Comparative transcriptomics and weighted gene co-expression correlation network analysis (WGCNA) reveal potential regulation mechanism of carotenoid accumulation in Chrysanthemum × morifolium

Lu, Chenfei; Pu, Yong; Liu, Yuting; Li, Yajun; Qu, Jiaping; Huang, He; Dai, Silan

doi:10.1016/j.plaphy.2019.07.023

Cited by 56 publications

(31 citation statements)

References 53 publications

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“…In other words, knowledge about the gene co-expression network involved in rose drought and recovery is very limited. WGCNA has become an important tool to identify gene co-expression related to associating with function, which has been widely used in the research of horticultural plants (Lu et al, 2019;Liu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Comparative Transcriptome and Weighted Gene Co-expression Network Analysis Identify Key Transcription Factors of Rosa chinensis ‘Old Blush’ After Exposure to a Gradual Drought Stress Followed by Recovery

Jia

Feng

et al. 2021

Front. Genet.

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Rose is one of the most fundamental ornamental crops, but its yield and quality are highly limited by drought. The key transcription factors (TFs) and co-expression networks during rose’s response to drought stress and recovery after drought stress are still limited. In this study, the transcriptomes of leaves of 2-year-old cutting seedlings of Rosa chinensis ‘Old Blush’ from three continuous droughted stages (30, 60, 90 days after full watering) and rewatering were analyzed using RNA sequencing. Weighted gene co-expression network analysis (WGCNA) was used to construct a co-expression network, which was associated with the physiological traits of drought response to discovering the hub TFs involved in drought response. More than 45 million high-quality clean reads were generated from the sample and used for comparison with the rose reference genome. A total of 46433 differentially expressed genes (DEGs) were identified. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that drought stress caused significant changes in signal transduction, plant hormones including ABA, auxin, brassinosteroid (BR), cytokinin, ethylene (ET), jasmonic acid (JA) and salicylic acid (SA), primary and secondary metabolism, and a certain degree of recovery after rewatering. Gene co-expression analysis identified 18 modules, in which four modules showed a high degree of correlation with physiological traits. In addition, 42 TFs including members of NACs, WRKYs, MYBs, AP2/ERFs, ARFs, and bHLHs with high connectivity in navajowhite1 and blue modules were screened. This study provides the transcriptome sequencing report of R. chinensis ‘Old Blush’ during drought stress and rewatering process. The study also identifies the response of candidate TFs to drought stress, providing guidelines for improving the drought tolerance of the rose through molecular breeding in the future.

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Section: Introductionmentioning

confidence: 99%

Comparative Transcriptome and Weighted Gene Co-expression Network Analysis Identify Key Transcription Factors of Rosa chinensis ‘Old Blush’ After Exposure to a Gradual Drought Stress Followed by Recovery

Jia

Feng

et al. 2021

Front. Genet.

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“…Transcriptome technology can provide unique insights into the molecular characteristics of nonmodel plants without a reference genome, especially in the study of flower color. It has been successful in many plants, for example, sheepgrass ( Leymus chinensis ), Siberian wildrye ( Elymus sibiricus ), ornamental crabapple ( Malus prunifolia ) and chrysanthemum ( Dendranthema morifolium ) [ 23 – 26 ]. As far as we know, there has no report of research employing RNA-Seq to study the color of sainfoin flowers.…”

Section: Introductionmentioning

confidence: 99%

Transcriptomic and chemical analyses to identify candidate genes involved in color variation of sainfoin flowers

et al. 2021

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Background Sainfoin (Onobrychis viciifolia Scop) is not only a high-quality legume forage, but also a nectar-producing plant. Therefore, the flower color of sainfoin is an important agronomic trait, but the factors affecting its flower phenotype are still unclear. To gain insights into the regulatory networks associated with metabolic pathways of coloration compounds (flavonoids or anthocyanins) and identify the key genes, we conducted a comprehensive analysis of the phenotype, metabolome and transcriptome of WF and AF of sainfoin. Results Delphinidin, petunidin and malvidin derivatives were the main anthocyanin compounds in the AF of sainfoin. These substances were not detected in the WF of sainfoin. The transcriptomes of WF and AF in sainfoin at the S1 and S3 stages were obtained using the Illumina HiSeq4000 platform. Overall, 10,166 (4273 upregulated and 5893 downregulated) and 15,334 (8174 upregulated and 7160 downregulated) DEGs were identified in flowers at S1 and S3 stages, respectively (WF-VS-AF). KEGG pathway annotations showed that 6396 unigenes were annotated to 120 pathways and contained 866 DEGs at S1 stages, and 6396 unigenes were annotated to 131 pathways and included 1546 DEGs at the S3 stage. Nine DEGs belonging to the “flavonoid biosynthesis”and “phenylpropanoid biosynthesis” pathways involved in flower color formation were identified and verified by RT-qPCR analyses. Among these DEGs, 4CL3, FLS, ANS, CHS, DFR and CHI2 exhibited downregulated expression, and F3H exhibited upregulated expression in the WF compared to the AF, resulting in a decrease in anthocyanin synthesis and the formation of WF in sainfoin. Conclusions This study is the first to use transcriptome technology to study the mechanism of white flower formation in sainfoin. Our transcriptome data will be a great enrichment of the genetic information for sainfoin. In addition, the data presented herein will provide valuable molecular information for genetic breeding and provide insight into the future study of flower color polymorphisms in sainfoin.

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“…The weighted gene co-expression network analysis (WGCNA) package of R language can search for highly related gene modules (Langfelder and Horvath, 2008), and then find the potential genes in a certain metabolic pathway, or find the key genes in the gene regulatory network . It can be used for medical diagnosis (Wan et al, 2018) or search for key genes related to a certain disease (Zhi et al, 2018), and can study the mechanism of plant response to environmental stress (Chih-Ta et al, 2019) or the structural genes and genes encoding transcription factors in a certain metabolic pathway (Lu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Algorithm optimization for weighted gene co-expression network analysis: accelerating the calculation of Topology Overlap Matrices with OpenMP and SQLite

Shuai

Chen

2021

Preprint

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MotivationWeighted gene co-expression network analysis (WGCNA) is an R package that can search highly related gene modules. The most time-consuming step of the whole analysis is to calculate the Topological Overlap Matrix (TOM) from the Adjacency Matrix in a single thread. This study changes it to multithreading.ResultsThis paper uses SQLite for multi-threaded data transfer between R and C++, uses OpenMP to enable multi-threading and calculates the TOM via an adjacency matrix on a Shared-memory MultiProcessor (SMP) system, where the calculation time decreases as the number of physical CPU cores increases.Availability and implementationThe source code is available at https://github.com/do-somethings-haha/fast_calculate_TOM_of_WGCNAContactchenxin@cdutcm.edu.cn

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Comparative transcriptomics and weighted gene co-expression correlation network analysis (WGCNA) reveal potential regulation mechanism of carotenoid accumulation in Chrysanthemum × morifolium

Cited by 56 publications

References 53 publications

Comparative Transcriptome and Weighted Gene Co-expression Network Analysis Identify Key Transcription Factors of Rosa chinensis ‘Old Blush’ After Exposure to a Gradual Drought Stress Followed by Recovery

Comparative Transcriptome and Weighted Gene Co-expression Network Analysis Identify Key Transcription Factors of Rosa chinensis ‘Old Blush’ After Exposure to a Gradual Drought Stress Followed by Recovery

Transcriptomic and chemical analyses to identify candidate genes involved in color variation of sainfoin flowers

Algorithm optimization for weighted gene co-expression network analysis: accelerating the calculation of Topology Overlap Matrices with OpenMP and SQLite

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