175 research outputs found

    Calorie Restriction Attenuates Terminal Differentiation of Immune Cells

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    Immune senescence is a natural consequence of aging and may contribute to frailty and loss of homeostasis in later life. Calorie restriction increases healthy life-span in C57BL/6J (but not DBA/2J) mice, but whether this is related to preservation of immune function, and how it interacts with aging, is unclear. We compared phenotypic and functional characteristics of natural killer (NK) cells and T cells, across the lifespan, of calorie-restricted (CR) and control C57BL/6 and DBA/2 mice. Calorie restriction preserves a naïve T cell phenotype and an immature NK cell phenotype as mice age. The splenic T cell populations of CR mice had higher proportions of CD11a-CD44locells, lower expression of TRAIL, KLRG1, and CXCR3, and higher expression of CD127, compared to control mice. Similarly, splenic NK cells from CR mice had higher proportions of less differentiated CD11b-CD27+cells and correspondingly lower proportions of highly differentiated CD11b+CD27-NK cells. Within each of these subsets, cells from CR mice had higher expression of CD127, CD25, TRAIL, NKG2A/C/E, and CXCR3 and lower expression of KLRG1 and Ly49 receptors compared to controls. The effects of calorie restriction on lymphoid cell populations in lung, liver, and lymph nodes were identical to those seen in the spleen, indicating that this is a system-wide effect. The impact of calorie restriction on NK cell and T cell maturation is much more profound than the effect of aging and, indeed, calorie restriction attenuates these age-associated changes. Importantly, the effects of calorie restriction on lymphocyte maturation were more marked in C57BL/6 than in DBA/2J mice indicating that delayed lymphocyte maturation correlates with extended lifespan. These findings have implications for understanding the interaction between nutritional status, immunity, and healthy lifespan in aging populations

    Pharmacological differentiation of opioid receptor antagonists by molecular and functional imaging of target occupancy and food reward-related brain activation in humans

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    Opioid neurotransmission has a key role in mediating reward-related behaviours. Opioid receptor (OR) antagonists, such as naltrexone (NTX), can attenuate the behaviour-reinforcing effects of primary (food) and secondary rewards. GSK1521498 is a novel OR ligand, which behaves as an inverse agonist at the μ-OR sub-type. In a sample of healthy volunteers, we used [11C]-carfentanil positron emission tomography to measure the OR occupancy and functional magnetic resonance imaging (fMRI) to measure activation of brain reward centres by palatable food stimuli before and after single oral doses of GSK1521498 (range, 0.4–100 mg) or NTX (range, 2–50 mg). GSK1521498 had high affinity for human brain ORs (GSK1521498 effective concentration 50=7.10 ng ml−1) and there was a direct relationship between receptor occupancy (RO) and plasma concentrations of GSK1521498. However, for both NTX and its principal active metabolite in humans, 6-β-NTX, this relationship was indirect. GSK1521498, but not NTX, significantly attenuated the fMRI activation of the amygdala by a palatable food stimulus. We thus have shown how the pharmacological properties of OR antagonists can be characterised directly in humans by a novel integration of molecular and functional neuroimaging techniques. GSK1521498 was differentiated from NTX in terms of its pharmacokinetics, target affinity, plasma concentration–RO relationships and pharmacodynamic effects on food reward processing in the brain. Pharmacological differentiation of these molecules suggests that they may have different therapeutic profiles for treatment of overeating and other disorders of compulsive consumption

    Efficient Protocols for Oblivious Linear Function Evaluation from Ring-LWE

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    An oblivious linear function evaluation protocol, or OLE, is a two-party protocol for the function f(x)=ax+bf(x) = ax + b, where a sender inputs the field elements a,ba,b, and a receiver inputs xx and learns f(x)f(x). OLE can be used to build secret-shared multiplication, and is an essential component of many secure computation applications including general-purpose multi-party computation, private set intersection and more. In this work, we present several efficient OLE protocols from the ring learning with errors (RLWE) assumption. Technically, we build two new passively secure protocols, which build upon recent advances in homomorphic secret sharing from (R)LWE (Boyle et al., Eurocrypt 2019), with optimizations tailored to the setting of OLE. We upgrade these to active security using efficient amortized zero-knowledge techniques for lattice relations (Baum et al., Crypto 2018), and design new variants of zero-knowledge arguments that are necessary for some of our constructions. Our protocols offer several advantages over existing constructions. Firstly, they have the lowest communication complexity amongst previous, practical protocols from RLWE and other assumptions; secondly, they are conceptually very simple, and have just one round of interaction for the case of OLE where bb is randomly chosen. We demonstrate this with an implementation of one of our passively secure protocols, which can perform more than 1 million OLEs per second over the ring Zm\mathbb{Z}_m, for a 120-bit modulus mm, on standard hardware

    Inheritance analysis and identification of SNP markers associated with ZYMV resistance in Cucurbita pepo

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    [EN] Cucurbit crops are economically important worldwide. One of the most serious threats to cucurbit production is Zucchini yellow mosaic virus (ZYMV). Several resistant accessions were identified in Cucurbita moschata and their resistance was introgressed into Cucurbita pepo. However, the mode of inheritance of ZYMV resistance in C. pepo presents a great challenge to attempts at introgressing resistance into elite germplasm. The main goal of this work was to analyze the inheritance of ZYMV resistance and to identify markers associated with genes conferring resistance. An Illumina GoldenGate assay allowed us to assess polymorphism among nine squash genotypes and to discover six polymorphic single-nucleotide polymorphisms (SNPs) between two near-isogenic lines, "True French" (susceptible to ZYMV) and Accession 381e (resistant to ZYMV). Two F-2 and three BC1 populations obtained from crossing the ZYMV-resistant Accession 381e with two susceptible ones, the zucchini True French and the cocozelle "San Pasquale," were assayed for ZYMV resistance. Molecular analysis revealed an approximately 90% association between SNP1 and resistance, which was confirmed using High Resolution Melt (HRM) and a CAPS marker. Co-segregation up to 72% in populations segregating for resistance was observed for two other SNP markers that could be potentially linked to genes involved in resistance expression. A functional prediction of proteins involved in the resistance response was performed on genome scaffolds containing the three SNPs of interest. Indeed, 16 full-length pathogen recognition genes (PRGs) were identified around the three SNP markers. In particular, we discovered that two nucleotide-binding site leucine-rich repeat (NBS-LRR) protein-encoding genes were located near the SNP1 marker. The investigation of ZYMV resistance in squash populations and the genomic analysis performed in this work could be useful for better directing the introgression of disease resistance into elite C. pepo germplasm.This work was supported by the Ministry of University and Research (GenHORT project).Capuozzo, C.; Formisano, G.; Iovieno, P.; Andolfo, G.; Tomassoli, L.; Barbella, M.; Picó Sirvent, MB.... (2017). Inheritance analysis and identification of SNP markers associated with ZYMV resistance in Cucurbita pepo. Molecular Breeding. 37(8). https://doi.org/10.1007/s11032-017-0698-5S378Addinsoft (2007) XLSTAT, Analyse de données et statistique avec MS Excel. Addinsoft, NYAndolfo G, Ercolano MR (2015) Plant innate immunity multicomponent model. Front Plant Sci 6:987Andolfo G, Sanseverino W, Rombauts S et al (2013) Overview of tomato (Solanum lycopersicum) candidate pathogen recognition genes reveals important Solanum R locus dynamics. New Phytol 197:223–237Andolfo G, Ferriello F, Tardella L et al (2014) Tomato genome-wide transcriptional responses to fusarium wilt, and tomato mosaic virus. PLoS One 9:e94963Blanca J, Cañizares J, Roig C, Ziarsolo P, Nuez F, Picó B (2011) Transcriptome characterization and high throughput SSRs and SNPs discovery in Cucurbita pepo (Cucurbitaceae). BMC Genomics 12:104Brown RN, Bolanos-Herrera A, Myers JR, Jahn MM (2003) Inheritance of resistance to four cucurbit viruses in Cucurbita moschata. Euphytica 129:253–258Burge CB, Karlin S (1998) Finding the genes in genomic DNA. Curr Opin Struct Biol 8:346–354Cipollini D (2008) Constitutive expression of methyl jasmonate-inducible responses delays reproduction and constrains fitness responses to nutrients in Arabidopsis thaliana. Evol Ecol 24:59–68Cohen R, Hanan A, Paris HS (2003) Single-gene resistance to powdery mildew in zucchini squash (Cucurbita pepo). Euphytica 130:433–441Collum TD, Padmanabhan MS, Hsieh YC, Culver JN (2016) Tobacco mosaic virus-directed reprogramming of auxin/indole acetic acid protein transcriptional responses enhances virus phloem loading. Proc Natl Acad Sci U S A 113:E2740–E2749Desbiez C, Lecoq H (1997) Zucchini yellow mosaic virus. Plant Pathol 46:809–829Ercolano MR, Sanseverino W, Carli P, Ferriello F, Frusciante L (2012) Genetic and genomic approaches for R-gene mediated disease resistance in tomato: retrospects and prospects. Plant Cell Rep 31:973–985Esteras C, Gómez P, Monforte AJ, Blanca J, Vicente-Dólera N, Roig C, Nuez F, Picó B (2012) High-throughput SNP genotyping in Cucurbita pepo for map construction and quantitative trait loci mapping. BMC Genomics 13:80Formisano G, Paris HS, Frusciante L, Ercolano MR (2010) Commercial Cucurbita pepo squash hybrids carrying disease resistance introgressed from Cucurbita moschata have high genetic similarity. Plant Genet Resour 8:198–203Fulton TM, Chunwongse J, Tanksley SD (1995) Microprep protocol for extraction of DNA from tomato and other herbaceous plants. Plant Mol Biol Report 13:207–209Gal-On A (2007) Zucchini yellow mosaic virus: insect transmission and pathogenicity—the tails of two proteins. Mol Plant Pathol 8:139–150Gilbert-Albertini F, Lecoq H, Pitrat M, Nicolet JL (1993) Resistance of Cucurbita moschata to watermelon mosaic virus type 2 and its genetic relation to resistance to zucchini yellow mosaic virus. Euphytica 69:231–237Gómez P, Rodríguez-Hernández AM, Moury B, Aranda MA (2009) Genetic resistance for the sustainable control of plant virus diseases: breeding, mechanisms and durability. Eur J Plant Pathol 125:1–22Gong L, Stift G, Kofler R, Pachner M, Lelley T (2008a) Microsatellites for the genus Cucurbita and an SSR-based genetic linkage map of Cucurbita pepo L. Theor Appl Genet 117:37–48Gong L, Pachner M, Kalai K, Lelley T (2008b) SSR-based genetic linkage map of Cucurbita moschata and its synteny with Cucurbita pepo. Genome 51:878–887Iovieno P, Andolfo G, Schiavulli A, Catalano D, Ricciardi L, Frusciante L et al. (2015) Structure, evolution and functional inference on the MildewLocusO (MLO) gene family in three cultivated Cucurbitaceae. BMC Genomics 16:1112. doi: 10.1186/s12864-015-2325-3Ishibashi K, Kezuka Y, Kobayashi C, Kato M, Inoue T, Nonaka T et al (2014) Structural basis for the recognition–evasion arms race between Tomato mosaic virus and the resistance gene Tm-1. PNAS 111:E3486–E3495Lecoq H, Pitrat M, Clément M (1981) Identification et caractérisation d’un potyvirus provoquant la maladie du rabougrissement jaune du melon. Agronomie 1:827–834Lefebvre V, Palloix A (1996) Both epistatic and additive effects of QTLs are involved in polygenic induced resistance to disease: a case study, the interaction pepper—Phytophthora capsici Leonian. Theor Appl Genet 93:503–511Levi A, Thomas CE, Newman M, Zhan X, Xu Y, Wehner TC (2003) Massive preferential segregation and nonrandom assortment of linkage-groups produce quasi-linkage in an F2 mapping population of watermelon. Hortscience 38:782Lisa V, Lecoq H (1984) Zucchini yellow mosaic virus. Descriptions of Plant Viruses, Commonwealth Mycological Institute and Association of Applied Biologists 282Lisa V, Boccardo G, D'Agostino G, Dellavalle G, d’Aquilio M (1981) Characterization of a potyvirus that causes zucchini yellow mosaic. Phytopathology 71:667–672MacQueen A, Bergelson J (2016) Modulation of R-gene expression across environments. J Exp Bot 67:2093–2105Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220Munger HM, Provvidenti R (1987) Inheritance of resistance to zucchini yellow mosaic virus in Cucurbita moschata. Cucurbit Genet Coop Rep 10:8–81Nameth ST, Dodds JA, Paulus AO, Laemmlen FF (1986) Cucurbit viruses of California: an ever-changing problem. Plant Dis 70:8–12Ott J, Wang J, Leal SM (2015) Genetic linkage analysis in the age of whole-genome sequencing. Nat Rev Genet 16(5):275–284Pachner M, Lelley T (2004) Different genes for resistance to zucchini yellow mosaic virus (ZYMV) in Cucurbita moschata. In: Lebeda A, Paris HS (eds) Progress in cucurbit genetics and breeding research: Proceedings of Cucurbitaceae 2004. Palacky University, Olomouc (Czech Republic), pp 237–243Pachner M, Paris HS, Lelley T (2011) Genes for resistance to zucchini yellow mosaic in tropical pumpkin. J Hered 102:330–335Pachner M, Paris HS, Winkler J, Lelley T (2015) Phenotypic and marker-assisted pyramiding of genes for resistance to zucchini yellow mosaic virus in oilseed pumpkin (Cucurbita pepo). Plant Breed 134:121–128Paris HS (1986) A proposed subspecific classification for Cucurbita pepo. Phytologia 61:133–138Paris HS (2001) Characterization of the Cucurbita pepo collection at the Newe Ya‘ar Research Center, Israel. Plant Genet Resour Newsl 126:41–45Paris HS (2008) Summer squash. In: Prohens J, Nuez F (eds) Handbook of plant breeding, Vegetables I: 351–379Paris HS, Cohen S (2000) Oligogenic inheritance for resistance to zucchini yellow mosaic virus in Cucurbita pepo. Ann Appl Biol 136:209–214Paris HS, Cohen S, Burger Y, Joseph R (1988) Single-gene resistance to zucchini yellow mosaic virus in Cucurbita moschata. Euphytica 37:27–29Peakall PE, Smouse R (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28:2537–2539Sakamoto T, Deguchi M, Brustolini OJ, Santos AA, Silva FF, Fontes EP (2012) The tomato RLK superfamily: phylogeny and functional predictions about the role of the LRRII-RLK subfamily in antiviral defense. BMC Plant Biol 12:229Sanseverino W, Ercolano MR (2012) In silico approach to predict candidate R proteins and to define their domain architecture. BMC Res Notes 5:678Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 28:2731–2739Teare MD, Santibanez Koref MF (2014) Linkage analysis and the study of Mendelian disease in the era of whole exome and genome sequencing. Brief Funct Genomics 13(5):378–383Valkonen JPT, Wiegmann K, Hämäläinen JH, Marczewski W, Watanabe KN (2008) Evidence for utility of the same PCR-based markers for selection of extreme resistance to Potato virus Y controlled by Rysto of Solanum stoloniferum derived from different sources. Ann Appl Biol 152:121–130Wessel-Beaver L (2005) Cultivar and germplasm release. Release of ‘Soler’ tropical pumpkin. J Agric Univ P R 89:263–266Whitaker TW, Davis GN (1962) Cucurbits: botany, cultivation and utilization. Interscience, New York, pp 105–116Whitaker TW, Robinson RW (1986) Squash breeding. In: Bassett MJ (ed) Breeding vegetable crops. Avi, Westport, pp 209–242Xu Y, Crouch JH (2008) Marker-assisted selection in plant breeding: from publications to practice. Crop Sci 48:391–407Xu R, Zhang S, Huang J, Zheng C (2013) Genome-wide comparative in silico analysis of the RNA helicase gene family in Zea mays and Glycine max: a comparison with Arabidopsis and Oryza sativa. PLoS One 8:e78982Ye G, Smith KF (2008) Marker-assisted gene pyramiding for inbred line development: basic principles and practical guidelines. Int J Plant Breed 2:1–10Zdobnov EM, Apweiler R (2001) InterProScan—an integration platform for the signature-recognition methods in InterPro. Bioinformatics 17:847–848Zraidi A, Stift G, Pachner M, Shojaeiyan A, Gong L, Lelley T (2007) A consensus map for Cucurbita pepo. Mol Breed 20:375–38

    The impact of viral mutations on recognition by SARS-CoV-2 specific T cells.

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    We identify amino acid variants within dominant SARS-CoV-2 T cell epitopes by interrogating global sequence data. Several variants within nucleocapsid and ORF3a epitopes have arisen independently in multiple lineages and result in loss of recognition by epitope-specific T cells assessed by IFN-γ and cytotoxic killing assays. Complete loss of T cell responsiveness was seen due to Q213K in the A∗01:01-restricted CD8+ ORF3a epitope FTSDYYQLY207-215; due to P13L, P13S, and P13T in the B∗27:05-restricted CD8+ nucleocapsid epitope QRNAPRITF9-17; and due to T362I and P365S in the A∗03:01/A∗11:01-restricted CD8+ nucleocapsid epitope KTFPPTEPK361-369. CD8+ T cell lines unable to recognize variant epitopes have diverse T cell receptor repertoires. These data demonstrate the potential for T cell evasion and highlight the need for ongoing surveillance for variants capable of escaping T cell as well as humoral immunity.This work is supported by the UK Medical Research Council (MRC); Chinese Academy of Medical Sciences(CAMS) Innovation Fund for Medical Sciences (CIFMS), China; National Institute for Health Research (NIHR)Oxford Biomedical Research Centre, and UK Researchand Innovation (UKRI)/NIHR through the UK Coro-navirus Immunology Consortium (UK-CIC). Sequencing of SARS-CoV-2 samples and collation of data wasundertaken by the COG-UK CONSORTIUM. COG-UK is supported by funding from the Medical ResearchCouncil (MRC) part of UK Research & Innovation (UKRI),the National Institute of Health Research (NIHR),and Genome Research Limited, operating as the Wellcome Sanger Institute. T.I.d.S. is supported by a Well-come Trust Intermediate Clinical Fellowship (110058/Z/15/Z). L.T. is supported by the Wellcome Trust(grant number 205228/Z/16/Z) and by theUniversity of Liverpool Centre for Excellence in Infectious DiseaseResearch (CEIDR). S.D. is funded by an NIHR GlobalResearch Professorship (NIHR300791). L.T. and S.C.M.are also supported by the U.S. Food and Drug Administration Medical Countermeasures Initiative contract75F40120C00085 and the National Institute for Health Research Health Protection Research Unit (HPRU) inEmerging and Zoonotic Infections (NIHR200907) at University of Liverpool inpartnership with Public HealthEngland (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford.L.T. is based at the University of Liverpool. M.D.P. is funded by the NIHR Sheffield Biomedical ResearchCentre (BRC – IS-BRC-1215-20017). ISARIC4C is supported by the MRC (grant no MC_PC_19059). J.C.K.is a Wellcome Investigator (WT204969/Z/16/Z) and supported by NIHR Oxford Biomedical Research Centreand CIFMS. The views expressed are those of the authors and not necessarily those of the NIHR or MRC
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