KORE-Map 1.0: Korean medicine Omics Resource Extension Map on transcriptome data of tonifying herbal medicine

Abstract

Traditional herbal medicine, rooted in a long history of use in East Asia, combines several herbs to create treatments showing high efficacy with minimal side effects, for specific diseases. Such combination therapies represent a potential reservoir of new drugs for treating multifactorial and incurable chronic diseases. However, the complexity of their mechanisms of action due to the combination of multiple compounds, has limited their research integration into modern pharmacological science. To address this challenge, we constructed drug-induced transcriptome data for herbal medicines through systematic experiments, analyzed with the aid of various omics databases. We introduce KORE-Map 1.0 (Korean medicine Omics Resource Extension Map), the first comprehensive resource of drug-derived transcriptome data for representative tonifying herbal medicines, effective in enhancing the immune system. This dataset aims to provide novel insights into the combinatorial mechanisms of these herbal medicines and to aid in the discovery of new therapeutic targets and indications for various incurable diseases.

Fig. 1

Introduction to transcriptomic data production protocols and herbal drugs used. (A) Overview of standard operating procedures for producing standardized transcriptome data. (B) List of herbal drugs processed for transcriptome data production.

Fig. 2

Quality assessment of RNA samples. (A) The A260/A280 ratio for individual samples grouped by four cell lines. The minimum of values widely recognized as indicative of high purity RNA is represented by the dotted line. (B) The 28 s/18 s rRNA ratio (left) and RNA integrity number (right) for individual samples grouped by four cell lines. Each dotted line represents a minimum value widely known to reflect high RNA quality and integrity.

Fig. 3

Quality evaluation of RNA sequencing data. (A) Representative FastQC report showing per sequence quality scores (left) and GC content (right) for A549 cell line treated with dimethyl sulfoxide (DMSO). (B) Summary of unmapped, multiple-mapped, and uniquely mapped reads against the reference genome for each cell line.

Fig. 4

Replicability of RNA-seq profiles. (A) Distribution of Pearson’s correlation coefficients for replicates (yellow) versus different samples (gray). (B) Heatmap of Pearson’s correlation coefficients among replicate samples across various sequencing batches.

Fig. 5

Transcriptome data comparisons with CMap. Distribution of Pearson’s correlation coefficients for samples under the same treatment condition (yellow) versus different conditions (gray).