The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations

Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

Novel miRNA-inducing drugs enable differentiation of retinoic acid-resistant neuroblastoma cells

Tumor cell heterogeneity in neuroblastoma, a pediatric cancer arising from neural crest-derived progenitor cells, poses a significant clinical challenge. In particular, unlike adrenergic (ADRN) neuroblastoma cells, mesenchymal (MES) cells are resistant to chemotherapy and retinoid therapy and thereby significantly contribute to relapses and treatment failures. Previous research suggested that overexpression or activation of miR-124, a neurogenic microRNA with tumor suppressor activity, can induce the differentiation of retinoic acid-resistant neuroblastoma cells. Leveraging our established screen for miRNA modulatory small molecules, we validated PP121, a dual inhibitor of tyrosine and phosphoinositide kinases, as a robust inducer of miR-124. A combination of PP121 and miR-132-inducing bufalin synergistically arrests proliferation, induces differentiation, and prolongs the survival of differentiated MES SK-N-AS cells for 8 weeks. RNA- seq and deconvolution analyses revealed a collapse of the ADRN core regulatory circuitry (CRC) and the emergence of novel CRCs associated with chromaffin cells and Schwann cell precursors. Using a similar protocol, we differentiated and maintained other MES neuroblastoma, as well as glioblastoma cells, over 16 weeks. In conclusion, our novel protocol suggests a promising treatment for therapy-resistant cancers of the nervous system. Moreover, these long-lived, differentiated cells provide valuable models for studying mechanisms underlying differentiation, maturation, and senescence.

NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders

Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.

Interactions between integrin α9β1 and VCAM-1 promote neutrophil hyperactivation and mediate poststroke DVT

ABSTRACT

Venous thromboembolic events are significant contributors to morbidity and mortality in patients with stroke. Neutrophils are among the first cells in the blood to respond to stroke and are known to promote deep vein thrombosis (DVT). Integrin α9 is a transmembrane glycoprotein highly expressed on neutrophils and stabilizes neutrophil adhesion to activated endothelium via vascular cell adhesion molecule 1 (VCAM-1). Nevertheless, the causative role of neutrophil integrin α9 in poststroke DVT remains unknown. Here, we found higher neutrophil integrin α9 and plasma VCAM-1 levels in humans and mice with stroke. Using mice with embolic stroke, we observed enhanced DVT severity in a novel model of poststroke DVT. Neutrophil-specific integrin α9-deficient mice (α9fl/flMrp8Cre+/-) exhibited a significant reduction in poststroke DVT severity along with decreased neutrophils and citrullinated histone H3 in thrombi. Unbiased transcriptomics indicated that α9/VCAM-1 interactions induced pathways related to neutrophil inflammation, exocytosis, NF-κB signaling, and chemotaxis. Mechanistic studies revealed that integrin α9/VCAM-1 interactions mediate neutrophil adhesion at the venous shear rate, promote neutrophil hyperactivation, increase phosphorylation of extracellular signal-regulated kinase, and induce endothelial cell apoptosis. Using pharmacogenomic profiling, virtual screening, and in vitro assays, we identified macitentan as a potent inhibitor of integrin α9/VCAM-1 interactions and neutrophil adhesion to activated endothelial cells. Macitentan reduced DVT severity in control mice with and without stroke, but not in α9fl/flMrp8Cre+/- mice, suggesting that macitentan improves DVT outcomes by inhibiting neutrophil integrin α9. Collectively, we uncovered a previously unrecognized and critical pathway involving the α9/VCAM-1 axis in neutrophil hyperactivation and DVT.

Focal adhesion kinase-YAP signaling axis drives drug-tolerant persister cells and residual disease in lung cancer

Targeted therapy is effective in many tumor types including lung cancer, the leading cause of cancer mortality. Paradigm defining examples are targeted therapies directed against non-small cell lung cancer (NSCLC) subtypes with oncogenic alterations in EGFR, ALK and KRAS. The success of targeted therapy is limited by drug-tolerant persister cells (DTPs) which withstand and adapt to treatment and comprise the residual disease state that is typical during treatment with clinical targeted therapies. Here, we integrate studies in patient-derived and immunocompetent lung cancer models and clinical specimens obtained from patients on targeted therapy to uncover a focal adhesion kinase (FAK)-YAP signaling axis that promotes residual disease during oncogenic EGFR-, ALK-, and KRAS-targeted therapies. FAK-YAP signaling inhibition combined with the primary targeted therapy suppressed residual drug-tolerant cells and enhanced tumor responses. This study unveils a FAK-YAP signaling module that promotes residual disease in lung cancer and mechanism-based therapeutic strategies to improve tumor response.

Nucleolar stress caused by arginine-rich peptides triggers a ribosomopathy and accelerates aging in mice

Nucleolar stress (NS) has been associated with age-related diseases such as cancer or neurodegeneration. To investigate how NS triggers toxicity, we used (PR)n arginine-rich peptides present in some neurodegenerative diseases as inducers of this perturbation. We here reveal that whereas (PR)n expression leads to a decrease in translation, this occurs concomitant with an accumulation of free ribosomal (r) proteins. Conversely, (PR)n-resistant cells have lower rates of r-protein synthesis, and targeting ribosome biogenesis by mTOR inhibition or MYC depletion alleviates (PR)n toxicity in vitro. In mice, systemic expression of (PR)97 drives widespread NS and accelerated aging, which is alleviated by rapamycin. Notably, the generalized accumulation of orphan r-proteins is a common outcome of chemical or genetic perturbations that induce NS. Together, our study presents a general model to explain how NS induces cellular toxicity and provides in vivo evidence supporting a role for NS as a driver of aging in mammals.

A Multistep In Silico Approach Identifies Potential Glioblastoma Drug Candidates via Inclusive Molecular Targeting of Glioblastoma Stem Cells

Glioblastoma (GBM) is the highest grade of glioma for which no effective therapy is currently available. Despite extensive research in diagnosis and therapy, there has been no significant improvement in GBM outcomes, with a median overall survival continuing at a dismal 15-18 months. In recent times, glioblastoma stem cells (GSCs) have been identified as crucial drivers of treatment resistance and tumor recurrence, and GBM therapies targeting GSCs are expected to improve patient outcomes. We used a multistep in silico screening strategy to identify repurposed candidate drugs against selected therapeutic molecular targets in GBM with potential to concomitantly target GSCs. Common differentially expressed genes (DEGs) were identified through analysis of multiple GBM and GSC datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). For identification of target genes, we selected the genes with most significant effect on overall patient survival. The relative mRNA and protein expression of the selected genes in TCGA control versus GBM samples was also validated and their cancer dependency scores were assessed. Drugs targeting these genes and their corresponding proteins were identified from LINCS database using Connectivity Map (CMap) portal and by in silico molecular docking against each individual target using FDA-approved drug library from the DrugBank database, respectively. The molecules thus obtained were further evaluated for their ability to cross blood brain barrier (BBB) and their likelihood of resulting in drug resistance by acting as p-glycoprotein (p-Gp) substrates. The growth inhibitory effect of these final shortlisted compounds was examined on a panel of GBM cell lines and compared with temozolomide through the drug sensitivity EC50 values and AUC from the PRISM Repurposing Secondary Screen, and the IC50 values were obtained from GDSC portal. We identified RPA3, PSMA2, PSMC2, BLVRA, and HUS1 as molecular targets in GBM including GSCs with significant impact on patient survival. Our results show GSK-2126458/omipalisib, linifanib, drospirenone, eltrombopag, nilotinib, and PD198306 as candidate drugs which can be further evaluated for their anti-tumor potential against GBM. Through this work, we identified repurposed candidate therapeutics against GBM utilizing a GSC inclusive targeting approach, which demonstrated high in vitro efficacy and can prospectively evade drug resistance. These drugs have the potential to be developed as individual or combination therapy to improve GBM outcomes.

AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data

Cancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties and represent a potentially effective therapeutic target toward long-term remission by means of differentiation induction. By leveraging an artificial intelligence approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human-induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the Library of Integrated Network-based Cellular Signatures (LINCS) database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of five out of six tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron and A-443654 offer a potential avenue for targeted therapies against breast CSCs.

ViCEKb: Vitiligo-linked Chemical Exposome Knowledgebase

Vitiligo is a complex disease wherein the environmental factors, in conjunction with the underlying genetic predispositions, trigger the autoimmune destruction of melanocytes, ultimately leading to depigmented patches on the skin. While genetic factors have been extensively studied, the knowledge on environmental triggers remains sparse and less understood. To address this knowledge gap, we present the first comprehensive knowledgebase of vitiligo-triggering chemicals namely, Vitiligo-linked Chemical Exposome Knowledgebase (ViCEKb). ViCEKb involves an extensive and systematic manual effort in curation of published literature and subsequent compilation of 113 unique chemical triggers of vitiligo. ViCEKb standardizes various chemical information, and categorizes the chemicals based on their evidences and sources of exposure. Importantly, ViCEKb contains a wide range of metrics necessary for different toxicological evaluations. Notably, we observed that ViCEKb chemicals are present in a variety of consumer products. For instance, Propyl gallate is present as a fragrance substance in various household products, and Flutamide is used in medication to treat prostate cancer. These two chemicals have the highest level of evidence in ViCEKb, but are not regulated for their skin sensitizing effects. Furthermore, an extensive cheminformatics-based investigation revealed that ViCEKb chemical space is structurally diverse and comprises unique chemical scaffolds in comparison with skin specific regulatory lists. For example, Neomycin and 2,3,5-Triglycidyl-4-aminophenol have unique chemical scaffolds and the highest level of evidence in ViCEKb, but are not regulated for their skin sensitizing effects. Finally, a transcriptomics-based analysis of ViCEKb chemical perturbations in skin cell samples highlighted the commonality in their linked biological processes. Overall, we present the first comprehensive effort in compilation and exploration of various chemical triggers of vitiligo. We believe such a resource will enable in deciphering the complex etiology of vitiligo and aid in the characterization of human chemical exposome. ViCEKb is freely available for academic research at: https://cb.imsc.res.in/vicekb.

Drug Mechanism: A bioinformatic update

A drug Mechanism of Action (MoA) is a complex biological phenomenon that describes how a bioactive compound produces a pharmacological effect. The complete knowledge of MoA is fundamental to fully understanding the drug activity. Over the years, many experimental methods have been developed and a huge quantity of data has been produced. Nowadays, considering the increasing omics data availability and the improvement of the accessible computational resources, the study of a drug MoA is conducted by integrating experimental and bioinformatics approaches. The development of new in silico solutions for this type of analysis is continuously ongoing; herein, an updating review on such bioinformatic methods is presented. The methodologies cited are based on multi-omics data integration in biochemical networks and Machine Learning (ML). The multiple types of usable input data and the advantages and disadvantages of each method have been analyzed, with a focus on their applications. Three specific research areas (i.e. cancer drug development, antibiotics discovery, and drug repurposing) have been chosen for their importance in the drug discovery fields in which the study of drug MoA, through novel bioinformatics approaches, is particularly productive.

A review of computational methods for predicting cancer drug response at the single-cell level through integration with bulk RNAseq data

Cancer treatment failure is often attributed to tumor heterogeneity, where diverse malignant cell clones exist within a patient. Despite a growing understanding of heterogeneous tumor cells depicted by single-cell RNA sequencing (scRNA-seq), there is still a gap in the translation of such knowledge into treatment strategies tackling the pervasive issue of therapy resistance. In this review, we survey methods leveraging large-scale drug screens to generate cellular sensitivities to various therapeutics. These methods enable efficient drug screens in scRNA-seq data and serve as the bedrock of drug discovery for specific cancer cell groups. We envision that they will become an indispensable tool for tailoring patient care in the era of heterogeneity-aware precision medicine.

Integration of single-nucleus RNA sequencing and network disturbance to elucidate crosstalk between multicomponent drugs and trigeminal ganglia cells in migraine

ETHNOPHARMACOLOGICAL RELEVANCE

Migraine is caused by hyperactivity of the trigeminovascular system, where trigeminal ganglia (TG) plays an important role. TG is composed of multiple neuronal and non-neuronal cell types, which is related to "neuro-inflammation-vascular" disorder in migraine. Tou Tong Ning capsule (TTNC), a CFDA-approved traditional Chinese medicine for treating migraine, has the characteristics of "multicomponents, multitargets, multipathways".

AIM OF THE STUDY

To clarify the mechanism of TTNC and elucidate crosstalk between multicomponent drugs and neuronal and non-neuronal functions and cells in migraine.

MATERIALS AND METHODS

We integrated single-nucleus RNA sequencing and a quantitative evaluation algorithm of the disturbance of multitarget drugs on the disease network and explored the specific pathology of migraine and corresponding compounds. A cerebrovascular smooth muscle spasmolytic activity experiment was carried out to verify the results of the bioinformatics analysis.

RESULTS

TTNC exhibited its regulation activities in neuronal and non-neuronal aspects based on drugs attack to four subnetworks and cell specific networks, which explored the MoA of TTNC in comprehensive and refined perspectives. Compared to neuronal regulation, TTNC showed more significant attack score on non-neuronal biological function (smooth muscle and vessel). And TTNC compound clusters C1, C6 and C7, targeting non-neuronal function and cells, had larger group area than C10, C4 and C6 for neuronal function and cell, which implied that TTNC may mainly regulate the non-neuronal function, e.g., vessel smooth muscle contraction. Contraction of cerebrovascular smooth muscle of mice ex vivo confirmed the vasodilation activity of TTNC and active compounds from C1, C6, C9 (Emodin, Luteolin and Levistilide A). Literature mining confirmed the vasospasmodolytic activity and neuroprotective effect of TTNC.

CONCLUSIONS

The study found that TTNC may primarily alleviate non-neuronal functional disorders in migraine by relaxing cerebral vascular smooth muscle cell spasm to alleviate migraine. Integrating single-nucleus RNA sequencing data and network disturbance tools provides a new strategy for the pharmacological mechanism of multicomponent drugs through cell subtyping.

Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks

As an alternative to target-driven drug discovery, phenotype-driven approaches identify compounds that counteract the overall disease effects by analyzing phenotypic signatures. Our study introduces a novel approach to this field, aiming to expand the search space for new therapeutic agents. We introduce PDGrapher, a causally-inspired graph neural network model designed to predict arbitrary perturbagens - sets of therapeutic targets - capable of reversing disease effects. Unlike existing methods that learn responses to perturbations, PDGrapher solves the inverse problem, which is to infer the perturbagens necessary to achieve a specific response - i.e., directly predicting perturbagens by learning which perturbations elicit a desired response. Experiments across eight datasets of genetic and chemical perturbations show that PDGrapher successfully predicted effective perturbagens in up to 9% additional test samples and ranked therapeutic targets up to 35% higher than competing methods. A key innovation of PDGrapher is its direct prediction capability, which contrasts with the indirect, computationally intensive models traditionally used in phenotypedriven drug discovery that only predict changes in phenotypes due to perturbations. The direct approach enables PDGrapher to train up to 30 times faster, representing a significant leap in efficiency. Our results suggest that PDGrapher can advance phenotype-driven drug discovery, offering a fast and comprehensive approach to identifying therapeutically useful perturbations.

Computational drug repurposing for viral infectious diseases: a case study on monkeypox

The traditional method of drug reuse or repurposing has significantly contributed to the identification of new antiviral compounds and therapeutic targets, enabling rapid response to developing infectious illnesses. This article presents an overview of how modern computational methods are used in drug repurposing for the treatment of viral infectious diseases. These methods utilize data sets that include reviewed information on the host's response to pathogens and drugs, as well as various connections such as gene expression patterns and protein-protein interaction networks. We assess the potential benefits and limitations of these methods by examining monkeypox as a specific example, but the knowledge acquired can be applied to other comparable disease scenarios.

IUPHAR review - Data-driven computational drug repurposing approaches for opioid use disorder

Opioid Use Disorder (OUD) is a chronic and relapsing condition characterized by the misuse of opioid drugs, causing significant morbidity and mortality in the United States. Existing medications for OUD are limited, and there is an immediate need to discover treatments with enhanced safety and efficacy. Drug repurposing aims to find new indications for existing medications, offering a time-saving and cost-efficient alternative strategy to traditional drug discovery. Computational approaches have been developed to further facilitate the drug repurposing process. In this paper, we reviewed state-of-the-art data-driven computational drug repurposing approaches for OUD and discussed their advantages and potential challenges. We also highlighted promising repurposed candidate drugs for OUD that were identified by computational drug repurposing techniques and reviewed studies supporting their potential mechanisms of action in treating OUD.

Identification of novel anti-ZIKV drugs from viral-infection temporal gene expression profiles

Zika virus (ZIKV) infections are typically asymptomatic but cause severe neurological complications (e.g. Guillain-Barré syndrome in adults, and microcephaly in newborns). There are currently no specific therapy or vaccine options available to prevent ZIKV infections. Temporal gene expression profiles of ZIKV-infected human brain microvascular endothelial cells (HBMECs) were used in this study to identify genes essential for viral replication. These genes were then used to identify novel anti-ZIKV agents and validated in publicly available data and functional wet-lab experiments. Here, we found that ZIKV effectively evaded activation of immune response-related genes and completely reprogrammed cellular transcriptional architectures. Knockdown of genes, which gradually upregulated during viral infection but showed distinct expression patterns between ZIKV- and mock infection, discovered novel proviral and antiviral factors. One-third of the 74 drugs found through signature-based drug repositioning and cross-reference with the Drug Gene Interaction Database (DGIdb) were known anti-ZIKV agents. In cellular assays, two promising antiviral candidates (Luminespib/NVP-AUY922, L-161982) were found to reduce viral replication without causing cell toxicity. Overall, our time-series transcriptome-based methods offer a novel and feasible strategy for antiviral drug discovery. Our strategies, which combine conventional and data-driven analysis, can be extended for other pathogens causing pandemics in the future.

EMNPD: a comprehensive endophytic microorganism natural products database for prompt the discovery of new bioactive substances

The discovery and utilization of natural products derived from endophytic microorganisms have garnered significant attention in pharmaceutical research. While remarkable progress has been made in this field each year, the absence of dedicated open-access databases for endophytic microorganism natural products research is evident. To address the increasing demand for mining and sharing of data resources related to endophytic microorganism natural products, this study introduces EMNPD, a comprehensive endophytic microorganism natural products database comprising manually curated data. Currently, EMNPD offers 6632 natural products from 1017 endophytic microorganisms, targeting 1286 entities (including 94 proteins, 282 cell lines, and 910 species) with 91 diverse bioactivities. It encompasses the physico-chemical properties of natural products, ADMET information, quantitative activity data with their potency, natural products contents with diverse fermentation conditions, systematic taxonomy, and links to various well-established databases. EMNPD aims to function as an open-access knowledge repository for the study of endophytic microorganisms and their natural products, thereby facilitating drug discovery research and exploration of bioactive substances. The database can be accessed at http://emnpd.idrblab.cn/ without the need for registration, enabling researchers to freely download the data. EMNPD is expected to become a valuable resource in the field of endophytic microorganism natural products and contribute to future drug development endeavors.

Mutant-Huntingtin Molecular Pathways Elucidate New Targets for Drug Repurposing

The spectrum of neurodegenerative diseases known today is quite extensive. The complexities of their research and treatment lie not only in their diversity. Even many years of struggle and narrowly focused research on common pathologies such as Alzheimer's, Parkinson's, and other brain diseases have not brought cures for these illnesses. What can be said about orphan diseases? In particular, Huntington's disease (HD), despite affecting a smaller part of the human population, still attracts many researchers. This disorder is known to result from a mutation in the HTT gene, but having this information still does not simplify the task of drug development and studying the mechanisms of disease progression. Nonetheless, the data accumulated over the years and their analysis provide a good basis for further research. Here, we review studies devoted to understanding the mechanisms of HD. We analyze genes and molecular pathways involved in HD pathogenesis to describe the action of repurposed drugs and try to find new therapeutic targets.

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

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

Applying a Gene Reversal Rate Computational Methodology to Identify Drugs for a Rare Cancer: Inflammatory Breast Cancer

The aim of this study was to utilize a computational methodology based on Gene Reversal Rate (GRR) scoring to repurpose existing drugs for a rare and understudied cancer: inflammatory breast cancer (IBC). This method uses IBC-related gene expression signatures (GES) and drug-induced gene expression profiles from the LINCS database to calculate a GRR score for each candidate drug, and is based on the idea that a compound that can counteract gene expression changes of a disease may have potential therapeutic applications for that disease. Genes related to IBC with associated differential expression data (265 up-regulated and 122 down-regulated) were collated from PubMed-indexed publications. Drug-induced gene expression profiles were downloaded from the LINCS database and candidate drugs to treat IBC were predicted using their GRR scores. Thirty-two (32) drug perturbations that could potentially reverse the pre-compiled list of 297 IBC genes were obtained using the LINCS Canvas Browser (LCB) analysis. Binary combinations of the 32 perturbations were assessed computationally to identify combined perturbations with the highest GRR scores, and resulted in 131 combinations with GRR greater than 80%, that reverse up to 264 of the 297 genes in the IBC-GES. The top 35 combinations involve 20 unique individual drug perturbations, and 19 potential drug candidates. A comprehensive literature search confirmed 17 of the 19 known drugs as having either anti-cancer or anti-inflammatory activities. AZD-7545, BMS-754807, and nimesulide target known IBC relevant genes: PDK, Met, and COX, respectively. AG-14361, butalbital, and clobenpropit are known to be functionally relevant in DNA damage, cell cycle, and apoptosis, respectively. These findings support the use of the GRR approach to identify drug candidates and potential combination therapies that could be used to treat rare diseases such as IBC.

PI3K signaling through a biochemical systems lens

Following 3 decades of extensive research into PI3K signaling, it is now evidently clear that the underlying network does not equate to a simple ON/OFF switch. This is best illustrated by the multifaceted nature of the many diseases associated with aberrant PI3K signaling, including common cancers, metabolic disease, and rare developmental disorders. However, we are still far from a complete understanding of the fundamental control principles that govern the numerous phenotypic outputs that are elicited by activation of this well-characterized biochemical signaling network, downstream of an equally diverse set of extrinsic inputs. At its core, this is a question on the role of PI3K signaling in cellular information processing and decision making. Here, we review the determinants of accurate encoding and decoding of growth factor signals and discuss outstanding questions in the PI3K signal relay network. We emphasize the importance of quantitative biochemistry, in close integration with advances in single-cell time-resolved signaling measurements and mathematical modeling.

Identifying shared transcriptional risk patterns between atherosclerosis and cancer

Cancer and cardiovascular disease (CVD) are the leading causes of death worldwide. Numerous overlapping pathophysiologic mechanisms have been hypothesized to drive the development of both diseases. Further investigation of these common pathways could allow for the identification of mutually detrimental processes and therapeutic targeting to derive mutual benefit. In this study, we intersect transcriptomic datasets correlated with disease severity or patient outcomes for both cancer and atherosclerotic CVD. These analyses confirmed numerous pathways known to underlie both diseases, such as inflammation and hypoxia, but also identified several novel shared pathways. We used these to explore common translational targets by applying the drug prediction software, OCTAD, to identify compounds that simultaneously reverse the gene expression signature for both diseases. These analyses suggest that certain tumor-specific therapeutic approaches may be implemented so that they avoid cardiovascular consequences, and in some cases may even be used to simultaneously target co-prevalent cancer and atherosclerosis.

Opioids and immune checkpoint inhibitors differentially regulate a common immune network in triple-negative breast cancer

Background

Opioids are the primary analgesics for cancer pain. Recent clinical evidence suggests opioids may counteract the effect of immune checkpoint inhibition (ICI) immunotherapy, but the mechanism for this interaction is unknown. The following experiments study how opioids and immunotherapy modulate a common RNA expression pathway in triple negative breast cancer (TNBC), a cancer subtype in which immunotherapy is increasingly used. This study identifies a mechanism by which opioids may decrease ICI efficacy, and compares ketamine, a non-opioid analgesic with emerging use in cancer pain, for potential ICI interaction.

Methods

Tumor RNA expression and clinicopathologic data from a large cohort with TNBC (N=286) was used to identify RNA expression signatures of disease. Various drug-induced RNA expression profiles were extracted from multimodal RNA expression datasets and analyzed to estimate the RNA expression effects of ICI, opioids, and ketamine on TNBC.

Results

We identified a RNA expression network in CD8+ T-cells that was relevant to TNBC pathogenesis and prognosis. Both opioids and anti-PD-L1 ICI regulated RNA expression in this network, suggesting a nexus for opioid-ICI interaction. Morphine and anti-PD-L1 therapy regulated RNA expression in opposing directions. By contrast, there was little overlap between the effect of ketamine and anti-PD-L1 therapy on RNA expression.

Conclusions

Opioids and ICI may target a common immune network in TNBC and regulate gene expression in opposing fashion. No available evidence supports a similar interaction between ketamine and ICI.

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

Unlocking therapeutic potential: integration of drug repurposing and immunotherapy for various disease targeting

Drug repurposing, also known as drug repositioning, entails the application of pre-approved or formerly assessed drugs having potentially functional therapeutic amalgams for curing various disorders or disease conditions distinctive from their original remedial indication. It has surfaced as a substitute for the development of drugs for treating cancer, cardiovascular diseases, neurodegenerative disorders, and various infectious diseases like Covid-19. Although the earlier lines of findings in this area were serendipitous, recent advancements are based on patient centered approaches following systematic, translational, drug targeting practices that explore pathophysiological ailment mechanisms. The presence of definite information and numerous records with respect to beneficial properties, harmfulness, and pharmacologic characteristics of repurposed drugs increase the chances of approval in the clinical trial stages. The last few years have showcased the successful emergence of repurposed drug immunotherapy in treating various diseases. In this light, the present review emphasises on incorporation of drug repositioning with Immunotherapy targeted for several disorders.

Proteomic profiling across breast cancer cell lines and models

We performed quantitative proteomics on 60 human-derived breast cancer cell line models to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics.

Antibody reliability influences observed mRNA-protein correlations in tumour samples

Reverse phase protein arrays (RPPA) have been used to quantify the abundance of hundreds of proteins across thousands of tumour samples in the Cancer Genome Atlas. By number of samples, this is the largest tumour proteomic dataset available and it provides an opportunity to systematically assess the correlation between mRNA and protein abundances. However, the RPPA approach is highly dependent on antibody reliability and approximately one-quarter of the antibodies used in the the Cancer Genome Atlas are deemed to be somewhat less reliable. Here, we assess the impact of antibody reliability on observed mRNA-protein correlations. We find that, in general, proteins measured with less reliable antibodies have lower observed mRNA-protein correlations. This is not true of the same proteins when measured using mass spectrometry. Furthermore, in cell lines, we find that when the same protein is quantified by both mass spectrometry and RPPA, the overall correlation between the two measurements is lower for proteins measured with less reliable antibodies. Overall our results reinforce the need for caution in using RPPA measurements from less reliable antibodies.

Toxicology knowledge graph for structural birth defects

BACKGROUND

Birth defects are functional and structural abnormalities that impact about 1 in 33 births in the United States. They have been attributed to genetic and other factors such as drugs, cosmetics, food, and environmental pollutants during pregnancy, but for most birth defects there are no known causes.

METHODS

To further characterize associations between small molecule compounds and their potential to induce specific birth abnormalities, we gathered knowledge from multiple sources to construct a reproductive toxicity Knowledge Graph (ReproTox-KG) with a focus on associations between birth defects, drugs, and genes. Specifically, we gathered data from drug/birth-defect associations from co-mentions in published abstracts, gene/birth-defect associations from genetic studies, drug- and preclinical-compound-induced gene expression changes in cell lines, known drug targets, genetic burden scores for human genes, and placental crossing scores for small molecules.

RESULTS

Using ReproTox-KG and semi-supervised learning (SSL), we scored >30,000 preclinical small molecules for their potential to cross the placenta and induce birth defects, and identified >500 birth-defect/gene/drug cliques that can be used to explain molecular mechanisms for drug-induced birth defects. The ReproTox-KG can be accessed via a web-based user interface available at https://maayanlab.cloud/reprotox-kg . This site enables users to explore the associations between birth defects, approved and preclinical drugs, and all human genes.

CONCLUSIONS

ReproTox-KG provides a resource for exploring knowledge about the molecular mechanisms of birth defects with the potential of predicting the likelihood of genes and preclinical small molecules to induce birth defects.

Donafenib and GSK-J4 Synergistically Induce Ferroptosis in Liver Cancer by Upregulating HMOX1 Expression

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide. Donafenib is a multi-receptor tyrosine kinase inhibitor approved for the treatment of patients with advanced HCC, but its clinical effect is very limited. Here, through integrated screening of a small-molecule inhibitor library and a druggable CRISPR library, that GSK-J4 is synthetically lethal with donafenib in liver cancer is shown. This synergistic lethality is validated in multiple HCC models, including xenograft, orthotopically induced HCC, patient-derived xenograft, and organoid models. Furthermore, co-treatment with donafenib and GSK-J4 resulted in cell death mainly via ferroptosis. Mechanistically, through integrated RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin with high throughput sequencing (ATAC-seq) analyses, that donafenib and GSK-J4 synergistically promoted the expression of HMOX1 and increased the intracellular Fe2+ level is found, eventually leading to ferroptosis. Additionally, through cleavage under targets & tagmentation followed by sequencing (CUT&Tag-seq), it is found that the enhancer regions upstream of HMOX1 promoter significantly increased under donafenib and GSK-J4 co-treatment. A chromosome conformation capture assay confirmed that the increased expression of HMOX1 is caused by the significantly enhanced interaction between the promoter and upstream enhancer under dual-drug combination. Taken together, this study elucidates a new synergistic lethal interaction in liver cancer.

Dissecting molecular, pathological, and clinical features associated with tumor neural/neuroendocrine heterogeneity

Lineage plasticity, especially transdifferentiation between neural/neuroendocrine (NE) and non-NE lineage, has been observed in multiple cancer types and linked to increased tumor aggressiveness. However, existing NE/non-NE subtype classifications in various cancer types were established through ad hoc approaches in different studies, making it difficult to align findings across cancer types and extend investigations to new datasets. To address this issue, we developed a generalized strategy to generate quantitative NE scores and a web application to facilitate its implementation. We applied this method to nine datasets covering seven cancer types, including two neural cancers, two neuroendocrine cancers, and three non-NE cancers. Our analysis revealed significant NE inter-tumoral heterogeneity and identified strong associations between NE scores and molecular, histological, and clinical features, including prognosis in different cancer types. These results support the translational utility of NE scores. Overall, our work demonstrated a broadly applicable strategy for determining the NE properties of tumors.

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

Morphodynamical cell state description via live-cell imaging trajectory embedding

Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of "trajectory embedding" to analyze cellular behavior using morphological feature trajectory histories-that is, multiple time points simultaneously, rather than the more common practice of examining morphological feature time courses in single timepoint (snapshot) morphological features. We apply this approach to analyze live-cell images of MCF10A mammary epithelial cells after treatment with a panel of microenvironmental perturbagens that strongly modulate cell motility, morphology, and cell cycle behavior. Our morphodynamical trajectory embedding analysis constructs a shared cell state landscape revealing ligand-specific regulation of cell state transitions and enables quantitative and descriptive models of single-cell trajectories. Additionally, we show that incorporation of trajectories into single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, (ii) better separation of phenotypes, and (iii) more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable to the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.

Restoration of aberrant gene expression of monocytes in systemic lupus erythematosus via a combined transcriptome-reversal and network-based drug repurposing strategy

BACKGROUND

Monocytes -key regulators of the innate immune response- are actively involved in the pathogenesis of systemic lupus erythematosus (SLE). We sought to identify novel compounds that might serve as monocyte-directed targeted therapies in SLE.

RESULTS

We performed mRNA sequencing in monocytes from 15 patients with active SLE and 10 healthy individuals. Disease activity was assessed with the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2 K). Leveraging the drug repurposing platforms iLINCS, CLUE and L1000CDS², we identified perturbagens capable of reversing the SLE monocyte signature. We identified transcription factors and microRNAs (miRNAs) that regulate the transcriptome of SLE monocytes, using the TRRUST and miRWalk databases, respectively. A gene regulatory network, integrating implicated transcription factors and miRNAs was constructed, and drugs targeting central components of the network were retrieved from the DGIDb database. Inhibitors of the NF-κB pathway, compounds targeting the heat shock protein 90 (HSP90), as well as a small molecule disrupting the Pim-1/NFATc1/NLRP3 signaling axis were predicted to efficiently counteract the aberrant monocyte gene signature in SLE. An additional analysis was conducted, to enhance the specificity of our drug repurposing approach on monocytes, using the iLINCS, CLUE and L1000CDS² platforms on publicly available datasets from circulating B-lymphocytes, CD4⁺ and CD8⁺ T-cells, derived from SLE patients. Through this approach we identified, small molecule compounds, that could potentially affect more selectively the transcriptome of SLE monocytes, such as, certain NF-κB pathway inhibitors, Pim-1 and SYK kinase inhibitors. Furthermore, according to our network-based drug repurposing approach, an IL-12/23 inhibitor and an EGFR inhibitor may represent potential drug candidates in SLE.

CONCLUSIONS

Application of two independent - a transcriptome-reversal and a network-based -drug repurposing strategies uncovered novel agents that might remedy transcriptional disturbances of monocytes in SLE.

Repurposing Approved Drugs for Sarcopenia Based on Transcriptomics Data in Humans

Sarcopenia, characterized by age-related loss of muscle mass, strength, and decreased physical performance, is a growing public health challenge amid the rapidly ageing population. As there are no approved drugs that target sarcopenia, it has become increasingly urgent to identify promising pharmacological interventions. In this study, we conducted an integrative drug repurposing analysis utilizing three distinct approaches. Firstly, we analyzed skeletal muscle transcriptomic sequencing data in humans and mice using gene differential expression analysis, weighted gene co-expression analysis, and gene set enrichment analysis. Subsequently, we employed gene expression profile similarity assessment, hub gene expression reversal, and disease-related pathway enrichment to identify and repurpose candidate drugs, followed by the integration of findings with rank aggregation algorithms. Vorinostat, the top-ranking drug, was also validated in an in vitro study, which demonstrated its efficacy in promoting muscle fiber formation. Although still requiring further validation in animal models and human clinical trials, these results suggest a promising drug repurposing prospect in the treatment and prevention of sarcopenia.

Combination of furosemide, gold, and dopamine as a potential therapy for breast cancer

Breast cancer is one of the leading causes of death in women worldwide. Initially, it develops in the epithelium of the ducts or lobules of the breast glandular tissues with limited growth and the potential to metastasize. It is a highly heterogeneous malignancy; however, the common molecular mechanisms could help identify new targeted drugs for treating its subtypes. This study uses computational drug repositioning approaches to explore fresh drug candidates for breast cancer treatment. We also implemented reversal gene expression and gene expression-based signatures to explore novel drug candidates computationally. The drug activity profiles and related gene expression changes were acquired from the DrugBank, PubChem, and LINCS databases, and then in silico drug screening, molecular dynamics (MD) simulation, replica exchange MD simulations, and simulated annealing molecular dynamics (SAMD) simulations were conducted to discover and verify the valid drug candidates. We have found that compounds like furosemide, gold, and dopamine showed significant outcomes. Furthermore, the expression of genes related to breast cancer was observed to be reversed by these shortlisted drugs. Therefore, we postulate that combining furosemide, gold, and dopamine would be a potential combination therapy measurement for breast cancer patients.

Identification of significant gene expression changes in multiple perturbation experiments using knockoffs

Large-scale multiple perturbation experiments have the potential to reveal a more detailed understanding of the molecular pathways that respond to genetic and environmental changes. A key question in these studies is which gene expression changes are important for the response to the perturbation. This problem is challenging because (i) the functional form of the nonlinear relationship between gene expression and the perturbation is unknown and (ii) identification of the most important genes is a high-dimensional variable selection problem. To deal with these challenges, we present here a method based on the model-X knockoffs framework and Deep Neural Networks to identify significant gene expression changes in multiple perturbation experiments. This approach makes no assumptions on the functional form of the dependence between the responses and the perturbations and it enjoys finite sample false discovery rate control for the selected set of important gene expression responses. We apply this approach to the Library of Integrated Network-Based Cellular Signature data sets which is a National Institutes of Health Common Fund program that catalogs how human cells globally respond to chemical, genetic and disease perturbations. We identified important genes whose expression is directly modulated in response to perturbation with anthracycline, vorinostat, trichostatin-a, geldanamycin and sirolimus. We compare the set of important genes that respond to these small molecules to identify co-responsive pathways. Identification of which genes respond to specific perturbation stressors can provide better understanding of the underlying mechanisms of disease and advance the identification of new drug targets.

Analysis of gene expression and use of connectivity mapping to identify drugs for treatment of human glomerulopathies

Background

Human glomerulonephritis (GN)—membranous nephropathy (MN), focal segmental glomerulosclerosis (FSGS) and IgA nephropathy (IgAN), as well as diabetic nephropathy (DN) are leading causes of chronic kidney disease. In these glomerulopathies, distinct stimuli disrupt metabolic pathways in glomerular cells. Other pathways, including the endoplasmic reticulum (ER) unfolded protein response (UPR) and autophagy, are activated in parallel to attenuate cell injury or promote repair.

Methods

We used publicly available datasets to examine gene transcriptional pathways in glomeruli of human GN and DN and to identify drugs.

Results

We demonstrate that there are many common genes upregulated in MN, FSGS, IgAN, and DN. Furthermore, these glomerulopathies were associated with increased expression of ER/UPR and autophagy genes, a significant number of which were shared. Several candidate drugs for treatment of glomerulopathies were identified by relating gene expression signatures of distinct drugs in cell culture with the ER/UPR and autophagy genes upregulated in the glomerulopathies (“connectivity mapping”). Using a glomerular cell culture assay that correlates with glomerular damage in vivo, we showed that one candidate drug – neratinib (an epidermal growth factor receptor inhibitor) is cytoprotective.

Conclusion

The UPR and autophagy are activated in multiple types of glomerular injury. Connectivity mapping identified candidate drugs that shared common signatures with ER/UPR and autophagy genes upregulated in glomerulopathies, and one of these drugs attenuated injury of glomerular cells. The present study opens the possibility for modulating the UPR or autophagy pharmacologically as therapy for GN.

lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs

Long non-coding ribonucleic acids (lncRNAs) account for the largest group of non-coding RNAs. However, knowledge about their function and regulation is limited. lncHUB2 is a web server database that provides known and inferred knowledge about the function of 18 705 human and 11 274 mouse lncRNAs. lncHUB2 produces reports that contain the secondary structure fold of the lncRNA, related publications, the most correlated coding genes, the most correlated lncRNAs, a network that visualizes the most correlated genes, predicted mouse phenotypes, predicted membership in biological processes and pathways, predicted upstream transcription factor regulators, and predicted disease associations. In addition, the reports include subcellular localization information; expression across tissues, cell types, and cell lines, and predicted small molecules and CRISPR knockout (CRISPR-KO) genes prioritized based on their likelihood to up- or downregulate the expression of the lncRNA. Overall, lncHUB2 is a database with rich information about human and mouse lncRNAs and as such it can facilitate hypothesis generation for many future studies. The lncHUB2 database is available at https://maayanlab.cloud/lncHUB2. Database URL: https://maayanlab.cloud/lncHUB2.

Repositioning linifanib as a potent anti-necroptosis agent for sepsis

Sepsis is a systemic inflammatory syndrome (SIRS) caused by acute microbial infection, and it has an extremely high mortality rate. Tumor necrosis factor-α (TNF-α)-induced necroptosis contributes to the pathophysiology of sepsis, so inhibiting necroptosis might be expected to improve clinical outcomes in septic patients. Here we predicted candidate drugs for treating sepsis in silico by combining genes differentially expressed in septic patients and controls combined with interrogation of the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 perturbation database. Sixteen candidate drugs were screened out through bioinformatics analysis, and the top candidate linifanib was validated in cellular and mouse models of TNF-α-induced necroptosis. Cell viability was measured using a luminescent ATP assay, while the effects of linifanib on necroptosis were investigated by western blotting, immunoprecipitation, and RIPK1 kinase assays. Linifanib effectively protected cells from necroptosis and rescued SIRS mice from TNF-α-induced shock and death. In vitro, linifanib directly suppressed RIPK1 kinase activity. In vivo, linifanib effectively reduced overexpressed IL-6, a marker of sepsis severity, in the lungs of SIRS mice. Our preclinical evidence using an integrated in silico and experimental drug repositioning approach supports the potential clinical utility of linifanib in septic patients. Further clinical validation is now warranted.

Localization of Noise in Biochemical Networks

Noise, or uncertainty in biochemical networks, has become an important aspect of many biological problems. Noise can arise and propagate from external factors and probabilistic chemical reactions occurring in small cellular compartments. For species survival, it is important to regulate such uncertainties in executing vital cell functions. Regulated noise can improve adaptability, whereas uncontrolled noise can cause diseases. Simulation can provide a detailed analysis of uncertainties, but parameters such as rate constants and initial conditions are usually unknown. A general understanding of noise dynamics from the perspective of network structure is highly desirable. In this study, we extended the previously developed law of localization for characterizing noise in terms of (co)variances and developed noise localization theory. With linear noise approximation, we can expand a biochemical network into an extended set of differential equations representing a fictitious network for pseudo-components consisting of variances and covariances, together with chemical species. Through localization analysis, perturbation responses at the steady state of pseudo-components can be summarized into a sensitivity matrix that only requires knowledge of network topology. Our work allows identification of buffering structures at the level of species, variances, and covariances and can provide insights into noise flow under non-steady-state conditions in the form of a pseudo-chemical reaction. We tested noise localization in various systems, and here we discuss its implications and potential applications. Results show that this theory is potentially applicable in discriminating models, scanning network topologies with interesting noise behavior, and designing and perturbing networks with the desired response.

Toxicogenomics scoring system: TGSS, a novel integrated risk assessment model for chemical carcinogenicity prediction

BACKGROUND

Given the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods.

OBJECTIVES

This study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets.

METHODS

Chemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts.

RESULTS

Forty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10^-5). ROC curves analysis indicated that C-score could distinguish "high-risk chemicals" from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts.

CONCLUSION

TGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.

NeuroLINCS Proteomics: Defining human-derived iPSC proteomes and protein signatures of pluripotency

The National Institute of Health (NIH) Library of integrated network-based cellular signatures (LINCS) program is premised on the generation of a publicly available data resource of cell-based biochemical responses or "signatures" to genetic or environmental perturbations. NeuroLINCS uses human inducible pluripotent stem cells (hiPSCs), derived from patients and healthy controls, and differentiated into motor neuron cell cultures. This multi-laboratory effort strives to establish i) robust multi-omic workflows for hiPSC and differentiated neuronal cultures, ii) public annotated data sets and iii) relevant and targetable biological pathways of spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS). Here, we focus on the proteomics and the quality of the developed workflow of hiPSC lines from 6 individuals, though epigenomics and transcriptomics data are also publicly available. Known and commonly used markers representing 73 proteins were reproducibly quantified with consistent expression levels across all hiPSC lines. Data quality assessments, data levels and metadata of all 6 genetically diverse human iPSCs analysed by DIA-MS are parsable and available as a high-quality resource to the public.

Evolution of precision oncology-guided treatment paradigms

Cancer treatment is gradually evolving from the classical use of nonspecific cytotoxic drugs targeting generic mechanisms of cell growth and proliferation. Instead, new "patient-specific treatment paradigms" that are based on an individual patient's tumor-specific molecular features are emerging, and these include "druggable" genomic alterations such as oncogenic driver mutations, downstream activities of cancer-signaling pathways, and the expression of specific genes involved in tumorigenesis and cancer progression. This evolving landscape of making evidence-based treatment decisions forms the foundation of precision oncology, which aims to deliver "the right drug, to the right patient and at the right time". The long-term vision for this approach is to maximize the treatment efficacy while minimizing exposure to ineffective therapy and reducing co-morbidity-related side effects. Successful clinical translation and implementation of this vision have the potential to revolutionize treatment paradigms from predominantly reactive, to more evidence-based, proactive and predictive care. In this article, we review the past and current approaches in precision oncology, and describe their remarkable power and limitations. We also speculate on the evolution of newly emerging methodologies of the future that can be used to address some of the key challenges associated with the existing paradigms. This article is categorized under: Cancer > Genetics/Genomics/Epigenetics Cancer > Molecular and Cellular Physiology Cancer > Computational Models.

Supervised chemical graph mining improves drug-induced liver injury prediction

Drug-induced liver injury (DILI) is the main cause of drug failure in clinical trials. The characterization of toxic compounds in terms of chemical structure is important because compounds can be metabolized to toxic substances in the liver. Traditional machine learning approaches have had limited success in predicting DILI, and emerging deep graph neural network (GNN) models are yet powerful enough to predict DILI. In this study, we developed a completely different approach, supervised subgraph mining (SSM), a strategy to mine explicit subgraph features by iteratively updating individual graph transitions to maximize DILI fidelity. Our method outperformed previous methods including state-of-the-art GNN tools in classifying DILI on two different datasets: DILIst and TDC-benchmark. We also combined the subgraph features by using SMARTS-based frequent structural pattern matching and associated them with drugs' ATC code.

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.

Artificial intelligence to guide precision anticancer therapy with multitargeted kinase inhibitors

BACKGROUND

Vast amounts of rapidly accumulating biological data related to cancer and a remarkable progress in the field of artificial intelligence (AI) have paved the way for precision oncology. Our recent contribution to this area of research is CancerOmicsNet, an AI-based system to predict the therapeutic effects of multitargeted kinase inhibitors across various cancers. This approach was previously demonstrated to outperform other deep learning methods, graph kernel models, molecular docking, and drug binding pocket matching.

METHODS

CancerOmicsNet integrates multiple heterogeneous data by utilizing a deep graph learning model with sophisticated attention propagation mechanisms to extract highly predictive features from cancer-specific networks. The AI-based system was devised to provide more accurate and robust predictions than data-driven therapeutic discovery using gene signature reversion.

RESULTS

Selected CancerOmicsNet predictions obtained for "unseen" data are positively validated against the biomedical literature and by live-cell time course inhibition assays performed against breast, pancreatic, and prostate cancer cell lines. Encouragingly, six molecules exhibited dose-dependent antiproliferative activities, with pan-CDK inhibitor JNJ-7706621 and Src inhibitor PP1 being the most potent against the pancreatic cancer cell line Panc 04.03.

CONCLUSIONS

CancerOmicsNet is a promising AI-based platform to help guide the development of new approaches in precision oncology involving a variety of tumor types and therapeutics.

Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities

Screening new compounds for potential bioactivities against cellular targets is vital for drug discovery and chemical safety. Transcriptomics offers an efficient approach for assessing global gene expression changes, but interpreting chemical mechanisms from these data is often challenging. Connectivity mapping is a potential data-driven avenue for linking chemicals to mechanisms based on the observation that many biological processes are associated with unique gene expression signatures (gene signatures). However, mining the effects of a chemical on gene signatures for biological mechanisms is challenging because transcriptomic data contain thousands of noisy genes. New connectivity mapping approaches seeking to distinguish signal from noise continue to be developed, spurred by the promise of discovering chemical mechanisms, new drugs, and disease targets from burgeoning transcriptomic data. Here, we analyze these approaches in terms of diverse transcriptomic technologies, public databases, gene signatures, pattern-matching algorithms, and statistical evaluation criteria. To navigate the complexity of connectivity mapping, we propose a harmonized scheme to coherently organize and compare published workflows. We first standardize concepts underlying transcriptomic profiles and gene signatures based on various transcriptomic technologies such as microarrays, RNA-Seq, and L1000 and discuss the widely used data sources such as Gene Expression Omnibus, ArrayExpress, and MSigDB. Next, we generalize connectivity mapping as a pattern-matching task for finding similarity between a query (e.g., transcriptomic profile for new chemical) and a reference (e.g., gene signature of known target). Published pattern-matching approaches fall into two main categories: vector-based use metrics like correlation, Jaccard index, etc., and aggregation-based use parametric and nonparametric statistics (e.g., gene set enrichment analysis). The statistical methods for evaluating the performance of different approaches are described, along with comparisons reported in the literature on benchmark transcriptomic data sets. Lastly, we review connectivity mapping applications in toxicology and offer guidance on evaluating chemical-induced toxicity with concentration-response transcriptomic data. In addition to serving as a high-level guide and tutorial for understanding and implementing connectivity mapping workflows, we hope this review will stimulate new algorithms for evaluating chemical safety and drug discovery using transcriptomic data.

Morphology and gene expression profiling provide complementary information for mapping cell state

Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb human A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOAs) and gene targets, we find that the two assays not only provide a partially shared but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations.