Hormone-regulated expansins: Expression, localization, and cell wall biomechanics in Arabidopsis root growth.

Expansins facilitate cell expansion by mediating pH-dependent cell wall (CW) loosening. However, the role of expansins in controlling CW biomechanical properties in specific tissues and organs remains elusive. We monitored hormonal responsiveness and spatial specificity of expression and localization of expansins predicted to be the direct targets of cytokinin signaling in Arabidopsis (Arabidopsis thaliana). We found EXPANSIN1 (EXPA1) homogenously distributed throughout the CW of columella/lateral root cap, while EXPA10 and EXPA14 localized predominantly at 3-cell boundaries in the epidermis/cortex in various root zones. EXPA15 revealed cell-type-specific combination of homogenous vs. 3-cell boundaries localization. By comparing Brillouin frequency shift and AFM-measured Young's modulus, we demonstrated Brillouin light scattering (BLS) as a tool suitable for non-invasive in vivo quantitative assessment of CW viscoelasticity. Using both BLS and AFM, we showed that EXPA1 overexpression upregulated CW stiffness in the root transition zone (TZ). The dexamethasone-controlled EXPA1 overexpression induced fast changes in the transcription of numerous CW-associated genes, including several EXPAs and XYLOGLUCAN:XYLOGLUCOSYL TRANSFERASEs (XTHs), and associated with rapid pectin methylesterification determined by in situ Fourier-transform infrared spectroscopy in the root TZ. The EXPA1-induced CW remodeling is associated with the shortening of the root apical meristem, leading to root growth arrest. Based on our results, we propose that expansins control root growth by a delicate orchestration of CW biomechanical properties, possibly regulating both CW loosening and CW remodeling.

WALTER: an easy way to online evaluate telomere lengths from terminal restriction fragment analysis.

BACKGROUND: Telomeres, nucleoprotein structures comprising short tandem repeats and delimiting the ends of linear eukaryotic chromosomes, play an important role in the maintenance of genome stability. Therefore, the determination of the length of telomeres is of high importance for many studies. Over the last years, new methods for the analysis of the length of telomeres have been developed, including those based on PCR or analysis of NGS data. Despite that, terminal restriction fragment (TRF) method remains the gold standard to this day. However, this method lacks universally accepted and precise tool capable to analyse and statistically evaluate TRF results. RESULTS: To standardize the processing of TRF results, we have developed WALTER, an online toolset allowing rapid, reproducible, and user-friendly analysis including statistical evaluation of the data. Given its web-based nature, it provides an easily accessible way to analyse TRF data without any need to install additional software. CONCLUSIONS: WALTER represents a major upgrade from currently available tools for the image processing of TRF scans. This toolset enables a rapid, highly reproducible, and user-friendly evaluation of almost any TRF scan including in-house statistical evaluation of the data. WALTER platform together with user manual describing the evaluation of TRF scans in detail and presenting tips and troubleshooting, as well as test data to demo the software are available at https://www.ceitec.eu/chromatin-molecular-complexes-jiri-fajkus/rg51/tab?tabId=125#WALTER and the source code at https://github.com/mlyc93/WALTER .

An Open-Source R Package for Detection of Adverse Events Under-Reporting in Clinical Trials: Implementation and Validation by the IMPALA (Inter coMPany quALity Analytics) Consortium.

Accurate and timely reporting of adverse events (AEs) in clinical trials is crucial to ensuring data integrity and patient safety. However, AE under-reporting remains a challenge, often highlighted in Good Clinical Practice (GCP) audits and inspections. Traditional detection methods, such as on-site investigator audits via manual source data verification (SDV), have limitations. Addressing this, the open-source R package {simaerep} was developed to facilitate rapid, comprehensive, and near-real-time detection of AE under-reporting at each clinical trial site. This package leverages patient-level AE and visit data for its analyses. To validate its efficacy, three member companies from the Inter coMPany quALity Analytics (IMPALA) consortium independently assessed the package. Results showed that {simaerep} consistently and effectively identified AE under-reporting across all three companies, particularly when there were significant differences in AE rates between compliant and non-compliant sites. Furthermore, {simaerep}'s detection rates surpassed heuristic methods, and it identified 50% of all detectable sites as early as 25% into the designated study duration. The open-source package can be embedded into audits to enable fast, holistic, and repeatable quality oversight of clinical trials.

iReenCAM: automated imaging system for kinetic analysis of photosynthetic pigment biosynthesis at high spatiotemporal resolution during early deetiolation.

Seedling de-etiolation is one of the key stages of the plant life cycle, characterized by a strong rearrangement of the plant development and metabolism. The conversion of dark accumulated protochlorophyllide to chlorophyll in etioplasts of de-etiolating plants is taking place in order of ns to µs after seedlings illumination, leading to detectable increase of chlorophyll levels in order of minutes after de-etiolation initiation. The highly complex chlorophyll biosynthesis integrates number of regulatory events including light and hormonal signaling, thus making de-etiolation an ideal model to study the underlying molecular mechanisms. Here we introduce the iReenCAM, a novel tool designed for non-invasive fluorescence-based quantitation of early stages of chlorophyll biosynthesis during de-etiolation with high spatial and temporal resolution. iReenCAM comprises customized HW configuration and optimized SW packages, allowing synchronized automated measurement and analysis of the acquired fluorescence image data. Using the system and carefully optimized protocol, we show tight correlation between the iReenCAM monitored fluorescence and HPLC measured chlorophyll accumulation during first 4h of seedling de-etiolation in wild type Arabidopsis and mutants with disturbed chlorophyll biosynthesis. Using the approach, we demonstrate negative effect of exogenously applied cytokinins and ethylene on chlorophyll biosynthesis during early de-etiolation. Accordingly, we identify type-B response regulators, the cytokinin-responsive transcriptional activators ARR1 and ARR12 as negative regulators of early chlorophyll biosynthesis, while contrasting response was observed in case of EIN2 and EIN3, the components of canonical ethylene signaling cascade. Knowing that, we propose the use of iReenCAM as a new phenotyping tool, suitable for quantitative and robust characterization of the highly dynamic response of seedling de-etiolation.

Long-Term High-Temperature Stress Impacts on Embryo and Seed Development in Brassica napus.

Brassica napus (rapeseed) is the second most important oilseed crop worldwide. Global rise in average ambient temperature and extreme weather severely impact rapeseed seed yield. However, fewer research explained the phenotype changes caused by moderate-to-high temperatures in rapeseed. To investigate these events, we determined the long-term response of three spring cultivars to different temperature regimes (21/18°C, 28/18°C, and 34/18°C) mimicking natural temperature variations. The analysis focused on the plant appearance, seed yield, quality and viability, and embryo development. Our microscopic observations suggest that embryonic development is accelerated and defective in high temperatures. Reduced viable seed yield at warm ambient temperature is due to a reduced fertilization rate, increased abortion rate, defective embryonic development, and pre-harvest sprouting. Reduced auxin levels in young seeds and low ABA and auxin levels in mature seeds may cause embryo pattern defects and reduced seed dormancy, respectively. Glucosinolates and oil composition measurements suggest reduced seed quality. These identified cues help understand seed thermomorphogenesis and pave the way to developing thermoresilient rapeseed.

Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences.

Plants adapt to continual changes in environmental conditions throughout their life spans. High-throughput phenotyping methods have been developed to noninvasively monitor the physiological responses to abiotic/biotic stresses on a scale spanning a long time, covering most of the vegetative and reproductive stages. However, some of the physiological events comprise almost immediate and very fast responses towards the changing environment which might be overlooked in long-term observations. Additionally, there are certain technical difficulties and restrictions in analyzing phenotyping data, especially when dealing with repeated measurements. In this study, a method for comparing means at different time points using generalized linear mixed models combined with classical time series models is presented. As an example, we use multiple chlorophyll time series measurements from different genotypes. The use of additional time series models as random effects is essential as the residuals of the initial mixed model may contain autocorrelations that bias the result. The nature of mixed models offers a viable solution as these can incorporate time series models for residuals as random effects. The results from analyzing chlorophyll content time series show that the autocorrelation is successfully eliminated from the residuals and incorporated into the final model. This allows the use of statistical inference.

Integration of Phenomics and Metabolomics Datasets Reveals Different Mode of Action of Biostimulants Based on Protein Hydrolysates in Lactuca sativa L. and Solanum lycopersicum L. Under Salinity.

Plant phenomics is becoming a common tool employed to characterize the mode of action of biostimulants. A combination of this technique with other omics such as metabolomics can offer a deeper understanding of a biostimulant effect in planta. However, the most challenging part then is the data analysis and the interpretation of the omics datasets. In this work, we present an example of how different tools, based on multivariate statistical analysis, can help to simplify the omics data and extract the relevant information. We demonstrate this by studying the effect of protein hydrolysate (PH)-based biostimulants derived from different natural sources in lettuce and tomato plants grown in controlled conditions and under salinity. The biostimulants induced different phenotypic and metabolomic responses in both crops. In general, they improved growth and photosynthesis performance under control and salt stress conditions, with better performance in lettuce. To identify the most significant traits for each treatment, a random forest classifier was used. Using this approach, we found out that, in lettuce, biomass-related parameters were the most relevant traits to evaluate the biostimulant mode of action, with a better response mainly connected to plant hormone regulation. However, in tomatoes, the relevant traits were related to chlorophyll fluorescence parameters in combination with certain antistress metabolites that benefit the electron transport chain, such as 4-hydroxycoumarin and vitamin K1 (phylloquinone). Altogether, we show that to go further in the understanding of the use of biostimulants as plant growth promotors and/or stress alleviators, it is highly beneficial to integrate more advanced statistical tools to deal with the huge datasets obtained from the -omics to extract the relevant information.

Steady-State Levels of Cytokinins and Their Derivatives May Serve as a Unique Classifier of Arabidopsis Ecotypes.

We determined steady-state (basal) endogenous levels of three plant hormones (abscisic acid, cytokinins and indole-3-acetic acid) in a collection of thirty different ecotypes of Arabidopsis that represent a broad genetic variability within this species. Hormone contents were analysed separately in plant shoots and roots after 21 days of cultivation on agar plates in a climate-controlled chamber. Using advanced statistical and machine learning methods, we tested if basal hormonal levels can be considered a unique ecotype-specific classifier. We also explored possible relationships between hormone levels and the prevalent environmental conditions in the site of origin for each ecotype. We found significant variations in basal hormonal levels and their ratios in both root and shoot among the ecotypes. We showed the prominent position of cytokinins (CK) among the other hormones. We found the content of CK and CK metabolites to be a reliable ecotype-specific identifier. Correlation with the mean temperature at the site of origin and the large variation in basal hormonal levels suggest that the high variability may potentially be in response to environmental factors. This study provides a starting point for ecotype-specific genetic maps of the CK metabolic and signalling network to explore its contribution to the adaptation of plants to local environmental conditions.

Evaluation of Bayesian classifiers in asthma exacerbation prediction after medication discontinuation.

OBJECTIVE: The achievement of the optimal control of the disease is of cardinal importance in asthma treatment. As the control of the disease is sustained the medication should be gradually reduced and then stopped. Nevertheless, the discontinuation of asthma medication may lead to loss of disease control and eventually to an exacerbation of the disease. The goal of this paper is to examine the performance of Bayesian network classifiers in predicting asthma exacerbation based on several patient's parameters such as objective measurements and medical history data. RESULTS: In this study several Bayesian network classifiers are presented and evaluated. It is shown that the proposed semi-naive network classifier with the use of Backward Sequential Elimination and Joining algorithm is able to predict if a patient will have an exacerbation of the disease after his last assessment with 93.84% accuracy and 90.9% sensitivity. In addition, the resulting structure and the conditional probability tables give a clear view of the probabilistic relationships between the used factors. This network may help the clinicians to identify the patients who are at high risk of having an exacerbation after stopping the medication and to confirm which factors are the most important.