Dataset Information

Combining protein and mRNA quantification to decipher transcriptional regulation.

ABSTRACT: We combine immunofluorescence and single-molecule fluorescence in situ hybridization (smFISH), followed by automated image analysis, to quantify the concentration of nuclear transcription factors, number of transcription factors bound, and number of nascent mRNAs synthesized at individual gene loci. A theoretical model is used to decipher how transcription factor binding modulates the stochastic kinetics of mRNA production. We demonstrate this approach by examining the regulation of hunchback in the early Drosophila embryo.

SUBMITTER: Xu H

PROVIDER: S-EPMC4521975 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Combining protein and mRNA quantification to decipher transcriptional regulation.

Xu Heng H Sepúlveda Leonardo A LA Figard Lauren L Sokac Anna Marie AM Golding Ido I

Nature methods 20150622 8

We combine immunofluorescence and single-molecule fluorescence in situ hybridization (smFISH), followed by automated image analysis, to quantify the concentration of nuclear transcription factors, number of transcription factors bound, and number of nascent mRNAs synthesized at individual gene loci. A theoretical model is used to decipher how transcription factor binding modulates the stochastic kinetics of mRNA production. We demonstrate this approach by examining the regulation of hunchback in ...[more]

PMID: 26098021

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Project description:The human ZFP36 zinc finger protein family consists of ZFP36, ZFP36L1, and ZFP36L2. These proteins regulate various cellular processes, including cell apoptosis, by binding to adenine uridine rich elements in the 3' untranslated regions of sets of target mRNAs to promote their degradation. The pro-apoptotic and other functions of ZFP36 family members have been implicated in the pathogenesis of lymphoid malignancies. To identify candidate mRNAs that are targeted in the pro-apoptotic response by ZFP36L1, we reverse-engineered a gene regulatory network for all three ZFP36 family members using the 'maximum information coefficient' (MIC) for target gene inference on a large microarray gene expression dataset representing cells of diverse histological origin. Of the three inferred ZFP36L1 mRNA targets that were identified, we focussed on experimental validation of mRNA for the pro-survival protein, BCL2, as a target for ZFP36L1. RNA electrophoretic mobility shift assay experiments revealed that ZFP36L1 interacted with the BCL2 adenine uridine rich element. In murine BCL1 leukemia cells stably transduced with a ZFP36L1 ShRNA lentiviral construct, BCL2 mRNA degradation was significantly delayed compared to control lentiviral expressing cells and ZFP36L1 knockdown in different cell types (BCL1, ACHN, Ramos), resulted in increased levels of BCL2 mRNA levels compared to control cells. 3' untranslated region luciferase reporter assays in HEK293T cells showed that wild type but not zinc finger mutant ZFP36L1 protein was able to downregulate a BCL2 construct containing the BCL2 adenine uridine rich element and removal of the adenine uridine rich core from the BCL2 3' untranslated region in the reporter construct significantly reduced the ability of ZFP36L1 to mediate this effect. Taken together, our data are consistent with ZFP36L1 interacting with and mediating degradation of BCL2 mRNA as an important target through which ZFP36L1 mediates its pro-apoptotic effects in malignant B-cells.

Project description:BACKGROUND:The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. RESULTS:We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. CONCLUSION:The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype.

Dataset Information

Combining protein and mRNA quantification to decipher transcriptional regulation.

Publications

Combining protein and mRNA quantification to decipher transcriptional regulation.

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