scholarly journals Evolution Analyses of CAMTA Transcription Factor in Plants and Its Enhancing Effect on Cold-tolerance

2021 ◽  
Vol 12 ◽  
Author(s):  
Peixuan Xiao ◽  
Jia-Wu Feng ◽  
Xi-Tong Zhu ◽  
Junxiang Gao
Keyword(s):  
Transcription Factor ◽  
Cold Tolerance ◽  
Regulatory Networks ◽  
Target Genes ◽  
Higher Plants ◽  
Expression Patterns ◽  
Evolutionary Process ◽  
Functional Differentiation ◽  
Transcription Activator ◽  
Structure Variation

The calmodulin binding transcription activator (CAMTA) is a transcription factor that is widely present in eukaryotes with conserved structure. It contributes to the response to biotic and abiotic stresses and promotes the growth and development of plants. Although previous studies have investigated the number and function of CAMTAs in some species, there is still a lack of comprehensive understanding of the evolutionary process, phylogenetic relationship, expression patterns, and functions of CAMTAs in plants. Here we identified 465 CMATA genes from 112 plants and systematically studied the origin of CAMTA family, gene expansion, functional differentiation, gene structure, and conservative motif distribution. Based on these analyses, we presented the evidence that CAMTA family was originated from chlorophyta, and we speculated that CAMTA might experience obvious structure variation during its early evolution, and that the number of CAMTA genes might gradually increase in higher plants. To reveal potential functions of CAMTA genes, we analyzed the expression patterns of 12 representative species and found significant species specificity, tissue specificity, and developmental stage specificity of CAMTAs. The results also indicated that the CAMTA genes might promote the maturation and senescence. The expression levels and regulatory networks of CAMTAs revealed that CAMTAs could enhance cold tolerance of rice by regulating carbohydrate metabolism-related genes to accumulate carbohydrates or by modulating target genes together with other transcription factors. Our study provides an insight into the molecular evolution of CAMTA family and lays a foundation for further study of related biological functions.

scholarly journals GhWRKY1-like, a WRKY transcription factor, mediates drought tolerance in Arabidopsis via modulating ABA biosynthesis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qin Hu ◽  
Chuanwei Ao ◽  
Xiaorui Wang ◽  
Yanfei Wu ◽  
Xuezhu Du
Keyword(s):  
Transcription Factor ◽  
Upland Cotton ◽  
Target Genes ◽  
Higher Plants ◽  
Wrky Transcription Factor ◽  
Abiotic And Biotic Stress ◽  
Plant Growth And Development ◽  
Negative Effects ◽  
Positive Regulator ◽  
Aba Biosynthesis

Abstract Background Drought stress has great negative effects on the plant growth and development. The tolerance of plants to such abiotic stress is triggered by complicated and multilayered signaling pathways to restore cellular homeostasis and to promote survival. The WRKY family is one of the largest transcription factor families in higher plants, and has been well recognized for the roles in regulating plants tolerance to abiotic and biotic stress. However, little is known about how the WRKY genes regulate drought resistance in cotton. Results In this work, we identified the WRKY transcription factor GhWRKY1-like from upland cotton as a positive regulator of tolerance to drought that directly manipulates abscisic acid (ABA) biosynthesis. Overexpression of GhWRKY1-like in Arabidopsis constitutively activated ABA biosynthesis genes, signaling genes, responsive genes and drought related maker genes, and led to enhanced tolerance to drought. Further analysis has shown that GhWRKY1-like can interact with “W-box” cis-elements of the promoters of AtNCED2, AtNCED5, AtNCED6 and AtNCED9 which are essential enzymes for ABA biosynthesis, and promotes the expression of those target genes. Conclusions In summary, our findings suggest that GhWRKY1-like may act as a positive regulator in Arabidopsis tolerance to drought via directly interacting with the promoters of AtNCED2, AtNCED5, AtNCED6 and AtNCED9 to promote ABA biosynthesis.

scholarly journals Gene Expression and Regulatory Webwork of POLR2K in Bladder Carcinogenesis by Integrated Bioinformatics Approaches

2020 ◽  
Author(s):  
Liliang Yang ◽  
Kaizhen Wang ◽  
Wenjing Guo ◽  
Xian Chen ◽  
Qinglong Guo ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Transcription Factor ◽  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Transcription Factor Binding ◽  
Sequencing Data ◽  
Factor Binding

Abstract Background：RNA polymerase II subunit K (POLR2K) belongs to one of the multiple subunits of RNA polymerase II (Pol II), whose biological function is to synthesize mRNA. Aberrant POLR2K expression is related to carcinogenesis. However, POLR2K’s underlying role in bladder cancer has not been explored. In the current study, we intend to analyze the function of POLR2K and its regulatory network within bladder cancer.Methods: Public sequencing data was obtained from GEO and TCGA to investigate POLR2K expression and regulatory network within bladder cancer (BLCA) by using GEPIA and Oncomine as well as cBioPortal online tool. LinkedOmics was employed to identify genes displaying significantly differential expression patterns and to perform GO and KEGG analyses. After differential genes was assigned and ranked, GSEA analyses was performed to obtain target networks for transcription factors, miRNAs, and kinases that could regulate POLR2K–associated gene network. Subsequent functional webwork analyses were used to identify cancer-relevant pathways Moreover, POLR2K gene is verified, by ChIP-seq in MCF-7 cell line , with transcription factor binding evidence in the ENCODE Transcription Factor Binding Site Profiles dataset.Conclusions: The current study implies that POLR2K gene is overexpressed and often amplified in BLCA, providing the first evidence that POLR2K deregulation, in particular increased transcription, may promote BLCA. These findings uncover a unique expression patterns of POLR2K and its potential regulatory networks in BLCA, contributing greatly to study of the role of POLR2K in cancer development.

scholarly journals MiRNA expression profiling and zeatin dynamic changes in a new model system of in vivo indirect regeneration of tomato

2020 ◽  
Author(s):  
Huiying Cao ◽  
Xinyue Zhang ◽  
Yanye Ruan ◽  
Lijun Zhang ◽  
Zhenhai Cui ◽  
...  
Keyword(s):  
Mirna Expression ◽  
Target Genes ◽  
Higher Plants ◽  
Callus Formation ◽  
Expression Patterns ◽  
Adventitious Shoot ◽  
Model System ◽  
Indirect Regeneration ◽  
Mirna Expression Profiling

AbstractCallus formation and adventitious shoot differentiation could be observed on the cut surface of completely decapitated tomato plants. We propose that this process can be used as a model system to investigate the mechanisms that regulate indirect regeneration of higher plants without the addition of exogenous hormones. This study analyzed the patterns of trans-zeatin and miRNA expression during in vivo regeneration of tomato. Analysis of trans-zeatin revealed that the hormone cytokinin played an important role in in vivo regeneration of tomato. Among 183 miRNAs and 1168 predicted target genes sequences identified, 93 miRNAs and 505 potential targets were selected based on differential expression levels for further characterization. Expression patterns of six miRNAs, including sly-miR166, sly-miR167, sly-miR396, sly-miR397, novel 156, and novel 128, were further validated by qRT-PCR. We speculate that sly-miR156, sly-miR160, sly-miR166, and sly-miR397 play major roles in callus formation of tomato during in vivo regeneration by regulating cytokinin, IAA, and laccase levels. Overall, our microRNA sequence and target analyses of callus formation during in vivo regeneration of tomato provide novel insights into the regulation of regeneration in higher plants.

scholarly journals The Wheat GENIE3 Network Provides Biologically-Relevant Information in Polyploid Wheat

2020 ◽  
Vol 10 (10) ◽  
pp. 3675-3686 ◽  
Author(s):  
Sophie A. Harrington ◽  
Anna E. Backhaus ◽  
Ajit Singh ◽  
Keywan Hassani-Pak ◽  
Cristobal Uauy
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Candidate Gene ◽  
Web Application ◽  
Regulatory Networks ◽  
Target Genes ◽  
Gene Prediction ◽  
Relevant Information ◽  
Rna Seq ◽  
Biologically Relevant

Gene regulatory networks are powerful tools which facilitate hypothesis generation and candidate gene discovery. However, the extent to which the network predictions are biologically relevant is often unclear. Recently a GENIE3 network which predicted targets of wheat transcription factors was produced. Here we used an independent RNA-Seq dataset to test the predictions of the wheat GENIE3 network for the senescence-regulating transcription factor NAM-A1 (TraesCS6A02G108300). We re-analyzed the RNA-Seq data against the RefSeqv1.0 genome and identified a set of differentially expressed genes (DEGs) between the wild-type and nam-a1 mutant which recapitulated the known role of NAM-A1 in senescence and nutrient remobilisation. We found that the GENIE3-predicted target genes of NAM-A1 overlap significantly with the DEGs, more than would be expected by chance. Based on high levels of overlap between GENIE3-predicted target genes and the DEGs, we identified candidate senescence regulators. We then explored genome-wide trends in the network related to polyploidy and found that only homeologous transcription factors are likely to share predicted targets in common. However, homeologs which vary in expression levels across tissues are less likely to share predicted targets than those that do not, suggesting that they may be more likely to act in distinct pathways. This work demonstrates that the wheat GENIE3 network can provide biologically-relevant predictions of transcription factor targets, which can be used for candidate gene prediction and for global analyses of transcription factor function. The GENIE3 network has now been integrated into the KnetMiner web application, facilitating its use in future studies.

Genome-Wide Identification and Characterization of the FAR1/FHY3 Family in Populus trichocarpa Torr. & Gray and Expression Analysis in Light Response

Forests ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1385
Author(s):  
Jiujun Du ◽  
Lei Zhang ◽  
Xiaolan Ge ◽  
Xiaodong Xiang ◽  
Demei Cao ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Higher Plants ◽  
Populus Trichocarpa ◽  
Expression Patterns ◽  
Light Response ◽  
Cis Acting ◽  
Poplar Genome ◽  
Genome Wide ◽  
Identification And Characterization

Light is an important environmental factor for plant growth, and in higher plants, phytochrome A (phyA) is the predominant far-red photoreceptor, involved in various photoresponses. The FAR1/FHY3 transcription factor family, derived from transposases, is able to regulate plant development in response to multiple photosensitizers phytochrome. In total, 51 PtrFRSs were identified in the poplar genome, and were divided into 4 subfamilies. Among them, 47 PtrFRSs are located on 17 chromosomes. Upstream cis-acting elements of the PtrFRS genes were classified into three categories: growth and metabolism, stress and hormone, and the hormone and stress categories contained most of the cis-acting elements. Analysis of the regulatory networks and expression patterns showed that most PtrFRSs responded to changes in light intensity and were involved in the regulation of phytochromes. In this study, 51 PtrFRSs were identified and comprehensively bioinformatically analyzed, and preliminary functional analysis and prediction of PtrFRSs was carried out.

scholarly journals Distinct miRNA Signatures and Networks Discern Fetal from Adult Erythroid Differentiation and Primary from Immortalized Erythroid Cells

2021 ◽  
Vol 22 (7) ◽  
pp. 3626
Author(s):  
Panayiota L. Papasavva ◽  
Nikoletta Y. Papaioannou ◽  
Petros Patsali ◽  
Ryo Kurita ◽  
Yukio Nakamura ◽  
...  
Keyword(s):  
Mirna Expression ◽  
Regulatory Networks ◽  
Target Genes ◽  
Human Peripheral Blood ◽  
Expression Patterns ◽  
Erythroid Differentiation ◽  
Erythroid Cell ◽  
Small Rna Sequencing ◽  
Erythroid Cells ◽  
Mirna Regulation

MicroRNAs (miRNAs) are small non-coding RNAs crucial for post-transcriptional and translational regulation of cellular and developmental pathways. The study of miRNAs in erythropoiesis elucidates underlying regulatory mechanisms and facilitates related diagnostic and therapy development. Here, we used DNA Nanoball (DNB) small RNA sequencing to comprehensively characterize miRNAs in human erythroid cell cultures. Based on primary human peripheral-blood-derived CD34+ (hCD34+) cells and two influential erythroid cell lines with adult and fetal hemoglobin expression patterns, HUDEP-2 and HUDEP-1, respectively, our study links differential miRNA expression to erythroid differentiation, cell type, and hemoglobin expression profile. Sequencing results validated by reverse-transcription quantitative PCR (RT-qPCR) of selected miRNAs indicate shared differentiation signatures in primary and immortalized cells, characterized by reduced overall miRNA expression and reciprocal expression increases for individual lineage-specific miRNAs in late-stage erythropoiesis. Despite the high similarity of same-stage hCD34+ and HUDEP-2 cells, differential expression of several miRNAs highlighted informative discrepancies between both cell types. Moreover, a comparison between HUDEP-2 and HUDEP-1 cells displayed changes in miRNAs, transcription factors (TFs), target genes, and pathways associated with globin switching. In resulting TF-miRNA co-regulatory networks, major therapeutically relevant regulators of globin expression were targeted by many co-expressed miRNAs, outlining intricate combinatorial miRNA regulation of globin expression in erythroid cells.

scholarly journals Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1933 ◽  
Author(s):  
Ruipeng Lu ◽  
Peter K. Rogan
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Transcription Factor ◽  
Binding Site ◽  
In Silico ◽  
Target Gene ◽  
Target Genes ◽  
Gene Prediction ◽  
Expression Patterns ◽  
Tree Classifier

Background:The distribution and composition ofcis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets.Methods:Genes with correlated expression patterns across 53 tissues and TF targets were respectively identified from Bray-Curtis Similarity and TF knockdown experiments. Corresponding promoter sequences were reduced to DNase I-accessible intervals; TFBSs were then identified within these intervals using information theory-based position weight matrices for each TF (iPWMs) and clustered. Features from information-dense TFBS clusters predicted these genes with machine learning classifiers, which were evaluated for accuracy, specificity and sensitivity. Mutations in TFBSs were analyzed toin silicoexamine their impact on cluster densities and the regulatory states of target genes.Results:  We initially chose the glucocorticoid receptor gene (NR3C1), whose regulation has been extensively studied, to test this approach.SLC25A32andTANKwere found to exhibit the most similar expression patterns toNR3C1. A Decision Tree classifier exhibited the largest area under the Receiver Operating Characteristic (ROC) curve in detecting such genes. Target gene prediction was confirmed using siRNA knockdown of TFs, which was found to be more accurate than those predicted after CRISPR/CAS9 inactivation.In-silicomutation analyses of TFBSs also revealed that one or more information-dense TFBS clusters in promoters are required for accurate target gene prediction. Conclusions: Machine learning based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.

scholarly journals Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations

F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 1933 ◽  
Author(s):  
Ruipeng Lu ◽  
Peter K. Rogan
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Site ◽  
Target Gene ◽  
Target Genes ◽  
Gene Prediction ◽  
Expression Patterns ◽  
Specificity And Sensitivity ◽  
Tree Classifier ◽  
Glucocorticoid Receptor Gene

Background:The distribution and composition ofcis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML).Methods:Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were selected within DNase I-accessible intervals of corresponding promoter sequences using information theory-based position weight matrices (iPWMs) for each TF. Features from information-dense clusters of TFBSs were input to ML classifiers which predict these gene targets along with their accuracy, specificity and sensitivity. Mutations in TFBSs were analyzedin silicoto examine their impact on TFBS clustering and predict changes in gene regulation.Results: The glucocorticoid receptor gene (NR3C1), whose regulation has been extensively studied, was selected to test this approach.SLC25A32andTANKexhibited the most similar expression patterns toNR3C1. A Decision Tree classifier exhibited the best performance in detecting such genes, based on Area Under the Receiver Operating Characteristic curve (ROC). TF target gene prediction was confirmed using siRNA knockdown, which was more accurate than CRISPR/CAS9 inactivation. TFBS mutation analyses revealed that accurate target gene prediction required  at least 1  information-dense TFBS cluster. Conclusions: ML based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.

scholarly journals Comprehensive Annotation and Functional Exploration of MicroRNAs in Lettuce

2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Deng ◽  
Yajuan Qin ◽  
Pan Yang ◽  
Jianjun Du ◽  
Zheng Kuang ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Computational Prediction ◽  
Regulatory Relationship ◽  
Specific Expression ◽  
Economic Significance ◽  
Specific Expression Pattern

MicroRNA (miRNA) is an important endogenous post-transcriptional regulator, while lettuce (Lactuca sativa) is a leafy vegetable of global economic significance. However, there are few studies on miRNAs in lettuce, and research on miRNA regulatory network in lettuce is absent. In this study, through deep sequencing of small RNAs in different tissues, together with a reference genome, 157 high-confidence miRNA loci in lettuce were comprehensively identified, and their expression patterns were determined. Using a combination of computational prediction and high-throughput experimental verification, a set of reliable lettuce miRNA targets were obtained. Furthermore, through RNA-Seq, the expression profiles of these targets and a comprehensive view of the negative regulatory relationship between miRNAs and their targets was acquired based on a correlation analysis. To further understand miRNA functions, a miRNA regulatory network was constructed, with miRNAs at the core and combining transcription factors and miRNA target genes. This regulatory network, mainly composed of feed forward loop motifs, greatly increases understanding of the potential functions of miRNAs, and many unknown potential regulatory links were discovered. Finally, considering its specific expression pattern, Lsa-MIR408 as a hub gene was employed to illustrate the function of the regulatory network, and genetic experiments revealed its ability to increase the fresh weight and achene size of lettuce. In short, this work lays a solid foundation for the study of miRNA functions and regulatory networks in lettuce.

scholarly journals Identification of transcription factors MYC and C/EBPβ mediated regulatory networks in heart failure

2019 ◽  
Author(s):  
Haiwei Wang ◽  
Xinrui Wang ◽  
Liangpu Xu ◽  
Hua Cao
Keyword(s):  
Heart Failure ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Signaling Pathway ◽  
Insulin Signaling ◽  
Regulatory Networks ◽  
Target Genes ◽  
Transcriptional Profiling ◽  
Normal Heart ◽  
Developmental Progress

Abstract Background: Heart failure is one of leading cause of death worldwide. However, the transcriptional profiling of heart failure is unclear. Moreover, the signaling pathways and transcription factors involving the heart failure developmental progress also are largely unclear.Methods: The transcriptional profiling of heart failure was identified from integrated gene expression datasets. The enriched pathways and transcription factors were analyzed using DAVID and GSEA assay. The transcriptional networks were created by Cytoscape.Results: Compared with the normal heart tissues, we found 90 genes were particularly differentially expressed in heart failing tissues, and those genes were associated with multiple metabolism pathways and insulin signaling pathway. Metabolism and insulin signaling pathway were both inactivated in heart failing tissues. Transcription factors MYC and C/EBPβ were both negatively associated with the expression profiling of heart failing tissues in GSEA assay. Moreover, compared with normal heart tissues, MYC and C/EBPβ were down regulated in heart failing tissues. Furthermore, MYC and C/EBPβ mediated downstream target genes were decreased in heart failing tissues. MYC and C/EBPβ were positively correlated with each other. At last, we constructed the transcription factor MYC and C/EBPβ mediated regulatory networks in heart failing tissues, and identified the MYC and C/EBPβ target genes which had been reported involving the failure developmental progress by literature research. Conclusions: Our results suggested that transcription factor MYC and C/EBPβ played critical roles in heart failure developmental progress. And new heart failure treatments may be developed by targeting MYC and C/EBPβ.

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