Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery

EGeRepDR: An enhanced genetic-based representation learning for drug repurposing using multiple biomedical sources

MOTIVATION

Drug repurposing (DR) is an imminent approach for identifying novel therapeutic indications for the available drugs and discovering novel drugs for previously untreatable diseases. Nowadays, DR has major attention in the pharmaceutical industry due to the high cost and time of launching new drugs to the market through traditional drug development. DR task majorly depends on genetic information since the drugs revert the modified Gene Expression (GE) of diseases to normal. Many of the existing studies have not considered the genetic importance of predicting the potential candidates.

METHOD

We proposed a novel multimodal framework that utilizes genetic aspects of drugs and diseases such as genes, pathways, gene signatures, or expression to enhance the performance of DR using various data sources. Firstly, the heterogeneous biological network (HBN) is constructed with three types of nodes namely drug, disease, and gene, and 4 types of edges similarities (drug, gene, and disease), drug-gene, gene-disease, and drug-disease. Next, a modified graph auto-encoder (GAE*) model is applied to learn the representation of drug and disease nodes using the topological structure and edge information. Secondly, the HBN is enhanced with the information extracted from biomedical literature and ontology using a novel semi-supervised pattern embedding-based bootstrapping model and novel DR perspective representation learning respectively to improve the prediction performance. Finally, our proposed system uses a neural network model to generate the probability score of drug-disease pairs.

RESULTS

We demonstrate the efficiency of the proposed model on various datasets and achieved outstanding performance in 5-fold cross-validation (AUC = 0.99, AUPR = 0.98). Further, we validated the top-ranked potential candidates using pathway analysis and proved that the known and predicted candidates share common genes in the pathways.

FAP promotes clear cell renal cell carcinoma progression via activating the PI3K/AKT/mTOR signaling pathway

OBJECTIVE

Herein, we aimed at exploring the FAP expression in clear cell renal cell carcinoma (ccRCC) along with its clinical implication.

METHODS

Using computational tools analysis of different freely accessible gene databases, the expression pattern, clinical importance, co-expressed genes, and signaling pathways of FAP in ccRCC were thoroughly investigated. FAP expression was examined in clinical ccRCC specimens through qRT-PCR, western blotting and immunohistochemistry. Furthermore, in vitro and in vivo experiments were carried out using flow cytometry, CCK-8, wound-healing and Transwell assays, as well as xenograft tumor model, respectively.

RESULTS

FAP levels were found to be significantly elevated in ccRCC based on bioinformatic data from public databases. Patients who exhibited higher expression levels of FAP had poorer prognoses, according to Kaplan-Meier analysis of survival data. In addition, diagnostic and prognostic value of FAP in ccRCC was figured out by ROC curve and prognostic nomogram model. In vitro study revealed that the over-expression FAP accelerated cell proliferation, migration as well as invasion, and suppressed cell apoptosis, but silencing of FAP had the opposite effect. FAP suppression reduced the PI3K/AKT/mTOR pathway's stimulation, whereas FAP up-regulation increased the stimulation of the pathway. Blocking the PI3K/AKT/mTOR signaling pathway with the dual PI3K/mTOR inhibitor BEZ235 repressesed cancer-promoting effect of FAP. Additionally, we found that the downregulation of FAP was effective at slowing tumor progression in vivo.

CONCLUSION

It is possible that FAP could be a reliable biomarker for the diagnosis and prognosis of ccRCC because of its role in the ccRCC progression via triggering the PI3K/AKT/mTOR signaling pathway.

Identification of Serpin peptidase inhibitor clade A member 1 (SERPINA1) might be a poor prognosis biomarker promoting the progression of papillary thyroid cancer

BACKGROUND

Papillary thyroid carcinoma (PTC) is the most prevalent malignancy within the endocrine system, exhibiting a rapid growth rate in recent years. Serpin peptidase inhibitor clade A member 1 (SERPINA1) has been previously proposed as a diagnostic biomarker; however, it's potential molecular relevance and biological function in PTC remains largely unexplored.

METHODS

Our study utilized multi-omics bioinformatic data from several public databases, supplemented with transcriptional profiles using our local cohort comprising 79 paired PTC samples.

RESULTS

Using multi-omics profiling of a PTC cohort, we have identified SERPINA1 as a potential oncogene involved in PTC progression. Our clinical analysis revealed a significant association between SERPINA1 expression and mutations in BRAFV600E and RAS. Furthermore, SERPINA1 level was correlated with clinicopathological factors in patients with PTC and with a worse prognosis in early-stage patients. Functionally, we found a strong correlation between SERPINA1 expression and increased infiltration of dendritic cells and regulatory T-cells, suggesting an elevated level of immune infiltration. Moreover, SERPINA1 knockdown reduced the proliferative and migrational ability of PTC cells in vitro.

CONCLUSION

Our study highlights the high expression of SERPINA1 in PTC and its potential role in shaping the immune microenvironment, thereby promoting disease progression. These findings suggest that SERPINA1 could serve as a promising therapeutic target for intervention in PTC.

A drug repurposing method based on inhibition effect on gene regulatory network

Numerous computational drug repurposing methods have emerged as efficient alternatives to costly and time-consuming traditional drug discovery approaches. Some of these methods are based on the assumption that the candidate drug should have a reversal effect on disease-associated genes. However, such methods are not applicable in the case that there is limited overlap between disease-related genes and drug-perturbed genes. In this study, we proposed a novel Drug Repurposing method based on the Inhibition Effect on gene regulatory network (DRIE) to identify potential drugs for cancer treatment. DRIE integrated gene expression profile and gene regulatory network to calculate inhibition score by using the shortest path in the disease-specific network. The results on eleven datasets indicated the superior performance of DRIE when compared to other state-of-the-art methods. Case studies showed that our method effectively discovered novel drug-disease associations. Our findings demonstrated that the top-ranked drug candidates had been already validated by CTD database. Additionally, it clearly identified potential agents for three cancers (colorectal, breast, and lung cancer), which was beneficial when annotating drug-disease relationships in the CTD. This study proposed a novel framework for drug repurposing, which would be helpful for drug discovery and development.

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 .

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

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

Overexpression of PKMYT1 associated with poor prognosis and immune infiltration may serve as a target in triple-negative breast cancer

Breast cancer (BC) is one of the most common malignancies among women worldwide. It is necessary to search for improvement in diagnosis and treatment methods to improve the prognosis. Protein kinase, membrane associated tyrosine/threonine 1 (PKMYT1), a member of the Wee family of protein kinases, has been studied in some tumors except BC. This study has explored that PKMYT1 functional role by bioinformatics methods combined with local clinical samples and experiments. Comprehensive analysis showed that PKMYT1 expression was higher in BC tissues, especially in advanced patients than that in normal breast tissues. The expression of PKMYT1 was an independent determinant for BC patients' prognosis when combined with the clinical features. In addition, based on multi-omics analysis, we found that the PKMYT1 expression was closely relevant to several oncogenic or tumor suppressor gene variants. The analysis of single-cell sequencing indicated that PKMYT1 expression was upregulated in triple-negative breast cancer (TNBC), consistent with the results of bulk RNA-sequencing. High PKMYT1 expression was correlated with a poor prognosis. Functional enrichment analysis revealed that PKMYT1 expression was associated with cell cycle-related, DNA replication-related, and cancer-related pathways. Further research revealed that PKMYT1 expression was linked to immune cell infiltration in the tumor microenvironment. Additionally, loss-of-function experiments in vitro were performed to investigate the role of PKMYT1. TNBC cell lines' proliferation, migration, and invasion were inhibited when PKMYT1 expression was knock-down. Besides, the down-regulation of PKMYT1 induced apoptosis in vitro. As a result, PKMYT1 might be a biomarker for prognosis and a therapeutic target for TNBC.

Folic acid: a potential inhibitor against SARS-CoV-2 nucleocapsid protein

CONTEXT

Coronavirus disease 2019 is a global pandemic. Studies suggest that folic acid has antiviral effects. Molecular docking shown that folic acid can act on SARS-CoV-2 Nucleocapsid Phosphoprotein (SARS-CoV-2 N).

OBJECTIVE

To identify novel molecular therapeutic targets for SARS-CoV-2.

MATERIALS AND METHODS

Traditional Chinese medicine targets and virus-related genes were identified with network pharmacology and big data analysis. Folic acid was singled out by molecular docking, and its potential target SARS-CoV-2 N was identified. Inhibition of SARS-CoV-2 N of folic acid was verified at the cellular level.

RESULTS

In total, 8355 drug targets were potentially involved in the inhibition of SARS-CoV-2. 113 hub genes were screened by further association analysis between targets and virus-related genes. The hub genes related compounds were analysed and folic acid was screened as a potential new drug. Moreover, molecular docking showed folic acid could target on SARS-CoV-2 N which inhibits host RNA interference (RNAi). Therefore, this study was based on RNAi to verify whether folic acid antagonises SARS-CoV-2 N. Cell-based experiments shown that RNAi decreased mCherry expression by 81.7% (p < 0.001). This effect was decreased by 8.0% in the presence of SARS-CoV-2 N, indicating that SARS-CoV-2 N inhibits RNAi. With increasing of folic acid concentration, mCherry expression decreased, indicating that folic acid antagonises the regulatory effect of SARS-CoV-2 N on host RNAi.

DISCUSSION AND CONCLUSIONS

Folic acid may be an antagonist of SARS-CoV-2 N, but its effect on viruses unclear. In future, the mechanisms of action of folic acid against SARS-CoV-2 N should be studied.

Artificial intelligence to guide precision anticancer therapy with multitargeted kinase inhibitors

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Transcriptome of pituitary function changes in rat model of high altitude cerebral edema

High altitude cerebral edema (HACE) is a serious subtype of acute mountain sickness (AMS). Studies have suggested that increased expression of corticotropin releasing hormone receptor 1 (CRFR1) in pituitary is related to the development of HACE, but no study has revealed the molecular landscape of pituitary function changes in this process. Rat model of HACE was established by simulating the high-altitude hypobaric hypoxia environment. Then RNA-sequencing was performed of rat pituitary gland (PG) in HACE and non-HACE groups. The function annotations, enrichment analysis, protein-protein interaction (PPI) network, chromosome location and drug repositioning of differentially expressed genes (DEGs) were explored based on the transcriptomic data. And we found pituitary secretion function was disordered in HACE, which was partly due to activated inflammation and oxidative stress. In addition, we identified potential biomarkers for early recognition of pituitary dysfunction and potential protective drugs for pituitary function in HACE.

Bioactive Microbial Metabolites in Cancer Therapeutics: Mining, Repurposing, and Their Molecular Targets

The persistence and resurgence of cancer, characterized by abnormal cell growth and differentiation, continues to be a serious public health concern critically affecting public health, social life, and the global economy. Hundreds of putative drug molecules of synthetic and natural origin were approved for anticancer therapy in the last few decades. Although conventional anticancer treatment strategies have promising aspects, several factors such as their limitations, drug resistance, and side effects associated with them demand more effort in repositioning or developing novel therapeutic regimens. The rich heritage of microbial bioactive components remains instrumental in providing novel avenues for cancer therapeutics. Actinobacteria, Firmicutes, and fungi have a plethora of bioactive compounds, which received attention for their efficacy in cancer treatment targeting different pathways responsible for abnormal cell growth and differentiation. Yet the full potential remains underexplored to date, and novel compounds from such microbes are reported regularly. In addition, the advent of computational tools has further augmented the mining of microbial secondary metabolites and identifying their molecular targets in cancer cells. Furthermore, the drug-repurposing strategy has facilitated the use of approved drugs of microbial origin in regulating cancer cell growth and progression. The wide diversity of microbial compounds, different mining approaches, and multiple modes of action warrant further investigations on the current status of microbial metabolites in cancer therapeutics. Hence, in this review, we have critically discussed the untapped potential of microbial products in mitigating cancer progression. The review also summarizes the impact of drug repurposing in cancer therapy and discusses the novel avenues for future therapeutic drug development against cancer.

Analysis of transcriptomic responses to SARS-CoV-2 reveals plausible defective pathways responsible for increased susceptibility to infection and complications and helps to develop fast-track repositioning of drugs against COVID-19

Background

To understand the transcriptomic response to SARS-CoV-2 infection, is of the utmost importance to design diagnostic tools predicting the severity of the infection.

Methods

We have performed a deep sampling analysis of the viral transcriptomic data oriented towards drug repositioning. Using different samplers, the basic principle of this methodology the biological invariance, which means that the pathways altered by the disease, should be independent on the algorithm used to unravel them.

Results

The transcriptomic analysis of the altered pathways, reveals a distinctive inflammatory response and potential side effects of infection. The virus replication causes, in some cases, acute respiratory distress syndrome in the lungs, and affects other organs such as heart, brain, and kidneys. Therefore, the repositioned drugs to fight COVID-19 should, not only target the interferon signalling pathway and the control of the inflammation, but also the altered genetic pathways related to the side effects of infection. We also show via Principal Component Analysis that the transcriptome signatures are different from influenza and RSV. The gene COL1A1, which controls collagen production, seems to play a key/vital role in the regulation of the immune system. Additionally, other small-scale signature genes appear to be involved in the development of other COVID-19 comorbidities.

Conclusions

Transcriptome-based drug repositioning offers possible fast-track antiviral therapy for COVID-19 patients. It calls for additional clinical studies using FDA approved drugs for patients with increased susceptibility to infection and with serious medical complications.

Identifying candidate genes and drug targets for Alzheimer's disease by an integrative network approach using genetic and brain region-specific proteomic data

Genome-wide association studies (GWAS) have identified more than 75 genetic variants associated with Alzheimer's disease (ad). However, how these variants function and impact protein expression in brain regions remain elusive. Large-scale proteomic datasets of ad postmortem brain tissues have become available recently. In this study, we used these datasets to investigate brain region-specific molecular pathways underlying ad pathogenesis and explore their potential drug targets. We applied our new network-based tool, Edge-Weighted Dense Module Search of GWAS (EW_dmGWAS), to integrate ad GWAS statistics of 472 868 individuals with proteomic profiles from two brain regions from two large-scale ad cohorts [parahippocampal gyrus (PHG), sample size n = 190; dorsolateral prefrontal cortex (DLPFC), n = 192]. The resulting network modules were evaluated using a scale-free network index, followed by a cross-region consistency evaluation. Our EW_dmGWAS analyses prioritized 52 top module genes (TMGs) specific in PHG and 58 TMGs in DLPFC, of which four genes (CLU, PICALM, PRRC2A and NDUFS3) overlapped. Those four genes were significantly associated with ad (GWAS gene-level false discovery rate < 0.05). To explore the impact of these genetic components on TMGs, we further examined their differentially co-expressed genes at the proteomic level and compared them with investigational drug targets. We pinpointed three potential drug target genes, APP, SNCA and VCAM1, specifically in PHG. Gene set enrichment analyses of TMGs in PHG and DLPFC revealed region-specific biological processes, tissue-cell type signatures and enriched drug signatures, suggesting potential region-specific drug repurposing targets for ad.

Prioritization of risk genes in multiple sclerosis by a refined Bayesian framework followed by tissue-specificity and cell type feature assessment

BACKGROUND

Multiple sclerosis (MS) is a debilitating immune-mediated disease of the central nervous system that affects over 2 million people worldwide, resulting in a heavy burden to families and entire communities. Understanding the genetic basis underlying MS could help decipher the pathogenesis and shed light on MS treatment. We refined a recently developed Bayesian framework, Integrative Risk Gene Selector (iRIGS), to prioritize risk genes associated with MS by integrating the summary statistics from the largest GWAS to date (n = 115,803), various genomic features, and gene-gene closeness.

RESULTS

We identified 163 MS-associated prioritized risk genes (MS-PRGenes) through the Bayesian framework. We replicated 35 MS-PRGenes through two-sample Mendelian randomization (2SMR) approach by integrating data from GWAS and Genotype-Tissue Expression (GTEx) expression quantitative trait loci (eQTL) of 19 tissues. We demonstrated that MS-PRGenes had more substantial deleterious effects and disease risk. Moreover, single-cell enrichment analysis indicated MS-PRGenes were more enriched in activated macrophages and microglia macrophages than non-activated ones in control samples. Biological and drug enrichment analyses highlighted inflammatory signaling pathways.

CONCLUSIONS

In summary, we predicted and validated a high-confidence MS risk gene set from diverse genomic, epigenomic, eQTL, single-cell, and drug data. The MS-PRGenes could further serve as a benchmark of MS GWAS risk genes for future validation or genetic studies.

Major Vault Protein (MVP) Associated With BRAF V600E Mutation Is an Immune Microenvironment-Related Biomarker Promoting the Progression of Papillary Thyroid Cancer via MAPK/ERK and PI3K/AKT Pathways

Papillary thyroid cancer (PTC) is the most common malignancy of the endocrine system, with an increase in incidence frequency. Major vault protein (MVP) is the main structural protein of the vault complex that has already been investigated in specific cancers. Yet the underlying biological functions and molecular mechanisms of MVP in PTC still remain considerably uncharacterized. Comprehensive analyses are predicated on several public datasets and local RNA-Seq cohort. Clinically, we found that MVP was upregulated in human PTC than in non-cancerous thyroid tissue and was correlated with vital clinicopathological parameters in PTC patients. MVP expression was associated with BRAF V600E, RAS, TERT, and RET status, and it was correlated with worse progression-free survival in PTC patients. Functionally, enrichment analysis provided new clues for the close relationship between MVP with cancer-related signaling pathways and the immune microenvironment in PTC. In PTC with high MVP expression, we found CD8+ T cells, regulatory T cells, and follicular helper T cells have a higher infiltration level. Intriguingly, MVP expression was positively correlated with multiple distinct phases of the anti-cancer immunity cycle. MVP knockdown significantly suppressed cell viability and colony formation, and promoted apoptosis. In addition, downregulated MVP markedly inhibited the migration and invasion potential of PTC cells. The rescue experiments showed that MVP could reverse the level of cell survival and migration. Mechanistically, MVP exerts its oncogenic function in PTC cells through activating PI3K/AKT/mTOR and MAPK/ERK pathways. These results point out that MVP is a reliable biomarker related to the immune microenvironment and provide a basis for elucidating the oncogenic roles of MVP in PTC progression.

Strategies to identify candidate repurposable drugs: COVID-19 treatment as a case example

Drug repurposing is an invaluable strategy to identify new uses for existing drug therapies that overcome many of the time and financial costs associated with novel drug development. The COVID-19 pandemic has driven an unprecedented surge in the development and use of bioinformatic tools to identify candidate repurposable drugs. Using COVID-19 as a case study, we discuss examples of machine-learning and signature-based approaches that have been adapted to rapidly identify candidate drugs. The Library of Integrated Network-based Signatures (LINCS) and Connectivity Map (CMap) are commonly used repositories and have the advantage of being amenable to use by scientists with limited bioinformatic training. Next, we discuss how these recent advances in bioinformatic drug repurposing approaches might be adapted to identify repurposable drugs for CNS disorders. As the development of novel therapies that successfully target the cause of neuropsychiatric and neurological disorders has stalled, there is a pressing need for innovative strategies to treat these complex brain disorders. Bioinformatic approaches to identify repurposable drugs provide an exciting avenue of research that offer promise for improved treatments for CNS disorders.

Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review

Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.

Transcriptional landscape in rat intestines under hypobaric hypoxia

Oxygen metabolism is closely related to the intestinal homeostasis environment, and the occurrence of many intestinal diseases is as a result of the destruction of oxygen gradients. The hypobaric hypoxic environment of the plateau can cause dysfunction of the intestine for humans, such as inflammation. The compensatory response of the small intestine cells to the harsh environment definitely changes their gene expression. How the small intestine cells response the hypobaric hypoxic environment is still unclear. We studied the rat small intestine under hypobaric hypoxic conditions to explore the transcriptional changes in rats under acute/chronic hypobaric hypoxic conditions. We randomly divided rats into three groups: normal control group (S), acute hypobaric hypoxia group, exposing to hypobaric hypoxic condition for 2 weeks (W2S) and chronic hypobaric hypoxia group, exposing to hypobaric hypoxic condition for 4 weeks (W4S). The RNA sequencing was performed on the small intestine tissues of the three groups of rats. The results of principal component analysis showed that the W4S and W2S groups were quite different from the control group. We identified a total of 636 differentially expressed genes, such as ATP binding cassette, Ace2 and Fabp. KEGG pathway analysis identified several metabolic and digestive pathways, such as PPAR signaling pathway, glycerolipid metabolism, fat metabolism, mineral absorption and vitamin metabolism. Cogena analysis found that up-regulation of digestive and metabolic functions began from the second week of high altitude exposure. Our study highlights the critical role of metabolic and digestive pathways of the intestine in response to the hypobaric hypoxic environment, provides new aspects for the molecular effects of hypobaric hypoxic environment on intestine, and raises further questions about between the lipid metabolism disorders and inflammation.

Drug Repurposing in Oncology, an Attractive Opportunity for Novel Combinatorial Regimens

The costs of developing, validating and buying new drugs are dramatically increasing. On the other hand, sobering economies have difficulties in sustaining their healthcare systems, particularly in countries with an elderly population requiring increasing welfare. This conundrum requires immediate action, and a possible option is to study the large, already present arsenal of drugs approved and to use them for innovative therapies. This possibility is particularly interesting in oncology, where the complexity of the cancer genome dictates in most patients a multistep therapeutic approach. In this review, we discuss a) Computational approaches; b) preclinical models; c) currently ongoing or already published clinical trials in the drug repurposing field in oncology; and d) drug repurposing to overcome resistance to previous therapies.

Scoring functions for drug-effect similarity

MOTIVATION

The difficulty to find new drugs and bring them to the market has led to an increased interest to find new applications for known compounds. Biological samples from many disease contexts have been extensively profiled by transcriptomics, and, intuitively, this motivates to search for compounds with a reversing effect on the expression of characteristic disease genes. However, disease effects may be cell line-specific and also depend on other factors, such as genetics and environment. Transcription profile changes between healthy and diseased cells relate in complex ways to profile changes gathered from cell lines upon stimulation with a drug. Despite these differences, we expect that there will be some similarity in the gene regulatory networks at play in both situations. The challenge is to match transcriptomes for both diseases and drugs alike, even though the exact molecular pathology/pharmacogenomics may not be known.

RESULTS

We substitute the challenge to match a drug effect to a disease effect with the challenge to match a drug effect to the effect of the same drug at another concentration or in another cell line. This is welldefined, reproducible in vitro and in silico and extendable with external data. Based on the Connectivity Map (CMap) dataset, we combined 26 different similarity scores with six different heuristics to reduce the number of genes in the model. Such gene filters may also utilize external knowledge e.g. from biological networks. We found that no similarity score always outperforms all others for all drugs, but the Pearson correlation finds the same drug with the highest reliability. Results are improved by filtering for highly expressed genes and to a lesser degree for genes with large fold changes. Also a network-based reduction of contributing transcripts was beneficial, here implemented by the FocusHeuristics. We found no drop in prediction accuracy when reducing the whole transcriptome to the set of 1000 landmark genes of the CMap's successor project Library of Integrated Network-based Cellular Signatures. All source code to re-analyze and extend the CMap data, the source code of heuristics, filters and their evaluation are available to propel the development of new methods for drug repurposing.

AVAILABILITY

https://bitbucket.org/ibima/moldrugeffectsdb.

CONTACT

steffen.moeller@uni-rostock.de.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Briefings in Bioinformatics online.

Up-regulation of L Antigen Family Member 3 Associates With Aggressive Progression of Breast Cancer

The role of L Antigen Family Member 3 (LAGE3) in breast cancer (BC) has not been sufficiently studied. In this study, we explored the clinical value and biological functions of LAGE3 in BC. Comprehensive analysis of LAGE3 was carried out on The Cancer Genome Atlas, Molecular Taxonomy of Breast Cancer International Consortium and Gene Expression Omnibus datasets. Results showed that LAGE3 expression was higher in BC tissues than in normal breast tissues of public datasets and our local cohort. Moreover, its expression was higher in BC patients with larger tumor size, significant lymph node metastasis, higher tumor grade, and more advanced disease stage. High expression of LAGE3 was correlated with poor prognosis, and LAGE3 could independently predict survival of BC patients. Functional enrichment analysis revealed a correlation between LAGE3 expression and biochemical metabolism and immune-related terms and cancer-related pathways. Analysis of tumor microenvironment indicated that LAGE3 expression was associated with the immune cell infiltration and anti-cancer immunity cycle. LAGE3 expression was higher in triple-negative breast cancer (TNBC) compared to hormone receptor-positive BC, but not HER2-positive subtype. Suppression of LAGE3 expression inhibited the proliferation and induced apoptosis of TNBC cell lines. Besides, the down-regulation of LAGE3 attenuated the migration and invasion but reduced the expression level of epithelial-mesenchymal-transition related proteins in TNBC cell lines. In conclusion, this study demonstrated for the first time that LAGE3 promotes the progression of BC. Therefore, it may be a potential diagnostic and prognostic biomarker, as well as a treatment target for BC.

Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2

BACKGROUND

The Coronavirus Disease 2019 (COVID-19) pandemic has infected over 10 million people globally with a relatively high mortality rate. There are many therapeutics undergoing clinical trials, but there is no effective vaccine or therapy for treatment thus far. After affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), molecular signaling pathways of host cells play critical roles during the life cycle of SARS-CoV-2. Thus, it is significant to identify the involved molecular signaling pathways within the host cells. Drugs targeting these molecular signaling pathways could be potentially effective for COVID-19 treatment.

METHODS

In this study, we developed a novel integrative analysis approach to identify the related molecular signaling pathways within host cells, and repurposed drugs as potentially effective treatments for COVID-19, based on the transcriptional response of host cells.

RESULTS

We identified activated signaling pathways associated with the infection caused SARS-CoV-2 in human lung epithelial cells through integrative analysis. Then, the activated gene ontologies (GOs) and super GOs were identified. Signaling pathways and GOs such as MAPK, JNK, STAT, ERK, JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were particularly activated. Based on the identified signaling pathways and GOs, a set of potentially effective drugs were repurposed by integrating the drug-target and reverse gene expression data resources. In addition to many drugs being evaluated in clinical trials, the dexamethasone was top-ranked in the prediction, which was the first reported drug to be able to significantly reduce the death rate of COVID-19 patients receiving respiratory support.

CONCLUSIONS

The integrative genomics data analysis and results can be helpful to understand the associated molecular signaling pathways within host cells, and facilitate the discovery of effective drugs for COVID-19 treatment.

Comparative transcriptome analysis of SARS-CoV, MERS-CoV, and SARS-CoV-2 to identify potential pathways for drug repurposing

The ongoing COVID-19 pandemic caused by the coronavirus, SARS-CoV-2, has already caused in excess of 1.25 million deaths worldwide, and the number is increasing. Knowledge of the host transcriptional response against this virus and how the pathways are activated or suppressed compared to other human coronaviruses (SARS-CoV, MERS-CoV) that caused outbreaks previously can help in the identification of potential drugs for the treatment of COVID-19. Hence, we used time point meta-analysis to investigate available SARS-CoV and MERS-CoV in-vitro transcriptome datasets in order to identify the significant genes and pathways that are dysregulated at each time point. The subsequent over-representation analysis (ORA) revealed that several pathways are significantly dysregulated at each time point after both SARS-CoV and MERS-CoV infection. We also performed gene set enrichment analyses of SARS-CoV and MERS-CoV with that of SARS-CoV-2 at the same time point and cell line, the results of which revealed that common pathways are activated and suppressed in all three coronaviruses. Furthermore, an analysis of an in-vivo transcriptomic dataset of COVID-19 patients showed that similar pathways are enriched to those identified in the earlier analyses. Based on these findings, a drug repurposing analysis was performed to identify potential drug candidates for combating COVID-19.

MNBDR: A Module Network Based Method for Drug Repositioning

Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein-protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein-protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.

T-Box Transcription Factor 22 Is an Immune Microenvironment-Related Biomarker Associated With the BRAF V600E Mutation in Papillary Thyroid Carcinoma

Papillary thyroid cancer (PTC) is the most common malignant disease in endocrine systems. T-box transcription factor 22 (TBX22) is a phylogenetically conserved family member that has not been widely characterized in cancers. In this study, we explored the potential clinical significance and biological functions of TBX22 in PTC. Comprehensive analyses of TBX22 were based on the public databases and our local qRT-PCR cohort. We observed that TBX22 was significantly downregulated in PTC compared with normal tissues. TBX22 was associated with several clinicopathological factors in PTC. Low TBX22 expression correlated with BRAF ^V600E and TERT mutation. Functional enrichment analysis revealed that cancer-related pathways and immune progress were closely associated with TBX22 in PTC. In TBX22-low PTC, high immune infiltration levels with increased CD8⁺ T cells, natural killer, M1 macrophages, and T-regulatory cells were observed. TBX22 was negatively correlated with the activity of different steps of the anticancer immunity cycle. Functionally, overexpression of TBX22 inhibited the proliferation, invasion, and migration in PTC cells, while knocking down of TBX22 showed the opposite effects. The present findings disclose that TBX22, as an immune microenvironment-related biomarker, could be an important tumor suppresser gene and might inform the management of PTC patients better.

Identification and validation of L Antigen Family Member 3 as an immune-related biomarker associated with the progression of papillary thyroid cancer

BACKGROUND

Papillary thyroid cancer (PTC) is heterogeneous cancer with many different immune cells involved in its pathogenesis. L Antigen Family Member 3 (LAGE3) is an ESO/LAGE gene family member that has not been extensively studied in PTC.

METHODS

Comprehensive bioinformatics analyses of LAGE3 were based on The Cancer Genome Atlas, Gene Expression Omnibus, and Genomics of Drug Sensitivity in Cancer (GDSC) databases. We also performed RNA-sequencing on 78 paired samples from local PTC patients.

RESULTS

We observed that LAGE3 was significantly up-regulated in most solid tumor types, including PTC compared with corresponding normal tissues. The high level of LAGE3 was also significantly associated with advanced malignancy. LAGE3 expression was significantly associated with cancer-related pathways, biochemical metabolism, and immune-related terms. Further, tumor microenvironment analysis indicated LAGE3 was positively correlated with different immune cells infiltrating levels and the activity of different steps of the cancer-immunity cycle. Analyses based on the GDSC database revealed that low levels of LAGE3 might be resistant to WZ3105, I-BET-762, and PHA-793887. In addition, the experimental results validated that knocking down LAGE3 could affect proliferation, migration, and invasion in the PTC cell lines.

CONCLUSION

This study discloses that LAGE3 plays an oncogenic and cancer-immunological role, also providing novel PTC biological and clinical implications.

Novel candidate drugs in anti-tumor necrosis factor refractory Crohn's diseases: in silico study for drug repositioning

Biologicals like anti-tumor necrosis factor (TNF) therapy for Crohn's disease (CD) are safe and effective but there is a significant rate of primary and secondary nonresponse in the patients. In this study, we applied a computational approach to discover novel drug therapies for anti-TNF refractory CD in silico. We use a transcriptome dataset (GSE100833) for the anti-TNF refractory CD patients from NCBI GEO. After co-expression analysis, we specifically investigated the extent of protein-protein interactions among genes in clusters based on a protein-protein interaction database, STRING. Pathway analysis was performed using the clEnrich function based on KEGG gene sets. Co-expressed genes in cluster 1, 2, 3, 4, up or down-regulated genes and all differentially expressed genes are highly connected. Among them, cluster 1, which is highly enriched for chemokine signaling, also showed enrichment for cytokine-cytokine receptor interaction and identifies several drugs including cyclosporin with known efficacy in CD. Vorinostat, histone deacetylase inhibitors, and piperlongumine, which is known to have inhibitory effect on activity of NF-κB, were also identified. Some alkaloids were also selected as potential therapeutic drugs. These finding suggest that they might serve as a novel therapeutic option for anti-TNF refractory CD and support the use of public molecular data and computational approaches to discover novel therapeutic options for CD.

Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing

Motivation

Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures.

Results

The novel approach Dr Insight implements a frame-breaking statistical model for the ‘hand-shake’ between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug–target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks.

Availability and implementation

Dr Insight R package is available at https://cran.r-project.org/web/packages/DrInsight/index.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

Transcriptome-based drug repositioning for coronavirus disease 2019 (COVID-19)

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) around the world has led to a pandemic with high morbidity and mortality. However, there are no effective drugs to prevent and treat the disease. Transcriptome-based drug repositioning, identifying new indications for old drugs, is a powerful tool for drug development. Using bronchoalveolar lavage fluid transcriptome data of COVID-19 patients, we found that the endocytosis and lysosome pathways are highly involved in the disease and that the regulation of genes involved in neutrophil degranulation was disrupted, suggesting an intense battle between SARS-CoV-2 and humans. Furthermore, we implemented a coexpression drug repositioning analysis, cogena, and identified two antiviral drugs (saquinavir and ribavirin) and several other candidate drugs (such as dinoprost, dipivefrine, dexamethasone and (-)-isoprenaline). Notably, the two antiviral drugs have also previously been identified using molecular docking methods, and ribavirin is a recommended drug in the diagnosis and treatment protocol for COVID pneumonia (trial version 5-7) published by the National Health Commission of the P.R. of China. Our study demonstrates the value of the cogena-based drug repositioning method for emerging infectious diseases, improves our understanding of SARS-CoV-2-induced disease, and provides potential drugs for the prevention and treatment of COVID-19 pneumonia.

Upregulation of LAGE3 correlates with prognosis and immune infiltrates in colorectal cancer: A bioinformatic analysis

BACKGROUND

Colorectal cancer (CRC) is the primary cause of cancer-related deaths worldwide. Identification of new CRC biomarkers is imperative to improve the prognosis and development of therapies against the disease. LAGE3 (L Antigen Family Member 3) functions as a tRNA modifier, although its potential role in CRC has not been fully elucidated.

METHODS

RNA-seq matrix and corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, then subjected to survival, enrichment, and tumor microenvironment analyses using packages implemented in R.

RESULTS

We found that LAGE3 was upregulated and significantly correlating with poor prognosis in multiple CRC cohorts. Additionally, multivariate Cox regression analysis revealed that LAGE3 was an independent prognostic factor in patients with CRC, whereas functional enrichment analysis indicated that it could regulate protein targeting, tRNA processing, and the PD-1/PD-L1 checkpoint pathway. Furthermore, CIBERSORT analysis indicated a negative relationship between LAGE3 and levels of infiltration for multiple immune cells, especially CD8 + T cells in CRC. Particularly, LAGE3 expression was inversely correlated with the expression of immune checkpoints as well as that of various immune cell types of signature genes.

CONCLUSION

Collectively, our results indicate that high LAGE3 expression correlates with adverse prognosis and poor immune infiltration in CRC patients.

Phyotochemical candidates repurposing for cancer therapy and their molecular mechanisms

Though limited success through chemotherapy, radiotherapy and surgery has been obtained for efficient cancer therapy for modern decades, cancers are still considered high burden to human health worldwide to date. Recently repurposing drugs are attractive with lower cost and shorter time compared to classical drug discovery, just as Metformin from Galega officinalis, originally approved for treating Type 2 diabetes by FDA, is globally valued at millions of US dollars for cancer therapy. As most previous reviews focused on FDA approved drugs and synthetic agents, current review discussed the anticancer potential of phytochemicals originally approved for treatment of cardiovascular diseases, diabetes, infectious diarrhea, depression and malaria with their molecular mechanisms and efficacies and suggested future research perspectives.

Transcriptome-Guided Drug Repositioning

Drug repositioning can save considerable time and resources and significantly speed up the drug development process. The increasing availability of drug action and disease-associated transcriptome data makes it an attractive source for repositioning studies. Here, we have developed a transcriptome-guided approach for drug/biologics repositioning based on multi-layer self-organizing maps (ml-SOM). It allows for analyzing multiple transcriptome datasets by segmenting them into layers of drug action- and disease-associated transcriptome data. A comparison of expression changes in clusters of functionally related genes across the layers identifies "drug target" spots in disease layers and evaluates the repositioning possibility of a drug. The repositioning potential for two approved biologics drugs (infliximab and brodalumab) confirmed the drugs' action for approved diseases (ulcerative colitis and Crohn's disease for infliximab and psoriasis for brodalumab). We showed the potential efficacy of infliximab for the treatment of sarcoidosis, but not chronic obstructive pulmonary disease (COPD). Brodalumab failed to affect dysregulated functional gene clusters in Crohn's disease (CD) and systemic juvenile idiopathic arthritis (SJIA), clearly indicating that it may not be effective in the treatment of these diseases. In conclusion, ml-SOM offers a novel approach for transcriptome-guided drug repositioning that could be particularly useful for biologics drugs.

Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases

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

Time-Course Transcriptome Analysis for Drug Repositioning in Fusobacterium nucleatum-Infected Human Gingival Fibroblasts

Fusobacterium nucleatum (F. nucleatum) is a crucial periodontal pathogen and human gingival fibroblasts (GFs) are the first line of defense against oral pathogens. However, the research on potential molecular mechanisms of host defense and effective treatment of F. nucleatum infection in GFs remains scarce. In this study, we undertook a time-series experiment and performed an RNA-seq analysis to explore gene expression profiles during the process of F. nucleatum infection in GFs. Differentially expressed genes (DEGs) could be divided into three coexpression clusters. Functional analysis revealed that the immune-related signaling pathways were more overrepresented at the early stage, while metabolic pathways were mainly enriched at the late stage. We computationally identified several U.S. Food and Drug Administration (FDA)-approved drugs that could protect the F. nucleatum infected GFs via a coexpression-based drug repositioning approach. Biologically, we confirmed that six drugs (etravirine, zalcitabine, wortmannin, calcium D-pantothenate, ellipticine, and tanespimycin) could significantly decrease F. nucleatum-induced reactive oxygen species (ROS) generation and block the Protein Kinase B (PKB/AKT)/mitogen-activated protein kinase signaling pathways. Our study provides more detailed molecular mechanisms of the process by which F. nucleatum infects GFs and illustrates the value of the cogena-based drug repositioning method and the potential therapeutic application of these tested drugs in the treatment of F. nucleatum infection.

Transcriptional profiling in the livers of rats after hypobaric hypoxia exposure

Ascent to high altitude feels uncomfortable in part because of a decreased partial pressure of oxygen due to the decrease in barometric pressure. The molecular mechanisms causing injury in liver tissue after exposure to a hypoxic environment are widely unknown. The liver must physiologically and metabolically change to improve tolerance to altitude-induced hypoxia. Since the liver is the largest metabolic organ and regulates many physiological and metabolic processes, it plays an important part in high altitude adaptation. The cellular response to hypoxia results in changes in the gene expression profile. The present study explores these changes in a rat model. To comprehensively investigate the gene expression and physiological changes under hypobaric hypoxia, we used genome-wide transcription profiling. Little is known about the genome-wide transcriptional response to acute and chronic hypobaric hypoxia in the livers of rats. In this study, we carried out RNA-Sequencing (RNA-Seq) of liver tissue from rats in three groups, normal control rats (L), rats exposed to acute hypobaric hypoxia for 2 weeks (W2L) and rats chronically exposed to hypobaric hypoxia for 4 weeks (W4L), to explore the transcriptional profile of acute and chronic mountain sickness in a mammal under a controlled time-course. We identified 497 differentially expressed genes between the three groups. A principal component analysis revealed large differences between the acute and chronic hypobaric hypoxia groups compared with the control group. Several immune-related and metabolic pathways, such as cytokine-cytokine receptor interaction and galactose metabolism, were highly enriched in the KEGG pathway analysis. Similar results were found in the Gene Ontology analysis. Cogena analysis showed that the immune-related pathways were mainly upregulated and enriched in the acute hypobaric hypoxia group.

Indirubin attenuates mouse psoriasis-like skin lesion in a CD274-dependent manner: an achievement of RNA sequencing

It was previously reported that the expression of CD274 was down-regulated in psoriatic epidermis, leading to immune disorders of psoriasis. However, the regulatory mechanisms of CD274 were rarely elucidated. We aimed to explore the regulatory mechanisms of CD274. Skin samples were collected from 18 patients with psoriasis vulgaris and 9 healthy participants for RNA sequencing. Candidate genes were chosen based on degree and k-core difference of genes in the co-expression network. The relations between candidate genes and CD274 were validated by flow cytometry and real-time PCR in primary human epidermal keratinocytes. The therapeutic effect of indirubin was assessed in an imiquimod-treated mouse model. Interferon-γ (IFN-γ), cyclin-dependent kinase (CDK) 1, Toll-like receptor 3 (TLR3), TLR4 and interleukin (IL)-17A were considered as candidate genes. In primary human epidermal keratinocytes, the level of CD274 was obviously increased under the stimulation of IFN-γ and CDK1 inhibitor (indirubin), independent of TLR4, TLR3 or IL-17A. Indirubin alleviated the severity of psoriatic mice in a CD274-dependent manner. Co-expression network analysis served as an effective method for the exploration of molecular mechanisms. We demonstrated for the first time that CD274 was the regulator of indirubin-mediated effect on mouse psoriasis-like skin lesion based on co-expression network analysis, contributing to the alleviation of mouse psoriasis-like skin lesion.

circlncRNAnet: an integrated web-based resource for mapping functional networks of long or circular forms of noncoding RNAs

Background

Despite their lack of protein-coding potential, long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) have emerged as key determinants in gene regulation, acting to fine-tune transcriptional and signaling output. These noncoding RNA transcripts are known to affect expression of messenger RNAs (mRNAs) via epigenetic and post-transcriptional regulation. Given their widespread target spectrum, as well as extensive modes of action, a complete understanding of their biological relevance will depend on integrative analyses of systems data at various levels.

Findings

While a handful of publicly available databases have been reported, existing tools do not fully capture, from a network perspective, the functional implications of lncRNAs or circRNAs of interest. Through an integrated and streamlined design, circlncRNAnet aims to broaden the understanding of ncRNA candidates by testing in silico several hypotheses of ncRNA-based functions, on the basis of large-scale RNA-seq data. This web server is implemented with several features that represent advances in the bioinformatics of ncRNAs: (1) a flexible framework that accepts and processes user-defined next-generation sequencing–based expression data; (2) multiple analytic modules that assign and productively assess the regulatory networks of user-selected ncRNAs by cross-referencing extensively curated databases; (3) an all-purpose, information-rich workflow design that is tailored to all types of ncRNAs. Outputs on expression profiles, co-expression networks and pathways, and molecular interactomes, are dynamically and interactively displayed according to user-defined criteria.

Conclusions

In short, users may apply circlncRNAnet to obtain, in real time, multiple lines of functionally relevant information on circRNAs/lncRNAs of their interest. In summary, circlncRNAnet provides a “one-stop” resource for in-depth analyses of ncRNA biology. circlncRNAnet is freely available at http://app.cgu.edu.tw/circlnc/.

Changing Trends in Computational Drug Repositioning

Efforts to maximize the indications potential and revenue from drugs that are already marketed are largely motivated by what Sir James Black, a Nobel Prize-winning pharmacologist advocated-"The most fruitful basis for the discovery of a new drug is to start with an old drug". However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Hence, most of the successfully repositioned drugs are the result of "serendipity", discovered during late phase clinical studies of unexpected but beneficial findings. The connections between drug candidates and their potential adverse drug reactions or new applications are often difficult to foresee because the underlying mechanism associating them is largely unknown, complex, or dispersed and buried in silos of information. Discovery of such multi-domain pharmacomodules-pharmacologically relevant sub-networks of biomolecules and/or pathways-from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.

Photodynamic therapy corrects abnormal cancer-associated gene expression observed in actinic keratosis lesions and induces a remodeling effect in photodamaged skin

BACKGROUND

Actinic keratoses (AK) are proliferations of neoplastic keratinocytes in the epidermis resulting from cumulative exposure to ultraviolet radiation (UVR), which are liable to transform into squamous cell carcinoma (SCC). Organ Transplant Recipients (OTR) have an increased risk of developing SCC as a consequence of long-term immunosuppressive therapy. The aim of this study was to determine the molecular signature of AKs from OTR prior to treatment with methyl aminolevulinate-photodynamic therapy (MAL-PDT), and to assess what impact the treatment has on promoting remodeling of the photo-damaged skin.

METHODS

Seven patients were enrolled on a clinical trial to assess the effect of MAL-PDT with biopsies taken at screening prior to the first treatment session (week 1), and six weeks after completion of final treatment (week 18). Whole-genome gene expression analysis was carried out on skin biopsies isolated from an AK lesion, an area surrounding the lesion, and a non-sun exposed region of the body. Quantitative PCR was utilized to confirm the differential expression of key genes.

RESULTS

MAL-PDT treatment corrected abnormal proliferation-related gene profiles, corrected aberrantly expressed cancer-associated genes and induced expression of dermal extracellular matrix genes in photo-exposed skin.

CONCLUSION

The efficacy of the MAL-PDT on AK lesions was confirmed at whole-genome gene expression level. A transcriptional signature of remodeling, identified through assessing the effect of MAL-PDT on photodamaged skin, supports the use of MAL-PDT for treating photodamaged skin and field cancerized areas.

Drug repurposing from the perspective of pharmaceutical companies

Drug repurposing holds the potential to bring medications with known safety profiles to new patient populations. Numerous examples exist for the identification of new indications for existing molecules, most stemming from serendipitous findings or focused recent efforts specifically limited to the mode of action of a specific drug. In recent years, the need for new approaches to drug research and development, combined with the advent of big data repositories and associated analytical methods, has generated interest in developing systematic approaches to drug repurposing. A variety of innovative computational methods to enable systematic repurposing screens, experimental as well as through in silico approaches, have emerged. An efficient drug repurposing pipeline requires the combination of access to molecular data, appropriate analytical expertise to enable robust insights, expertise and experimental set-up for validation and clinical development know-how. In this review, we describe some of the main approaches to systematic repurposing and discuss the various players in this field and the need for strategic collaborations to increase the likelihood of success in bringing existing molecules to new indications, as well as the current advantages, considerations and challenges in repurposing as a drug development strategy pursued by pharmaceutical companies.

LINKED ARTICLES

This article is part of a themed section on Inventing New Therapies Without Reinventing the Wheel: The Power of Drug Repurposing. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.2/issuetoc.

Laying in silico pipelines for drug repositioning: a paradigm in ensemble analysis for neurodegenerative diseases

When faced with time- and money-consuming problems, new practices in pharmaceutical R&D arose when trying to alleviate them. Drug repositioning has great promise and when combined with today's computational power and intelligence it becomes more precise and potent. This work showcases current approaches of creating a computational pipeline for drug repositioning, along with an extensive example of how researchers can influence therapeutic approaches and further understanding, through either single or multiple disease studies. This paradigm is based on three neurodegenerative diseases with pathophysiological similarities. It is our goal to provide the readers with all the information needed to enrich their research and note expectations along the way.