Mantra 2.0: an online collaborative resource for drug mode of action and repurposing by network analysis

Carrella, Diego; Napolitano, Francesco; Rispoli, Rossella; Miglietta, Mario Marcello; Carissimo, Annamaria; Cutillo, Luisa; Sirci, Francesco; Gregoretti, Francesco; Bernardo, Diego di

doi:10.1093/bioinformatics/btu058

Cited by 79 publications

(62 citation statements)

References 7 publications

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“…Although the overlap between the four target sets was minimal, with only 2 genes being common targets for rapamycin and for LY‐294002 ( let‐363 , a homologue of mTOR , and rps‐6 ), it is still possible that all mechanisms converge to a downstream signature. This idea is further supported by our MANTRA (Carrella et al ., 2014) analysis, which allows the visualization of the similarity in induced gene expression for a set of drugs using a network (Fig. S17).…”

Section: Resultsmentioning

confidence: 99%

A network pharmacology approach reveals new candidate caloric restriction mimetics in C. elegans

et al. 2015

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SummaryCaloric restriction (CR), a reduction in calorie intake without malnutrition, retards aging in several animal models from worms to mammals. Developing CR mimetics, compounds that reproduce the longevity benefits of CR without its side effects, is of widespread interest. Here, we employed the Connectivity Map to identify drugs with overlapping gene expression profiles with CR. Eleven statistically significant compounds were predicted as CR mimetics using this bioinformatics approach. We then tested rapamycin, allantoin, trichostatin A, LY‐294002 and geldanamycin in Caenorhabditis elegans. An increase in lifespan and healthspan was observed for all drugs except geldanamycin when fed to wild‐type worms, but no lifespan effects were observed in eat‐2 mutant worms, a genetic model of CR, suggesting that life‐extending effects may be acting via CR‐related mechanisms. We also treated daf‐16 worms with rapamycin, allantoin or trichostatin A, and a lifespan extension was observed, suggesting that these drugs act via DAF‐16‐independent mechanisms, as would be expected from CR mimetics. Supporting this idea, an analysis of predictive targets of the drugs extending lifespan indicates various genes within CR and longevity networks. We also assessed the transcriptional profile of worms treated with either rapamycin or allantoin and found that both drugs use several specific pathways that do not overlap, indicating different modes of action for each compound. The current work validates the capabilities of this bioinformatic drug repositioning method in the context of longevity and reveals new putative CR mimetics that warrant further studies.

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Section: Resultsmentioning

confidence: 99%

A network pharmacology approach reveals new candidate caloric restriction mimetics in C. elegans

et al. 2015

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“…DrugSig is different from the existing webservers or databases for drug repositioning. Although several drug repositioning related webservers or databases exist such as CMap, PREDICT [18], PROMISCUOUS [19], INDI [20] and Mantra [21], each has certain shortcomings, such as covering a limited collection of drug response microarray or only containing a computational framework. These shortcomings limit the accurate and scope of computational drug repurposing also increase the difficulties in using these data for scientists.…”

Section: Discussionmentioning

confidence: 99%

DrugSig: A resource for computational drug repositioning utilizing gene expression signatures

Huang

Zhong

et al. 2017

PLoS ONE

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Computational drug repositioning has been proved as an effective approach to develop new drug uses. However, currently existing strategies strongly rely on drug response gene signatures which scattered in separated or individual experimental data, and resulted in low efficient outputs. So, a fully drug response gene signatures database will be very helpful to these methods. We collected drug response microarray data and annotated related drug and targets information from public databases and scientific literature. By selecting top 500 up-regulated and down-regulated genes as drug signatures, we manually established the DrugSig database. Currently DrugSig contains more than 1300 drugs, 7000 microarray and 800 targets. Moreover, we developed the signature based and target based functions to aid drug repositioning. The constructed database can serve as a resource to quicken computational drug repositioning. Database URL: http://biotechlab.fudan.edu.cn/database/drugsig/.

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“…Investigators searching the CMap enter over-and underexpressed genes into the search engine, ranking chemotherapeutic agents on whether they regulate the same genes, either in an opposite or in similar fashion. The Mode of Action by NeTwoRk Analysis Mantra 2.0 database has clustered the CMap database and annotated the MoA of each compound, enabling determination of an unknown compound's MoA by referring to the neighbouring compounds in the network [58]. Connectivity mapping has been implemented for the TGGATEs data set in the liver toxicity map service; the Toxygates interface to the TG-GATEs data also enables ranking of compounds based on the genes that they regulate [54,57].…”

Section: Surveying the Landscape Of Databases For Cancer Biology And mentioning

confidence: 99%

“…The US Environmental Protection Agency aggregated computational toxicology resource (AcToR) database, PubChem and chEMBL databases as well as the Comparative Toxicogenomics Database connects chemicals with gene expression changes and disease or toxicity associations [50,52,56,59]. But the toxicology field would benefit from even further developed integrated tools such as the Mantra 2.0 or the cBio Cancer genomics portal that integrates gene expression information, genomic alterations, cancer survival analysis and antibody-based pathway assays from more than 20 different cancer types and 1000 cellular models [24,58]. The European SEURAT-1 (Safety Evaluation Ultimately Replacing Animal Testing 1) including the ToxBank consortium (www.toxbank.net) has created a cross-cluster data warehouse to facilitate storage and analysis of all data generated by the more than 70 research groups that form the SEURAT-1 cluster [28,53].…”

Section: Surveying the Landscape Of Databases For Cancer Biology And mentioning

confidence: 99%

Cancer Biology, Toxicology and Alternative Methods Development Go Hand‐in‐Hand

Kohonen

Ceder

Smit

et al. 2014

Basic Clin Pharma Tox

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Toxicological research faces the challenge of integrating knowledge from diverse fields and novel technological developments generally in the biological and medical sciences. We discuss herein the fact that the multiple facets of cancer research, including discovery related to mechanisms, treatment and diagnosis, overlap many up and coming interest areas in toxicology, including the need for improved methods and analysis tools. Common to both disciplines, in vitro and in silico methods serve as alternative investigation routes to animal studies. Knowledge on cancer development helps in understanding the relevance of chemical toxicity studies in cell models, and many bioinformatics-based cancer biomarker discovery tools are also applicable to computational toxicology. Robotics-aided, cell-based, high-throughput screening, microscale immunostaining techniques and gene expression profiling analyses are common tools in cancer research, and when sequentially combined, form a tiered approach to structured safety evaluation of thousands of environmental agents, novel chemicals or engineered nanomaterials. Comprehensive tumour data collections in databases have been translated into clinically useful data, and this concept serves as template for computer-driven evaluation of toxicity data into meaningful results. Future 'cancer research-inspired knowledge management' of toxicological data will aid the translation of basic discovery results and chemicals-and materials-testing data to information relevant to human health and environmental safety.Assessing the intrinsic toxicological properties of environmental agents is central to defining hazard within the paradigm of human risk assessment used now for decades [1]. A constantly increasing number of chemicals and engineered nanomaterials (ENMs) emphasize the need of applying novel tools and screening technologies outside of the standard, both lengthy and expensive, rodent toxicological tests traditionally used in such work [1][2][3][4][5][6][7][8][9][10][11][12][13]. Especially considered for the future innovation of novel ENMs, safety evaluations should be integrated proactively and efficiently already in the material and product development phase [9,12]. Summarized under a concept termed 'Toxicity Testing in the 21st Century' or 'Tox21', biochemical and cell-based in vitro assays coupled with bioinformatics and modelling-driven in silico assays are now considered key to transforming toxicology from a previously animal-based testing practice into a computational science built on systems biology [2,4,6,[9][10][11]13]. The resulting novel research field is variably termed 'systems toxicology', 'toxicogenomics' or 'computational toxicology' [3,4,8,14,15]. Such effort typically relies on informatics-driven and modelling-based analyses of results from several experimental systems and data-rich technologies for measuring pools of biological molecules, such as mRNAs, the overall aim being to interdependently analyse toxicity data and profiling results for understanding...

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Mantra 2.0: an online collaborative resource for drug mode of action and repurposing by network analysis

Abstract: The web tool is freely available for academic use at http://mantra.tigem.it.

Cited by 79 publications

References 7 publications

A network pharmacology approach reveals new candidate caloric restriction mimetics in C. elegans

A network pharmacology approach reveals new candidate caloric restriction mimetics in C. elegans

DrugSig: A resource for computational drug repositioning utilizing gene expression signatures

Cancer Biology, Toxicology and Alternative Methods Development Go Hand‐in‐Hand

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