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A gene expression map of shoot domains reveals regulatory mechanisms.

ABSTRACT: Gene regulatory networks control development via domain-specific gene expression. In seed plants, self-renewing stem cells located in the shoot apical meristem (SAM) produce leaves from the SAM peripheral zone. After initiation, leaves develop polarity patterns to form a planar shape. Here we compare translating RNAs among SAM and leaf domains. Using translating ribosome affinity purification and RNA sequencing to quantify gene expression in target domains, we generate a domain-specific translatome map covering representative vegetative stage SAM and leaf domains. We discuss the predicted cellular functions of these domains and provide evidence that dome seemingly unrelated domains, utilize common regulatory modules. Experimental follow up shows that the RABBIT EARS and HANABA TARANU transcription factors have roles in axillary meristem initiation. This dataset provides a community resource for further study of shoot development and response to internal and environmental signals.

SUBMITTER: Tian C

PROVIDER: S-EPMC6329838 | biostudies-other | 2019 Jan

REPOSITORIES: biostudies-other

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A gene expression map of shoot domains reveals regulatory mechanisms.

Tian Caihuan C Wang Ying Y Yu Haopeng H He Jun J Wang Jin J Shi Bihai B Du Qingwei Q Provart Nicholas J NJ Meyerowitz Elliot M EM Jiao Yuling Y

Nature communications 20190111 1

Gene regulatory networks control development via domain-specific gene expression. In seed plants, self-renewing stem cells located in the shoot apical meristem (SAM) produce leaves from the SAM peripheral zone. After initiation, leaves develop polarity patterns to form a planar shape. Here we compare translating RNAs among SAM and leaf domains. Using translating ribosome affinity purification and RNA sequencing to quantify gene expression in target domains, we generate a domain-specific translat ...[more]

PMID: 30635575

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Project description:Approximately half of known human miRNAs are located in the introns of protein coding genes. Some of these intronic miRNAs are only expressed when their host gene is and, as such, their steady state expression levels are highly correlated with those of the host gene's mRNA. Recently host gene expression levels have been used to predict the targets of intronic miRNAs by identifying other mRNAs that they have consistent negative correlation with. This is a potentially powerful approach because it allows a large number of expression profiling studies to be used but needs refinement because mRNAs can be targeted by multiple miRNAs and not all intronic miRNAs are co-expressed with their host genes.Here we introduce InMiR, a new computational method that uses a linear-Gaussian model to predict the targets of intronic miRNAs based on the expression profiles of their host genes across a large number of datasets. Our method recovers nearly twice as many true positives at the same fixed false positive rate as a comparable method that only considers correlations. Through an analysis of 140 Affymetrix datasets from Gene Expression Omnibus, we build a network of 19,926 interactions among 57 intronic miRNAs and 3,864 targets. InMiR can also predict which host genes have expression profiles that are good surrogates for those of their intronic miRNAs. Host genes that InMiR predicts are bad surrogates contain significantly more miRNA target sites in their 3' UTRs and are significantly more likely to have predicted Pol II and Pol III promoters in their introns.We provide a dataset of 1,935 predicted mRNA targets for 22 intronic miRNAs. These prediction are supported both by sequence features and expression. By combining our results with previous reports, we distinguish three classes of intronic miRNAs: Those that are tightly regulated with their host gene; those that are likely to be expressed from the same promoter but whose host gene is highly regulated by miRNAs; and those likely to have independent promoters.

Project description:BACKGROUND: Pre-implantation development is a crucial step in successful implantation and pregnancy in mammals. It has been studied in depth, but mostly in laboratory animal models. Less is known about the regulatory mechanism involved in the pre-implantation development in humans and about the comparative aspects. METHODS: Here, we employed the microarray datasets from the public database library of GEO and applied comparative analysis of genome wide temporal gene expression data based on statistical analysis and functional annotation for both mouse and human, demonstrating the discordance between the regulatory mechanisms of both mouse and human pre-implantation development. RESULTS: There were differences between mouse and human pre-implantation development both in the global gene expression pattern and in the expression changes of individual genes at each stage, including different major transient waves of transcription profiles and some stage-specific genes and significantly related pathways. There also appeared to be different functional changes from one stage to another between mouse and human. CONCLUSIONS: The analysis presented here lead to interesting and complementary conclusions that the regulatory mechanism of human pre-implantation development is not completely the same as the mouse. Not as the fact that 1-cell to 2-cell stage is important for mouse pre-implantation development, the 4-cell stage and 8-cell stage are both essential for human. Unlike in mouse, of which most of pathways found were related to energy, RNA and protein metabolism, the identified pathways in human were mostly disease-related and associated with human pre-implantation embryonic development. All of these suggest that a further comparative analysis should be required for applying the result of mouse expression data to human research or therapy, particularly in pre-implantation developments. Our study provides several potential targets of genes and pathways for studying the regulatory mechanism of human pre-implantation development using mouse model.

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A gene expression map of shoot domains reveals regulatory mechanisms.

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A gene expression map of shoot domains reveals regulatory mechanisms.

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