Occupational exposure to gases/fumes and mineral dust affect DNA methylation levels of genes regulating expression

Many workers are daily exposed to occupational agents like gases/fumes, mineral dust or biological dust, which could induce adverse health effects. Epigenetic mechanisms, such as DNA methylation, have been suggested to play a role. We therefore aimed to identify differentially methylated regions (DMRs) upon occupational exposures in never-smokers and investigated if these DMRs associated with gene expression levels. To determine the effects of occupational exposures independent of smoking, 903 never-smokers of the LifeLines cohort study were included. We performed three genome-wide methylation analyses (Illumina 450 K), one per occupational exposure being gases/fumes, mineral dust and biological dust, using robust linear regression adjusted for appropriate confounders. DMRs were identified using comb-p in Python. Results were validated in the Rotterdam Study (233 never-smokers) and methylation-expression associations were assessed using Biobank-based Integrative Omics Study data (n = 2802). Of the total 21 significant DMRs, 14 DMRs were associated with gases/fumes and 7 with mineral dust. Three of these DMRs were associated with both exposures (RPLP1 and LINC02169 (2x)) and 11 DMRs were located within transcript start sites of gene expression regulating genes. We replicated two DMRs with gases/fumes (VTRNA2-1 and GNAS) and one with mineral dust (CCDC144NL). In addition, nine gases/fumes DMRs and six mineral dust DMRs significantly associated with gene expression levels. Our data suggest that occupational exposures may induce differential methylation of gene expression regulating genes and thereby may induce adverse health effects. Given the millions of workers that are exposed daily to occupational exposures, further studies on this epigenetic mechanism and health outcomes are warranted

Refining Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Genetic Loci by Integrating Summary Data From Genome-wide Association, Gene Expression, and DNA Methylation Studies

Background: Recent genome-wide association studies (GWASs) identified the first genetic loci associated with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The next step is to use these results to increase our understanding of the biological mechanisms involved. Most of the identified variants likely influence gene regulation. The aim of the current study is to shed light on the mechanisms underlying the genetic signals and prioritize genes by integrating GWAS results with gene expression and DNA methylation (DNAm) levels. Methods: We applied summary-data–based Mendelian randomization to integrate ADHD and ASD GWAS data with fetal brain expression and methylation quantitative trait loci, given the early onset of these disorders. We also analyzed expression and methylation quantitative trait loci datasets of adult brain and blood, as these provide increased statistical power. We subsequently used summary-data–based Mendelian randomization to investigate if the same variant influences both DNAm and gene expression levels. Results: We identified multiple gene expression and DNAm levels in fetal brain at chromosomes 1 and 17 that were associated with ADHD and ASD, respectively, through pleiotropy at shared genetic variants. The analyses in brain and blood showed additional associated gene expression and DNAm levels at the same and additional loci, likely because of increased statistical power. Several of the associated genes have not been identified in ADHD and ASD GWASs before. Conclusions: Our findings identified the genetic variants associated with ADHD and ASD that likely act through gene regulation. This facilitates prioritization of candidate genes for functional follow-up studies

Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

Genome-wide association studies (GWAS) have identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs, indicating their potential involvement in regulation of gene expression. Here, we propose a transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) that uses multiple SNPs as instruments and multiple gene expression traits as exposures, simultaneously. Applied to 43 human phenotypes, it uncovers 3,913 putatively causal gene-trait associations, 36% of which have no genome-wide significant SNP nearby in previous GWAS. Using independent association summary statistics, we find that the majority of these loci were missed by GWAS due to power issues. Noteworthy among these links is educational attainment-associated BSCL2, known to carry mutations leading to a Mendelian form of encephalopathy. We also find pleiotropic causal effects suggestive of mechanistic connections. TWMR better accounts for pleiotropy and has the potential to identify biological mechanisms underlying complex traits

Effects of Fluids on the Macro- and Microcirculations.

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2018. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2018. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901

DNA methylation signatures of aggression and closely related constructs : A meta-analysis of epigenome-wide studies across the lifespan

DNA methylation profiles of aggressive behavior may capture lifetime cumulative effects of genetic, stochastic, and environmental influences associated with aggression. Here, we report the first large meta-analysis of epigenome-wide association studies (EWAS) of aggressive behavior (N = 15,324 participants). In peripheral blood samples of 14,434 participants from 18 cohorts with mean ages ranging from 7 to 68 years, 13 methylation sites were significantly associated with aggression (alpha = 1.2 x 10(-7); Bonferroni correction). In cord blood samples of 2425 children from five cohorts with aggression assessed at mean ages ranging from 4 to 7 years, 83% of these sites showed the same direction of association with childhood aggression (r = 0.74, p = 0.006) but no epigenome-wide significant sites were found. Top-sites (48 at a false discovery rate of 5% in the peripheral blood meta-analysis or in a combined meta-analysis of peripheral blood and cord blood) have been associated with chemical exposures, smoking, cognition, metabolic traits, and genetic variation (mQTLs). Three genes whose expression levels were associated with top-sites were previously linked to schizophrenia and general risk tolerance. At six CpGs, DNA methylation variation in blood mirrors variation in the brain. On average 44% (range = 3-82%) of the aggression-methylation association was explained by current and former smoking and BMI. These findings point at loci that are sensitive to chemical exposures with potential implications for neuronal functions. We hope these results to be a starting point for studies leading to applications as peripheral biomarkers and to reveal causal relationships with aggression and related traits.Peer reviewe

Symposium on the Scottish labour market

In the post-war period, up to the late 1960s, Britain enjoyed a modicum of unemployment and government policies which were geared to producing Full Employment were considered a success. It was simple - boost demand and more people would find work. But the mid 1970s the economic regency enjoyed by those advocating demand sided policies fell into disrepute as the OPEC nations raised prices dramatically and brought in a new era of both rising prices and unemployment. The laws of economics, which previously had viewed policy decisions as the choice between lower unemployment and higher inflation were now redundant. Both unemployment and inflation were moving in the same direction. The era of stagflation had begun

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