Estimating the impact of divergent mating phenology between residents and migrants on the potential for gene flow

Gene flow between populations can allow the spread of beneficial alleles and genetic diversity between populations, with importance to conservation, invasion biology, and agriculture. Levels of gene flow between populations vary not only with distance, but also with divergence in reproductive phenology. Since phenology is often locally adapted, arriving migrants may be reproductively out of synch with residents, which can depress realized gene flow. In flowering plants, the potential impact of phenological divergence on hybridization between populations can be predicted from overlap in flowering schedules—the daily count of flowers capable of pollen exchange—between a resident and migrant population. The accuracy of this prospective hybridization estimate, based on parental phenotypes, rests upon the assumptions of unbiased pollen transfer between resident and migrant active flowers. We tested the impact of phenological divergence on resident–migrant mating frequencies in experiments that mimicked a single large gene flow event. We first prospectively estimated mating frequencies two lines of Brassica rapaselected or early and late flowering. We then estimated realized mating frequencies retrospectively through progeny testing. The two estimates strongly agreed in a greenhouse experiment, where procedures ensured saturating, unbiased pollination. Under natural pollination in the field, the rate of resident–migrant mating, was lower than estimated by phenological divergence alone, although prospective and retrospective estimates were correlated. In both experiments, differences between residents and migrants in flowering schedule shape led to asymmetric hybridization. Results suggest that a prospective estimate of hybridization based on mating schedules can be a useful, although imperfect, tool for evaluating potential gene flow. They also illustrate the impact of mating phenology on the magnitude and symmetry of reproductive isolation.     

Selective use of the primary literature transforms the classroom into a virtual laboratory

CREATE (consider, read, elucidate hypotheses, analyze and interpret the data, and think of the next experiment) is a new method for teaching science and the nature of science through primary literature. CREATE uses a unique combination of novel pedagogical tools to guide undergraduates through analysis of journal articles, highlighting the evolution of scientific ideas by focusing on a module of four articles from the same laboratory. Students become fluent in the universal language of data analysis as they decipher the figures, interpret the findings, and propose and defend further experiments to test their own hypotheses about the system under study. At the end of the course students gain insight into the individual experiences of article authors by reading authors' responses to an e-mail questionnaire generated by CREATE students. Assessment data indicate that CREATE students gain in ability to read and critically analyze scientific data, as well as in their understanding of, and interest in, research and researchers. The CREATE approach demystifies the process of reading a scientific article and at the same time humanizes scientists. The positive response of students to this method suggests that it could make a significant contribution to retaining undergraduates as science majors.     

The essential Drosophila ATP-binding cassette domain protein, pixie, binds the 40 S ribosome in an ATP-dependent manner and is required for translation initiation

The Drosophila gene, pixie, is an essential gene required for normal growth and translation. Pixie is the fly ortholog of human RLI, which was first identified as an RNase L inhibitor, and yeast Rli1p, which has recently been shown to play a role in translation initiation and ribosome biogenesis. These proteins are all soluble ATP-binding cassette proteins with two N-terminal iron-sulfur clusters. Here we demonstrate that Pixie can be isolated from cells in complex with eukaryotic translation initiation factor 3 and ribosomal proteins of the small subunit. In addition, our analysis of polysome profiles reveals that double-stranded RNA interference-mediated depletion of Pixie results in an increase in empty 80 S ribosomes and a corresponding decrease in polysomes. Thus Pixie is required for normal levels of translation initiation. We also find that Pixie associates with the 40 S subunit on sucrose density gradients in an ATP-dependent manner. Our observations are consistent with Pixie playing a catalytic role in the assembly of complexes required for translation initiation. Thus, the function of this soluble ATP-binding cassette domain protein family in translation initiation has been conserved from yeast through to higher eukaryotes.     

shRNA knockdown of Bmi-1 reveals a critical role for p21-Rb pathway in NSC self-renewal during development

Knockout studies have shown that the polycomb gene Bmi-1 is important for postnatal, but not embryonic, neural stem cell (NSC) self-renewal and have identified the cell-cycle inhibitors p16/p19 as molecular targets. Here, using lentiviral-delivered shRNAs in vitro and in vivo, we determined that Bmi-1 is also important for NSC self-renewal in the embryo. We found that neural progenitors depend increasingly on Bmi-1 for proliferation as development proceeds from embryonic through adult stages. Acute shRNA-mediated Bmi-1 reduction causes defects in embryonic and adult NSC proliferation and self-renewal that, unexpectedly, are mediated by a different cell-cycle inhibitor, p21. Gene array analyses revealed developmental differences in Bmi-1-controlled expression of genes in the p21-Rb cell cycle regulatory pathway. Our data therefore implicate p21 as an important Bmi-1 target in NSCs, potentially with stage-related differences. Understanding stage-related mechanisms underlying NSC self-renewal has important implications for development of stem cell-based therapies.     

Proteomic patterns for classification of ovarian cancer and CTCL serum samples utilizing peak pairs indicative of post-translational modifications

Proteomic patterns as a potential diagnostic technology has been well established for several cancer conditions and other diseases. The use of machine learning techniques such as decision trees, neural networks, genetic algorithms, and other methods has been the basis for pattern determination. Cancer is known to involve signaling pathways that are regulated through PTM of proteins. These modifications are also detectable with high confidence using high-resolution MS. We generated data using a prOTOF mass spectrometer on two sets of patient samples: ovarian cancer and cutaneous t-cell lymphoma (CTCL) with matched normal samples for each disease. Using the knowledge of mass shifts caused by common modifications, we built models using peak pairs and compared this to a conventional technique using individual peaks. The results for each disease showed that a small number of peak pairs gave classification equal to or better than the conventional technique that used multiple individual peaks. This simple peak picking technique could be used to guide identification of important peak pairs involved in the disease process.     

Inundation,Hydrodynamics and Vegetation Influence Carbon Dioxide Concentrations in Amazon Floodplain Lakes

Ecosystems - Extensive floodplains and numerous lakes in the Amazon basin are well suited to examine the role of floodable lands within the context of the sources and processing of carbon within...