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1.
Vibrio cholerae is autochthonous to various aquatic niches and is the etiological agent of the life-threatening diarrheal disease cholera. The persistence of V. cholerae in natural habitats is a crucial factor in the epidemiology of cholera. In contrast to the well-studied V. cholerae-chitin connection, scarce information is available about the factors employed by the bacteria for the interaction with collagens. Collagens might serve as biologically relevant substrates, because they are the most abundant protein constituents of metazoan tissues and V. cholerae has been identified in association with invertebrate and vertebrate marine animals, as well as in a benthic zone of the ocean where organic matter, including collagens, accumulates. Here, we describe the characterization of the V. cholerae putative collagenase, VchC, encoded by open reading frame VC1650 and belonging to the subfamily M9A peptidases. Our studies demonstrate that VchC is an extracellular collagenase degrading native type I collagen of fish and mammalian origin. Alteration of the predicted catalytic residues coordinating zinc ions completely abolished the protein enzymatic activity but did not affect the translocation of the protease by the type II secretion pathway into the extracellular milieu. We also show that the protease undergoes a maturation process with the aid of a secreted factor(s). Finally, we propose that V. cholerae is a collagenovorous bacterium, as it is able to utilize collagen as a sole nutrient source. This study initiates new lines of investigations aiming to uncover the structural and functional components of the V. cholerae collagen utilization program.  相似文献   
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High-throughput phenotyping is emerging as an important technology to dissect phenotypic components in plants. Efficient image processing and feature extraction are prerequisites to quantify plant growth and performance based on phenotypic traits. Issues include data management, image analysis, and result visualization of large-scale phenotypic data sets. Here, we present Integrated Analysis Platform (IAP), an open-source framework for high-throughput plant phenotyping. IAP provides user-friendly interfaces, and its core functions are highly adaptable. Our system supports image data transfer from different acquisition environments and large-scale image analysis for different plant species based on real-time imaging data obtained from different spectra. Due to the huge amount of data to manage, we utilized a common data structure for efficient storage and organization of data for both input data and result data. We implemented a block-based method for automated image processing to extract a representative list of plant phenotypic traits. We also provide tools for build-in data plotting and result export. For validation of IAP, we performed an example experiment that contains 33 maize (Zea mays ‘Fernandez’) plants, which were grown for 9 weeks in an automated greenhouse with nondestructive imaging. Subsequently, the image data were subjected to automated analysis with the maize pipeline implemented in our system. We found that the computed digital volume and number of leaves correlate with our manually measured data in high accuracy up to 0.98 and 0.95, respectively. In summary, IAP provides a multiple set of functionalities for import/export, management, and automated analysis of high-throughput plant phenotyping data, and its analysis results are highly reliable.Plant bioinformatics faces the challenge of integrating information from the related “omics” fields to elucidate the functional relationship between genotype and observed phenotype (Edwards and Batley, 2004), known as the genotype-phenotype map (Houle et al., 2010). One of the main obstacles is our currently limited ability of systemic depiction and quantification of plant phenotypes, representing the so-called phenotyping bottleneck phenomenon (Furbank and Tester, 2011). To get a comprehensive genotype-phenotype map, more accurate and precise phenotyping strategies are required to empower high-resolution linkage mapping and genome-wide association studies in order to uncover underlying genetic variants associated with complex phenotypic traits, which aim to improve the efficiency, effectiveness, and economy of cultivars in plant breeding (Cobb et al., 2013). In the era of phenomics, automatic high-throughput phenotyping in a noninvasive manner is applied to identify and quantify plant phenotypic traits. Plants are bred in fully automated greenhouses under predefined environmental conditions with controlled temperature, watering, and humidity. To meet the demand of data access, exchange, and sharing, several phenomics-related projects in the context of several consortia have been launched, such as the International Plant Phenotyping Network (http://www.plantphenomics.com/), the European Plant Phenotyping Network (http://www.plant-phenotyping-network.eu/), and the German Plant Phenotyping Network (http://www.dppn.de/).Thanks to the development of new imaging and transport systems, various automated or semiautomated high-throughput plant phenotyping systems are being developed and used to examine plant function and performance under controlled conditions. PHENOPSIS (Granier et al., 2006) is one of the pioneering platforms that was developed to dissect genotype-environment effects on plant growth in Arabidopsis (Arabidopsis thaliana). GROWSCREEN (Walter et al., 2007; Biskup et al., 2009; Jansen et al., 2009; Nagel et al., 2012) was designed for rapid optical phenotyping of different plant species with respect to different biological aspects. Other systems in the context of high-throughput phenotyping include Phenodyn/Phenoarch (Sadok et al., 2007), TraitMill (Reuzeau et al., 2005; Reuzeau, 2007), Phenoscope (Tisné et al., 2013), RootReader3D (Clark et al., 2011), GROW Map (http://www.fz-juelich.de/ibg/ibg-2/EN/methods_jppc/methods_node.html), and LemnaTec Scanalyzer 3D. These developments enable the phenotyping of specific organs (e.g. leaf, root, and shoot) or of whole plants. Some of them are even used for three-dimensional plant analysis (Clark et al., 2011). Consequently, several specific software applications (a comprehensive list can be found at http://www.phenomics.cn/links.php), such as HYPOTrace (Wang et al., 2009), HTPheno (Hartmann et al., 2011), LAMINA (Bylesjö et al., 2008), PhenoPhyte (Green et al., 2012), Rosette Tracker (De Vylder et al., 2012), LeafAnalyser (Weight et al., 2008), RootNav (Pound et al., 2013), SmartGrain (Tanabata et al., 2012), and LemnaGrid, were designed to extract a wide range of measurements, such as height/length, width, shape, projected area, digital volume, compactness, relative growth rate, and colorimetric analysis.The huge amount of generated image data from various phenotyping systems requires appropriate data management as well as an appropriate analytical framework for data interpretation (Fiorani and Schurr, 2013). However, most of the developed image-analysis tools are designed for a specific task, for specific plant species, or are not freely available to the research community. They lack flexibility in terms of needed adaptations to meet new analysis requirements. For example, it would be desirable that a system could handle imaging data from different sources (either from fully automated high-throughput phenotyping systems or from setups where images are acquired manually), different imaging modalities (fluorescence, near-infrared, and thermal imaging), and/or different species (wheat [Triticum aestivum], barley [Hordeum vulgare], maize [Zea mays], and Arabidopsis).In this work, we present Integrated Analysis Platform (IAP), a scalable open-source framework, for high-throughput plant phenotyping data processing. IAP handles different image sources and helps to organize phenotypic data by retaining the metadata from the input in the result data set. In order to measure phenotypic traits in new or modified setups, users can easily create new analysis pipelines or modify the predefined ones. IAP provides various user-friendly interfaces at different system levels to meet the demands of users (e.g. software developers, bioinformaticians, and biologists) with different experiences in software programming.  相似文献   
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Meiotic crossovers (COs) are crucial for ensuring accurate homologous chromosome segregation during meiosis I. Because the double-strand breaks (DSBs) that initiate meiotic recombination greatly outnumber eventual COs, this process requires exquisite regulation to narrow down the pool of DSB intermediates that may form COs. In this paper, we identify a cyclin-related protein, CNTD1, as a critical mediator of this process. Disruption of Cntd1 results in failure to localize CO-specific factors MutLγ and HEI10 at designated CO sites and also leads to prolonged high levels of pre-CO intermediates marked by MutSγ and RNF212. These data show that maturation of COs is intimately coupled to deselection of excess pre-CO sites to yield a limited number of COs and that CNTD1 coordinates these processes by regulating the association between the RING finger proteins HEI10 and RNF212 and components of the CO machinery.  相似文献   
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Plasma lipidome is now increasingly recognized as a potentially important marker of chronic diseases, but the exact extent of its contribution to the interindividual phenotypic variability in family studies is unknown. Here, we used the rich data from the ongoing San Antonio Family Heart Study (SAFHS) and developed a novel statistical approach to quantify the independent and additive value of the plasma lipidome in explaining metabolic syndrome (MS) variability in Mexican American families recruited in the SAFHS. Our analytical approach included two preprocessing steps: principal components analysis of the high-resolution plasma lipidomics data and construction of a subject-subject lipidomic similarity matrix. We then used the Sequential Oligogenic Linkage Analysis Routines software to model the complex family relationships, lipidomic similarities, and other important covariates in a variance components framework. Our results suggested that even after accounting for the shared genetic influences, indicators of lipemic status (total serum cholesterol, TGs, and HDL cholesterol), and obesity, the plasma lipidome independently explained 22% of variability in the homeostatic model of assessment-insulin resistance trait and 16% to 22% variability in glucose, insulin, and waist circumference. Our results demonstrate that plasma lipidomic studies can additively contribute to an understanding of the interindividual variability in MS.  相似文献   
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In these studies, the role of ceramide-1-phosphate (C1P) in the wound-healing process was investigated. Specifically, fibroblasts isolated from mice with the known anabolic enzyme for C1P, ceramide kinase (CERK), ablated (CERK−/− mice) and their wild-type littermates (CERK+/+) were subjected to in vitro wound-healing assays. Simulation of mechanical trauma of a wound by scratching a monolayer of fibroblasts from CERK+/+ mice demonstrated steadily increasing levels of arachidonic acid in a time-dependent manner in stark contrast to CERK−/− fibroblasts. This observed difference was reflected in scratch-induced eicosanoid levels. Similar, but somewhat less intense, changes were observed in a more complex system utilizing skin biopsies obtained from CERK-null mice. Importantly, C1P levels increased during the early stages of human wound healing correlating with the transition from the inflammatory stage to the peak of the fibroplasia stage (e.g., proliferation and migration of fibroblasts). Finally, the loss of proper eicosanoid response translated into an abnormal migration pattern for the fibroblasts isolated from CERK−/−. As the proper migration of fibroblasts is one of the necessary steps of wound healing, these studies demonstrate a novel requirement for the CERK-derived C1P in the proper healing response of wounds.  相似文献   
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Clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR-associated (cas) genes constitute the adaptive immune system in bacteria and archaea. Although the CRISPR-Cas systems have been hypothesized to encode potential toxins, no experimental data supporting the hypothesis are available in the literature. In this work, we provide the first experimental evidence for the presence of a toxin gene in the type I-A CRISPR system of hyperthermophilic archaeon Sulfolobus. csa5, under the control of its native promoter in a shuttle vector, could not be transformed into CRISPR-deficient mutant Sulfolobus solfataricus Sens1, demonstrating a strong toxicity in the cells. A single-amino-acid mutation destroying the intersubunit bridge of Csa5 attenuated the toxicity, indicative of the importance of Csa5 oligomerization for its toxicity. In line with the absence of Csa5 toxicity in S. solfataricus InF1 containing functional CRISPR systems, the expression of csa5 is repressed in InF1 cells. Induced from the arabinose promoter in Sens1 cells, Csa5 oligomers resistant to 1% SDS co-occur with chromosome degradation and cell death, reinforcing the connection between Csa5 oligomerization and its toxicity. Importantly, a rudivirus was shown to induce Csa5 expression and the formation of SDS-resistant Csa5 oligomers in Sulfolobus cells. This demonstrates that the derepression of csa5 and the subsequent Csa5 oligomerization take place in native virus-host systems. Thus, csa5 is likely to act as a suicide gene under certain circumstances to inhibit virus spreading.  相似文献   
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