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.

Carbon usage in yellow-fleshed Manihot esculenta storage roots shifts from starch biosynthesis to cell wall and raffinose biosynthesis via the myo-inositol pathway.

Cassava is a crucial staple crop for smallholder farmers in tropical Asia and Sub-Saharan Africa. Although high yield remains the top priority for farmers, the significance of nutritional values has increased in cassava breeding programs. A notable negative correlation between provitamin A and starch accumulation poses a significant challenge for breeding efforts. The negative correlation between starch and carotenoid levels in conventional and genetically modified cassava plants implies the absence of a direct genomic connection between the two traits. The competition among various carbon pathways seems to account for this relationship. In this study, we conducted a thorough analysis of 49 African cassava genotypes with varying levels of starch and provitamin A. Our goal was to identify factors contributing to differential starch accumulation. Considering carotenoid levels as a confounding factor in starch production, we found that yellow- and white-fleshed storage roots did not differ significantly in most measured components of starch or de novo fatty acid biosynthesis. However, genes and metabolites associated with myo-inositol synthesis and cell wall polymer production were substantially enriched in high provitamin A genotypes. These results indicate that yellow-fleshed cultivars, in comparison to their white-fleshed counterparts, direct more carbon toward the synthesis of raffinose and cell wall components. This finding is underlined by a significant rise in cell wall components measured within the 20 most contrasting genotypes for carotenoid levels. Our findings enhance the comprehension of the biosynthesis of starch and carotenoids in the storage roots of cassava.

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.

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.

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.

The synthesis of xyloglucan, an abundant plant cell wall polysaccharide, requires CSLC function.

Xyloglucan (XyG) is an abundant component of the primary cell walls of most plants. While the structure of XyG has been well studied, much remains to be learned about its biosynthesis. Here we employed reverse genetics to investigate the role of Arabidopsis cellulose synthase like-C (CSLC) proteins in XyG biosynthesis. We found that single mutants containing a T-DNA in each of the five Arabidopsis CSLC genes had normal levels of XyG. However, higher-order cslc mutants had significantly reduced XyG levels, and a mutant with disruptions in all five CSLC genes had no detectable XyG. The higher-order mutants grew with mild tissue-specific phenotypes. Despite the apparent lack of XyG, the cslc quintuple mutant did not display significant alteration of gene expression at the whole-genome level, excluding transcriptional compensation. The quintuple mutant could be complemented by each of the five CSLC genes, supporting the conclusion that each of them encodes a XyG glucan synthase. Phylogenetic analyses indicated that the CSLC genes are widespread in the plant kingdom and evolved from an ancient family. These results establish the role of the CSLC genes in XyG biosynthesis, and the mutants described here provide valuable tools with which to study both the molecular details of XyG biosynthesis and the role of XyG in plant cell wall structure and function.

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.

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 mixed-linkage (1,3;1,4)-ß-D-glucan specific hydrolase mediates dark-triggered degradation of this plant cell wall polysaccharide.

The presence of mixed-linkage (1,3;1,4)-ß-d-glucan (MLG) in plant cell walls is a key feature of grass species such as cereals, the main source of calorie intake for humans and cattle. Accumulation of this polysaccharide involves the coordinated regulation of biosynthetic and metabolic machineries. While several components of the MLG biosynthesis machinery have been identified in diverse plant species, degradation of MLG is poorly understood. In this study, we performed a large-scale forward genetic screen for maize (Zea mays) mutants with altered cell wall polysaccharide structural properties. As a result, we identified a maize mutant with increased MLG content in several tissues, including adult leaves and senesced organs, where only trace amounts of MLG are usually detected. The causative mutation was found in the GRMZM2G137535 gene, encoding a GH17 licheninase as demonstrated by an in vitro activity assay of the heterologously expressed protein. In addition, maize plants overexpressing GRMZM2G137535 exhibit a 90% reduction in MLG content, indicating that the protein is not only required, but its expression is sufficient to degrade MLG. Accordingly, the mutant was named MLG hydrolase 1 (mlgh1). mlgh1 plants show increased saccharification yields upon enzymatic digestion. Stacking mlgh1 with lignin-deficient mutations results in synergistic increases in saccharification. Time profiling experiments indicate that wall MLG content is modulated during day/night cycles, inversely associated with MLGH1 transcript accumulation. This cycling is absent in the mlgh1 mutant, suggesting that the mechanism involved requires MLG degradation, which may in turn regulate MLGH1 gene expression.

Which test for crossing survival curves? A user's guideline.

BACKGROUND: The exchange of knowledge between statisticians developing new methodology and clinicians, reviewers or authors applying them is fundamental. This is specifically true for clinical trials with time-to-event endpoints. Thereby, one of the most commonly arising questions is that of equal survival distributions in two-armed trial. The log-rank test is still the gold-standard to infer this question. However, in case of non-proportional hazards, its power can become poor and multiple extensions have been developed to overcome this issue. We aim to facilitate the choice of a test for the detection of survival differences in the case of crossing hazards. METHODS: We restricted the review to the most recent two-armed clinical oncology trials with crossing survival curves. Each data set was reconstructed using a state-of-the-art reconstruction algorithm. To ensure reproduction quality, only publications with published number at risk at multiple time points, sufficient printing quality and a non-informative censoring pattern were included. This article depicts the p-values of the log-rank and Peto-Peto test as references and compares them with nine different tests developed for detection of survival differences in the presence of non-proportional or crossing hazards. RESULTS: We reviewed 1400 recent phase III clinical oncology trials and selected fifteen studies that met our eligibility criteria for data reconstruction. After including further three individual patient data sets, for nine out of eighteen studies significant differences in survival were found using the investigated tests. An important point that reviewers should pay attention to is that 28% of the studies with published survival curves did not report the number at risk. This makes reconstruction and plausibility checks almost impossible. CONCLUSIONS: The evaluation shows that inference methods constructed to detect differences in survival in presence of non-proportional hazards are beneficial and help to provide guidance in choosing a sensible alternative to the standard log-rank test.

Mechanistic insights from plant heteromannan synthesis in yeast.

Heteromannan (HM) is one of the most ancient cell wall polymers in the plant kingdom, consisting of ß-(1-4)-linked backbones of glucose (Glc) and mannose (Man) units. Despite the widespread distribution of HM polysaccharides, their biosynthesis remains mechanistically unclear. HM is elongated by glycosyltransferases (GTs) from the cellulose synthase-like A (CSLA) family. MANNAN-SYNTHESIS RELATED (MSR) putative GTs have also been implicated in (gluco)mannan synthesis, but their roles have been difficult to decipher in planta and in vitro. To further characterize the products of the HM synthases and accessory proteins, we chose a synthetic biology approach to synthesize plant HM in yeast. The expression of a CSLA protein in Pichia pastoris led to the abundant production of plant HM: up to 30% of glycans in the yeast cell wall. Based on sequential chemical and enzymatic extractions, followed by detailed structural analyses, the newly produced HM polymers were unbranched and could be larger than 270 kDa. Using CSLAs from different species, we programmed yeast cells to produce an HM backbone composed exclusively of Man or also incorporating Glc. We demonstrate that specific MSR cofactors were indispensable for mannan synthase activity of a coffee CSLA or modulated a functional CSLA enzyme to produce glucomannan instead of mannan. Therefore, this powerful platform yields functional insight into the molecular machinery required for HM biosynthesis in plants.

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.

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.

A cautionary tale on using imputation methods for inference in matched-pairs design.

MOTIVATION: Imputation procedures in biomedical fields have turned into statistical practice, since further analyses can be conducted ignoring the former presence of missing values. In particular, non-parametric imputation schemes like the random forest have shown favorable imputation performance compared to the more traditionally used MICE procedure. However, their effect on valid statistical inference has not been analyzed so far. This article closes this gap by investigating their validity for inferring mean differences in incompletely observed pairs while opposing them to a recent approach that only works with the given observations at hand. RESULTS: Our findings indicate that machine-learning schemes for (multiply) imputing missing values may inflate type I error or result in comparably low power in small-to-moderate matched pairs, even after modifying the test statistics using Rubin's multiple imputation rule. In addition to an extensive simulation study, an illustrative data example from a breast cancer gene study has been considered. AVAILABILITY AND IMPLEMENTATION: The corresponding R-code can be accessed through the authors and the gene expression data can be downloaded at www.gdac.broadinstitute.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Asymptotic-based bootstrap approach for matched pairs with missingness in a single arm.

The issue of missing values is an arising difficulty when dealing with paired data. Several test procedures are developed in the literature to tackle this problem. Some of them are even robust under deviations and control type-I error quite accurately. However, most of these methods are not applicable when missing values are present only in a single arm. For this case, we provide asymptotic correct resampling tests that are robust under heteroskedasticity and skewed distributions. The tests are based on a meaningful restructuring of all observed information in quadratic form-type test statistics. An extensive simulation study is conducted exemplifying the tests for finite sample sizes under different missingness mechanisms. In addition, illustrative data examples based on real life studies are analyzed.

Permutation tests are robust and powerful at 0.5% and 5% significance levels.

Recent replication crisis has led to a number of ad hoc suggestions to decrease the chance of making false positive findings. Among them, Johnson (Proceedings of the National Academy of Sciences, 110, 19313-19317, 2013) and Benjamin et al. (Nature Human Behaviour, 2, 6-10 2018) recommend using the significance level of α = 0.005 (0.5%) as opposed to the conventional 0.05 (5%) level. Even though their suggestion is easy to implement, it is unclear whether or not the commonly used statistical tests are robust and/or powerful at such a small significance level. Therefore, the main aim of our study is to investigate the robustness and power curve behaviors of independent (unpaired) two-sample tests for metric and ordinal data at nominal significance levels of α = 0.005 and α = 0.05. Through an extensive simulation study, it is found that the permutation versions of the Welch t-test and the Brunner-Munzel test are particularly robust and powerful while the commonly used two-sample tests which utilize t-distribution tend to be either liberal or conservative, and have peculiar power curve behaviors under skewed distributions with variance heterogeneity.

The Maize MID-COMPLEMENTING ACTIVITY Homolog CELL NUMBER REGULATOR13/NARROW ODD DWARF Coordinates Organ Growth and Tissue Patterning.

Organogenesis occurs through cell division, expansion, and differentiation. How these cellular processes are coordinated remains elusive. The maize (Zea mays) leaf provides a robust system to study cellular differentiation due to its distinct tissues and cell types. The narrow odd dwarf (nod) mutant displays defects at both the cellular and tissue level that increase in severity throughout growth. nod mutant leaves have reduced size due to fewer and smaller cells compared with the wild type. The juvenile-to-adult transition is delayed, and proximal distal-patterning is abnormal in this mutant. Differentiation of specialized cells such as those forming stomata and trichomes is incomplete. Analysis of nod-1 sectors suggests that NOD plays a cell-autonomous function in the leaf. We cloned nod positionally and found that it encodes CELL NUMBER REGULATOR13 (CNR13), the maize MID-COMPLEMENTING ACTIVITY homolog. CNR13/NOD is localized to the membrane and is enriched in dividing tissues. Transcriptome analysis of nod mutants revealed overrepresentation of cell wall, hormone metabolism, and defense gene categories. We propose that NOD coordinates cell activity in response to intrinsic and extrinsic cues.

Pectin Biosynthesis Is Critical for Cell Wall Integrity and Immunity in Arabidopsis thaliana.

Plant cell walls are important barriers against microbial pathogens. Cell walls of Arabidopsis thaliana leaves contain three major types of polysaccharides: cellulose, various hemicelluloses, and pectins. UDP-D-galacturonic acid, the key building block of pectins, is produced from the precursor UDP-D-glucuronic acid by the action of glucuronate 4-epimerases (GAEs). Pseudomonas syringae pv maculicola ES4326 (Pma ES4326) repressed expression of GAE1 and GAE6 in Arabidopsis, and immunity to Pma ES4326 was compromised in gae6 and gae1 gae6 mutant plants. These plants had brittle leaves and cell walls of leaves had less galacturonic acid. Resistance to specific Botrytis cinerea isolates was also compromised in gae1 gae6 double mutant plants. Although oligogalacturonide (OG)-induced immune signaling was unaltered in gae1 gae6 mutant plants, immune signaling induced by a commercial pectinase, macerozyme, was reduced. Macerozyme treatment or infection with B. cinerea released less soluble uronic acid, likely reflecting fewer OGs, from gae1 gae6 cell walls than from wild-type Col-0. Although both OGs and macerozyme-induced immunity to B. cinerea in Col-0, only OGs also induced immunity in gae1 gae6. Pectin is thus an important contributor to plant immunity, and this is due at least in part to the induction of immune responses by soluble pectin, likely OGs, that are released during plant-pathogen interactions.

Confidence bands for multiplicative hazards models: Flexible resampling approaches.

We propose new resampling-based approaches to construct asymptotically valid time-simultaneous confidence bands for cumulative hazard functions in multistate Cox models. In particular, we exemplify the methodology in detail for the simple Cox model with time-dependent covariates, where the data may be subject to independent right-censoring or left-truncation. We use simulations to investigate their finite sample behavior. Finally, the methods are utilized to analyze two empirical examples with survival and competing risks data.

Multiplication-combination tests for incomplete paired data.

We consider statistical procedures for hypothesis testing of real valued functionals of matched pairs with missing values. In order to improve the accuracy of existing methods, we propose a novel multiplication combination procedure. Dividing the observed data into dependent (completely observed) pairs and independent (incompletely observed) components, it is based on combining separate results of adequate tests for the two sub data sets. Our methods can be applied for parametric as well as semiparametric and nonparametric models and make use of all available data. In particular, the approaches are flexible and can be used to test different hypotheses in various models of interest. This is exemplified by a detailed study of mean- as well as rank-based approaches under different missingness mechanisms with different amount of missing data. Extensive simulations show that in most considered situations, the proposed procedures are more accurate than existing competitors particularly for the nonparametric Behrens-Fisher problem. A real data set illustrates the application of the methods.