Drug-set enrichment analysis: a novel tool to investigate drug mode of action

Choline-deficient, high-fat diet-induced MASH in Göttingen Minipigs: characterization and effects of a chow reversal period

The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH) is increasing, and translational animal models are needed to develop novel treatments for this disease. The physiology and metabolism of pigs have a relatively high resemblance to humans, and the present study aimed to characterize choline-deficient and high-fat diet (CDAHFD)-fed Göttingen Minipigs as a novel animal model of MASLD/MASH. Göttingen Minipigs were fed CDAHFD for up to 5 mo, and the phenotype was investigated by the analysis of plasma parameters and repeated collection of liver biopsies. Furthermore, changes in hepatic gene expression during the experiment were explored by RNA sequencing. For a subset of the minipigs, the diet was changed from CDAHFD back to chow to investigate whether the liver pathology was reversible. Göttingen Minipigs on CDAHFD gained body weight, and plasma levels of cholesterol, AST, ALT, ALP, and GGT were increased. CDAHFD-fed minipigs developed hepatic steatosis, inflammation, and fibrosis, which in 5 of 16 animals progressed to cirrhosis. During an 11-wk chow reversal period, steatosis regressed, while fibrosis persisted. Regarding inflammation, the findings were less clear, depending on the type of readout. MASH Human Proximity Scoring (combined evaluation of transcriptional, phenotypic, and histopathological parameters) showed that CDAHFD-fed Göttingen Minipigs resemble human MASLD/MASH better than most rodent models. In conclusion, CDAHFD-fed minipigs develop a MASH-like phenotype, which, in several aspects, resembles the changes observed in human patients with MASLD/MASH. Furthermore, repeated collection of liver biopsies allows detailed characterization of histopathological changes over time in individual animals.NEW & NOTEWORTHY The physiology and metabolism of pigs have a relatively high resemblance to humans. This study characterizes a new animal model of MASLD/MASH using CDAHFD-fed Göttingen Minipigs. Göttingen Minipigs fed CDAHFD gained weight and developed hepatic steatosis, inflammation, fibrosis, and cirrhosis. After an 11-wk chow-reversal period, hepatic steatosis and some inflammatory parameters reversed. Combined evaluation of phenotypic, transcriptional, and histological parameters revealed the minipig model showed a higher resemblance to human disease than many rodent models.

An unbiased ranking of murine dietary models based on their proximity to human metabolic dysfunction-associated steatotic liver disease (MASLD)

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease, encompasses steatosis and metabolic dysfunction-associated steatohepatitis (MASH), leading to cirrhosis and hepatocellular carcinoma. Preclinical MASLD research is mainly performed in rodents; however, the model that best recapitulates human disease is yet to be defined. We conducted a wide-ranging retrospective review (metabolic phenotype, liver histopathology, transcriptome benchmarked against humans) of murine models (mostly male) and ranked them using an unbiased MASLD 'human proximity score' to define their metabolic relevance and ability to induce MASH-fibrosis. Here, we show that Western diets align closely with human MASH; high cholesterol content, extended study duration and/or genetic manipulation of disease-promoting pathways are required to intensify liver damage and accelerate significant (F2+) fibrosis development. Choline-deficient models rapidly induce MASH-fibrosis while showing relatively poor translatability. Our ranking of commonly used MASLD models, based on their proximity to human MASLD, helps with the selection of appropriate in vivo models to accelerate preclinical research.

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.

Identifying novel radioprotective drugs via salivary gland tissue chip screening

During head and neck cancer treatment, off-target ionizing radiation damage to the salivary glands commonly causes a permanent loss of secretory function. Due to the resulting decrease in saliva production, patients have trouble eating, speaking and are predisposed to oral infections and tooth decay. While the radioprotective antioxidant drug Amifostine is FDA approved to prevent radiation-induced hyposalivation, it has intolerable side effects that limit its use, motivating the discovery of alternative therapeutics. To address this issue, we previously developed a salivary gland mimetic (SGm) tissue chip platform. Here, we leverage this SGm tissue chip for high-content drug discovery. First, we developed in-chip assays to quantify glutathione and cellular senescence (β-galactosidase), which are biomarkers of radiation damage, and we validated radioprotection using WR-1065, the active form of Amifostine. Other reported radioprotective drugs including Edaravone, Tempol, N-acetylcysteine (NAC), Rapamycin, Ex-Rad, and Palifermin were also tested to validate the ability of the assays to detect cell damage and radioprotection. All of the drugs except NAC and Ex-Rad exhibited robust radioprotection. Next, a Selleck Chemicals library of 438 FDA-approved drugs was screened for radioprotection. We discovered 25 hits, with most of the drugs identified exhibiting mechanisms of action other than antioxidant activity. Hits were down-selected using EC50 values and pharmacokinetic and pharmacodynamic data from the PubChem database. This led us to test Phenylbutazone (anti-inflammatory), Enoxacin (antibiotic), and Doripenem (antibiotic) for in vivo radioprotection in mice using retroductal injections. Results confirm that Phenylbutazone and Enoxacin exhibited radioprotection equivalent to Amifostine. This body of work demonstrates the development and validation of assays using a SGm tissue chip platform for high-content drug screening and the successful in vitro discovery and in vivo validation of novel radioprotective drugs with non-antioxidant primary indications pointing to possible, yet unknown novel mechanisms of radioprotection.

Identifying novel radioprotective drugs via salivary gland tissue chip screening

During head and neck cancer treatment, off-target ionizing radiation damage to the salivary glands commonly causes a permanent loss of secretory function. Due to the resulting decrease in saliva production, patients have trouble eating, speaking and are predisposed to oral infections and tooth decay. While the radioprotective antioxidant drug Amifostine is approved to prevent radiation-induced hyposalivation, it has intolerable side effects that limit its use, motivating the discovery of alternative therapeutics. To address this issue, we previously developed a salivary gland mimetic (SGm) tissue chip platform. Here, we leverage this SGm tissue chip for high-content drug discovery. First, we developed in-chip assays to quantify glutathione and cellular senescence (β-galactosidase), which are biomarkers of radiation damage, and we validated radioprotection using WR-1065, the active form of Amifostine. Following validation, we tested other reported radioprotective drugs, including, Edaravone, Tempol, N-acetylcysteine (NAC), Rapamycin, Ex-Rad, and Palifermin, confirming that all drugs but NAC and Ex-Rad exhibited robust radioprotection. Next, a Selleck Chemicals library of 438 FDA-approved drugs was screened for radioprotection. We discovered 25 hits, with most of the drugs identified with mechanisms of action other than antioxidant activity. Hits were down-selected using EC 50 values and pharmacokinetics and pharmacodynamics data from the PubChem database leading to testing of Phenylbutazone (anti-inflammatory), Enoxacin (antibiotic), and Doripenem (antibiotic) for in vivo radioprotection in mice using retroductal injections. Results confirm that Phenylbutazone and Enoxacin exhibited equivalent radioprotection to Amifostine. This body of work demonstrates the development and validation of assays using a SGm tissue chip platform for high-content drug screening and the successful in vitro discovery and in vivo validation of novel radioprotective drugs with nonantioxidant primary indications pointing to possible, yet unknown novel mechanisms of radioprotection.

Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing

BACKGROUND

There is a pressing need for improved methods to identify effective therapeutics for diseases. Many computational approaches have been developed to repurpose existing drugs to meet this need. However, these tools often output long lists of candidate drugs that are difficult to interpret, and individual drug candidates may suffer from unknown off-target effects. We reasoned that an approach which aggregates information from multiple drugs that share a common mechanism of action (MOA) would increase on-target signal compared to evaluating drugs on an individual basis. In this study, we present drug mechanism enrichment analysis (DMEA), an adaptation of gene set enrichment analysis (GSEA), which groups drugs with shared MOAs to improve the prioritization of drug repurposing candidates.

RESULTS

First, we tested DMEA on simulated data and showed that it can sensitively and robustly identify an enriched drug MOA. Next, we used DMEA on three types of rank-ordered drug lists: (1) perturbagen signatures based on gene expression data, (2) drug sensitivity scores based on high-throughput cancer cell line screening, and (3) molecular classification scores of intrinsic and acquired drug resistance. In each case, DMEA detected the expected MOA as well as other relevant MOAs. Furthermore, the rankings of MOAs generated by DMEA were better than the original single-drug rankings in all tested data sets. Finally, in a drug discovery experiment, we identified potential senescence-inducing and senolytic drug MOAs for primary human mammary epithelial cells and then experimentally validated the senolytic effects of EGFR inhibitors.

CONCLUSIONS

DMEA is a versatile bioinformatic tool that can improve the prioritization of candidates for drug repurposing. By grouping drugs with a shared MOA, DMEA increases on-target signal and reduces off-target effects compared to analysis of individual drugs. DMEA is publicly available as both a web application and an R package at https://belindabgarana.github.io/DMEA .

Distinct genetic liability profiles define clinically relevant patient strata across common diseases

Genome-wide association studies have unearthed a wealth of genetic associations across many complex diseases. However, translating these associations into biological mechanisms contributing to disease etiology and heterogeneity has been challenging. Here, we hypothesize that the effects of disease-associated genetic variants converge onto distinct cell type specific molecular pathways within distinct subgroups of patients. In order to test this hypothesis, we develop the CASTom-iGEx pipeline to operationalize individual level genotype data to interpret personal polygenic risk and identify the genetic basis of clinical heterogeneity. The paradigmatic application of this approach to coronary artery disease and schizophrenia reveals a convergence of disease associated variant effects onto known and novel genes, pathways, and biological processes. The biological process specific genetic liabilities are not equally distributed across patients. Instead, they defined genetically distinct groups of patients, characterized by different profiles across pathways, endophenotypes, and disease severity. These results provide further evidence for a genetic contribution to clinical heterogeneity and point to the existence of partially distinct pathomechanisms across patient subgroups. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine concepts.

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.

Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications

De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.

High-Content Drug Discovery Targeting Molecular Bladder Cancer Subtypes

Molecular subtypes of muscle-invasive bladder cancer (MIBC) display differential survival and drug sensitivities in clinical trials. To date, they have not been used as a paradigm for phenotypic drug discovery. This study aimed to discover novel subtype-stratified therapy approaches based on high-content screening (HCS) drug discovery. Transcriptome expression data of CCLE and BLA-40 cell lines were used for molecular subtype assignment in basal, luminal, and mesenchymal-like cell lines. Two independent HCSs, using focused compound libraries, were conducted to identify subtype-specific drug leads. We correlated lead drug sensitivity data with functional genomics, regulon analysis, and in-vitro drug response-based enrichment analysis. The basal MIBC subtype displayed sensitivity to HDAC and CHK inhibitors, while the luminal subtype was sensitive to MDM2 inhibitors. The mesenchymal-like cell lines were exclusively sensitive to the ITGAV inhibitor SB273005. The role of integrins within this mesenchymal-like MIBC subtype was confirmed via its regulon activity and gene essentiality based on CRISPR–Cas9 knock-out data. Patients with high ITGAV expression showed a significant decrease in the median overall survival. Phenotypic high-content drug screens based on bladder cancer cell lines provide rationales for novel stratified therapeutic approaches as a framework for further prospective validation in clinical trials.

From GWAS to drug screening: repurposing antipsychotics for glioblastoma

BACKGROUND

Glioblastoma is currently an incurable cancer. Genome-wide association studies have demonstrated that 41 genetic variants are associated with glioblastoma and may provide an option for drug development.

METHODS

We investigated FDA-approved antipsychotics for their potential treatment of glioblastoma based on genome-wide association studies data using a 'pathway/gene-set analysis' approach.

RESULTS

The in-silico screening led to the discovery of 12 candidate drugs. DepMap portal revealed that 42 glioma cell lines show higher sensitivities to 12 candidate drugs than to Temozolomide, the current standard treatment for glioblastoma.

CONCLUSION

In particular, cell lines showed significantly higher sensitivities to Norcyclobenzaprine and Protriptyline which were predicted to bind targets to disrupt a certain molecular function such as DNA repair, response to hormones, or DNA-templated transcription, and may lead to an effect on survival-related pathways including cell cycle arrest, response to ER stress, glucose transport, and regulation of autophagy. However, it is recommended that their mechanism of action and efficacy are further determined.

Existing Drug Repurposing for Glioblastoma to Discover Candidate Drugs as a New a Approach

Aims:

Repurposing of drugs has been hypothesized as a means of identifying novel treatment methods for certain diseases.

Background:

Glioblastoma (GB) is an aggressive type of human cancer; the most effective treatment for glioblastoma is chemotherapy, whereas, when repurposing drugs, a lot of time and money can be saved.

Objective:

Repurposing of the existing drug may be used to discover candidate drugs for individualized treatments of GB.

Method:

We used the bioinformatics method to obtain the candidate drugs. In addition, the drugs were verified by MTT assay, Transwell® assays, TUNEL staining, and in vivo tumor formation experiments, as well as statistical analysis.

Result:

We obtained 4 candidate drugs suitable for the treatment of glioma, camptothecin, doxorubicin, daunorubicin and mitoxantrone, by the expression spectrum data IPAS algorithm analysis and drug-pathway connectivity analysis. These validation experiments showed that camptothecin was more effective in treating the GB, such as MTT assay, Transwell® assays, TUNEL staining, and in vivo tumor formation.

Conclusion:

With regard to personalized treatment, this present study may be used to guide the research of new drugs via verification experiments and tumor formation. The present study also provides a guide to systematic, individualized drug discovery for complex diseases and may contribute to the future application of individualized treatments.

ACE2 Netlas: In silico Functional Characterization and Drug-Gene Interactions of ACE2 Gene Network to Understand Its Potential Involvement in COVID-19 Susceptibility

Angiotensin-converting enzyme-2 (ACE2) receptor has been identified as the key adhesion molecule for the transmission of the SARS-CoV-2. However, there is no evidence that human genetic variation in ACE2 is singularly responsible for COVID-19 susceptibility. Therefore, we performed an integrative multi-level characterization of genes that interact with ACE2 (ACE2-gene network) for their statistically enriched biological properties in the context of COVID-19. The phenome-wide association of 51 genes including ACE2 with 4,756 traits categorized into 26 phenotype categories, showed enrichment of immunological, respiratory, environmental, skeletal, dermatological, and metabolic domains (p < 4e-4). Transcriptomic regulation of ACE2-gene network was enriched for tissue-specificity in kidney, small intestine, and colon (p < 4.7e-4). Leveraging the drug-gene interaction database we identified 47 drugs, including dexamethasone and spironolactone, among others. Considering genetic variants within ± 10 kb of ACE2-network genes we identified miRNAs whose binding sites may be altered as a consequence of genetic variation. The identified miRNAs revealed statistical over-representation of inflammation, aging, diabetes, and heart conditions. The genetic variant associations in RORA, SLC12A6, and SLC6A19 genes were observed in genome-wide association study (GWAS) of COVID-19 susceptibility. We also report the GWAS-identified variant in 3p21.31 locus, serves as trans-QTL for RORA and RORC genes. Overall, functional characterization of ACE2-gene network highlights several potential mechanisms in COVID-19 susceptibility. The data can also be accessed at https://gpwhiz.github.io/ACE2Netlas/.

Automatic identification of small molecules that promote cell conversion and reprogramming

Controlling cell fate has great potential for regenerative medicine, drug discovery, and basic research. Although transcription factors are able to promote cell reprogramming and transdifferentiation, methods based on their upregulation often show low efficiency. Small molecules that can facilitate conversion between cell types can ameliorate this problem working through safe, rapid, and reversible mechanisms. Here, we present DECCODE, an unbiased computational method for identification of such molecules based on transcriptional data. DECCODE matches a large collection of drug-induced profiles for drug treatments against a large dataset of primary cell transcriptional profiles to identify drugs that either alone or in combination enhance cell reprogramming and cell conversion. Extensive validation in the context of human induced pluripotent stem cells shows that DECCODE is able to prioritize drugs and drug combinations enhancing cell reprogramming. We also provide predictions for cell conversion with single drugs and drug combinations for 145 different cell types.

Pleiotropic effects of telomere length loci with brain morphology and brain tissue expression

Several studies have reported association between leukocyte telomere length (LTL) and neuropsychiatric disorders. Although telomere length is affected by environmental factors, genetic variants in certain loci are strongly associated with LTL. Thus, we aimed to identify the genomic relationship between genetic variants of LTL with brain-based regulatory changes and brain volume. We tested genetic colocalization of seven and nine LTL loci in two ancestry groups, European (EUR) and East-Asian (EAS), respectively, with brain morphology measures for 101 T1-magnetic resonance imaging-based region of interests (n = 21 821). The posterior probability (>90%) was observed for 'fourth ventricle', 'gray matter' and 'cerebellar vermal lobules I-IV' volumes. We then tested causal relationship using LTL loci for gene and methylation expression. We found causal pleiotropy for gene (EAS = four genes; EUR = five genes) and methylation expression (EUR = 17 probes; EAS = 4 probes) of brain tissues (P ≤ 2.47 × 10-6). Integrating chromatin profiles with LTL-single nucleotide polymorphisms identified 45 genes (EUR) and 79 genes (EAS) (P ≤ 9.78×10-7). We found additional 38 LTL-genes using chromatin-based gene mapping for EUR ancestry population. Gene variants in three LTL-genes-GPR37, OBFC1 and RTEL1/RTEL1-TNFRSF6B-show convergent evidence of pleiotropy with brain morphology, gene and methylation expression and chromatin association. Mapping gene functions to drug-gene interactions, we identified process 'transmission across chemical synapses' (P < 2.78 × 10-4). This study provides evidence that genetic variants of LTL have pleiotropic roles with brain-based effects that could explain the phenotypic association of LTL with several neuropsychiatric traits.

The anti-tumour activity of DNA methylation inhibitor 5-aza-2'-deoxycytidine is enhanced by the common analgesic paracetamol through induction of oxidative stress

The DNA demethylating agent 5-aza-2'-deoxycytidine (DAC, decitabine) has anti-cancer therapeutic potential, but its clinical efficacy is hindered by DNA damage-related side effects and its use in solid tumours is debated. Here we describe how paracetamol augments the effects of DAC on cancer cell proliferation and differentiation, without enhancing DNA damage. Firstly, DAC specifically upregulates cyclooxygenase-2-prostaglandin E2 pathway, inadvertently providing cancer cells with survival potential, while the addition of paracetamol offsets this effect. Secondly, in the presence of paracetamol, DAC treatment leads to glutathione depletion and finally to accumulation of ROS and/or mitochondrial superoxide, both of which have the potential to restrict tumour growth. The benefits of combined treatment are demonstrated here in head and neck squamous cell carcinoma (HNSCC) and acute myeloid leukaemia cell lines, further corroborated in a HNSCC xenograft mouse model and through mining of publicly available DAC and paracetamol responses. The sensitizing effect of paracetamol supplementation is specific to DAC but not its analogue 5-azacitidine. In summary, the addition of paracetamol could allow for DAC dose reduction, widening its clinical usability and providing a strong rationale for consideration in cancer therapy.

Drugmonizome and Drugmonizome-ML: integration and abstraction of small molecule attributes for drug enrichment analysis and machine learning

Understanding the underlying molecular and structural similarities between seemingly heterogeneous sets of drugs can aid in identifying drug repurposing opportunities and assist in the discovery of novel properties of preclinical small molecules. A wealth of information about drug and small molecule structure, targets, indications and side effects; induced gene expression signatures; and other attributes are publicly available through web-based tools, databases and repositories. By processing, abstracting and aggregating information from these resources into drug set libraries, knowledge about novel properties of drugs and small molecules can be systematically imputed with machine learning. In addition, drug set libraries can be used as the underlying database for drug set enrichment analysis. Here, we present Drugmonizome, a database with a search engine for querying annotated sets of drugs and small molecules for performing drug set enrichment analysis. Utilizing the data within Drugmonizome, we also developed Drugmonizome-ML. Drugmonizome-ML enables users to construct customized machine learning pipelines using the drug set libraries from Drugmonizome. To demonstrate the utility of Drugmonizome, drug sets from 12 independent SARS-CoV-2 in vitro screens were subjected to consensus enrichment analysis. Despite the low overlap among these 12 independent in vitro screens, we identified common biological processes critical for blocking viral replication. To demonstrate Drugmonizome-ML, we constructed a machine learning pipeline to predict whether approved and preclinical drugs may induce peripheral neuropathy as a potential side effect. Overall, the Drugmonizome and Drugmonizome-ML resources provide rich and diverse knowledge about drugs and small molecules for direct systems pharmacology applications. Database URL: https://maayanlab.cloud/drugmonizome/.

Drug perturbation gene set enrichment analysis (dpGSEA): a new transcriptomic drug screening approach

Background

In this study, we demonstrate that our modified Gene Set Enrichment Analysis (GSEA) method, drug perturbation GSEA (dpGSEA), can detect phenotypically relevant drug targets through a unique transcriptomic enrichment that emphasizes biological directionality of drug-derived gene sets.

Results

We detail our dpGSEA method and show its effectiveness in detecting specific perturbation of drugs in independent public datasets by confirming fluvastatin, paclitaxel, and rosiglitazone perturbation in gastroenteropancreatic neuroendocrine tumor cells. In drug discovery experiments, we found that dpGSEA was able to detect phenotypically relevant drug targets in previously published differentially expressed genes of CD4+T regulatory cells from immune responders and non-responders to antiviral therapy in HIV-infected individuals, such as those involved with virion replication, cell cycle dysfunction, and mitochondrial dysfunction. dpGSEA is publicly available at https://github.com/sxf296/drug_targeting.

Conclusions

dpGSEA is an approach that uniquely enriches on drug-defined gene sets while considering directionality of gene modulation. We recommend dpGSEA as an exploratory tool to screen for possible drug targeting molecules.

ACE2 Netlas: In-silico functional characterization and drug-gene interactions of ACE2 gene network to understand its potential involvement in COVID-19 susceptibility

Angiotensin-converting enzyme-2 ( ACE2 ) receptor has been identified as the key adhesion molecule for the transmission of the SARS-CoV-2. However, there is no evidence that human genetic variation in ACE2 is singularly responsible for COVID-19 susceptibility. Therefore, we performed a multi-level characterization of genes that interact with ACE2 (ACE2-gene network) for their over-represented biological properties in the context of COVID-19. The phenome-wide association of 51 genes including ACE2 with 4,756 traits categorized into 26 phenotype categories, showed enrichment of immunological, respiratory, environmental, skeletal, dermatological, and metabolic domains (p<4e-4). Transcriptomic regulation of ACE2-gene network was enriched for tissue-specificity in kidney, small intestine, and colon (p<4.7e-4). Leveraging the drug-gene interaction database we identified 47 drugs, including dexamethasone and spironolactone, among others. Considering genetic variants within ± 10 kb of ACE2-network genes we characterized functional consequences (among others) using miRNA binding-site targets. MiRNAs affected by ACE2-network variants revealed statistical over-representation of inflammation, aging, diabetes, and heart conditions. With respect to variants mapped to the ACE2-network, we observed COVID-19 related associations in RORA, SLC12A6 and SLC6A19 genes. Overall, functional characterization of ACE2-gene network highlights several potential mechanisms in COVID-19 susceptibility. The data can also be accessed at https://gpwhiz.github.io/ACE2Netlas/.

Evaluating the consistency of large-scale pharmacogenomic studies

Recent years have seen an increase in the availability of pharmacogenomic databases such as Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) that provide genomic and functional characterization information for multiple cell lines. Studies have alluded to the fact that specific characterizations may be inconsistent between different databases. Analysis of the potential discrepancies in the different databases is highly significant, as these sources are frequently used to analyze and validate methodologies for personalized cancer therapies. In this article, we review the recent developments in investigating the correspondence between different pharmacogenomics databases and discuss the potential factors that require attention when incorporating these sources in any modeling analysis. Furthermore, we explored the consistency among these databases using copulas that can capture nonlinear dependencies between two sets of data.

Impaired mitophagy links mitochondrial disease to epithelial stress in methylmalonyl-CoA mutase deficiency

Deregulation of mitochondrial network in terminally differentiated cells contributes to a broad spectrum of disorders. Methylmalonic acidemia (MMA) is one of the most common inherited metabolic disorders, due to deficiency of the mitochondrial methylmalonyl-coenzyme A mutase (MMUT). How MMUT deficiency triggers cell damage remains unknown, preventing the development of disease-modifying therapies. Here we combine genetic and pharmacological approaches to demonstrate that MMUT deficiency induces metabolic and mitochondrial alterations that are exacerbated by anomalies in PINK1/Parkin-mediated mitophagy, causing the accumulation of dysfunctional mitochondria that trigger epithelial stress and ultimately cell damage. Using drug-disease network perturbation modelling, we predict targetable pathways, whose modulation repairs mitochondrial dysfunctions in patient-derived cells and alleviate phenotype changes in mmut-deficient zebrafish. These results suggest a link between primary MMUT deficiency, diseased mitochondria, mitophagy dysfunction and epithelial stress, and provide potential therapeutic perspectives for MMA.

Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases

The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.

Individualized Drug Repositioning For Rheumatoid Arthritis Using Weighted Kolmogorov-Smirnov Algorithm

Background

Existing drugs are far from enough for investigators and patients to administrate the therapy of rheumatoid arthritis. Drug repositioning has drawn broad attention by reusing marketed drugs and clinical candidates for new uses.

Purpose

This study attempted to predict candidate drugs for rheumatoid arthritis treatment by mining the similarities of pathway aberrance induced by disease and various drugs, on a personalized or customized basis.

Methods

We firstly measured the individualized pathway aberrance induced by rheumatoid arthritis based on the microarray data and various drugs from CMap database, respectively. Then, the similarities of pathway aberrances between RA and various drugs were calculated using a Kolmogorov–Smirnov weighted enrichment score algorithm.

Results

Using this method, we identified 4 crucial pathways involved in rheumatoid arthritis development and predicted 9 underlying candidate drugs for rheumatoid arthritis treatment. Some candidates with current indications to treat other diseases might be repurposed to treat rheumatoid arthritis and complement the drug group for rheumatoid arthritis.

Conclusion

This study predicts candidate drugs for rheumatoid arthritis treatment through mining the similarities of pathway aberrance induced by disease and various drugs, on a personalized or customized basis. Our framework will provide novel insights in personalized drug discovery for rheumatoid arthritis and contribute to the future application of custom therapeutic decisions.

gep2pep: a Bioconductor package for the creation and analysis of pathway-based expression profiles

SUMMARY

Pathway-based expression profiles allow for high-level interpretation of transcriptomic data and systematic comparison of dysregulated cellular programs. We have previously demonstrated the efficacy of pathway-based approaches with two different applications: the Drug Set Enrichment Analysis and the Gene2drug analysis. Here we present a software tool that allows to easily convert gene-based profiles to pathway-based profiles and analyze them within the popular R framework. We also provide pre-computed profiles derived from the original Connectivity Map and its next generation release, i.e. the LINCS database.

AVAILABILITY AND IMPLEMENTATION

the tool is implemented as the R/Bioconductor package gep2pep and can be freely downloaded from https://bioconductor.org/packages/gep2pep.

SUPPLEMENTARY INFORMATION

Supplementary data are available at http://dsea.tigem.it/lincs.

Meta-path Based Prioritization of Functional Drug Actions with Multi-Level Biological Networks

Functional drug actions refer to drug-affected GO terms. They aid in the investigation of drug effects that are therapeutic or adverse. Previous studies have utilized the linkage information between drugs and functions in molecular level biological networks. Since the current knowledge of molecular level mechanisms of biological functions is still limited, such previous studies were incomplete. We expected that the multi-level biological networks would allow us to more completely investigate the functional drug actions. We constructed multi-level biological networks with genes, GO terms, and diseases. Meta-paths were utilized to extract the features of each GO term. We trained 39 SVM models to prioritize the functional drug actions of the various 39 drugs. Through the multi-level networks, more functional drug actions were utilized for the 39 models and inferred by the models. Multi-level based features improved the performance of the models, and the average AUROC value in the cross-validation was 0.86. Moreover, 60% of the candidates were true.

FoPA: identifying perturbed signaling pathways in clinical conditions using formal methods

BACKGROUND

Accurate identification of perturbed signaling pathways based on differentially expressed genes between sample groups is one of the key factors in the understanding of diseases and druggable targets. Most pathway analysis methods prioritize impacted signaling pathways by incorporating pathway topology using simple graph-based models. Despite their relative success, these models are limited in describing all types of dependencies and interactions that exist in biological pathways.

RESULTS

In this work, we propose a new approach based on the formal modeling of signaling pathways. Signaling pathways are formally modeled, and then model checking tools are applied to find the likelihood of perturbation for each pathway in a given condition. By adopting formal methods, various complex interactions among biological parts are modeled, which can contribute to reducing the false-positive rate of the proposed approach. We have developed a tool named Formal model checking based pathway analysis (FoPA) based on this approach. FoPA is compared with three well-known pathway analysis methods: PADOG, CePa, and SPIA on the benchmark of 36 GEO datasets from various diseases by applying the target pathway technique. This validation technique eliminates the need for possibly biased human assessments of results. In the cases that, there is no apriori knowledge of all relevant pathways, simulated false inputs (permuted class labels and decoy pathways) are chosen as a set of negative controls to test the false positive rate of the methods. Finally, to further evaluate the efficiency of FoPA, it is applied to a list of autism-related genes.

CONCLUSIONS

The results obtained by the target pathway technique demonstrate that FoPA is able to prioritize target pathways as well as PADOG but better than CePa and SPIA. Also, the false-positive rate of finding significant pathways using FoPA is lower than other compared methods. Also, FoPA can detect more consistent relevant pathways than other methods. The results of FoPA on autism-related genes highlight the role of "Renin-angiotensin system" pathway. This pathway has been supposed to have a pivotal role in some neurodegenerative diseases, while little attention has been paid to its impact on autism development so far.

INSIdE NANO: a systems biology framework to contextualize the mechanism-of-action of engineered nanomaterials

Engineered nanomaterials (ENMs) are widely present in our daily lives. Despite the efforts to characterize their mechanism of action in multiple species, their possible implications in human pathologies are still not fully understood. Here we performed an integrated analysis of the effects of ENMs on human health by contextualizing their transcriptional mechanism-of-action with respect to drugs, chemicals and diseases. We built a network of interactions of over 3,000 biological entities and developed a novel computational tool, INSIdE NANO, to infer new knowledge about ENM behavior. We highlight striking association of metal and metal-oxide nanoparticles and major neurodegenerative disorders. Our novel strategy opens possibilities to achieve fast and accurate read-across evaluation of ENMs and other chemicals based on their biosignatures.

Revisiting Connectivity Map from a gene co-expression network analysis

The Connectivity Map (CMap) is a tool that has been extensively utilized to study drug repositioning and side-effect prediction. However, most of these analyses rely on signature genes, ignoring the pathways by which those genes are regulated, as well as the functional overlap of redundant genes. The present study utilized a systems biology approach referred to as Weighted Gene Co-expression Network Analysis (WGCNA) to dissect the transcriptional profiles of CMap and reveal these hidden factors. Seven common modules associated with protein binding, extracellular matrix organization and translation were identified. Furthermore, drugs were clustered based on module expression to infer their mechanism of action (MoA) based on common activity profiles. As an extension of this, an example of disease-based module projection to identify novel drugs was provided. The analysis developed in the present study may provide a novel framework for drug repositioning or discovering MoAs.

From gene networks to drugs: systems pharmacology approaches for AUD

The alcohol research field has amassed an impressive number of gene expression datasets spanning key brain areas for addiction, species (humans as well as multiple animal models), and stages in the addiction cycle (binge/intoxication, withdrawal/negative effect, and preoccupation/anticipation). These data have improved our understanding of the molecular adaptations that eventually lead to dysregulation of brain function and the chronic, relapsing disorder of addiction. Identification of new medications to treat alcohol use disorder (AUD) will likely benefit from the integration of genetic, genomic, and behavioral information included in these important datasets. Systems pharmacology considers drug effects as the outcome of the complex network of interactions a drug has rather than a single drug-molecule interaction. Computational strategies based on this principle that integrate gene expression signatures of pharmaceuticals and disease states have shown promise for identifying treatments that ameliorate disease symptoms (called in silico gene mapping or connectivity mapping). In this review, we suggest that gene expression profiling for in silico mapping is critical to improve drug repurposing and discovery for AUD and other psychiatric illnesses. We highlight studies that successfully apply gene mapping computational approaches to identify or repurpose pharmaceutical treatments for psychiatric illnesses. Furthermore, we address important challenges that must be overcome to maximize the potential of these strategies to translate to the clinic and improve healthcare outcomes.

MiR-211 is essential for adult cone photoreceptor maintenance and visual function

MicroRNAs (miRNAs) are key post-transcriptional regulators of gene expression that play an important role in the control of fundamental biological processes in both physiological and pathological conditions. Their function in retinal cells is just beginning to be elucidated, and a few have been found to play a role in photoreceptor maintenance and function. MiR-211 is one of the most abundant miRNAs in the developing and adult eye. However, its role in controlling vertebrate visual system development, maintenance and function so far remain incompletely unexplored. Here, by targeted inactivation in a mouse model, we identify a critical role of miR-211 in cone photoreceptor function and survival. MiR-211 knockout (-/-) mice exhibited a progressive cone dystrophy accompanied by significant alterations in visual function. Transcriptome analysis of the retina from miR-211-/- mice during cone degeneration revealed significant alteration of pathways related to cell metabolism. Collectively, this study highlights for the first time the impact of miR-211 function in the retina and significantly contributes to unravelling the role of specific miRNAs in cone photoreceptor function and survival.

Statistically controlled identification of differentially expressed genes in one-to-one cell line comparisons of the CMAP database for drug repositioning

BACKGROUND

The Connectivity Map (CMAP) database, an important public data source for drug repositioning, archives gene expression profiles from cancer cell lines treated with and without bioactive small molecules. However, there are only one or two technical replicates for each cell line under one treatment condition. For such small-scale data, current fold-changes-based methods lack statistical control in identifying differentially expressed genes (DEGs) in treated cells. Especially, one-to-one comparison may result in too many drug-irrelevant DEGs due to random experimental factors. To tackle this problem, CMAP adopts a pattern-matching strategy to build "connection" between disease signatures and gene expression changes associated with drug treatments. However, many drug-irrelevant genes may blur the "connection" if all the genes are used instead of pre-selected DEGs induced by drug treatments.

METHODS

We applied OneComp, a customized version of RankComp, to identify DEGs in such small-scale cell line datasets. For a cell line, a list of gene pairs with stable relative expression orderings (REOs) were identified in a large collection of control cell samples measured in different experiments and they formed the background stable REOs. When applying OneComp to a small-scale cell line dataset, the background stable REOs were customized by filtering out the gene pairs with reversal REOs in the control samples of the analyzed dataset.

RESULTS

In simulated data, the consistency scores of overlapping genes between DEGs identified by OneComp and SAM were all higher than 99%, while the consistency score of the DEGs solely identified by OneComp was 96.85% according to the observed expression difference method. The usefulness of OneComp was exemplified in drug repositioning by identifying phenformin and metformin related genes using small-scale cell line datasets which helped to support them as a potential anti-tumor drug for non-small-cell lung carcinoma, while the pattern-matching strategy adopted by CMAP missed the two connections. The implementation of OneComp is available at https://github.com/pathint/reoa .

CONCLUSIONS

OneComp performed well in both the simulated and real data. It is useful in drug repositioning studies by helping to find hidden "connections" between drugs and diseases.

Identifying new antiepileptic drugs through genomics-based drug repurposing

Currently available antiepileptic drugs (AEDs) fail to control seizures in 30% of patients. Genomics-based drug repurposing (GBR) offers the potential of savings in the time and cost of developing new AEDs. In the current study, we used published data and software to identify the transcriptomic signature of chornic temporal lobe epilepsy and the drugs that reverse it. After filtering out compounds based on exclusion criteria, such as toxicity, 36 drugs were retained. 11 of the 36 drugs identified (>30%) have published evidence of the antiepileptic efficacy (for example, curcumin) or antiepileptogenic affect (for example, atorvastatin) in recognised rodent models or patients. By objectively annotating all ∼20,000 compounds in the LINCS database as either having published evidence of antiepileptic efficacy or lacking such evidence, we demonstrated that our set of repurposable drugs is ∼6-fold more enriched with drugs having published evidence of antiepileptic efficacy in animal models than expected by chance (P-value <0.006). Further, we showed that another of our GBR-identified drugs, the commonly-used well-tolerated antihyperglycemic sitagliptin, produces a dose-dependent reduction in seizures in a mouse model of pharmacoresistant epilepsy. In conclusion, GBR successfully identifies compounds with antiepileptic efficacy in animal models and, hence, it is an appealing methodology for the discovery of potential AEDs.

Comparing structural and transcriptional drug networks reveals signatures of drug activity and toxicity in transcriptional responses

We performed an integrated analysis of drug chemical structures and drug-induced transcriptional responses. We demonstrated that a network representing three-dimensional structural similarities among 5452 compounds can be used to automatically group together drugs with similar scaffolds, physicochemical parameters and mode-of-action. We compared the structural network to a network representing transcriptional similarities among a subset of 1309 drugs for which transcriptional response were available in the Connectivity Map data set. Analysis of structurally similar, but transcriptionally different drugs sharing the same MOA enabled us to detect and remove weak and noisy transcriptional responses, greatly enhancing the reliability of transcription-based approaches to drug discovery and drug repositioning. Cardiac glycosides exhibited the strongest transcriptional responses with a significant induction of pathways related to epigenetic regulation, which suggests an epigenetic mechanism of action for these drugs. Drug classes with the weakest transcriptional responses tended to induce expression of cytochrome P450 enzymes, hinting at drug-induced drug resistance. Analysis of transcriptionally similar, but structurally different drugs with unrelated MOA, led us to the identification of a 'toxic' transcriptional signature indicative of lysosomal stress (lysosomotropism) and lipid accumulation (phospholipidosis) partially masking the target-specific transcriptional effects of these drugs. We found that this transcriptional signature is shared by 258 compounds and it is associated to the activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy. Finally, we built a predictive Random Forest model of these 258 compounds based on 128 physicochemical parameters, which should help in the early identification of potentially toxic drug candidates.

Transcriptional responses to drug treatment can reveal mechanism of action and off-target effects thus enabling drug repositioning, but only if measured in the appropriate cells at clinically relevant concentrations. A team led by Diego di Bernardo and Diego Medina generated a network representing structural similarities among compounds to automatically group together drugs with similar scaffolds and MOA. By comparing the structural drug network with a transcriptional drug network based on similarities in transcriptional response, the team observed broad differences between the two. This observation led to the identification of a transcriptional signature related lysosomal stress and phospholipidosis, and a physicochemical model to identify such compounds. These results provide general guidelines to prevent erroneous conclusion when using transcriptional responses of small molecules for drug discovery and drug repositioning

Personalized Drug Analysis in B Cell Chronic Lymphocytic Leukemia Patients

Background

B cell chronic lymphocytic leukemia (B-CLL) is the most common adult leukemia in the Western world. Although therapeutic advances have notably improved the outcome for many patients, B-CLL remains an incurable disease. The purpose of this study was to search for therapeutic drugs based on altered pathways in individual patients.

Material/Methods

Genes from microarray data were mapped to 300 Homo sapiens-related pathways. Individual pathway aberrance analysis was used to identify altered pathways. Drug data, obtained from connectivity map (cMAP), were subjected to drug-set enrichment analysis. To analyze the relations between drug-induced pathways and disease-induced altered pathways in individuals, Pearson correlation analysis was applied.

Results

The disease-induced pathways with P-values <0.05 in individual samples were recorded and presented in a heatmap. Drug-induced pathways were analyzed in the 104 samples. After Pearson correlation analysis between altered pathways and drug, the 20 top-ranked drugs that were most relevant to disease were obtained. There were 9 drugs with positive scores and 11 with negative scores.

Conclusions

With this method, we identified the 20 top-ranked drugs that were most relevant to disease. The drugs with negative scores may play therapeutic roles in B-CLL.

The impact of microRNAs on transcriptional heterogeneity and gene co-expression across single embryonic stem cells

MicroRNAs act posttranscriptionally to suppress multiple target genes within a cell population. To what extent this multi-target suppression occurs in individual cells and how it impacts transcriptional heterogeneity and gene co-expression remains unknown. Here we used single-cell sequencing combined with introduction of individual microRNAs. miR-294 and let-7c were introduced into otherwise microRNA-deficient Dgcr8 knockout mouse embryonic stem cells. Both microRNAs induce suppression and correlated expression of their respective gene targets. The two microRNAs had opposing effects on transcriptional heterogeneity within the cell population, with let-7c increasing and miR-294 decreasing the heterogeneity between cells. Furthermore, let-7c promotes, whereas miR-294 suppresses, the phasing of cell cycle genes. These results show at the individual cell level how a microRNA simultaneously has impacts on its many targets and how that in turn can influence a population of cells. The findings have important implications in the understanding of how microRNAs influence the co-expression of genes and pathways, and thus ultimately cell fate.

MicroRNAs can posttranscriptionally repress multiple targets in a cell population. Here the authors use single-cell sequencing to investigate the effects of an individual miRNA on transcriptional heterogeneity and gene co-expression

A Systematic Framework for Drug Repositioning from Integrated Omics and Drug Phenotype Profiles Using Pathway-Drug Network

Drug repositioning offers new clinical indications for old drugs. Recently, many computational approaches have been developed to repurpose marketed drugs in human diseases by mining various of biological data including disease expression profiles, pathways, drug phenotype expression profiles, and chemical structure data. However, despite encouraging results, a comprehensive and efficient computational drug repositioning approach is needed that includes the high-level integration of available resources. In this study, we propose a systematic framework employing experimental genomic knowledge and pharmaceutical knowledge to reposition drugs for a specific disease. Specifically, we first obtain experimental genomic knowledge from disease gene expression profiles and pharmaceutical knowledge from drug phenotype expression profiles and construct a pathway-drug network representing a priori known associations between drugs and pathways. To discover promising candidates for drug repositioning, we initialize node labels for the pathway-drug network using identified disease pathways and known drugs associated with the phenotype of interest and perform network propagation in a semisupervised manner. To evaluate our method, we conducted some experiments to reposition 1309 drugs based on four different breast cancer datasets and verified the results of promising candidate drugs for breast cancer by a two-step validation procedure. Consequently, our experimental results showed that the proposed framework is quite useful approach to discover promising candidates for breast cancer treatment.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.