Guard cell and subsidiary cell sizes are key determinants for stomatal kinetics and drought adaptation in cereal crops.

Climate change-induced drought is a major threat to agriculture. C4 crops have a higher water use efficiency (WUE) and better adaptability to drought than C3 crops due to their smaller stomatal morphology and faster response. However, our understanding of stomatal behaviours in both C3 and C4 Poaceae crops is limited by knowledge gaps in physical traits of guard cell (GC) and subsidiary cell (SC). We employed infrared gas exchange analysis and a stomatal assay to explore the relationship between GC/SC sizes and stomatal kinetics across diverse drought conditions in two C3 (wheat and barley) and three C4 (maize, sorghum and foxtail millet) upland Poaceae crops. Through statistical analyses, we proposed a GCSC-τ model to demonstrate how morphological differences affect stomatal kinetics in C4 Poaceae crops. Our findings reveal that morphological variations specifically correlate with stomatal kinetics in C4 Poaceae crops, but not in C3 ones. Subsequent modelling and experimental validation provide further evidence that GC/SC sizes significantly impact stomatal kinetics, which affects stomatal responses to different drought conditions and thereby WUE in C4 Poaceae crops. These findings emphasize the crucial advantage of GC/SC morphological characteristics and stomatal kinetics for the drought adaptability of C4 Poaceae crops, highlighting their potential as future climate-resilient crops.

RAVAR: a curated repository for rare variant-trait associations.

Rare variants contribute significantly to the genetic causes of complex traits, as they can have much larger effects than common variants and account for much of the missing heritability in genome-wide association studies. The emergence of UK Biobank scale datasets and accurate gene-level rare variant-trait association testing methods have dramatically increased the number of rare variant associations that have been detected. However, no systematic collection of these associations has been carried out to date, especially at the gene level. To address the issue, we present the Rare Variant Association Repository (RAVAR), a comprehensive collection of rare variant associations. RAVAR includes 95 047 high-quality rare variant associations (76186 gene-level and 18 861 variant-level associations) for 4429 reported traits which are manually curated from 245 publications. RAVAR is the first resource to collect and curate published rare variant associations in an interactive web interface with integrated visualization, search, and download features. Detailed gene and SNP information are provided for each association, and users can conveniently search for related studies by exploring the EFO tree structure and interactive Manhattan plots. RAVAR could vastly improve the accessibility of rare variant studies. RAVAR is freely available for all users without login requirement at http://www.ravar.bio.

Plantorganelle Hunter is an effective deep-learning-based method for plant organelle phenotyping in electron microscopy.

Accurate delineation of plant cell organelles from electron microscope images is essential for understanding subcellular behaviour and function. Here we develop a deep-learning pipeline, called the organelle segmentation network (OrgSegNet), for pixel-wise segmentation to identify chloroplasts, mitochondria, nuclei and vacuoles. OrgSegNet was evaluated on a large manually annotated dataset collected from 19 plant species and achieved state-of-the-art segmentation performance. We defined three digital traits (shape complexity, electron density and cross-sectional area) to track the quantitative features of individual organelles in 2D images and released an open-source web tool called Plantorganelle Hunter for quantitatively profiling subcellular morphology. In addition, the automatic segmentation method was successfully applied to a serial-sectioning scanning microscope technique to create a 3D cell model that offers unique views of the morphology and distribution of these organelles. The functionalities of Plantorganelle Hunter can be easily operated, which will increase efficiency and productivity for the plant science community, and enhance understanding of subcellular biology.

Comparative Microscopic, Transcriptome and IAA Content Analyses Reveal the Stem Growth Variations in Two Cultivars Ilex verticillata.

Ilex verticillata is not only an excellent ornamental tree species for courtyards, but it is also a popular bonsai tree. 'Oosterwijk' and 'Red sprite' are two varieties of Ilex verticillata. The former has a long stem with few branches, while the latter has a short stem. In order to explain the stem growth differences between the two cultivars 'Oosterwijk' and 'Red sprite', determination of the microstructure, transcriptome sequence and IAA content was carried out. The results showed that the xylem thickness, vessel area and vessel number of 'Oosterwijk' were larger than in 'Red sprite'. In addition, our analysis revealed that the differentially expressed genes which were enriched in phenylpropanoid biosynthesis; phenylalanine metabolism and phenylalanine, tyrosine and tryptophan biosynthesis in the black and tan modules of the two varieties. We found that AST, HCT and bHLH 94 may be key genes in the formation of shoot difference. Moreover, we found that the IAA content and auxin-related DEGs GH3.6, GH3, ATRP5, IAA27, SAUR36-like, GH3.6-like and AIP 10A5-like may play important roles in the formation of shoot differences. In summary, these results indicated that stem growth variations of 'Oosterwijk' and 'Red sprite' were associated with DEGs related to phenylpropanoid biosynthesis, phenylalanine metabolism and phenylalanine, tyrosine and tryptophan biosynthesis, as well as auxin content and DEGs related to the auxin signaling pathway.

Transcriptome Analysis Reveals the Response Mechanism of Digitaria sanguinalis, Arabidopsis thaliana and Poa annua under 4,8-Dihydroxy-1-tetralone Treatment.

4,8-dihydroxy-l-tetralone (4,8-DHT) is an allelochemical isolated from the outer bark of Carya cathayensis that acts as a plant growth inhibitor. In order to explore the mechanism of 4,8-DHT inhibiting weed activity, we treated three species of Digitaria sanguinalis, Arabidopsis thaliana, and Poa annua with different concentrations of 4,8-DHT and performed phenotype observation and transcriptome sequencing. The results showed that with an increase in 4,8-DHT concentration, the degree of plant damage gradually deepened. Under the same concentration of 4,8-DHT, the damage degree of leaves and roots of Digitaria sanguinalis was the greatest, followed by Arabidopsis thaliana, while Poa annua had the least damage, and the leaves turned slightly yellow. Transcriptome data showed that 24536, 9913, and 1662 differentially expressed genes (DEGs) were identified in Digitaria sanguinalis, Arabidopsis thaliana, and Poa annua, respectively. These DEGs were significantly enriched in photosynthesis, carbon fixation, glutathione metabolism, phenylpropanoid biosynthesis, and oxidative phosphorylation pathways. In addition, DEGs were also enriched in plant hormone signal transduction and the MAPK signal pathway in Arabidopsis thaliana. Further analysis showed that after 4,8-DHT treatment, the transcript levels of photosynthesis PSI- and PSII-related genes, LHCA/B-related genes, Rubisco, and PEPC were significantly decreased in Digitaria sanguinalis and Arabidopsis thaliana. At the same time, the transcription levels of genes related to glutathione metabolism and the phenylpropanoid biosynthesis pathway in Digitaria sanguinalis were also significantly decreased. However, the expression of these genes was upregulated in Arabidopsis thaliana and Poa annua. These indicated that 4,8-DHT affected the growth of the three plants through different physiological pathways, and then played a role in inhibiting plant growth. Simultaneously, the extent to which plants were affected depended on the tested plants and the content of 4,8-DHT. The identification of weed genes that respond to 4,8-DHT has helped us to further understand the inhibition of plant growth by allelochemicals and has provided a scientific basis for the development of allelochemicals as herbicides.

Machine learning models for predicting steroid-resistant of nephrotic syndrome.

Background: In the absence of effective measures to predict steroid responsiveness, patients with nonhereditary steroid-resistant nephrotic syndrome (SRNS) have a significantly increased risk of progression to end-stage renal disease. In view of the poor outcomes of SRNS, it is urgent to identify the steroid responsiveness of idiopathic nephrotic syndrome (INS) early. Methods: To build a prediction model for SRNS, we collected 91 subjects; 57 of them had steroid-sensitive nephrotic syndrome, and the others had SRNS. For each subject, 87 clinical variables were measured. In general, only a small part of these variables is informative to SRNS. Thus, we proposed a new variable selection framework including a penalized regression approach (named MLR+TLP) to select variables having a linear effect on the SRNS and a nonparametric screening method (MAC) to select variables having a nonlinear marginal (joint) effect on the SRNS. Thereafter, considering the correlation between selected clinical variables, we used a stepwise method to build our final model for predicting SRNS. In addition, a statistical testing procedure is proposed to test the overfitting of the proposed model. Results: Twenty-six clinical variables were selected to be informative to SRNS, and an SVM model was built to predict SRNS with a leave-one-out cross-validation (LOO-CV) accuracy of 95.2% (overfitting p value<0.005). To make the model more useful, we incorporate prior medical information into the model and consider the correlation between selected variables. Then, a reduced SVM model including only eight clinical variables (erythrocyte sedimentation rate, urine occult blood, percentage of neutrophils, immunoglobulin A, cholesterol, vinculin autoantibody, aspartate aminotransferase, and prolonged prothrombin time) was built to have a LOO-CV accuracy of 92.8% (overfitting p value<0.005). The validation cohort showed that the reduced model obtained an accuracy of 94.0% (overfitting p value<0.005), with a sensitivity of 90.0% and a specificity of 96.7%. Notably, vinculin autoantibody is the only podocyte autoantibody included in this model. It is linearly related to steroid responsiveness. Finally, our model is freely available as a user-friendly web tool at https://datalinkx.shinyapps.io/srns/. Conclusion: The SRNS prediction model constructed in this study comprehensively and objectively evaluates the internal conditions and disease status of INS patients and will provide scientific guidance for selecting treatment methods for children with nonhereditary SRNS.

Revealing Free Energy Landscape From MD Data via Conditional Angle Partition Tree.

Deciphering the free energy landscape of biomolecular structure space is crucial for understanding many complex molecular processes, such as protein-protein interaction, RNA folding, and protein folding. A major source of current dynamic structure data is Molecular Dynamics (MD) simulations. Several methods have been proposed to investigate the free energy landscape from MD data, but all of them rely on the assumption that kinetic similarity is associated with global geometric similarity, which may lead to unsatisfactory results. In this paper, we proposed a new method called Conditional Angle Partition Tree to reveal the hierarchical free energy landscape by correlating local geometric similarity with kinetic similarity. Its application on the benchmark alanine dipeptide MD data showed a much better performance than existing methods in exploring and understanding the free energy landscape. We also applied it to the MD data of Villin HP35. Our results are more reasonable on various aspects than those from other methods and very informative on the hierarchical structure of its energy landscape.

Quantitative System Modeling Bridges the Gap between Macro- and Microscopic Stomatal Model.

An understanding of stomatal function is vital for the carbon and water cycle in nature. In the past decades, various stomatal models with different functions have been established to investigate and predict stomatal behavior and its association with plants' responses to the changing climate, but with limited biological information provided. On the other hand, many stomatal models at the molecular level focus on simulating and predicting molecular practices and ignore the dynamic quantitative information. As a result, stomatal models are often divided between the microscopic and macroscopic scales. Quantitative systems analysis offers an effective in silico approach to explore the link between microscopic gene function and macroscopic physiological traits. As a first step, a systems model, OnGuard, is developed for the investigation of guard cell ion homeostasis and its relevance to the dynamic stomatal movements. The system model has already yielded a series of important predictions to guide molecular physiological studies in stomata. It also exhibits great potential in breeding practice, which represents a key step toward "Breeding by design" of improving plant carbon-water use efficiency. Here, the development of stomatal models is reviewed, and the future perspectives on stomatal modeling for agricultural and ecological applications are discussed.

Recent Progress on Systems and Synthetic Biology of Diatoms for Improving Algal Productivity.

Microalgae have drawn much attention for their potential applications as a sustainable source for developing bioactive compounds, functional foods, feeds, and biofuels. Diatoms, as one major group of microalgae with high yields and strong adaptability to the environment, have shown advantages in developing photosynthetic cell factories to produce value-added compounds, including heterologous bioactive products. However, the commercialization of diatoms has encountered several obstacles that limit the potential mass production, such as the limitation of algal productivity and low photosynthetic efficiency. In recent years, systems and synthetic biology have dramatically improved the efficiency of diatom cell factories. In this review, we discussed first the genome sequencing and genome-scale metabolic models (GEMs) of diatoms. Then, approaches to optimizing photosynthetic efficiency are introduced with a focus on the enhancement of biomass productivity in diatoms. We also reviewed genome engineering technologies, including CRISPR (clustered regularly interspaced short palindromic repeats) gene-editing to produce bioactive compounds in diatoms. Finally, we summarized the recent progress on the diatom cell factory for producing heterologous compounds through genome engineering to introduce foreign genes into host diatoms. This review also pinpointed the bottlenecks in algal engineering development and provided critical insights into the future direction of algal production.

Defining upstream enhancing and inhibiting sequence patterns for plant peroxisome targeting signal type 1 using large-scale in silico and in vivo analyses.

Peroxisomes are universal eukaryotic organelles essential to plants and animals. Most peroxisomal matrix proteins carry peroxisome targeting signal type 1 (PTS1), a C-terminal tripeptide. Studies from various kingdoms have revealed influences from sequence upstream of the tripeptide on peroxisome targeting, supporting the view that positive charges in the upstream region are the major enhancing elements. However, a systematic approach to better define the upstream elements influencing PTS1 targeting capability is needed. Here, we used protein sequences from 177 plant genomes to perform large-scale and in-depth analysis of the PTS1 domain, which includes the PTS1 tripeptide and upstream sequence elements. We identified and verified 12 low-frequency PTS1 tripeptides and revealed upstream enhancing and inhibiting sequence patterns for peroxisome targeting, which were subsequently validated in vivo. Follow-up analysis revealed that nonpolar and acidic residues have relatively strong enhancing and inhibiting effects, respectively, on peroxisome targeting. However, in contrast to the previous understanding, positive charges alone do not show the anticipated enhancing effect and that both the position and property of the residues within these patterns are important for peroxisome targeting. We further demonstrated that the three residues immediately upstream of the tripeptide are the core influencers, with a 'basic-nonpolar-basic' pattern serving as a strong and universal enhancing pattern for peroxisome targeting. These findings have significantly advanced our knowledge of the PTS1 domain in plants and likely other eukaryotic species as well. The principles and strategies employed in the present study may also be applied to deciphering auxiliary targeting signals for other organelles.

MiRLoc: predicting miRNA subcellular localization by incorporating miRNA-mRNA interactions and mRNA subcellular localization.

Subcellular localization of microRNAs (miRNAs) is an important reflection of their biological functions. Considering the spatio-temporal specificity of miRNA subcellular localization, experimental detection techniques are expensive and time-consuming, which strongly motivates an efficient and economical computational method to predict miRNA subcellular localization. In this paper, we describe a computational framework, MiRLoc, to predict the subcellular localization of miRNAs. In contrast to existing methods, MiRLoc uses the functional similarity between miRNAs instead of sequence features and incorporates information about the subcellular localization of the corresponding target mRNAs. The results show that miRNA functional similarity data can be effectively used to predict miRNA subcellular localization, and that inclusion of subcellular localization information of target mRNAs greatly improves prediction performance.

Combining power of different methods to detect associations in large data sets.

Exploring the relationship between factors of interest is a fundamental step for further analysis on various scientific problems such as understanding the genetic mechanism underlying specific disease, brain functional connectivity analysis. There are many methods proposed for association analysis and each has its own advantages, but none of them is suitable for all kinds of situations. This brings difficulties and confusions to practitioner on which one to use when facing a real problem. In this paper, we propose to combine power of different methods to detect associations in large data sets. It goes as combining the weaker to be stronger. Numerical results from simulation study and real data applications show that our new framework is powerful. Importantly, the framework can also be applied to other problems. Availability: The R script is available at https://jiangdata.github.io/resources/DM.zip.

Increased urbanization reduced the effectiveness of school closures on seasonal influenza epidemics in China.

BACKGROUND: School closure is a common mitigation strategy during severe influenza epidemics and pandemics. However, the effectiveness of this strategy remains controversial. In this study, we aimed to explore the effectiveness of school closure on seasonal influenza epidemics in provincial-level administrative divisions (PLADs) with varying urbanization rates in China. METHODS: This study analyzed influenza surveillance data between 2010 and 2019 provided by the Chinese National Influenza Center. Taking into consideration the climate, this study included a region with 3 adjacent PLADs in Northern China and another region with 4 adjacent PLADs in Southern China. The effect of school closure on influenza transmission was evaluated by the reduction of the effective reproductive number of seasonal influenza during school winter breaks compared with that before school winter breaks. An age-structured Susceptible-Infected-Recovered-Susceptible (SIRS) model was built to model influenza transmission in different levels of urbanization. Parameters were determined using the surveillance data via robust Bayesian method. RESULTS: Between 2010 and 2019, in the less urbanized provinces: Hebei, Zhejiang, Jiangsu and Anhui, during school winter breaks, the effective reproductive number of seasonal influenza epidemics reduced 14.6% [95% confidential interval (CI): 6.2-22.9%], 9.6% (95% CI: 2.5-16.6%), 7.3% (95% CI: 0.1-14.4%) and 8.2% (95% CI: 1.1-15.3%) respectively. However, in the highly urbanized cities: Beijing, Tianjin and Shanghai, it reduced only 5.2% (95% CI: -0.7-11.2%), 4.1% (95% CI: -0.9-9.1%) and 3.9% (95% CI: -1.6-9.4%) respectively. In China, urbanization is associated with decreased proportion of children and increased social contact. According to the SIRS model, both factors could reduce the impact of school closure on seasonal influenza epidemics, and the proportion of children in the population is thought to be the dominant influencing factor. CONCLUSIONS: Effectiveness of school closure on the epidemics varies with the age structure in the population and social contact patterns. School closure should be recommended in the low urbanized regions in China in the influenza seasons.

The Two-Step Clustering Approach for Metastable States Learning.

Understanding the energy landscape and the conformational dynamics is crucial for studying many biological or chemical processes, such as protein-protein interaction and RNA folding. Molecular Dynamics (MD) simulations have been a major source of dynamic structure. Although many methods were proposed for learning metastable states from MD data, some key problems are still in need of further investigation. Here, we give a brief review on recent progresses in this field, with an emphasis on some popular methods belonging to a two-step clustering framework, and hope to draw more researchers to contribute to this area.

Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species.

N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA's biological functions. However, the existing experimental techniques for detecting 6mA sites are cost-ineffective, which implies the great need of developing new computational methods for this problem. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species: Arabidopsis thaliana, Fragaria vesca and Rosa chinensis with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression.

Detect differentially methylated regions using non-homogeneous hidden Markov model for bisulfite sequencing data.

DNA methylation plays an important role in many biological processes and diseases. With the rise of the whole genome bisulfite sequencing technique, aberrant methylation patterns can now be detected by comparing paired normal and disease samples at the single nucleotide level. We develop a novel Bayesian method for detecting differentially methylated regions from paired bisulfite sequencing data, and implement it as a R package called BSDMR. Based on a non-homogeneous hidden Markov model, BSDMR provides a better modeling strategy for the spatial correlation between CpG sites and takes into consideration the relationship between methylation signals from normal and disease samples. Simulations show that BSDMR performs well even under low read depth and has a smaller false discovery rates than existing methods. We also apply BSDMR to the colon cancer data from Gene Expression Omnibus. The detected DMRs are well supported by existing biomedical literatures.

Robust estimation for panel count data with informative observation times and censoring times.

We consider the semiparametric regression of panel count data occurring in longitudinal follow-up studies that concern occurrence rate of certain recurrent events. The analysis of panel count data involves two processes, i.e, a recurrent event process of interest and an observation process controlling observation times. However, the model assumptions of existing methods, such as independent censoring time and Poisson assumption, are restrictive and questionable. In this paper, we propose new joint models for panel count data by considering both informative observation times and censoring times. The asymptotic normality of the proposed estimators are established. Numerical results from simulation studies and a real data example show the advantage of the proposed method.