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Important Roles of Key Genes and Transcription Factors in Flower Color Differences of Nicotianaalata.

ABSTRACT: Nicotiana alata is an ornamental horticultural plant with a variety of flower colors and a long flowering period. The genes in four different colored N. alata (white, purple, red, and lemon green) were analyzed to explain the differences in flower color using transcriptomes. A total of 32 differential expression genes in the chlorophyll biosynthesis pathway and 41 in the anthocyanin biosynthesis pathway were identified. The enrichment analysis showed that the chlorophyll biosynthesis pathway and anthocyanin biosynthesis pathway play critical roles in the color differences of N. alata. The HEMA of the chlorophyll biosynthesis pathway was up-regulated in lemon green flowers. Compared with white flowers, in the red and purple flowers, F3H, F3'5'H and DFR were significantly up-regulated, while FLS was significantly down-regulated. Seventeen differential expression genes homologous to transcription factor coding genes were obtained, and the homologues of HY5, MYB12, AN1 and AN4 were also involved in flower color differences. The discovery of these candidate genes related to flower color differences is significant for further research on the flower colors formation mechanism and color improvements of N. alata.

SUBMITTER: Zheng Y

PROVIDER: S-EPMC8701347 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Important Roles of Key Genes and Transcription Factors in Flower Color Differences of Nicotianaalata.

Zheng Yalin Y Chen Yudong Y Liu Zhiguo Z Wu Hui H Jiao Fangchan F Xin Haiping H Zhang Li L Yang Long L

Genes 20211210 12

Nicotiana alata is an ornamental horticultural plant with a variety of flower colors and a long flowering period. The genes in four different colored N. alata (white, purple, red, and lemon green) were analyzed to explain the differences in flower color using transcriptomes. A total of 32 differential expression genes in the chlorophyll biosynthesis pathway and 41 in the anthocyanin biosynthesis pathway were identified. The enrichment analysis showed that the chlorophyll biosynthes ...[more]

PMID: 34946925

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Project description:BackgroundPaphiopedilum hirsutissimum is a member of Orchidaceae family that is famous for its ornamental value around the globe, it is vulnerable due to over-exploitation and was listed in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora, which prevents its trade across borders. Variation in flower color that gives rise to different flower patterns is a major trait contributing to its high ornamental value. However, the molecular mechanism underlying color formation in P. hirsutissimum still remains unexplored. In the present study, we exploited natural variation in petal and labellum color of Paphiopedilum plants and used comparative transcriptome analysis as well as pigment measurements to explore the important genes, metabolites and regulatory pathways linked to flower color variation in P. hirsutissimum.ResultWe observed that reduced anthocyanin and flavonoid contents along with slightly higher carotenoids are responsible for albino flower phenotype. Comparative transcriptome analysis identified 3287 differentially expressed genes (DEGs) among normal and albino labellum, and 3634 DEGs between normal and albino petals. Two genes encoding for flavanone 3-hydroxylase (F3H) and one gene encoding for chalcone synthase (CHS) were strongly downregulated in albino labellum and petals compared to normal flowers. As both F3H and CHS catalyze essentially important steps in anthocyanin biosynthesis pathway, downregulation of these genes is probably leading to albino flower phenotype via down-accumulation of anthocyanins. However, we observed the downregulation of major carotenoid biosynthesis genes including VDE, NCED and ABA2 which was inconsistent with the increased carotenoid accumulation in albino flowers, suggesting that carotenoid accumulation was probably controlled at post-transcriptional or translational level. In addition, we identified several key transcription factors (MYB73, MYB61, bHLH14, bHLH106, MADS-SOC1, AP2/ERF1, ERF26 and ERF87) that may regulate structural genes involved in flower color formation in P. hirsutissimum. Importantly, over-expression of some of these candidate TFs increased anthocyanin accumulation in tobacco leaves which provided important evidence for the role of these TFs in flower color formation probably via regulating key structural genes of the anthocyanin pathway.ConclusionThe genes identified here could be potential targets for breeding P. hirsutissimum with different flower color patterns by manipulating the anthocyanin and carotenoid biosynthesis pathways.

Project description:Sepsis is a life-threatening organ malfunction induced by an imbalanced immunological reaction to infection in the host. Many studies have utilized traditional RNA sequencing (RNA-seq) data to identify important biological targets to predict sepsis prognosis. However, alterations in core cells and functional status cannot be effectively detected in sepsis patients. The goal of this study was to identify key cells through single-cell RNA-seq (scRNA-seq), and combine bulk RNA-seq data and multiple algorithm analysis to construct a stable prognostic model for sepsis. The scRNA-seq and bulk RNA-seq data from sepsis patients were collected from the Gene Expression Omnibus (GEO) database. The R package "Seurat" was used to process the scRNA-seq data. Cell communication was investigated using the R package "CellChat". The pseudo-time of the cells was calculated using the R package "monocle". The R package "limma" was used to identify differentially expressed genes (DEGs) between the sepsis group and the control group. Weighted gene correlation network analysis (WGCNA) was used to identify critical modules. Eight kinds of machine learning and 90 algorithm combinations were used to construct the prognostic model for sepsis. Quantitative real-time PCR (qRT‒PCR) was performed to determine the expression of key genes in the cecal ligation and puncture (CLP)-induced sepsis mouse model. The immunological status and related properties of DEGs were then investigated in the high- and low-risk groups delineated by the model. By combining the scRNA-seq data from nine samples, 13 clusters and 9 cell types were identified. CellChat analysis revealed that the number and strength of interactions between platelets and a variety of cells increased. We identified key platelet genes from the scRNA-seq data and combined these genes and the results of differential analysis and WGCNA of the bulk RNA-seq data. After univariate Cox regression analysis, we calculated the Cindex of the model constructed by the combination of 90 algorithms, and we finally determined the "CoxBoost + Lasso" combination. Multivariate Cox regression was used to construct the final prognostic model. The qRT-PCR results revealed significant differences in five key prognostic genes between the CLP and sham groups. The data was classified into high- and low-risk groups based on the model score. The high-risk group had a poorer survival rate and less immune infiltration. We identified the importance of platelets in sepsis patients through scRNA-seq, and established prognostic models with key genes that were identified via scRNA-seq combined with bulk RNA-seq analysis. The results of this model were closely associated with patient survival rates and immunological status and this model is useful for the prognostic management of sepsis.

Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most common cancers worldwide. The etiology and pathophysiology of LUAD remain unclear. The aim of the present study was to identify the key genes, miRNAs and transcription factors (TFs) associated with the pathogenesis and prognosis of LUAD.MethodsThree gene expression profiles (GSE43458, GSE32863, GSE74706) of LUAD were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by GEO2R.The Gene Ontology (GO) terms, pathways, and protein-protein interactions (PPIs) of these DEGs were analyzed. Bases on DEGs, the miRNAs and TFs were predicted. Furthermore, TF-gene-miRNA co-expression network was constructed to identify key genes, miRNAs and TFs by bioinformatic methods. The expressions and prognostic values of key genes, miRNAs and TFs were carried out through The Cancer Genome Atlas (TCGA) database and Kaplan Meier-plotter (KM) online dataset.ResultsA total of 337 overlapped DEGs (75 upregulated and 262 downregulated) of LUAD were identified from the three GSE datasets. Moreover, 851 miRNAs and 29 TFs were identified to be associated with these DEGs. In total, 10 hub genes, 10 key miRNAs and 10 key TFs were located in the central hub of the TF-gene-miRNA co-expression network, and validated using The Cancer Genome Atlas (TCGA) database. Specifically, seven genes (PHACTR2, MSRB3, GHR, PLSCR4, EPB41L2, NPNT, FBXO32), two miRNAs (hsa-let-7e-5p, hsa-miR-17-5p) and four TFs (STAT6, E2F1, ETS1, JUN) were identified to be associated with prognosis of LUAD, which have significantly different expressions between LUAD and normal lung tissue. Additionally, the miRNA/gene co-expression analysis also revealed that hsa-miR-17-5p and PLSCR4 have a significant negative co-expression relationship (r=-0.33, P=1.67e-14) in LUAD.ConclusionsOur study constructed a regulatory network of TF-gene-miRNA in LUAD, which may provide new insights about the interaction between genes, miRNAs and TFs in the pathogenesis of LUAD, and identify potential biomarkers or therapeutic targets for LUAD.

Dataset Information

Important Roles of Key Genes and Transcription Factors in Flower Color Differences of Nicotianaalata.

Publications

Important Roles of Key Genes and Transcription Factors in Flower Color Differences of <i>Nicotiana</i><i>alata</i>.

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