Fig. 2.

Inferring a TR-metabolite association network by integrating transcriptome and metabolome data. a Schematic overview of the computational framework for finding TR-metabolite associations. Activity of 728 human TRs was calculated using Network Component Analysis²⁹ on expression profiles of the NCI-60 cell lines¹⁰ and the TRRUST transcriptional regulatory network²⁴. Correlation analysis was used to find associations between TRs and metabolites. b Correlations between metabolite levels and TR activity profiles in relation to the distance between TRs and enzyme targets in the metabolic network²¹. The black line represents average TR-metabolite distance (y-axis) at different levels of absolute TR-metabolite correlation (x-axis). The blue line represents average TR-metabolite distance in 10.000 randomized networks. Dot color indicates significance (q-value, corrected for multiple tests) of TR-metabolite proximity compared to the randomized networks. c Distribution of TR-metabolite associations (0.1% FDR, left section) and TR-gene regulatory interactions (right section) across KEGG pathways. Edge-size connecting the TR hub and metabolic pathways reflects the number of links found in either network. d Glucose uptake vs lactate secretion. Mean ± standard deviation of rates estimated over three biological replicates and five time points. e, f Metabolites whose abundance correlated with glycolytic flux (i.e., glucose uptake and lactate secretion, e) were tested for significant (q-value, corrected for multiple tests) enrichment in KEGG metabolic pathways (f), separately for positively and negatively correlated metabolites (yellow to red and purple to blue color ranges, respectively). g Boxplot of the correlation between glucose uptake/lactate secretion and sensitivity (i.e., GI₅₀, drug concentration causing 50% growth rate reduction) to 430 drugs³¹ grouped by mechanism of action. Box edges reflect 25th and 75th percentiles, center dots indicate medians, whiskers include extreme data points, and outliers are gray circles. h Association between TR activity and metabolites reporting on glycolytic flux. TR names are shown for the 10 TRs with the highest score. i For each KEGG pathway we calculated the average association score (h) among TRs with known enzyme targets in the pathway (x-axis). Significance (y-axis) is estimated using a permutation test and corrected for multiple tests (q-value). Only pathways with a q-value ≤ 0.01 are labeled