22 research outputs found

    The genome and transcriptome of Haemonchus contortus, a key model parasite for drug and vaccine discovery

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    <p>Background: The small ruminant parasite Haemonchus contortus is the most widely used parasitic nematode in drug discovery, vaccine development and anthelmintic resistance research. Its remarkable propensity to develop resistance threatens the viability of the sheep industry in many regions of the world and provides a cautionary example of the effect of mass drug administration to control parasitic nematodes. Its phylogenetic position makes it particularly well placed for comparison with the free-living nematode Caenorhabditis elegans and the most economically important parasites of livestock and humans.</p> <p>Results: Here we report the detailed analysis of a draft genome assembly and extensive transcriptomic dataset for H. contortus. This represents the first genome to be published for a strongylid nematode and the most extensive transcriptomic dataset for any parasitic nematode reported to date. We show a general pattern of conservation of genome structure and gene content between H. contortus and C. elegans, but also a dramatic expansion of important parasite gene families. We identify genes involved in parasite-specific pathways such as blood feeding, neurological function, and drug metabolism. In particular, we describe complete gene repertoires for known drug target families, providing the most comprehensive understanding yet of the action of several important anthelmintics. Also, we identify a set of genes enriched in the parasitic stages of the lifecycle and the parasite gut that provide a rich source of vaccine and drug target candidates.</p> <p>Conclusions: The H. contortus genome and transcriptome provides an essential platform for postgenomic research in this and other important strongylid parasites. </p&gt

    Brain imaging of the cortex in ADHD: a coordinated analysis of large-scale clinical and population-based samples

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    Objective: Neuroimaging studies show structural alterations of various brain regions in children and adults with attention deficit hyperactivity disorder (ADHD), although nonreplications are frequent. The authors sought to identify cortical characteristics related to ADHD using large-scale studies. Methods: Cortical thickness and surface area (based on the Desikan–Killiany atlas) were compared between case subjects with ADHD (N=2,246) and control subjects (N=1,934) for children, adolescents, and adults separately in ENIGMA-ADHD, a consortium of 36 centers. To assess familial effects on cortical measures, case subjects, unaffected siblings, and control subjects in the NeuroIMAGE study (N=506) were compared. Associations of the attention scale from the Child Behavior Checklist with cortical measures were determined in a pediatric population sample (Generation-R, N=2,707). Results: In the ENIGMA-ADHD sample, lower surface area values were found in children with ADHD, mainly in frontal, cingulate, and temporal regions; the largest significant effect was for total surface area (Cohen’s d=−0.21). Fusiform gyrus and temporal pole cortical thickness was also lower in children with ADHD. Neither surface area nor thickness differences were found in the adolescent or adult groups. Familial effects were seen for surface area in several regions. In an overlapping set of regions, surface area, but not thickness, was associated with attention problems in the Generation-R sample. Conclusions: Subtle differences in cortical surface area are widespread in children but not adolescents and adults with ADHD, confirming involvement of the frontal cortex and highlighting regions deserving further attention. Notably, the alterations behave like endophenotypes in families and are linked to ADHD symptoms in the population, extending evidence that ADHD behaves as a continuous trait in the population. Future longitudinal studies should clarify individual lifespan trajectories that lead to nonsignificant findings in adolescent and adult groups despite the presence of an ADHD diagnosis

    Analysis of structural brain asymmetries in attention-deficit/hyperactivity disorder in 39 datasets

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    Objective Some studies have suggested alterations of structural brain asymmetry in attention-deficit/hyperactivity disorder (ADHD), but findings have been contradictory and based on small samples. Here, we performed the largest ever analysis of brain left-right asymmetry in ADHD, using 39 datasets of the ENIGMA consortium. Methods We analyzed asymmetry of subcortical and cerebral cortical structures in up to 1,933 people with ADHD and 1,829 unaffected controls. Asymmetry Indexes (AIs) were calculated per participant for each bilaterally paired measure, and linear mixed effects modeling was applied separately in children, adolescents, adults, and the total sample, to test exhaustively for potential associations of ADHD with structural brain asymmetries. Results There was no evidence for altered caudate nucleus asymmetry in ADHD, in contrast to prior literature. In children, there was less rightward asymmetry of the total hemispheric surface area compared to controls (t = 2.1, p = .04). Lower rightward asymmetry of medial orbitofrontal cortex surface area in ADHD (t = 2.7, p = .01) was similar to a recent finding for autism spectrum disorder. There were also some differences in cortical thickness asymmetry across age groups. In adults with ADHD, globus pallidus asymmetry was altered compared to those without ADHD. However, all effects were small (Cohen’s d from −0.18 to 0.18) and would not survive study-wide correction for multiple testing. Conclusion Prior studies of altered structural brain asymmetry in ADHD were likely underpowered to detect the small effects reported here. Altered structural asymmetry is unlikely to provide a useful biomarker for ADHD, but may provide neurobiological insights into the trait

    Subcortical brain volume, regional cortical thickness, and cortical surface area across disorders: findings from the ENIGMA ADHD, ASD, and OCD Working Groups

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    Objective Attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) are common neurodevelopmental disorders that frequently co-occur. We aimed to directly compare all three disorders. The ENIGMA consortium is ideally positioned to investigate structural brain alterations across these disorders. Methods Structural T1-weighted whole-brain MRI of controls (n=5,827) and patients with ADHD (n=2,271), ASD (n=1,777), and OCD (n=2,323) from 151 cohorts worldwide were analyzed using standardized processing protocols. We examined subcortical volume, cortical thickness and surface area differences within a mega-analytical framework, pooling measures extracted from each cohort. Analyses were performed separately for children, adolescents, and adults using linear mixed-effects models adjusting for age, sex and site (and ICV for subcortical and surface area measures). Results We found no shared alterations among all three disorders, while shared alterations between any two disorders did not survive multiple comparisons correction. Children with ADHD compared to those with OCD had smaller hippocampal volumes, possibly influenced by IQ. Children and adolescents with ADHD also had smaller ICV than controls and those with OCD or ASD. Adults with ASD showed thicker frontal cortices compared to adult controls and other clinical groups. No OCD-specific alterations across different age-groups and surface area alterations among all disorders in childhood and adulthood were observed. Conclusion Our findings suggest robust but subtle alterations across different age-groups among ADHD, ASD, and OCD. ADHD-specific ICV and hippocampal alterations in children and adolescents, and ASD-specific cortical thickness alterations in the frontal cortex in adults support previous work emphasizing neurodevelopmental alterations in these disorders

    A chemical genetic roadmap to improved tomato flavor

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    [EN] Modern commercial tomato varieties are substantially less flavorful than heirloom varieties. To understand and ultimately correct this deficiency, we quantified flavor-associated chemicals in 398 modern, heirloom, and wild accessions. A subset of these accessions was evaluated in consumer panels, identifying the chemicals that made the most important contributions to flavor and consumer liking. We found that modern commercial varieties contain significantly lower amounts of many of these important flavor chemicals than older varieties. Whole-genome sequencing and a genome-wide association study permitted identification of genetic loci that affect most of the target flavor chemicals, including sugars, acids, and volatiles. Together, these results provide an understanding of the flavor deficiencies in modern commercial varieties and the information necessary for the recovery of good flavor through molecular breeding.This work was supported by the NSF (grant IOS-0923312 to H.K.), the China National Key Research and Development Program for Crop Breeding (grant 2016YFD0100307 to S.H.), the Leading Talents of Guangdong Province Program (grant 00201515 to S.H.), the National Natural Science Foundation of China (grant 31601756 to G.Z.), the European Research Council (grant ERC-2011-AdG 294691 YIELD to D.Z.), and the European Commission Horizon 2020 program (TRADITOM grant 634561 to A.G. and D.Z.) This work was also supported by the Chinese Academy of Agricultural Science (ASTIP-CAAS) and the Shenzhen municipal and Dapeng district governments. We acknowledge the assistance of L. Kates in fieldwork and volatile, sugar, and acid quantification.Tieman, D.; Zhu, G.; Resende, MFR.; Lin, T.; Nguyen, C.; Bies, D.; Rambla Nebot, JL.... (2017). A chemical genetic roadmap to improved tomato flavor. Science. 355(6323):391-394. https://doi.org/10.1126/science.aal1556S3913943556323Food and Agriculture Organization of the United Nations; http://faostat.fao.org/site/339/default.aspx.Tieman, D., Bliss, P., McIntyre, L. M., Blandon-Ubeda, A., Bies, D., Odabasi, A. Z., … Klee, H. J. (2012). The Chemical Interactions Underlying Tomato Flavor Preferences. Current Biology, 22(11), 1035-1039. doi:10.1016/j.cub.2012.04.016R. G. Buttery, R. Teranishi, R. A. Flath, L. C. Ling, Fresh tomato volatiles: Composition and sensory studies. Am. Chem. Soc. Symp. 388, 213–222 (1987).Baldwin, E. A., Scott, J. W., Shewmaker, C. K., & Schuch, W. (2000). Flavor Trivia and Tomato Aroma: Biochemistry and Possible Mechanisms for Control of Important Aroma Components. HortScience, 35(6), 1013-1022. doi:10.21273/hortsci.35.6.1013Vogel, J. T., Tieman, D. M., Sims, C. A., Odabasi, A. Z., Clark, D. G., & Klee, H. J. (2010). Carotenoid content impacts flavor acceptability in tomato (Solanum lycopersicum). Journal of the Science of Food and Agriculture, 90(13), 2233-2240. doi:10.1002/jsfa.4076Zhang, B., Tieman, D. M., Jiao, C., Xu, Y., Chen, K., Fei, Z., … Klee, H. J. (2016). Chilling-induced tomato flavor loss is associated with altered volatile synthesis and transient changes in DNA methylation. Proceedings of the National Academy of Sciences, 113(44), 12580-12585. doi:10.1073/pnas.1613910113Lin, T., Zhu, G., Zhang, J., Xu, X., Yu, Q., Zheng, Z., … Huang, S. (2014). Genomic analyses provide insights into the history of tomato breeding. Nature Genetics, 46(11), 1220-1226. doi:10.1038/ng.3117Fridman, E. (2004). Zooming In on a Quantitative Trait for Tomato Yield Using Interspecific Introgressions. Science, 305(5691), 1786-1789. doi:10.1126/science.1101666Zanor, M. I., Osorio, S., Nunes-Nesi, A., Carrari, F., Lohse, M., Usadel, B., … Fernie, A. R. (2009). RNA Interference of LIN5 in Tomato Confirms Its Role in Controlling Brix Content, Uncovers the Influence of Sugars on the Levels of Fruit Hormones, and Demonstrates the Importance of Sucrose Cleavage for Normal Fruit Development and Fertility. Plant Physiology, 150(3), 1204-1218. doi:10.1104/pp.109.136598Tieman, D., Zeigler, M., Schmelz, E., Taylor, M. G., Rushing, S., Jones, J. B., & Klee, H. J. (2010). Functional analysis of a tomato salicylic acid methyl transferase and its role in synthesis of the flavor volatile methyl salicylate. The Plant Journal, 62(1), 113-123. doi:10.1111/j.1365-313x.2010.04128.xZanor, M. I., Rambla, J.-L., Chaïb, J., Steppa, A., Medina, A., Granell, A., … Causse, M. (2009). Metabolic characterization of loci affecting sensory attributes in tomato allows an assessment of the influence of the levels of primary metabolites and volatile organic contents. Journal of Experimental Botany, 60(7), 2139-2154. doi:10.1093/jxb/erp086Zierler, B., Siegmund, B., & Pfannhauser, W. (2004). Determination of off-flavour compounds in apple juice caused by microorganisms using headspace solid phase microextraction–gas chromatography–mass spectrometry. Analytica Chimica Acta, 520(1-2), 3-11. doi:10.1016/j.aca.2004.03.084Deikman, J., Kline, R., & Fischer, R. L. (1992). Organization of Ripening and Ethylene Regulatory Regions in a Fruit-Specific Promoter from Tomato (Lycopersicon esculentum). Plant Physiology, 100(4), 2013-2017. doi:10.1104/pp.100.4.2013Penarrubia, L., Aguilar, M., Margossian, L., & Fischer, R. L. (1992). An Antisense Gene Stimulates Ethylene Hormone Production during Tomato Fruit Ripening. The Plant Cell, 681-687. doi:10.1105/tpc.4.6.681Lewinsohn, E., Sitrit, Y., Bar, E., Azulay, Y., Meir, A., Zamir, D., & Tadmor, Y. (2005). Carotenoid Pigmentation Affects the Volatile Composition of Tomato and Watermelon Fruits, As Revealed by Comparative Genetic Analyses. Journal of Agricultural and Food Chemistry, 53(8), 3142-3148. doi:10.1021/jf047927tOltman, A. E., Jervis, S. M., & Drake, M. A. (2014). Consumer Attitudes and Preferences for Fresh Market Tomatoes. Journal of Food Science, 79(10), S2091-S2097. doi:10.1111/1750-3841.12638Tieman, D. M., Zeigler, M., Schmelz, E. A., Taylor, M. G., Bliss, P., Kirst, M., & Klee, H. J. (2006). Identification of loci affecting flavour volatile emissions in tomato fruits. Journal of Experimental Botany, 57(4), 887-896. doi:10.1093/jxb/erj074Rambla, J. L., Alfaro, C., Medina, A., Zarzo, M., Primo, J., & Granell, A. (2015). Tomato fruit volatile profiles are highly dependent on sample processing and capturing methods. Metabolomics, 11(6), 1708-1720. doi:10.1007/s11306-015-0824-5Bartoshuk, L. ., Duffy, V. ., Fast, K., Green, B. ., Prutkin, J., & Snyder, D. . (2003). Labeled scales (e.g., category, Likert, VAS) and invalid across-group comparisons: what we have learned from genetic variation in taste. Food Quality and Preference, 14(2), 125-138. doi:10.1016/s0950-3293(02)00077-0A. B. Gilmour, B. Gogel, B. Cullis, R. Thompson R ASReml User Guide Release 3.0. VSN International. Hemel Hempstead, UK (2009).Li, R., Yu, C., Li, Y., Lam, T.-W., Yiu, S.-M., Kristiansen, K., & Wang, J. (2009). SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics, 25(15), 1966-1967. doi:10.1093/bioinformatics/btp336(2012). The tomato genome sequence provides insights into fleshy fruit evolution. Nature, 485(7400), 635-641. doi:10.1038/nature11119Li, Y., Chen, W., Liu, E. Y., & Zhou, Y.-H. (2012). Single Nucleotide Polymorphism (SNP) Detection and Genotype Calling from Massively Parallel Sequencing (MPS) Data. Statistics in Biosciences, 5(1), 3-25. doi:10.1007/s12561-012-9067-4Jombart, T., Devillard, S., & Balloux, F. (2010). Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genetics, 11(1), 94. doi:10.1186/1471-2156-11-94Zheng, X., Levine, D., Shen, J., Gogarten, S. M., Laurie, C., & Weir, B. S. (2012). A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics, 28(24), 3326-3328. doi:10.1093/bioinformatics/bts606Jombart, T. (2008). adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics, 24(11), 1403-1405. doi:10.1093/bioinformatics/btn129Kang, H. M., Sul, J. H., Service, S. K., Zaitlen, N. A., Kong, S., Freimer, N. B., … Eskin, E. (2010). Variance component model to account for sample structure in genome-wide association studies. Nature Genetics, 42(4), 348-354. doi:10.1038/ng.548Li, M.-X., Yeung, J. M. Y., Cherny, S. S., & Sham, P. C. (2011). Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Human Genetics, 131(5), 747-756. doi:10.1007/s00439-011-1118-2Barrett, J. C., Fry, B., Maller, J., & Daly, M. J. (2004). Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics, 21(2), 263-265. doi:10.1093/bioinformatics/bth457Craig, D. W., Pearson, J. V., Szelinger, S., Sekar, A., Redman, M., Corneveaux, J. J., … Huentelman, M. J. (2008). Identification of genetic variants using bar-coded multiplexed sequencing. Nature Methods, 5(10), 887-893. doi:10.1038/nmeth.1251Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25(14), 1754-1760. doi:10.1093/bioinformatics/btp324Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., … Homer, N. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16), 2078-2079. doi:10.1093/bioinformatics/btp35

    Prospective and longitudinal natural history study of patients with Type 2 and 3 spinal muscular atrophy: Baseline data NatHis-SMA study

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    Spinal muscular atrophy (SMA) is a monogenic disorder caused by loss of function mutations in the survival motor neuron 1 gene, which results in a broad range of disease severity, from neonatal to adult onset. There is currently a concerted effort to define the natural history of the disease and develop outcome measures that accurately capture its complexity. As several therapeutic strategies are currently under investigation and both the FDA and EMA have recently approved the first medical treatment for SMA, there is a critical need to identify the right association of responsive outcome measures and biomarkers for individual patient follow-up. As an approved treatment becomes available, untreated patients will soon become rare, further intensifying the need for a rapid, prospective and longitudinal study of the natural history of SMA Type 2 and 3. Here we present the baseline assessments of 81 patients aged 2 to 30 years of which 19 are non-sitter SMA Type 2, 34 are sitter SMA Type 2, 9 non-ambulant SMA Type 3 and 19 ambulant SMA Type 3. Collecting these data at nine sites in France, Germany and Belgium established the feasibility of gathering consistent data from numerous and demanding assessments in a multicenter SMA study. Most assessments discriminated between the four groups well. This included the Motor Function Measure (MFM), pulmonary function testing, strength, electroneuromyography, muscle imaging and workspace volume. Additionally, all of the assessments showed good correlation with the MFM score. As the untreated patient population decreases, having reliable and valid multi-site data will be imperative for recruitment in clinical trials. The pending two-year study results will evaluate the sensitivity of the studied outcomes and biomarkers to disease progression. TRIAL REGISTRATION: ClinicalTrials.gov (NCT02391831).status: publishe

    Evidence for similar structural brain anomalies in youth and adult attention-deficit/hyperactivity disorder: a machine learning analysis

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    Attention-deficit/hyperactivity disorder (ADHD) affects 5% of children world-wide. Of these, two-thirds continue to have impairing symptoms of ADHD into adulthood. Although a large literature implicates structural brain differences of the disorder, it is not clear if adults with ADHD have similar neuroanatomical differences as those seen in children with recent reports from the large ENIGMA-ADHD consortium finding structural differences for children but not for adults. This paper uses deep learning neural network classification models to determine if there are neuroanatomical changes in the brains of children with ADHD that are also observed for adult ADHD, and vice versa. We found that structural MRI data can significantly separate ADHD from control participants for both children and adults. Consistent with the prior reports from ENIGMA-ADHD, prediction performance and effect sizes were better for the child than the adult samples. The model trained on adult samples significantly predicted ADHD in the child sample, suggesting that our model learned anatomical features that are common to ADHD in childhood and adulthood. These results support the continuity of ADHD’s brain differences from childhood to adulthood. In addition, our work demonstrates a novel use of neural network classification models to test hypotheses about developmental continuity.publishedVersio
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