Ingenuity pathways analysis

To explore the biological interpretation of the transcriptome data, pathway analysis was performed using knowledge-based functional analysis software Ingenuity Pathways Analysis (Ingenuity Systems, Mountain View, CA, USA)^{56 (link)} as previously described^{57 (link)}. With details, “Disease and bio function analysis”, “Canonical pathway analysis” and “Upstream regulator analysis” were performed to identify upstream regulatory molecules and associated pathways of the observed expression changes. Upstream regulator analysis is one of the causal analytics algorithms in IPA that was developed to identify the upstream molecules in the data set that can explain the observed expression changes. One of the statistical measures of IPA is the activation z-score, which can be used to find likely regulating molecules based on a statistically significant pattern match of up- and down-regulation, and also to predict the activation state (either activated or inhibited) of a putative regulator. An absolute z-score more than 2 was considered as significant. Unsupervised principle component analysis (PCA) was run using R.

100 ng of total RNA from PBMC from 4 controls and 5 PE at week 22–24 was subjected to GeneChip HT One-Cycle cDNA Synthesis Kit and GeneChip HT IVT Labeling Kit, following the manufacturer’s protocol for whole genome gene expression analysis (Affymetrix, Santa Clara, CA). Labeled and fragmented single stranded cDNAs were hybridized to the GeneChip Human Gene 1.0 ST Arrays (28,869 transcripts) (Affymetrix). The arrays were washed and stained using FS-450 fluidics station (Affymetrix). Signal intensities were detected by Hewlett Packard Gene Array Scanner 3000 7G (Hewlett Packard, Palo Alto, CA). The scanned images were processed using Affymetrix GeneChip Command Console (AGCC). The CEL files were imported into Partek Genomics Suite software (Partek, Inc. MO). Robust microarray analysis (RMA) was applied for normalization. Differentially expressed genes between groups were identified using one-way ANOVA analysis. Cluster analysis were generated in Partek Genomics Suite. Further bioinformatics analysis was conducted on the significant genes to identify functional significance by means of Ingenuity Pathways Analysis (Ingenuity Systems, Redwood City, CA).

Ingenuity Pathways Analysis (Ingenuity Systems, www.ingenuity.com) was used to identify the upstream transcriptional regulators that can explain the gene expression differences between GC subtypes. The expression log ratio of PAM genes in the YCC cohort was used for analysis. The Connectivity Map (https://clue.io/) was queried to identify reference perturbagen signatures most similar (positive score) or dissimilar (negative score) to each GC module.

For prediction of target genes of differentially expressed miRNAs, we first used TargetScan 6.1 (http://www.targetscan.org/) to identify the potential mRNA targets. MeSH database (http://www.nlm.nih.gov/mesh/meshhome.html) was then employed to identify the molecules relevant to eosinophil biology by exact syntax matching. MiRanda (http://www.microrna.org/) was also used to refine the predicted targets. Ingenuity Pathways Analysis (Ingenuity Systems, Redwood City, CA) software was further used to identify canonical signaling pathways containing the miRNA-associated eosinophil-associated molecules, and to establish network connections between miRNAs and their respective predicted targets.

Database of Genomic Variants (DGV): http://projects.tcag.ca/variation/.
DatabasE of Chromosomal Imbalance and Phenotype in Humans using Ensembl Resources (DECIPHER): http://decipher.sanger.ac.uk/.
BioGPS website (http://www.biogps.org/).
Gene Relationships Across Implicated Loci (http://www.broadinstitute.org/mpg/grail/.
Ingenuity Pathways Analysis (Ingenuity Systems Inc., RedwoodCity, California, USA; http://www.ingenuity.com/).