Tyrosine phosphorylation controls brassinosteroid receptor activation by triggering membrane release of its kinase inhibitor

Receptor tyrosine kinases control many critical processes in metazoans, but these enzymes appear to be absent in plants. Recently, two Arabidopsis receptor kinases-BRASSINOSTEROID INSENSITIVE 1 (BRI1) and BRI1-ASSOCIATED KINASE1 (BAK1), the receptor and coreceptor for brassinosteroids-were shown to autophosphorylate on tyrosines. However, the cellular roles for tyrosine phosphorylation in plants remain poorly understood. Here, we report that the BRI1 KINASE INHIBITOR 1 (BKI1) is tyrosine phosphorylated in response to brassinosteroid perception. Phosphorylation occurs within a reiterated [KR][KR] membrane targeting motif, releasing BKI1 into the cytosol and enabling formation of an active signaling complex. Our work reveals that tyrosine phosphorylation is a conserved mechanism controlling protein localization in all higher organisms

Spike-and-Slab Priors for Function Selection in Structured Additive Regression Models

Structured additive regression provides a general framework for complex Gaussian and non-Gaussian regression models, with predictors comprising arbitrary combinations of nonlinear functions and surfaces, spatial effects, varying coefficients, random effects and further regression terms. The large flexibility of structured additive regression makes function selection a challenging and important task, aiming at (1) selecting the relevant covariates, (2) choosing an appropriate and parsimonious representation of the impact of covariates on the predictor and (3) determining the required interactions. We propose a spike-and-slab prior structure for function selection that allows to include or exclude single coefficients as well as blocks of coefficients representing specific model terms. A novel multiplicative parameter expansion is required to obtain good mixing and convergence properties in a Markov chain Monte Carlo simulation approach and is shown to induce desirable shrinkage properties. In simulation studies and with (real) benchmark classification data, we investigate sensitivity to hyperparameter settings and compare performance to competitors. The flexibility and applicability of our approach are demonstrated in an additive piecewise exponential model with time-varying effects for right-censored survival times of intensive care patients with sepsis. Geoadditive and additive mixed logit model applications are discussed in an extensive appendix

Two new approaches to improve the analysis of BALB/c 3T3 cell transformation assay data

Validation activities of the BALB/c 3T3 cell transformation assay (CTA) – a test method used for the assessment of the carcinogenic potential of compounds – have revealed the need for statistical analysis tailored to specific features of BALB/c 3T3 CTA data. Whereas a standard statistical approach for the Syrian hamster embryo (SHE) CTA was considered sufficient, an international expert group was gathered by the European Centre for the Validation of Alternative Methods (ECVAM) to review commonly applied statistical approaches for BALB/c 3T3 CTA. As it was concluded that none of the commonly applied approaches is entirely appropriate, two novel statistical approaches were found to be recommended for the evaluation of BALB/c 3T3 CTA data accounting for possible non-monotone concentration–response relationship and variance heterogeneity: a negative binomial generalised linear model with William's-type downturn-protected trend tests and a normalisation of the data by a specific transformation allowing for application of a general linear model that estimates effects assuming a normal distribution with William's-type protected tests. Both approaches are described in this article and their performance and the quality of the results they generate is demonstrated using exemplary data. Our work confirmed that both approaches are suitable for the statistical analysis of BALB/c 3T3 CTA data and that each of them is superior to commonly used methods. Furthermore, a procedure dichotomising data into negatives and positives is proposed which allows re-testing in cases where inconclusive data are encountered. The scripts of the statistical evaluation programs written in R – a freely available statistical software – are appended including exemplary outputs

Ensemble of a subset of kNN classifiers

Combining multiple classifiers, known as ensemble methods, can give substantial improvement in prediction performance of learning algorithms especially in the presence of non-informative features in the data sets. We propose an ensemble of subset of kNN classifiers, ESkNN, for classification task in two steps. Firstly, we choose classifiers based upon their individual performance using the out-of-sample accuracy. The selected classifiers are then combined sequentially starting from the best model and assessed for collective performance on a validation data set. We use bench mark data sets with their original and some added non-informative features for the evaluation of our method. The results are compared with usual kNN, bagged kNN, random kNN, multiple feature subset method, random forest and support vector machines. Our experimental comparisons on benchmark classification problems and simulated data sets reveal that the proposed ensemble gives better classification performance than the usual kNN and its ensembles, and performs comparable to random forest and support vector machines

CLERK is a novel receptor kinase required for sensing of root-active CLE peptides in <i>Arabidopsis</i>.

CLAVATA3/EMBRYO SURROUNDING REGION (CLE) peptides are secreted endogenous plant ligands that are sensed by receptor kinases (RKs) to convey environmental and developmental inputs. Typically, this involves an RK with narrow ligand specificity that signals together with a more promiscuous co-receptor. For most CLEs, biologically relevant (co-)receptors are unknown. The dimer of the receptor-like protein CLAVATA 2 (CLV2) and the pseudokinase CORYNE (CRN) conditions perception of so-called root-active CLE peptides, the exogenous application of which suppresses root growth by preventing protophloem formation in the meristem. &lt;i&gt;clv2&lt;/i&gt; as well as &lt;i&gt;crn&lt;/i&gt; null mutants are resistant to root-active CLE peptides, possibly because CLV2-CRN promotes expression of their cognate receptors. Here, we have identified the &lt;i&gt;CLE-RESISTANT RECEPTOR KINASE&lt;/i&gt; ( &lt;i&gt;CLERK&lt;/i&gt; ) gene, which is required for full sensing of root-active CLE peptides in early developing protophloem. CLERK protein can be replaced by its close homologs, SENESCENCE-ASSOCIATED RECEPTOR-LIKE KINASE (SARK) and NSP-INTERACTING KINASE 1 (NIK1). Yet neither CLERK nor NIK1 ectodomains interact biochemically with described CLE receptor ectodomains. Consistently, &lt;i&gt;CLERK&lt;/i&gt; also acts genetically independently of &lt;i&gt;CLV2-CRN&lt;/i&gt; We, thus, have discovered a novel hub for redundant CLE sensing in the root

An AUC-based Permutation Variable Importance Measure for Random Forests

The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance. We investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/070_drittmittel/janitza/index.html

Prediction intervals for future BMI values of individual children - a non-parametric approach by quantile boosting

Background: The construction of prediction intervals (PIs) for future body mass index (BMI) values of individual children based on a recent German birth cohort study with n = 2007 children is problematic for standard parametric approaches, as the BMI distribution in childhood is typically skewed depending on age. Methods: We avoid distributional assumptions by directly modelling the borders of PIs by additive quantile regression, estimated by boosting. We point out the concept of conditional coverage to prove the accuracy of PIs. As conditional coverage can hardly be evaluated in practical applications, we conduct a simulation study before fitting child- and covariate-specific PIs for future BMI values and BMI patterns for the present data. Results: The results of our simulation study suggest that PIs fitted by quantile boosting cover future observations with the predefined coverage probability and outperform the benchmark approach. For the prediction of future BMI values, quantile boosting automatically selects informative covariates and adapts to the age-specific skewness of the BMI distribution. The lengths of the estimated PIs are child-specific and increase, as expected, with the age of the child. Conclusions: Quantile boosting is a promising approach to construct PIs with correct conditional coverage in a non-parametric way. It is in particular suitable for the prediction of BMI patterns depending on covariates, since it provides an interpretable predictor structure, inherent variable selection properties and can even account for longitudinal data structures

Prediction of peptide and protein propensity for amyloid formation

Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔGº values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation