3 research outputs found
ABA-regulated promoter activity in stomatal guard cells.
CDeT6-19 is an ABA-regulated gene which has been isolated from Craterostigma plantagineum. The CDeT6-19 gene promoter has been fused to the β-glucuronidase reporter gene (GUS) and used to stably transform Arabidopsis thaliana and Nicotiana tabacum. This construct has been shown to be expressed in stomatal guard cells and often in the adjacent epidermal cells of both species in response to both exogenous ABA and drought stress. These results indicate that the stomatal guard cell is competent to relay an ABA signal to the nucleus. In contrast GUS expression directed by the promoter from a predominantly seed-specific, ABA-regulated gene, Em, or the promoter from the ABA-regulated CDeT27-45 gene is not detectable in the epidermal or guard cells of tobacco or Arabidopsis in response to ABA. The fact that not all ABA-regulated gene promoters are active in stomatal guard cells suggests that effective transduction of the signal is dependent upon particular regions within the gene promoter or that guard cells lack all or part of the specific transduction apparatus required to couple the ABA signal to these promoters. This suggests that there are multiple ABA stimulus response coupling pathways. The identification of a regulatory sequence from an ABA-induced gene which is expressed in stomatal guard cells creates the possibility of examining the role of Ca2+ and other second messengers in ABA-induced gene expression
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers