To identify potential drug targets among the Set A differentially expressed genes, we have used the drug target selection tool via gene list analysis by MetaCore™ (Thomson Reuters) (Ekins et al., 2007 (link)) and Thomson Reuters Cortellis Drug Viewer tool (also available on MetaCore™ platform) via pathway analysis. The search has been performed among human primary/direct (Table 3 ) and secondary/indirect (Table 4 ) drug targets (see OrthoDB Kriventseva et al., 2015 (link), for the comparison between human and mouse orthologs). The drugs have been taken into account for their targets and not for their use, so not only anti-neoplastic agents are listed in Tables 3 –6 . Cortellis™ drugs results were compared with records contained in public databases such as DrugBank version 4.2 (Knox et al., 2011 (link); Law et al., 2014 (link)), PubChem Compound by NIH (Bolton et al., 2010 (link)) and Naturally Occurring Plant based Anticancerous Compound-Activity-Target (Mangal et al., 2013 (link)). Finally, to further annotate Set A list genes with respect to known drug-gene interactions and potential druggability, in both mouse and human, we have used the search tools on The Drug Gene Interaction Database (DGIdb) (Griffith et al., 2013 (link)) via gene list (Figure 3 , Tables 4 , 5 ).
Metacore
Manufactured by Thomson Reuters
Sourced in United States, United Kingdom, Canada
MetaCore is a comprehensive computational biology software suite developed by Thomson Reuters. It provides a platform for the analysis and interpretation of high-throughput experimental data, such as gene expression, proteomics, and metabolomics data. The core function of MetaCore is to enable researchers to explore and understand complex biological systems and pathways.
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Lab products found in correlation
Ingenuity pathway analysis ipa, by Qiagen (4 mentions)
Hiseq 4000, by Illumina (3 mentions)
Raybio human matrix metalloproteinase antibody array aah mmp 1, by RayBiotech (2 mentions)
Trizol reagent, by Thermo Fisher Scientific (2 mentions)
Bioanalyzer, by Agilent Technologies (2 mentions)
Whole human genome oligo microarray g4851a, by Agilent Technologies (2 mentions)
Rneasy mini kit, by Qiagen (2 mentions)
Ingenuity pathway analysis (ipa), by Qiagen (2 mentions)
Metacore 6, by Thomson Reuters (2 mentions)
Genego, by Thomson Reuters (2 mentions)
Mouse gene 1.0 st array, by Thermo Fisher Scientific (1 mentions)
Prism 6, by GraphPad (1 mentions)
Omics explorer 3, by Qlucore (1 mentions)
Genomestudio, by Illumina (1 mentions)
Omics explorer, by Qlucore (1 mentions)
Rnaeasy kit, by Qiagen (1 mentions)
Rna screentape assay, by Agilent Technologies (1 mentions)
Rnase free dnase set, by Qiagen (1 mentions)
Human gene 2.1 st array, by Thermo Fisher Scientific (1 mentions)
Genechip fluidics station 450, by Thermo Fisher Scientific (1 mentions)
136 protocols using metacore
1
Identifying Potential Drug Targets via Gene Expression
2
Phosphoproteomics Analysis of AuNR Treatment
3
SNO-Proteins Bioinformatics Analysis
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Cellular Compartments (CC), Biological Processes (BP), and pathways analysis were conducted for the systems biology analysis. The lists of all SNO-proteins was uploaded into MetaCore from Thomson Reuters (MetaCore™ version 6.34 build 69200) and into the Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Recourses (version 6.8, https://david.ncifcrf.gov ) [35 (link)]. Functional annotation tool in DAVID was used for GO terms and KEGG pathway enrichment analysis [35 (link)]. The Benjamini–Hochberg correction [36 (link)] was used to calculate the p value and generate FDR, and terms with FDR values below 0.05 were accepted. The search tool for the interacting proteins (STRING, version 10.0) was used to analyze BP and pathway enrichment (http://string-db.org ) [37 (link)]. MetaCore from Thomson Reuters (MetaCore™ version 6.34 build 69200) was used for the networks generation after submitting the lists of SNO-proteins. For this, we also used Benjamini–Hochberg correction [36 (link)] to calculate the p value and generate FDR. The processes/terms with the FDR values of below 0.05 were included.
4
Quantifying Matrix Metalloproteinase Levels
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MMP levels were measured using RayBio Human Matrix Metalloproteinase Antibody Array (AAH-MMP-1, Ray-Biotech, Inc.). Cells were seeded on top of collagen in 10% FCS-containing medium, allowed to adhere for 24 h and then grown in serum-free medium for 48 h. Media were collected, spun down to discard debris and probed onto the protein array membranes following the manufacturer’s instructions. Chemiluminescence signal was further analysed using the Protein Array Analyzer plugin for ImageJ (http://image.bio.methods.free.fr/ImageJ/?Protein-Array-Analyzer-for-ImageJ.html ). Heatmaps were generated using MultiExperiment Viewer software (Microarray Software Suite, http://www.tm4.org/mev.html ). MMP/TIMP networks were analysed using MetaCore (Thomson Reuters, MetaCore/">http://thomsonreuters.com/MetaCore/ ).
5
SNO-Protein Interaction Network Analysis
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GO processes and networks were generated after submitting the lists of SNO-proteins to “MetaCore from Thomson Reuters”, MetaCore™ version 6.34 build 69,200. We used Benjamini-Hochberg correction [53 (link)] on the p-value to generate FDR, and processes/terms with FDR values below 0.05 were included. We used STRING (version 10.0) to analyze the protein-protein interaction of SNO-proteins (http://string-db.org ) [54 (link)].
6
Quantifying Matrix Metalloproteinase Levels
7
Microarray Analysis of Gene Expression
8
Multivariate Analysis of Gene Expression
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Gene expression data were log 2 transformed and analyzed by using general linear model-based statistical tests adjusting for age, sex, and site code with Qlucore Omics Explorer 3.3 (Qlucore). Benjamini-Hochberg multiple correction was used to control for the rate of false-positive results (referred to as q value). Statistical analysis of clinical variables and biomarker data was performed with Kruskal-Wallis tests in Spotfire 7.0.2 (TIBCO Spotfire). The P value for MetaCore (MetaCore; Thomson Reuters, Toronto, Ontario, Canada) pathway analysis and Ingenuity Pathway Analysis (IPA; Qiagen, Hilden, Germany) was calculated by using the right-tailed Fisher exact test. All statistical analyses of in vitro data were performed with the 2-tailed unpaired t test. All data analyses, except analysis of gene expression data, were considered hypothesis based, and significance was reached at a P value of .05 or less. Correlations were tested with Spearman r statistics. Prism 6.0 software (GraphPad Software, La Jolla, Calif) was used for data analysis and graphic representation.
9
Transcriptomic Analysis of Intervention Effects
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After correcting for background noise, using normexp background correction (neqc filtration, Limma), quantile normalization of the data was performed using the Illumina GenomeStudio software, version 1.7.0. Data were log2-transformed and exported raw (non-normalized) to R (http://www.r-project.org/ ) for biostatistical analysis using the Linear Models for Microarray Data (Limma) Bioconductor package version 1.1.0. Differential gene expression was estimated by a moderated paired t test (Limma) by comparing the relative change from baseline to after the intervention using R software. Gene transcripts that were significantly regulated during the intervention (nominal p value < 0.05) were subjected to further gene pathway analysis using MetaCoreTM (GeneGo, Thomson Reuters, Michigan, USA). Pathways identified in MetaCoreTM with a FDR q-value < 0.05 were considered significantly modulated.
10
Mapping MAPK-Wnt Signaling Crosstalk
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Key MAPK-signaling and canonical Wnt-signaling cascade components and their crosstalk components were considered for the model setup.
The model was manually curated based on a literature search [28] . Here, first main components of the investigated crosstalk were evaluated and included via a Google Scholar and PubMed search. Interactions were included, prioritizing results driven from the CRC context, integrating both in vitro and in vivo information. When available, also interactions observed in patients-derived tumoral tissues were included. Different regulatory levels were also considered, ranging from regulation of expression to protein alterations. Finally, interactions were refined via screening of curated databases BioGRID [25] (link) and MetacoreTM (Thomson Reuters Inc., Carlsbad, CA). A detailed description of the model setup rationale and dynamic analysis is available in theAppendix Method A.4 .
The model was manually curated based on a literature search [28] . Here, first main components of the investigated crosstalk were evaluated and included via a Google Scholar and PubMed search. Interactions were included, prioritizing results driven from the CRC context, integrating both in vitro and in vivo information. When available, also interactions observed in patients-derived tumoral tissues were included. Different regulatory levels were also considered, ranging from regulation of expression to protein alterations. Finally, interactions were refined via screening of curated databases BioGRID [25] (link) and MetacoreTM (Thomson Reuters Inc., Carlsbad, CA). A detailed description of the model setup rationale and dynamic analysis is available in the
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