Ingenuity pathways analysis software

HBMEC and MDA-MB231 cells were cultured in 6 and 4 well plates, respectively, and treated with SGP40, LVSe-MR, or M-Se-A at the concentration normalized to 5 μM Se at 37°C in 5% CO₂ for 24 h. Then, TNF-α (10 ng/mL) was added for the additional 20 h. Total RNA was isolated and purified using RNeasy Mini Kit (Qiagen, Valencia, CA) following the manufacturer’s protocol.
Microarray gene expression analysis was performed using the Affymetrix GeneChip^® Human Gene 1.0 ST Array (Affymetrix, Santa Clara, CA). The labeling of RNA samples, hybridization and array scanning were performed following the standard protocols recommended by Affymetrix. Microarray data analysis was conducted as described earlier [21 (link)]. GeneSpring GX 12.5 (Silicon Genetics, Redwood, CA) software was employed to normalize the data and to perform statistical and gene expression pattern analyses, where genes that differed from the control with a p ≤ 0.01 and corresponding signal intensity fold change (FC) ≥ 1.2 or FC ≤ −1.2 were deemed as changed. Ingenuity® pathways analysis software (Ingenuity® Systems Inc., Redwood City, CA) was used to dissect the biological pathways and processes in which differentially expressed genes were involved.

All differentially expressed proteins from two proteomic analysis approaches with their gene symbol names as identifiers were subjected to Ingenuity Pathways Analysis software (Ingenuity Systems, www.ingenuity.com). Filters and general settings were set to consider all molecules as well as both direct and indirect relationships. The taxae of human and mouse and all data sources were selected. Networks and canonical pathways of focus genes and associations to biological functions (and/or diseases) were then algorithmically generated. Toxicity analysis function incorporated in IPA software was utilized to assess the secondary physiologic impact of primary ACADS gene deficiency. In this analysis, we essentially defined SCAD deficiency as a “toxin”, assessed secondary changes identified in mitochondrial proteome, and assigned them to functional pathways as with more traditional small molecule studies. Biomarker analysis function was originally incorporated in IPA software to identify disease or predict the development of symptoms. We utilized this function to identify and prioritize promising molecular biomarker candidates from differentially expressed proteins in SCAD deficient mice. A biomarker filter was selected to focus on contextual information related to human and mouse genes associated with known diseases.

Ingenuity pathways analysis software (Ingenuity Systems) was used to gain insight into the cellular location and molecular/cellular functional characteristics of the identified proteins listed in Supplemental Tables 1–5. An independent analysis of the biological pathways associated with each peptidome set was performed with the Database for Annotation, Visualization and Integrated Discovery v6.7 (http://www.genome.jp/kegg/pathway.html). In addition, the Generic Gene Ontology (GO) Term Mapper from Princeton. edu (http://go.princeton.edu/cgi-bin/GOTermMapper) was used to generate the GO terms associated with the global proteome depicted in Supplemental Table 1.