale investigation of the reasons why potentially important genes are ignored

Biomedical research has been previously reported to primarily focus on a minority of all known genes. Here, we demonstrate that these differences in attention can be explained, to a large extent, exclusively from a small set of identifiable chemical, physical, and biological properties of genes. Together with knowledge about homologous genes from model organisms, these features allow us to accurately predict the number of publications on individual human genes, the year of their first report, the levels of funding awarded by the National Institutes of Health (NIH), and the development of drugs against disease-associated genes. By explicitly identifying the reasons for gene-specific bias and performing a meta-analysis of existing computational and experimental knowledge bases, we describe gene-specific strategies for the identification of important but hitherto ignored genes that can open novel directions for future investigation.