915 research outputs found

    Nesting biology of the bee Caupolicana yarrowi.

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    20 pages : illustrations (some color), color maps ; 26 cm. Appendix: Use of nectar by the desert bee Caupolicana yarrowi (Colletidae) in cell construction / James H. Cane and Jerome G. Rozen, Jr.The first part of this publication, written by a group of participants in Bee Course 2018, results from the discovery of three nests of Caupolicana yarrowi (Cresson, 1875) at the base of the Chiricahua Mountains in southeastern Arizona. The nests are deep with branching laterals that usually connect to large vertical brood cells by an upward turn before curving downward and attaching to the top of the chambers. This loop of the lateral thus seems to serve as a "sink trap," excluding rainwater from reaching open cells during provisioning. Although mature larvae had not yet developed, an egg of C. yarrowi was discovered floating on the provisions allowing an SEM examination of its chorion, the first such study for any egg of the Diphaglossinae. Larval food for this species at this site came from Solanum elaeagnifolium Cav. (Solanaceae). Nests were parasitized by Triepeolus grandis (Friese, 1917) (Epeolini), which previously was known to attack only Ptiloglossa (Diphaglossinae: Caupolicanini). The subterranean nest cells of the desert bee Caupolicana yarrowi (Colletidae), which are enveloped by a casing of hardened soil that easily separates from the surrounding matrix, are discussed in a separate appendix. Chemical analysis revealed the casing to be rich in reducing sugars, indicating that the mother bee had regurgitated floral nectar onto the rough interior walls of the cell cavity before smoothing and waterproofing them. This novel use of nectar in nest construction is compared with that of other bee species that bring water to a nest site to soften soil for excavation

    HECTD2 Is Associated with Susceptibility to Mouse and Human Prion Disease

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    Prion diseases are fatal transmissible neurodegenerative disorders, which include Scrapie, Bovine Spongiform Encephalopathy (BSE), Creutzfeldt-Jakob Disease (CJD), and kuru. They are characterised by a prolonged clinically silent incubation period, variation in which is determined by many factors, including genetic background. We have used a heterogeneous stock of mice to identify Hectd2, an E3 ubiquitin ligase, as a quantitative trait gene for prion disease incubation time in mice. Further, we report an association between HECTD2 haplotypes and susceptibility to the acquired human prion diseases, vCJD and kuru. We report a genotype-associated differential expression of Hectd2 mRNA in mouse brains and human lymphocytes and a significant up-regulation of transcript in mice at the terminal stage of prion disease. Although the substrate of HECTD2 is unknown, these data highlight the importance of proteosome-directed protein degradation in neurodegeneration. This is the first demonstration of a mouse quantitative trait gene that also influences susceptibility to human prion diseases. Characterisation of such genes is key to understanding human risk and the molecular basis of incubation periods

    Crystal Structure of EHEC Intimin: Insights into the Complementarity between EPEC and EHEC

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    Enterohaemorrhagic E. coli (EHEC) O157:H7 is a primary food-borne bacterial pathogen capable of causing life-threatening human infections which poses a serious challenge to public health worldwide. Intimin, the bacterial outer-membrane protein, plays a key role in the initiating process of EHEC infection. This activity is dependent upon translocation of the intimin receptor (Tir), the intimin binding partner of the bacteria-encoded host cell surface protein. Intimin has attracted considerable attention due to its potential function as an antibacterial drug target. Here, we report the crystal structure of the Tir-binding domain of intimin (Int188) from E. coli O157:H7 at 2.8 Ã… resolution, together with a mutant (IntN916Y) at 2.6 Ã…. We also built the structural model of EHEC intimin-Tir complex and analyzed the key binding residues. It suggested that the binding pattern of intimin and Tir between EHEC and Enteropathogenic E. coli (EPEC) adopt a similar mode and they can complement with each other. Detailed structural comparison indicates that there are four major points of structural variations between EHEC and EPEC intimins: one in Domain I (Ig-like domain), the other three located in Domain II (C-type lectin-like domain). These variations result in different binding affinities. These findings provide structural insight into the binding pattern of intimin to Tir and the molecular mechanism of EHEC O157: H7

    Crystal Structure of EHEC Intimin: Insights into the Complementarity between EPEC and EHEC

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    Enterohaemorrhagic E. coli (EHEC) O157:H7 is a primary food-borne bacterial pathogen capable of causing life-threatening human infections which poses a serious challenge to public health worldwide. Intimin, the bacterial outer-membrane protein, plays a key role in the initiating process of EHEC infection. This activity is dependent upon translocation of the intimin receptor (Tir), the intimin binding partner of the bacteria-encoded host cell surface protein. Intimin has attracted considerable attention due to its potential function as an antibacterial drug target. Here, we report the crystal structure of the Tir-binding domain of intimin (Int188) from E. coli O157:H7 at 2.8 Ã… resolution, together with a mutant (IntN916Y) at 2.6 Ã…. We also built the structural model of EHEC intimin-Tir complex and analyzed the key binding residues. It suggested that the binding pattern of intimin and Tir between EHEC and Enteropathogenic E. coli (EPEC) adopt a similar mode and they can complement with each other. Detailed structural comparison indicates that there are four major points of structural variations between EHEC and EPEC intimins: one in Domain I (Ig-like domain), the other three located in Domain II (C-type lectin-like domain). These variations result in different binding affinities. These findings provide structural insight into the binding pattern of intimin to Tir and the molecular mechanism of EHEC O157: H7

    Mapping and Functional Characterisation of a CTCF-Dependent Insulator Element at the 3′ Border of the Murine Scl Transcriptional Domain

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    The Scl gene encodes a transcription factor essential for haematopoietic development. Scl transcription is regulated by a panel of cis-elements spread over 55 kb with the most distal 3′ element being located downstream of the neighbouring gene Map17, which is co-regulated with Scl in haematopoietic cells. The Scl/Map17 domain is flanked upstream by the ubiquitously expressed Sil gene and downstream by a cluster of Cyp genes active in liver, but the mechanisms responsible for delineating the domain boundaries remain unclear. Here we report identification of a DNaseI hypersensitive site at the 3′ end of the Scl/Map17 domain and 45 kb downstream of the Scl transcription start site. This element is located at the boundary of active and inactive chromatin, does not function as a classical tissue-specific enhancer, binds CTCF and is both necessary and sufficient for insulator function in haematopoietic cells in vitro. Moreover, in a transgenic reporter assay, tissue-specific expression of the Scl promoter in brain was increased by incorporation of 350 bp flanking fragments from the +45 element. Our data suggests that the +45 region functions as a boundary element that separates the Scl/Map17 and Cyp transcriptional domains, and raise the possibility that this element may be useful for improving tissue-specific expression of transgenic constructs

    Network Inference Algorithms Elucidate Nrf2 Regulation of Mouse Lung Oxidative Stress

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    A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods. Nuclear factor erythroid 2-related factor (Nrf2) regulates the transcription of several gene products involved in the protective response to oxidative stress. The transcriptional regulatory and signaling relationships linking gene products involved in the response to oxidative stress are, currently, only partially resolved. Microarray data constitute RNA abundance measures representing gene expression patterns. In some cases, these patterns can identify the molecular interactions of gene products. They can be, in effect, proxies for protein–protein and protein–DNA interactions. Traditional techniques used for clustering coregulated genes on high-throughput gene arrays are rarely capable of distinguishing between direct transcriptional regulatory interactions and indirect ones. In this study, newly developed information-theoretic algorithms that employ the concept of mutual information were used: the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Context Likelihood of Relatedness (CLR). These algorithms captured dependencies in the gene expression profiles of the mouse lung, allowing the regulatory effect of Nrf2 in response to oxidative stress to be determined more precisely. In addition, a characterization of promoter sequences of Nrf2 regulatory targets was conducted using a Support Vector Machine classification algorithm to corroborate ARACNE and CLR predictions. Inferred networks were analyzed, compared, and integrated using the Collective Analysis of Biological Interaction Networks (CABIN) plug-in of Cytoscape. Using the two network inference algorithms and one machine learning algorithm, a number of both previously known and novel targets of Nrf2 transcriptional activation were identified. Genes predicted as novel Nrf2 targets include Atf1, Srxn1, Prnp, Sod2, Als2, Nfkbib, and Ppp1r15b. Furthermore, microarray and quantitative RT-PCR experiments following cigarette-smoke-induced oxidative stress in Nrf2+/+ and Nrf2−/− mouse lung affirmed many of the predictions made. Several new potential feed-forward regulatory loops involving Nrf2, Nqo1, Srxn1, Prdx1, Als2, Atf1, Sod1, and Park7 were predicted. This work shows the promise of network inference algorithms operating on high-throughput gene expression data in identifying transcriptional regulatory and other signaling relationships implicated in mammalian disease
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