PlantRegMap: charting functional regulatory maps in plants

Tian, Feng; Yang, De-Chang; Meng, Yan; Jin, Jinpu; Gao, Ge

doi:10.1093/nar/gkz1020

Cited by 508 publications

(555 citation statements)

References 40 publications

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“…Binding site motifs for N. benthamiana transcription factors were obtained from the PlantTFDB, 17 and FIMO 18 was used to predict their occurrence in the 35S core enhancer. Fragments of pZS*11_4enh were aligned to the reference sequence using Bowtie2 (ref.…”

Section: Methodsmentioning

confidence: 99%

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Identification of plant enhancers and their constituent elements by STARR-seq in tobacco leaves

Jores

Tonnies

Dorrity

et al. 2020

Preprint

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In a time of climate change and increasing human population, crop plants with higher yields and improved response to abiotic stresses will be required to ensure food security. As many of the beneficial traits in domesticated crops are caused by mutations in cis-regulatory elements, especially enhancers, genetic engineering of such elements is a promising strategy for improving crops 1,2 . However, such engineering is currently hindered by our limited knowledge of plant cis-regulatory elements. Here, we adapted STARRseq -a technology for the high-throughput identification of enhancers -for its use in transiently transfected tobacco leaves. We demonstrate that the optimal placement in the reporter construct of enhancer sequences from a plant virus, pea and wheat was just upstream of a minimal promoter, and that none of these four known enhancers was active in the 3′-UTR of the reporter gene. The optimized assay sensitively identified small DNA regions containing each of the four enhancers, including two whose activity was stimulated by light. Furthermore, we coupled the assay to saturation mutagenesis to pinpoint functional regions within an enhancer, which we recombined to create synthetic enhancers. Our results describe an approach to define enhancer properties that can be performed in any plant species or tissue transformable by Agrobacterium and that can use regulatory DNA derived from any plant. MainEnhancers can be identified by self-transcribing active regulatory region sequencing (STARR-seq), a massively parallel reporter assay 3 . Here, candidate enhancer sequences are inserted into the 3' untranslated region (3′-UTR) of a reporter gene under the control of a minimal promoter. If an insert has enhancer activity, it can upregulate its own transcription. The resulting transcript can be detected by next generation sequencing and linked to its corresponding enhancer element, which is incorporated within the mRNA. This method has been widely used in Drosophila and human cells. In plants, STARR-seq has been described in only two studies that apply the method to the monocot species rice and maize 4,5 . Both studies relied on species-specific protoplasts as recipient cells for the assay. Our approach bypasses the need for a species-specific protoplasting protocol by using transient expression of diverse STARR-seq libraries in tobacco leaves. As transcription factors are highly conserved among plant species 6,7 , the versatile tobacco system can serve as a proxy for many plant species, including crops.We used a green fluorescent protein (GFP) reporter gene under control of the Cauliflower mosaic virus 35S minimal promoter and the 35S core enhancer 8,9 (subdomains A1 and B1-3) to benchmark the assay.We systematically analyzed the position-and orientation-dependency of the enhancer (Fig. 1a). To this end, we used a more generalized version of STARR-seq in which we placed a barcode in the GFP open reading frame. This barcode is linked to the corresponding enhancer variant by next generation sequencing and serves as a ...

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Section: Methodsmentioning

confidence: 99%

“…3c,d, Supplementary Table 2). These regions co-localize with predicted transcription factor binding sites 17,18 . Mutations in positions 116-135 were especially deleterious.…”

mentioning

confidence: 92%

Identification of plant enhancers and their constituent elements by STARR-seq in tobacco leaves

Jores

Tonnies

Dorrity

et al. 2020

Preprint

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“…Protein sequences of TFs were subsequently submitted to footprintDB to predict their interacting DNA-binding site. To functionally interpret the co-expressed modules, Gene Ontology (GO) enrichment was conducted on PlantRegMap / PlantTFDB portal v5.0 (http://planttfdb.gao-lab.org/, last accessed January, 2020) (Tian et al, 2019). P-value of 0.01 was set to retain the significant GO terms.…”

Section: Methodsmentioning

confidence: 99%

“…TFs may act as either activators or repressors of gene expression, leading to dynamic changes of the cellular pathways. For peach, annotation of TFs is available in the plant transcription factor database (plantTFDB) (Tian et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Motif analysis in co-expression networks reveals regulatory elements in plants: The peach as a model

Ksouri

Montardit-Tardà

et al. 2020

Preprint

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Identification of functional regulatory elements encoded in plant genomes is a fundamental need to understand gene regulation. While much attention has been given to model species as Arabidopsis thaliana, little is known about regulatory motifs in other plant genera. Here, we describe an accurate bottom-up approach using the online workbench RSAT::Plants for a versatile ab-initio motif discovery taking Prunus persica as a model. These predictions rely on the construction of a co-expression network to generate modules with similar expression trends and assess the effect of increasing upstream region length on the sensitivity of motif discovery. Applying two discovery algorithms, 18 out of 45 modules were found to be enriched in motifs typical of well-known transcription factor families (bHLH, bZip, BZR, CAMTA, DOF, E2FE, AP2-ERF, Myb-like, NAC, TCP, WRKY) and a novel motif. Our results indicate that small number of input sequences and short promoter length are preferential to minimize the amount of uninformative signals in peach. The spatial distribution of TF binding sites revealed an unbalanced distribution where motifs tend to lie around the transcriptional start site region. The reliability of this approach was also benchmarked in Arabidopsis thaliana, where it recovered the expected motifs from promoters of genes containing ChIPseq peaks. Overall, this paper presents a glimpse of the peach regulatory components at genome scale and provides a general protocol that can be applied to many other species. Additionally, a RSAT Docker container was released to facilitate similar analyses on other species or to reproduce our results.One sentence summaryMotifs prediction depends on the promoter size. A proximal promoter region defined as an interval of -500 bp to +200 bp seems to be the adequate stretch to predict de novo regulatory motifs in peach

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“…To generate features from polymorphisms in non-coding sequences, we used two types of predicted non-coding sequences in PlantRegMap (Tian et al 2019): Conserved Elements (CE) and functional Transcription Factor Binding Sites (TFBS). In Arabidopsis and rice, there are significantly more SNPs and Indels in the CEs nearby causal genes than in the CEs nearby an average gene in the genome (Mann-Whitney U Test, p-value <0.05, Figure 5A, Supplemental Tables S2 and S3).…”

Section: Resultsmentioning

confidence: 99%

QTG-Finder2: a generalized machine-learning algorithm for prioritizing QTL causal genes in plants

Lin

Lazarus

Rhee

2020

Preprint

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Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. In addition, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.

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PlantRegMap: charting functional regulatory maps in plants

Cited by 508 publications

References 40 publications

Identification of plant enhancers and their constituent elements by STARR-seq in tobacco leaves

Identification of plant enhancers and their constituent elements by STARR-seq in tobacco leaves

Motif analysis in co-expression networks reveals regulatory elements in plants: The peach as a model

QTG-Finder2: a generalized machine-learning algorithm for prioritizing QTL causal genes in plants

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