Dynamic modeling of yeast meiotic initiation

Abstract Background Meiosis is the sexual reproduction process common to eukaryotes. The diploid yeast Saccharomyces cerevisiae undergoes meiosis in sporulation medium to form four haploid spores. Initiation of the process is tightly controlled by intricate networks of positive and negative feedback loops. Intriguingly, expression of early meiotic proteins occurs within a narrow time window. Further, sporulation efficiency is strikingly different for yeast strains with distinct mutations or genetic backgrounds. To investigate signal transduction pathways that regulate transient protein expression and sporulation efficiency, we develop a mathematical model using ordinary differential equations. The model describes early meiotic events, particularly feedback mechanisms at the system level and phosphorylation of signaling molecules for regulating protein activities. Results The mathematical model is capable of simulating the orderly and transient dynamics of meiotic proteins including Ime1, the master regulator of meiotic initiation, and Ime2, a kinase encoded by an early gene. The model is validated by quantitative sporulation phenotypes of single-gene knockouts. Thus, we can use the model to make novel predictions on the cooperation between proteins in the signaling pathway. Virtual perturbations on feedback loops suggest that both positive and negative feedback loops are required to terminate expression of early meiotic proteins. Bifurcation analyses on feedback loops indicate that multiple feedback loops are coordinated to modulate sporulation efficiency. In particular, positive auto-regulation of Ime2 produces a bistable system with a normal meiotic state and a more efficient meiotic state. Conclusions By systematically scanning through feedback loops in the mathematical model, we demonstrate that, in yeast, the decisions to terminate protein expression and to sporulate at different efficiencies stem from feedback signals toward the master regulator Ime1 and the early meiotic protein Ime2. We argue that the architecture of meiotic initiation pathway generates a robust mechanism that assures a rapid and complete transition into meiosis. This type of systems-level regulation is a commonly used mechanism controlling developmental programs in yeast and other organisms. Our mathematical model uncovers key regulations that can be manipulated to enhance sporulation efficiency, an important first step in the development of new strategies for producing gametes with high quality and quantity. </jats:sec

ELUCIDATING GENES AND PATHWAYS REQUIRED FOR MEIOSIS IN YEAST AND MAMMALS

Meiosis is an important developmental program that determines the quality and quantity of the next generation. The process is conserved in a range of organisms from unicellular budding yeast to multi-cellular mammals. Errors during this process can lead to infertility, miscarriages, or birth defect.The main goal of the dissertation is to develop new mathematical models and machine learning tools to study the biological networks and pathways involved in meiosis. These tools would be further used to indentify important genes and cellular interactions controlling meiosis. The potential meiosis specific genes would be studied further and experimentally verified to solve problems related to infertility. The study would enhance better understanding of meiosis and open up avenues for pathway and genetic engineering. My thesis addresses four related projects on meiotic process in yeast and mammals:1. A metabolic network specific for the Saccharomyces cerevisiae meiosis is constructed and used to indentify the cellular objective of the cell. Novel sporulation deficient genes were indentified that contribute to the efficiency of the meiosis process.2. The genetic regulation is an important factor that controls the precise expression of important gene that initiate the meiosis process. Feedback loops forms a robust mechanism that assures a rapid and complete transition into meiosis. We formulated a dynamic model to understand how feedback loops control yeast meiotic initiation.3. The expression and localization of potential meiosis specific genes were identified using support vector machine learning methods. Groups of novel genes associated with male and female meiosis process were correctly identified.4. The cellular behaviors in mammalian germ cells are coordinated to produce testicular morphology and generate male gametes. We aim to identify the cellular behaviors that have major influence on the developmental process. We can understand cellular processes in normal spermatogenesis and predict the casual cellular events in the various spermatogenic defects.Overall, this thesis work contributes to the development of nethodology to indentify important genes and processes that contribute to the success of meiosis process

Characterization of the Metabolic Requirements in Yeast Meiosis

<div><p>The diploid yeast <i>Saccharomyces cerevisiae</i> undergoes mitosis in glucose-rich medium but enters meiosis in acetate sporulation medium. The transition from mitosis to meiosis involves a remarkable adaptation of the metabolic machinery to the changing environment to meet new energy and biosynthesis requirements. Biochemical studies indicate that five metabolic pathways are active at different stages of sporulation: glutamate formation, tricarboxylic acid cycle, glyoxylate cycle, gluconeogenesis, and glycogenolysis. A dynamic synthesis of macromolecules, including nucleotides, amino acids, and lipids, is also observed. However, the metabolic requirements of sporulating cells are poorly understood. In this study, we apply flux balance analyses to uncover optimal principles driving the operation of metabolic networks over the entire period of sporulation. A meiosis-specific metabolic network is constructed, and flux distribution is simulated using ten objective functions combined with time-course expression-based reaction constraints. By systematically evaluating the correlation between computational and experimental fluxes on pathways and macromolecule syntheses, the metabolic requirements of cells are determined: sporulation requires maximization of ATP production and macromolecule syntheses in the early phase followed by maximization of carbohydrate breakdown and minimization of ATP production in the middle and late stages. Our computational models are validated by <i>in silico</i> deletion of enzymes known to be essential for sporulation. Finally, the models are used to predict novel metabolic genes required for sporulation. This study indicates that yeast cells have distinct metabolic requirements at different phases of meiosis, which may reflect regulation that realizes the optimal outcome of sporulation. Our meiosis-specific network models provide a framework for an in-depth understanding of the roles of enzymes and reactions, and may open new avenues for engineering metabolic pathways to improve sporulation efficiency.</p></div

Robustness analyses on reactions catalyzed by predicted genes required for sporulation.

<p>The objective function value is computed as the flux through the reactions varies. The best objective function combined with expression-based constraints at a specific time is used for the robustness analysis.</p

Flux changes in metabolic pathways and macromolecule syntheses when individually deleting known sporulation-deficient genes.

<p><b>A</b>. <i>IDH2</i> deletion at 0.033 hour. Single gene KOs of <i>KGD1</i>, <i>KGD2</i>, <i>LSC1</i>, and <i>LSC2</i> show similar flux changes at the same time. <b>B</b>. <i>SDH1</i> deletion at 0.033 hour. Single gene KOs of <i>SDH2</i>, <i>MDH1</i>, and <i>ADY2</i> show similar flux changes at the same time. <b>C</b>. <i>ICL1</i> deletion at 1.5 hours. Single gene KOs of <i>SDH1</i>, <i>SDH2</i>, <i>MDH1</i>, <i>MLS1</i>, <i>ADY2</i>, <i>MDH2</i>, and <i>ICL1</i> show similar flux changes at 0.5, 1, and 1.5 hours.</p

Performance comparison between the meiosis-specific network and iMM904 in predicting sporulation-deficient genes using hypergeometric P-values.

&<p>The Pearson correlation between <i>in silico</i> fluxes and biochemical values on eight pathways is calculated for each gene KO and WT. A z-score is computed to measure the difference in correlation coefficient between a KO and WT. A KO with z-score≤−2 for at least one time point is predicted to be a sporulation-deficient gene.</p>*<p>The optimal objective value is obtained for each gene KO and WT. A z-score is computed to measure the difference in optimal objective value between a KO and WT. A KO with z-score≤−2 for at least one time point is predicted to be a sporulation-deficient gene.</p>#<p>The total flux difference between a gene KO and WT is obtained from linear MOMA. A KO with flux difference≥1000 for at least one time point is predicted to be a sporulation-deficient gene.</p

Scaled biochemical data on metabolic pathways and macromolecule syntheses during yeast meiosis.

<p>The time scale of sporulation (12 hours) is defined by the SK1 strain. Datasets obtained using other strains are standardized to the SK1 time scale based on the duration when the ascus level reaches a steady state. Activities of metabolic pathways and macromolecule syntheses are further scaled to the range of 0 and 1. Raw and scaled biochemical data are summarized in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063707#pone.0063707.s009" target="_blank">Table S3</a>. <b>A.</b> Pathway activity: glutamate formation <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063707#pone.0063707-Dickinson1" target="_blank">[9]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063707#pone.0063707-Esposito1" target="_blank">[10]</a>, TCA cycle <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063707#pone.0063707-Hopper2" target="_blank">[13]</a>, gluconeogenesis <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063707#pone.0063707-Kane1" target="_blank">[11]</a>, and glycogenolysis <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063707#pone.0063707-Colonna1" target="_blank">[8]</a>. <b>B.</b> Macromolecule synthesis: DNA <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063707#pone.0063707-Hopper2" target="_blank">[13]</a>, RNA <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063707#pone.0063707-Hopper2" target="_blank">[13]</a>, protein <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063707#pone.0063707-Hopper2" target="_blank">[13]</a>, and lipid <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0063707#pone.0063707-Henry1" target="_blank">[15]</a>.</p

Model prediction of novel genes required for sporulation.

<p>Every gene in the meiosis-specific network is deleted <i>in silico</i>; optimal fluxes are obtained using the best objective function combined with expression-based constraints at each time point. Pearson correlations are calculated between optimal fluxes and biochemical data on eight pathways for gene KOs. Deviation from the WT correlation is quantified by a z-score. Genes previously unknown to be required for sporulation and having a z-score ≤−2 for at least one time point are predicted to be novel sporulation-deficient genes.</p