Genome-Wide Binding Analysis of the Transcription Activator IDEAL PLANT ARCHITECTURE1 Reveals a Complex Network Regulating Rice Plant Architecture
TL;DR: The results demonstrated that IPA1 could directly bind to the promoter of rice TEOSINTE BRANCHED1, a negative regulator of tiller bud outgrowth, to suppress rice tillering, and directly and positively regulate DENSE AND ERECT PANicle1, an important gene regulating panicle architecture, to influence plant height and panicle length.
Abstract: IDEAL PLANT ARCHITECTURE1 (IPA1) is critical in regulating rice (Oryza sativa) plant architecture and substantially enhances grain yield. To elucidate its molecular basis, we first confirmed IPA1 as a functional transcription activator and then identified 1067 and 2185 genes associated with IPA1 binding sites in shoot apices and young panicles, respectively, through chromatin immunoprecipitation sequencing assays. The SQUAMOSA PROMOTER BINDING PROTEIN-box direct binding core motif GTAC was highly enriched in IPA1 binding peaks; interestingly, a previously uncharacterized indirect binding motif TGGGCC/T was found to be significantly enriched through the interaction of IPA1 with proliferating cell nuclear antigen PROMOTER BINDING FACTOR1 or PROMOTER BINDING FACTOR2. Genome-wide expression profiling by RNA sequencing revealed IPA1 roles in diverse pathways. Moreover, our results demonstrated that IPA1 could directly bind to the promoter of rice TEOSINTE BRANCHED1, a negative regulator of tiller bud outgrowth, to suppress rice tillering, and directly and positively regulate DENSE AND ERECT PANICLE1, an important gene regulating panicle architecture, to influence plant height and panicle length. The elucidation of target genes of IPA1 genome-wide will contribute to understanding the molecular mechanisms underlying plant architecture and to facilitating the breeding of elite varieties with ideal plant architecture.
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TL;DR: Manipulation of the OsSPL16-GW7 module represents a new strategy to simultaneously improve rice yield and grain quality and is associated with higher grain quality without the yield penalty imposed by the Basmati gw8 allele.
Abstract: The deployment of heterosis in the form of hybrid rice varieties has boosted grain yield, but grain quality improvement still remains a challenge. Here we show that a quantitative trait locus for rice grain quality, qGW7, reflects allelic variation of GW7, a gene encoding a TONNEAU1-recruiting motif protein with similarity to C-terminal motifs of the human centrosomal protein CAP350. Upregulation of GW7 expression was correlated with the production of more slender grains, as a result of increased cell division in the longitudinal direction and decreased cell division in the transverse direction. OsSPL16 (GW8), an SBP-domain transcription factor that regulates grain width, bound directly to the GW7 promoter and repressed its expression. The presence of a semidominant GW7(TFA) allele from tropical japonica rice was associated with higher grain quality without the yield penalty imposed by the Basmati gw8 allele. Manipulation of the OsSPL16-GW7 module thus represents a new strategy to simultaneously improve rice yield and grain quality.
453 citations
TL;DR: Key advances include elucidation of mechanisms by which strigolactones, auxins, and genes such as IDEAL PLANT ARCHITECTURE1 and TEOSINTE BRANCHED1 control shoot architecture.
Abstract: Shoot architecture is determined by the organization and activities of apical, axillary, intercalary, secondary, and inflorescence meristems and by the subsequent development of stems, leaves, shoot branches, and inflorescences. In this review, we discuss the unifying principles of hormonal and genetic control of shoot architecture including advances in our understanding of lateral branch outgrowth; control of stem elongation, thickness, and angle; and regulation of inflorescence development. We focus on recent progress made mainly in Arabidopsis thaliana, rice, pea, maize, and tomato, including the identification of new genes and mechanisms controlling shoot architecture. Key advances include elucidation of mechanisms by which strigolactones, auxins, and genes such as IDEAL PLANT ARCHITECTURE1 and TEOSINTE BRANCHED1 control shoot architecture. Knowledge now available provides a foundation for rational approaches to crop breeding and the generation of ideotypes with defined architectural features to impro...
363 citations
Cites background from "Genome-Wide Binding Analysis of the..."
...IPA1 also binds to TGGGCC/T motifs through its interaction with PROLIFERATING CELL FACTOR 1 or 2 (98)....
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...Because IPA1 functions as a central component of a regulatory network shaping the ideal plant architecture in rice (69, 98, 111, 177, 209), beneficial ipa1-1D alleles have been introduced into indica or japonica cultivars by molecular marker-assisted selection....
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...A point mutation in the OsmiR156 recognition site relieves OsmiR156-mediated repression on IPA1, leading to an “ideal” rice plant with fewer tillers, increased plant height, lodging resistance, and enhanced grain yield (69, 98, 111)....
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TL;DR: The balance of DELLA and GRF4 proteins in plants ensures the co-regulation of growth with metabolism and tipping this balance towardsGRF4 leads to higher efficiency of nitrogen use.
Abstract: Enhancing global food security by increasing the productivity of green revolution varieties of cereals risks increasing the collateral environmental damage produced by inorganic nitrogen fertilizers. Improvements in the efficiency of nitrogen use of crops are therefore essential; however, they require an in-depth understanding of the co-regulatory mechanisms that integrate growth, nitrogen assimilation and carbon fixation. Here we show that the balanced opposing activities and physical interactions of the rice GROWTH-REGULATING FACTOR 4 (GRF4) transcription factor and the growth inhibitor DELLA confer homeostatic co-regulation of growth and the metabolism of carbon and nitrogen. GRF4 promotes and integrates nitrogen assimilation, carbon fixation and growth, whereas DELLA inhibits these processes. As a consequence, the accumulation of DELLA that is characteristic of green revolution varieties confers not only yield-enhancing dwarfism, but also reduces the efficiency of nitrogen use. However, the nitrogen-use efficiency of green revolution varieties and grain yield are increased by tipping the GRF4-DELLA balance towards increased GRF4 abundance. Modulation of plant growth and metabolic co-regulation thus enables novel breeding strategies for future sustainable food security and a new green revolution.
358 citations
TL;DR: These findings have paved new ways for understanding the molecular basis of grain size and have substantial implications for genetic improvement of crops.
Abstract: Grain size is one of the most important factors determining rice yield. As a quantitative trait, grain size is predominantly and tightly controlled by genetic factors. Several quantitative trait loci (QTLs) for grain size have been molecularly identified and characterized. These QTLs may act in independent genetic pathways and, along with other identified genes for grain size, are mainly involved in the signaling pathways mediated by proteasomal degradation, phytohormones, and G proteins to regulate cell proliferation and cell elongation. Many of these QTLs and genes have been strongly selected for enhanced rice productivity during domestication and breeding. These findings have paved new ways for understanding the molecular basis of grain size and have substantial implications for genetic improvement of crops.
317 citations
TL;DR: It is proposed that dissection of broad spectrum stress tolerance conferred by priming chemicals may provide an insight on stress cross regulation and additional candidate genes for improving crop performance under combined stress.
Abstract: Plants growing in their natural habitats are often challenged simultaneously by multiple stress factors, both abiotic and biotic. Research has so far been limited to responses to individual stresses, and understanding of adaptation to combinatorial stress is limited, but indicative of non-additive interactions. Omics data analysis and functional characterization of individual genes has revealed a convergence of signaling pathways for abiotic and biotic stress adaptation. Taking into account that most data originate from imposition of individual stress factors, this review summarizes these findings in a physiological context, following the pathogenesis timeline and highlighting potential differential interactions occurring between abiotic and biotic stress signaling across the different cellular compartments and at the whole plant level. Potential effects of abiotic stress on resistance components such as extracellular receptor proteins, R-genes and systemic acquired resistance will be elaborated, as well as crosstalk at the levels of hormone, reactive oxygen species, and redox signaling. Breeding targets and strategies are proposed focusing on either manipulation and deployment of individual common regulators such as transcription factors or pyramiding of non- (negatively) interacting components such as R-genes with abiotic stress resistance genes. We propose that dissection of broad spectrum stress tolerance conferred by priming chemicals may provide an insight on stress cross regulation and additional candidate genes for improving crop performance under combined stress. Validation of the proposed strategies in lab and field experiments is a first step toward the goal of achieving tolerance to combinatorial stress in crops.
283 citations
References
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TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Abstract: Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
35,225 citations
TL;DR: The results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.
Abstract: High-throughput mRNA sequencing (RNA-Seq) promises simultaneous transcript discovery and abundance estimation. However, this would require algorithms that are not restricted by prior gene annotations and that account for alternative transcription and splicing. Here we introduce such algorithms in an open-source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed >430 million paired 75-bp RNA-Seq reads from a mouse myoblast cell line over a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Over the time series, 330 genes showed complete switches in the dominant transcription start site (TSS) or splice isoform, and we observed more subtle shifts in 1,304 other genes. These results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.
13,337 citations
"Genome-Wide Binding Analysis of the..." refers methods in this paper
...Cuffdiff (Trapnell et al., 2009, 2010) was used to calculate the fragments per kilobase of exon per million fragments mapped of each gene and identify the differentially expressed genes between NIL-ipa1 and NIL-IPA1 in SAs and YPs....
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TL;DR: This work presents Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer, and uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions.
Abstract: We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.
13,008 citations
"Genome-Wide Binding Analysis of the..." refers methods in this paper
...Only uniquely mapped reads were used for peak identification, and IPA1 binding peaks were obtained by model-based analysis of ChIP-seq (Zhang et al., 2008) with default parameters....
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...Only uniquely mapped reads were used for peak identification, and IPA1 binding peaks were obtained by model-based analysis of ChIP-seq (Zhang et al., 2008) with default parameters....
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TL;DR: The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer.
Abstract: Motivation: A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites.
Results: We mapped the RNA-Seq reads from a recent mammalian RNA-Seq experiment and recovered more than 72% of the splice junctions reported by the annotation-based software from that study, along with nearly 20 000 previously unreported junctions. The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer. We describe several challenges unique to ab initio splice site discovery from RNA-Seq reads that will require further algorithm development.
Availability: TopHat is free, open-source software available from http://tophat.cbcb.umd.edu
Contact: ude.dmu.sc@eloc
Supplementary information: Supplementary data are available at Bioinformatics online.
11,473 citations
"Genome-Wide Binding Analysis of the..." refers methods in this paper
...The raw reads of RNA-seq were mapped to Release 6 of the Michigan State University Rice Genome (Ouyang et al., 2007) by Tophat (Trapnell et al., 2009)....
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...Cuffdiff (Trapnell et al., 2009, 2010) was used to calculate the fragments per kilobase of exon per million fragments mapped of each gene and identify the differentially expressed genes between NIL-ipa1 and NIL-IPA1 in SAs and YPs....
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TL;DR: This protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results, which takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
Abstract: Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
10,913 citations
"Genome-Wide Binding Analysis of the..." refers methods in this paper
...Analysis of RNA-Seq Data Analysis of RNA-seq data followed the standard protocol described by Trapnell et al. (2012)....
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