Yeast biological networks unfold the interplay of antioxidants, genome and phenotype, and reveal a novel regulator of the oxidative stress response

Abstract

Background: Identifying causative biological networks associated with relevant phenotypes is essential in the field of systems biology. We used ferulic acid (FA) as a model antioxidant to characterize the global expression programs triggered by this small molecule and decipher the transcriptional network controlling the phenotypic adaptation of the yeast Saccharomyces cerevisiae.

Methodology/principal findings: By employing a strict cut off value during gene expression data analysis, 106 genes were found to be involved in the cell response to FA, independent of aerobic or anaerobic conditions. Network analysis of the system guided us to a key target node, the FMP43 protein, that when deleted resulted in marked acceleration of cellular growth (∼15% in both minimal and rich media). To extend our findings to human cells and identify proteins that could serve as drug targets, we replaced the yeast FMP43 protein with its human ortholog BRP44 in the genetic background of the yeast strain Δfmp43. The conservation of the two proteins was phenotypically evident, with BRP44 restoring the normal specific growth rate of the wild type. We also applied homology modeling to predict the 3D structure of the FMP43 and BRP44 proteins. The binding sites in the homology models of FMP43 and BRP44 were computationally predicted, and further docking studies were performed using FA as the ligand. The docking studies demonstrated the affinity of FA towards both FMP43 and BRP44.

Conclusions: This study proposes a hypothesis on the mechanisms yeast employs to respond to antioxidant molecules, while demonstrating how phenome and metabolome yeast data can serve as biomarkers for nutraceutical discovery and development. Additionally, we provide evidence for a putative therapeutic target, revealed by replacing the FMP43 protein with its human ortholog BRP44, a brain protein, and functionally characterizing the relevant mutant strain.

Figure 1. Application of bottom-up and top-down systems biology approaches for the development of functional foods that target specific body functions through either individual ingredients (e.g., resveratrol, ferulic acid, epigallocatechin gallate, EGCG) or specially designed mixtures (e.g., food ingestion).

A discovery process that derives molecular markers for the bioactivity of defined foods through a human intervention trial, and the use of pathway models (bottom-up) should be followed by an animal model study to verify the markers in vivo. Similarly, the biomarker signature identification of specific nutrients using nutri-omics technology in yeast (top-down) will eventually be tested by an intervention study in an animal model. The output of this process, which might well be iterative, is new knowledge obtained for the biological system, as well as the potential for predictive understanding of that system; in the nutritional arena this would lead to personalized nutrition.

Figure 2. Understanding the dynamic programs that a yeast cell utilizes in response to the external stimulus of an antioxidant compound.

(A) The primary FA-specific protein-protein interaction (PPI) network (3,251 nodes, 12,462 edges) constructed by the list of the 64 genes that significantly responded to the FA environmental perturbation, independently of the aeration level, (B) High-scoring active modules identified in the primary FA-specific PPI network. Bold font indicates genes that belong to the list of the 64 genes, (C) The ACTMOD network, consisting of 167 nodes and 1,651 edges, using the “organic layout” in Cytoscape, (D) Sub-cellular localization of the 167 nodes of the ACTMOD network, (E) Functional enrichment (biological process, BP) of the ACTMOD network. For some nodes no functional annotation could be retrieved. During visualization feature assignment, color and shape codes have been defined as follows. Shape code: OCTAGON was used to indicate genes with significant differential expression in the (+)FA(+)O₂ vs. (+)FA(−)O₂ t-test, TRIANGLE for genes with significant differential expression in the (+)FA(−)O₂ vs. (−)FA(−)O₂ t-test, ROUND RECTANGULAR for genes with significant differential expression in the ANOVA statistic, and PARALLELOGRAM for genes with significant differential expression in the (+)FA(+)O₂ vs. (−)FA(+)O₂ and (+)FA(−)O₂ vs. (−)FA(−)O₂ t-tests. Color code: GREEN indicates down-regulation, while RED indicates up-regulation.

Figure 3. Integrating information from network connectivity and gene expression data.

(A) The Table contains 13 genes that were present in our initial FA-specific gene list (ANOVA), and simultaneously in the significant MCODE clusters and the identified high-scoring active modules. (B) The transcriptional regulatory network (24 TFs) controlling the expression of the 13 genes. (C) A sub-network with the 7 TFs that regulate the expression of FMP43 protein. Grey font: Transcription factors, White font: Genes regulated by these TFs.

Figure 4. Homology modeling of the human brain protein BRP44 and ligand binding site prediction.

(A) Molecular surface structure of BRP44 showing predicted binding site-1 in cyan. (B) Ramachandran plot of BRP44 structure obtained from Procheck program. None of the amino acid residues are in disallowed regions.