Transcriptomic profiling reveals candidate allelopathic genes in rice responsible for interactions with barnyardgrass.

Echinochloa crus-galli (barnyardgrass) is one of the most damaging weeds in rice fields worldwide. Allelopathy has been considered a possible application for weed management. Thus understanding its molecular mechanisms is important for rice production. This study generated transcriptomes from rice under mono- and co-culture with barnyardgrass at two-time points to identify the candidate genes controlling allelopathic interactions between rice and barnyardgrass. A total of 5,684 differentially expressed genes (DEGs) were detected, amongst which 388 genes were transcription factors. These DEGs include genes associated with momilactone and phenolic acid biosynthesis, which play critical roles in allelopathy. Additionally, we found significantly more DEGs at 3 hours than at 3 days, suggesting a quick allelopathic response in rice. Up-regulated DEGs involve diverse biological processes, such as response to stimulus and pathways related to phenylpropanoid and secondary metabolites biosynthesis. Down-regulated DEGs were involved in developmental processes, indicating a balance between growth and stress response to allelopathy from barnyardgrass. Comparison of DEGs between rice and barnyardgrass shows few common genes, suggesting different mechanisms underlying allelopathic interaction in these two species. Our results offer an important basis for identifying of candidate genes responsible for rice and barnyardgrass interactions and contribute valuable resources for revealing its molecular mechanisms.

Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS.

Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene-gene interaction, gene-environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society.

Robustification of Linear Regression and Its Application in Genome-Wide Association Studies.

Regression analysis is one of the most popular statistical techniques that attempt to explore the relationships between a response (dependent) variable and one or more explanatory (independent) variables. To test the overall significance of regression, F-statistic is used if the parameters are estimated by the least-squares estimators (LSEs), while if the parameters are estimated by the maximum likelihood estimators (MLEs), the likelihood ratio test (LRT) statistic is used. However, both procedures produce misleading results and often fail to provide good fits to the reasonable space of the dataset in the presence of outlying observations. Moreover, outliers occur very frequently in any real datasets as well as in the molecular OMICS datasets. Hence, an effort is made in this study to robustify MLE based regression analysis by maximizing the ß-likelihood function. The tuning parameter ß is selected by cross-validation. For ß = 0, the proposed method reduces to the classical MLE based regression analysis. We inspect the performance of the proposed method using both synthetic and real data analysis. The results of simulations indicate that the proposed method performs better than traditional methods in both outliers and high leverage points to estimate the parameters and mean square errors. The results of relative efficiency analysis show that the proposed estimator is relatively less affected than the popular estimators, including S, MM, and fast-S for normal error distribution in case high dimension and outliers. Also, real data analysis results demonstrated that the proposed method shows robust properties with respect to data contaminations, overcome the drawback of the traditional methods. Genome-wide association studies (GWAS) by the proposed method identify the vital gene influencing hypertension and iron level in the liver and spleen of mice. Furthermore, we have identified 15 and 21 significant SNPs for chalkiness degree and chalkiness percentage, respectively, by GWAS based on the proposed method. The variant of the SNPs might be provided the new resources for grain quality traits and could be used for further molecular and physiological analysis to enhance the better quality of rice grain. These results offer an important basis for further understanding of the robust regression analysis, which might be applied in various fields, including business, genetics, and bioinformatics.

RiceRelativesGD: a genomic database of rice relatives for rice research.

Rice (Oryza sativa L.) is one of the most important crops worldwide. Its relatives, including phylogenetically related species of rice and paddy weeds with a similar ecological niche, can provide crucial genetic resources (such as resistance to biotic and abiotic stresses and high photosynthetic efficiency) for rice research. Although many rice genomic databases have been constructed, a database providing large-scale curated genomic data from rice relatives and offering specific gene resources is still lacking. Here, we present RiceRelativesGD, a user-friendly genomic database of rice relatives. RiceRelativesGD integrates large-scale genomic resources from 2 cultivated rice and 11 rice relatives, including 208 321 specific genes and 13 643 genes related to photosynthesis and responsive to external stimuli. Diverse bioinformatics tools are embedded in the database, which allow users to search, visualize and download the information of interest. To our knowledge, this is the first genomic database providing a centralized genetic resource of rice relatives. RiceRelativesGD will serve as a significant and comprehensive knowledgebase for the rice community.

Gene Modules Co-regulated with Biosynthetic Gene Clusters for Allelopathy between Rice and Barnyardgrass.

Allelopathy is a central process in crop-weed interactions and is mediated by the release of allelochemicals that result in adverse growth effects on one or the other plant in the interaction. The genomic mechanism for the biosynthesis of many critical allelochemicals is unknown but may involve the clustering of non-homologous biosynthetic genes involved in their formation and regulatory gene modules involved in controlling the coordinated expression within these gene clusters. In this study, we used the transcriptomes from mono- or co-cultured rice and barnyardgrass to investigate the nature of the gene clusters and their regulatory gene modules involved in the allelopathic interactions of these two plants. In addition to the already known biosynthetic gene clusters in barnyardgrass we identified three potential new clusters including one for quercetin biosynthesis and potentially involved in allelopathic interaction with rice. Based on the construction of gene networks, we identified one gene regulatory module containing hub transcription factors, significantly positively co-regulated with both the momilactone A and phytocassane clusters in rice. In barnyardgrass, gene modules and hub genes co-expressed with the gene clusters responsible for 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA) biosynthesis were also identified. In addition, we found three genes in barnyardgrass encoding indole-3-glycerolphosphate synthase that regulate the expression of the DIMBOA cluster. Our findings offer new insights into the regulatory mechanisms of biosynthetic gene clusters involved in allelopathic interactions between rice and barnyardgrass, and have potential implications in controlling weeds for crop protection.

Rice SDSFL1 plays a critical role in the regulation of plant structure through the control of different phytohormones and altered cell structure.

Semi-dwarfism is one of the most important agronomic traits for many cereal crops. In the present study, a mutant with semi-dwarf and short flag leaf 1, sdsfl1, was identified and characterized. The sdsfl1 mutant demonstrated some distinguished structural alterations, including shorter plant height and flag leaf length, increased tiller numbers and flag leaf width, and decreased panicle length compared with those of wild type (WT). Genetic analysis suggested that the mutant traits were completely controlled by a single recessive gene. The SDSFL1 gene was mapped to the long arm of chromosome 3 within a region of 44.6 kb between InDel markers A3P8.3 and A3P8.4. The DNA sequence analysis revealed that there was only a T to C substitution in the coding region of LOC_Os03g63970, resulting in the substitution of Tryptophan (Try) to Arginine (Arg) and encoding a GA 20 oxidase 1 protein of 372 amino acid residues. Photosynthesis analysis showed that the photosynthetic rate (Pn), stomatal conductance (Gs), and intercellular CO2 concentration (Ci) were significantly increased in sdsfl1. Chlorophyll a (Chl a), total Chl, and carotenoid contents were significantly increased in sdsfl1 compared with those in WT. sdsfl1 carried a reduced level of GA3 but reacted to exogenously applied gibberellins (GA). Moreover, the levels of abscisic acid (ABA), indole 3-acetic acid (IAA), and salicylic acid (SA) were notably improved in sdsfl1, whereas there was no noteworthy change in jasmonic acid (JA). The results thus offer a visible foundation for the molecular and physiological analysis of the SDSFL1 gene, which might participate in various functional pathways for controlling plant height and leaf length in rice breeding.

Genomic variation associated with local adaptation of weedy rice during de-domestication.

De-domestication is a unique evolutionary process by which domesticated crops are converted into 'wild predecessor like' forms. Weedy rice (Oryza sativa f. spontanea) is an excellent model to dissect the molecular processes underlying de-domestication. Here, we analyse the genomes of 155 weedy and 76 locally cultivated rice accessions from four representative regions in China that were sequenced to an average 18.2 × coverage. Phylogenetic and demographic analyses indicate that Chinese weedy rice was de-domesticated independently from cultivated rice and experienced a strong genetic bottleneck. Although evolving from multiple origins, critical genes underlying convergent evolution of different weedy types can be found. Allele frequency analyses suggest that standing variations and new mutations contribute differently to japonica and indica weedy rice. We identify a Mb-scale genomic region present in weedy rice but not cultivated rice genomes that shows evidence of balancing selection, thereby suggesting that there might be more complexity inherent to the process of de-domestication.