Abstract
It has been difficult to elucidate the structure of gene regulatory networks under anticancer drug treatment. Here, we developed an algorithm to highlight the hub genes that play a major role in creating the upstream and downstream relationships within a given set of differentially expressed genes. The directionality of the relationships between genes was defined using information from comprehensive collections of transcriptome profiles after gene knockdown and overexpression. As expected, among the drug-perturbed genes, our algorithm tended to derive plausible hub genes, such as transcription factors. Our validation experiments successfully showed the anticipated activity of certain hub gene in establishing the gene regulatory network that was associated with cell growth inhibition. Notably, giving such top priority to the hub gene was not achieved by ranking fold change in expression and by the conventional gene set enrichment analysis of drug-induced transcriptome data. Thus, our data-driven approach can facilitate to understand drug-induced gene regulatory networks for finding potential functional genes.
Highlights
Comparative gene expression analysis defines differentially expressed genes (DEGs) under certain conditions of interest
InDePTH involves four steps for the identification of influential genes from among query DEGs (Figure 1a). It calculates similarity scores between patterns of query DEGs and those of perturbed genes from each of the genetic perturbations in Library of Integrated Network-Based Cellular Signatures (LINCS), using the connectivity map (CMap) algorithm [4] (Figure 1b). If these similarity scores are above the predetermined cut-off point and if a gene subjected to the genetic perturbation satisfies the condition that the direction of change of its expression due to the perturbation is the same as that of the query DEGs, the gene is selected as an upstream gene
InDePTH searches for downstream genes (genes whose expression change by an upstream gene perturbation is significant (z-score ≥ 2 or ≤ −2), as recorded in LINCS) whose direction of change in expression is the same as that of the query DEGs, and upstream and downstream genes are connected by arrows (Figure 1c)
Summary
Comparative gene expression analysis defines differentially expressed genes (DEGs) under certain conditions of interest. To interpret DEGs from biological aspects, they have been compared with gene sets from curated databases of molecular functions [1,2,3]. We have constructed a transcriptome database focusing on anticancer compounds and related compounds, mainly using colon adenocarcinoma HT-29 cells [6, 7]. These drug-induced transcriptome databases are useful as reference databases of gene expression change. Further prior knowledge and summarizing techniques are required to extract underlying biological information from these gene expression signatures [8]
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