Overview of the integrative systems approach. The lipidomics, transcriptomic, and genomic data were collected from experiments and databases; (A, B, C) example data points or data matrix. Integrating the matching lipidomic and transcriptomic data in a correlation analysis lead to a gene-versus-lipid correlation coefficient matrix shown as a heat map shown in (D). Genomic and transcriptomic data were combined to infer the activation states of TFs under each experiment, shown as a TF-versus-condition heat map representing the activation states in (E). The inferred activation sates of TFs from (E) were combined with lipidomic data (A) to model the relationship between lipid mass and activation of TFs, shown as a heat map representing the significant logistic parameters in (F). The results from (E) and (F) resulted in the hypothesis that PHS1P mediated regulation of a subset of genes through activation of the HAP complex, which was tested in a series of genetic and pharmacological experiments (G).

Summary of major sphingolipid biosynthetic pathways in Saccharomyces cerevisiae.

Effects of mutations in specific sphingolipid metabolic genes on gene expression and total sphingolipid profiles. (A) Overrepresented Gene Ontology annotations for genes aberrantly regulated during heat stress in the lcb4Δ/lcb5Δ mutant strain. (B) Heat map depicting changes in sphingolipid profiles over a time course of heat stress. Data are shown as a pseudo-colored heat map reflecting the logarithms of lipid mass measurements normalized to total phospholipid content of the sample. Log values of normalized measurements are color coded as indicated in the scale to the left of the heat map. (C) A double-sided clustering map depicting relationships between specific lipid–gene pairs over heat stress. A statistically significant (P-value ⩽0.05 and q-value <0.1) positive correlation coefficient between a gene and a lipid is shown as a red bar; a significant negative one is shown as a green bar; the value of correlation coefficient is pseudo-color coded. In the map, rows represent genes, and columns represent lipids. The clustering tree on the left side of the map indicate gene clusters; a block across rows in the map represents a group of genes sharing similar information with respect to lipids; lipids with similar information with respect to gene expression (columns with similar color pattern) are grouped close to each other.

PHS1P-mediated gene expression regulation. (A) Treatment with PHS1P in the absence of heat stress did not induce all genes aberrantly regulated in the lcb4Δ/lcb5Δ strain. (B) Treatment with PHS1P in the absence of heat stress induces gene expression of the putative PHS1P-dependent genes identified by the integromics analysis. (C) The metabolic precursor of PHS1P, PHS, upregulated PHS1P-dependent genes in the wild-type strain, but not in the lcb4Δ/lcb5Δ mutant, which cannot phosphorylate PHS. Experiments were performed two to three times in triplicate and represented as mean±s.e.m.

Modeling information flow from lipid, to TFs, and to gene expression. (A) Inferred TF activation states through integrating genomic and transcriptomic data. Red color indicates activated state and black denotes inactivate states. The TFs were grouped according to their state across experiment; the yellow block indicates a group of TFs ‘turned off' after heat stress; the purple box outlines the TFs ‘turned on' after heat stress. (B) Logistic regression modeling of the relationship between sphingolipids and TF states. Statistically significant regression parameters are shown as a TF-versus-lipid heat map. The orange box indicates the significant parameters associated with PHS1P with respect to Hap2p and Hap4p. (C) The ability of PHS1P treatment to induce PHS1P-dependent genes in the absence of HAP4 was determined. The experiment was performed three times in triplicate and represented as mean±s.e.m.