ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells

Abstract

Prior knowledge of perturbation data can significantly assist in inferring the relationship between chemical perturbations and their specific transcriptional response. However, current databases mostly contain cancer cell lines, which are unsuitable for the aforementioned inference in non-cancer cells, such as cells related to non-cancer disease, immunology and aging. Here, we present ChemPert (https://chempert.uni.lu/), a database consisting of 82 270 transcriptional signatures in response to 2566 unique perturbagens (drugs, small molecules and protein ligands) across 167 non-cancer cell types, as well as the protein targets of 57 818 perturbagens. In addition, we develop a computational tool that leverages the non-cancer cell datasets, which enables more accurate predictions of perturbation responses and drugs in non-cancer cells compared to those based onto cancer databases. In particular, ChemPert correctly predicted drug effects for treating hepatitis and novel drugs for osteoarthritis. The ChemPert web interface is user-friendly and allows easy access of the entire datasets and the computational tool, providing valuable resources for both experimental researchers who wish to find datasets relevant to their research and computational researchers who need comprehensive non-cancer perturbation transcriptomics datasets for developing novel algorithms. Overall, ChemPert will facilitate future in silico compound screening for non-cancer cells.

On the correspondence between the transcriptomic response of a compound and its effects on its targets

“…Figure 1 illustrates the different analyses conducted. All analyses are based on transcriptomic and target data for over two thousand compounds retrieved from ChemPert (Zheng et al, 2022) (see subsection 2.1). These two modalities are represented as vectors as we intend to evaluate their correspondence for each compound.…”

“…We leveraged data from ChemPert (Zheng et al ., 2022), a database containing transcriptional data from thousands of experiments where cell lines and tissues were perturbed by 2,508 unique perturbagens (details in Supplementary Text 1). Furthermore, this database contains target information from Drug Repurposing Hub (Corsello et al ., 2017), DrugBank (Wishart et al ., 2018), and STITCH (Szklarczyk et al ., 2016).…”

“…Recently, a new database called ChemPert integrated protein target information as well as thousands of transcriptomic experiments from over one hundred non-cancer cell types (Zheng et al ., 2022). Leveraging this resource, we investigated the correspondence between transcriptomic and target data in over 2,000 compounds.…”

TranSiGen: Deep representation learning of chemical-induced transcriptional profile

“…In addition, there are ongoing efforts to collect and organize gene expression profiles from publicly available databases to create standardized and unified transcriptional data. ARCHS4 is a web resource containing RNA-seq data from human and mouse 4 , while ChemPert focuses on RNA-seq data for non-cancer cell lines 5 .…”

On the correspondence between the transcriptomic response of a compound and its effects on its targets

“…Recently, a new database called ChemPert integrated protein target information and thousands of transcriptomic experiments from over one hundred non-cancer cell types [ 28 ]. Leveraging this resource, we systematically investigated the correspondence between transcriptomic and target data with a large-scale dataset containing over 2000 compounds.…”