Soybean steroids improve crop abiotic stress tolerance and increase yield.

Sterols have long been associated with diverse fields, such as cancer treatment, drug development, and plant growth; however, their underlying mechanisms and functions remain enigmatic. Here, we unveil a critical role played by a GmNF-YC9-mediated CCAAT-box transcription complex in modulating the steroid metabolism pathway within soybeans. Specifically, this complex directly activates squalene monooxygenase (GmSQE1), which is a rate-limiting enzyme in steroid synthesis. Our findings demonstrate that overexpression of either GmNF-YC9 or GmSQE1 significantly enhances soybean stress tolerance, while the inhibition of SQE weakens this tolerance. Field experiments conducted over two seasons further reveal increased yields per plant in both GmNF-YC9 and GmSQE1 overexpressing plants under drought stress conditions. This enhanced stress tolerance is attributed to the reduction of abiotic stress-induced cell oxidative damage. Transcriptome and metabolome analyses shed light on the upregulation of multiple sterol compounds, including fucosterol and soyasaponin II, in GmNF-YC9 and GmSQE1 overexpressing soybean plants under stress conditions. Intriguingly, the application of soybean steroids, including fucosterol and soyasaponin II, significantly improves drought tolerance in soybean, wheat, foxtail millet, and maize. These findings underscore the pivotal role of soybean steroids in countering oxidative stress in plants and offer a new research strategy for enhancing crop stress tolerance and quality from gene regulation to chemical intervention.

Negligible Impact of Drought-Resistant Genetically Modified Maize on Arthropod Community Structure Observed in a 2-Year Field Investigation.

Dehydration-responsive element-binding (DREB) transcription factors regulate diverse processes during plant development. Here, a 2-year field study was conducted to assess the potential effects of DREB-genetically modified maize (GM1) on arthropod species and ecological communities. Arthropod abundance, diversity, and community composition in GM1 and its non-transformed counterpart maize variety, Chang 7-2, were compared using whole plant inspection, pitfall trap, and suction sampler methods. Based on Shannon-Wiener diversity, Simpson's diversity, Pielou's indexes, number of species, and total number of individuals, GM1 had a negligible effect on arthropod abundance and diversity. Redundancy analysis indicated that the composition of arthropod community was not associated with maize type in the three investigation methods, while it exhibited significant correlation with year and sampling time in whole plant inspection and suction sample methods, and distinctly correlated with sampling time in the pitfall trap method. Nonmetric multidimensional scaling analysis of variable factors in the three investigation methods showed that sampling time, rather than maize type or year, was closely related to the composition of arthropod community in the field. Our results provide direct evidence to support that DREB-GM maize had negligible effects on arthropods in the Jilin Province under natural conditions.

Overexpression of ZmWRKY65 transcription factor from maize confers stress resistances in transgenic Arabidopsis.

Plant-specific WRKY transcription factors play important roles in regulating the expression of defense-responsive genes against pathogen attack. A multiple stress-responsive WRKY gene, ZmWRKY65, was identified in maize by screening salicylic acid (SA)-induced de novo transcriptomic sequences. The ZmWRKY65 protein was localized in the nucleus of mesophyll protoplasts. The analysis of the ZmWRKY65 promoter sequence indicated that it contains several stress-related transcriptional regulatory elements. Many environmental factors affecting the transcription of ZmWRKY65 gene, such as drought, salinity, high temperature and low temperature stress. Moreover, the transcription of ZmWRKY65 gene was also affected by the induction of defense related plant hormones such as SA and exogenous ABA. The results of seed germination and stomatal aperture assays indicated that transgenic Arabidopsis plants exhibit enhanced sensitivity to ABA and high concentrations of SA. Overexpression of ZmWRKY65 improved tolerance to both pathogen attack and abiotic stress in transgenic Arabidopsis plants and activated several stress-related genes such as RD29A, ERD10, and STZ as well as pathogenesis-related (PR) genes such as PR1, PR2 and PR5; these genes are involved in resistance to abiotic and biotic stresses in Arabidopsis. Together, this evidence implies that the ZmWRKY65 gene is involved in multiple stress signal transduction pathways.

Use of Taxonomic and Trait-Based Approaches to Evaluate the Effect of Bt maize Expressing Cry1Ie Protein on Non-Target Collembola: A Case Study in Northeast China.

To evaluate the effect of Bt maize expressing Cry1Ie protein on non-target soil Collembola, a two-year field study was conducted in Northeast China. Bt maize line IE09S034 and its near isoline Zong 31 were selected as experimental crops; we investigated the collembolan community using both taxonomic and trait-based approaches, and elucidated the relationship between environmental variables and the collembolan community using redundancy analysis (RDA).The ANOVA results showed that maize variety neither had significant effect on the parameters based on taxonomic approach (abundance, species richness, Shannon-Wiener index, Pielou's evenness index), nor on the parameters based on trait-based approach (ocelli number, body length, pigmentation level, and furcula development) in either year. The results of RDA also showed that maize variety did not affect collembolan community significantly. These results suggest that two years cultivation of cry1Ie maize does not affect collembolan community in Northeast China.

Genome-Wide Analysis of the Shi-Related Sequence Family and Functional Identification of GmSRS18 Involving in Drought and Salt Stresses in Soybean.

The plant-special SHI-RELATED SEQUENCE (SRS) family plays vital roles in various biological processes. However, the genome-wide analysis and abiotic stress-related functions of this family were less reported in soybean. In this work, 21 members of soybean SRS family were identified, which were divided into three groups (Group I, II, and III). The chromosome location and gene structure were analyzed, which indicated that the members in the same group may have similar functions. The analysis of stress-related cis-elements showed that the SRS family may be involved in abiotic stress signaling pathway. The analysis of expression patterns in various tissues demonstrated that SRS family may play crucial roles in special tissue-dependent regulatory networks. The data based on soybean RNA sequencing (RNA-seq) and quantitative Real-Time PCR (qRT-PCR) proved that SRS genes were induced by drought, NaCl, and exogenous abscisic acid (ABA). GmSRS18 significantly induced by drought and NaCl was selected for further functional verification. GmSRS18, encoding a cell nuclear protein, could negatively regulate drought and salt resistance in transgenic Arabidopsis. It can affect stress-related physiological index, including chlorophyll, proline, and relative electrolyte leakage. Additionally, it inhibited the expression levels of stress-related marker genes. Taken together, these results provide valuable information for understanding the classification of soybean SRS transcription factors and indicates that SRS plays important roles in abiotic stress responses.

Genome-Wide Analysis of the DYW Subgroup PPR Gene Family and Identification of GmPPR4 Responses to Drought Stress.

Pentatricopeptide-repeat (PPR) proteins were identified as a type of nucleus coding protein that is composed of multiple tandem repeats. It has been reported that PPR genes play an important role in RNA editing, plant growth and development, and abiotic stresses in plants. However, the functions of PPR proteins remain largely unknown in soybean. In this study, 179 DYW subgroup PPR genes were identified in soybean genome (Glycine max Wm82.a2.v1). Chromosomal location analysis indicated that DYW subgroup PPR genes were mapped to all 20 chromosomes. Phylogenetic relationship analysis revealed that DYW subgroup PPR genes were categorized into three distinct Clusters (I to III). Gene structure analysis showed that most PPR genes were featured by a lack of intron. Gene duplication analysis demonstrated 30 PPR genes (15 pairs; ~35.7%) were segmentally duplicated among Cluster I PPR genes. Furthermore, we validated the mRNA expression of three genes that were highly up-regulated in soybean drought- and salt-induced transcriptome database and found that the expression levels of GmPPR4 were induced under salt and drought stresses. Under drought stress condition, GmPPR4-overexpressing (GmPPR4-OE) plants showed delayed leaf rolling; higher content of proline (Pro); and lower contents of H2O2, O2- and malondialdehyde (MDA) compared with the empty vector (EV)-control plants. GmPPR4-OE plants exhibited increased transcripts of several drought-inducible genes compared with EV-control plants. Our results provided a comprehensive analysis of the DYW subgroup PPR genes and an insight for improving the drought tolerance in soybean.

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When the genetically modified soybean is planted in the field, the expression product of exogenous gene could be exposed in the soil ecosystem and bring potential risk to the soil fauna, with the form of leaves and other debris. A few of genetically modified soybeans developed by China independently were used in our study as materials. They were Phytophthora-resistant soybean harboring hrpZm gene (B4J8049), leaf-feeding insect-resistant soybean harboring Cry1C gene (A2A8001) and Leguminivora glycinivorella-resistant soybean harboring Cry1Iem gene (C802). By feeding Folsomia candida with the three genetically modified soybeans for continuous 60 days, the surviving rate, reproductive rate and changes on the body length of F. candida were studied. The results showed that all the three genetically modified soybeans of B4J8049, A2A8001 and C802 had no significant adverse effects on the growth of F. candida, as an environmental indicator organism. It was initially inferred that they were environmentally safe under short-term exposure, which provided basic data of ecological safety for their wide cultivation.

Bayesian analysis of transformation latent variable models with multivariate censored data.

Transformation latent variable models are proposed in this study to analyze multivariate censored data. The proposed models generalize conventional linear transformation models to semiparametric transformation models that accommodate latent variables. The characteristics of the latent variables were assessed based on several correlated observed indicators through measurement equations. A Bayesian approach was developed with Bayesian P-splines technique and the Markov chain Monte Carlo algorithm to estimate the unknown parameters and transformation functions. Simulation shows that the performance of the proposed methodology is satisfactory. The proposed method was applied to analyze a cardiovascular disease data set.

Structure detection of semiparametric structural equation models with Bayesian adaptive group lasso.

Structural equation models (SEMs) are widely recognized as the most important statistical tool for assessing the interrelationships among latent variables. This study develops a Bayesian adaptive group least absolute shrinkage and selection operator procedure to perform simultaneous model selection and estimation for semiparametric SEMs, wherein the structural equation is formulated using the additive nonparametric functions of observed and latent variables. We propose the use of basis expansions to approximate the unknown functions. By introducing adaptive penalties to the groups of basis expansions, the nonlinear, linear, or non-existent effects of observed and latent variables in the structural equation can be automatically detected. A simulation study demonstrates that the proposed method performs satisfactorily. This paper presents an application of revealing the observed and latent risk factors of diabetic kidney disease.

A Bayesian modeling approach for generalized semiparametric structural equation models.

In behavioral, biomedical, and psychological studies, structural equation models (SEMs) have been widely used for assessing relationships between latent variables. Regression-type structural models based on parametric functions are often used for such purposes. In many applications, however, parametric SEMs are not adequate to capture subtle patterns in the functions over the entire range of the predictor variable. A different but equally important limitation of traditional parametric SEMs is that they are not designed to handle mixed data types-continuous, count, ordered, and unordered categorical. This paper develops a generalized semiparametric SEM that is able to handle mixed data types and to simultaneously model different functional relationships among latent variables. A structural equation of the proposed SEM is formulated using a series of unspecified smooth functions. The Bayesian P-splines approach and Markov chain Monte Carlo methods are developed to estimate the smooth functions and the unknown parameters. Moreover, we examine the relative benefits of semiparametric modeling over parametric modeling using a Bayesian model-comparison statistic, called the complete deviance information criterion (DIC). The performance of the developed methodology is evaluated using a simulation study. To illustrate the method, we used a data set derived from the National Longitudinal Survey of Youth.

A Bayesian analysis of mixture structural equation models with non-ignorable missing responses and covariates.

In behavioral, biomedical, and social-psychological sciences, it is common to encounter latent variables and heterogeneous data. Mixture structural equation models (SEMs) are very useful methods to analyze these kinds of data. Moreover, the presence of missing data, including both missing responses and missing covariates, is an important issue in practical research. However, limited work has been done on the analysis of mixture SEMs with non-ignorable missing responses and covariates. The main objective of this paper is to develop a Bayesian approach for analyzing mixture SEMs with an unknown number of components, in which a multinomial logit model is introduced to assess the influence of some covariates on the component probability. Results of our simulation study show that the Bayesian estimates obtained by the proposed method are accurate, and the model selection procedure via a modified DIC is useful in identifying the correct number of components and in selecting an appropriate missing mechanism in the proposed mixture SEMs. A real data set related to a longitudinal study of polydrug use is employed to illustrate the methodology.

A semiparametric Bayesian approach for structural equation models.

In the development of structural equation models (SEMs), observed variables are usually assumed to be normally distributed. However, this assumption is likely to be violated in many practical researches. As the non-normality of observed variables in an SEM can be obtained from either non-normal latent variables or non-normal residuals or both, semiparametric modeling with unknown distribution of latent variables or unknown distribution of residuals is needed. In this article, we find that an SEM becomes nonidentifiable when both the latent variable distribution and the residual distribution are unknown. Hence, it is impossible to estimate reliably both the latent variable distribution and the residual distribution without parametric assumptions on one or the other. We also find that the residuals in the measurement equation are more sensitive to the normality assumption than the latent variables, and the negative impact on the estimation of parameters and distributions due to the non-normality of residuals is more serious. Therefore, when there is no prior knowledge about parametric distributions for either the latent variables or the residuals, we recommend making parametric assumption on latent variables, and modeling residuals nonparametrically. We propose a semiparametric Bayesian approach using the truncated Dirichlet process with a stick breaking prior to tackle the non-normality of residuals in the measurement equation. Simulation studies and a real data analysis demonstrate our findings, and reveal the empirical performance of the proposed methodology. A free WinBUGS code to perform the analysis is available in Supporting Information.

Bayesian variable selection for disease classification using gene expression data.

MOTIVATION: An important application of gene expression microarray data is the classification of samples into categories. Accurate classification depends upon the method used to identify the most relevant genes. Owing to the large number of genes and relatively small sample size, the selection process can be unstable. Modification of existing methods for achieving better analysis of microarray data is needed. RESULTS: We propose a Bayesian stochastic variable selection approach for gene selection based on a probit regression model with a generalized singular g-prior distribution for regression coefficients. Using simulation-based Markov chain Monte Carlo methods for simulating parameters from the posterior distribution, an efficient and dependable algorithm is implemented. It is also shown that this algorithm is robust to the choices of initial values, and produces posterior probabilities of related genes for biological interpretation. The performance of the proposed approach is compared with other popular methods in gene selection and classification via the well-known colon cancer and leukemia datasets in microarray literature. AVAILABILITY: A free Matlab code to perform gene selection is available at http://www.sta.cuhk.edu.hk/xysong/geneselection/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bayesian analysis of mixtures in structural equation models with non-ignorable missing data.

Structural equation models (SEMs) have become widely used to determine the interrelationships between latent and observed variables in social, psychological, and behavioural sciences. As heterogeneous data are very common in practical research in these fields, the analysis of mixture models has received a lot of attention in the literature. An important issue in the analysis of mixture SEMs is the presence of missing data, in particular of data missing with a non-ignorable mechanism. However, only a limited amount of work has been done in analysing mixture SEMs with non-ignorable missing data. The main objective of this paper is to develop a Bayesian approach for analysing mixture SEMs with an unknown number of components and non-ignorable missing data. A simulation study shows that Bayesian estimates obtained by the proposed Markov chain Monte Carlo methods are accurate and the Bayes factor computed via a path sampling procedure is useful for identifying the correct number of components, selecting an appropriate missingness mechanism, and investigating various effects of latent variables in the mixture SEMs. A real data set on a study of job satisfaction is used to demonstrate the methodology.

Bayesian Analysis of Multivariate Latent Curve Models With Nonlinear Longitudinal Latent Effects.

In longitudinal studies, investigators often measure multiple variables at multiple time points and are interested in investigating individual differences in patterns of change on those variables. Furthermore, in behavioral, social, psychological, and medical research, investigators often deal with latent variables that cannot be observed directly and should be measured by 2 or more manifest variables. Longitudinal latent variables occur when the corresponding manifest variables are measured at multiple time points. Our primary interests are in studying the dynamic change of longitudinal latent variables and exploring the possible interactive effect among the latent variables.Much of the existing research in longitudinal studies focuses on studying change in a single observed variable at different time points. In this article, we propose a novel latent curve model (LCM) for studying the dynamic change of multivariate manifest and latent variables and their linear and interaction relationships. The proposed LCM has the following useful features: First, it can handle multivariate variables for exploring the dynamic change of their relationships, whereas conventional LCMs usually consider change in a univariate variable. Second, it accommodates both first- and second-order latent variables and their interactions to explore how changes in latent attributes interact to produce a joint effect on the growth of an outcome variable. Third, it accommodates both continuous and ordered categorical data, and missing data.

Bayesian semiparametric analysis of structural equation models with mixed continuous and unordered categorical variables.

Recently, structural equation models (SEMs) have been applied for analyzing interrelationships among observed and latent variables in biological and medical research. Latent variables in these models are typically assumed to have a normal distribution. This article considers a Bayesian semparametric SEM with covariates, and mixed continuous and unordered categorical variables, in which the explanatory latent variables in the structural equation are modeled via an appropriate truncated Dirichlet process with a stick-breaking procedure. Results obtained from a simulation study and an analysis of a real medical data set are presented to illustrate the methodology.

Robust model fitting for the non linear structural equation model under normal theory.

Structural equation modelling has been widely applied in behavioural, educational, medical, social, and psychological research. The classical maximum likelihood estimate is vulnerable to outliers and non-normal data. In this paper, a robust estimation method for the nonlinear structural equation model is proposed. This method gives more weight to data that are likely to occur based on the structure of the posited model, and effectively downweights the influence of outliers. An algorithm is proposed to obtain the robust estimator. Asymptotic properties of the proposed method are investigated, which include the asymptotic distribution of the estimator, and some statistics for hypothesis testing. Results from a simulation study and a real data example show that our procedure is effective.

Non-linear structural equation models with correlated continuous and discrete data.

Structural equation models (SEMs) have been widely applied to examine interrelationships among latent and observed variables in social and psychological research. Motivated by the fact that correlated discrete variables are frequently encountered in practical applications, a non-linear SEM that accommodates covariates, and mixed continuous, ordered, and unordered categorical variables is proposed. Maximum likelihood methods for estimation and model comparison are discussed. One real-life data set about cardiovascular disease is used to illustrate the methodologies.

Longitudinal analysis of quality of life for stroke survivors using latent curve models.

BACKGROUND AND PURPOSE: For the survivors, activities of daily living, handicap, and depression have a significant impact on health-related quality of life (HRQOL). How the dynamic changes of these variables relate to HRQOL over time in the subacute phase of stroke recovery has not been investigated. The objective of this study was to study longitudinal behaviors of HRQOL of the stroke survivors in relation to the changes in activities of daily living, handicap, and depression after stroke. METHODS: This was a prospective cohort study of first disabling patients with stroke. Subjects were interviewed at 3, 6, and 12 months after stroke for modified Barthel Index, London Handicap Scale, Geriatric Depression Scale, and the World Health Organization Quality of Life questionnaire (abbreviated Hong Kong version). A latent curve model was developed to analyze how the dynamic changes in activities of daily living, handicap, and depressive mood related to the changes in HRQOL. RESULTS: Two hundred forty-seven of 303 patients (82%) followed up at 3 months after stroke could complete the quality-of-life questionnaire. Their mean age was 68.8 years. The latent curve model analysis revealed that initial physical health HRQOL was independently associated with activities of daily living, handicap, and depression. The other 3 HRQOL domain scores were primarily associated with depression only. The rates of change in all 4 domains of HRQOL were significantly and inversely associated with rate of change in the Geriatric Depression Scale only. CONCLUSIONS: Change in mood in the postacute phase of stroke recovery is the most significant determinant of change in HRQOL. More attention should be paid to the detection and management of poststroke depression.