Interplay of EXO70 and MLO proteins modulates trichome cell wall composition and susceptibility to powdery mildew.

Exocyst component of 70-kDa (EXO70) proteins are constituents of the exocyst complex implicated in vesicle tethering during exocytosis. MILDEW RESISTANCE LOCUS O (MLO) proteins are plant-specific calcium channels and some MLO isoforms enable fungal powdery mildew pathogenesis. We here detected an unexpected phenotypic overlap of Arabidopsis thaliana exo70H4 and mlo2 mlo6 mlo12 triple mutant plants regarding the biogenesis of leaf trichome secondary cell walls. Biochemical and Fourier transform infrared spectroscopic analyses corroborated deficiencies in the composition of trichome cell walls in these mutants. Transgenic lines expressing fluorophore-tagged EXO70H4 and MLO exhibited extensive colocalization of these proteins. Furthermore, mCherry-EXO70H4 mislocalized in trichomes of the mlo triple mutant and, vice versa, MLO6-GFP mislocalized in trichomes of the exo70H4 mutant. Expression of GFP-marked PMR4 callose synthase, a known cargo of EXO70H4-dependent exocytosis, revealed reduced cell wall delivery of GFP-PMR4 in trichomes of mlo triple mutant plants. In vivo protein-protein interaction assays in plant and yeast cells uncovered isoform-preferential interactions between EXO70.2 subfamily members and MLO proteins. Finally, exo70H4 and mlo6 mutants, when combined, showed synergistically enhanced resistance to powdery mildew attack. Taken together, our data point to an isoform-specific interplay of EXO70 and MLO proteins in the modulation of trichome cell wall biogenesis and powdery mildew susceptibility.

A GH81-type ß-glucan-binding protein enhances colonization by mutualistic fungi in barley.

Cell walls are important interfaces of plant-fungal interactions, acting as robust physical and chemical barriers against invaders. Upon fungal colonization, plants deposit phenolics and callose at the sites of fungal penetration to prevent further fungal progression. Alterations in the composition of plant cell walls significantly impact host susceptibility. Furthermore, plants and fungi secrete glycan hydrolases acting on each other's cell walls. These enzymes release various sugar oligomers into the apoplast, some of which activate host immunity via surface receptors. Recent characterization of cell walls from plant-colonizing fungi has emphasized the abundance of ß-glucans in different cell wall layers, which makes them suitable targets for recognition. To characterize host components involved in immunity against fungi, we performed a protein pull-down with the biotinylated ß-glucan laminarin. Thereby, we identified a plant glycoside hydrolase family 81-type glucan-binding protein (GBP) as a ß-glucan interactor. Mutation of GBP1 and its only paralog, GBP2, in barley led to decreased colonization by the beneficial root endophytes Serendipita indica and S. vermifera, as well as the arbuscular mycorrhizal fungus Rhizophagus irregularis. The reduction of colonization was accompanied by enhanced responses at the host cell wall, including an extension of callose-containing cell wall appositions. Moreover, GBP mutation in barley also reduced fungal biomass in roots by the hemibiotrophic pathogen Bipolaris sorokiniana and inhibited the penetration success of the obligate biotrophic leaf pathogen Blumeria hordei. These results indicate that GBP1 is involved in the establishment of symbiotic associations with beneficial fungi-a role that has potentially been appropriated by barley-adapted pathogens.

Identification of a xyloglucan beta-xylopyranosyltransferase from Vaccinium corymbosum.

Plant cell walls contain the hemicellulose xyloglucan, whose fine structure may vary depending on cell type, tissue, and/or plant species. Most but not all of the glycosyltransferases involved in the biosynthesis of xyloglucan sidechains have been identified. Here, we report the identification of several functional glycosyltransferases from blueberry (Vaccinium corymbosum bluecrop). Among those transferases is a hitherto elusive Xyloglucan:Beta-xylosylTransferase (XBT). Heterologous expression of VcXBT in the Arabidopsis thaliana double mutant mur3 xlt2, where xyloglucan consists only of an unsubstituted xylosylated glucan core structure, results in the production of the xylopyranose-containing "U" sidechain as characterized by mass spectrometry, glycosidic linkage, and NMR analysis. The introduction of the additional xylopyranosyl residue rescues the dwarfed phenotype of the untransformed Arabidopsis mur3 xlt2 mutant to wild-type height. Structural protein analysis using Alphafold of this and other related xyloglucan glycosyltransferase family 47 proteins not only identifies potential domains that might influence the regioselectivity of these enzymes but also gives hints to specific amino acids that might determine the donor-substrate specificity of these glycosyltransferases.

The consequences of neglected confounding and interactions in mixed-effects meta-regression: An illustrative example.

Analysts seldom include interaction terms in their meta-regression model, which can introduce bias if an interaction is present. We illustrate this by reanalysing a meta-regression study in acute heart failure. Based on a total of 285 studies, the 1-year mortality rate related to acute heart failure is considered and the connection to the study-level covariates year of recruitment and average age of study participants are of interest. We show that neglecting a possibly confounding variable and an interaction term might lead to erroneous inference and conclusions. Based on our results and accompanying simulations, we recommend to include possible confounders and interaction terms, whenever they are plausible, in mixed-effects meta-regression models.

Analyzing the Effect of Imputation on Classification Performance under MCAR and MAR Missing Mechanisms.

Many datasets in statistical analyses contain missing values. As omitting observations containing missing entries may lead to information loss or greatly reduce the sample size, imputation is usually preferable. However, imputation can also introduce bias and impact the quality and validity of subsequent analysis. Focusing on binary classification problems, we analyzed how missing value imputation under MCAR as well as MAR missingness with different missing patterns affects the predictive performance of subsequent classification. To this end, we compared imputation methods such as several MICE variants, missForest, Hot Deck as well as mean imputation with regard to the classification performance achieved with commonly used classifiers such as Random Forest, Extreme Gradient Boosting, Support Vector Machine and regularized logistic regression. Our simulation results showed that Random Forest based imputation (i.e., MICE Random Forest and missForest) performed particularly well in most scenarios studied. In addition to these two methods, simple mean imputation also proved to be useful, especially when many features (covariates) contained missing values.

A large-scale forward genetic screen for maize mutants with altered lignocellulosic properties.

The development of efficient pipelines for the bioconversion of grass lignocellulosic feedstocks is challenging due to the limited understanding of the molecular mechanisms controlling the synthesis, deposition, and degradation of the varying polymers unique to grass cell walls. Here, we describe a large-scale forward genetic approach resulting in the identification of a collection of chemically mutagenized maize mutants with diverse alterations in their cell wall attributes such as crystalline cellulose content or hemicellulose composition. Saccharification yield, i.e. the amount of lignocellulosic glucose (Glc) released by means of enzymatic hydrolysis, is increased in two of the mutants and decreased in the remaining six. These mutants, termed candy-leaf (cal), show no obvious plant growth or developmental defects despite associated differences in their lignocellulosic composition. The identified cal mutants are a valuable tool not only to understand recalcitrance of grass lignocellulosics to enzymatic deconstruction but also to decipher grass-specific aspects of cell wall biology once the genetic basis, i.e. the location of the mutation, has been identified.

A comparative study to alternatives to the log-rank test.

BACKGROUND: Studies to compare the survival of two or more groups using time-to-event data are of high importance in medical research. The gold standard is the log-rank test, which is optimal under proportional hazards. As the latter is no simple regularity assumption, we are interested in evaluating the power of various statistical tests under different settings including proportional and non-proportional hazards with a special emphasis on crossing hazards. This challenge has been going on for many years now and multiple methods have already been investigated in extensive simulation studies. However, in recent years new omnibus tests and methods based on the restricted mean survival time appeared that have been strongly recommended in biometric literature. METHODS: Thus, to give updated recommendations, we perform a vast simulation study to compare tests that showed high power in previous studies with these more recent approaches. We thereby analyze various simulation settings with varying survival and censoring distributions, unequal censoring between groups, small sample sizes and unbalanced group sizes. RESULTS: Overall, omnibus tests are more robust in terms of power against deviations from the proportional hazards assumption. CONCLUSION: We recommend considering the more robust omnibus approaches for group comparison in case of uncertainty about the underlying survival time distributions.

Populus endo-glucanase 16 localizes to the cell walls of developing tissues.

The hemicelluloses comprise a group of matrix glycans that interact with cellulose microfibrils in plant cell walls and play important roles in establishing wall architecture. The structures of hemicelluloses are determined by carbohydrate-active enzymes (CAZymes) that synthesize, integrate, and break down these polymers. Specifically, endo-glucanase 16 (EG16) enzymes, which are related to the well-known xyloglucan endotransglycosylase/hydrolase (XTH) gene products in Glycoside Hydrolase Family 16 (GH16), have been implicated in the degradation of the ß(1,4)-linked backbone of mixed-linkage ß(1,3);ß(1,4)-glucans (MLG) and xyloglucans. EG16 members are single-copy genes found in most plant clades but are absent from many eudicots, including the model plant Arabidopsis thaliana. Until recently, EG16 members had only been characterized in vitro, establishing their substrate specificity, protein structure, and phylogenetic history, but their biological function was unknown. Here we used a hybrid polar, Populus alba × Populus grandidentata (P39), as a model to examine EG16 expression, subcellular localization, and pheno- and chemotypes of EG16-downregulated P39 plants. Populus EG16 expression is strong in young tissues, but RNAi-mediated downregulation did not impact plant growth nor the fine structure of the hemicellulose xyloglucan, suggesting a restricted or currently unknown role in angiosperm physiology.

Epigenetics of traumatic stress: The association of NR3C1 methylation and posttraumatic stress disorder symptom changes in response to narrative exposure therapy.

Epigenetic processes allow plasticity in gene regulation in response to significant environmental events. Accumulating evidence suggests that effective psychotherapy is accompanied by epigenetic changes, rendering DNA methylation a potential biomarker of therapy success. Due to the central role of glucocorticoid dynamics in stress regulation and the alteration of aversive memories, glucocorticoid receptors are likely involved in the molecular processes that are required to successfully treat Posttraumatic Stress Disorder (PTSD). This study aimed to investigate the relationship between methylation at the glucocorticoid receptor gene (NR3C1) and PTSD treatment success of evidence-based psychotherapy. A sample of N = 153 conflict survivors from Northern Uganda (98 females and 55 males) with PTSD were treated with Narrative Exposure Therapy (NET). Diagnostic interviews and saliva sampling took place at pretreatment and 4 and 10 months after treatment completion. We investigated potential associations between PTSD symptom development and methylation changes at 38 CpG sites spanning NR3C1 over the three times of measurement using the repeated measures correlation. After accounting for multiple comparisons, DNA methylation at CpG site cg25535999 remained negatively associated with PTSD symptoms. These results were followed up by mixed models as well as structural equation modelling. These analyses revealed that treatment responders had a significant cg25535999 methylation increase after treatment with NET. Furthermore, lower methylation at cg25535999 pretreatment predicted a higher symptom improvement. Our results suggest different epigenetic profile dynamics at NR3C1 cg25535999 in therapy responders compared to non-responders and underscore the central role of glucocorticoid signaling in trauma-focused therapy.

CASANOVA: Permutation inference in factorial survival designs.

We propose inference procedures for general factorial designs with time-to-event endpoints. Similar to additive Aalen models, null hypotheses are formulated in terms of cumulative hazards. Deviations are measured in terms of quadratic forms in Nelson-Aalen-type integrals. Different from existing approaches, this allows to work without restrictive model assumptions as proportional hazards. In particular, crossing survival or hazard curves can be detected without a significant loss of power. For a distribution-free application of the method, a permutation strategy is suggested. The resulting procedures' asymptotic validity is proven and small sample performances are analyzed in extensive simulations. The analysis of a data set on asthma illustrates the applicability.

Regularization approaches in clinical biostatistics: A review of methods and their applications.

A range of regularization approaches have been proposed in the data sciences to overcome overfitting, to exploit sparsity or to improve prediction. Using a broad definition of regularization, namely controlling model complexity by adding information in order to solve ill-posed problems or to prevent overfitting, we review a range of approaches within this framework including penalization, early stopping, ensembling and model averaging. Aspects of their practical implementation are discussed including available R-packages and examples are provided. To assess the extent to which these approaches are used in medicine, we conducted a review of three general medical journals. It revealed that regularization approaches are rarely applied in practical clinical applications, with the exception of random effects models. Hence, we suggest a more frequent use of regularization approaches in medical research. In situations where also other approaches work well, the only downside of the regularization approaches is increased complexity in the conduct of the analyses which can pose challenges in terms of computational resources and expertise on the side of the data analyst. In our view, both can and should be overcome by investments in appropriate computing facilities and educational resources.

Estimating Gaussian Copulas with Missing Data with and without Expert Knowledge.

In this work, we present a rigorous application of the Expectation Maximization algorithm to determine the marginal distributions and the dependence structure in a Gaussian copula model with missing data. We further show how to circumvent a priori assumptions on the marginals with semiparametric modeling. Further, we outline how expert knowledge on the marginals and the dependency structure can be included. A simulation study shows that the distribution learned through this algorithm is closer to the true distribution than that obtained with existing methods and that the incorporation of domain knowledge provides benefits.

A conserved enzyme of smut fungi facilitates cell-to-cell extension in the plant bundle sheath.

Smut fungi comprise one of the largest groups of fungal plant pathogens causing disease in all cereal crops. They directly penetrate host tissues and establish a biotrophic interaction. To do so, smut fungi secrete a wide range of effector proteins, which suppress plant immunity and modulate cellular functions as well as development of the host, thereby determining the pathogen's lifestyle and virulence potential. The conserved effector Erc1 (enzyme required for cell-to-cell extension) contributes to virulence of the corn smut Ustilago maydis in maize leaves but not on the tassel. Erc1 binds to host cell wall components and displays 1,3-ß-glucanase activity, which is required to attenuate ß-glucan-induced defense responses. Here we show that Erc1 has a cell type-specific virulence function, being necessary for fungal cell-to-cell extension in the plant bundle sheath and this function is fully conserved in the Erc1 orthologue of the barley pathogen Ustilago hordei.

Explosive seed dispersal depends on SPL7 to ensure sufficient copper for localized lignin deposition via laccases.

Exploding seed pods evolved in the Arabidopsis relative Cardamine hirsuta via morphomechanical innovations that allow the storage and rapid release of elastic energy. Asymmetric lignin deposition within endocarpb cell walls is one such innovation that is required for explosive seed dispersal and evolved in association with the trait. However, the genetic control of this novel lignin pattern is unknown. Here, we identify three lignin-polymerizing laccases, LAC4, 11, and 17, that precisely colocalize with, and are redundantly required for, asymmetric lignification of endocarpb cells. By screening for C. hirsuta mutants with less lignified fruit valves, we found that loss of function of the transcription factor gene SQUAMOSA PROMOTER-BINDING PROTEIN-LIKE 7 (SPL7) caused a reduction in endocarpb cell-wall lignification and a consequent reduction in seed dispersal range. SPL7 is a conserved regulator of copper homeostasis and is both necessary and sufficient for copper to accumulate in the fruit. Laccases are copper-requiring enzymes. We discovered that laccase activity in endocarpb cell walls depends on the SPL7 pathway to acclimate to copper deficiency and provide sufficient copper for lignin polymerization. Hence, SPL7 links mineral nutrition to efficient dispersal of the next generation.

Emerging Roles of ß-Glucanases in Plant Development and Adaptative Responses.

Plant ß-glucanases are enzymes involved in the synthesis, remodelling and turnover of cell wall components during multiple physiological processes. Based on the type of the glycoside bond they cleave, plant ß-glucanases have been grouped into three categories: (i) ß-1,4-glucanases degrade cellulose and other polysaccharides containing 1,4-glycosidic bonds to remodel and disassemble the wall during cell growth. (ii) ß-1,3-glucanases are responsible for the mobilization of callose, governing the symplastic trafficking through plasmodesmata. (iii) ß-1,3-1,4-glucanases degrade mixed linkage glucan, a transient wall polysaccharide found in cereals, which is broken down to obtain energy during rapid seedling growth. In addition to their roles in the turnover of self-glucan structures, plant ß-glucanases are crucial in regulating the outcome in symbiotic and hostile plant-microbe interactions by degrading non-self glucan structures. Plants use these enzymes to hydrolyse ß-glucans found in the walls of microbes, not only by contributing to a local antimicrobial defence barrier, but also by generating signalling glucans triggering the activation of global responses. As a counterpart, microbes developed strategies to hijack plant ß-glucanases to their advantage to successfully colonize plant tissues. This review outlines our current understanding on plant ß-glucanases, with a particular focus on the latest advances on their roles in adaptative responses.

On the role of data, statistics and decisions in a pandemic.

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

Fungi hijack a ubiquitous plant apoplastic endoglucanase to release a ROS scavenging ß-glucan decasaccharide to subvert immune responses.

Plant pathogenic and beneficial fungi have evolved several strategies to evade immunity and cope with host-derived hydrolytic enzymes and oxidative stress in the apoplast, the extracellular space of plant tissues. Fungal hyphae are surrounded by an inner insoluble cell wall layer and an outer soluble extracellular polysaccharide (EPS) matrix. Here, we show by proteomics and glycomics that these two layers have distinct protein and carbohydrate signatures, and hence likely have different biological functions. The barley (Hordeum vulgare) ß-1,3-endoglucanase HvBGLUII, which belongs to the widely distributed apoplastic glycoside hydrolase 17 family (GH17), releases a conserved ß-1,3;1,6-glucan decasaccharide (ß-GD) from the EPS matrices of fungi with different lifestyles and taxonomic positions. This low molecular weight ß-GD does not activate plant immunity, is resilient to further enzymatic hydrolysis by ß-1,3-endoglucanases due to the presence of three ß-1,6-linked glucose branches and can scavenge reactive oxygen species. Exogenous application of ß-GD leads to enhanced fungal colonization in barley, confirming its role in the fungal counter-defensive strategy to subvert host immunity. Our data highlight the hitherto undescribed capacity of this often-overlooked EPS matrix from plant-associated fungi to act as an outer protective barrier important for fungal accommodation within the hostile environment at the apoplastic plant-microbe interface.

On the Relation between Prediction and Imputation Accuracy under Missing Covariates.

Missing covariates in regression or classification problems can prohibit the direct use of advanced tools for further analysis. Recent research has realized an increasing trend towards the use of modern Machine-Learning algorithms for imputation. This originates from their capability of showing favorable prediction accuracy in different learning problems. In this work, we analyze through simulation the interaction between imputation accuracy and prediction accuracy in regression learning problems with missing covariates when Machine-Learning-based methods for both imputation and prediction are used. We see that even a slight decrease in imputation accuracy can seriously affect the prediction accuracy. In addition, we explore imputation performance when using statistical inference procedures in prediction settings, such as the coverage rates of (valid) prediction intervals. Our analysis is based on empirical datasets provided by the UCI Machine Learning repository and an extensive simulation study.

High-sensitivity cardiac troponin I after coronary artery bypass grafting for post-operative decision-making.

AIMS: Current troponin cut-offs suggested for the post-operative workup of patients following coronary artery bypass graft (CABG) surgery are based on studies using non-high-sensitive troponin assays or are arbitrarily chosen. We aimed to identify an optimal cut-off and timing for a proprietary high-sensitivity cardiac troponin I (hs-cTnI) assay to facilitate post-operative clinical decision-making. METHODS AND RESULTS: We performed a retrospective analysis of all patients undergoing elective isolated CABG at our centre between January 2013 and May 2019. Of 4684 consecutive patients, 161 patients (3.48%) underwent invasive coronary angiography after surgery, of whom 86 patients (53.4%) underwent repeat revascularization. We found an optimal cut-off value for peak hs-cTnI of >13 000 ng/L [>500× the upper reference limit (URL)] to be significantly associated with repeat revascularization within 48 h after surgery, which was internally validated through random repeated sampling with 1000 iterations. The same cut-off also predicted 30-day major adverse cardiovascular events and all-cause mortality after a median follow-up of 3.1 years, which was validated in an external cohort. A decision tree analysis of serial hs-cTnI measurements showed no added benefit of hs-cTnI measurements in patients with electrocardiographic or echocardiographic abnormalities or haemodynamic instability. Likewise, early post-operative hs-cTnI elevations had a low yield for clinical decision-making and only later elevations (at 12-16 h post-operatively) using a threshold of 8000 ng/L (307× URL) were significantly associated with repeat revascularization with an area under the curve of 0.92 (95% confidence interval 0.88-0.95). CONCLUSION: Our data suggest that for hs-cTnI, higher cut-offs than currently recommended should be used in the post-operative management of patients following CABG.