Expression of Toll-Like Receptors in the Developing Brain

Toll-like receptors (TLR) are key players of the innate and adaptive immune response in vertebrates. The original protein Toll in Drosophila melanogaster regulates both host defense and morphogenesis during development. Making use of real-time PCR, in situ hybridization, and immunohistochemistry we systematically examined the expression of TLR1–9 and the intracellular adaptor molecules MyD88 and TRIF during development of the mouse brain. Expression of TLR7 and TLR9 in the brain was strongly regulated during different embryonic, postnatal, and adult stages. In contrast, expression of TLR1–6, TLR8, MyD88, and TRIF mRNA displayed no significant changes in the different phases of brain development. Neurons of various brain regions including the neocortex and the hippocampus were identified as the main cell type expressing both TLR7 and TLR9 in the developing brain. Taken together, our data reveal specific expression patterns of distinct TLRs in the developing mouse brain and lay the foundation for further investigation of the pathophysiological significance of these receptors for developmental processes in the central nervous system of vertebrates

Defining strawberry shape uniformity using 3D imaging and genetic mapping

Strawberry shape uniformity is a complex trait, influenced by multiple genetic and environmental components. To complicate matters further, the phenotypic assessment of strawberry uniformity is confounded by the difficulty of quantifying geometric parameters ‘by eye’ and variation between assessors. An in-depth genetic analysis of strawberry uniformity has not been undertaken to date, due to the lack of accurate and objective data. Nonetheless, uniformity remains one of the most important fruit quality selection criteria for the development of a new variety. In this study, a 3D-imaging approach was developed to characterise berry shape uniformity. We show that circularity of the maximum circumference had the closest predictive relationship with the manual uniformity score. Combining five or six automated metrics provided the best predictive model, indicating that human assessment of uniformity is highly complex. Furthermore, visual assessment of strawberry fruit quality in a multi-parental QTL mapping population has allowed the identification of genetic components controlling uniformity. A “regular shape” QTL was identified and found to be associated with three uniformity metrics. The QTL was present across a wide array of germplasm, indicating a potential candidate for marker-assisted breeding, while the potential to implement genomic selection is explored. A greater understanding of berry uniformity has been achieved through the study of the relative impact of automated metrics on human perceived uniformity. Furthermore, the comprehensive definition of strawberry shape uniformity using 3D imaging tools has allowed precision phenotyping, which has improved the accuracy of trait quantification and unlocked the ability to accurately select for uniform berries

First RNA-seq approach to study fruit set and parthenocarpy in zucchini (Cucurbita pepo L.)

[EN] Background: Zucchini fruit set can be limited due to unfavourable environmental conditions in off-seasons crops that caused ineffective pollination/fertilization. Parthenocarpy, the natural or artificial fruit development without fertilization, has been recognized as an important trait to avoid this problem, and is related to auxin signalling. Nevertheless, differences found in transcriptome analysis during early fruit development of zucchini suggest that other complementary pathways could regulate fruit formation in parthenocarpic cultivars of this species. The development of next-generation sequencing technologies (NGS) as RNA-sequencing (RNA-seq) opens a new horizon for mapping and quantifying transcriptome to understand the molecular basis of pathways that could regulate parthenocarpy in this species. The aim of the current study was to analyze fruit transcriptome of two cultivars of zucchini, a non-parthenocarpic cultivar and a parthenocarpic cultivar, in an attempt to identify key genes involved in parthenocarpy. Results: RNA-seq analysis of six libraries (unpollinated, pollinated and auxin treated fruit in a non-parthenocarpic and parthenocarpic cultivar) was performed mapping to a new version of C. pepo transcriptome, with a mean of 92% success rate of mapping. In the non-parthenocarpic cultivar, 6479 and 2186 genes were differentially expressed (DEGs) in pollinated fruit and auxin treated fruit, respectively. In the parthenocarpic cultivar, 10,497 in pollinated fruit and 5718 in auxin treated fruit. A comparison between transcriptome of the unpollinated fruit for each cultivar has been performed determining that 6120 genes were differentially expressed. Annotation analysis of these DEGs revealed that cell cycle, regulation of transcription, carbohydrate metabolism and coordination between auxin, ethylene and gibberellin were enriched biological processes during pollinated and parthenocarpic fruit set. Conclusion: This analysis revealed the important role of hormones during fruit set, establishing the activating role of auxins and gibberellins against the inhibitory role of ethylene and different candidate genes that could be useful as markers for parthenocarpic selection in the current breeding programs of zucchini.Research worked is supported by the project RTA2014-00078 from the Spanish Institute of Agronomy Research INIA (Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria) and also PP.AVA.AVA201601.7, FEDER y FSE (Programa Operativo FSE de Andalucia 2007-2013 "Andalucia se mueve con Europa"). TPV is supported by a FPI scholarship from RTA2011-00044-C02-01/02 project of INIA. The funding agencies were not involved in the design of the study, collection, analysis, and interpretation of data and in writing the manuscript.Pomares-Viciana, T.; Del Rio-Celestino, M.; Roman, B.; Die, J.; Picó Sirvent, MB.; Gómez, P. (2019). First RNA-seq approach to study fruit set and parthenocarpy in zucchini (Cucurbita pepo L.). BMC Plant Biology. 19:1-20. https://doi.org/10.1186/s12870-019-1632-2S12019Varga A, Bruinsma J. Tomato. In: Monselise SP, editor. CRC Handbook of Fruit Set and Development. Boca Raton: CRC Press; 1986. p. 461–80.Nepi M, Cresti L, Guarnieri M, Pacini E. Effect of relative humidity on water content, viability and carbohydrate profile of Petunia hybrid and Cucurbita pepo pollen. Plant Syst Evol. 2010;284:57–64.Gustafson FG. Parthenocarpy: natural and artificial. Bot Rev. 1942;8:599–654.Robinson RW, Reiners S. Parthenocarpy in summer squash. Hortscience. 1999;34:715–7.Pomares-Viciana T, Die J, Del Río-Celestino M, Román B, Gómez P. Auxin signalling regulation during induced and parthenocarpic fruit set in zucchini. Mol Breeding. 2017;37:56.Ozga JA, Reinecke DM. Hormonal interactions in fruit development. J Plant Growth Regul. 2003;22:73–81.Kim IS, Okubo H, Fujieda K. Endogenous levels of IAA in relation to parthenocarpy in cucumber (Cucumis sativus L). Sci Hortic. 1992;52:1–8.Olimpieri I, Siligato F, Caccia R, Mariotti L, Ceccarelli N, Soressi GP, et al. Tomato fruit set driven by pollination or by the parthenocarpic fruit allele are mediated by transcriptionally regulated gibberellin biosynthesis. Planta. 2007;226:877–88.Cui L, Zhang T, Li J, Lou Q, Chen J. Cloning and expression analysis of Cs-TIR1/AFB2: the fruit development-related genes of cucumber (Cucumis sativus L.). Acta Physiol Plant. 2014;36:139–49.De Jong M, Wolters-Arts J, Feron R, Mariani C, Vriezen WH. The Solanum lycopersicum auxin response factor 7 (SlARF7) regulates auxin signalling during tomato fruit set and development. Plant J. 2009;57:160–70.Wang H, Jones B, Li Z, Frasse P, Delalande C, Regad F, Chaabouni S, Latché A, Pech JC, Bouzayen M. The tomato aux/IAA transcription factor IAA9 is involved in fruit development and leaf morphogenesis. Plant Cell. 2005;17(10):2676–92.Goetz M, Vivian-Smith A, Johnson SD, Koltunow AM. AUXIN RESPONSE FACTOR 8 is a negative regulator of fruit initiation in Arabidopsis. Plant Cell. 2006;18(8):1873–86.Mazzucato A, Cellini F, Bouzayen M, Zouine M, Mila I, Minoia S et al. A TILLING allele of the tomato aux/IAA9 gene offers new insights into fruit set mechanisms and perspectives for breeding seedless tomatoes. Mol Breeding. 2015; 35(22):1-15.Blanca J, Cañizares J, Roig C, Ziarsolo P, Nuez F, Picó B. Transcriptome characterization and high throughput SSRs and SNPs discovery in Cucurbita pepo (Cucurbitaceae). BMC Genomics. 2011;12:104.Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10(1):57–63.Da Fonseca RR, Albrechtsen A, Themudo GE, Ramos-Madrigal J, Sibbesen JA, Maretty L, et al. Next-generation biology: sequencing and data analysis approaches for non-model organisms. Mar Genomics. 2016;30:3–13.Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17:13.Li J, Cui ZWJ, Zhang T, Guo Q, Xu J, Li J, et al. Transcriptome comparison of global distinctive features between pollination and parthenocarpic fruit set reveals transcriptional phytohormone cross-talk in cucumber (Cucumis sativus L). Plant Cell Physiol. 2014;55(7):1325–42.Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28(23):3150–2.Montero-Pau J, Blanca J, Bombarely A, Ziarsolo P, Esteras C, Martí-Gómez C, et al. De novo assembly of the zucchini genome reveals a whole genome duplication associated with the origin of the Cucurbita genus. Plant Biotechnol J. 2017. https://doi.org/10.1111/pbi.12860 .Vriezen WH, Feron R, Maretto F, Keijman J, Mariani C. Changes in tomato ovary transcriptome demonstrate complex hormonal regulation of fruit set. New Phytol. 2008;177:60–76.Tang N, Deng W, Hu G, Hu N, Li Z. Transcriptome profiling reveals the regulatory mechanism underlying pollination dependent and parthenocarpic fruit set mainly mediated by auxin and gibberellin. PLoS One. 2015;10(4):e0125355.Li J, Yan S, Yang W, Li Y, Xia M, Chen Z, et al. Transcriptomic analysis reveals the roles of microtubule-related genes and transcription factors in fruit length regulation in cucumber (Cucumis sativus L.). Sci Rep. 2015;26(5):8031.Mironov V, De Veylder L, Van Montagu M, Inze D. Cyclin-dependent kinases and cell division in plants- the nexus. Plant Cell. 1999;11(4):509–22.Perrot-Rechenmann C. Cellular responses to auxin: division versus expansion. Cold Spring Harb Perspect Biol. 2010;2(5):a001446.De Veylder L, Beeckman T, Beemster GT, Krols L, Terras F, Landrieu I, et al. Functional analysis of cyclin-dependent kinase inhibitors of Arabidopsis. Plant Cell. 2001;13:1653–68.Nieuwland J, Menges M, Murray JAH. The plant cyclins. In: Inze D, editor. Cell cycle control and plant development, vol. 2007. Oxford: Wiley-Blackwell Publishing; 2007. p. 33–61.Menges M, Samland AK, Planchais S, Murray JA. The D-type cyclin CYCD3;1 is limiting for the G1-to-S-phasetransition in Arabidopsis. Plant Cell. 2006;18:893–906.Boruc J, Mylle E, Duda M, De Clercq R, Rombauts S, Geelen D, et al. Systematic localization of the Arabidopsis core cell cycle proteins reveals novel cell division complexes. Plant Physiol. 2010;152(2):553–65.Sampedro J, Cosgrove DJ. The expansin superfamily. Genome Biol. 2005;6:242.Esmon CA, Tinsley AG, Ljung K, Sandberg G, Hearne LB, Liscum E. A gradient of auxin and auxin-dependent transcription precedes tropic growth responses. Proc Natl Acad Sci. 2006;103:236–41.De Folter S, Busscher J, Colombo L, Losa A, Angenent GC. Transcript profiling of transcription factors genes during siliques development in Arabidopsis. Plant Mol Bio. 2004;56:351–3662004.Son O, Cho HY, Kim MR, Lee H, Lee MS, Song E, et al. Induction of a homeodomain-leucine zipper gene by auxin is inhibited by cytokinin in Arabidopsis roots. Biochem Biophys Res Commun. 2005;326(1):203–9.Olsson ASB, Engstroem P, Seoderman E. The homeobox genes ATHB12 and ATHB7 encode potential regulators of growth in response to water deficit in Arabidopsis. Plant Mol Biol. 2004;55:663–77.Merrow SB, Hopp RJ. Storage effects on winter squashes. Associations between the sugar and starch content of and the degree of preference for winter squashes. J Agric Food Chem. 1961;9:321–6.Berg JM, Tymoczko JL, Stryer L. Carbohydrates. In: Freeman WH, editor. Biochemistry. 5th ed. New York: W H Freeman; 2002.Prabhakar V, Löttgert T, Gigolashvili T, Bell K, Flügge UI, Häusler RE. Molecular and functional characterization of the plastid-localized phosphoenolpyruvate enolase (ENO1) from Arabidopsis thaliana. FEBS Lett. 2009;583(6):983–91.Rius SP, Casati P, Iglesias AA, Gomez-Casati DF. Characterization of Arabidopsis lines deficient in GAPC-1, a cytosolic NAD-dependent glyceraldehyde-3-phosphate dehydrogenase. Plant Physiol. 2008;148(3):1655–67.Van der Linde K, Gutsche N, Leffers HM, Lindermayr C, Müller B, Holtgrefe S, et al. Regulation of plant cytosolic aldolase functions by redox-modifications. Plant Physiol Biochem. 2011;49(9):946–57.Lim H, Cho MH, Jeon JS, Bhoo SH, Kwon YK, Hahn TR. Altered expression of pyrophosphate: fructose-6-phosphate 1-phosphotransferase affects the growth of transgenic Arabidopsis plants. Mol Cells. 2009;27(6):641–9.Baud S, Wuillème S, Dubreucq B, De Almeida A, Vuagnat C, Lepiniec L, et al. Function of plastidial pyruvate kinases in seeds of Arabidopsis thaliana. Plant J. 2007;52:405–19.De Jong M, Mariani C, Vriezen WH. The role of auxin and gibberellin in tomato fruit set. J Exp Bot. 2009;60(5):1523–32.Martínez C, Manzano S, Megías Z, Garrido D, Picó B, Jamilena M. Involvement of ethylene biosynthesis and signalling in fruit set and early fruit development in zucchini squash (Cucurbita pepo L.). BMC Plant Biol. 2013;13:139.Serrani JC, Fos M, Atarés A, Garcia-martinez JL. Effect of gibberellin and auxin on parthenocarpic fruit growth induction in the cv. micro-tom of tomato. J Plant Growth Regul. 2007;26:211–21.Mapelli S. Changes in cytokinin in the fruits of parthenocarpic and normal tomatoes. Plant Sci Lett. 1981;22:227–33.Ulmasov T, Hagen G, Guilfoyle TJ. Activation and repression of transcription by auxin-response factors. Proc Natl Acad Sci U S A. 1999;96:5844–9.Ulmasov T, Hagen G, Guilfoyle TJ. Dimerization and DNA binding of auxin response factors. Plant J. 1999;19:309–19.Tiwari SB, Hagen G, Guilfoyle TJ. Aux/IAA proteins contain a potent transcriptional repression domain. Plant Cell. 2004;16:533–43.Switzenberg JA, Beaudry RM, Grumet R. Effect of CRC:: etr1-1 transgene expression on ethylene production, sex expression, fruit set and fruit ripening in transgenic melon (Cucumis melo L.). Transgenic Res. 2015;24(3):497-507.Nitsch LM, Oplaat C, Feron R, Ma Q, Wolters-Arts M, Hedden P, et al. Abscisic acid levels in tomato ovaries are regulated by LeNCED1 and SlCYP707A1. Planta. 2009;229(6):1335–46.Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat Methods. 2008;5(7):621–8.Robinson MD, McCarthy DJ, Smyth GK. Edger: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2008;26(1):139–40.Raza K, Mishra A. A novel anticlustering filtering algorithm for the prediction of genes as a drug target. Am J Bio Engi. 2012;2(5):206–11.Van Iterson M, Boer JM, Menezes RX. Filtering, FDR and power. BMCBioinformatics. 2010;11:450.Conesa A, Götz S, Garcia-Gomez JM, Terol J, Talon M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21:3674–6.Berardini TZ, Reiser L, Li D, Mezheritsky Y, Muller R, Strait E, Huala E. The Arabidopsis information resource: making and mining the “gold standard” annotated reference plant genome. Genesis. 2015. https://doi.org/10.1002/dvg.22877 .Bairoch A, Apweiler R. The SWISS-PROT protein sequence database and its supplement TrEMBL. Nucleic Acids Res. 2000;28(1):45–8.Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL. NCBI BLAST: a better web interface. Nucleic Acids Res. 2008;36:W5–9.Wyatt LE, Strickler SR, Mueller LA, Mazourek M. An acorn squash (Cucurbita pepo ssp. ovifera) fruit and seed transcriptome as a resource for the study of fruit traits in Cucurbita. Hortic Res. 2015;2:14070. https://doi.org/10.1038/hortres.2014.70 .Zhang A, Ren GA, Sun YA, Guo H, Zhang SA, Zhang FA, et al. A high-density genetic map for anchoring genome sequences and identifying QTLs associated with dwarf vine in pumpkin (Cucurbita maxima Duch.). BMC Genomics. 2015;16:1101.Finn RD, Attwood TK, Babbit AB, Bork P, Bridge AJ, Chang HY. InterPro in 2017-beyond protein family and domain annotations. Nucleic Acids Res. https://doi.org/10.1093/nar/gkw1107 .Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Sherlock G. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25–9.Kanehisa M, Araki M, Goto S, et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008;36:480–4

RETRACTED ARTICLE: Age-dependent Increase in Desmosterol Restores DRM Formation and Membrane-related Functions in Cholesterol-free DHCR24−/− Mice

Cholesterol is a prominent modulator of the integrity and functional activity of physiological membranes and the most abundant sterol in the mammalian brain. DHCR24-knock-out mice lack cholesterol and accumulate desmosterol with age. Here we demonstrate that brain cholesterol deficiency in 3-week-old DHCR24−/− mice was associated with altered membrane composition including disrupted detergent-resistant membrane domain (DRM) structure. Furthermore, membrane-related functions differed extensively in the brains of these mice, resulting in lower plasmin activity, decreased β-secretase activity and diminished Aβ generation. Age-dependent accumulation and integration of desmosterol in brain membranes of 16-week-old DHCR24−/− mice led to the formation of desmosterol-containing DRMs and rescued the observed membrane-related functional deficits. Our data provide evidence that an alternate sterol, desmosterol, can facilitate processes that are normally cholesterol-dependent including formation of DRMs from mouse brain extracts, membrane receptor ligand binding and activation, and regulation of membrane protein proteolytic activity. These data indicate that desmosterol can replace cholesterol in membrane-related functions in the DHCR24−/− mouse

Mild-to-Moderate Kidney Dysfunction and Cardiovascular Disease: Observational and Mendelian Randomization Analyses

BACKGROUND: End-stage renal disease is associated with a high risk of cardiovascular events. It is unknown, however, whether mild-to-moderate kidney dysfunction is causally related to coronary heart disease (CHD) and stroke. METHODS: Observational analyses were conducted using individual-level data from 4 population data sources (Emerging Risk Factors Collaboration, EPIC-CVD [European Prospective Investigation into Cancer and Nutrition-Cardiovascular Disease Study], Million Veteran Program, and UK Biobank), comprising 648 135 participants with no history of cardiovascular disease or diabetes at baseline, yielding 42 858 and 15 693 incident CHD and stroke events, respectively, during 6.8 million person-years of follow-up. Using a genetic risk score of 218 variants for estimated glomerular filtration rate (eGFR), we conducted Mendelian randomization analyses involving 413 718 participants (25 917 CHD and 8622 strokes) in EPIC-CVD, Million Veteran Program, and UK Biobank. RESULTS: There were U-shaped observational associations of creatinine-based eGFR with CHD and stroke, with higher risk in participants with eGFR values 105 mL·min-1·1.73 m-2, compared with those with eGFR between 60 and 105 mL·min-1·1.73 m-2. Mendelian randomization analyses for CHD showed an association among participants with eGFR 105 mL·min-1·1.73 m-2. Results were not materially different after adjustment for factors associated with the eGFR genetic risk score, such as lipoprotein(a), triglycerides, hemoglobin A1c, and blood pressure. Mendelian randomization results for stroke were nonsignificant but broadly similar to those for CHD. CONCLUSIONS: In people without manifest cardiovascular disease or diabetes, mild-to-moderate kidney dysfunction is causally related to risk of CHD, highlighting the potential value of preventive approaches that preserve and modulate kidney function

Like mother, like child : investigating perinatal and maternal health stress in post-medieval London.

Post-Medieval London (sixteenth-nineteenth centuries) was a stressful environment for the poor. Overcrowded and squalid housing, physically demanding and risky working conditions, air and water pollution, inadequate diet and exposure to infectious diseases created high levels of morbidity and low life expectancy. All of these factors pressed with particular severity on the lowest members of the social strata, with burgeoning disparities in health between the richest and poorest. Foetal, perinatal and infant skeletal remains provide the most sensitive source of bioarchaeological information regarding past population health and in particular maternal well-being. This chapter examined the evidence for chronic growth and health disruption in 136 foetal, perinatal and infant skeletons from four low-status cemetery samples in post-medieval London. The aim of this study was to consider the impact of poverty on the maternal-infant nexus, through an analysis of evidence of growth disruption and pathological lesions. The results highlight the dire consequences of poverty in London during this period from the very earliest moments of life