22 research outputs found

    Identification of intragenic mutations in the Hansenula polymorpha PEX6 gene that affect peroxisome biogenesis and methylotrophic growth

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    Two interacting AAA ATPases, Pex1p and Pex6p, are indispensable for peroxisome biogenesis in different organisms. Mutations affecting corresponding genes are the most common cause of the peroxisome biogenesis disorders in humans. By UV mutagenesis of the Hansenula polymorpha pex6 mutant, deficient in peroxisome biogenesis, we isolated a conditional cold-sensitive strain with restored ability to grow in methanol medium at 37degreesC but not at 28degreesC. Sequencing of the pex6 allele revealed a point mutation in the first AAA module of the PEX6 gene that leads to substitution of a conserved amino acid residue (G737E). An additional intragenic mutation identified in the cold-sensitive pex6 allele leads to a conserved amino acid substitution in the second AAA domain (R1000G). Electron microscopic analysis revealed restored peroxisomes in methanol-induced cold-sensitive pex6 cells at both permissive and restrictive temperatures. If separated, the secondary mutation did not affect methylotrophic growth. Our data suggest that H. polymorpha Pex6p may have a complex function in peroxisome biogenesis in which identified amino acid residues are involved. (C) 2003 Published by Elsevier B.V. on behalf of the Federation of European Microbiological Societies

    The Adaptor Function of TRAPPC2 in Mammalian TRAPPs Explains TRAPPC2-Associated SEDT and TRAPPC9-Associated Congenital Intellectual Disability

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    Background: The TRAPP (Transport protein particle) complex is a conserved protein complex functioning at various steps in vesicle transport. Although yeast has three functionally and structurally distinct forms, TRAPPI, II and III, emerging evidence suggests that mammalian TRAPP complex may be different. Mutations in the TRAPP complex subunit 2 (TRAPPC2) cause X-linked spondyloepiphyseal dysplasia tarda, while mutations in the TRAPP complex subunit 9 (TRAPPC9) cause postnatal mental retardation with microcephaly. The structural interplay between these subunits found in mammalian equivalent of TRAPPI and those specific to TRAPPII and TRAPPIII remains largely unknown and we undertook the present study to examine the interaction between these subunits. Here, we reveal that the mammalian equivalent of the TRAPPII complex is structurally distinct from the yeast counterpart thus leading to insight into mechanism of disease. Principal Findings: We analyzed how TRAPPII- or TRAPPIII- specific subunits interact with the six-subunit core complex of TRAPP by co-immunoprecipitation in mammalian cells. TRAPPC2 binds to TRAPPII-specific subunit TRAPPC9, which in turn binds to TRAPPC10. Unexpectedly, TRAPPC2 can also bind to the putative TRAPPIII-specific subunit, TRAPPC8. Endogenous TRAPPC9-positive TRAPPII complex does not contain TRAPPC8, suggesting that TRAPPC2 binds to either TRAPPC9 or TRAPPC8 during the formation of the mammalian equivalents of TRAPPII or TRAPPIII, respectively. Therefore, TRAPPC2 serves as an adaptor for the formation of these complexes. A disease-causing mutation of TRAPPC2, D47Y, failed to interact with either TRAPPC9 or TRAPPC8, suggesting that aspartate 47 in TRAPPC2 is at or near the site of interaction with TRAPPC9 or TRAPPC8, mediating the formation of TRAPPII and/or TRAPPIII. Furthermore, disease-causing deletional mutants of TRAPPC9 all failed to interact with TRAPPC2 and TRAPPC10. Conclusions: TRAPPC2 serves as an adaptor for the formation of TRAPPII or TRAPPIII in mammalian cells. The mammalian equivalent of TRAPPII is likely different from the yeast TRAPPII structurally. © 2011 Zong et al.published_or_final_versio

    Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

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    We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

    Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

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    In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field
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