Network-based feature selection reveals substructures of gene modules responding to salt stress in rice

Chalkiness and premature controlled by energy homeostasis in OsNAC02 Ko-mutant during vegetative endosperm development

BACKGROUND

Chalkiness is a common phenotype induced by various reasons, such as abiotic stress or the imbalance of starch synthesis and metabolism during the development period. However, the reason mainly for one gene losing its function such as NAC (TFs has a large family in rice) which may cause premature is rarely known to us.

RESULTS

The Ko-Osnac02 mutant demonstrated an obviously early maturation stage compared to the wild type (WT) with 15 days earlier. The result showed that the mature endosperm of Ko-Osnac02 mutant exhibited chalkiness, characterized by white-core and white-belly in mature endosperm. As grain filling rate is a crucial factor in determining the yield and quality of rice (Oryza sativa, ssp. japonica), it's significant that mutant has a lower amylose content (AC) and higher soluble sugar content in the mature endosperm. Interestingly among the top DEGs in the RNA sequencing of N2 (3DAP) and WT seeds revealed that the OsBAM2 (LOC_Os10g32810) expressed significantly high in N2 mutant, which involved in Maltose up-regulated by the starch degradation. As Prediction of Protein interaction showed in the chalky endosperm formation in N2 seeds (3 DAP), seven genes were expressed at a lower-level which should be verified by a heatmap diagrams based on DEGs of N2 versus WT. The Tubulin genes controlling cell cycle are downregulated together with the MCM family genes MCM4 ( ↓), MCM7 ( ↑), which may cause white-core in the early endosperm development. In conclusion, the developing period drastically decreased in the Ko-Osnac02 mutants, which might cause the chalkiness in seeds during the early endosperm development.

CONCLUSIONS

The gene OsNAC02 which controls a great genetic co-network for cell cycle regulation in early development, and KO-Osnac02 mutant shows prematurity and white-core in endosperm.

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

Plants intricately deploy defense systems to counter diverse biotic and abiotic stresses. Omics technologies, spanning genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the exploration of plant defense mechanisms, unraveling molecular intricacies in response to various stressors. However, the complexity and scale of omics data necessitate sophisticated analytical tools for meaningful insights. This review delves into the application of artificial intelligence algorithms, particularly machine learning and deep learning, as promising approaches for deciphering complex omics data in plant defense research. The overview encompasses key omics techniques and addresses the challenges and limitations inherent in current AI-assisted omics approaches. Moreover, it contemplates potential future directions in this dynamic field. In summary, AI-assisted omics techniques present a robust toolkit, enabling a profound understanding of the molecular foundations of plant defense and paving the way for more effective crop protection strategies amidst climate change and emerging diseases.

Genomic variations combined with epigenetic modifications rewire open chromatin in rice

Cis-regulatory elements (CREs) fine-tune gene transcription in eukaryotes. CREs with sequence variations play vital roles in driving plant or crop domestication. However, how global sequence and structural variations (SVs) are responsible for multilevel changes between indica and japonica rice (Oryza sativa) is still not fully elucidated. To address this, we conducted multiomic studies using MNase hypersensitivity sequencing (MH-seq) in combination with RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), and bisulfite sequencing (BS-seq) between the japonica rice variety Nipponbare (NIP) and indica rice variety 93-11. We found that differential MNase hypersensitive sites (MHSs) exhibited some distinct intrinsic genomic sequence features between NIP and 93-11. Notably, through MHS-genome-wide association studies (GWAS) integration, we found that key sequence variations may be associated with differences of agronomic traits between NIP and 93-11, which is partly achieved by MHSs harboring CREs. In addition, SV-derived differential MHSs caused by transposable element (TE) insertion, especially by noncommon TEs among rice varieties, were associated with genes with distinct functions, indicating that TE-driven gene neo- or subfunctionalization is mediated by changes of chromatin openness. This study thus provides insights into how sequence and genomic SVs control agronomic traits of NIP and 93-11; it also provides genome-editing targets for molecular breeding aiming at improving favorable agronomic properties.

Variety-Specific Transcriptional and Alternative Splicing Regulations Modulate Salt Tolerance in Rice from Early Stage of Stress

Salt stress poses physiological drought, ionic toxicity and oxidative stress to plants, which causes premature senescence and death of the leaves if the stress sustained. Salt tolerance varied between different rice varieties, but how different rice varieties respond at the early stage of salt stress has been seldom studied comprehensively. By employing third generation sequencing technology, we compared gene expressional changes in leaves of three rice varieties that varied in their level of tolerance after salt stress treatment for 6 h. Commonly up-regulated genes in all rice varieties were related to water shortage response and carbon and amino acids metabolism at the early stage of salt stress, while reactive oxygen species cleavage genes were induced more in salt-tolerant rice. Unexpectedly, genes involved in chloroplast development and photosynthesis were more significantly down-regulated in the two salt tolerant rice varieties 'C34' and 'Nona Bokra'. At the same time, genes coding ribosomal protein were suppressed to a more severe extent in the salt-sensitive rice variety 'IR29'. Interestingly, not only variety-specific gene transcriptional regulation, but also variety-specific mRNA alternative splicing, on both coding and long-noncoding genes, were found at the early stage of salt stress. In summary, differential regulation in gene expression at both transcriptional and post-transcriptional levels, determine and fine-tune the observed response in level of damage in leaves of specific rice genotypes at early stage of salt stress.

Identification of major candidate genes for multiple abiotic stress tolerance at seedling stage by network analysis and their validation by expression profiling in rice (Oryza sativa L.)

A wealth of microarray and RNA-seq data for studying abiotic stress tolerance in rice exists but only limited studies have been carried out on multiple stress-tolerance responses and mechanisms. In this study, we identified 6657 abiotic stress-responsive genes pertaining to drought, salinity and heat stresses from the seedling stage microarray data of 83 samples and used them to perform unweighted network analysis and to identify key hub genes or master regulators for multiple abiotic stress tolerance. Of the total 55 modules identified from the analysis, the top 10 modules with 8-61 nodes comprised 239 genes. From these 10 modules, 10 genes common to all the three stresses were selected. Further, based on the centrality properties and highly dense interactions, we identified 7 intra-modular hub genes leading to a total of 17 potential candidate genes. Out of these 17 genes, 15 were validated by expression analysis using a panel of 4 test genotypes and a pair of standard check genotypes for each abiotic stress response. Interestingly, all the 15 genes showed upregulation under all stresses and in all the genotypes, suggesting that they could be representing some of the core abiotic stress-responsive genes. More pertinently, eight of the genes were found to be co-localized with the stress-tolerance QTL regions. Thus, in conclusion, our study not only provided an effective approach for studying abiotic stress tolerance in rice, but also identified major candidate genes which could be further validated by functional genomics for abiotic stress tolerance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03182-7.

Deciphering Haplotypic Variation and Gene Expression Dynamics Associated with Nutritional and Cooking Quality in Rice

Nutritional quality improvement of rice is the key to ensure global food security. Consequently, enormous efforts have been made to develop genomics and transcriptomics resources for rice. The available omics resources along with the molecular understanding of trait development can be utilized for efficient exploration of genetic resources for breeding programs. In the present study, 80 genes known to regulate the nutritional and cooking quality of rice were extensively studied to understand the haplotypic variability and gene expression dynamics. The haplotypic variability of selected genes were defined using whole-genome re-sequencing data of ~4700 diverse genotypes. The analytical workflow identified 133 deleterious single-nucleotide polymorphisms, which are predicted to affect the gene function. Furthermore, 788 haplotype groups were defined for 80 genes, and the distribution and evolution of these haplotype groups in rice were described. The nucleotide diversity for the selected genes was significantly reduced in cultivated rice as compared with that in wild rice. The utility of the approach was successfully demonstrated by revealing the haplotypic association of chalk5 gene with the varying degree of grain chalkiness. The gene expression atlas was developed for these genes by analyzing RNA-Seq transcriptome profiling data from 102 independent sequence libraries. Subsequently, weighted gene co-expression meta-analyses of 11,726 publicly available RNAseq libraries identified 19 genes as the hub of interactions. The comprehensive analyses of genetic polymorphisms, allelic distribution, and gene expression profiling of key quality traits will help in exploring the most desired haplotype for grain quality improvement. Similarly, the information provided here will be helpful to understand the molecular mechanism involved in the development of nutritional and cooking quality traits in rice.

Assessment of Greenhouse Tomato Anthesis Rate Through Metabolomics Using LASSO Regularized Linear Regression Model

While the high year-round production of tomatoes has been facilitated by solar greenhouse cultivation, these yields readily fluctuate in response to changing environmental conditions. Mathematic modeling has been applied to forecast phenotypes of tomatoes using environmental measurements (e.g., temperature) as indirect parameters. In this study, metabolome data, as direct parameters reflecting plant internal status, were used to construct a predictive model of the anthesis rate of greenhouse tomatoes. Metabolome data were obtained from tomato leaves and used as variables for linear regression with the least absolute shrinkage and selection operator (LASSO) for prediction. The constructed model accurately predicted the anthesis rate, with an R² value of 0.85. Twenty-nine of the 161 metabolites were selected as candidate markers. The selected metabolites were further validated for their association with anthesis rates using the different metabolome datasets. To assess the importance of the selected metabolites in cultivation, the relationships between the metabolites and cultivation conditions were analyzed via correspondence analysis. Trigonelline, whose content did not exhibit a diurnal rhythm, displayed major contributions to the cultivation, and is thus a potential metabolic marker for predicting the anthesis rate. This study demonstrates that machine learning can be applied to metabolome data to identify metabolites indicative of agricultural traits.

Identifying stress responsive genes using overlapping communities in co-expression networks

Background

This paper proposes a workflow to identify genes that respond to specific treatments in plants. The workflow takes as input the RNA sequencing read counts and phenotypical data of different genotypes, measured under control and treatment conditions. It outputs a reduced group of genes marked as relevant for treatment response. Technically, the proposed approach is both a generalization and an extension of WGCNA. It aims to identify specific modules of overlapping communities underlying the co-expression network of genes. Module detection is achieved by using Hierarchical Link Clustering. The overlapping nature of the systems’ regulatory domains that generate co-expression can be identified by such modules. LASSO regression is employed to analyze phenotypic responses of modules to treatment.

Results

The workflow is applied to rice (Oryza sativa), a major food source known to be highly sensitive to salt stress. The workflow identifies 19 rice genes that seem relevant in the response to salt stress. They are distributed across 6 modules: 3 modules, each grouping together 3 genes, are associated to shoot K content; 2 modules of 3 genes are associated to shoot biomass; and 1 module of 4 genes is associated to root biomass. These genes represent target genes for the improvement of salinity tolerance in rice.

Conclusions

A more effective framework to reduce the search-space for target genes that respond to a specific treatment is introduced. It facilitates experimental validation by restraining efforts to a smaller subset of genes of high potential relevance.

Genome-wide discovery of natural variation in pre-mRNA splicing and prioritising causal alternative splicing to salt stress response in rice

Pre-mRNA splicing is an essential step for the regulation of gene expression. In order to specifically capture splicing variants in plants for genome-wide association studies (GWAS), we developed a software tool to quantify and visualise Variations of Splicing in Population (VaSP). VaSP can quantify splicing variants from short-read RNA-seq datasets and discover genotype-specific splicing (GSS) events, which can be used to prioritise causal pre-mRNA splicing events in GWAS. We applied our method to an RNA-seq dataset with 328 samples from 82 genotypes from a rice diversity panel exposed to optimal and saline growing conditions. In total, 764 significant GSS events were identified in salt stress conditions. GSS events were used as markers for a GWAS with the shoot Na⁺ accumulation, which identified six GSS events in five genes significantly associated with the shoot Na⁺ content. Two of these genes, OsNUC1 and OsRAD23 emerged as top candidate genes with splice variants that exhibited significant divergence between the variants for shoot growth under salt stress conditions. VaSP is a versatile tool for alternative splicing analysis in plants and a powerful tool for prioritising candidate causal pre-mRNA splicing and corresponding genomic variations in GWAS.

Network Analysis of Gene Transcriptions of Arabidopsis thaliana in Spaceflight Microgravity

The transcriptomic datasets of the plant model organism Arabidopsis thaliana grown in the International Space Station provided by GeneLab have been mined to isolate the impact of spaceflight microgravity on gene expressions related to root growth. A set of computational tools is used to identify the hub genes that respond differently in spaceflight with controlled lighting compared to on the ground. These computational tools based on graph-theoretic approaches are used to infer gene regulatory networks from the transcriptomic datasets. The three main algorithms used for network analyses are Least Absolute Shrinkage and Selection Operator (LASSO), Pearson correlation, and the Hyperlink-Induced Topic Search (HITS) algorithm. Graph-based spectral analyses reveal distinct properties of the spaceflight microgravity networks for the Wassilewskija (WS), Columbia (Col)-0, and mutant phytochromeD (phyD) ecotypes. The set of hub genes that are significantly altered in spaceflight microgravity are mainly involved in cell wall synthesis, protein transport, response to auxin, stress responses, and catabolic processes. Network analysis highlights five important root growth-regulating hub genes that have the highest outdegree distribution in spaceflight microgravity networks. These concerned genes coding for proteins are identified from the Gene Regulatory Networks (GRNs) corresponding to spaceflight total light environment. Furthermore, network analysis uncovers genes that encode nucleotide-diphospho-sugar interconversion enzymes that have higher transcriptional regulation in spaceflight microgravity and are involved in cell wall biosynthesis.