Exclusive neuronal expression of SUCLA2 in the human brain

SUCLA2 encodes the ATP-forming subunit (A-SUCL-) of succinyl-CoA ligase, an enzyme of the citric acid cycle. Mutations in SUCLA2 lead to a mitochondrial disorder manifesting as encephalomyopathy with dystonia, deafness and lesions in the basal ganglia. Despite the distinct brain pathology associated with SUCLA2 mutations, the precise localization of SUCLA2 protein has never been investigated. Here we show that immunoreactivity of A-SUCL- in surgical human cortical tissue samples was present exclusively in neurons, identified by their morphology and visualized by double labeling with a fluorescent Nissl dye. A-SUCL- immunoreactivity co-localized >99% with that of the d subunit of the mitochondrial F0-F1 ATP synthase. Specificity of the anti-A-SUCL- antiserum was verified by the absence of labeling in fibroblasts from a patient with a complete deletion of SUCLA2. A-SUCL- immunoreactivity was absent in glial cells, identified by antibodies directed against the glial markers GFAP and S100. Furthermore, in situ hybridization histochemistry demonstrated that SUCLA2 mRNA was present in Nissl-labeled neurons but not glial cells labeled with S100. Immunoreactivity of the GTP-forming subunit (G-SUCL-) encoded by SUCLG2, or in situ hybridization histochemistry for SUCLG2 mRNA could not be demonstrated in either neurons or astrocytes. Western blotting of post mortem brain samples revealed minor G-SUCL- immunoreactivity that was however, not upregulated in samples obtained from diabetic versus non-diabetic patients, as has been described for murine brain. Our work establishes that SUCLA2 is expressed exclusively in neurons in the human cerebral cortex

Dual requirement of cytokine and activation receptor triggering for cytotoxic control of murine cytomegalovirus by NK cells

Natural killer (NK) cells play a critical role in controlling murine cytomegalovirus (MCMV) and can mediate both cytokine production and direct cytotoxicity. The NK cell activation receptor, Ly49H, is responsible for genetic resistance to MCMV in C57BL/6 mice. Recognition of the viral m157 protein by Ly49H is sufficient for effective control of MCMV infection. Additionally, during the host response to infection, distinct immune and non-immune cells elaborate a variety of pleiotropic cytokines which have the potential to impact viral pathogenesis, NK cells, and other immune functions, both directly and indirectly. While the effects of various immune deficiencies have been examined for general antiviral phenotypes, their direct effects on Ly49H-dependent MCMV control are poorly understood. To specifically interrogate Ly49H-dependent functions, herein we employed an in vivo viral competition approach to show Ly49H-dependent MCMV control is specifically mediated through cytotoxicity but not IFNγ production. Whereas m157 induced Ly49H-dependent degranulation, efficient cytotoxicity also required either IL-12 or type I interferon (IFN-I) which acted directly on NK cells to produce granzyme B. These studies demonstrate that both of these distinct NK cell-intrinsic mechanisms are integrated for optimal viral control by NK cells

Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data

<p>Abstract</p> <p>Background</p> <p><it>Saccharomyces cerevisiae </it>is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., <it>i</it>MM904) remains significantly less predictive than the latest <it>E. coli </it>model (i.e., <it>i</it>AF1260). This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the <it>E. coli </it>model.</p> <p>Results</p> <p>In this paper we make use of the automated GrowMatch procedure for restoring consistency with single gene deletion experiments in yeast and extend the procedure to make use of synthetic lethality data using the genome-scale model <it>i</it>MM904 as a basis. We identified and vetted using literature sources 120 distinct model modifications including various regulatory constraints for minimal and YP media. The incorporation of the suggested modifications led to a substantial increase in the fraction of correctly predicted lethal knockouts (i.e., specificity) from 38.84% (87 out of 224) to 53.57% (120 out of 224) for the minimal medium and from 24.73% (45 out of 182) to 40.11% (73 out of 182) for the YP medium. Synthetic lethality predictions improved from 12.03% (16 out of 133) to 23.31% (31 out of 133) for the minimal medium and from 6.96% (8 out of 115) to 13.04% (15 out of 115) for the YP medium.</p> <p>Conclusions</p> <p>Overall, this study provides a roadmap for the computationally driven correction of multi-compartment genome-scale metabolic models and demonstrates the value of synthetic lethals as curation agents.</p

Shedding Light on the Galaxy Luminosity Function

From as early as the 1930s, astronomers have tried to quantify the statistical nature of the evolution and large-scale structure of galaxies by studying their luminosity distribution as a function of redshift - known as the galaxy luminosity function (LF). Accurately constructing the LF remains a popular and yet tricky pursuit in modern observational cosmology where the presence of observational selection effects due to e.g. detection thresholds in apparent magnitude, colour, surface brightness or some combination thereof can render any given galaxy survey incomplete and thus introduce bias into the LF. Over the last seventy years there have been numerous sophisticated statistical approaches devised to tackle these issues; all have advantages -- but not one is perfect. This review takes a broad historical look at the key statistical tools that have been developed over this period, discussing their relative merits and highlighting any significant extensions and modifications. In addition, the more generalised methods that have emerged within the last few years are examined. These methods propose a more rigorous statistical framework within which to determine the LF compared to some of the more traditional methods. I also look at how photometric redshift estimations are being incorporated into the LF methodology as well as considering the construction of bivariate LFs. Finally, I review the ongoing development of completeness estimators which test some of the fundamental assumptions going into LF estimators and can be powerful probes of any residual systematic effects inherent magnitude-redshift data.Comment: 95 pages, 23 figures, 3 tables. Now published in The Astronomy & Astrophysics Review. This version: bring in line with A&AR format requirements, also minor typo corrections made, additional citations and higher rez images adde