ArticlePublisher preview available

Yield-associated putative gene regulatory networks in Oryza sativa L. subsp. indica and their association with high-yielding genotypes

Authors:
Aparna Eragam
Aparna Eragam
Aparna Eragam
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Vishnu Shukla at Indian Institute of Science Education and Research, Tirupati
Vishnu Shukla
Vijaya Sudhakararao Kola
Vijaya Sudhakararao Kola
Vijaya Sudhakararao Kola
  • This person is not on ResearchGate, or hasn't claimed this research yet.
P. Latha at Acharya N G Ranga Agricultural University
P. Latha
Show all 8 authorsHide
Read publisher preview
Download citation
To read the full-text of this research, you can request a copy directly from the authors.

Abstract and Figures

Background With the increase in population and economies of developing countries in Asia and Africa, the research towards securing future food demands is an imminent need. Among japonica and indica genotypes, indica rice varieties are largely cultivated across the globe. However, our present understanding of yield-contributing gene information stems mainly from japonica and studies on the yield potential of indica genotypes are limited. Methods and results In the present study, yield contributing orthologous genes previously characterized from japonica varieties were identified in the indica genome and analysed with binGO tool for GO biological processes categorization. Transcription factor binding site enrichment analysis in the promoters of yield-related genes of indica was performed with MEME-AME tool that revealed putative common TF regulators are enriched in flower development, two-component signalling and water deprivation biological processes. Gene regulatory networks revealed important TF-target interactions that might govern yield-related traits. Some of the identified candidate genes were validated by qRT-PCR analysis for their expression and association with yield-related traits among 16 widely cultivated popular indica genotypes. Further, SNP-metabolite-trait association analysis was performed using high-yielding indica variety Rasi. This resulted in the identification of putative SNP variations in TF regulators and targeted yield genes significantly linked with metabolite accumulation. Conclusions The study suggests some of the high yielding indica genotypes such as Ravi003, Rasi and Kavya could be used as potential donors in breeding programs based on yield gene expression analysis and SNP-metabolites associations.
Transcription factor motif enrichment of yield trait-related genes in Oryza sativa (indica group). Transcription factor binding site (motif) enrichment stipulated conserved group of Transcription factor (TFs) as significant regulators of yield trait-related genes. The enrichment analysis was categorized in a heat map which compares TF binding site enrichment (columns) in promoter region of yield-related genes belonging to yield trait components (row). TF family groups are annotated with distinct colors on the left panel. Heatmap scale at the top indicates the range of significant adjusted p-values (-log10). Briefly, different rice genes (indica group) representative of yield component traits (PN panicle, PR productivity, DW dwarf, SD seed and GR grain) were selected from previous studies (Table S1a). Further, 2 kb upstream region of transcription start site was searched for the enrichment of transcription factor (TF) motifs in MEME Suite AME tool
Transcription factor motif enrichment of yield trait-related genes in Oryza sativa (indica group). Transcription factor binding site (motif) enrichment stipulated conserved group of Transcription factor (TFs) as significant regulators of yield trait-related genes. The enrichment analysis was categorized in a heat map which compares TF binding site enrichment (columns) in promoter region of yield-related genes belonging to yield trait components (row). TF family groups are annotated with distinct colors on the left panel. Heatmap scale at the top indicates the range of significant adjusted p-values (-log10). Briefly, different rice genes (indica group) representative of yield component traits (PN panicle, PR productivity, DW dwarf, SD seed and GR grain) were selected from previous studies (Table S1a). Further, 2 kb upstream region of transcription start site was searched for the enrichment of transcription factor (TF) motifs in MEME Suite AME tool
… 
Conserved Biological processes enriched for yield trait regulation in rice indica group. The heat map (upper panel) indicates similar enrichment significance values (-log10 adjusted p-value) of TFs targeting different yield trait-associated genes (PN panicle, PR productivity, DW dwarf, SD seed and GR grain). The TF family grouping is kept identical with Fig. 1. The enrichment network (lower panel) showed significant association of putative yield trait-TFs with biological processes involved in regulation of hormone stimulus, carbohydrate stimulus, stress response, seed germination and flower development. For functional categorization, each TF was assigned their respective GO term using binGO V.3.0.3 by mapping onto rice-specific annotation (prepared using RAP-DB and TBtools). The network was constructed in cytoscape with EnrichmentMap plugin [30]. The circles are labelled with enriched GO ID numbers. The color intensity (scale bar given) depicts p-value significance for enrichment and size of the circle denotes gene count. The biological processes are grouped according to their functional associations
Conserved Biological processes enriched for yield trait regulation in rice indica group. The heat map (upper panel) indicates similar enrichment significance values (-log10 adjusted p-value) of TFs targeting different yield trait-associated genes (PN panicle, PR productivity, DW dwarf, SD seed and GR grain). The TF family grouping is kept identical with Fig. 1. The enrichment network (lower panel) showed significant association of putative yield trait-TFs with biological processes involved in regulation of hormone stimulus, carbohydrate stimulus, stress response, seed germination and flower development. For functional categorization, each TF was assigned their respective GO term using binGO V.3.0.3 by mapping onto rice-specific annotation (prepared using RAP-DB and TBtools). The network was constructed in cytoscape with EnrichmentMap plugin [30]. The circles are labelled with enriched GO ID numbers. The color intensity (scale bar given) depicts p-value significance for enrichment and size of the circle denotes gene count. The biological processes are grouped according to their functional associations
… 
Putative yield-associated regulatory TF-target interaction network in rice indica group. Putative common TFs categorized to a water deprivation, b two-component signalling and c flower development, were found to significantly target yield trait-associated genes either in exclusive or shared manner. For interaction network, the conserved TFs and their targets in 2 kb promoter region from rice indica genome were merged in a network file (Table S1b, S1c and S1d) and visualized in cytoscape [56]. The TFs belonging to specific class of transcription factor family, mainly AP2ERBP, MYB, C2C2DOF, LOBAS2, BBRBPC and ND are denoted by colour palette arranged in bottom right corner with number of target interactions (1 to 7) of TFs at the top and known targets (red bordered circle) at the bottom
Putative yield-associated regulatory TF-target interaction network in rice indica group. Putative common TFs categorized to a water deprivation, b two-component signalling and c flower development, were found to significantly target yield trait-associated genes either in exclusive or shared manner. For interaction network, the conserved TFs and their targets in 2 kb promoter region from rice indica genome were merged in a network file (Table S1b, S1c and S1d) and visualized in cytoscape [56]. The TFs belonging to specific class of transcription factor family, mainly AP2ERBP, MYB, C2C2DOF, LOBAS2, BBRBPC and ND are denoted by colour palette arranged in bottom right corner with number of target interactions (1 to 7) of TFs at the top and known targets (red bordered circle) at the bottom
… 
Grain yield-based phenotypic association of 16 Oryza sativa (indica group) genotypes. Correlation of phenotypic variations related to different yield attributes classified 16 rice indica genotypes into high, medium and low yield. a Pearson correlation coefficients among 9 yield attributes (DTM days to maturity, PH plant height, PL panicle length, NP number of panicles, NFG, number of filled grains, TSW thousand grain weight, NSP number of spikelets/plant, SF spikelet fertility and GYP grain yield/plant) during the plant growth. Positive correlations are denoted in red and negative correlations in blue color. Circle size and color intensity represents correlation coefficients. b Comparative plot of NFG and NSP phenotypic attributes in 16 rice indica genotypes. The X-axis and Y-axis represents rice indica genotype names and quantitative value, respectively. Based on the significant variations in the phenotypic attributes, 16 genotypes were considered as high, medium and low yield. The color palette is indicated on the right of the plot. The represented data are means of three biological replicates. Vertical bars indicate the standard error. The complete statistical analyses are provided in Table S2b. c Expression validation of known candidate target genes was performed using qPCR in flag leaf and young panicle of 16 indica rice genotypes. The specific primers used are listed in Table S2e. The colour scale for transcript abundance levels are shown at the right side of the figure along with genotype and tissue information. The represented transcript abundances are means of three biological replicates. The complete statistical analyses are provided in Table S2c and Table S2d
Grain yield-based phenotypic association of 16 Oryza sativa (indica group) genotypes. Correlation of phenotypic variations related to different yield attributes classified 16 rice indica genotypes into high, medium and low yield. a Pearson correlation coefficients among 9 yield attributes (DTM days to maturity, PH plant height, PL panicle length, NP number of panicles, NFG, number of filled grains, TSW thousand grain weight, NSP number of spikelets/plant, SF spikelet fertility and GYP grain yield/plant) during the plant growth. Positive correlations are denoted in red and negative correlations in blue color. Circle size and color intensity represents correlation coefficients. b Comparative plot of NFG and NSP phenotypic attributes in 16 rice indica genotypes. The X-axis and Y-axis represents rice indica genotype names and quantitative value, respectively. Based on the significant variations in the phenotypic attributes, 16 genotypes were considered as high, medium and low yield. The color palette is indicated on the right of the plot. The represented data are means of three biological replicates. Vertical bars indicate the standard error. The complete statistical analyses are provided in Table S2b. c Expression validation of known candidate target genes was performed using qPCR in flag leaf and young panicle of 16 indica rice genotypes. The specific primers used are listed in Table S2e. The colour scale for transcript abundance levels are shown at the right side of the figure along with genotype and tissue information. The represented transcript abundances are means of three biological replicates. The complete statistical analyses are provided in Table S2c and Table S2d
… 
Figures - available from: Molecular Biology Reports
This content is subject to copyright. Terms and conditions apply.
A preview of this full-text is provided by Springer Nature.
Learn more
Content available from Molecular Biology Reports
This content is subject to copyright. Terms and conditions apply.
Vol.:(0123456789)
1 3
Molecular Biology Reports (2022) 49:7649–7663
https://doi.org/10.1007/s11033-022-07581-0
ORIGINAL ARTICLE
Yield‑associated putative gene regulatory networks inOryza sativa L.
subsp. indica andtheir association withhigh‑yielding genotypes
AparnaEragam1,2· VishnuShukla2· VijayaSudhakararaoKola2· P.Latha3· SrividhyaAkkareddy3·
MadhaviL.Kommana4· EswarayyaRamireddy2 · LakshminarayanaR.Vemireddy1
Received: 27 January 2022 / Accepted: 6 May 2022 / Published online: 25 May 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 2022
Abstract
Background With the increase in population and economies of developing countries in Asia and Africa, the research towards
securing future food demands is an imminent need. Among japonica and indica genotypes, indica rice varieties are largely
cultivated across the globe. However, our present understanding of yield-contributing gene information stems mainly from
japonica and studies on the yield potential of indica genotypes are limited.
Methods and results In the present study, yield contributing orthologous genes previously characterized from japonica
varieties were identified in the indica genome and analysed with binGO tool for GO biological processes categorization.
Transcription factor binding site enrichment analysis in the promoters of yield-related genes of indica was performed with
MEME-AME tool that revealed putative common TF regulators are enriched in flower development, two-component sig-
nalling and water deprivation biological processes. Gene regulatory networks revealed important TF-target interactions
that might govern yield-related traits. Some of the identified candidate genes were validated by qRT-PCR analysis for their
expression and association with yield-related traits among 16 widely cultivated popular indica genotypes. Further, SNP-
metabolite-trait association analysis was performed using high-yielding indica variety Rasi. This resulted in the identifica-
tion of putative SNP variations in TF regulators and targeted yield genes significantly linked with metabolite accumulation.
Conclusions The study suggests some of the high yielding indica genotypes such as Ravi003, Rasi and Kavya could be
used as potential donors in breeding programs based on yield gene expression analysis and SNP-metabolites associations.
Keywords Rice· Oryza sativa L.· Gene regulatory networks· Gene expression· SNP-metabolite associations· Yield and
high-yielding genotypes
* Eswarayya Ramireddy
eswar.ramireddy@iisertirupati.ac.in
* Lakshminarayana R. Vemireddy
vlnreddy@angrau.ac.in
Aparna Eragam
aparnareddy003@gmail.com
Vishnu Shukla
vishnushukla@iisertirupati.ac.in
Vijaya Sudhakararao Kola
vijayasudhakarkola@rediffmail.com
P. Latha
p.latha@angrau.ac.in
Srividhya Akkareddy
a.srividhya@angrau.ac.in
Madhavi L. Kommana
madhavi2011040@gmail.com
1 Department ofMolecular Biology andBiotechnology, S.V.
Agricultural College, Acharya NG Ranga Agricultural
University (ANGRAU), Tirupati517502, India
2 Biology Division, Indian Institute ofScience Education
andResearch Tirupati (IISER Tirupati), Tirupati517507,
India
3 Regional Agricultural Research Station (RARS), ANGRAU ,
Tirupati, India
4 Department ofGenetics andPlant Breeding,
S.V. Agricultural College, Acharya NG Ranga Agricultural
University (ANGRAU), Tirupati517502, India
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Genetic and functional mechanisms of yield-related genes in rice
Article
  • Feb 2024
Rice yield potentiality has been enhanced much after incorporating semi-dwarf trait in rice, which has led to the Green Revolution worldwide, but afterward, yield potentiality has increased marginally. To keep pace with increasing food grain demand due to increasing population, rice production needs to be enhanced even though available land, water, and other natural resources are limited. Thus, the only option is to improve intrinsic yield potentiality. Generally, rice yield is determined by different direct and indirect source and sink size-related components such as grain number per panicle, weight of grains, number of tillers, panicle architecture, heading date, etc. During the last two decades, QTL mapping and map-based cloning have identified several QTLs and genes controlling intrinsic and extrinsic factors of yield, followed by proper validation for their yield-enhancing ability in diverse rice backgrounds of indica and japonica subspecies. Because of the character's complexity, identifying favorable alleles of these genes, followed by gene pyramiding is the main strategy for high-yielding variety development. Any update regarding the molecular mechanism, gene functions and gene-to-gene interaction will assist a breeder to move a step ahead toward yield improvement. This review summarizes the recent progress of these genes and their contributions to yield and this information will further advance and facilitate rice breeding with more precision and efficiency.
View
CORRELATION AND PATH ANALYSIS FOR YIELD AND YIELD COMPONENTS IN RICE (ORYZA SATIVA L.)
Article
Full-text available
  • Jan 2021
Correlation studies in rice revealed that genotypic correlation coefficients were higher than phenotypic correlation coefficients for most of the characters under study. Character association analysis revealed significantly positive association of grain yield per plant with number of productive tillers per plant, 1000-grain weight, panicle length and number of grains per panicle. Correlations among yield components were positive, encouraging rapid improvement of yield. Path analysis revealed that number of grains per panicle, days to 50 per cent flowering, 1000-grain weight and number of productive tillers per plant have shown high positive direct effects on grain yield.
View
Regulator Network Analysis of Rice and Maize Yield-Related Genes
Article
Full-text available
  • Dec 2020
Rice and maize are the principal food crop species worldwide. The mechanism of gene regulation for the yield of rice and maize is still the research focus at present. Seed size, weight and shape are important traits of crop yield in rice and maize. Most members of three gene families, APETALA2/ethylene response factor, auxin response factors and MADS, were identified to be involved in yield traits in rice and maize. Analysis of molecular regulation mechanisms related to yield traits provides theoretical support for the improvement of crop yield. Genetic regulatory network analysis can provide new insights into gene families with the improvement of sequencing technology. Here, we analyzed the evolutionary relationships and the genetic regulatory network for the gene family members to predicted genes that may be involved in yield-related traits in rice and maize. The results may provide some theoretical and application guidelines for future investigations of molecular biology, which may be helpful for developing new rice and maize varieties with high yield traits.
View
Rice OsLHT1 Functions in Leaf-to-Panicle Nitrogen Allocation for Grain Yield and Quality
Article
Full-text available
  • Jul 2020
Proper allocation of nitrogen (N) from source leaves to grains is essential step for high crop grain yield and N use efficiency. In rice (Oryza sativa) grown in flooding paddy field, amino acids are the major N compounds for N distribution and re-allocation. We have recently identified that Lysine-Histidine-type Transporter 1 (OsLHT1) is the major transporter for root uptake and root-to-shoot allocation of amino acids in rice. In this study, we planted knockout mutant lines of OsLHT1 together wild-type (WT) in paddy field for evaluating OsLHT1 function in N redistribution and grain production. OsLHT1 is expressed in vascular bundles of leaves, rachis, and flowering organs. Oslht1 plants showed lower panicle length and seed setting rate, especially lower grain number per panicle and total grain weight. The concentrations of both total N and free amino acids in the flag leaf were similar at anthesis between Oslht1 lines and WT while significantly higher in the mutants than WT at maturation. The Oslht1 seeds contained higher proteins and most of the essential free amino acids, similar total starch but less amylose with lower paste viscosity than WT seeds. The mutant seeds showed lower germination rate than WT. Knockout of OsLHT1 decreased N uptake efficiency and physiological utilization efficiency (kg-grains/kg-N) by about 55% and 72%, respectively. Taken together, we conclude that OsLHT1 plays critical role in the translocation of amino acids from vegetative to reproductive organs for grain yield and quality of nutrition and functionality.
View
Genome-wide analysis and transcript profiling of PSKR gene family members in Oryza sativa
Article
Full-text available
Peptide signalling is an integral part of cell-to-cell communication which helps to relay the information responsible for coordinating cell proliferation and differentiation. Phytosulfokine Receptor (PSKR) is a transmembrane LRR-RLK family protein with a binding site for small signalling peptide, phytosulfokine (PSK). PSK signalling through PSKR promotes normal growth and development and also plays a role in defense responses. Like other RLKs, these PSKRs might have a role in signal transduction pathways related to abiotic stress responses. Genome-wide analysis of phytosulfokine receptor gene family has led to the identification of fifteen putative members in the Oryza sativa genome. The expression analysis of OsPSKR genes done using RNA-seq data, showed that these genes were differentially expressed in different tissues and responded specifically to heat, salt, drought and cold stress. Furthermore, the real-time quantitative PCR for fifteen OsPSKR genes revealed temporally and spatially regulated gene expression corresponding to salinity and drought stress. Our results provide useful information for a better understanding of OsPSKR genes and provide the foundation for additional functional exploration of the rice PSKR gene family in development and stress response.
View
Identification of QTLs for high grain yield and component traits in new plant types of rice
Article
Full-text available
A panel of 60 genotypes comprising New Plant Types (NPTs) along with indica, tropical and temperate japonica genotypes was phenotypically evaluated for four seasons in irrigated situation for grain yield per se and component traits. Twenty NPT genotypes were found promising with an average grain yield varying from 5.45 to 8.8 t/ha. A total of 85 SSR markers were used in the study to identify QTLs associated with grain yield per se and related traits. Sixty-six (77.65%) markers were found to be polymorphic. The PIC values varied from 0.516 to 0.92 with an average of 0.704. A moderate level of genetic diversity (0.39) was detected among genotypes. Variation to the tune of 8% within genotypes, 68% among the genotypes within the population and 24% among the populations were observed (AMOVA). This information may help in identification of potential parents for development of transgressive segregants with very high yield. The association analysis using GLM and MLM models led to the identification of 30 and 10 SSR markers associated with 70 and 16 QTLs, respectively. Thirty novel QTLs linked with 16 SSRs were identified to be associated with eleven traits, namely tiller number (qTL-6.1, qTL-11.1, qTL-4.1), panicle length (qPL-1.1, qPL-5.1, qPL-7.1, qPL-8.1), flag leaf length (qFLL-8.1, qFLL-9.1), flag leaf width (qFLW-6.2, qFLW-5.1, qFLW-8.1, qFLW-7.1), total no. of grains (qTG-2.2, qTG-a7.1), thousand-grain weight (qTGW-a1.1, qTGW-a9.2, qTGW-5.1, qTGW-8.1), fertile grains (qFG-7.1), seed length-breadth ratio (qSlb-3.1), plant height (qPHT-6.1, qPHT-9.1), days to 50% flowering (qFD-1.1) and grain yield per se (qYLD-5.1, qYLD-6.1a, qYLD-11.1).Some of the SSRs were co-localized with more than two traits. The highest co-localization was identified with RM5709 linked to nine traits, followed by RM297 with five traits. Similarly, RM5575, RM204, RM168, RM112, RM26499 and RM22899 were also recorded to be co-localized with more than one trait and could be rated as important for marker-assisted backcross breeding programs, for pyramiding of these QTLs for important yield traits, to produce new-generation rice for prospective increment in yield potentiality and breaking yield ceiling.
View
Rice gene, OsCKX2-2, regulates inflorescence and grain size by increasing endogenous cytokinin content
Article
Full-text available
  • Nov 2020
  • PLANT GROWTH REGUL
Panicle size is one of the yield determining traits in rice (Oryza sativa L.). In the current study, a rice mutant named cytokinin oxidase/dehydrogenase (Osckx2-2) was developed from the indica cultivar 9311 by ethyl methanesulfonate (EMS) mutagenesis. A comparison of agronomic traits showed that the panicle of the Osckx2-2 mutant was longer with dense branching pattern and higher seed setting rate compared to the wild-type (WT). In addition, the grains in the Osckx2-2 mutant were markedly longer, wider, and heavier compared to the WT. Histological analysis revealed that a higher number of the spikelet cells with bigger size resulted in larger grains in the Osckx2-2 mutant. By map-based cloning, SNP was detected in the 3′-UTR of the OsCKX2-2 gene which encodes cytokinin dehydrogenase and the cleaved amplified polymorphic sequence (CAPS) marker was developed based on the detected SNP. The RT-qPCR analysis displayed a decreased OsCKX2-2 expression in the Osckx2-2 mutant panicles. Meanwhile, the enzymatic activity assay showed the reduced cytokinin dehydrogenase enzyme activity, whereas the enzyme-linked immunosorbent assay (ELISA) revealed remarkably higher endogenous cytokinin content in the Osckx2-2 mutant in contrast to WT. The analysis of grain quality parameters showed that the degree of chalkiness and gel consistency decreased, while the amylose content and alkaline spreading value increased in the Osckx2-2 mutant. The OsCKX2-2 gene and Osckx2-2 mutant would be beneficial in marker-assisted backcrossing to improve yield in rice.
View
Natural Sequence Variations and Combinations of GNP1 and NAL1 Determine the Grain Number per Panicle in Rice
Article
Full-text available
  • Feb 2020
Background: The grain number per panicle (GNP), which is one of three grain yield components, is an important trait for the genetic improvement of rice. Although the NAL1 and GNP1 genes regulating the rice GNP and grain yield have been cloned, their allelic diversity, functional differences in rice germplasms, and effects of their combination on GNP and grain yield remain unclear. Results: Based on DNA sequences of these two genes in 198 cultivated rice (Oryza sativa) and 8-10 wild rice (Oryza rufipogon) germplasms, 16 and 14 haplotypes were identified for NAL1 and GNP1, respectively. The NAL1 gene had the strongest effects on GNP in indica (xian) and japonica (geng) subpopulations. In contrast, GNP1 had no significant effects in the geng subpopulation and was rare in the xian background, in which the superior GNP1 allele (GNP1-6) was detected in only 4.0% of the 198 germplasms. Compared with the transgenic lines with GNP1 or NAL1, the transgenic lines with both genes had a higher GNP (15.5%-25.4% and 11.6%-15.9% higher, respectively) and grain yield (5.7%-9.0% and 8.3%-12.3% higher, respectively) across 3 years. The two genes combined in the introgression lines in Lemont background resulted in especially favorable effects on the GNP. Conclusions: Our results indicated that the GNP1 and NAL1 exhibited obvious differentiation and their combinations can significantly increase the grain yield in geng rice cultivars. These observations provide insights into the molecular basis of the GNP and may be useful for rice breeding of high yield potential by pyramiding GNP1 and NAL1.
View
Integrating dynamics of yield traits in rice responding to environmental changes
Article
Full-text available
  • Aug 2019
  • J EXP BOT
Crop yield reduction, as a consequence of global climate change, threatens worldwide food security. Hence, it is imperative to develop high-yielding crop plants that show sustainable production under stress conditions. For achieving this target through breeding or genetic engineering, a complete and comprehensive understanding of the molecular basis of plant architecture and regulation of its sub-components, contributing to yield under stress is crucial. Rice is one of the most widely consumed crops adversely influenced by abiotic stresses such as drought and salinity. Taking rice as a model system, in the present review, we present a consolidated account on the current understanding of the physiological and molecular architecture determining its yield traits under optimal as well as sub-optimal growth conditions. Based on the physiological function of these genes, we also project the best possible combination of genes which may improve the grain yield under optimal as well as environmental stress conditions. This information on rice system is very critical and will contribute to ensuring the availability of 'food for all' and the knowledge presented here will also be useful for similar studies in other grain crops.
View
ZmRAP2.7, an AP2 Transcription Factor, Is Involved in Maize Brace Roots Development
Article
Full-text available
  • Jul 2019
In maize, shoot-borne roots dominate the whole root system and play essential roles in water and nutrient acquisition and lodging tolerance. Shoot-borne roots initiate at shoot nodes, including crown roots from the belowground nodes and brace roots from aboveground nodes. In contrast to crown roots, few genes for brace roots development have been identified. Here, we characterized a maize AP2/ERF transcription factor, ZmRAP2.7, to be involved in brace roots development. ZmRAP2.7 expressed in all types of roots, and the encoded protein localized in the nucleus with transcriptional activation activity. A maize transposon insert mutant RAP2.7-Mu defective in ZmRAP2.7 expression revealed a decreased number of brace roots but not crown roots. Maize Corngrass1 mutant, which showed an elevated expression of ZmRAP2.7, however, revealed an increased number of brace roots. The ZmRAP2.7-based association analysis in a maize panel further identified a SNP marker at the fifth exon of gene to be associated with number of brace roots. These results uncovered a function of ZmRAP2.7 in brace roots development and provided the valuable gene and allele for genetic improvement of maize root systems.
View
TBtools: An Integrative Toolkit Developed for Interactive Analyses of Big Biological Data
Article
  • Jun 2020
The rapid development of high-throughput sequencing (HTS) techniques has led biology into the big-data era. Data analyses using various bioinformatics tools rely on programming and command-line environments, which are challenging and time-consuming for most wet-lab biologists. Here, we present TBtools (a Toolkit for Biologists integrating various biological data handling tools), a stand-alone software with a user-friendly interface. The toolkit incorporates over 130 functions, which are designed to meet the increasing demand for big-data analyses, ranging from bulk sequence processing to interactive data visualization. A wide variety of graphs can be prepared in TBtools, with a new plotting engine (“JIGplot”) developed to maximum their interactive ability, which allows quick point-and-click modification to almost every graphic feature. TBtools is a platform-independent software that can be run under all operating systems with Java Runtime Environment 1.6 or newer. It is freely available to non-commercial users at https://github.com/CJ-Chen/TBtools/releases.
View