Genome-Wide Analysis of Yeast Metabolic Cycle through Metabolic Network Models Reveals Superiority of Integrated ATAC-seq Data over RNA-seq Data

Abstract

Saccharomyces cerevisiae undergoes robust oscillations to regulate its physiology for adaptation and survival under nutrient-limited conditions. Environmental cues can induce rhythmic metabolic alterations in order to facilitate the coordination of dynamic metabolic behaviors. Of such metabolic processes, the yeast metabolic cycle enables adaptation of the cells to varying nutritional status through oscillations in gene expression and metabolite production levels. In this process, yeast metabolism is altered between diverse cellular states based on changing oxygen consumption levels: quiescent (reductive charging [RC]), growth (oxidative [OX]), and proliferation (reductive building [RB]) phases. We characterized metabolic alterations during the yeast metabolic cycle using a variety of approaches. Gene expression levels are widely used for condition-specific metabolic simulations, whereas the use of epigenetic information in metabolic modeling is still limited despite the clear relationship between epigenetics and metabolism. This prompted us to investigate the contribution of epigenomic information to metabolic predictions for progression of the yeast metabolic cycle. In this regard, we determined altered pathways through the prediction of regulated reactions and corresponding model genes relying on differential chromatin accessibility levels. The predicted metabolic alterations were confirmed via data analysis and literature. We subsequently utilized RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) data sets in the contextualization of the yeast model. The use of ATAC-seq data considerably enhanced the predictive capability of the model. To the best of our knowledge, this is the first attempt to use genome-wide chromatin accessibility data in metabolic modeling. The preliminary results showed that epigenomic data sets can pave the way for more accurate metabolic simulations. IMPORTANCE Dynamic chromatin organization mediates the emergence of condition-specific phenotypes in eukaryotic organisms. Saccharomyces cerevisiae can alter its metabolic profile via regulation of genome accessibility and robust transcriptional oscillations under nutrient-limited conditions. Thus, both epigenetic information and transcriptomic information are crucial in the understanding of condition-specific metabolic behavior in this organism. Based on genome-wide alterations in chromatin accessibility and transcription, we investigated the yeast metabolic cycle, which is a remarkable example of coordinated and dynamic yeast behavior. In this regard, we assessed the use of ATAC-seq and RNA-seq data sets in condition-specific metabolic modeling. To our knowledge, this is the first attempt to use chromatin accessibility data in the reconstruction of context-specific metabolic models, despite the extensive use of transcriptomic data. As a result of comparative analyses, we propose that the incorporation of epigenetic information is a promising approach in the accurate prediction of metabolic dynamics.

Keywords: ATAC-seq; RNA-seq; Saccharomyces cerevisiae; flux balance analysis; genome-scale metabolic network; yeast metabolic cycle.

FIG 1

General flowchart for the analysis of ATAC-seq and RNA-seq data sets in addition to model-based analyses of the yeast metabolic cycle.

FIG 2

(A and B) Hierarchical clustering analysis of RNA-seq (A) and ATAC-seq (B) samples. Three gene clusters (designated I, II, and III) are selected based on the differential gene expression and chromatin accessibility levels. They are highlighted with bars in different colors. Clustered samples are also represented by the dendrograms. (C) Venn diagrams showing the numbers of common and distinct genes in each differentially expressed/accessible gene cluster.

FIG 3

Bar plot of significantly enriched KEGG pathways that are regulated during the metabolic shift from early RC phase to OX (mid and late) and RB (early and late) phases. For each pathway, the horizontal axis (count) shows the number of the genes involved in that pathway.

FIG 4

Reconstruction process of the contextualized models. (A) Feeding-aided induced YMC phases (growth-related OX phase, proliferation-related RB phase, and stress/quiescence-related RC phase). (B) Incorporation of RNA-seq or ATAC-seq data sets dedicated to each YMC phase into the Yeast8 model allowed the reconstruction of RNA-seq- and ATAC-seq-based yeast models. Using the reaction activity information in these models, intersection and union models were also reconstructed for each YMC phase. (C) Thus, each YMC phase is represented by four diverse models.

FIG 5

Comparison of the predicted and measured flux distributions for different GMN models (I, generic Yeast8 model; II, RNA-seq-based model; III, ATAC-seq-based model; IV, intersection model; V, union model) across early RC, mid OX, and late RB phases (colored blue, gray, and red). Mean squared error (MSE) and Pearson’s correlation coefficient (r) are shown for each model.

FIG 6

Comparison of the predicted and measured flux distributions for different GMN models based on filtered data (I, generic Yeast8 model; II, RNA-seq-based model; III, ATAC-seq-based model; IV, intersection model; V, union model) across early RC, mid OX, and late RB phases (colored blue, gray, and red). Mean squared error (MSE) and Pearson’s correlation coefficient (r) are shown for each model.