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

Validation of the meiosis-specific network models using known sporulation-deficient genes.

<p>A total of 16 enzymes in the meiosis-specific network are known to be essential for sporulation. These genes are individually 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. Gene KOs with a z-score ≤−2 for at least one time point are considered to validate the models (underlined).</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

A meiosis-specific metabolic network in yeast.

<p>Five metabolic pathways are active during meiosis when acetate serves as the external carbon source: glutamate formation, TCA cycle, glyoxylate cycle, gluconeogenesis, and glycogenolysis. Metabolites in these pathways are utilized to synthesize macromolecules: nucleotides, amino acids, and lipids.</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

Evaluation of objective functions using the meiosis-specific network models.

<p>The Pearson correlation is calculated between predicted fluxes and biochemical data on eight pathways when maximizing or minimizing each of the ten objective functions at each of the 18 time points. The best objective function for each time point is the one with the maximum Pearson correlation coefficient. Close circle: maximization of an objective function; open circle: minimization of an objective function. Undefined correlation coefficients due to zero variance of predicted pathway fluxes are not shown in the figure.</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

Erupted maxillary conical mesiodens in deciduous dentition in a Bengali girl - A case report

Mesiodens is a midline supernumerary tooth commonly seen in the maxillary arch. It is the most significant dental anomaly affecting permanent dentition mainly and primary dentition rarely. It may occur as an isolated dental anomalous condition or may be associated with a syndrome. Many theories have been promulgated to explain its etiology. But an exact etiology is still obscure. Incidence of mesiodens in children varies from 0.15 to 3.8&#x0025;. Boys are affected more (2 : 1) than Girls. Morphologically, mesiodens may be of three types: the most commonly seen is conical, while tuberculate and supplementary types

Experimental Validation of Ankrd17 and Anapc10, Two Novel Meiotic Genes Predicted by Computational Models in Mice1

Prophase is a critical stage of meiosis, during which recombination—the landmark event of meiosis—exchanges information between homologous chromosomes. The intractability of mammalian gonads has limited our knowledge on genes or interactions between genes during this key stage. Microarray profiling of gonads in both sexes has generated genome-scale information. However, the asynchronous development of germ cells and the mixed germ/somatic cell population complicate the use of this resource. To elucidate functional networks of meiotic prophase, we have integrated global gene expression with other genome-scale datasets either within or across species. Our computational approaches provide a comprehensive understanding of interactions between genes and can prioritize candidates for targeted experiments. Here, we examined two novel prophase genes predicted by computational models: Ankrd17 and Anapc10. Their expression and localization were characterized in the developing mouse testis using in situ hybridization and immunofluorescence. We found ANKRD17 expression was predominantly restricted to pachytene spermatocytes and round spermatids. ANKRD17 was diffusely distributed throughout the nucleus of pachytene cells but excluded from the XY body and other heterochromatic regions. ANAPC10 was mainly expressed in the cytoplasm of spermatogonia and leptotene and pachytene spermatocytes. These experiments support our computational predictions of Ankrd17 and Anapc10 as potential prophase genes. More importantly, they serve as a proof of concept of our integrative computational and experimental approach, which has delivered a larger candidate gene set to the broader reproductive community