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Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain

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Abstract

With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize (Zea mays L.) grain is low. Several large-effect genes with cis-acting variants affecting messenger RNA (mRNA) expression are mostly responsible for tocochromanol variation in maize grain, with other relevant associated quantitative trait loci (QTL) yet to be fully resolved. Leveraging existing genomic and transcriptomic information for maize inbreds could improve prediction when selecting for higher vitamin E content. Here, we first evaluated a multikernel genomic best linear unbiased prediction (MK-GBLUP) approach for modeling known QTL in the prediction of nine tocochromanol grain phenotypes (12–21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines. On average, MK-GBLUP models improved predictive abilities by 7.0–13.6% when compared with GBLUP. In a second approach with a subset of 545 lines from the larger panel, the highest average improvement in predictive ability relative to GBLUP was achieved with a multi-trait GBLUP model (15.4%) that had a tocochromanol phenotype and transcript abundances in developing grain for a few large-effect candidate causal genes (1–3 genes per trait) as multiple response variables. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes.

Long noncoding RNA transcriptome analysis reveals novel lncRNAs in Morus alba ‘Yu‐711’ response to drought stress

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Abstract

Drought stress has been a key environmental factor affecting plant growth and development. The plant genome is capable of producing long noncoding RNAs (lncRNAs). To better understand white mulberry (Morus alba L.) drought response mechanism, we conducted a comparative transcriptome study comparing two treatments: drought-stressed (EG) and well-watered (CK) plants. A total of 674 differentially expressed lncRNAs (DElncRNAs) were identified. In addition, 782 differentially expressed messenger RNAs (DEmRNAs) were identified. We conducted Gene Ontology (GO) and KEGG enrichment analyses focusing on the differential lncRNAs cis-target genes. The target genes of the DElncRNAs were most significantly involved in the biosynthesis of secondary metabolites. Gene regulatory networks of the target genes involving DElncRNAs–mRNAs–DEmRNAs and DElncRNA–miRNA–DEmRNA were constructed. In the DElncRNAs–DEmRNAs network, 30 DEmRNAs involved in the biosynthesis of secondary metabolites are collocated with 46 DElncRNAs. The interaction between DElncRNAs and candidate genes was identified using LncTar. In summary, quantitative real-time polymerase chain reaction (qRT-PCR) validated nine candidate genes and seven target lncRNAs including those identified by LncTar. We predicted that the DElncRNAs–DEmRNAs might recruit microRNAs (miRNAs) to interact with gene regulatory networks under the drought stress response in mulberry. The findings will contribute to our understanding of the regulatory functions of lncRNAs under drought stress and will shed new light on the mulberry–drought stress interactions.

Genome‐wide identification of R2R3‐MYB family genes and gene response to stress in ginger

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Abstract

Ginger (Zingiber officinale Roscoe) is an important plant used worldwide for medicine and food. The R2R3-MYB transcription factor (TF) family has essential roles in plant growth, development, and stresses resistance, and the number of genes in the family varies greatly among different types of plants. However, genome-wide discovery of ZoMYBs and gene responses to stresses have not been reported in ginger. Therefore, genome-wide analysis of R2R3-MYB genes in ginger was conducted in this study. Protein phylogenetic relations and conserved motifs and chromosome localization and duplication, structure, and cis-regulatory elements were analyzed. In addition, the expression patterns of selected genes were analyzed under two different stresses. A total of 299 candidate ZoMYB genes were discovered in ginger. Based on groupings of R2R3-MYB genes in the model plant Arabidopsis thaliana (L.) Heynh., ZoMYBs were divided into eight groups. Genes were distributed across 22 chromosomes at uneven densities. In gene duplication analysis, 120 segmental duplications were identified in the ginger genome. Gene expression patterns of 10 ZoMYBs in leaves of ginger under abscisic acid (ABA) and low-temperature stress treatments were different. The results will help to determine the exact roles of ZoMYBs in anti-stress responses in ginger.

Genetic factors underlying anaerobic germination in rice: Genome‐wide association study and transcriptomic analysis

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Abstract

The success of rice (Oryza sativa L.) germination and survival under submerged conditions is mainly determined by the rapid growth of the coleoptile to reach the water surface. Previous reports have shown the presence of genetic variability within rice accessions in the levels of flooding tolerance during germination or anaerobic germination (AG). Although many studies have focused on the physiological mechanisms of oxygen stress, few studies have explored the breadth of natural variation in AG tolerance-related traits in rice. In this study, we evaluated the coleoptile lengths of a geographically diverse rice panel of 241 accessions, including global accessions along with elite breeding lines and released cultivars from the United States, under the normal and flooded conditions in laboratory and greenhouse environments. A genome-wide association study (GWAS) was performed using a 7K single-nucleotide polymorphism (SNP) array and the phenotypic data of normal coleoptile length, flooded coleoptile length, flooding tolerance index, and survival at 14 d after seeding (DAS). Out of the 30 significant GWAS quantitative trait loci (QTL) regions identified, 14 colocalized with previously identified candidate genes of AG tolerance, whereas 16 were potentially novel. Two rice accessions showing contrasting phenotypic responses to AG stress were selected for the transcriptomics study. The combined approach of GWAS and transcriptomics analysis identified 77 potential candidate genes related to AG tolerance. The findings of our study may assist rice improvement programs in developing rice cultivars with robust tolerance under flooding stress during germination and the early seedling stage.

ASRpro: A machine‐learning computational model for identifying proteins associated with multiple abiotic stress in plants

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Abstract

One of the thrust areas of research in plant breeding is to develop crop cultivars with enhanced tolerance to abiotic stresses. Thus, identifying abiotic stress-responsive genes (SRGs) and proteins is important for plant breeding research. However, identifying such genes via established genetic approaches is laborious and resource intensive. Although transcriptome profiling has remained a reliable method of SRG identification, it is species specific. Additionally, identifying multistress responsive genes using gene expression studies is cumbersome. Thus, endorsing the need to develop a computational method for identifying the genes associated with different abiotic stresses. In this work, we aimed to develop a computational model for identifying genes responsive to six abiotic stresses: cold, drought, heat, light, oxidative, and salt. The predictions were performed using support vector machine (SVM), random forest, adaptive boosting (ADB), and extreme gradient boosting (XGB), where the autocross covariance (ACC) and K-mer compositional features were used as input. With ACC, K-mer, and ACC + K-mer compositional features, the overall accuracy of ∼60–77, ∼75–86, and ∼61–78% were respectively obtained using the SVM algorithm with fivefold cross-validation. The SVM also achieved higher accuracy than the other three algorithms. The proposed model was also assessed with an independent dataset and obtained an accuracy consistent with cross-validation. The proposed model is the first of its kind and is expected to serve the requirement of experimental biologists; however, the prediction accuracy was modest. Given its importance for the research community, the online prediction application, ASRpro, is made freely available (https://iasri-sg.icar.gov.in/asrpro/) for predicting abiotic SRGs and proteins.

Changes in epigenetic features in legumes under abiotic stresses

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Abstract

Legume crops are rich in nutritional value for human and livestock consumption. With global climate change, developing stress-resilient crops is crucial for ensuring global food security. Because of their nitrogen-fixing ability, legumes are also important for sustainable agriculture. Various abiotic stresses, such as salt, drought, and elevated temperatures, are known to adversely affect legume production. The responses of plants to abiotic stresses involve complicated cellular processes including stress hormone signaling, metabolic adjustments, and transcriptional regulations. Epigenetic mechanisms play a key role in regulating gene expressions at both transcriptional and posttranscriptional levels. Increasing evidence suggests the importance of epigenetic regulations of abiotic stress responses in legumes, and recent investigations have extended the scope to the epigenomic level using next-generation sequencing technologies. In this review, the current knowledge on the involvement of epigenetic features, including DNA methylation, histone modification, and noncoding RNAs, in abiotic stress responses in legumes is summarized and discussed. Since most of the available information focuses on a single aspect of these epigenetic features, integrative analyses involving omics data in multiple layers are needed for a better understanding of the dynamic chromatin statuses and their roles in transcriptional regulation. The inheritability of epigenetic modifications should also be assessed in future studies for their applications in improving stress tolerance in legumes through the stable epigenetic optimization of gene expressions.