173 research outputs found

    Influence of Ketotifen, Cromolyn Sodium, and Compound 48/80 on the survival rates after intestinal ischemia reperfusion injury in rats

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>Mast cells were associated with intestinal ischemia-reperfusion injury, the study was to observe the influence of Ketotifen, Cromolyn Sdium(CS), and Compound 48/80(CP) on the survival rates on the third day after intestinal ischemia-reperfusion injury in rats.</p> <p>Methods</p> <p>120 healthy Sprague-Dawley rats were randomly divided into 5 groups, Sham-operated group (group S), model group (group M), group K, group C and group CP. Intestinal damage was triggered by clamping the superior mesenteric artery for 75 minutes, group K, C, and CP were treated with kotifen 1 mg·kg<sup>-1</sup>, CS 50 mg·kg<sup>-1</sup>, and CP 0.75 mg·kg<sup>-1 </sup>i.v. at 5 min before reperfusion and once daily for three days following reperfusion respectively. Survival rate in each group was recorded during the three days after reperfusion. All the surviving rats were killed for determining the concentration of serum glutamic-oxaloacetic transaminase(AST), glutamic pyruvic transaminase(ALT), the ratio of AST compare ALT(S/L), total protein(TP), albumin(ALB), globulin(GLB), the ratio of ALB compare GLB(A/G), phosphocreatine kinase(CK), lactate dehydrogenase(LDH), urea nitrogen(BUN) and creatinine(CRE) at the 3<sup>rd </sup>day after reperfusion. And ultrastructure of IMMC, Chiu's score, lung histology, IMMC counts, the levels of TNF-α, IL-1ÎČ, IL-6 and IL-10 of the small intestine were detected at the same time.</p> <p>Results</p> <p>Intestinal ischemia-reperfusion injury reduced the survival rate. The concentrations of TP, ALB and level of IL-10 in intestine in group M decreased significantly while the concentrations of S/L, LDH and the levels of IL-6 and TNF-α in intestine increased significantly compared with group S (<it>P </it>< 0.05). Treatment with Ketotifen and CS increased the survival rate compared with group M (<it>P </it>< 0.05), attenuated the down-regulation or up-regulation of the above index (<it>P </it>< 0.05). Treatment with CP decreased the survival rate on the 3<sup>rd </sup>day after reperfusion compared with group M(<it>P </it>< 0.05). Group K and C had better morphology in IMMC in the small intestine and in the lungs than in group M and CP, although the Chiu's score and IMMC counts remained the same in the five groups(<it>P </it>> 0.05).</p> <p>Conclusion</p> <p>Mast cell inhibition after ischemia prior to reperfusion and following reperfusion may decrease the multi-organ injury induced by intestine ischemia reperfusion, and increase the survival rates.</p

    Prevalence and demographics of anxiety disorders: a snapshot from a community health centre in Pakistan

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>The developing world is faced with a high burden of anxiety disorders. The exact prevalence of anxiety disorders in Pakistan is not known. There is a need to develop an evidence base to aid policy development on tackling anxiety and depressive disorders in the country. This is the first pilot study to address the prevalence of anxiety disorders and their association with sociodemographic factors in Pakistan.</p> <p>Methods</p> <p>A cross-sectional study was conducted among people visiting Aga Khan University Hospital (AKUH), a tertiary care facility in Karachi, Pakistan. The point prevalence of anxiety amongst the sample population, which comprised of patients and their attendants, excluding all health care personnel, was assessed using the validated Urdu version of the Hospital Anxiety and Depression Scale (HADS). The questionnaire was administered to 423 people. Descriptive statistics were performed for mean scores and proportions.</p> <p>Results</p> <p>The mean anxiety score of the population was 5.7 ± 3.86. About 28.3% had borderline or pathological anxiety. The factors found to be independently predicted with anxiety were, female sex (odds ratio (OR) = 2.14, 95% CI 1.36–3.36, p = 0.01); physical illness (OR = 1.67, 95% CI 1.06–2.64, p = 0.026); and psychiatric illness (OR = 1.176, 95% CI 1.0–3.1, p = 0.048). In the final multivariate model, female sex (adjusted odds ratio (AOR) = 2, 95% CI 1.28–3.22) and physical illness (AOR = 1.56, 95% CI 0.97–2.48) were found to be significant.</p> <p>Conclusion</p> <p>Further studies via nationally representative surveys need to be undertaken to fully grasp the scope of this emerging public health issue in Pakistan.</p

    Comparison of transcriptome-derived simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers for genetic fingerprinting, diversity evaluation, and establishment of relationships in eggplants

    Full text link
    [EN] Simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers are amongst the most common markers of choice for studies of diversity and relationships in horticultural species. We have used 11 SSR and 35 SNP markers derived from transcriptome sequencing projects to fingerprint 48 accessions of a collection of brinjal (Solanum melongena), gboma (S. macrocarpon) and scarlet (S. aethiopicum) eggplant complexes, which also include their respective wild relatives S. incanum, S. dasyphyllum and S. anguivi. All SSR and SNP markers were polymorphic and 34 and 36 different genetic fingerprints were obtained with SSRs and SNPs, respectively. When combining both markers all accessions but two had different genetic profiles. Although on average SSRs were more informative than SNPs, with a higher number of alleles, genotypes and polymorphic information content (PIC), and expected heterozygosity (He) values, SNPs have proved highly informative in our materials. Low observed heterozygosity (Ho) and high fixation index (f) values confirm the high degree of homozygosity of eggplants. Genetic identities within groups of each complex were higher than with groups of other complexes, although differences in the ranks of genetic identity values among groups were observed between SSR and SNP markers. For low and intermediate values of pair-wise SNP genetic distances, a moderate correlation between SSR and SNP genetic distances was observed (r(2) = 0.592), but for high SNP genetic distances the correlation was low (r(2) = 0.080). The differences among markers resulted in different phenogram topologies, with a different eggplant complex being basal (gboma eggplant for SSRs and brinjal eggplant for SNPs) to the two others. Overall the results reveal that both types of markers are complementary for eggplant fingerprinting and that interpretation of relationships among groups may be greatly affected by the type of marker used.This work has been funded by European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 677379 (G2P-SOL project: Linking genetic resources, genomes and phenotypes of Solanaceous crops) and by Spanish Ministerio de Economia y Competitividad and Fondo Europeo de Desarrollo Regional (Grant AGL2015-64755-R from MINECO/FEDER). Pietro Gramazio is grateful to Universitat Politecnica de Valencia for a pre-doctoral contract (Programa FPI de la UPV-Subprograma 1/2013 call). Mariola Plazas is grateful to Spanish Ministerio de Economia, Industria y Competitividad for a post-doctoral grant within the Juan de la Cierva-Formacion programme (FJCI-2015-24835).Gramazio, P.; Prohens TomĂĄs, J.; Borras, D.; Plazas Ávila, MDLO.; Herraiz GarcĂ­a, FJ.; Vilanova Navarro, S. (2017). Comparison of transcriptome-derived simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers for genetic fingerprinting, diversity evaluation, and establishment of relationships in eggplants. Euphytica. 213(264):1-18. https://doi.org/10.1007/s10681-017-2057-3S118213264Acquadro A, Barchi L, Gramazio P et al (2017) Coding SNPs analysis highlights genetic relationships and evolution pattern in eggplant complexes. PLoS ONE 12:e0180774. https://doi.org/10.1371/journal.pone.0180774Adeniji O, Kusolwa P, Reuben S (2013) Morphological descriptors and micro satellite diversity among scarlet eggplant groups. Afr Crop Sci J 21(1):37–49Aguoru C, Omoigui L, Olasan J (2015) Molecular characterization of Solanum species (Solanum aethiopicum complex; Solanum macrocarpon and Solanum anguivi) using multiplex RAPD primers. J Plant Stud 4:27–34. https://doi.org/10.5539/jps.v4n1p27Arumuganathan K, Earle E (1991) Nuclear DNA content of some important plant species. Plant Mol Biol Rep 9(3):208–218Ashrafi H, Hill T, Stoffel K et al (2012) De novo assembly of the pepper transcriptome (Capsicum annuum): a benchmark for in silico discovery of SNPs, SSRs and candidate genes. BMC Genom 13:1–15. https://doi.org/10.1186/1471-2164-13-571Augustinos AA, Petropoulos C, Karasoulou V et al (2016) Assessing diversity among traditional Greek and foreign eggplant cultivars using molecular markers and morphometrical descriptors. Span J Agric Res 14:e0710. https://doi.org/10.5424/sjar/2016144-9020Avise JC (2012) Molecular markers, natural history and evolution. Springer Science & Business Media, Berlin. https://doi.org/10.1007/978-1-4615-2381-9Blanca J, Cañizares J, Roig C et al (2011) Transcriptome characterization and high throughput SSRs and SNPs discovery in Cucurbita pepo (Cucurbitaceae). BMC Genom 12:104. https://doi.org/10.1186/1471-2164-12-104Botstein D, White RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32(3):314–331Bukenya Z, Carasco J (1994) Biosystematic study of Solanum macrocarpon—S. dasyphyllum complex in Uganda and relations with Solanum linnaeanum. East Afr Agric For J 59(3):187–204Castillo A, Budak H, Varshney RK et al (2008) Transferability and polymorphism of barley EST-SSR markers used for phylogenetic analysis in Hordeum chilense. BMC Plant Biol 8:97. https://doi.org/10.1186/1471-2229-8-97Choudhary S, Sethy NK, Shokeen B, Bhatia S (2009) Development of chickpea EST-SSR markers and analysis of allelic variation across related species. Theor Appl Genet 118:591–608. https://doi.org/10.1007/s00122-008-0923-zCoates BS, Sumerford DV, Miller NJ et al (2009) Comparative performance of single nucleotide polymorphism and microsatellite markers for population genetic analysis. J Hered 100:556–564. https://doi.org/10.1093/jhered/esp028D’Agostino N, Golas T, van de Geest H et al (2013) Genomic analysis of the native European Solanum species, S. dulcamara. BMC Genom 14:356. https://doi.org/10.1186/1471-2164-14-356Daunay MC, Hazra P (2012) Eggplant. In: Peter KV, Hazra P (eds) Handbook of Vegetables. Studium Press, Houston, pp 257–322Davey J, Hohenlohe P, Etter P et al (2011) Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet 12:499–510. https://doi.org/10.1038/nrg3012De Barba M, Miquel C, LobrĂ©aux S et al (2016) High-throughput microsatellite genotyping in ecology: improved accuracy, efficiency, standardization and success with low-quantity and degraded DNA. Mol Ecol Resour 17(3):492–507. https://doi.org/10.1111/1755-0998.12594Doyle J, Doyle J (1987) A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bull 19:11–15Ellegren H (2004) Microsatellites: simple sequences with complex evolution. Nat Rev Genet 5:435–445. https://doi.org/10.1038/nrg1348Felsenstein, J (2007). PHYLIP (Phylogeny Inference Package) Version 3.67. Department of Genome Sciences, University of Washington, Seattle, WA, USAFernandez-Silva I, Whitney J, Wainwright B (2013) Microsatellites for next-generation ecologists: a post-sequencing bioinformatics pipeline. PLoS ONE 8(2):e55990Filippi CV, Aguirre N, Rivas JG et al (2015) Population structure and genetic diversity characterization of a sunflower association mapping population using SSR and SNP markers. BMC Plant Biol 15:52. https://doi.org/10.1186/s12870-014-0360-xFischer MC, Rellstab C, Leuzinger M et al (2017) Estimating genomic diversity and population differentiation—an empirical comparison of microsatellite and SNP variation in Arabidopsis halleri. BMC Genom 18:69. https://doi.org/10.1186/s12864-016-3459-7Furini A, Wunder J (2004) Analysis of eggplant (Solanum melongena)-related germplasm: morphological and AFLP data contribute to phylogenetic interpretations and germplasm utilization. Theor Appl Genet 108:197–208. https://doi.org/10.1007/s00122-003-1439-1Gadaleta A, Giancaspro A, Zacheo S et al (2011) Comparison of genomic and EST-derived SSR markers in phylogenetic analysis of wheat. Plant Genet Resour 9:243–246. https://doi.org/10.1017/S147926211100030XGe H, Liu Y, Jiang M et al (2013) Analysis of genetic diversity and structure of eggplant populations (Solanum melongena L.) in China using simple sequence repeat markers. Sci Hortic 162:71–75. https://doi.org/10.1016/j.scienta.2013.08.004Gonzaga ZJ (2015) Evaluation of SSR and SNP Markers for Molecular Breeding in Rice. Plant Breed Biotechnol 3:139–152. https://doi.org/10.9787/PBB.2015.3.2.139Goodwin S, McPherson J, McCombie W (2016) Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 17(6):333–351Gramazio P, Blanca J, Ziarsolo P et al (2016) Transcriptome analysis and molecular marker discovery in Solanum incanum and S. aethiopicum, two close relatives of the common eggplant (Solanum melongena) with interest for breeding. BMC Genom 17:300. https://doi.org/10.1186/s12864-016-2631-4Grover A, Sharma PC (2014) Development and use of molecular markers: past and present. Crit Rev Biotechnol 8551:1–13. https://doi.org/10.3109/07388551.2014.959891Hamblin MT, Warburton ML, Buckler ES (2007) Empirical comparison of simple sequence repeats and single nucleotide polymorphisms in assessment of maize diversity and relatedness. PLoS ONE 2:e1367. https://doi.org/10.1371/journal.pone.0001367Hess JE, Matala AP (2011) Comparison of SNPs and microsatellites for fine-scale application of genetic stock identification of Chinook salmon in the Columbia River Basin Comparison of SNPs and microsatellites for fine-scale application of genetic stock identification of Chinook salmon in the Columbia River Basin. Mol Ecol Resour. https://doi.org/10.1111/j.1755-0998.2010.02958.xHighton R (1993) The relationship between the number of loci and the statistical support for the topology of UPGMA trees obtained from genetic distance data. Mol Phylogenet Evol 2:337–343Hirakawa H, Shirasawa K, Miyatake K, Nunome, T et al (2014) Draft genome sequence of eggplant (Solanum melongena L.): the representative solanum species indigenous to the old world. DNA Res 21:649–660. https://doi.org/10.1093/dnares/dsu027Hong CP, Piao ZY, Kang TW et al (2007) Genomic distribution of simple sequence repeats in Brassica rapa. Mol Cells 23:349–356.Hu J, Wang L, Li J (2011) Comparison of genomic SSR and EST-SSR markers for estimating genetic diversity in cucumber. Biol Plant 55:577–580. https://doi.org/10.1007/s10535-011-0129-0Isshiki S, Iwata N, Khan MMR (2008) ISSR variations in eggplant (Solanum melongena L.) and related Solanum species. Sci Hortic 117:186–190. https://doi.org/10.1016/j.scienta.2008.04.003Jones ES, Sullivan H, Bhattramakki D, Smith JSC (2007) A comparison of simple sequence repeat and single nucleotide polymorphism marker technologies for the genotypic analysis of maize (Zea mays L.). Theor Appl Genet 115:361–371. https://doi.org/10.1007/s00122-007-0570-9Kalia RK, Rai MK, Kalia S et al (2011) Microsatellite markers: an overview of the recent progress in plants. Euphytica 177:309–334Kashi Y, King DG (2006) Simple sequence repeats as advantageous mutators in evolution. Trends Genet 22:253–259. https://doi.org/10.1016/j.tig.2006.03.005Kaushik P, Prohens J, Vilanova S et al (2016) Phenotyping of eggplant wild relatives and interspecific hybrids with conventional and phenomics descriptors provides insight for their potential utilization in breeding. Front Plant Sci 7:677Kim C, Guo H, Kong W et al (2016) Application of genotyping by sequencing technology to a variety of crop breeding programs. Plant Sci 242:14–22Knapp S, Vorontsova MS, Prohens J (2013) Wild relatives of the eggplant (Solanum melongena L.: Solanaceae): new understanding of species names in a complex group. PLoS ONE 8:e57039Kruglyak S, Durrett RT, Schug MD, Aquadro CF (1998) Equilibrium distributions of microsatellite repeat length resulting from a balance between slippage events and point mutations. Proc Natl Acad Sci USA 95:10774–10778. https://doi.org/10.1073/pnas.95.18.10774Lester RN, Daunay MC (2003) Diversity of African vegetable Solanum species and its implications for a better understanding of plant domestication. Schriften zu Genetischen Ressourcen 22:137–152Lester RN, Niakan L (1986) Origin and domestication of the scarlet eggplant, Solanum aethiopicum, from S. anguivi in Africa. In: D’Arcy WG (ed) Solanaceae: biology and systematics. Columbia University Press, New York, pp 433–456Lester RN, Jaeger PML, Bleijendaal-Spierings BHM et al (1990) African eggplants-a review of collecting in West Africa. Plant Genet Resour Newsl 81:17–26Levin R, Myers N, Bohs L (2006) Phylogenetic relationships among the ‘spiny solanums’ (Solanum subgenus Leptostemonum, Solanaceae). Am J Bot 93(1):157–169Li WH, Gojobori T, Nei M (1981) Pseudogenes as a paradigm of neutral evolution. Nature 292:237–239Li YC, Korol AB, Fahima T et al (2002) Microsatellites: genomic distribution, putative functions and mutational mechanisms: a review. Mol Ecol 11:2453–2465Liu K, Muse S (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21:2128–2129Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220. https://doi.org/10.1038/214637b0MartĂ­nez-Arias R, Calafell F, Mateu E et al (2001) Sequence variability of a human pseudogene. Genome Res 11:1071–1085. https://doi.org/10.1101/gr.167701Meyer RS, Karol KG, Little DP et al (2012) Phylogeographic relationships among Asian eggplants and new perspectives on eggplant domestication. Mol Phylogenet Evol 63:685–701. https://doi.org/10.1016/j.ympev.2012.02.006Muñoz-FalcĂłn J, Prohens J, Vilanova S, Nuez F (2009) Diversity in commercial varieties and landraces of black eggplants and implications for broadening the breeders’ gene pool. Ann Appl Biol 154(3):453–465Nandha PS, Singh J (2014) Comparative assessment of genetic diversity between wild and cultivated barley using gSSR and EST-SSR markers. Plant Breed 133:28–35. https://doi.org/10.1111/pbr.12118Nei M (1972) Genetic distance between populations. Am Nat 106:283–292. https://doi.org/10.1086/282771Nunome T, Negoro S, Kono I et al (2009) Development of SSR markers derived from SSR-enriched genomic library of eggplant (Solanum melongena L.). Theor Appl Genet 119:1143–1153. https://doi.org/10.1007/s00122-009-1116-0Page R (2001) TreeView. Glasgow University, GlasgowPeakall P, Smouse R (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research an update. Bioinformatics 28:2537–2539Pessarakli M, Dris R (2004) Pollination and breeding of eggplants. J Food Agric Environ 2:218–219Plazas M, AndĂșjar I, Vilanova S et al (2014) Conventional and phenomics characterization provides insight into the diversity and relationships of hypervariable scarlet (Solanum aethiopicum L.) and gboma (S. macrocarpon L.) eggplant complexes. Front. Plant Sci 5:318Ranil R, Niran H, Plazas M et al (2015) Improving seed germination of the eggplant rootstock Solanum torvum by testing multiple factors using an orthogonal array design. Sci Hortic 193:174–181. https://doi.org/10.1016/j.scienta.2015.07.030Sakata Y, Lester RN (1997) Chloroplast DNA diversity in brinjal eggplant (Solanum melongena L.) and related species. Euphytica 97:295–301. https://doi.org/10.1023/A:1003000612441Sakata Y, Nishio T, Matthews PJ (1991) Chloroplast DNA analysis of eggplant (Solanum melongena) and related species for their taxonomic affinity. Euphytica 55:21–26SĂ€rkinen T, Bohs L, Olmstead RG, Knapp S (2013) A phylogenetic framework for evolutionary study of the nightshades (Solanaceae): a dated 1000-tip tree. BMC Evol Biol 13:214. https://doi.org/10.1186/1471-2148-13-214Scheben A, Batley J, Edwards D (2017) Genotyping-by-sequencing approaches to characterize crop genomes: choosing the right tool for the right application. Plant Biotechnol J 15:149–161Sneath P, Sokal R (1973) Numerical taxonomy. The principles and practice of numerical classification. W H Freeman Limited, San FranciscoStĂ gel A, Portis E, Toppino L et al (2008) Gene-based microsatellite development for mapping and phylogeny studies in eggplant. BMC Genom 9:357. https://doi.org/10.1186/1471-2164-9-357Sunseri F, Polignano GB, Alba V et al (2010) Genetic diversity and characterization of African eggplant germplasm collection. Afr J Plant Sci 4:231–241Syfert MM, Castañeda-Álvarez NP, Khoury CK et al (2016) Crop wild relatives of the brinjal eggplant (Solanum melongena): poorly represented in genebanks and many species at risk of extinction. Am J Bot 103:635–651. https://doi.org/10.3732/ajb.1500539Thiel T, Michalek W, Varshney R, Graner A (2003) Exploiting EST databases for the development and characterization of gene-derived SSR-markers in barley (Hordeum vulgare L.). Theor Appl Genet 106:411–422. https://doi.org/10.1007/s00122-002-1031-0Thomson MJ, Alfred J, Dangl J et al (2014) High-throughput SNP genotyping to accelerate crop improvement. Plant Breed Biotechnol 2:195–212. https://doi.org/10.9787/PBB.2014.2.3.195ThorvaldsdĂłttir H, Robinson JT, Mesirov JP (2013) Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14:178–192. https://doi.org/10.1093/bib/bbs017Tumbilen Y, Frary A, Daunay MC, Doganlar S (2011) Application of EST-SSRs to examine genetic diversity in eggplant and its close relatives. Turk J Biol 35:125–136. https://doi.org/10.3906/biy-0906-57van Inghelandt D, Melchinger AE, Lebreton C, Stich B (2010) Population structure and genetic diversity in a commercial maize breeding program assessed with SSR and SNP markers. Theor Appl Genet 120:1289–1299. https://doi.org/10.1007/s00122-009-1256-2Van Tassell CP, Smith TPL, Matukumalli LK et al (2008) SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat Methods 5:247–252. https://doi.org/10.1038/nmeth.1185Varshney R, Graner A, Sorrells M (2005) Genic microsatellite markers in plants: features and applications. Trends Biotechnol 23(1):48–55Varshney RK, Chabane K, Hendre PS et al (2007) Comparative assessment of EST-SSR, EST-SNP and AFLP markers for evaluation of genetic diversity and conservation of genetic resources using wild, cultivated and elite barleys. Plant Sci 173:638–649. https://doi.org/10.1016/j.plantsci.2007.08.010Vilanova S, Manzur JP, Prohens J (2012) Development and characterization of genomic simple sequence repeat markers in eggplant and their application to the study of diversity and relationships in a collection of different cultivar types and origins. Mol Breed 30:647–660. https://doi.org/10.1007/s11032-011-9650-2Vilanova S, Hurtado M, Cardona A (2014) Genetic diversity and relationships in local varieties of eggplant from different cultivar groups as assessed by genomic SSR markers. Not Bot Horti Agrobo Cluj-Napoca 42:59–65Vogel JP, Gu YQ, Twigg P et al (2006) EST sequencing and phylogenetic analysis of the model grass Brachypodium distachyon. Theor Appl Genet 113:186–195. https://doi.org/10.1007/s00122-006-0285-3Vorontsova MS, Stern S, Bohs L, Knapp S (2013) African spiny solanum (subgenus Leptostemonum, Solanaceae): a thorny phylogenetic tangle. Bot J Linn Soc 173:176–193. https://doi.org/10.1111/boj.12053Weese TL, Bohs L (2010) Eggplant origins: out of Africa, into the Orient. Taxon 59:49–56. https://doi.org/10.2307/27757050Wright S (1965) The interpretation of population structure by F-statistics with special regard to systems of mating. Evolution 19:395–420. https://doi.org/10.2307/2406450Xiao M, Zhang Y, Chen X et al (2013) Transcriptome analysis based on next-generation sequencing of non-model plants producing specialized metabolites of biotechnological interest. J Biotechnol 166:122–134. https://doi.org/10.1016/j.jbiotec.2013.04.004Yan J, Yang X, Shah T et al (2010) High-throughput SNP genotyping with the Goldengate assay in maize. Mol Breed 25:441–451. https://doi.org/10.1007/s11032-009-9343-2Yang X, Xu Y, Shah T et al (2011) Comparison of SSRs and SNPs in assessment of genetic relatedness in maize. Genetica 139:1045–1054. https://doi.org/10.1007/s10709-011-9606-9Yu J, Zhang Z, Zhu C et al (2009) Simulation appraisal of the adequacy of number of background markers for relationship estimation in association mapping. Plant Genome 2:63. https://doi.org/10.3835/plantgenome2008.09.0009Zhan L, Paterson I, Fraser B (2016) MEGASAT: automated inference of microsatellite genotypes from sequence data. Ecol Resour, Mol. https://doi.org/10.1111/1755-0998.1256

    Phylogeny in Aid of the Present and Novel Microbial Lineages: Diversity in Bacillus

    Get PDF
    Bacillus represents microbes of high economic, medical and biodefense importance. Bacillus strain identification based on 16S rRNA sequence analyses is invariably limited to species level. Secondly, certain discrepancies exist in the segregation of Bacillus subtilis strains. In the RDP/NCBI databases, out of a total of 2611 individual 16S rDNA sequences belonging to the 175 different species of the genus Bacillus, only 1586 have been identified up to species level. 16S rRNA sequences of Bacillus anthracis (153 strains), B. cereus (211 strains), B. thuringiensis (108 strains), B. subtilis (271 strains), B. licheniformis (131 strains), B. pumilus (83 strains), B. megaterium (47 strains), B. sphaericus (42 strains), B. clausii (39 strains) and B. halodurans (36 strains) were considered for generating species-specific framework and probes as tools for their rapid identification. Phylogenetic segregation of 1121, 16S rDNA sequences of 10 different Bacillus species in to 89 clusters enabled us to develop a phylogenetic frame work of 34 representative sequences. Using this phylogenetic framework, 305 out of 1025, 16S rDNA sequences presently classified as Bacillus sp. could be identified up to species level. This identification was supported by 20 to 30 nucleotides long signature sequences and in silico restriction enzyme analysis specific to the 10 Bacillus species. This integrated approach resulted in identifying around 30% of Bacillus sp. up to species level and revealed that B. subtilis strains can be segregated into two phylogenetically distinct groups, such that one of them may be renamed

    A functional Kv1.2-hERG chimaeric channel expressed in Pichia pastoris.

    Get PDF
    Members of the six-transmembrane segment family of ion channels share a common structural design. However, there are sequence differences between the members that confer distinct biophysical properties on individual channels. Currently, we do not have 3D structures for all members of the family to help explain the molecular basis for the differences in their biophysical properties and pharmacology. This is due to low-level expression of many members in native or heterologous systems. One exception is rat Kv1.2 which has been overexpressed in Pichia pastoris and crystallised. Here, we tested chimaeras of rat Kv1.2 with the hERG channel for function in Xenopus oocytes and for overexpression in Pichia. Chimaera containing the S1-S6 transmembrane region of HERG showed functional and pharmacological properties similar to hERG and could be overexpressed and purified from Pichia. Our results demonstrate that rat Kv1.2 could serve as a surrogate to express difficult-to-overexpress members of the six-transmembrane segment channel family

    Ischaemic conditioning and targeting reperfusion injury: a 30 year voyage of discovery

    Get PDF
    To commemorate the auspicious occasion of the 30th anniversary of IPC, leading pioneers in the field of cardioprotection gathered in Barcelona in May 2016 to review and discuss the history of IPC, its evolution to IPost and RIC, myocardial reperfusion injury as a therapeutic target, and future targets and strategies for cardioprotection. This article provides an overview of the major topics discussed at this special meeting and underscores the huge importance and impact, the discovery of IPC has made in the field of cardiovascular research

    Why Functional Pre-Erythrocytic and Bloodstage Malaria Vaccines Fail: A Meta-Analysis of Fully Protective Immunizations and Novel Immunological Model

    Get PDF
    Background: Clinically protective malaria vaccines consistently fail to protect adults and children in endemic settings, and at best only partially protect infants. Methodology/Principal Findings: We identify and evaluate 1916 immunization studies between 1965-February 2010, and exclude partially or nonprotective results to find 177 completely protective immunization experiments. Detailed reexamination reveals an unexpectedly mundane basis for selective vaccine failure: live malaria parasites in the skin inhibit vaccine function. We next show published molecular and cellular data support a testable, novel model where parasite-host interactions in the skin induce malaria-specific regulatory T cells, and subvert early antigen-specific immunity to parasite-specific immunotolerance. This ensures infection and tolerance to reinfection. Exposure to Plasmodium-infected mosquito bites therefore systematically triggers immunosuppression of endemic vaccine-elicited responses. The extensive vaccine trial data solidly substantiate this model experimentally. Conclusions/Significance: We conclude skinstage-initiated immunosuppression, unassociated with bloodstage parasites, systematically blocks vaccine function in the field. Our model exposes novel molecular and procedural strategies to significantly and quickly increase protective efficacy in both pipeline and currently ineffective malaria vaccines, and forces fundamental reassessment of central precepts determining vaccine development. This has major implications fo

    Integrated systems for biopolymers and bioenergy production from organic waste and by-products: a review of microbial processes

    Full text link

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

    Get PDF
    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
    • 

    corecore