Connectivity Map-based discovery of parbendazole reveals targetable human osteogenic pathway

Osteoporosis is a common skeletal disorder characterized by low bone mass leading to increased bone fragility and fracture susceptibility. In this study, we have identified pathways that stimulate differentiation of bone forming osteoblasts from human mesenchymal stromal cells (hMSCs). Gene expression profiling was performed in hMSCs differentiated toward osteoblasts (at 6 h). Significantly regulated genes were analyzed in silico, and the Connectivity Map (CMap) was used to identify candidate bone stimulatory compounds. The signature of parbendazole matches the expression changes observed for osteogenic hMSCs. Parbendazole stimulates osteoblast differentiation as indicated by increased alkaline phosphatase activity, mineralization, and up-regulation of bone marker genes (alkaline phosphatase/ALPL, osteopontin/SPP1, and bone sialoprotein II/IBSP) in a subset of the hMSC population resistant to the apoptotic effects of parbendazole. These osteogenic effects are independent of glucocorticoids because parbendazole does not up-regulate glucocorticoid receptor (GR) target genes and is not inhibited by the GR antagonist mifepristone. Parbendazole causes profound cytoskeletal changes including degradation of microtubules and increased focal adhesions. Stabilization of microtubules by pretreatment with Taxol inhibits osteoblast differentiation. Parbendazole up-regulates bone morphogenetic protein 2 (BMP-2) gene expression and activity. Cotreatment with the BMP-2 antagonist DMH1 limits, but does not block, parbendazole-induced mineralization. Using the CMap we have identified a previously unidentified lineage-specific, bone anabolic compound, parbendazole, which induces osteogenic differentiation through a combination of cytoskeletal changes and increased BMP-2 activity.

Bioinformatics methods in drug repurposing for Alzheimer's disease

Alarming epidemiological features of Alzheimer's disease impose curative treatment rather than symptomatic relief. Drug repurposing, that is reappraisal of a substance's indications against other diseases, offers time, cost and efficiency benefits in drug development, especially when in silico techniques are used. In this study, we have used gene signatures, where up- and down-regulated gene lists summarize a cell's gene expression perturbation from a drug or disease. To cope with the inherent biological and computational noise, we used an integrative approach on five disease-related microarray data sets of hippocampal origin with three different methods of evaluating differential gene expression and four drug repurposing tools. We found a list of 27 potential anti-Alzheimer agents that were additionally processed with regard to molecular similarity, pathway/ontology enrichment and network analysis. Protein kinase C, histone deacetylase, glycogen synthase kinase 3 and arginase inhibitors appear consistently in the resultant drug list and may exert their pharmacologic action in an epidermal growth factor receptor-mediated subpathway of Alzheimer's disease.

Targeting of multiple oncogenic signaling pathways by Hsp90 inhibitor alone or in combination with berberine for treatment of colorectal cancer

There is a wide range of drugs and combinations under investigation and/or approved over the last decade to treat colorectal cancer (CRC), but the 5-year survival rate remains poor at stages II-IV. Therefore, new, more-efficient drugs still need to be developed that will hopefully be included in first-line therapy or overcome resistance when it appears, as part of second- or third-line treatments in the near future. In this study, we revealed that heat shock protein 90 (Hsp90) inhibitors have high therapeutic potential in CRC according to combinative analysis of NCBI's Gene Expression Omnibus (GEO) repository and chemical genomic database of Connectivity Map (CMap). We found that second generation Hsp90 inhibitor, NVP-AUY922, significantly downregulated the activities of a broad spectrum of kinases involved in regulating cell growth arrest and death of NVP-AUY922-sensitive CRC cells. To overcome NVP-AUY922-induced upregulation of survivin expression which causes drug insensitivity, we found that combining berberine (BBR), a herbal medicine with potency in inhibiting survivin expression, with NVP-AUY922 resulted in synergistic antiproliferative effects for NVP-AUY922-sensitive and -insensitive CRC cells. Furthermore, we demonstrated that treatment of NVP-AUY922-insensitive CRC cells with the combination of NVP-AUY922 and BBR caused cell growth arrest through inhibiting CDK4 expression and induction of microRNA-296-5p (miR-296-5p)-mediated suppression of Pin1-β-catenin-cyclin D1 signaling pathway. Finally, we found that the expression level of Hsp90 in tumor tissues of CRC was positively correlated with CDK4 and Pin1 expression levels. Taken together, these results indicate that combination of NVP-AUY922 and BBR therapy can inhibit multiple oncogenic signaling pathways of CRC.

Depiction of tumor stemlike features and underlying relationships with hazard immune infiltrations based on large prostate cancer cohorts

Prostate cancer stemness (PCS) cells have been reported to drive tumor progression, recurrence and drug resistance. However, there is lacking systematical assessment of stemlike indices and associations with immunological properties in prostate adenocarcinoma (PRAD). We thus collected 7 PRAD cohorts with 1465 men and calculated the stemlike indices for each sample using one-class logistic regression machine learning algorithm. We selected the mRNAsi to quantify the stemlike indices that correlated significantly with prognosis and accordingly identified 21 PCS-related CpG loci and 13 pivotal signature. The 13-gene based PCS model possessed high predictive significance for progression-free survival (PFS) that was trained and validated in 7 independent cohorts. Meanwhile, we conducted consensus clustering and classified the total cohorts into 5 PCS clusters with distinct outcomes. Samples in PCScluster5 possessed the highest stemness fractions and suffered from the worst prognosis. Additionally, we implemented the CIBERSORT algorithm to infer the differential abundance across 5 PCS clusters. The activated immune cells (CD8+ T cell and dendritic cells) infiltrated significantly less in PCScluster5 than other clusters, supporting the negative regulations between stemlike indices and anticancer immunity. High mRNAsi was also found to be associated with up-regulation of immunosuppressive checkpoints, like PDL1. Lastly, we used the Connectivity Map (CMap) resource to screen potential compounds for targeting PRAD stemness, including the top hits of cell cycle inhibitor and FOXM1 inhibitor. Taken together, our study comprehensively evaluated the PRAD stemlike indices based on large cohorts and established a 13-gene based classifier for predicting prognosis or potential strategies for stemness treatment.

Systematic polypharmacology and drug repurposing via an integrated L1000-based Connectivity Map database mining

Drug repurposing aims to find novel indications of clinically used or experimental drugs. Because drug data already exist, drug repurposing may save time and cost, and bypass safety concerns. Polypharmacology, one drug with multiple targets, serves as a basis for drug repurposing. Large-scale databases have been accumulated in recent years, and utilization and integration of these databases would be highly helpful for polypharmacology and drug repurposing. The Connectivity Map (CMap) is a database collecting gene-expression profiles of drug-treated human cancer cells, which has been widely used for investigation of polypharmacology and drug repurposing. In this study, we integrated the next-generation L1000-based CMap and an analytic Web tool, the L1000FWD, for systematic analyses of polypharmacology and drug repurposing. Two different types of anti-cancer drugs were used as proof-of-concept examples, including histone deacetylase (HDAC) inhibitors and topoisomerase inhibitors. We identified KM-00927 and BRD-K75081836 as novel HDAC inhibitors and mitomycin C as a topoisomerase IIB inhibitor. Our study provides a prime example of utilization and integration of the freely available public resources for systematic polypharmacology analysis and drug repurposing.

Using the Connectivity Map to discover compounds influencing human osteoblast differentiation

Osteoporosis is a common skeletal disorder characterized by low bone mass leading to increased bone fragility and fracture susceptibility. Identification of factors influencing osteoblast differentiation and bone formation is very important. Previously, we identified parbendazole to be a novel compound that stimulates osteogenic differentiation of human mesenchymal stromal cells (hMSCs), using gene expression profiling and bioinformatic analyzes, including the Connectivity Map (CMap), as an in-silico approach. The aim for this paper is to identify additional compounds affecting osteoblast differentiation using the CMap. Gene expression profiling was performed on hMSCs differentiated to osteoblasts using Illumina microarrays. Our osteoblast gene signature, the top regulated genes 6 hr after induction by dexamethasone, was uploaded into CMap (www.broadinstitute.org/cmap/). Through this approach we identified compounds with gene signatures positively correlating (withaferin-A, calcium folinate, amylocaine) or negatively correlating (salbutamol, metaraminol, diprophylline) to our osteoblast gene signature. All positively correlating compounds stimulated osteogenic differentiation, as indicated by increased mineralization compared to control treated cells. One of three negatively correlating compounds, salbutamol, inhibited dexamethasone-induced osteoblastic differentiation, while the other two had no effect. Based on gene expression data of withaferin-A and salbutamol, we identified HMOX1 and STC1 as being strongly differentially expressed . shRNA knockdown of HMOX1 or STC1 in hMSCs inhibited osteoblast differentiation. These results confirm that the CMap is a powerful approach to identify positively compounds that stimulate osteogenesis of hMSCs, and through this approach we can identify genes that play an important role in osteoblast differentiation and could be targets for novel bone anabolic therapies.

In Vitro and In Silico Mechanistic Insights into miR-21-5p-Mediated Topoisomerase Drug Resistance in Human Colorectal Cancer Cells

Although chemotherapy for treating colorectal cancer has had some success, drug resistance and metastasis remain the major causes of death for colorectal cancer patients. MicroRNA-21-5p (hereafter denoted as miR-21) is one of the most abundant miRNAs in human colorectal cancer. A Kaplan-Meier survival analysis found a negative prognostic correlation of miR-21 and metastasis-free survival in colorectal cancer patients (The Cancer Genome Atlas Colon Adenocarcinoma/TCGA-COAD cohort). To explore the role of miR-21 overexpression in drug resistance, a stable miR-21-overexpressing clone in a human DLD-1 colorectal cancer cell line was established. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) cell viability assay found that miR-21 overexpression induced drug resistance to topoisomerase inhibitors (SN-38, doxorubicin, and etoposide/VP-16). Mechanistically, we showed that miR-21 overexpression reduced VP-16-induced apoptosis and concomitantly enhanced pro-survival autophagic flux without the alteration of topoisomerase expression and activity. Bioinformatics analyses suggested that miR-21 overexpression induced genetic reprogramming that mimicked the gene signature of topoisomerase inhibitors and downregulated genes related to the proteasome pathway. Taken together, our results provide a novel insight into the role of miR-21 in the development of drug resistance in colorectal cancer.

Using Pharmacogenomic Databases for Discovering Patient-Target Genes and Small Molecule Candidates to Cancer Therapy

With multiple omics strategies being applied to several cancer genomics projects, researchers have the opportunity to develop a rational planning of targeted cancer therapy. The investigation of such numerous and diverse pharmacogenomic datasets is a complex task. It requires biological knowledge and skills on a set of tools to accurately predict signaling network and clinical outcomes. Herein, we describe Web-based in silico approaches user friendly for exploring integrative studies on cancer biology and pharmacogenomics. We briefly explain how to submit a query to cancer genome databases to predict which genes are significantly altered across several types of cancers using CBioPortal. Moreover, we describe how to identify clinically available drugs and potential small molecules for gene targeting using CellMiner. We also show how to generate a gene signature and compare gene expression profiles to investigate the complex biology behind drug response using Connectivity Map. Furthermore, we discuss on-going challenges, limitations and new directions to integrate molecular, biological and epidemiological information from oncogenomics platforms to create hypothesis-driven projects. Finally, we discuss the use of Patient-Derived Xenografts models (PDXs) for drug profiling in vivo assay. These platforms and approaches are a rational way to predict patient-targeted therapy response and to develop clinically relevant small molecules drugs.

Transcriptomic Data Mining and Repurposing for Computational Drug Discovery

Conventional drug discovery in general is costly and time-consuming with extremely low success and relatively high attrition rates. The disparity between high cost of drug discovery and vast unmet medical needs resulted in advent of an increasing number of computational approaches that can "connect" disease with a candidate therapeutic. This includes computational drug repurposing or repositioning wherein the goal is to discover a new indication for an approved drug. Computational drug discovery approaches that are commonly used are similarity-based wherein network analysis or machine learning-based methods are used. One such approach is matching gene expression signatures from disease to those from small molecules, commonly referred to as connectivity mapping. In this chapter, we will focus on how publicly available existing transcriptomic data from diseases can be reused to identify novel candidate therapeutics and drug repositioning candidates. To elucidate these, we will present two case studies: (1) using transcriptional signature similarity or positive correlation to identify novel small molecules that are similar to an approved drug and (2) identifying candidate therapeutics via reciprocal connectivity or negative correlation between transcriptional signatures from a disease and small molecule.

Decoding Connectivity Map-based drug repurposing for oncotherapy

The rising global burden of cancer has driven considerable efforts into the research and development of effective anti-cancer agents. Fortunately, with impressive advances in transcriptome profiling technology, the Connectivity Map (CMap) database has emerged as a promising and powerful drug repurposing approach. It provides an important platform for systematically discovering of the associations among genes, small-molecule compounds and diseases, and elucidating the mechanism of action of drug, contributing toward efficient anti-cancer pharmacotherapy. Moreover, CMap-based computational drug repurposing is gaining attention because of its potential to overcome the bottleneck constraints faced by traditional drug discovery in terms of cost, time and risk. Herein, we provide a comprehensive review of the applications of drug repurposing for anti-cancer drug discovery and summarize approaches for computational drug repurposing. We focus on the principle of the CMap database and novel CMap-based software/algorithms as well as their progress achieved for drug repurposing in the field of oncotherapy. This article is expected to illuminate the emerging potential of CMap in discovering effective anti-cancer drugs, thereby promoting efficient healthcare for cancer patients.

Connectivity mapping uncovers small molecules that modulate neurodegeneration in Huntington's disease models

Huntington's disease (HD) is a genetic disease caused by a CAG trinucleotide repeat expansion encoding a polyglutamine tract in the huntingtin (HTT) protein, ultimately leading to neuronal loss and consequent cognitive decline and death. As no treatments for HD currently exist, several chemical screens have been performed using cell-based models of mutant HTT toxicity. These screens measured single disease-related endpoints, such as cell death, but had low 'hit rates' and limited dimensionality for therapeutic detection. Here, we have employed gene expression microarray analysis of HD samples--a snapshot of the expression of 25,000 genes--to define a gene expression signature for HD from publically available data. We used this information to mine a database for chemicals positively and negatively correlated to the HD gene expression signature using the Connectivity Map, a tool for comparing large sets of gene expression patterns. Chemicals with negatively correlated expression profiles were highly enriched for protective characteristics against mutant HTT fragment toxicity in in vitro and in vivo models. This study demonstrates the potential of using gene expression to mine chemical activity, guide chemical screening, and detect potential novel therapeutic compounds.

KEY MESSAGES

Single-endpoint chemical screens have low therapeutic discovery hit-rates. In the context of HD, we guided a chemical screen using gene expression data. The resulting chemicals were highly enriched for suppressors of mutant HTT fragment toxicity. This study provides a proof of concept for wider usage in all chemical screening.

Gene expression profiling coupled with Connectivity Map database mining reveals potential therapeutic drugs for Hirschsprung disease

BACKGROUND

Hirschsprung disease (HD) is a congenital intestinal anomaly resulting from a failure to form enteric ganglia in the lower bowel. Surgery is the main therapeutic strategy, although neural stem cell transplantation has recently shown promise. However, HD remains a challenging disorder to treat. Our aim was to identify drugs that could counteract the dysregulated pathways in HD and could thus be potential novel therapies.

METHODS

We used microarray analysis to identify genes differentially expressed in ganglionic and aganglionic bowel samples from eight children with HD. The signature of differentially expressed genes was then used as a search query to explore the Connectivity Map (cMAP), a transcriptional expression database that catalogs gene signatures elicited by chemical perturbagens.

RESULTS

We uncovered several dysregulated signaling pathways, and in particular regulation of neuron development, in HD. The cMAP search identified some compounds with the potential to counteract the effects of the dysregulated molecular signature in this disease. One of these, pepstatin A, was recently shown to rescue the migration defects observed in a mouse model of HD, providing strong support for our findings.

CONCLUSIONS

This study advances our understanding of the molecular changes in HD and identifies several potential pharmacological interventions. Further testing of the identified compounds is warranted.

An Integrated Bioinformatics Analysis Repurposes an Antihelminthic Drug Niclosamide for Treating HMGA2-Overexpressing Human Colorectal Cancer

Aberrant overexpression of high mobility group AT-hook 2 (HMGA2) is frequently found in cancers and HMGA2 has been considered an anticancer therapeutic target. In this study, a pan-cancer genomics survey based on Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) data indicated that HMGA2 was mainly overexpressed in gastrointestinal cancers including colorectal cancer. Intriguingly, HMGA2 overexpression had no prognostic impacts on cancer patients’ overall and disease-free survivals. In addition, HMGA2-overexpressing colorectal cancer cell lines did not display higher susceptibility to a previously identified HMGA2 inhibitor (netroposin). By microarray profiling of HMGA2-driven gene signature and subsequent Connectivity Map (CMap) database mining, we identified that S100 calcium-binding protein A4 (S100A4) may be a druggable vulnerability for HMGA2-overexpressing colorectal cancer. A repurposing S100A4 inhibitor, niclosamide, was found to reverse the HMGA2-driven gene signature both in colorectal cancer cell lines and patients’ tissues. In vitro and in vivo experiments validated that HMGA2-overexpressing colorectal cancer cells were more sensitive to niclosamide. However, inhibition of S100A4 by siRNAs and other inhibitors was not sufficient to exert effects like niclosamide. Further RNA sequencing analysis identified that niclosamide inhibited more cell-cycle-related gene expression in HMGA2-overexpressing colorectal cancer cells, which may explain its selective anticancer effect. Together, our study repurposes an anthelminthic drug niclosamide for treating HMGA2-overexpression colorectal cancer.

A genomic signature approach to rescue ΔF508-cystic fibrosis transmembrane conductance regulator biosynthesis and function

The most common cystic fibrosis (CF) mutation, ΔF508, causes protein misfolding, leading to proteosomal degradation. We recently showed that expression of miR-138 enhances CF transmembrane conductance regulator (CFTR) biogenesis and partially rescues ΔF508-CFTR function in CF airway epithelia. We hypothesized that a genomic signature approach can be used to identify new bioactive small molecules affecting ΔF508-CFTR rescue. The Connectivity Map was used to identify 27 small molecules with potential to restore ΔF508-CFTR function in airway epithelia. The molecules were screened in vitro for efficacy in improving ΔF508-CFTR trafficking, maturation, and chloride current. We identified four small molecules that partially restore ΔF508-CFTR function in primary CF airway epithelia. Of these, pyridostigmine showed cooperativity with corrector compound 18 in improving ΔF508-CFTR function. There are few CF therapies based on new molecular insights. Querying the Connectivity Map with relevant genomic signatures offers a method to identify new candidates for rescuing ΔF508-CFTR function.

Predicting Drug-Induced Liver Injury Using Machine Learning on a Diverse Set of Predictors

A major challenge in drug development is safety and toxicity concerns due to drug side effects. One such side effect, drug-induced liver injury (DILI), is considered a primary factor in regulatory clearance. The Critical Assessment of Massive Data Analysis (CAMDA) 2020 CMap Drug Safety Challenge goal was to develop prediction models based on gene perturbation of six preselected cell-lines (CMap L1000), extended structural information (MOLD2), toxicity data (TOX21), and FDA reporting of adverse events (FAERS). Four types of DILI classes were targeted, including two clinically relevant scores and two control classifications, designed by the CAMDA organizers. The L1000 gene expression data had variable drug coverage across cell lines with only 247 out of 617 drugs in the study measured in all six cell types. We addressed this coverage issue by using Kru-Bor ranked merging to generate a singular drug expression signature across all six cell lines. These merged signatures were then narrowed down to the top and bottom 100, 250, 500, or 1,000 genes most perturbed by drug treatment. These signatures were subject to feature selection using Fisher's exact test to identify genes predictive of DILI status. Models based solely on expression signatures had varying results for clinical DILI subtypes with an accuracy ranging from 0.49 to 0.67 and Matthews Correlation Coefficient (MCC) values ranging from -0.03 to 0.1. Models built using FAERS, MOLD2, and TOX21 also had similar results in predicting clinical DILI scores with accuracy ranging from 0.56 to 0.67 with MCC scores ranging from 0.12 to 0.36. To incorporate these various data types with expression-based models, we utilized soft, hard, and weighted ensemble voting methods using the top three performing models for each DILI classification. These voting models achieved a balanced accuracy up to 0.54 and 0.60 for the clinically relevant DILI subtypes. Overall, from our experiment, traditional machine learning approaches may not be optimal as a classification method for the current data.

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.

The Utility of Resolving Asthma Molecular Signatures Using Tissue-Specific Transcriptome Data

An integrative analysis focused on multi-tissue transcriptomics has not been done for asthma. Tissue-specific DEGs remain undetected in many multi-tissue analyses, which influences identification of disease-relevant pathways and potential drug candidates. Transcriptome data from 609 cases and 196 controls, generated using airway epithelium, bronchial, nasal, airway macrophages, distal lung fibroblasts, proximal lung fibroblasts, CD4+ lymphocytes, CD8+ lymphocytes from whole blood and induced sputum samples, were retrieved from Gene Expression Omnibus (GEO). Differentially regulated asthma-relevant genes identified from each sample type were used to identify (a) tissue-specific and tissue-shared asthma pathways, (b) their connection to GWAS-identified disease genes to identify candidate tissue for functional studies, (c) to select surrogate sample for invasive tissues, and finally (d) to identify potential drug candidates via connectivity map analysis. We found that inter-tissue similarity in gene expression was more pronounced at pathway/functional level than at gene level with highest similarity between bronchial epithelial cells and lung fibroblasts, and lowest between airway epithelium and whole blood samples. Although public-domain gene expression data are limited by inadequately annotated per-sample demographic and clinical information which limited the analysis, our tissue-resolved analysis clearly demonstrated relative importance of unique and shared asthma pathways, At the pathway level, IL-1b signaling and ERK signaling were significant in many tissue types, while Insulin-like growth factor and TGF-beta signaling were relevant in only airway epithelial tissue. IL-12 (in macrophages) and Immunoglobulin signaling (in lymphocytes) and chemokines (in nasal epithelium) were the highest expressed pathways. Overall, the IL-1 signaling genes (inflammatory) were relevant in the airway compartment, while pro-Th2 genes including IL-13 and STAT6 were more relevant in fibroblasts, lymphocytes, macrophages and bronchial biopsies. These genes were also associated with asthma in the GWAS catalog. Support Vector Machine showed that DEGs based on macrophages and epithelial cells have the highest and lowest discriminatory accuracy, respectively. Drug (entinostat, BMS-345541) and genetic perturbagens (KLF6, BCL10, INFB1 and BAMBI) negatively connected to disease at multi-tissue level could potentially repurposed for treating asthma. Collectively, our study indicates that the DEGs, perturbagens and disease are connected differentially depending on tissue/cell types. While most of the existing literature describes asthma transcriptome data from individual sample types, the present work demonstrates the utility of multi-tissue transcriptome data. Future studies should focus on collecting transcriptomic data from multiple tissues, age and race groups, genetic background, disease subtypes and on the availability of better-annotated data in the public domain.

Gene Co-Expression Analysis Identified Preserved and Survival-Related Modules in Severe Blunt Trauma, Burns, Sepsis, and Systemic Inflammatory Response Syndrome

Background

Severe trauma and burns accompanied by sepsis are associated with high morbidity and mortality. Little is known about the transcriptional similarity between trauma, burns, sepsis, and systemic inflammatory response syndrome (SIRS). Uncovering key genes and molecular networks is critical to understanding the biology of disease. Conventional analysis of gene changes (fold change) analysis is difficult for time-serial data such as post-injury blood transcriptome.

Methods

Weighted gene co-expression network analysis (WGCNA) was applied to the trauma dataset to identify modules and hub genes. Module stability was tested by half sampling. Module preservations of burns, sepsis, and SIRS were calculated using trauma as reference. Module functional enrichment was analyzed in gProfiler server. Candidate drugs were screened using Connectivity Map based on hub genes. The modules were visualized in Cytoscape.

Results

Seventeen modules were identified. The modules were robust to the exclusion of half the sample. They were involved in lymphocyte and platelet activation, erythrocyte differentiation, cell cycle, translation, and interferon signaling. In addition, highly connected hub genes were identified in each module, such as GUCY1B1, BCL11B, HMMR, and CEACAM6. High BCL11B (M13) or low CEACAM6 (M27) expression indicates better survival prognosis in sepsis patients regardless of endotype class and age. Network preservation in burns, sepsis, and SIRS showed a general similarity between trauma and burns. M4, M5, M13, M16, M20, and M27 were significantly associated with injury time in trauma and burns. High M13 (T cell activation), low M15 (cell cycle), and low M27 (neutrophil activation) indicate better survival of sepsis patients, regardless of endotype class and age. Moreover, the modules can efficiently separate patients with different diseases. Some modules and hub genes have good prognostic performance in sepsis. Based on the hub genes of each module, six candidate drugs were screened.

Conclusion

This study first compared the gene co-expression modules in trauma, burns, sepsis, and SIRS. The identified modules are useful for disease prognosis and drug discovery. BCL11B and CEACAM6 may be promising biomarkers for sepsis risk assessment.

Predicting Agents That Can Overcome 5-FU Resistance in Colorectal Cancers via Pharmacogenomic Analysis

5-Fluorouracil (5-FU) is one of several chemotherapeutic agents in clinical use as a standard of care to treat colorectal cancers (CRCs). As an antimetabolite, 5-FU inhibits thymidylate synthase to disrupt the synthesis and repair of DNA and RNA. However, only a small proportion of patients benefit from 5-FU treatment due to the development of drug resistance. This study applied pharmacogenomic analysis using two public resources, the Genomics of Drug Sensitivity in Cancer (GDSC) and the Connectivity Map, to predict agents overcoming 5-FU resistance in CRC cells based on their genetic background or gene expression profile. Based on the genetic status of adenomatous polyposis coli (APC), the most frequent mutated gene found in CRC, we found that combining a MEK inhibitor with 5-FU exhibited synergism effects on CRC cells with APC truncations. While considering the gene expression in 5-FU resistant cells, we demonstrated that targeting ROCK is a potential avenue to restore 5-FU response to resistant cells with wild-type APC background. Our results reveal MEK signaling plays a pivotal role in loss-of-function, APC-mediated 5-FU resistance, and ROCK activation serves as a signature in APC-independent 5-FU resistance. Through the use of these available database resources, we highlight possible approaches to predict potential drugs for combinatorial therapy for patients developing resistance to 5-FU treatment.

Neuropathological stage-dependent proteome mapping of the olfactory tract in Alzheimer's disease: From early olfactory-related omics signatures to computational repurposing of drug candidates

Alzheimer's disease (AD) is the most common form of dementia, characterized by an early olfactory dysfunction, progressive memory loss, and behavioral deterioration. Albeit substantial progress has been made in characterizing AD-associated molecular and cellular events, there is an unmet clinical need for new therapies. In this study, olfactory tract proteotyping performed in controls and AD subjects (n = 17/group) showed a Braak stage-dependent proteostatic impairment accompanied by the progressive modulation of amyloid precursor protein and tau functional interactomes. To implement a computational repurposing of drug candidates with the capacity to reverse early AD-related olfactory omics signatures (OMSs), we generated a consensual OMSs database compiling differential omics datasets obtained by mass-spectrometry or RNA-sequencing derived from initial AD across the olfactory axis. Using the Connectivity Map-based drug repurposing approach, PKC, EGFR, Aurora kinase, Glycogen synthase kinase, and CDK inhibitors were the top pharmacologic classes capable to restore multiple OMSs, whereas compounds with targeted activity to inhibit PI3K, Insulin-like growth factor 1 (IGF-1), microtubules, and Polo-like kinase (PLK) represented a family of drugs with detrimental potential to induce olfactory AD-associated gene expression changes. To validate the potential therapeutic effects of the proposed drugs, in vitro assays were performed. These validation experiments revealed that pretreatment of human neuron-like SH-SY5Y cells with the EGFR inhibitor AG-1478 showed a neuroprotective effect against hydrogen peroxide-induced damage while the pretreatment with the Aurora kinase inhibitor Reversine reduced amyloid-beta (Aβ)-induced neurotoxicity. Taken together, our data pointed out that OMSs may be useful as substrates for drug repurposing to propose novel neuroprotective treatments against AD.

Identification of small molecules as novel anti-adipogenic compounds based on Connectivity Map

Several physiological and pathological conditions such as aging, obesity, diabetes, anorexia nervosa are associated with increased adipogenesis in the bone marrow. A lack of effective drugs hinder the improved treatment for aberrant accumulation of bone marrow adipocytes. Given the higher costs, longer duration and sometimes lack of efficacy in drug discovery, computational and experimental strategies have been used to identify previously approved drugs for the treatment of diseases, also known as drug repurposing. Here, we describe the method of small molecule-prioritization by employing adipocyte-specific genes using the connectivity map (CMap). We then generated transcriptomic profiles using human mesenchymal stromal cells under adipogenic differentiation with the treatment of prioritized compounds, and identified emetine and kinetin-riboside to have a potent inhibitory effect on adipogenesis. Overall, we demonstrated a proof-of-concept method to identify repurposable drugs capable of inhibiting adipogenesis, using the Connectivity Map.

Quantitative Analysis of Cell Aggregation Dynamics Identifies HDAC Inhibitors as Potential Regulators of Cancer Cell Clustering

Simple Summary

Metastases formation involves the formation, circulation and seeding of cohesive group of tumor cells called circulating tumors cells clusters at distant organs from the primary tumor. These clusters have a much higher metastatic potential than individual circulating tumor cell, it is therefore important to understand the molecular mechanisms involved in their formation. To this aim, in this study, from the analysis of the relationship between in vitro aggregation quantitative characterization of 25 cancer cell lines and their expression data, we identified genes significantly associated with aggregation. Interestingly, we found that these genes were strongly correlated with the transcriptional signature induced by HDAC inhibitors treatment and we finally showed experimentally that two HDAC inhibitors inhibits tumor cells cluster formation in vitro. These results open new therapeutic perspectives to prevent metastasis formation.

Abstract

Characterization of the molecular mechanisms involved in tumor cell clustering could open the way to new therapeutic strategies. Towards this aim, we used an in vitro quantitative procedure to monitor the anchorage-independent cell aggregation kinetics in a panel of 25 cancer cell lines. The analysis of the relationship between selected aggregation dynamic parameters and the gene expression data for these cell lines from the CCLE database allowed identifying genes with expression significantly associated with aggregation parameter variations. Comparison of these transcripts with the perturbagen signatures from the Connectivity Map resource highlighted that they were strongly correlated with the transcriptional signature of most histone deacetylase (HDAC) inhibitors. Experimental evaluation of two HDAC inhibitors (SAHA and ISOX) showed that they inhibited the initial step of in vitro tumor cell aggregation. This validates our findings and reinforces the potential interest of HDCA inhibitors to prevent metastasis spreading.

Wnt dose escalation during the exit from pluripotency identifies tranilast as a regulator of cardiac mesoderm

Wnt signaling is a critical determinant of cell lineage development. This study used Wnt dose-dependent induction programs to gain insights into molecular regulation of stem cell differentiation. We performed single-cell RNA sequencing of hiPSCs responding to a dose escalation protocol with Wnt agonist CHIR-99021 during the exit from pluripotency to identify cell types and genetic activity driven by Wnt stimulation. Results of activated gene sets and cell types were used to build a multiple regression model that predicts the efficiency of cardiomyocyte differentiation. Cross-referencing Wnt-associated gene expression profiles to the Connectivity Map database, we identified the small-molecule drug, tranilast. We found that tranilast synergistically activates Wnt signaling to promote cardiac lineage differentiation, which we validate by in vitro analysis of hiPSC differentiation and in vivo analysis of developing quail embryos. Our study provides an integrated workflow that links experimental datasets, prediction models, and small-molecule databases to identify drug-like compounds that control cell differentiation.