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

Mechanism-free repurposing of drugs for C9orf72-related ALS/FTD using large-scale genomic data

Repeat expansions in the C9orf72 gene are the most common genetic cause of (ALS) and frontotemporal dementia (FTD). Like other genetic forms of neurodegeneration, pinpointing the precise mechanism(s) by which this mutation leads to neuronal death remains elusive, and this lack of knowledge hampers the development of therapy for C9orf72-related disease. We used an agnostic approach based on genomic data (n = 41,273 ALS and healthy samples, and n = 1,516 C9orf72 carriers) to overcome these bottlenecks. Our drug-repurposing screen, based on gene- and expression-pattern matching and information about the genetic variants influencing onset age among C9orf72 carriers, identified acamprosate, a γ-aminobutyric acid analog, as a potentially repurposable treatment for patients carrying C9orf72 repeat expansions. We validated its neuroprotective effect in cell models and showed comparable efficacy to riluzole, the current standard of care. Our work highlights the potential value of genomics in repurposing drugs in situations where the underlying pathomechanisms are inherently complex. VIDEO ABSTRACT.

Cancer pharmacoinformatics: Databases and analytical tools

Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.

A cellular senescence-related signature for predicting prognosis, immunotherapy response, and candidate drugs in patients treated with transarterial chemoembolization (TACE)

BACKGROUND

Cellular senescence is essential to TME development, progression, and remodeling. Few studies have examined cellular senescence in HCC after TACE. Investigating the relationship between cellular senescence, post-TACE prognosis, the TME, and immune treatment responses is crucial.

METHODS

We analyzed the GSE104580 dataset to identify DEGs. A cellular senescence-related signature was developed using LASSO Cox regression in the GSE14520 dataset and validated in the ICGC dataset. High- and low-risk subgroups were compared using GSVA and GSEA. Correlation studies were conducted to explore the relationship between the prognostic model, immune infiltration, immunotherapy response, and drug sensitivity.

RESULTS

A cellular senescence-related signature comprising FOXM1, CDK1, CHEK1, and SERPINE1 was created and validated. High-risk patients showed significantly lower OS than low-risk patients. High-risk patients had carcinogenetic pathways activated, immunosuppressive cells infiltrated, and immunomodulatory genes overexpressed. They also showed higher sensitivity to EPZ004777_1237 and MK-2206_1053 and potential benefits from GSK-3 inhibitor IX, nortriptyline, lestaurtinib, and JNK-9L.

CONCLUSIONS

This study constructed a cellular senescence-related signature that could be used to predict HCC patients' responses to and prognosis after TACE treatment, aiding in the development of personalized treatment plans.

Synergizing drug repurposing and target identification for neurodegenerative diseases

Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.

The Potential of a Stratified Approach to Drug Repurposing in Alzheimer's Disease

Alzheimer's disease (AD) is a complex neurodegenerative condition that is characterized by the build-up of amyloid-beta plaques and neurofibrillary tangles. While multiple theories explaining the aetiology of the disease have been suggested, the underlying cause of the disease is still unknown. Despite this, several modifiable and non-modifiable factors that increase the risk of developing AD have been identified. To date, only eight AD drugs have ever gained regulatory approval, including six symptomatic and two disease-modifying drugs. However, not all are available in all countries and high costs associated with new disease-modifying biologics prevent large proportions of the patient population from accessing them. With the current patient population expected to triple by 2050, it is imperative that new, effective, and affordable drugs become available to patients. Traditional drug development strategies have a 99% failure rate in AD, which is far higher than in other disease areas. Even when a drug does reach the market, additional barriers such as high cost and lack of accessibility prevent patients from benefiting from them. In this review, we discuss how a stratified medicine drug repurposing approach may address some of the limitations and barriers that traditional strategies face in relation to drug development in AD. We believe that novel, stratified drug repurposing studies may expedite the discovery of alternative, effective, and more affordable treatment options for a rapidly expanding patient population in comparison with traditional drug development methods.

Repurposing fluphenazine as an autophagy modulator for treating liver cancer

Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system with a low early diagnosis rate. Owing to the side effects, tolerance, and patient contraindications of existing therapies, effective drug treatments for HCC remain a major clinical challenge. However, using approved or investigational drugs not initially intended for cancer therapy is a promising strategy for resolving this problem because their safety have been tested in clinic. Therefore, this study evaluated differentially expressed genes between liver cancer and normal tissues in a cohort of patients with HCC from The Cancer Genome Atlas and applied them to query a connectivity map to identify candidate anti-HCC drugs. As a result, fluphenazine was identified as a candidate for anti-HCC therapy in vitro and in vivo. Fluphenazine suppressed HCC cell proliferation and migration and induced cell cycle arrest and apoptosis, possibly owing to disrupted lysosomal function, blocking autophagy flux. Additionally, in vivo studies demonstrated that fluphenazine suppresses HCC subcutaneous xenografts growth without causing severe side effects. Strikingly, fluphenazine could be used as an analgesic to alleviate oxaliplatin-induced pain as well as pain related anxiety-like behavior. Therefore, fluphenazine could be a novel liver cancer treatment candidate.

Neurodevelopmental deficits and cell-type-specific transcriptomic perturbations in a mouse model of HNRNPU haploinsufficiency

Heterozygous de novo loss-of-function mutations in the gene expression regulator HNRNPU cause an early-onset developmental and epileptic encephalopathy. To gain insight into pathological mechanisms and lay the potential groundwork for developing targeted therapies, we characterized the neurophysiologic and cell-type-specific transcriptomic consequences of a mouse model of HNRNPU haploinsufficiency. Heterozygous mutants demonstrated global developmental delay, impaired ultrasonic vocalizations, cognitive dysfunction and increased seizure susceptibility, thus modeling aspects of the human disease. Single-cell RNA-sequencing of hippocampal and neocortical cells revealed widespread, yet modest, dysregulation of gene expression across mutant neuronal subtypes. We observed an increased burden of differentially-expressed genes in mutant excitatory neurons of the subiculum-a region of the hippocampus implicated in temporal lobe epilepsy. Evaluation of transcriptomic signature reversal as a therapeutic strategy highlights the potential importance of generating cell-type-specific signatures. Overall, this work provides insight into HNRNPU-mediated disease mechanisms and provides a framework for using single-cell RNA-sequencing to study transcriptional regulators implicated in disease.

Ataluren prevented bone loss induced by ovariectomy and aging in mice through the BMP-SMAD signaling pathway

Both estrogen deficiency and aging may lead to osteoporosis. Developing novel drugs for treating osteoporosis is a popular research direction. We screened several potential therapeutic agents through a new deep learning-based efficacy prediction system (DLEPS) using transcriptional profiles for osteoporosis. DLEPS screening led to a potential novel drug examinee, ataluren, for treating osteoporosis. Ataluren significantly reversed bone loss in ovariectomized mice. Next, ataluren significantly increased human bone marrow-derived mesenchymal stem cell (hBMMSC) osteogenic differentiation without cytotoxicity, indicated by the high expression index of osteogenic differentiation genes (OCN , BGLAP, ALP, COL1A, BMP2, RUNX2). Mechanistically, ataluren exerted its function through the BMP-SMAD pathway. Furthermore, it activated SMAD phosphorylation but osteogenic differentiation was attenuated by BMP2-SMAD inhibitors or small interfering RNA of BMP2. Finally, ataluren significantly reversed bone loss in aged mice. In summary, our findings suggest that the DLEPS-screened ataluren may be a therapeutic agent against osteoporosis by aiding hBMMSC osteogenic differentiation.

Identification of a novel competing endogenous RNA network and candidate drugs associated with ferroptosis in aldosterone-producing adenomas

Aldosterone-producing adenoma (APA), characterized by unilaterally excessive aldosterone production, is a common cause of primary aldosteronism. Ferroptosis, a recently raised iron-dependent mode of programmed cell death, has been involved in the development and therapy of various diseases. This study obtained datasets of the mRNA and lncRNA expression profiles for APA and adjacent adrenal gland (AAG) from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and lncRNAs (DE lncRNAs) associated with ferroptosis were identified. Enrichment analyses indicated 89 ferroptosis-related DEGs were primarily enriched in ROS related processes and ferroptosis. Two physical cores, and one combined core were identified in the protein-protein interaction (PPI). DEGs and clinical traits were used in conjunction to screen eight hub genes from two hub modules and 89 DEGs. A competitive endogenous RNA (ceRNA) network was constructed via co-express analysis. Thereafter, molecular docking was used to identify potential targets. Two active compounds, QL-X-138 and MK-1775, bound to AURKA and DUOX1, respectively, with the lowest binding energies. Molecular dynamics simulation verified the stability of the two complexes. In summary, our studies identified eight hub genes and a novel ceRNA regulatory network associated with ferroptosis, wherein QL-X-138 and MK-1775 were considered to be potential drugs for treating APA.

Tensin-3 is involved in osteogenic versus adipogenic fate of human bone marrow stromal cells

BACKGROUND

The tightly controlled balance between osteogenic and adipogenic differentiation of human bone marrow-derived stromal cells (BMSCs) is critical to maintain bone homeostasis. Age-related osteoporosis is characterized by low bone mass with excessive infiltration of adipose tissue in the bone marrow compartment. The shift of BMSC differentiation from osteoblasts to adipocytes could result in bone loss and adiposity.

METHODS

TNS3 gene expression during osteogenic and adipogenic differentiation of BMSCs was evaluated by qPCR and Western blot analyses. Lentiviral-mediated knockdown or overexpression of TNS3 was used to assess its function. The organization of cytoskeleton was examined by immunofluorescent staining at multiple time points. The role of TNS3 and its domain function in osteogenic differentiation were evaluated by ALP activity, calcium assay, and Alizarin Red S staining. The expression of Rho-GTP was determined using the RhoA pull-down activation assay.

RESULTS

Loss of TNS3 impaired osteogenic differentiation of BMSCs but promoted adipogenic differentiation. Conversely, TNS3 overexpression hampered adipogenesis while enhancing osteogenesis. The expression level of TNS3 determined cell shape and cytoskeletal reorganization during osteogenic differentiation. TNS3 truncation experiments revealed that for optimal osteogenesis to occur, all domains proved essential. Pull-down and immunocytochemical experiments suggested that TNS3 mediates osteogenic differentiation through RhoA.

CONCLUSIONS

Here, we identify TNS3 to be involved in BMSC fate decision. Our study links the domain structure in TNS3 to RhoA activity via actin dynamics and implicates an important role for TNS3 in regulating osteogenesis and adipogenesis from BMSCs. Furthermore, it supports the critical involvement of cytoskeletal reorganization in BMSC differentiation.

Regulating the cell shift of endothelial cell-like myofibroblasts in pulmonary fibrosis

Pulmonary fibrosis is a common and severe fibrotic lung disease with high morbidity and mortality. Recent studies have reported a large number of unwanted myofibroblasts appearing in pulmonary fibrosis, and shown that the sustained activation of myofibroblasts is essential for unremitting interstitial fibrogenesis. However, the origin of these myofibroblasts remains poorly understood. Here, we create new mouse models of pulmonary fibrosis and identify a previously unknown population of endothelial cell (EC)-like myofibroblasts in normal lung tissue. We show that these EC-like myofibroblasts significantly contribute myofibroblasts to pulmonary fibrosis, which is confirmed by single-cell RNA sequencing of human pulmonary fibrosis. Using the transcriptional profiles, we identified a small molecule that redirects the differentiation of EC-like myofibroblasts and reduces pulmonary fibrosis in our mouse models. Our study reveals the mechanistic underpinnings of the differentiation of EC-like myofibroblasts in pulmonary fibrosis and may provide new strategies for therapeutic interventions.

Bioinformatics exploration of potential common therapeutic targets for systemic and pulmonary arterial hypertension-induced myocardial hypertrophy

Systemic and pulmonary arterial hypertension (PAH) can induce left and right ventricular hypertrophy, respectively, but common therapeutic targets for both left and right hypertrophy are limited. In this study, we attempt to explore potential common therapeutic targets and screen out potential target drugs for further study. Cardiac mRNA expression profiles in mice with transverse aortic constriction (TAC) and pulmonary arterial constriction (PAC) are obtained from online databases. After bioinformatics analyses, we generate TAC and PAC mouse models to validate the phenotypes of cardiac remodelling as well as the identified hub genes. Bioinformatics analyses show that there are 214 independent differentially expressed genes (DEGs) in GSE136308 (TAC related) and 2607 independent DEGs in GSE30922 (PAC related), while 547 shared DEGs are associated with the function of the extracellular matrix (ECM) or involved in the PI3K-Akt signaling pathway, cytokine-cytokine receptor interactions, and ECM-receptor interactions. We identifyd Fn1, Il6, Col1a1, Igf1, Col1a2, Timp1, Col3a1, Cd44, Ctgf and Postn as hub genes of the shared DEGs, and most of them are associated with myocardial fibrosis. Those hub genes and phenotypes of cardiac remodelling are validated in our TAC and PAC mouse models. Furthermore, we identify dehydroisoandrosterone (DHEA), iloprost and 4,5-dianilinophthalimide (DAPH) as potential therapeutic drugs targeting both left and right ventricular hypertrophy and validate the effect of DHEA. These findings suggest that DHEA could be an effective drug for pressure overload-induced left or right ventricular hypertrophy by regulating the shared hub differentially expressed genes associated with fibrosis.

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 .

HSPB7 oppositely regulates human mesenchymal stromal cell-derived osteogenesis and adipogenesis

BACKGROUND

Recent evidence suggests that accumulation of marrow adipose tissue induced by aberrant lineage allocation of bone marrow-derived mesenchymal stromal cells (BMSCs) contributes to the pathophysiologic processes of osteoporosis. Although master regulators of lineage commitment have been well documented, molecular switches between osteogenesis and adipogenesis are largely unknown.

METHODS

HSPB7 gene expression during osteogenic and adipogenic differentiation of BMSCs was evaluated by qPCR and Western blot analyses. Lentiviral-mediated knockdown or overexpression of HSPB7 and its deletion constructs were used to assess its function. The organization of cytoskeleton was examined by immunofluorescent staining. ALP activity, calcium assay, Alizarin Red S staining and Oil Red O staining were performed in vitro during osteoblast or adipocyte differentiation. SB431542 and Activin A antibody were used to identify the mechanism of Activin A in the regulation of osteogenic differentiation in BMSCs.

RESULTS

In this study, we identified HSPB7 capable of oppositely regulating osteogenic and adipogenic differentiation of BMSCs. HSPB7 silencing promoted adipogenesis while reducing osteogenic differentiation and mineralization. Conversely, overexpression of HSPB7 strongly enhanced osteogenesis, but no effect was observed on adipogenic differentiation. Deletion of the N-terminal or C-terminal domain of HSPB7 led to decreased osteoblastic potency and mineralization. Mechanistically, our data showed that Activin A is a downstream target participating in HSPB7 knockdown-mediated osteogenic inhibition.

CONCLUSIONS

Our findings suggest that HSPB7 plays a positive role in driving osteoblastic differentiation, and with the capability in maintaining the osteo-adipogenesis balance. It holds great promise as a potential therapeutic target in the treatment of bone metabolic diseases.

An Innovative Drug Repurposing Approach to Restrain Endometrial Cancer Metastatization

BACKGROUND

Endometrial cancer (EC) is the most common gynecologic tumor and the world's fourth most common cancer in women. Most patients respond to first-line treatments and have a low risk of recurrence, but refractory patients, and those with metastatic cancer at diagnosis, remain with no treatment options. Drug repurposing aims to discover new clinical indications for existing drugs with known safety profiles. It provides ready-to-use new therapeutic options for highly aggressive tumors for which standard protocols are ineffective, such as high-risk EC.

METHODS

Here, we aimed at defining new therapeutic opportunities for high-risk EC using an innovative and integrated computational drug repurposing approach.

RESULTS

We compared gene-expression profiles, from publicly available databases, of metastatic and non-metastatic EC patients being metastatization the most severe feature of EC aggressiveness. A comprehensive analysis of transcriptomic data through a two-arm approach was applied to obtain a robust prediction of drug candidates.

CONCLUSIONS

Some of the identified therapeutic agents are already successfully used in clinical practice to treat other types of tumors. This highlights the potential to repurpose them for EC and, therefore, the reliability of the proposed approach.

Zika virus alters osteogenic lineage progression of human mesenchymal stromal cells

Arboviruses target bone forming osteoblasts and perturb bone remodeling via paracrine factors. We previously reported that Zika virus (ZIKV) infection of early-stage human mesenchymal stromal cells (MSCs) inhibited the osteogenic lineage commitment of MSCs. To understand the physiological interplay between bone development and ZIKV pathogenesis, we employed a primary in vitro model to examine the biological responses of MSCs to ZIKV infection at different stages of osteogenesis. Precommitted MSCs were infected at the late stage of osteogenic stimulation (Day 7) with ZIKV (multiplicity of infection of 5). We observe that MSCs infected at the late stage of differentiation are highly susceptible to ZIKV infection similar to previous observations with early stage infected MSCs (Day 0). However, in contrast to ZIKV infection at the early stage of differentiation, infection at a later stage significantly elevates the key osteogenic markers and calcium content. Comparative RNA sequencing (RNA-seq) of early and late stage infected MSCs reveals that ZIKV infection alters the mRNA transcriptome during osteogenic induction of MSCs (1251 genes). ZIKV infection provokes a robust antiviral response at both stages of osteogenic differentiation as reflected by the upregulation of interferon responsive genes (n > 140). ZIKV infection enhances the expression of immune-related genes in early stage MSCs while increasing cell cycle genes in late stage MSCs. Remarkably, ZIKA infection in early stage MSCs also activates lipid metabolism-related pathways. In conclusion, ZIKV infection has differentiation stage-dependent effects on MSCs and this mechanistic understanding may permit the development of new therapeutic or preventative measures for bone-related effects of ZIKV infection.

Specific gene module pair-based target identification and drug discovery

Identification of the biological targets of a compound is of paramount importance for the exploration of the mechanism of action of drugs and for the development of novel drugs. A concept of the Connectivity Map (CMap) was previously proposed to connect genes, drugs, and disease states based on the common gene-expression signatures. For a new query compound, the CMap-based method can infer its potential targets by searching similar drugs with known targets (reference drugs) and measuring the similarities into their specific transcriptional responses between the query compound and those reference drugs. However, the available methods are often inefficient due to the requirement of the reference drugs as a medium to link the query agent and targets. Here, we developed a general procedure to extract target-induced consensus gene modules from the transcriptional profiles induced by the treatment of perturbagens of a target. A specific transcriptional gene module pair (GMP) was automatically identified for each target and could be used as a direct target signature. Based on the GMPs, we built the target network and identified some target gene clusters with similar biological mechanisms. Moreover, a gene module pair-based target identification (GMPTI) approach was proposed to predict novel compound-target interactions. Using this method, we have discovered novel inhibitors for three PI3K pathway proteins PI3Kα/β/δ, including PU-H71, alvespimycin, reversine, astemizole, raloxifene HCl, and tamoxifen.

Saracatinib, a Selective Src Kinase Inhibitor, Blocks Fibrotic Responses in Preclinical Models of Pulmonary Fibrosis

Rationale: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and often fatal disorder. Two U.S. Food and Drug Administration-approved antifibrotic drugs, nintedanib and pirfenidone, slow the rate of decline in lung function, but responses are variable and side effects are common. Objectives: Using an in silico data-driven approach, we identified a robust connection between the transcriptomic perturbations in IPF disease and those induced by saracatinib, a selective Src kinase inhibitor originally developed for oncological indications. Based on these observations, we hypothesized that saracatinib would be effective at attenuating pulmonary fibrosis. Methods: We investigated the antifibrotic efficacy of saracatinib relative to nintedanib and pirfenidone in three preclinical models: 1) in vitro in normal human lung fibroblasts; 2) in vivo in bleomycin and recombinant Ad-TGF-β (adenovirus transforming growth factor-β) murine models of pulmonary fibrosis; and 3) ex vivo in mice and human precision-cut lung slices from these two murine models as well as patients with IPF and healthy donors. Measurements and Main Results: In each model, the effectiveness of saracatinib in blocking fibrogenic responses was equal or superior to nintedanib and pirfenidone. Transcriptomic analyses of TGF-β-stimulated normal human lung fibroblasts identified specific gene sets associated with fibrosis, including epithelial-mesenchymal transition, TGF-β, and WNT signaling that was uniquely altered by saracatinib. Transcriptomic analysis of whole-lung extracts from the two animal models of pulmonary fibrosis revealed that saracatinib reverted many fibrogenic pathways, including epithelial-mesenchymal transition, immune responses, and extracellular matrix organization. Amelioration of fibrosis and inflammatory cascades in human precision-cut lung slices confirmed the potential therapeutic efficacy of saracatinib in human lung fibrosis. Conclusions: These studies identify novel Src-dependent fibrogenic pathways and support the study of the therapeutic effectiveness of saracatinib in IPF treatment.

A cellular senescence-related classifier based on a tumorigenesis- and immune infiltration-guided strategy can predict prognosis, immunotherapy response, and candidate drugs in hepatocellular carcinoma

BACKGROUND

Cellular senescence plays an irreplaceable role in tumorigenesis, progression, and tumor microenvironment (TME) remodeling. However, to date, there is limited research delineating the landscape of cellular senescence in hepatocellular carcinoma (HCC), and an improved understanding on the interaction of tumor-associated cellular senescence with HCC prognosis, TME, and response to immunotherapy is warrant.

METHODS

Tumorigenic and immune infiltration-associated senescence genes were determined by weighted gene co-expression network analysis (WGCNA) and the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm, and subsequently, a prognostic scoring model (named TIS) was constructed using multiple survival analysis algorithms to classify the senescence-related subtypes of HCC. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were conducted to identify the distinct hallmark pathways between high- and low-risk subtypes. Additionally, we carried out correlation analyses for TIS and clinical traits, senescence-associated secretory phenotype (SASP), immune infiltration and evasion, immune checkpoint factors, drug response, and immunotherapeutic efficacy. External experimental validation was conducted to delineate the association of CPEP3 (a TIS gene) with HCC phenotypes through assays of proliferation, colony formation, and invasion.

RESULTS

A five-gene TIS, composed of NET1, ATP6V0B, MMP1, GTDC1, and CPEB3, was constructed and validated using TCGA and ICGC datasets, respectively, and showed a highly robust and plausible signature for overall survival (OS) prediction of HCC in both training and validation cohorts. Patients in the TIS-high group were accompanied by worse OS, activation of carcinogenetic pathways, infiltration of immunosuppressive cells, exclusion of effector killing cells, overexpression of immunomodulatory genes and SASP, and unsatisfied response to immunotherapy. In response to anticancer drugs, patients in the TIS-high group exhibited enhanced susceptibility to several conventional chemotherapeutic agents (5-fluorouracil, docetaxel, doxorubicin, gemcitabine, and etoposide), as well as several inhibitors of pathways involved in cellular senescence (cell-cycle inhibitors, bromodomain and extraterminal domain family (BET) inhibitors, PI3K-AKT pathway inhibitors, and multikinase inhibitors). Additionally, four putative drugs (palbociclib, JAK3 inhibitor VI, floxuridine, and lestaurtinib) were identified as potential compounds for patients in the TIS-high group. Notably, in vitro functional validation showed that CPEB3 knockdown boosted the phenotypes of proliferation, clonogenicity, and invasion in HCC cells, whereas CPEB3 overexpression attenuated these phenotypes.

CONCLUSIONS

Our study provides comprehensive clues demonstrating the role of novel TIS in predicting HCC prognosis, immunotherapeutic response, and candidate drugs. This work highlights the significance of tumorigenesis- and immune infiltration-related cellular senescence in cancer therapy.

Methylglyoxal - an advanced glycation end products (AGEs) precursor - Inhibits differentiation of human MSC-derived osteoblasts in vitro independently of receptor for AGEs (RAGE)

A major precursor of advanced glycation end-products (AGEs) - methylglyoxal (MG) - is a reactive carbonyl metabolite that originates from glycolytic pathways. MG formation and accumulation has been implicated in the pathogenesis of diabetes and age-related chronic musculoskeletal disorders. Human bone marrow-derived stromal cells (BMSCs) are multipotent cells that have the potential to differentiate into cells of mesenchymal origin including osteoblasts, but the role of MG on their differentiation is unclear. We therefore evaluated the effect of MG on proliferation and differentiation of BMSC-derived osteoblasts. Cells were treated with different concentrations of MG (600, 800 and 1000 μM). Cell viability was assessed using a Cell Counting Kit-8 assay. Alkaline phosphatase (ALP) activity and calcium deposition assays were performed to evaluate osteoblast differentiation and mineralization. Gene expression was measured using qRT-PCR, whereas AGE specific receptor (RAGE) and collagen 1 were examined by immunocytochemistry and Western blotting. RAGE knockdown was performed by transducing RAGE specific short hairpin RNAs (shRNAs) using lentivirus. During osteogenic differentiation, MG treatment resulted in reduction of cell viability (27.7 %), ALP activity (45.5 %) and mineralization (82.3 %) compared to untreated cells. MG significantly decreased expression of genes involved in osteogenic differentiation - RUNX2 (2.8 fold), ALPL (3.2 fold), MG detoxification through glyoxalase - GLO1 (3 fold) and collagen metabolism - COL1A1 (4.9 fold), COL1A2 (6.8 fold), LOX (5.4 fold) and PLOD1 (1.7 fold). MG significantly reduced expression of collagen 1 (53.3 %) and RAGE (43.1 %) at protein levels. Co-treatment with a MG scavenger - aminoguanidine - prevented all negative effects of MG. RAGE-specific knockdown during MG treatment did not reverse the effects on cell viability, osteogenic differentiation or collagen metabolism. In conclusion, MG treatment can negatively influence the collagen metabolism and differentiation of BMSCs-derived osteoblasts through a RAGE independent mechanism.

Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection

Mitochondrial toxicity is an important safety endpoint in drug discovery. Models based solely on chemical structure for predicting mitochondrial toxicity are currently limited in accuracy and applicability domain to the chemical space of the training compounds. In this work, we aimed to utilize both -omics and chemical data to push beyond the state-of-the-art. We combined Cell Painting and Gene Expression data with chemical structural information from Morgan fingerprints for 382 chemical perturbants tested in the Tox21 mitochondrial membrane depolarization assay. We observed that mitochondrial toxicants differ from non-toxic compounds in morphological space and identified compound clusters having similar mechanisms of mitochondrial toxicity, thereby indicating that morphological space provides biological insights related to mechanisms of action of this endpoint. We further showed that models combining Cell Painting, Gene Expression features and Morgan fingerprints improved model performance on an external test set of 244 compounds by 60% (in terms of F1 score) and improved extrapolation to new chemical space. The performance of our combined models was comparable with dedicated in vitro assays for mitochondrial toxicity. Our results suggest that combining chemical descriptors with biological readouts enhances the detection of mitochondrial toxicants, with practical implications in drug discovery.

Microfluidic evidence of synergistic effects between mesenchymal stromal cell-derived biochemical factors and biomechanical forces to control endothelial cell function

A functional vascular system is a prerequisite for bone repair as disturbed angiogenesis often causes non-union. Paracrine factors released from human bone marrow derived mesenchymal stromal cells (BMSCs) have angiogenic effects on endothelial cells. However, whether these paracrine factors participate in blood flow dynamics within bone capillaries remains poorly understood. Here, we used two different microfluidic designs to investigate critical steps during angiogenesis and found pronounced effects of endothelial cell proliferation as well as chemotactic and mechanotactic migration induced by BMSC conditioned medium (CM). The application of BMSC-CM in dynamic cultures demonstrates that bioactive factors in combination with fluidic flow-induced biomechanical signals significantly enhanced endothelial cell migration. Transcriptional analyses of endothelial cells demonstrate the induction of a unique gene expression profile related to tricarboxylic acid cycle and energy metabolism by the combination of BMSC-CM factors and shear stress, which opens an interesting avenue to explore during fracture healing. Our results stress the importance of in vivo - like microenvironments simultaneously including biochemical, biomechanical and oxygen levels when investigating key events during vessel repair. STATEMENT OF SIGNIFICANCE: Our results demonstrate the importance of recapitulating in vivo - like microenvironments when investigating key events during vessel repair. Endothelial cells exhibit enhanced angiogenesis characteristics when simultaneous exposing them to hMSC-CM, mechanical forces and biochemical signals simultaneously. The improved angiogenesis may not only result from the direct effect of growth factors, but also by reprogramming of endothelial cell metabolism. Moreover, with this model we demonstrated a synergistic impact of mechanical forces and biochemical factors on endothelial cell behavior and the expression of genes involved in the TCA cycle and energy metabolism, which opens an interesting new avenue to stimulate angiogenesis during fracture healing.

Cells Dynamically Adapt to Surface Geometry by Remodeling Their Focal Adhesions and Actin Cytoskeleton

Cells probe their environment and adapt their shape accordingly via the organization of focal adhesions and the actin cytoskeleton. In an earlier publication, we described the relationship between cell shape and physiology, for example, shape-induced differentiation, metabolism, and proliferation in mesenchymal stem cells and tenocytes. In this study, we investigated how these cells organize their adhesive machinery over time when exposed to microfabricated surfaces of different topographies and adhesive island geometries. We further examined the reciprocal interaction between stress fiber and focal adhesion formation by pharmacological perturbations. Our results confirm the current literature that spatial organization of adhesive sites determines the ability to form focal adhesions and stress fibers. Therefore, cells on roughened surfaces have smaller focal adhesion and fewer stress fibers. Our results further highlight the importance of integrin-mediated adhesion in the adaptive properties of cells and provide clear links to the development of bioactive materials.

Vitamin D Receptor Activation Attenuates Hippo Pathway Effectors and Cell Survival in Metastatic Neuroblastoma

Survival for high-risk neuroblastoma remains poor. Most patients who recur, present with metastatic disease, and few targetable pathways that govern spread to distant sites are currently known. We previously developed a metastatic mouse model to select cells with enhanced ability to spread to the bone and brain and identified a signature based on differentially expressed genes, which also predicted patient survival. To discover new neuroblastoma therapies, we utilized the Connectivity Map to identify compounds that can reverse this metastatic transcriptional signature and found calcipotriol, a vitamin D3 analog, to be a compound that selectively targets cell lines with enhanced metastatic potential. Calcipotriol treatment of enhanced metastatic, but not parental, cells reduces proliferation and survival via vitamin D receptor (VDR) signaling, increases the expression of RASSF2, a negative regulator of the Hippo signaling pathway, and reduces the levels of the Hippo pathway effectors YAP and TAZ. RASSF2 is required for the effects of calcipotriol and for the reduction of levels and nuclear localization of YAP/TAZ. Migration of the enhanced metastatic cells and YAP/TAZ levels are reduced after calcipotriol treatment and YAP overexpression reduces calcipotriol sensitivity. Furthermore, metastatic cells that overexpress VDR also showed lower tumor burden in vivo.

IMPLICATIONS

This newly identified link between VDR signaling and the Hippo pathway could inform treatment strategies for metastatic neuroblastoma.

Identification of anthelmintic parbendazole as a therapeutic molecule for HNSCC through connectivity map-based drug repositioning

Head and neck squamous cell carcinoma (HNSCC) is one of the most common human cancers; however, its outcome of pharmacotherapy is always very limited. Herein, we performed a batch query in the connectivity map (cMap) based on bioinformatics, queried out 35 compounds with therapeutic potential, and screened out parbendazole as a most promising compound, which had an excellent inhibitory effect on the proliferation of HNSCC cell lines. In addition, tubulin was identified as a primary target of parbendazole, and the direct binding between them was further verified. Parbendazole was further proved as an effective tubulin polymerization inhibitor, which can block the cell cycle, cause apoptosis and prevent cell migration, and it exhibited reasonable therapeutic effect and low toxicity in the in vivo and in vitro anti-tumor evaluation. Our study repositioned an anthelmintic parbendazole to treat HNSCC, which revealed a therapeutic utility and provided a new treatment option for human cancers.

Integrative network analysis of early-stage lung adenocarcinoma identifies aurora kinase inhibition as interceptor of invasion and progression

Here we focus on the molecular characterization of clinically significant histological subtypes of early-stage lung adenocarcinoma (esLUAD), which is the most common histological subtype of lung cancer. Within lung adenocarcinoma, histology is heterogeneous and associated with tumor invasion and diverse clinical outcomes. We present a gene signature distinguishing invasive and non-invasive tumors among esLUAD. Using the gene signatures, we estimate an Invasiveness Score that is strongly associated with survival of esLUAD patients in multiple independent cohorts and with the invasiveness phenotype in lung cancer cell lines. Regulatory network analysis identifies aurora kinase as one of master regulators of the gene signature and the perturbation of aurora kinases in vitro and in a murine model of invasive lung adenocarcinoma reduces tumor invasion. Our study reveals aurora kinases as a therapeutic target for treatment of early-stage invasive lung adenocarcinoma.

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.

The Role of Natural Products as Sources of Therapeutic Agents for Innovative Drug Discovery

Emerging threats to human health require a concerted effort in search of both preventive and treatment strategies, placing natural products at the center of efforts to obtain new therapies and reduce disease spread and associated mortality. The therapeutic value of compounds found in plants has been known for ages, resulting in their utilization in homes and in clinics for the treatment of many ailments ranging from common headache to serious conditions such as wounds. Despite the advancement observed in the world, plant based medicines are still being used to treat many pathological conditions or are used as alternatives to modern medicines. In most cases, these natural products or plant-based medicines are used in an un-purified state as extracts. A lot of research is underway to identify and purify the active compounds responsible for the healing process. Some of the current drugs used in clinics have their origins as natural products or came from plant extracts. In addition, several synthetic analogues are natural product-based or plant-based. With the emergence of novel infectious agents such as the SARS-CoV-2 in addition to already burdensome diseases such as diabetes, cancer, tuberculosis and HIV/AIDS, there is need to come up with new drugs that can cure these conditions. Natural products offer an opportunity to discover new compounds that can be converted into drugs given their chemical structure diversity. Advances in analytical processes make drug discovery a multi-dimensional process involving computational designing and testing and eventual laboratory screening of potential drug candidates. Lead compounds will then be evaluated for safety, pharmacokinetics and efficacy. New technologies including Artificial Intelligence, better organ and tissue models such as organoids allow virtual screening, automation and high-throughput screening to be part of drug discovery. The use of bioinformatics and computation means that drug discovery can be a fast and efficient process and enable the use of natural products structures to obtain novel drugs. The removal of potential bottlenecks resulting in minimal false positive leads in drug development has enabled an efficient system of drug discovery. This review describes the biosynthesis and screening of natural products during drug discovery as well as methods used in studying natural products.

Identification and drug-induced reversion of molecular signatures of Alzheimer's disease onset and progression in AppNL-G-F, AppNL-F, and 3xTg-AD mouse models

BACKGROUND

In spite of many years of research, our understanding of the molecular bases of Alzheimer's disease (AD) is still incomplete, and the medical treatments available mainly target the disease symptoms and are hardly effective. Indeed, the modulation of a single target (e.g., β-secretase) has proven to be insufficient to significantly alter the physiopathology of the disease, and we should therefore move from gene-centric to systemic therapeutic strategies, where AD-related changes are modulated globally.

METHODS

Here we present the complete characterization of three murine models of AD at different stages of the disease (i.e., onset, progression and advanced). We combined the cognitive assessment of these mice with histological analyses and full transcriptional and protein quantification profiling of the hippocampus. Additionally, we derived specific Aβ-related molecular AD signatures and looked for drugs able to globally revert them.

RESULTS

We found that AD models show accelerated aging and that factors specifically associated with Aβ pathology are involved. We discovered a few proteins whose abundance increases with AD progression, while the corresponding transcript levels remain stable, and showed that at least two of them (i.e., lfit3 and Syt11) co-localize with Aβ plaques in the brain. Finally, we found two NSAIDs (dexketoprofen and etodolac) and two anti-hypertensives (penbutolol and bendroflumethiazide) that overturn the cognitive impairment in AD mice while reducing Aβ plaques in the hippocampus and partially restoring the physiological levels of AD signature genes to wild-type levels.

CONCLUSIONS

The characterization of three AD mouse models at different disease stages provides an unprecedented view of AD pathology and how this differs from physiological aging. Moreover, our computational strategy to chemically revert AD signatures has shown that NSAID and anti-hypertensive drugs may still have an opportunity as anti-AD agents, challenging previous reports.

Evaluation of connectivity map shows limited reproducibility in drug repositioning

The Connectivity Map (CMap) is a popular resource designed for data-driven drug repositioning using a large transcriptomic compendium. However, evaluations of its performance are limited. We used two iterations of CMap (CMap 1 and 2) to assess their comparability and reliability. We queried CMap 2 with CMap 1-derived signatures, expecting CMap 2 would highly prioritize the queried compounds; the success rate was 17%. Analysis of previously published prioritizations yielded similar results. Low recall is caused by low differential expression (DE) reproducibility both between CMaps and within each CMap. DE strength was predictive of reproducibility, and is influenced by compound concentration and cell-line responsiveness. Reproducibility of CMap 2 sample expression levels was also lower than expected. We attempted to identify the "better" CMap by comparison with a third dataset, but they were mutually discordant. Our findings have implications for CMap usage and we suggest steps for investigators to limit false positives.

Long non-coding RNA exploration for mesenchymal stem cell characterisation

BACKGROUND

The development of RNA sequencing (RNAseq) and the corresponding emergence of public datasets have created new avenues of transcriptional marker search. The long non-coding RNAs (lncRNAs) constitute an emerging class of transcripts with a potential for high tissue specificity and function. Therefore, we tested the biomarker potential of lncRNAs on Mesenchymal Stem Cells (MSCs), a complex type of adult multipotent stem cells of diverse tissue origins, that is frequently used in clinics but which is lacking extensive characterization.

RESULTS

We developed a dedicated bioinformatics pipeline for the purpose of building a cell-specific catalogue of unannotated lncRNAs. The pipeline performs ab initio transcript identification, pseudoalignment and uses new methodologies such as a specific k-mer approach for naive quantification of expression in numerous RNAseq data. We next applied it on MSCs, and our pipeline was able to highlight novel lncRNAs with high cell specificity. Furthermore, with original and efficient approaches for functional prediction, we demonstrated that each candidate represents one specific state of MSCs biology.

CONCLUSIONS

We showed that our approach can be employed to harness lncRNAs as cell markers. More specifically, our results suggest different candidates as potential actors in MSCs biology and propose promising directions for future experimental investigations.

Development of omics biomarkers for estrogen exposure using mRNA, miRNA and piRNAs

The number of chemicals requiring risk evaluation exceeds our capacity to provide the underlying data using traditional methodology. This has led to an increased focus on the development of novel approach methodologies. This work aimed to expand the panel of gene expression-based biomarkers to include responses to estrogens, to identify training strategies that maximize the range of applicable concentrations, and to evaluate the potential for two classes of small non-coding RNAs (sncRNAs), microRNA (miRNA) and piwi-interacting RNA (piRNA), as biomarkers. To this end larval Pimephales promelas (96 hpf +/- 1h) were exposed to 5 concentrations of 17α- ethinylestradiol (0.12, 1.25, 2.5, 5.0, 10.0 ng/L) for 48 h. For mRNA-based biomarker development, RNA-seq was conducted across all concentrations. For sncRNA biomarkers, small RNA libraries were prepared only for the control and 10.0 ng/L EE2 treatment. In order to develop an mRNA classifier that remained accurate over the range of exposure concentrations, three different training strategies were employed that focused on 10 ng/L, 2.5 ng/L or a combination of both. Classifiers were tested against an independent test set of individuals exposed to the same concentrations used in training and subsequently against concentrations not included in model training. Both random forest (RF) and logistic regression with elastic net regularizations (glmnet) models trained on 10 ng/L EE2 performed poorly when applied to lower concentrations. RF models trained with either the 2.5 ng/L or combination (2.5 + 10 ng/L) treatments were able to accurately discriminate exposed vs. non-exposed across all but the lowest concentrations. glmnet models were unable to accurately classify below 5 ng/L. With the exception of the 10 ng/L treatment, few mRNA differentially expressed genes (DEG) were observed, however, there was marked overlap of DEGs across treatments. Overlapping DEGs have well established linkages to estrogen and several of the 81 DEGs identified in the 10 ng/L treatment have been previously utilized as estrogenic biomarkers (vitellogenin, estrogen receptor-β). Following multiple test correction, no sncRNAs were found to be differentially expressed, however, both miRNA and piRNA classifiers were able to accurately discriminate control and 10 ng/L exposed organisms with AUCs of 0.83 and 1.0 respectively. We have developed a highly discriminative estrogenic mRNA biomarker that is accurate over a range of concentrations likely to be found in real-world exposures. We have demonstrated that both miRNA and piRNA are responsive to estrogenic exposure, suggesting the need to further investigate their regulatory roles in the estrogenic response.

Harnessing Drug Repurposing for Exploration of New Diseases: An Insight to Strategies and Case Studies

BACKGROUND

Traditional drug discovery is time consuming, costly, and risky process. Owing to the large investment, excessive attrition, and declined output, drug repurposing has become a blooming approach for the identification and development of new therapeutics. The method has gained momentum in the past few years and has resulted in many excellent discoveries. Industries are resurrecting the failed and shelved drugs to save time and cost. The process accounts for approximately 30% of the new US Food and Drug Administration approved drugs and vaccines in recent years.

METHODS

A systematic literature search using appropriate keywords were made to identify articles discussing the different strategies being adopted for repurposing and various drugs that have been/are being repurposed.

RESULTS

This review aims to describe the comprehensive data about the various strategies (Blinded search, computational approaches, and experimental approaches) used for the repurposing along with success case studies (treatment for orphan diseases, neglected tropical disease, neurodegenerative diseases, and drugs for pediatric population). It also inculcates an elaborated list of more than 100 drugs that have been repositioned, approaches adopted, and their present clinical status. We have also attempted to incorporate the different databases used for computational repurposing.

CONCLUSION

The data presented is proof that drug repurposing is a prolific approach circumventing the issues poised by conventional drug discovery approaches. It is a highly promising approach and when combined with sophisticated computational tools, it also carries high precision. The review would help researches in prioritizing the drugrepositioning method much needed to flourish the drug discovery research.

Nanocellulose-Reinforced Hydroxyapatite Nanobelt Membrane as a Stem Cell Multi-Lineage Differentiation Platform for Biomimetic Construction of Bioactive 3D Osteoid Tissue In Vitro

Severe bone defects, especially accompanied by vascular and peripheral nerve injuries, remain a massive challenge. Most studies related to bone tissue engineering have focused on osteogenic differentiation of mesenchymal stem cells (MSCs), and ignored the formation of blood vessels and nerves in the newly generated bone owing to the lack of proper materials and methodology for tuning stem cells differentiated into osteogenic, neuronal, and endothelial cells (ECs) in the same scaffold system. Herein, a nanocellulose-reinforced hybrid membrane with good mechanical properties and control over biodegradation by assembling ultralong hydroxyapatite nanobelts in a bacterial nanocellulose hydrogel is designed and synthesized. Osteogenic, neuronal cells are successfully differentiated on this hybrid membrane. Based on the multi-lineage differentiation property of the membrane, a bioactive 3D osteoid tissue (osteogenic, neural, and ECs) is mimetically constructed in vitro using layer-by-layer culture and integration. The bone regeneration ability of the as-prepared bioactive osteoid tissue is assessed in vivo via heterotopic osteogenesis experiments for eight weeks. The rapid new bone growth and formation of blood capillaries and nerve fibers prove that the hybrid membrane can be universally applied as a stem cell multi-lineage differentiation platform, which has significant applications in bone tissue engineering.

The Advantages of Connectivity Map Applied in Traditional Chinese Medicine

Amid the establishment and optimization of Connectivity Map (CMAP), the functional relationships among drugs, genes, and diseases are further explored. This biological database has been widely used to identify drugs with common mechanisms, repurpose existing drugs, discover the molecular mechanisms of unknown drugs, and find potential drugs for some diseases. Research on traditional Chinese medicine (TCM) has entered a new era in the wake of the development of bioinformatics and other subjects including network pharmacology, proteomics, metabolomics, herbgenomics, and so on. TCM gradually conforms to modern science, but there is still a torrent of limitations. In recent years, CMAP has shown its distinct advantages in the study of the components of TCM and the synergetic mechanism of TCM formulas; hence, the combination of them is inevitable.

A Transcriptome-Based Drug Discovery Paradigm for Neurodevelopmental Disorders

Advances in genetic discoveries have created substantial opportunities for precision medicine in neurodevelopmental disorders. Many of the genes implicated in these diseases encode proteins that regulate gene expression, such as chromatin-associated proteins, transcription factors, and RNA-binding proteins. The identification of targeted therapeutics for individuals carrying mutations in these genes remains a challenge, as the encoded proteins can theoretically regulate thousands of downstream targets in a considerable number of cell types. Here, we propose the application of a drug discovery approach originally developed for cancer called "transcriptome reversal" for these neurodevelopmental disorders. This approach attempts to identify compounds that reverse gene-expression signatures associated with disease states. ANN NEUROL 2021;89:199-211.

Machine and deep learning approaches for cancer drug repurposing

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.

Identification of securinine as vascular protective agent targeting atherosclerosis in vascular endothelial cells, smooth muscle cells, and apolipoprotein E deficient mice

BACKGROUND

Atherosclerosis is a chronic vascular disease and characterized by accumulation within the intima of inflammatory cells, smooth muscle cells, lipid, and connective tissue.

PURPOSE

The purpose of the present study was to identify natural agents that commonly reverse advanced atherosclerotic plaque to early atherosclerotic plaque.

METHODS

Differentially expressed genes (DEGs) were analyzed in silico. The differentially expressed genes from 9 intimal thickening and 8 fibrous cap atheroma tissue which were collected from GEO data were assessed by the connectivity map. Natural candidate securinine, a main compound from Securinega suffruticosa, was selected and administrated 1, 5 mg/kg/day in apolipoprotein-E-deficient (ApoE KO) mice for 18 weeks.

RESULTS

Securinine significantly showed lowered blood pressure and improvement of metabolic parameters with hyperlipidemia. The impairment in vasorelaxation was remarkably decreased by treatment with securinine. H&E staining revealed that treatment with securinine reduced atherosclerotic lesions. Securinine suppressed the expression of adhesion molecules and matrix metalloproteinase-2/-9 in both ApoE KO and vascular endothelial cells (HUVEC). In HUVEC pretreatment with securinine significantly inhibited ROS generation and NF-κB activation. Growth curve assays using the real-time cell analyzer showed that securinine significantly decreased TNF-α-induced aortic smooth muscle cell proliferation and migration in a dose-dependent manner.

CONCLUSION

Securinine may be a potential natural candidate for the treatment of atherosclerosis because it attenuates vascular inflammation and dysfunction as well as vascular lesion.

OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features

As the field of precision medicine progresses, treatments for patients with cancer are starting to be tailored to their molecular as well as their clinical features. The emerging cancer subtypes defined by these molecular features require that dedicated resources be used to assist the discovery of drug candidates for preclinical evaluation. Voluminous gene expression profiles of patients with cancer have been accumulated in public databases, enabling the creation of cancer-specific expression signatures. Meanwhile, large-scale gene expression profiles of cellular responses to chemical compounds have also recently became available. By matching the cancer-specific expression signature to compound-induced gene expression profiles from large drug libraries, researchers can prioritize small molecules that present high potency to reverse expression of signature genes for further experimental testing of their efficacy. This approach has proven to be an efficient and cost-effective way to identify efficacious drug candidates. However, the success of this approach requires multiscale procedures, imposing considerable challenges to many labs. To address this, we developed Open Cancer TherApeutic Discovery (OCTAD; http://octad.org ): an open workspace for virtually screening compounds targeting precise groups of patients with cancer using gene expression features. Its database includes 19,127 patient tissue samples covering more than 50 cancer types and expression profiles for 12,442 distinct compounds. The program is used to perform deep-learning-based reference tissue selection, disease gene expression signature creation, drug reversal potency scoring and in silico validation. OCTAD is available as a web portal and a standalone R package to allow experimental and computational scientists to easily navigate the tool.

PRRX1 promotes malignant properties in human osteosarcoma

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PRRX1 is a poor-prognosis marker of human osteosarcoma.

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PRRX1 promotes proliferation, invasion, and drug resistance in human osteosarcoma.

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Forskolin was identified using RNA expression signatures of PRRX1 knockdown.

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Forskolin decreased proliferation and migration in human osteosarcoma.

Paired related homeobox 1 (PRRX1) is a marker of limb bud mesenchymal cells, and deficiency of p53 or Rb in Prrx1-positive cells induces osteosarcoma in several mouse models. However, the regulatory roles of PRRX1 in human osteosarcoma have not been defined. In this study, we performed PRRX1 immunostaining on 35 human osteosarcoma specimens to assess the correlation between PRRX1 level and overall survival. In patients with osteosarcoma, the expression level of PRRX1 positively correlated with poor prognosis or the ratio of lung metastasis. Additionally, we found PRRX1 expression on in 143B cells, a human osteosarcoma line with a high metastatic capacity. Downregulation of PRRX1 not only suppressed proliferation and invasion but also increased the sensitivity to cisplatin and doxorubicin. When 143B cells were subcutaneously transplanted into nude mice, PRRX1 knockdown decreased tumor sizes and rates of lung metastasis. Interestingly, forskolin, a chemical compound identified by Connectivity Map analysis using RNA expression signatures during PRRX1 knockdown, decreased tumor proliferation and cell migration to the same degree as PRRX1 knockdown. These results demonstrate that PRRX1 promotes tumor malignancy in human osteosarcoma.

Bio-inspired polymeric iron-doped hydroxyapatite microspheres as a tunable carrier of rhBMP-2

Hybrid superparamagnetic microspheres with bone-like composition, previously developed by a bio-inspired assembling/mineralization process, are evaluated for their ability to uptake and deliver recombinant human bone morphogenetic protein-2 (rhBMP-2) in therapeutically-relevant doses along with prolonged release profiles. The comparison with hybrid non-magnetic and with non-mineralized microspheres highlights the role of nanocrystalline, nanosize mineral phases when they exhibit surface charged groups enabling the chemical linking with the growth factor and thus moderating the release kinetics. All the microspheres show excellent osteogenic ability with human mesenchymal stem cells whereas the hybrid mineralized ones show a slow and sustained release of rhBMP-2 along 14 days of soaking into cell culture medium with substantially bioactive effect, as reported by assay with C2C12 BRE-Luc cell line. It is also shown that the release extent can be modulated by the application of pulsed electromagnetic field, thus showing the potential of remote controlling the bioactivity of the new micro-devices which is promising for future application of hybrid biomimetic microspheres in precisely designed and personalized therapies.

Harnessing big 'omics' data and AI for drug discovery in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common form of primary adult liver cancer. After nearly a decade with sorafenib as the only approved treatment, multiple new agents have demonstrated efficacy in clinical trials, including the targeted therapies regorafenib, lenvatinib and cabozantinib, the anti-angiogenic antibody ramucirumab, and the immune checkpoint inhibitors nivolumab and pembrolizumab. Although these agents offer new promise to patients with HCC, the optimal choice and sequence of therapies remains unknown and without established biomarkers, and many patients do not respond to treatment. The advances and the decreasing costs of molecular measurement technologies enable profiling of HCC molecular features (such as genome, transcriptome, proteome and metabolome) at different levels, including bulk tissues, animal models and single cells. The release of such data sets to the public enhances the ability to search for information from these legacy studies and provides the opportunity to leverage them to understand HCC mechanisms, rationally develop new therapeutics and identify candidate biomarkers of treatment response. Here, we provide a comprehensive review of public data sets related to HCC and discuss how emerging artificial intelligence methods can be applied to identify new targets and drugs as well as to guide therapeutic choices for improved HCC treatment.

Network-based method for drug target discovery at the isoform level

Identification of primary targets associated with phenotypes can facilitate exploration of the underlying molecular mechanisms of compounds and optimization of the structures of promising drugs. However, the literature reports limited effort to identify the target major isoform of a single known target gene. The majority of genes generate multiple transcripts that are translated into proteins that may carry out distinct and even opposing biological functions through alternative splicing. In addition, isoform expression is dynamic and varies depending on the developmental stage and cell type. To identify target major isoforms, we integrated a breast cancer type-specific isoform coexpression network with gene perturbation signatures in the MCF7 cell line in the Connectivity Map database using the ‘shortest path’ drug target prioritization method. We used a leukemia cancer network and differential expression data for drugs in the HL-60 cell line to test the robustness of the detection algorithm for target major isoforms. We further analyzed the properties of target major isoforms for each multi-isoform gene using pharmacogenomic datasets, proteomic data and the principal isoforms defined by the APPRIS and STRING datasets. Then, we tested our predictions for the most promising target major protein isoforms of DNMT1, MGEA5 and P4HB4 based on expression data and topological features in the coexpression network. Interestingly, these isoforms are not annotated as principal isoforms in APPRIS. Lastly, we tested the affinity of the target major isoform of MGEA5 for streptozocin through in silico docking. Our findings will pave the way for more effective and targeted therapies via studies of drug targets at the isoform level.

Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data

Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important question is which features for targets are most relevant to DTI prediction. As an early attempt to answer this question, we objectively compared three canonical target features extracted from: (i) the expression profiles by gene knockdown (GEPs); (ii) the protein–protein interaction network (PPI network); and (iii) the pathway membership (PM) of a target gene. For drug features, the large-scale drug-induced transcriptome dataset, or the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset was used. All these features are closely related to protein function or drug MoA, of which utility is only sparsely investigated. In particular, few studies have compared the three types of target features in DNN-based DTI prediction under the same evaluation scheme. Among the three target features, the PM and the PPI network show similar performances superior to GEPs. DNN models based on both features consistently outperformed other machine learning methods such as naïve Bayes, random forest, or logistic regression.

Insights into Computational Drug Repurposing for Neurodegenerative Disease

Computational drug repurposing has the ability to remarkably reduce drug development time and cost in an era where these factors are prohibitively high. Several examples of successful repurposed drugs exist in fields such as oncology, diabetes, leprosy, inflammatory bowel disease, among others, however computational drug repurposing in neurodegenerative disease has presented several unique challenges stemming from the lack of validation methods and difficulty in studying heterogenous diseases of aging. Here, we examine existing approaches to computational drug repurposing, including molecular, clinical, and biophysical methods, and propose data sources and methods to advance computational drug repurposing in neurodegenerative disease using Alzheimer's disease as an example.

Connectivity Mapping: Methods and Applications

Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.

Identification of Pyrvinium, an Anthelmintic Drug, as a Novel Anti-Adipogenic Compound Based on the Gene Expression Microarray and Connectivity Map

Obesity is a serious health problem, while the current anti-obesity drugs are not very effective. The Connectivity Map (C-Map), an in-silico drug screening approach based on gene expression profiles, has recently been indicated as a promising strategy for drug repositioning. In this study, we performed mRNA expression profile analysis using microarray technology and identified 435 differentially expressed genes (DEG) during adipogenesis in both C3H10T1/2 and 3T3-L1 cells. Then, DEG signature was uploaded into C-Map, and using pattern-matching methods we discovered that pyrvinium, a classical anthelminthic, is a novel anti-adipogenic differentiation agent. Pyrvinium suppressed adipogenic differentiation in a dose-dependent manner, as evidenced by Oil Red O staining and the mRNA levels of adipogenic markers. Furthermore, we identified that the inhibitory effect of pyrvinium was resulted primarily from the early stage of adipogenesis. Molecular studies showed that pyrvinium downregulated the expression of key transcription factors C/EBPa and PPARγ. The mRNA levels of notch target genes Hes1 and Hey1 were obviously reduced after pyrvinium treatment. Taken together, this study identified many differentially expressed genes involved in adipogenesis and demonstrated for the first time that pyrvinium is a novel anti-adipogenic compound for obesity therapy. Meanwhile, we provided a new strategy to explore potential anti-obesity drugs.

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

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