Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer.

To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC. VIDEO ABSTRACT.

Engineered chromosome-based genetic mapping establishes a 3.7 Mb critical genomic region for Down syndrome-associated heart defects in mice.

Trisomy 21 (Down syndrome, DS) is the most common human genetic anomaly associated with heart defects. Based on evolutionary conservation, DS-associated heart defects have been modeled in mice. By generating and analyzing mouse mutants carrying different genomic rearrangements in human chromosome 21 (Hsa21) syntenic regions, we found the triplication of the Tiam1-Kcnj6 region on mouse chromosome 16 (Mmu16) resulted in DS-related cardiovascular abnormalities. In this study, we developed two tandem duplications spanning the Tiam1-Kcnj6 genomic region on Mmu16 using recombinase-mediated genome engineering, Dp(16)3Yey and Dp(16)4Yey, spanning the 2.1 Mb Tiam1-Il10rb and 3.7 Mb Ifnar1-Kcnj6 regions, respectively. We found that Dp(16)4Yey/+, but not Dp(16)3Yey/+, led to heart defects, suggesting the triplication of the Ifnar1-Kcnj6 region is sufficient to cause DS-associated heart defects. Our transcriptional analysis of Dp(16)4Yey/+ embryos showed that the Hsa21 gene orthologs located within the duplicated interval were expressed at the elevated levels, reflecting the consequences of the gene dosage alterations. Therefore, we have identified a 3.7 Mb genomic region, the smallest critical genomic region, for DS-associated heart defects, and our results should set the stage for the final step to establish the identities of the causal gene(s), whose elevated expression(s) directly underlie this major DS phenotype.

Identification of germ cell-specific genes in mammalian meiotic prophase.

BACKGROUND: Mammalian germ cells undergo meiosis to produce sperm or eggs, haploid cells that are primed to meet and propagate life. Meiosis is initiated by retinoic acid and meiotic prophase is the first and most complex stage of meiosis when homologous chromosomes pair to exchange genetic information. Errors in meiosis can lead to infertility and birth defects. However, despite the importance of this process, germ cell-specific gene expression patterns during meiosis remain undefined due to difficulty in obtaining pure germ cell samples, especially in females, where prophase occurs in the embryonic ovary. Indeed, mixed signals from both germ cells and somatic cells complicate gonadal transcriptome studies. RESULTS: We developed a machine-learning method for identifying germ cell-specific patterns of gene expression in microarray data from mammalian gonads, specifically during meiotic initiation and prophase. At 10% recall, the method detected spermatocyte genes and oocyte genes with 90% and 94% precision, respectively. Our method outperformed gonadal expression levels and gonadal expression correlations in predicting germ cell-specific expression. Top-predicted spermatocyte and oocyte genes were both preferentially localized to the X chromosome and significantly enriched for essential genes. Also identified were transcription factors and microRNAs that might regulate germ cell-specific expression. Finally, we experimentally validated Rps6ka3, a top-predicted X-linked spermatocyte gene. Protein localization studies in the mouse testis revealed germ cell-specific expression of RPS6KA3, mainly detected in the cytoplasm of spermatogonia and prophase spermatocytes. CONCLUSIONS: We have demonstrated that, through the use of machine-learning methods, it is possible to detect germ cell-specific expression from gonadal microarray data. Results from this study improve our understanding of the transition from germ cells to meiocytes in the mammalian gonad. Further, this approach is applicable to other tissues for which isolating cell populations remains difficult.

Experimental validation of Ankrd17 and Anapc10, two novel meiotic genes predicted by computational models in mice.

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.

Genome Sequence of the Historical Clinical Isolate Burkholderia pseudomallei PHLS 6.

Here, we present the draft genome sequence of Burkholderia pseudomallei PHLS 6, a virulent clinical strain isolated from a melioidosis patient in Bangladesh in 1960. The draft genome consists of 39 contigs and is 7,322,181 bp long.

Computer simulations of the mouse spermatogenic cycle.

The spermatogenic cycle describes the periodic development of germ cells in the testicular tissue. The temporal-spatial dynamics of the cycle highlight the unique, complex, and interdependent interaction between germ and somatic cells, and are the key to continual sperm production. Although understanding the spermatogenic cycle has important clinical relevance for male fertility and contraception, there are a number of experimental obstacles. For example, the lengthy process cannot be visualized through dynamic imaging, and the precise action of germ cells that leads to the emergence of testicular morphology remains uncharacterized. Here, we report an agent-based model that simulates the mouse spermatogenic cycle on a cross-section of the seminiferous tubule over a time scale of hours to years, while considering feedback regulation, mitotic and meiotic division, differentiation, apoptosis, and movement. The computer model is able to elaborate the germ cell dynamics in a time-lapse movie format, allowing us to trace individual cells as they change state and location. More importantly, the model provides mechanistic understanding of the fundamentals of male fertility, namely how testicular morphology and sperm production are achieved. By manipulating cellular behaviors either individually or collectively in silico, the model predicts causal events for the altered arrangement of germ cells upon genetic or environmental perturbations. This in silico platform can serve as an interactive tool to perform long-term simulation and to identify optimal approaches for infertility treatment and contraceptive development.

Characterization of the metabolic requirements in yeast meiosis.

The diploid yeast Saccharomyces cerevisiae 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 in silico 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.

Dynamic modeling of yeast meiotic initiation.

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.

Eco-friendly synthesis and study of new plant growth promoters: 3,3'-Diindolylmethane and its derivatives.

3,3'-Diindolylmethane (DIM) derivatives 3a-k, prepared in one-pot from indoles 1a-k and hexamethylenetetramine (2) using ionic liquid [Bmim]BF(4) as eco-friendly recyclable solvent as well as catalyst, showed good plant growth promoting activity on Oryza sativa. Among the DIM derivatives synthesized 3c shows potent auxin like growth promoting activity.

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%. 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.