Conserved WCPL and CX4C Domains Mediate Several Mating Adhesin Interactions in Saccharomyces cerevisiae

Several adhesins are induced by pheromones during mating in Saccharomyces cerevisiae, including Aga1p, Aga2p, Sag1p (Agα1p), and Fig2p. These four proteins all participate in or influence a well-studied agglutinin interaction mediated by Aga1p–Aga2p complexes and Sag1p; however, they also play redundant and essential roles in mating via an unknown mechanism. Aga1p and Fig2p both contain repeated, conserved WCPL and CX₄C domains. This study was directed toward understanding the mechanism underlying the collective requirement of agglutinins and Fig2p for mating. Apart from the well-known agglutinin interaction between Aga2p and Sag1p, three more pairs of interactions in cells of opposite mating type were revealed by this study, including bilateral heterotypic interactions between Aga1p and Fig2p and a homotypic interaction between Fig2p and Fig2p. These four pairs of adhesin interactions are collectively required for maximum mating efficiency and normal zygote morphogenesis. GPI-less, epitope-tagged forms of Aga1p and Fig2p can be co-immunoprecipitated from the culture medium of mating cells in a manner dependent on the WCPL and CX₄C domains in the R1 repeat of Aga1p. Using site-directed mutagenesis, the conserved residues in Aga1p that interact with Fig2p were identified. Aga1p is involved in two distinct adhesive functions that are independent of each other, which raises the possibility for combinatorial interactions of this protein with its different adhesion receptors, Sag1 and Fig2p, a property of many higher eukaryotic adhesins.     

In vivo analysis of a new lacZ retrovirus vector suitable for cell lineage marking in avian and other species

To obtain a replication-defective retrovirus vector well suited for cell lineage marking in early avian embryos, we have constructed and tested a derivative of the avian spleen necrosis virus (SNV) carrying the marker gene lacZ. Consistently high titers of this virus, designated CXL, were produced from retroviral packaging cells with no evidence of contaminating helper virus even after 12 months of continuous culture. CXL expresses lacZ strongly and stably in avian cells and has a host range that extends to other avian and some mammalian species. We show that CXL has the potential to mark a wide variety of chick embryo cell types by infection in ovo. The high titers obtainable with this virus can provide a significant advantage over alternative lacZ vectors, especially in lineage marking of early stage embryos. As an example of this, we show that CXL can be used to mark cells of the precardiac mesoderm in stage 4-5 chick embryos.     

Multiple Phenotypic Changes Associated with Large-Scale Horizontal Gene Transfer

Horizontal gene transfer often leads to phenotypic changes within recipient organisms independent of any immediate evolutionary benefits. While secondary phenotypic effects of horizontal transfer (i.e., changes in growth rates) have been demonstrated and studied across a variety of systems using relatively small plasmids and phage, little is known about the magnitude or number of such costs after the transfer of larger regions. Here we describe numerous phenotypic changes that occur after a large-scale horizontal transfer event (∼1 Mb megaplasmid) within Pseudomonas stutzeri including sensitization to various stresses as well as changes in bacterial behavior. These results highlight the power of horizontal transfer to shift pleiotropic relationships and cellular networks within bacterial genomes. They also provide an important context for how secondary effects of transfer can bias evolutionary trajectories and interactions between species. Lastly, these results and system provide a foundation to investigate evolutionary consequences in real time as newly acquired regions are ameliorated and integrated into new genomic contexts.     

Tract Profiles of White Matter Properties: Automating Fiber-Tract Quantification

Tractography based on diffusion weighted imaging (DWI) data is a method for identifying the major white matter fascicles (tracts) in the living human brain. The health of these tracts is an important factor underlying many cognitive and neurological disorders. In vivo, tissue properties may vary systematically along each tract for several reasons: different populations of axons enter and exit the tract, and disease can strike at local positions within the tract. Hence quantifying and understanding diffusion measures along each fiber tract (Tract Profile) may reveal new insights into white matter development, function, and disease that are not obvious from mean measures of that tract. We demonstrate several novel findings related to Tract Profiles in the brains of typically developing children and children at risk for white matter injury secondary to preterm birth. First, fractional anisotropy (FA) values vary substantially within a tract but the Tract FA Profile is consistent across subjects. Thus, Tract Profiles contain far more information than mean diffusion measures. Second, developmental changes in FA occur at specific positions within the Tract Profile, rather than along the entire tract. Third, Tract Profiles can be used to compare white matter properties of individual patients to standardized Tract Profiles of a healthy population to elucidate unique features of that patient''s clinical condition. Fourth, Tract Profiles can be used to evaluate the association between white matter properties and behavioral outcomes. Specifically, in the preterm group reading ability is positively correlated with FA measured at specific locations on the left arcuate and left superior longitudinal fasciculus and the magnitude of the correlation varies significantly along the Tract Profiles. We introduce open source software for automated fiber-tract quantification (AFQ) that measures Tract Profiles of MRI parameters for 18 white matter tracts. With further validation, AFQ Tract Profiles have potential for informing clinical management and decision-making.     

Participation of lymphoid cells in the withdrawal syndrome of opiate dependent rats

Treatment of rats with 500 Rads whole-body ionizing irradiation prior to chronic administration of morphine reduced the severity of the naloxone induced withdrawal signs. In contrast, adoptive transfer of 2-6 X 10(8) lymphoid cells to irradiated rats prior to chronic morphine treatment completely restored the ability to manifest the withdrawal signs precipitated by naloxone. These observations offer the possibility that the immune system participates in opiate addiction.     

The first annulus of otoliths: a tool for studying intra-annual growth of walleye pollock (Theragra chalcogramma)

We quantified the growing season of yearling walleye pollock (Theragra chalcogramma) and related it to annual cycles of water temperature and day length. The study was restricted to members of the 2000 year class and thereby controlled for inter-annual variability. Juveniles were sampled from the year class during 10 cruises in the western Gulf of Alaska (GOA). Fifty percent of juveniles exhibited an annulus on 16 March 2001 (± 11 days 95% confidence interval). No regional difference was detected in the timing of annulus formation or in post-annulus growth trajectories. A model, derived from growth trajectories, estimated that the growing season lasted 204 days (22 March to 13 October 2001) and that growth rate peaked at 0.59 mm day⁻¹ on 2 July 2001. Growth rate increased with day length and water temperature during spring and decreased in late summer possibly due to thermal stress. Secondarily, we explored the utility of otolith size at the first annulus as a natural tag to identify nursery area, but this potential was curtailed by overlap in length among regions. Our results indicate that the first annulus can be used to advance our understanding of climate forcing on marine fish growth by providing fine temporal resolution of the growing season.     

Accelerating Atomic Catalyst Discovery by Theoretical Calculations‐Machine Learning Strategy

Atomic catalysts (AC) are emerging as a highly attractive research topic, especially in sustainable energy fields. Lack of a full picture of the hydrogen evolution reaction (HER) impedes the future development of potential electrocatalysts. In this work, the systematic investigation of the HER process in graphdyine (GDY) based AC is presented in terms of the adsorption energies, adsorption trend, electronic structures, reaction pathway, and active sites. This comprehensive work innovatively reveals GDY based AC for HER covering all the transition metals (TM) and lanthanide (Ln) metals, enabling the screening of potential catalysts. The density functional theory (DFT) calculations carefully explore the HER performance beyond the comparison of sole H adsorption. Therefore, the screened catalysts candidates not only match with experimental results but also provide significant references for novel catalysts. Moreover, the machine learning (ML) technique bag‐tree approach is innovatively utilized based on the fuzzy model for data separation and converse prediction of the HER performance, which indicates a similar result to the theoretical calculations. From two independent theoretical perspectives (DFT and ML), this work proposes pivotal guidelines for experimental catalyst design and synthesis. The proposed advanced research strategy shows great potential as a general approach in other energy‐related areas.     

Accurate and Reliable Cancer Classification Based on Probabilistic Inference of Pathway Activity

With the advent of high-throughput technologies for measuring genome-wide expression profiles, a large number of methods have been proposed for discovering diagnostic markers that can accurately discriminate between different classes of a disease. However, factors such as the small sample size of typical clinical data, the inherent noise in high-throughput measurements, and the heterogeneity across different samples, often make it difficult to find reliable gene markers. To overcome this problem, several studies have proposed the use of pathway-based markers, instead of individual gene markers, for building the classifier. Given a set of known pathways, these methods estimate the activity level of each pathway by summarizing the expression values of its member genes, and use the pathway activities for classification. It has been shown that pathway-based classifiers typically yield more reliable results compared to traditional gene-based classifiers. In this paper, we propose a new classification method based on probabilistic inference of pathway activities. For a given sample, we compute the log-likelihood ratio between different disease phenotypes based on the expression level of each gene. The activity of a given pathway is then inferred by combining the log-likelihood ratios of the constituent genes. We apply the proposed method to the classification of breast cancer metastasis, and show that it achieves higher accuracy and identifies more reproducible pathway markers compared to several existing pathway activity inference methods.