Oryzabase. An integrated biological and genome information database for rice

β-Tyrosine and its biosynthetic enzyme TAM1 are predominantly distributed in the ancestral subpopulation of japonica rice in Oryza rufipogon

Intraspecific variation in specialized metabolites plays a crucial role in the adaptive response to diverse environments. Two major subspecies, japonica and indica, are observed in Asian cultivated rice (Oryza sativa L.). Previously, we identified (3R)-β-tyrosine, a novel nonproteinogenic β-amino acid in plants, along with the enzyme tyrosine aminomutase (TAM1), which is required for β-tyrosine biosynthesis, in the japonica cultivar Nipponbare. Notably, TAM1 and β-tyrosine were preferentially distributed in japonica cultivars compared with indica cultivars. Considering its phytotoxicity and antimicrobial activity, intraspecific variation in β-tyrosine may contribute to the defensive potential of japonica rice. Investigation of the evolutionary trajectory of TAM1 and β-tyrosine should enhance our understanding of the evolution of rice defense. However, their distribution patterns in O. rufipogon, the direct ancestor of O. sativa, remain unclear. Therefore, in this study, we extensively examined TAM1 presence/absence and β-tyrosine content in 110 genetically and geographically diverse O. rufipogon accessions and revealed that they are characteristically observed in the ancestral subpopulation of japonica rice, while being absent or slightly accumulated in other subpopulations. Thus, we conclude that TAM1 and β-tyrosine in japonica rice are likely derived from its ancestral subpopulation. Furthermore, the high and low TAM1 possession rates and β-tyrosine content in japonica and indica rice, respectively, could be attributed to distribution patterns of TAM1 and β-tyrosine in their ancestral subpopulations. This study provides fundamental insights into the evolution of rice defense.

Phenotypic, Physiological, and Gene Expression Analysis for Nitrogen and Phosphorus Use Efficienies in Three Popular Genotypes of Rice (Oryza sativa Indica)

Crop nitrogen (N) and phosphorus (P) use efficiencies (NUE/PUE) are important to minimize wastage and nutrient pollution, but no improved crop for both is currently available. We addressed them together in rice, in the view of its high consumption of NPK fertilizers. We analyzed 46 morphophysiological parameters for the N/P response in three popular indica genotypes, namely, BPT 5204, Panvel 1, and CR Dhan 301 at low, medium, and normal N/P doses. They include 18 vegetative, 15 physiological, and 13 reproductive parameters. The segregation of significantly N/P-responsive parameters correlating with NUE/PUE revealed 21 NUE, 22 PUE, and 12 common parameters. Feature selection analyses revealed the common high-ranking parameters including the photosynthetic rate at the reproductive stage, tiller number, root-shoot ratio, culm thickness, and flag leaf width. The venn selection using the reported NUE/PUE-related candidate genes in rice revealed five genes in common for both, namely OsIAA3, OsEXPA10, OsCYP75B4, OsSultr3;4, and OsFER2, which were associated with three of the common traits for NUE/PUE. Their expression studies using qRT-PCR revealed the opposite regulation in contrasting genotypes for OsSultr3;4 and OsEXPA10 in N-response and for OsFER2 in P-response, indicating their role in contrasting N/P use efficiencies. Overall, CR Dhan 301 has the highest NUE and PUE followed by Panvel 1 and BPT5204 among the studied genotypes.

Meta-Analysis of Rice Phosphoproteomics Data to Understand Variation in Cell Signaling Across the Rice Pan-Genome

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have reanalyzed publicly available mass spectrometry proteomics data sets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,565 phosphosites on serine, threonine, and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety and clustered the data to identify groups of sites with similar patterns across rice family groups. The data has been loaded into UniProt Knowledge-Base─enabling researchers to visualize sites alongside other data on rice proteins, e.g., structural models from AlphaFold2, PeptideAtlas, and the PRIDE database─enabling visualization of source evidence, including scores and supporting mass spectra.

Transcriptional networks revealed late embryogenesis abundant genes regulating drought mitigation in aromatic Keteki Joha rice

Climate change has become increasingly intertwined with the occurrence and severity of droughts. As global temperatures rise due to greenhouse gas emissions, weather patterns are altered, leading to shifts in precipitation levels and distribution. These exacerbate the risk of drought in many regions, with potentially devastating consequences. A comprehensive transcriptome analysis was performed on Keteki Joha, an aromatic rice from North East India, with the aim of elucidating molecular responses to drought. Numerous genes linked to drought were activated, with both ABA-dependent and ABA-independent pathways playing crucial roles. Upregulated genes were enriched with gene ontology terms with response to abscisic acid and abscisic acid-activated signalling pathway, suggesting the existence of an ABA-dependent pathway for drought mitigation. The upregulated genes were also enriched with responses to stress, water, heat, jasmonic acid, and hydrogen peroxide, indicating the presence of an ABA-independent pathway alongside the ABA-dependent mechanism. Weighted Correlation Network Analysis (WGCNA) identified 267 genes that specifically govern drought mitigation in Keteki Joha. The late embryogenesis abundant (LEA) gene family emerges as the most overrepresented in both RNA sequencing data and WGCNA analysis, suggesting their dominant role in mitigating drought. Notably, 31 LEA genes were induced in seedlings and 32 in mature stages under drought stress. The LEA3-1, LEA14/WSI18, RAB16A, RAB16B, DHN1, DHN6, LEA1, LEA3, LEA17, and LEA33 exhibited and established co-expression with numerous other drought stress-related genes, indicating their inseparable role in alleviating drought. Consequently, LEA genes have been proposed to be primary and crucial responders to drought in Keteki Joha.

"Genome-wide identification of bZIP gene family in Pearl millet and transcriptional profiling under abiotic stress, phytohormonal treatments; and functional characterization of PgbZIP9"

Abiotic stresses are major constraints in crop production, and are accountable for more than half of the total crop loss. Plants overcome these environmental stresses using coordinated activities of transcription factors and phytohormones. Pearl millet an important C4 cereal plant having high nutritional value and climate resilient features is grown in marginal lands of Africa and South-East Asia including India. Among several transcription factors, the basic leucine zipper (bZIP) is an important TF family associated with diverse biological functions in plants. In this study, we have identified 98 bZIP family members (PgbZIP) in pearl millet. Phylogenetic analysis divided these PgbZIP genes into twelve groups (A-I, S, U and X). Motif analysis has shown that all the PgbZIP proteins possess conserved bZIP domains and the exon-intron organization revealed conserved structural features among the identified genes. Cis-element analysis, RNA-seq data analysis, and real-time expression analysis of PgbZIP genes suggested the potential role of selected PgbZIP genes in growth/development and abiotic stress responses in pearl millet. Expression profiling of selected PgbZIPs under various phytohormones (ABA, SA and MeJA) treatment showed differential expression patterns of PgbZIP genes. Further, PgbZIP9, a homolog of AtABI5 was found to localize in the nucleus and modulate gene expression in pearl millet under stresses. Our present findings provide a better understanding of bZIP genes in pearl millet and lay a good foundation for the further functional characterization of multi-stress tolerant PgbZIP genes, which could become efficient tools for crop improvement.

RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci

BACKGROUND

As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources.

RESULTS

We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs.

CONCLUSIONS

RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.

A meta-analysis of rice phosphoproteomics data to understand variation in cell signalling across the rice pan-genome

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have re-analysed publicly available mass spectrometry proteomics datasets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,522 phosphosites on serine, threonine and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety. The data was clustered to identify groups of sites with similar patterns across rice family groups, for example those highly conserved in Japonica, but mostly absent in Aus type rice varieties - known to have different responses to drought. These resources can assist rice researchers to discover alleles with significantly different functional effects across rice varieties. The data has been loaded into UniProt Knowledge-Base - enabling researchers to visualise sites alongside other data on rice proteins e.g. structural models from AlphaFold2, PeptideAtlas and the PRIDE database - enabling visualisation of source evidence, including scores and supporting mass spectra.

OsBTBZ1 Confers Salt Stress Tolerance in Arabidopsis thaliana

Rice (Oryza sativa L.), one of the most important commodities and a primary food source worldwide, can be affected by adverse environmental factors. The chromosome segment substitution line 16 (CSSL16) of rice is considered salt-tolerant. A comparison of the transcriptomic data of the CSSL16 line under normal and salt stress conditions revealed 511 differentially expressed sequence (DEseq) genes at the seedling stage, 520 DEseq genes in the secondary leaves, and 584 DEseq genes in the flag leaves at the booting stage. Four BTB genes, OsBTBZ1, OsBTBZ2, OsBTBN3, and OsBTBN7, were differentially expressed under salt stress. Interestingly, only OsBTBZ1 was differentially expressed at the seedling stage, whereas the other genes were differentially expressed at the booting stage. Based on the STRING database, OsBTBZ1 was more closely associated with other abiotic stress-related proteins than other BTB genes. The highest expression of OsBTBZ1 was observed in the sheaths of young leaves. The OsBTBZ1-GFP fusion protein was localized to the nucleus, supporting the hypothesis of a transcriptionally regulatory role for this protein. The bt3 Arabidopsis mutant line exhibited susceptibility to NaCl and abscisic acid (ABA) but not to mannitol. NaCl and ABA decreased the germination rate and growth of the mutant lines. Moreover, the ectopic expression of OsBTBZ1 rescued the phenotypes of the bt3 mutant line and enhanced the growth of wild-type Arabidopsis under stress conditions. These results suggest that OsBTBZ1 is a salt-tolerant gene functioning in ABA-dependent pathways.

Weighted gene co-expression network analysis of nitrogen (N)-responsive genes and the putative role of G-quadruplexes in N use efficiency (NUE) in rice

Rice is an important target to improve crop nitrogen (N) use efficiency (NUE), and the identification and shortlisting of the candidate genes are still in progress. We analyzed data from 16 published N-responsive transcriptomes/microarrays to identify, eight datasets that contained the maximum number of 3020 common genes, referred to as N-responsive genes. These include different classes of transcription factors, transporters, miRNA targets, kinases and events of post-translational modifications. A Weighted gene co-expression network analysis (WGCNA) with all the 3020 N-responsive genes revealed 15 co-expression modules and their annotated biological roles. Protein-protein interaction network analysis of the main module revealed the hub genes and their functional annotation revealed their involvement in the ubiquitin process. Further, the occurrences of G-quadruplex sequences were examined, which are known to play important roles in epigenetic regulation but are hitherto unknown in N-response/NUE. Out of the 3020 N-responsive genes studied, 2298 contained G-quadruplex sequences. We compared these N-responsive genes containing G-quadruplex sequences with the 3601 genes we previously identified as NUE-related (for being both N-responsive and yield-associated). This analysis revealed 389 (17%) NUE-related genes containing G-quadruplex sequences. These genes may be involved in the epigenetic regulation of NUE, while the rest of the 83% (1811) genes may regulate NUE through genetic mechanisms and/or other epigenetic means besides G-quadruplexes. A few potentially important genes/processes identified as associated with NUE were experimentally validated in a pair of rice genotypes contrasting for NUE. The results from the WGCNA and G4 sequence analysis of N-responsive genes helped identify and shortlist six genes as candidates to improve NUE. Further, the hitherto unavailable segregation of genetic and epigenetic gene targets could aid in informed interventions through genetic and epigenetic means of crop improvement.

Exploring the crop epigenome: a comparison of DNA methylation profiling techniques

Epigenetic modifications play a vital role in the preservation of genome integrity and in the regulation of gene expression. DNA methylation, one of the key mechanisms of epigenetic control, impacts growth, development, stress response and adaptability of all organisms, including plants. The detection of DNA methylation marks is crucial for understanding the mechanisms underlying these processes and for developing strategies to improve productivity and stress resistance of crop plants. There are different methods for detecting plant DNA methylation, such as bisulfite sequencing, methylation-sensitive amplified polymorphism, genome-wide DNA methylation analysis, methylated DNA immunoprecipitation sequencing, reduced representation bisulfite sequencing, MS and immuno-based techniques. These profiling approaches vary in many aspects, including DNA input, resolution, genomic region coverage, and bioinformatics analysis. Selecting an appropriate methylation screening approach requires an understanding of all these techniques. This review provides an overview of DNA methylation profiling methods in crop plants, along with comparisons of the efficacy of these techniques between model and crop plants. The strengths and limitations of each methodological approach are outlined, and the importance of considering both technical and biological factors are highlighted. Additionally, methods for modulating DNA methylation in model and crop species are presented. Overall, this review will assist scientists in making informed decisions when selecting an appropriate DNA methylation profiling method.

Gene based markers improve precision of genome-wide association studies and accuracy of genomic predictions in rice breeding

It is hypothesized that the genome-wide genic markers may increase the prediction accuracy of genomic selection for quantitative traits. To test this hypothesis, a set of candidate gene-based markers for yield and grain traits-related genes cloned across the rice genome were custom-designed. A multi-model, multi-locus genome-wide association study (GWAS) was performed using new genic markers developed to test their effectiveness for gene discovery. Two multi-locus models, FarmCPU and mrMLM, along with a single-locus mixed linear model (MLM), identified 28 significant marker-trait associations. These associations revealed novel causative alleles for grain weight and pleiotropic associations with other traits. For instance, the marker YD91 derived from the gene OsAAP3 on chromosome 1 was consistently associated with grain weight, while the gene has a significant effect on grain yield. Furthermore, nine genomic selection methods, including regression-based and machine learning-based models, were used to predict grain weight using a leave-one-out five-fold cross-validation approach to optimize the genomic selection model with genic markers. Among nine prediction models, Kernel Hilbert Space Regression (RKHS) is the best among regression-based models, and Random Forest Regression (RFR) is the best among machine learning-based models. Genomic prediction accuracies with and without GWAS significant markers were compared to assess the effectiveness of markers. The rapid decreases in prediction accuracy upon dropping GWAS significant markers indicate the effectiveness of new genic markers in genomic selection. Apart from that, the candidate gene-based markers were found to be more effective in genomic selection programs for better accuracy.

Comparative transcriptomics and co-expression networks reveal cultivar-specific molecular signatures associated with reproductive-stage cold stress in rice

The resistance of Huaidao5 results from the high constitutive expression of tolerance genes, while that of Huaidao9 is due to the cold-induced resistance in flag leaves and panicles. The regulation mechanism of rice seedlings' cold tolerance is relatively clear, and knowledge of its underlying mechanisms at the reproductive stage is limited. We performed differential expression and co-expression network analyses to transcriptomes from panicle and flag leaf tissues of a cold-tolerant cultivar (Huaidao5), and a sensitive cultivar (Huaidao9), under reproductive-stage cold stress. The results revealed that the expression levels of genes in stress-related pathways such as MAPK signaling pathway, diterpenoid biosynthesis, glutathione metabolism, plant-pathogen interaction and plant hormone signal transduction were constitutively highly expressed in Huaidao5, especially in panicles. Moreover, the Hudaidao5's panicle sample-specific (under cold) module contained some genes related to rice yield, such as GW5L, GGC2, SG1 and CTPS1. However, the resistance of Huaidao9 was derived from the induced resistance to cold in flag leaves and panicles. In the flag leaves, the responses included a series of stress response and signal transduction, while in the panicles nitrogen metabolism was severely affected, especially 66 endosperm-specific genes. Through integrating differential expression with co-expression networks, we predicted 161 candidate genes (79 cold-responsive genes common to both cultivars and 82 cold-tolerance genes associated with differences in cold tolerance between cultivars) potentially affecting cold response/tolerance, among which 85 (52.80%) were known to be cold-related genes. Moreover, 52 (65.82%) cold-responsive genes (e.g., TIFY11C, LSK1 and LPA) could be confirmed by previous transcriptome studies and 72 (87.80%) cold-tolerance genes (e.g., APX5, OsFbox17 and OsSTA109) were located within QTLs associated with cold tolerance. This study provides an efficient strategy for further discovery of mechanisms of cold tolerance in rice.

A wheat integrative regulatory network from large-scale complementary functional datasets enables trait-associated gene discovery for crop improvement

Gene regulation is central to all aspects of organism growth, and understanding it using large-scale functional datasets can provide a whole view of biological processes controlling complex phenotypic traits in crops. However, the connection between massive functional datasets and trait-associated gene discovery for crop improvement is still lacking. In this study, we constructed a wheat integrative gene regulatory network (wGRN) by combining an updated genome annotation and diverse complementary functional datasets, including gene expression, sequence motif, transcription factor (TF) binding, chromatin accessibility, and evolutionarily conserved regulation. wGRN contains 7.2 million genome-wide interactions covering 5947 TFs and 127 439 target genes, which were further verified using known regulatory relationships, condition-specific expression, gene functional information, and experiments. We used wGRN to assign genome-wide genes to 3891 specific biological pathways and accurately prioritize candidate genes associated with complex phenotypic traits in genome-wide association studies. In addition, wGRN was used to enhance the interpretation of a spike temporal transcriptome dataset to construct high-resolution networks. We further unveiled novel regulators that enhance the power of spike phenotypic trait prediction using machine learning and contribute to the spike phenotypic differences among modern wheat accessions. Finally, we developed an interactive webserver, wGRN (http://wheat.cau.edu.cn/wGRN), for the community to explore gene regulation and discover trait-associated genes. Collectively, this community resource establishes the foundation for using large-scale functional datasets to guide trait-associated gene discovery for crop improvement.

Co-expression network of heat-response transcripts: A glimpse into how splicing factors impact rice basal thermotolerance

To identify novel solutions to improve rice yield under rising temperatures, molecular components of thermotolerance must be better understood. Alternative splicing (AS) is a major post-transcriptional mechanism impacting plant tolerance against stresses, including heat stress (HS). AS is largely regulated by splicing factors (SFs) and recent studies have shown their involvement in temperature response. However, little is known about the splicing networks between SFs and AS transcripts in the HS response. To expand this knowledge, we constructed a co-expression network based on a publicly available RNA-seq dataset that explored rice basal thermotolerance over a time-course. Our analyses suggest that the HS-dependent control of the abundance of specific transcripts coding for SFs might explain the widespread, coordinated, complex, and delicate AS regulation of critical genes during a plant's inherent response to extreme temperatures. AS changes in these critical genes might affect many aspects of plant biology, from organellar functions to cell death, providing relevant regulatory candidates for future functional studies of basal thermotolerance.

Unraveling the genetics underlying micronutrient signatures of diversity panel present in brown rice through genome-ionome linkages

Rice (Oryza sativa) is an important staple crop to address the Hidden Hunger problem not only in Asia but also in Africa where rice is fast becoming an important source of calories. The brown rice (whole grain with bran) is known to be more nutritious due to elevated mineral composition. The genetics underlying brown rice ionome (sum total of such mineral composition) remains largely unexplored. Hence, we conducted a comprehensive study to dissect the genetic architecture of the brown rice ionome. We used genome-wide association studies, gene set analysis, and targeted association analysis for 12 micronutrients in the brown rice grains. A diverse panel of 300 resequenced indica accessions, with more than 1.02 million single nucleotide polymorphisms, was used. We identified 109 candidate genes with 5-20% phenotypic variation explained for the 12 micronutrients and identified epistatic interactions with multiple micronutrients. Pooling all candidate genes per micronutrient exhibited phenotypic variation explained values ranging from 11% to almost 40%. The key donor lines with larger concentrations for most of the micronutrients possessed superior alleles, which were absent in the breeding lines. Through gene regulatory networks we identified enriched functional pathways for central regulators that were detected as key candidate genes through genome-wide association studies. This study provided important insights on the ionome variations in rice, on the genetic basis of the genome-ionome relationships and on the molecular mechanisms underlying micronutrient signatures.

A comprehensive analysis of NAC gene family in Oryza sativa japonica: a structural and functional genomics approach

NAC gene family regulates diverse aspects of plant growth and developmental processes. The NAC DNA binding domains together with cis-acting elements play inter-related roles in regulating gene expression. In this study, an in silico approach for genome wide analysis of NAC gene in Oryza sativa japonica lead to an identification of 11 NAC genes, distributed over 12 chromosomes. A detailed analysis of phylogenetic relationship, motifs, gene structure, duplication patterns, positive-selection pressure and cis-elements of 11 OsNAC genes were performed. Three pairs of NAC genes with a high degree of homology in terminal nodes were observed and were inferred to be paralogous pairs. One conserved NAC domain was analyzed in all the NAC proteins. Only one gene was studied to be intronless and the majority had 2 introns. Segmental gene duplication pattern was predominant in 11 NAC genes. Ka/Ks ratio of 3 pairs of segmentally duplicated gene was substantially lower than 1, suggesting that the OsNAC sequences are under strong purifying selection pressure. NAC74 and NAC71 gene showed the maximum responsiveness for several factors. The paralogous genes, NAC2 and NAC67 were found to have maximum mya values, respectively. They showed maximum difference amongst themselves in all the categories of responsiveness. Responsiveness towards abscisic acid was observed to be absent in NAC67, but present in NAC2, while responsiveness to meristem inducibility was observed to remain absent in NAC2 but present in NAC67. These results would provide a platform for the future identification and analysis of NAC genes in Oryza sativa japonica.Communicated by Ramaswamy H. Sarma.

cgSSR marker-based genome-wide association study identified genomic regions for panicle characters and yield in rice (Oryza sativa L.)

BACKGROUND

To improve production efficiency, positive alleles corresponding to yield-related attributes must be accumulated in a single elite background. We designed and used cgSSR markers, which are superior to random SSR markers in genome-wide association study, to identify genomic regions that contribute to panicle characters and grain yield in this study.

RESULTS

As evidenced by the high polymorphic information content value and gene diversity coefficient, the new cgSSR markers were determined to be highly informative. These cgSSR markers were employed to generate genotype data for an association panel evaluated for four panicle characters and grain yield over three seasons. For five traits, 17 significant marker-trait associations on six chromosomes were discovered. The percentage of phenotypic variance that could be explained ranged from 4% to 13%. Unrelated gene-derived markers had a strong association with target traits as well.

CONCLUSION

Trait-associated cgSSR markers derived from corresponding or related genes ensure their utility in direct allele selection, while other linked markers aid in allele selection indirectly by altering the phenotype of interest. Through a marker-assisted breeding approach, these marker-trait associations can be leveraged to accumulate favourable alleles for yield enhancement in rice. © 2022 Society of Chemical Industry.

MYB Transcription Factor Family in Pearl Millet: Genome-Wide Identification, Evolutionary Progression and Expression Analysis under Abiotic Stress and Phytohormone Treatments

Transcription factors (TFs) are the regulatory proteins that act as molecular switches in controlling stress-responsive gene expression. Among them, the MYB transcription factor family is one of the largest TF family in plants, playing a significant role in plant growth, development, phytohormone signaling and stress-responsive processes. Pearl millet (Pennisetum glaucum L.) is one of the most important C4 crop plants of the arid and semi-arid regions of Africa and Southeast Asia for sustaining food and fodder production. To explore the evolutionary mechanism and functional diversity of the MYB family in pearl millet, we conducted a comprehensive genome-wide survey and identified 279 MYB TFs (PgMYB) in pearl millet, distributed unevenly across seven chromosomes of pearl millet. A phylogenetic analysis of the identified PgMYBs classified them into 18 subgroups, and members of the same group showed a similar gene structure and conserved motif/s pattern. Further, duplication events were identified in pearl millet that indicated towards evolutionary progression and expansion of the MYB family. Transcriptome data and relative expression analysis by qRT-PCR identified differentially expressed candidate PgMYBs (PgMYB2, PgMYB9, PgMYB88 and PgMYB151) under dehydration, salinity, heat stress and phytohormone (ABA, SA and MeJA) treatment. Taken together, this study provides valuable information for a prospective functional characterization of the MYB family members of pearl millet and their application in the genetic improvement of crop plants.

Leveraging class hierarchy for detecting missing annotations on hierarchical multi-label classification

With the development of new sequencing technologies, availability of genomic data has grown exponentially. Over the past decade, numerous studies have used genomic data to identify associations between genes and biological functions. While these studies have shown success in annotating genes with functions, they often assume that genes are completely annotated and fail to take into account that datasets are sparse and noisy. This work proposes a method to detect missing annotations in the context of hierarchical multi-label classification. More precisely, our method exploits the relations of functions, represented as a hierarchy, by computing probabilities based on the paths of functions in the hierarchy. By performing several experiments on a variety of rice (Oriza sativa Japonica), we showcase that the proposed method accurately detects missing annotations and yields superior results when compared to state-of-art methods from the literature.

Multi-model genome-wide association studies of leaf anatomical traits and vein architecture in rice

Introduction

The anatomy of rice leaves is closely related to photosynthesis and grain yield. Therefore, exploring insight into the quantitative trait loci (QTLs) and alleles related to rice flag leaf anatomical and vein traits is vital for rice improvement.

Methods

Here, we aimed to explore the genetic architecture of eight flag leaf traits using one single-locus model; mixed-linear model (MLM), and two multi-locus models; fixed and random model circulating probability unification (FarmCPU) and Bayesian information and linkage disequilibrium iteratively nested keyway (BLINK). We performed multi-model GWAS using 329 rice accessions of RDP1 with 700K single-nucleotide polymorphisms (SNPs) markers.

Results

The phenotypic correlation results indicated that rice flag leaf thickness was strongly correlated with leaf mesophyll cells layer (ML) and thickness of both major and minor veins. All three models were able to identify several significant loci associated with the traits. MLM identified three non-synonymous SNPs near NARROW LEAF 1 (NAL1) in association with ML and the distance between minor veins (IVD) traits.

Discussion

Several numbers of significant SNPs associated with known gene function in leaf development and yield traits were detected by multi-model GWAS performed in this study. Our findings indicate that flag leaf traits could be improved via molecular breeding and can be one of the targets in high-yield rice development.

Genome-wide identification and expression profiling of WUSCHEL-related homeobox (WOX) genes confer their roles in somatic embryogenesis, growth and abiotic stresses in banana

Plant-specific WUSCHEL-related homeobox (WOX) transcription factors are known to be involved in plant developmental processes, especially in embryogenesis. In this study, a total of thirteen WOX members were identified in the banana (Musa acuminata) genome (MaWOX) and characterized for in-silico analysis. Phylogenetic analysis revealed that these genes were divided into three clades (ancient, intermediate and modern) which reflected the evolutionary history of WOX families. Furthermore, modern clade members have shown higher variations in gene structural features and carried unique conserved motifs (motif 3 and motif 4) when compared to the members of other clades. The differential expression of all 13 MaWOX was observed in early (embryogenic cell suspension (ECS), multiplying ECS, germinating embryos, young leaflet and node of germinated plantlets) and late (unripe fruit peel and pulp, ripe fruit peel and pulp) developmental stages of banana cultivar Grand Naine. The maximum expression of MaWOX6 (18 fold) and MaWOX13 (120 fold) was found during somatic embryogenesis and in unripe fruit pulp, respectively. Moreover, numerous cis-elements responsive to drought, cold, ethylene, methyl jasmonate (MeJA), abscisic acid (ABA) and gibberellic acid (GA) were observed in all MaWOX promoter regions. The subsequent expression analysis under various abiotic stresses (cold, drought and salt) revealed maximum expression of the MaWOX3 (830 fold), MaWOX8a (30 fold) and MaWOX11b (105 fold) in salt stress. It gives evidence about their possible role in salt stress tolerance in banana. Hence, the present study provides precise information on the MaWOX gene family and their expression in various tissues and stressful environmental conditions that may help to develop climate-resilient banana plants.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03387-w.

Rice co-expression network analysis identifies gene modules associated with agronomic traits

Identifying trait-associated genes is critical for rice (Oryza sativa) improvement, which usually relies on map-based cloning, quantitative trait locus analysis, or genome-wide association studies. Here we show that trait-associated genes tend to form modules within rice gene co-expression networks, a feature that can be exploited to discover additional trait-associated genes using reverse genetics. We constructed a rice gene co-expression network based on the graphical Gaussian model using 8,456 RNA-seq transcriptomes, which assembled into 1,286 gene co-expression modules functioning in diverse pathways. A number of the modules were enriched with genes associated with agronomic traits, such as grain size, grain number, tiller number, grain quality, leaf angle, stem strength, and anthocyanin content, and these modules are considered to be trait-associated gene modules. These trait-associated gene modules can be used to dissect the genetic basis of rice agronomic traits and to facilitate the identification of trait genes. As an example, we identified a candidate gene, OCTOPUS-LIKE 1 (OsOPL1), a homolog of the Arabidopsis (Arabidopsis thaliana) OCTOPUS gene, from a grain size module and verified it as a regulator of grain size via functional studies. Thus, our network represents a valuable resource for studying trait-associated genes in rice.

Genome-wide analysis of sulfur-encoding biosynthetic genes in rice (Oryza sativa L.) with Arabidopsis as the sulfur-dependent model plant

Sulfur is an essential element required for plant growth and development, physiological processes and stress responses. Sulfur-encoding biosynthetic genes are involved in the primary sulfur assimilation pathway, regulating various mechanisms at the gene, cellular and system levels, and in the biosynthesis of sulfur-containing compounds (SCCs). In this study, the SCC-encoding biosynthetic genes in rice were identified using a sulfur-dependent model plant, the Arabidopsis. A total of 139 AtSCC from Arabidopsis were used as reference sequences in search of putative rice SCCs. At similarity index > 30%, the similarity search against Arabidopsis SCC query sequences identified 665 putative OsSCC genes in rice. The gene synteny analysis showed a total of 477 syntenic gene pairs comprised of 89 AtSCC and 265 OsSCC biosynthetic genes in Arabidopsis and rice, respectively. Phylogenetic tree of the collated (AtSCCs and OsSCCs) SCC-encoding biosynthetic genes were divided into 11 different clades of various sizes comprised of branches of subclades. In clade 1, nearing equal representation of OsSCC and AtSCC biosynthetic genes imply the most ancestral lineage. A total of 25 candidate Arabidopsis SCC homologs were identified in rice. The gene ontology enrichment analysis showed that the rice-Arabidopsis SCC homologs were significantly enriched in the following terms at false discovery rate (FDR) < 0.05: (i) biological process; sulfur compound metabolic process and organic acid metabolic processes, (ii) molecular function; oxidoreductase activity, acting on paired donors with incorporation or reduction of molecular oxygen and (iii) KEGG pathway; metabolic pathways and biosynthesis of secondary metabolites. At less than five duplicated blocks of separation, no tandem duplications were observed among the SCC biosynthetic genes distributed in rice chromosomes. The comprehensive rice SCC gene description entailing syntenic events with Arabidopsis, motif distribution and chromosomal mapping of the present findings offer a foundation for rice SCC gene functional studies and advanced strategic rice breeding.

The ties of brotherhood between japonica and indica rice for regional adaptation

Selection of beneficial genomic variants was crucial for regional adaptation of crops during domestication, but the underlying genomic basis remains largely unexplored. Here we report a genome-wide selective-sweep analysis of 655 japonica and 1,205 indica accessions selected from 2,673 landraces through principal component analysis to identify 5,636 non-synonymous single nucleotide polymorphisms (SNPs) fixed in at least one subspecies. We classified these SNPs into three groups, jiS (japonica- and indica-selected), jS (japonica-selected only), and iS (indica-selected only), and documented evidence for selection acting on these groups, their relation to yield-related traits, such as heading date, and their practical value in cropping area prediction. We also demonstrated the role of a jiS-SNP-containing gene in temperature adaptability. Our study informs genes underpinning adaptation that may shape Green Super Rice and proposes a time-saving, cost-reducing selection strategy of genomic breeding, sweep-SNP-guided selection, for developing regionally-adapted heterosis.

Phenotypic Variation and the Impact of Admixture in the Oryza rufipogon Species Complex (ORSC)

Crop wild relatives represent valuable reservoirs of variation for breeding, but their populations are threatened in natural habitats, are sparsely represented in genebanks, and most are poorly characterized. The focus of this study is the Oryza rufipogon species complex (ORSC), wild progenitor of Asian rice (Oryza sativa L.). The ORSC comprises perennial, annual and intermediate forms which were historically designated as O. rufipogon, O. nivara, and O. sativa f. spontanea (or Oryza spp., an annual form of mixed O. rufipogon/O. nivara and O. sativa ancestry), respectively, based on non-standardized morphological, geographical, and/or ecologically-based species definitions and boundaries. Here, a collection of 240 diverse ORSC accessions, characterized by genotyping-by-sequencing (113,739 SNPs), was phenotyped for 44 traits associated with plant, panicle, and seed morphology in the screenhouse at the International Rice Research Institute, Philippines. These traits included heritable phenotypes often recorded as characterization data by genebanks. Over 100 of these ORSC accessions were also phenotyped in the greenhouse for 18 traits in Stuttgart, Arkansas, and 16 traits in Ithaca, New York, United States. We implemented a Bayesian Gaussian mixture model to infer accession groups from a subset of these phenotypic data and ascertained three phenotype-based group assignments. We used concordance between the genotypic subpopulations and these phenotype-based groups to identify a suite of phenotypic traits that could reliably differentiate the ORSC populations, whether measured in tropical or temperate regions. The traits provide insight into plant morphology, life history (perenniality versus annuality) and mating habit (self- versus cross-pollinated), and are largely consistent with genebank species designations. One phenotypic group contains predominantly O. rufipogon accessions characterized as perennial and largely out-crossing and one contains predominantly O. nivara accessions characterized as annual and largely inbreeding. From these groups, 42 "core" O. rufipogon and 25 "core" O. nivara accessions were identified for domestication studies. The third group, comprising 20% of our collection, has the most accessions identified as Oryza spp. (51.2%) and levels of O. sativa admixture accounting for more than 50% of the genome. This third group is potentially useful as a "pre-breeding" pool for breeders attempting to incorporate novel variation into elite breeding lines.

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.

XSim version 2: simulation of modern breeding programs

Simulation can be an efficient approach to design, evaluate, and optimize breeding programs. In the era of modern agriculture, breeding programs can benefit from a simulator that integrates various sources of big data and accommodates state-of-the-art statistical models. The initial release of XSim, in which stochastic descendants can be efficiently simulated with a drop-down strategy, has mainly been used to validate genomic selection results. In this article, we present XSim Version 2 that is an open-source tool and has been extensively redesigned with additional features to meet the needs in modern breeding programs. It seamlessly incorporates multiple statistical models for genetic evaluations, such as GBLUP, Bayesian alphabets, and neural networks, and it can effortlessly simulate successive generations of descendants based on complex mating schemes by the aid of its modular design. Case studies are presented to demonstrate the flexibility of XSim Version 2 in simulating crossbreeding in animal and plant populations. Modern biotechnology, including double haploids and embryo transfer, can all be simultaneously integrated into the mating plans that drive the simulation. From a computing perspective, XSim Version 2 is implemented in Julia, which is a computer language that retains the readability of scripting languages (e.g. R and Python) without sacrificing much computational speed compared to compiled languages (e.g. C). This makes XSim Version 2 a simulation tool that is relatively easy for both champions and community members to maintain, modify, or extend in order to improve their breeding programs. Functions and operators are overloaded for a better user interface so they may concatenate, subset, summarize, and organize simulated populations at each breeding step. With the strong and foreseeable demands in the community, XSim Version 2 will serve as a modern simulator bridging the gaps between theories and experiments with its flexibility, extensibility, and friendly interface.

Identification of Potential Cytokinin Responsive Key Genes in Rice Treated With Trans-Zeatin Through Systems Biology Approach

Rice is an important staple food grain consumed by most of the population around the world. With climate and environmental changes, rice has undergone a tremendous stress state which has impacted crop production and productivity. Plant growth hormones are essential component that controls the overall outcome of the growth and development of the plant. Cytokinin is a hormone that plays an important role in plant immunity and defense systems. Trans-zeatin is an active form of cytokinin that can affect plant growth which is mediated by a multi-step two-component phosphorelay system that has different roles in various developmental stages. Systems biology is an approach for pathway analysis to trans-zeatin treated rice that could provide a deep understanding of different molecules associated with them. In this study, we have used a weighted gene co-expression network analysis method to identify the functional modules and hub genes involved in the cytokinin pathway. We have identified nine functional modules comprising of different hub genes which contribute to the cytokinin signaling route. The biological significance of these identified hub genes has been tested by applying well-proven statistical techniques to establish the association with the experimentally validated QTLs and annotated by the DAVID server. The establishment of key genes in different pathways has been confirmed. These results will be useful to design new stress-resistant cultivars which can provide sustainable yield in stress-specific conditions.

Plant Reactome and PubChem: The Plant Pathway and (Bio)Chemical Entity Knowledgebases

Plant Reactome (https://plantreactome.gramene.org) and PubChem ( https://pubchem.ncbi.nlm.nih.gov ) are two reference data portals and resources for curated plant pathways, small molecules, metabolites, gene products, and macromolecular interactions. Plant Reactome knowledgebase, a conceptual plant pathway network, is built by biocuration and integrating (bio)chemical entities, gene products, and macromolecular interactions. It provides manually curated pathways for the reference species Oryza sativa (rice) and gene orthology-based projections that extend pathway knowledge to 106 plant species. Currently, it hosts 320 reference pathways for plant metabolism, hormone signaling, transport, genetic regulation, plant organ development and differentiation, and biotic and abiotic stress responses. In addition to the pathway browsing and search functions, the Plant Reactome provides the analysis tools for pathway comparison between reference and projected species, pathway enrichment in gene expression data, and overlay of gene-gene interaction data on pathways. PubChem, a popular reference database of (bio)chemical entities, provides information on small molecules and other types of chemical entities, such as siRNAs, miRNAs, lipids, carbohydrates, and chemically modified nucleotides. The data in PubChem is collected from hundreds of data sources, including Plant Reactome. This chapter provides a brief overview of the Plant Reactome and the PubChem knowledgebases, their association to other public resources providing accessory information, and how users can readily access the contents.

Structural property, molecular regulation, and functional diversity of glutamine synthetase in higher plants: a data-mining bioinformatics approach

Glutamine synthetase (GS; E.C.6.3.1.2) is a key enzyme in higher plants with two isozymes, cytosolic GS1 and plastidic GS2, and involves in the assimilation and recycling of NH₄⁺ ions and maintenance of complex traits such as crop nitrogen-use efficiency and yield. Our present understanding of crop nitrogen-use efficiency and its correlation with the functional role of the GS family genes is inadequate, which delays harnessing the benefit of this key enzyme in crop improvement. In this report, we performed a comprehensive investigation on the phylogenetic relationship, structural properties, complex multilevel gene regulation, and expression patterns of the GS genes to enrich present understanding about the enzyme. Our Gene Ontology and protein-protein interactions analysis revealed the functional aspects of GS isozymes in stress mitigation, aging, nucleotide biosynthesis/transport, DNA repair and response to metals. The insight gained here contributes to the future research strategies in developing climate-smart crops for global sustainability.

Identification of QTLs for stagnant flooding tolerance in rice employing genotyping by sequencing of a RIL population derived from Swarna × Rashpanjor

In lowland rice ecosystems stagnant flooding or partial submergence has a significant negative impact on important yield attributing traits resulting in substantial grain yield reduction. Genetics of this stress is not yet studied intensively. Rashpanjor (IC 575321), a landrace from India, was identified and used as the tolerant donor for stagnant flooding and was crossed with high yielding variety Swarna to develop the RIL population for the present investigation. Yield and yield attributing traits of 180 F_2:8 lines in rainfed non-stressed and stressed (stagnant flooding with 45 ± 5 cm standing water) conditions were recorded in the wet season of 2018 and stress susceptibility and tolerance indices of yield component traits were deduced. Homo-polymorphic high-quality SNPs between two parents derived from genotyping by sequencing were employed and 17 putative QTLs for plant height, shoot elongation, panicle number, grain weight, panicle length in control and stagnant flooding conditions were identified. Tolerance and susceptibility indexes for these traits were detected in chromosomes 1, 3, 4, 5, 6, 10, 11, and 12 with PVE ranging from 6.53 to 57.89%. Two major QTLs clusters were found for stress susceptibility index of grain and panicle weight on chromosome 1 and plant height in non-stress condition and stress tolerance index of elongation ability on chromosome 3. Putative functional genes present either in associated non-synonymous SNPs or inside the QTL regions were also predicted. Some of them were directly associated with ethylene biosynthesis and encoding auxin responsive factors for better adaptation under stagnant flooding and also coded for different transcription factors viz. NAC domain-binding protein, WRKY gene family, and MYB class known for ROS scavenging and production of metabolites to enhance tolerance to stagnant flooding.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12298-021-01107-x.

OryzaGP 2021 update: a rice gene and protein dataset for named-entity recognition

Due to the rapid evolution of high-throughput technologies, a tremendous amount of data is being produced in the biological domain, which poses a challenging task for information extraction and natural language understanding. Biological named entity recognition (NER) and named entity normalisation (NEN) are two common tasks aiming at identifying and linking biologically important entities such as genes or gene products mentioned in the literature to biological databases. In this paper, we present an updated version of OryzaGP, a gene and protein dataset for rice species created to help natural language processing (NLP) tools in processing NER and NEN tasks. To create the dataset, we selected more than 15,000 abstracts associated with articles previously curated for rice genes. We developed four dictionaries of gene and protein names associated with database identifiers. We used these dictionaries to annotate the dataset. We also annotated the dataset using pre-trained NLP models. Finally, we analysed the annotation results and discussed how to improve OryzaGP.

Comparative genome-wide analysis of WRKY, MADS-box and MYB transcription factor families in Arabidopsis and rice

Transcription factors (TFs) form the major class of regulatory genes and play key roles in multiple plant stress responses. In most eukaryotic plants, transcription factor (TF) families (WRKY, MADS-box and MYB) activate unique cellular-level abiotic and biotic stress-responsive strategies, which are considered as key determinants for defense and developmental processes. Arabidopsis and rice are two important representative model systems for dicot and monocot plants, respectively. A comprehensive comparative study on 101 OsWRKY, 34 OsMADS box and 122 OsMYB genes (rice genome) and, 71 AtWRKY, 66 AtMADS box and 144 AtMYB genes (Arabidopsis genome) showed various relationships among TFs across species. The phylogenetic analysis clustered WRKY, MADS-box and MYB TF family members into 10, 7 and 14 clades, respectively. All clades in WRKY and MYB TF families and almost half of the total number of clades in the MADS-box TF family are shared between both species. Chromosomal and gene structure analysis showed that the Arabidopsis-rice orthologous TF gene pairs were unevenly localized within their chromosomes whilst the distribution of exon–intron gene structure and motif conservation indicated plausible functional similarity in both species. The abiotic and biotic stress-responsive cis-regulatory element type and distribution patterns in the promoter regions of Arabidopsis and rice WRKY, MADS-box and MYB orthologous gene pairs provide better knowledge on their role as conserved regulators in both species. Co-expression network analysis showed the correlation between WRKY, MADs-box and MYB genes in each independent rice and Arabidopsis network indicating their role in stress responsiveness and developmental processes.

MedProDB: A database of Mediator proteins

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Mediator complex is a key component of transcriptional regulation in eukaryotes.

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Identification of Mediator subunits was done by using computational approaches.

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Different physicochemical properties, and functions of Mediators were discussed.

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We have developed first database of Mediator proteins e.g. MedProDB.

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MedProDB contains different types of search and browse options, and various tools.

In the last three decades, the multi-subunit Mediator complex has emerged as the key component of transcriptional regulation of eukaryotic gene expression. Although there were initial hiccups, recent advancements in bioinformatics tools contributed significantly to in-silico prediction and characterization of Mediator subunits from several organisms belonging to different eukaryotic kingdoms. In this study, we have developed the first database of Mediator proteins named MedProDB with 33,971 Mediator protein entries. Out of those, 12531, 11545, and 9895 sequences belong to metazoans, plants, and fungi, respectively. Apart from the core information consisting of sequence, length, position, organism, molecular weight, and taxonomic lineage, additional information of each Mediator sequence like aromaticity, hydropathy, instability index, isoelectric point, functions, interactions, repeat regions, diseases, sequence alignment to Mediator subunit family, Intrinsically Disordered Regions (IDRs), Post-translation modifications (PTMs), and Molecular Recognition Features (MoRFs) may be of high utility to the users. Furthermore, different types of search and browse options with four different tools namely BLAST, Smith-Waterman Align, IUPred, and MoRF-Chibi_Light are provided at MedProDB to perform different types of analysis. Being a critical component of the transcriptional machinery and regulating almost all the aspects of transcription, it generated lots of interest in structural and functional studies of Mediator functioning. So, we think that the MedProDB database will be very useful for researchers studying the process of transcription. This database is freely available at www.nipgr.ac.in/MedProDB.

Comprehensive identification and expression analysis of GRAS gene family under abiotic stress and phytohormone treatments in Pearl millet

Pearl millet is an important C4 cereal plant that possesses enormous capacity to survive under extreme climatic conditions. It serves as a major food source for people in arid and semiarid regions of south-east Asia and Africa. GRAS is an important transcription factor gene family of plant that play a critical role in regulating developmental processes, stress responses and phytohormonal signalling. In the present study, we have identified a total number of 57 GRAS members in pearl millet. Phylogenetic analysis clustered all the PgGRAS genes into eight groups (GroupI-GroupVIII). Motif analysis has shown that all the PgGRAS proteins had conserved GRAS domains and gene structure analysis revealed a high structural diversity among PgGRAS genes. Expression patterns of PgGRAS genes in different tissues (leaf, stem and root) and under various abiotic stress (drought, heat and salinity) were determined. Further, expression analysis was also carried out in response to various hormones (SA, MeJA, GA and ABA). The results provide a clear understanding of GRAS transcription factor family in pearl millet, and lay a good foundation for the functional characterisation of GRAS genes in pearl millet.

Reading between the Lines: RNA-seq Data Mining Reveals the Alternative Message of the Rice Leaf Transcriptome in Response to Heat Stress

Rice (Oryza sativa L.) is a major food crop but heat stress affects its yield and grain quality. To identify mechanistic solutions to improve rice yield under rising temperatures, molecular responses of thermotolerance must be understood. Transcriptional and post-transcriptional controls are involved in a wide range of plant environmental responses. Alternative splicing (AS), in particular, is a widespread mechanism impacting the stress defence in plants but it has been completely overlooked in rice genome-wide heat stress studies. In this context, we carried out a robust data mining of publicly available RNA-seq datasets to investigate the extension of heat-induced AS in rice leaves. For this, datasets of interest were subjected to filtering and quality control, followed by accurate transcript-specific quantifications. Powerful differential gene expression (DE) and differential AS (DAS) identified 17,143 and 2162 heat response genes, respectively, many of which are novel. Detailed analysis of DAS genes coding for key regulators of gene expression suggests that AS helps shape transcriptome and proteome diversity in response to heat. The knowledge resulting from this study confirmed a widespread transcriptional and post-transcriptional response to heat stress in plants, and it provided novel candidates for rapidly advancing rice breeding in response to climate change.

Two NLR immune receptors acquired high-affinity binding to a fungal effector through convergent evolution of their integrated domain

A subset of plant NLR immune receptors carry unconventional integrated domains in addition to their canonical domain architecture. One example is rice Pik-1 that comprises an integrated heavy metal-associated (HMA) domain. Here, we reconstructed the evolutionary history of Pik-1 and its NLR partner, Pik-2, and tested hypotheses about adaptive evolution of the HMA domain. Phylogenetic analyses revealed that the HMA domain integrated into Pik-1 before Oryzinae speciation over 15 million years ago and has been under diversifying selection. Ancestral sequence reconstruction coupled with functional studies showed that two Pik-1 allelic variants independently evolved from a weakly binding ancestral state to high-affinity binding of the blast fungus effector AVR-PikD. We conclude that for most of its evolutionary history the Pik-1 HMA domain did not sense AVR-PikD, and that different Pik-1 receptors have recently evolved through distinct biochemical paths to produce similar phenotypic outcomes. These findings highlight the dynamic nature of the evolutionary mechanisms underpinning NLR adaptation to plant pathogens.

Two NLR immune receptors acquired high-affinity binding to a fungal effector through convergent evolution of their integrated domain

A subset of plant NLR immune receptors carry unconventional integrated domains in addition to their canonical domain architecture. One example is rice Pik-1 that comprises an integrated heavy metal-associated (HMA) domain. Here, we reconstructed the evolutionary history of Pik-1 and its NLR partner, Pik-2, and tested hypotheses about adaptive evolution of the HMA domain. Phylogenetic analyses revealed that the HMA domain integrated into Pik-1 before Oryzinae speciation over 15 million years ago and has been under diversifying selection. Ancestral sequence reconstruction coupled with functional studies showed that two Pik-1 allelic variants independently evolved from a weakly binding ancestral state to high-affinity binding of the blast fungus effector AVR-PikD. We conclude that for most of its evolutionary history the Pik-1 HMA domain did not sense AVR-PikD, and that different Pik-1 receptors have recently evolved through distinct biochemical paths to produce similar phenotypic outcomes. These findings highlight the dynamic nature of the evolutionary mechanisms underpinning NLR adaptation to plant pathogens.

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.

PyRice: a Python package for querying Oryza sativa databases

SUMMARY

Currently, gene information available for Oryza sativa species is located in various online heterogeneous data sources. Moreover, methods of access are also diverse, mostly web-based and sometimes query APIs, which might not always be straightforward for domain experts. The challenge is to collect information quickly from these applications and combine it logically, to facilitate scientific research. We developed a Python package named PyRice, a unified programing API to access all supported databases at the same time with consistent output. PyRice design is modular and implements a smart query system, which fits the computing resources to optimize the query speed. As a result, PyRice is easy to use and produces intuitive results.

AVAILABILITY AND IMPLEMENTATION

https://github.com/SouthGreenPlatform/PyRice.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Collection, preservation and distribution of Oryza genetic resources by the National Bioresource Project RICE (NBRP-RICE)

Biological resources are the basic infrastructure of bioscience research. Rice (Oryza sativa L.) is a good experimental model for research in cereal crops and monocots and includes important genetic materials used in breeding. The availability of genetic materials, including mutants, is important for rice research. In addition, Oryza species are attractive to researchers for both finding useful genes for breeding and for understanding the mechanism of genome evolution that enables wild plants to adapt to their own habitats. NBRP-RICE contributes to rice research by promoting the usage of genetic materials, especially wild Oryza accessions and mutant lines. Our activity includes collection, preservation and distribution of those materials and the provision of basic information on them, such as morphological and physiological traits and genomic information. In this review paper, we introduce the activities of NBRP-RICE and our database, Oryzabase, which facilitates the access to NBRP-RICE resources and their genomic sequences as well as the current situation of wild Oryza genome sequencing efforts by NBRP-RICE and other institutes.

UV-B signalling in rice: Response identification, gene expression profiling and mutant isolation

Responses of rice seedlings to UV-B radiation (UV-B) were investigated, aiming to establish rice as a model plant for UV-B signalling studies. The growth of japonica rice coleoptiles, grown under red light, was inhibited by brief irradiation with UV-B, but not with blue light. The effective UV-B fluences (10^-1 -10³ μmol m^-2 ) were much lower than those reported in Arabidopsis. The response was much less in indica rice cultivars and its extent varied among Oryza species. We next identified UV-B-specific anthocyanin accumulation in the first leaf of purple rice and used this visible phenotype to isolate mutants. Some isolated mutants were further characterized, and one was found to have a defect in the growth response. Using microarrays, we identified a number of genes that are regulated by low-fluence-rate UV-B in japonica coleoptiles. Some up-regulated genes were analysed by real-time PCR for UV-B specificity and the difference between japonica and indica. More than 70% of UV-B-regulated rice genes had no homologs in UV-B-regulated Arabidopsis genes. Many UV-B-regulated rice genes are related to plant hormones and especially to jasmonate biosynthetic and responsive genes in apparent agreement with the growth response. Possible involvement of two rice homologs of UVR8, a UV-B photoreceptor, is discussed.

OryzaGenome2.1: Database of Diverse Genotypes in Wild Oryza Species

BACKGROUND

OryzaGenome ( http://viewer.shigen.info/oryzagenome21detail/index.xhtml ), a feature within Oryzabase ( https://shigen.nig.ac.jp/rice/oryzabase/ ), is a genomic database for wild Oryza species that provides comparative and evolutionary genomics approaches for the rice research community.

RESULTS

Here we release OryzaGenome2.1, the first major update of OryzaGenome. The main feature in this version is the inclusion of newly sequenced genotypes and their meta-information, giving a total of 217 accessions of 19 wild Oryza species (O. rufipogon, O. barthii, O. longistaminata, O. meridionalis, O. glumaepatula, O. punctata, O. minuta, O. officinalis, O. rhizomatis, O. eichingeri, O. latifolia, O. alta, O. grandiglumis, O. australiensis, O. brachyantha, O. granulata, O. meyeriana, O. ridleyi, and O. longiglumis). These 19 wild species belong to 9 genome types (AA, BB, CC, BBCC, CCDD, EE, FF, GG, and HHJJ), representing wide genomic diversity in the genus. Using the genotype information, we analyzed the genome diversity of Oryza species. Other features of OryzaGenome facilitate the use of information on single nucleotide polymorphisms (SNPs) between O. sativa and its wild progenitor O. rufipogon in rice research, including breeding as well as basic science. For example, we provide Variant Call Format (VCF) files for genome-wide SNPs of 33 O. rufipogon accessions against the O. sativa reference genome, IRGSP1.0. In addition, we provide a new SNP Effect Table function, allowing users to identify SNPs or small insertion/deletion polymorphisms in the 33 O. rufipogon accessions and to search for the effect of these polymorphisms on protein function if they reside in the coding region (e.g., are missense or nonsense mutations). Furthermore, the SNP Viewer for 446 O. rufipogon accessions was updated by implementing new tracks for possible selective sweep regions and highly mutated regions that were potentially exposed to selective pressures during the process of domestication.

CONCLUSION

OryzaGenome2.1 focuses on comparative genomic analysis of diverse wild Oryza accessions collected around the world and on the development of resources to speed up the identification of critical trait-related genes, especially from O. rufipogon. It aims to promote the use of genotype information from wild accessions in rice breeding and potential future crop improvements. Diverse genotypes will be a key resource for evolutionary studies in Oryza, including polyploid biology.

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.

A Coordinated Suite of Wild-Introgression Lines in Indica and Japonica Elite Backgrounds

Rice, Oryza sativa L., is a cultivated, inbreeding species that serves as the staple food for the largest number of people on earth. It has two strongly diverged varietal groups, Indica and Japonica, which result from a combination of natural and human selection. The genetic divergence of these groups reflects the underlying population structure of their wild ancestors, and suggests that a pre-breeding strategy designed to take advantage of existing genetic, geographic and ecological substructure may provide a rational approach to the utilization of crop wild ancestors in plant improvement. Here we describe the coordinated development of six introgression libraries (n = 63 to 81 lines per library) in both Indica (cv. IR64) and Japonica (cv. Cybonnet) backgrounds using three bio-geographically diverse wild donors representing the Oryza rufipogon Species Complex from China, Laos and Indonesia. The final libraries were genotyped using an Infinium 7K rice SNP array (C7AIR) and analyzed under greenhouse conditions for several simply inherited (Mendelian) traits. These six interspecific populations can be used as individual Chromosome Segment Substitution Line libraries and, when considered together, serve as a powerful genetic resource for systematic genetic dissection of agronomic, physiological and developmental traits in rice.

Profiling of rice Cd-tolerant genes through yeast-based cDNA library survival screening

The bioaccumulation of cadmium (Cd) in crop and the subsequent food chain has aroused extensive concerns. However, the underlying molecular mechanisms of plant Cd tolerance remain to be clarified from the viewpoint of novel candidate genes. Here we described a highly efficient approach for preliminary identifying rice Cd-tolerant genes through the yeast-based cDNA library survival screening combined with high-throughput sequencing strategy. About 690 gene isoforms were identified as being Cd-tolerant candidates using this shotgun approach. Among the Cd-tolerant genes identified, several categories of genes such as BAX inhibitor (BI), NAC transcription factors and Rapid ALkalinization Factors (RALFs) were of particular interest, and their function of Cd tolerance was further validated via heterologous expression, which suggested that SNAC1, RALF12, OsBI-1 can confer Cd tolerance in yeast and tobacco leaves. Regarding the genes involved in ion transport, the validated Cd-tolerant heavy metal-associated domain (HMAD) isoprenylated protein HIPP42 was particularly noteworthy. Further elucidation of these genes associated with Cd tolerance in rice will benefit agricultural activities.

Light-induced chloroplast movements in Oryza species

Light-induced chloroplast movements control efficient light utilization in leaves, and thus, are essential for leaf photosynthesis and biomass production under fluctuating light conditions. Chloroplast movements have been intensively analyzed using wild-type and mutant plants of Arabidopsis thaliana. The molecular mechanism and the contribution to biomass production were elucidated. However, the knowledge of chloroplast movements is very scarce in other plant species, especially grass species including crop plants. Because chloroplast movements are efficient strategy to optimize light capture in leaves and thus promote leaf photosynthesis and biomass, analysis of chloroplast movements in crops is required for biomass production. Here, we analyzed chloroplast movements in a wide range of cultivated and wild species of genus Oryza. All examined Oryza species showed the blue-light-induced chloroplast movements. However, O. sativa and its ancestral species O. rufipogon, both of which are AA-genome species and usually grown in open condition where plants are exposed to full sunlight, showed the much weaker chloroplast movements than Oryza species that are usually grown under shade or semi-shade conditions, including O. officinalis, O. eichingeri, and O. granulata. Further detailed analyses of different O. officinalis accessions, including sun, semi-shade, and shade accessions, indicated that the difference in chloroplast movement strength between domesticated rice plants and wild species might result from the difference in habitat, and the shape of mesophyll chlorenchyma cells. The findings of this study provide useful information for optimizing Oryza growth conditions, and lay the groundwork for improving growth and yield in staple food crop Oryza sativa.

Heavy Metal Stress-Associated Proteins in Rice and Arabidopsis: Genome-Wide Identification, Phylogenetics, Duplication, and Expression Profiles Analysis

Heavy metal exposure is a serious environmental stress in plants. However, plants have evolved several strategies to improve their heavy metal tolerance. Heavy metal-associated proteins (HMPs) participate in heavy metal detoxification. Here, we identified 46 and 55 HMPs in rice and Arabidopsis, respectively, and named them OsHMP 1–46 and AtHMP 1–55 according to their chromosomal locations. The HMPs from both plants were divided into six clades based on the characteristics of their heavy metal-associated domains (HMA). The HMP gene structures and motifs varied greatly among the different classifications. The HMPs had high collinearity and were segmentally duplicated. A cis-element analysis revealed that the HMPs may be regulated by different transcription factors. An expression profile analysis disclosed that only eight OsHMPs were constitutive in rice tissues. Of these, the expression of OsHMP37 was far higher than that of the other seven genes while OsHMP28 was expressed exclusively in the roots. For Arabidopsis, nine AtHMPs presented with very high transcript levels in all organs. Most of the selected OsHMPs were differentially expressed in various tissues under different heavy metal stresses. Only OsHMP09, OsHMP18, and OsHMP22 showed higher expression levels in all tissues under different heavy metal stresses. In contrast, most of the selected AtHMPs had nearly constant expression levels in different tissues under various heavy metal stresses. The AtHMP20, AtHMP23, AtHMP25, AtHMP31, AtHMP35, AtHMP46 expression levels under different heavy metal stresses were higher in the leaves and roots. The foregoing discoveries elucidated HMP evolution in monocotyledonous and dicotyledonous plants and may helpful functionally characterize HMPs in the future.