Lrp6 is a target of the PTH-activated αNAC transcriptional coregulator

Pellicelli, Martin; Hariri, Hadla; Miller, Julie Ann; St‐Arnaud, René

doi:10.1016/j.bbagrm.2018.01.008

Cited by 14 publications

(26 citation statements)

References 49 publications

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“…The selective enrichment of antisense versus sense GT insertions in the region upstream of the LRP6 promoter in cells sorted for low WNT reporter fluorescence (Fig 5A and 5B) suggests that such insertions are not merely disrupting an enhancer or promoter. A prior study narrowed down the location of the minimal promoter for LRP6 to the region 242 to 352 bp upstream of the annotated TSS [16], whereas the region enriched for antisense GT insertions that we identified is located 1124 to 2123 bp upstream of the annotated TSS, supporting our conclusion that these antisense GT insertions do not disrupt the activity of the LRP6 promoter. Furthermore, that same study observed no change in LRP6 transcription when the region from 352 to 2523 bp upstream of the transcriptional start site was deleted.…”

Section: Discussionsupporting

confidence: 86%

Discovery of gene regulatory elements through a new bioinformatics analysis of haploid genetic screens

Patel¹,

Lebensohn²,

Pusapati³

et al. 2019

PLoS ONE

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The systematic identification of regulatory elements that control gene expression remains a challenge. Genetic screens that use untargeted mutagenesis have the potential to identify protein-coding genes, non-coding RNAs and regulatory elements, but their analysis has mainly focused on identifying the former two. To identify regulatory elements, we conducted a new bioinformatics analysis of insertional mutagenesis screens interrogating WNT signaling in haploid human cells. We searched for specific patterns of retroviral gene trap integrations (used as mutagens in haploid screens) in short genomic intervals overlapping with introns and regions upstream of genes. We uncovered atypical patterns of gene trap insertions that were not predicted to disrupt coding sequences, but caused changes in the expression of two key regulators of WNT signaling, suggesting the presence of cis-regulatory elements. Our methodology extends the scope of haploid genetic screens by enabling the identification of regulatory elements that control gene expression.

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Section: Discussionsupporting

confidence: 86%

Discovery of gene regulatory elements through a new bioinformatics analysis of haploid genetic screens

Patel¹,

Lebensohn²,

Pusapati³

et al. 2019

PLoS ONE

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“…The down-regulation of LRP5 in aggressive HCC was validated in HCC data but was not significant for LRP6 probably due to the low level of gene expression (Additional file 1: Figure S8B). According with this, the up-regulation of LRP6 through JUND::NACA complexes was clearly demonstrated in osteoblasts [33].…”

Section: Activation Of Nfκb Signaling In Aggressive Hccmentioning

confidence: 53%

A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma

et al. 2020

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Background: Integrating genome-wide gene expression patient profiles with regulatory knowledge is a challenging task because of the inherent heterogeneity, noise and incompleteness of biological data. From the computational side, several solvers for logic programs are able to perform extremely well in decision problems for combinatorial search domains. The challenge then is how to process the biological knowledge in order to feed these solvers to gain insights in a biological study. It requires formalizing the biological knowledge to give a precise interpretation of this information; currently, very few pathway databases offer this possibility. Results: The presented work proposes an automatic pipeline to extract automatically regulatory knowledge from pathway databases and generate novel computational predictions related to the state of expression or activity of biological molecules. We applied it in the context of hepatocellular carcinoma (HCC) progression, and evaluate the precision and the stability of these computational predictions. Our working base is a graph of 3383 nodes and 13,771 edges extracted from the KEGG database, in which we integrate 209 differentially expressed genes between low and high aggressive HCC across 294 patients. Our computational model predicts the shifts of expression of 146 initially non-observed biological components. Our predictions were validated at 88% using a larger experimental dataset and cross-validation techniques. In particular, we focus on the protein complexes predictions and show for the first time that NFKB1/BCL-3 complexes are activated in aggressive HCC. In spite of the large dimension of the reconstructed models, our analyses over the computational predictions discover a well constrained region where KEGG regulatory knowledge constrains gene expression of several biomolecules. These regions can offer interesting windows to perturb experimentally such complex systems. Conclusion: This new pipeline allows biologists to develop their own predictive models based on a list of genes. It facilitates the identification of new regulatory biomolecules using knowledge graphs and predictive computational methods. Our workflow is implemented in an automatic python pipeline which is publicly available at https://github. com/LokmaneChebouba/key-pipe and contains as testing data all the data used in this paper.

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“…In one study, the PTH (1–34) stimulation of osteoblasts induces the phosphorylation of NACA at S99, its subsequent nuclear translocation and binding to the Bglap2 promoter 14 . Another study has highlighted the role of NACA along with JUND in Lrp6 transcriptional regulation in MC3T3-E1 cells 18 .…”

Section: Resultsmentioning

confidence: 99%

“…In addition to Lrp6 18 , Bglap2 14 , and Nfil3 19 , we have added Usp53 to the list of target genes regulated by the PTH-NACA pathway in osteoblasts. This finding is significant as the literature lacks any characterization of Usp53 regulation or function in any tissue.…”

Section: Discussionmentioning

confidence: 99%

“…NACA (alpha chain of the nascent polypeptide-associated complex, αNAC) is a physiologically relevant transcriptional coregulator of osteogenic targets in bone tissue 14 , 15 . NACA has been found to specifically bind DNA and assemble with transcription factors such as JUN and JUND homodimers to enhance the transcription of the osteocalcin ( Bglap2 ) and Lrp6 genes 15 – 18 . More recently, we have shown that NACA is required for the CREB-mediated activation of the Nfil3 gene in osteoblasts 19 .…”

Section: Introductionmentioning

confidence: 99%

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Ubiquitin specific peptidase Usp53 regulates osteoblast versus adipocyte lineage commitment

Hariri

Addison

St‐Arnaud

2021

Sci Rep

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We have previously shown that parathyroid hormone (PTH) induces the phosphorylation of the DNA-binding protein Nascent polypeptide associated complex And Coregulator alpha (NACA), leading to nuclear translocation of NACA and activation of target genes. Using ChIP-Seq against NACA in parallel with RNA-sequencing, we report the identification of Ubiquitin Specific Peptidase 53 (Usp53) as a target gene of PTH-activated NACA in osteoblasts. A binding site for NACA within the ChIP fragment from the Usp53 promoter was confirmed by electrophoretic mobility shift assay. Activity of the Usp53 promoter (− 2325/+ 238 bp) was regulated by the JUN-CREB complex and this activation relied on activated PKA and the presence of NACA. Usp53 knockdown in ST2 stromal cells stimulated expression of the osteoblastic markers Bglap2 (Osteocalcin) and Alpl (Alkaline phosphatase) and inhibited expression of the adipogenic markers Pparg and Cebpa. A similar effect was measured when knocking down Naca. During osteoblastogenesis, the impact of Usp53 knockdown on PTH responses varied depending on the maturation stage of the cells. In vivo implantation of Usp53-knockdown bone marrow stromal cells in immunocompromised mice showed an increase in osteoblast number and a decrease in adipocyte counts. Our data suggest that Usp53 modulates the fate of mesenchymal cells by impacting lineage selection.

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Lrp6 is a target of the PTH-activated αNAC transcriptional coregulator

Cited by 14 publications

References 49 publications

Discovery of gene regulatory elements through a new bioinformatics analysis of haploid genetic screens

Discovery of gene regulatory elements through a new bioinformatics analysis of haploid genetic screens

A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma

Ubiquitin specific peptidase Usp53 regulates osteoblast versus adipocyte lineage commitment

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