ChemDIS-Mixture: an online tool for analyzing potential interaction effects of chemical mixtures

The assessment of bioactivity and toxicity for mixtures remains a challenging work. Although several computational models have been developed to accelerate the evaluation of chemical-chemical interaction, a specific biological endpoint should be defined before applying the models that usually relies on clinical and experimental data. The development of computational methods is desirable for identifying potential biological endpoints of mixture interactions. To facilitate the identification of potential effects of mixture interactions, a novel online system named ChemDIS-Mixture is proposed to analyze the shared target proteins, and common enriched functions, pathways, and diseases affected by multiple chemicals. Venn diagram tools have been implemented for easy analysis and visualization of interaction targets and effects. Case studies have been provided to demonstrate the capability of ChemDIS-Mixture for identifying potential effects of mixture interactions in clinical studies. ChemDIS-Mixture provides useful functions for the identification of potential effects of coexposure to multiple chemicals. ChemDIS-Mixture is freely accessible at http://cwtung.kmu.edu.tw/chemdis/mixture.

[1]  N. Ives,et al.  Treatment of tuberculosis in HIV-infected persons in the era of highly active antiretroviral therapy , 2002, AIDS.

[2]  S. Cobbina,et al.  A review of toxicity and mechanisms of individual and mixtures of heavy metals in the environment , 2016, Environmental Science and Pollution Research.

[3]  K. Audouze,et al.  Human Environmental Disease Network: A computational model to assess toxicology of contaminants. , 2017, ALTEX.

[4]  Thomas C. Wiegers,et al.  The Comparative Toxicogenomics Database: update 2017 , 2016, Nucleic Acids Res..

[5]  C. Tung,et al.  Profiling transcriptomes of human SH-SY5Y neuroblastoma cells exposed to maleic acid , 2017, PeerJ.

[6]  Susumu Goto,et al.  Data, information, knowledge and principle: back to metabolism in KEGG , 2013, Nucleic Acids Res..

[7]  Gang Feng,et al.  From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations , 2009, Bioinform..

[8]  Lu Xie,et al.  Biomolecular Network-Based Synergistic Drug Combination Discovery , 2016, BioMed research international.

[9]  Henning Hermjakob,et al.  The Reactome pathway Knowledgebase , 2015, Nucleic acids research.

[10]  J. Streibig,et al.  A review of independent action compared to concentration addition as reference models for mixtures of compounds with different molecular target sites , 2008, Environmental toxicology and chemistry.

[11]  David S. Wishart,et al.  DrugBank 4.0: shedding new light on drug metabolism , 2013, Nucleic Acids Res..

[12]  Lei Huang,et al.  DrugComboRanker: drug combination discovery based on target network analysis , 2014, Bioinform..

[13]  Timothy M. D. Ebbels,et al.  Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA , 2011 .

[14]  Gang Fu,et al.  Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data , 2014, Nucleic Acids Res..

[15]  K. Setchell,et al.  Dietary inclusion of whole soy foods results in significant reductions in clinical risk factors for osteoporosis and cardiovascular disease in normal postmenopausal women , 2001, Menopause.

[16]  Yi Xiong,et al.  A Hadoop-Based Method to Predict Potential Effective Drug Combination , 2014, BioMed research international.

[17]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[18]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[19]  Atul K Patel,et al.  Safety and Antiretroviral Effectiveness of Concomitant Use of Rifampicin and Efavirenz for Antiretroviral-Naive Patients in India Who Are Coinfected With Tuberculosis and HIV-1 , 2004, Journal of acquired immune deficiency syndromes.

[20]  M. Junghans,et al.  A decision tree for assessing effects from exposures to multiple substances , 2012, Environmental Sciences Europe.

[21]  R. Bojalil,et al.  Acute coronary syndrome and acute kidney injury: role of inflammation in worsening renal function , 2017, BMC Cardiovascular Disorders.

[22]  C. D’Adamo,et al.  Soy foods and supplementation: a review of commonly perceived health benefits and risks. , 2014, Alternative therapies in health and medicine.

[23]  N. Cedergreen,et al.  Mixture effects of dietary flavonoids on steroid hormone synthesis in the human adrenocortical H295R cell line. , 2010, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[24]  C. Tung,et al.  An in silico toxicogenomics approach for inferring potential diseases associated with maleic acid. , 2014, Chemico-biological interactions.

[25]  George Papadatos,et al.  The ChEMBL database in 2017 , 2016, Nucleic Acids Res..

[26]  Philippe Bardou,et al.  jvenn: an interactive Venn diagram viewer , 2014, BMC Bioinformatics.

[27]  Huamin Zhang,et al.  Synergy evaluation by a pathway-pathway interaction network: a new way to predict drug combination. , 2016, Molecular bioSystems.

[28]  Yi Xiong,et al.  PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm. , 2017, Journal of theoretical biology.

[29]  J. Erdman,et al.  Soy protein and isoflavones: their effects on blood lipids and bone density in postmenopausal women. , 1998, The American journal of clinical nutrition.

[30]  M. Messina Legumes and soybeans: overview of their nutritional profiles and health effects. , 1999, The American journal of clinical nutrition.

[31]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[32]  Rajarshi Guha,et al.  Synergy Maps: exploring compound combinations using network-based visualization , 2015, Journal of Cheminformatics.

[33]  Damian Szklarczyk,et al.  STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data , 2015, Nucleic Acids Res..

[34]  H. Orimo,et al.  Effect of Soy Protein on Bone Metabolism in Postmenopausal Japanese Women , 2000, Osteoporosis International.

[35]  Marc C. Nicklaus,et al.  QSAR Modeling and Prediction of Drug-Drug Interactions. , 2016, Molecular pharmaceutics.

[36]  Chun-Wei Tung,et al.  ChemDIS: a chemical–disease inference system based on chemical–protein interactions , 2015, Journal of Cheminformatics.

[37]  S. Mohapatra,et al.  : DISEASE ONTOLOGY , 2014 .

[38]  David S. Wishart,et al.  DrugBank 5.0: a major update to the DrugBank database for 2018 , 2017, Nucleic Acids Res..

[39]  David S. Wishart,et al.  SMPDB 2.0: Big Improvements to the Small Molecule Pathway Database , 2013, Nucleic Acids Res..

[40]  F. Gobbi,et al.  Drug–drug interactions and tolerance in combining antituberculosis and antiretroviral therapy , 2005, Expert opinion on drug safety.

[41]  M. Brandi,et al.  Phytoestrogens: food or drug? , 2007, Clinical cases in mineral and bone metabolism : the official journal of the Italian Society of Osteoporosis, Mineral Metabolism, and Skeletal Diseases.

[42]  G. Rice,et al.  Effects of dietary phytoestrogens in postmenopausal women. , 1998, Climacteric : the journal of the International Menopause Society.