Identification of novel Sp1 targets involved in proliferation and cancer by functional genomics

Oleaga, Carlota; Welten, S.; Belloc, A; Solé, Anna; Rodrı̀guez, Laura; Mencia, Núria; Selga, Elisabet; Tapias, Alicia; Noé, Verónique; Ciudad, Carlos J.

doi:10.1016/j.bcp.2012.09.014

Cited by 29 publications

(31 citation statements)

References 49 publications

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“…Microarray data for the knockdown of SP1 gene in HeLa cells were taken from GSE37935 [10]. The knockdown was carried out by treating HeLa cells with an siRNA directed against the SP1 mRNA.…”

Section: Resultsmentioning

confidence: 99%

“…In the first dataset GSE61973 [9], PARK2 gene was overexpressed in human glioma cell line (U251). In the second study GSE37935 [10], SP1 gene was knocked down using siRNA in HeLa cells. These two case studies were selected to demonstrate the general applicability of the EpiTracer algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…Analysis of the immediate influence zone of the top-ranked epicenter (PARK2) showed that it was enriched in genes involved in cell-cycle regulation. The second case study attempts to identify the target genes regulated by transcription factor SP1 by knocking down the expression of SP1 in HeLa cells (GSE37935) [10]. In this study, EpiTracer was able to identify SP1 among the top ranked epicenters.…”

Section: Introductionmentioning

confidence: 99%

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EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks

2016

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BackgroundIn biological systems, diseases are caused by small perturbations in a complex network of interactions between proteins. Perturbations typically affect only a small number of proteins, which go on to disturb a larger part of the network. To counteract this, a stress-response is launched, resulting in a complex pattern of variations in the cell. Identifying the key players involved in either spreading the perturbation or responding to it can give us important insights.ResultsWe develop an algorithm, EpiTracer, which identifies the key proteins, or epicenters, from which a large number of changes in the protein-protein interaction (PPI) network ripple out. We propose a new centrality measure, ripple centrality, which measures how effectively a change at a particular node can ripple across the network by identifying highest activity paths specific to the condition of interest, obtained by mapping gene expression profiles to the PPI network.We demonstrate the algorithm using an overexpression study and a knockdown study. In the overexpression study, the gene that was overexpressed (PARK2) was highlighted as the most important epicenter specific to the perturbation. The other top-ranked epicenters were involved in either supporting the activity of PARK2, or counteracting it. Also, 5 of the identified epicenters showed no significant differential expression, showing that our method can find information which simple differential expression analysis cannot. In the second dataset (SP1 knockdown), alternative regulators of SP1 targets were highlighted as epicenters. Also, the gene that was knocked down (SP1) was picked up as an epicenter specific to the control condition. Sensitivity analysis showed that the genes identified as epicenters remain largely unaffected by small changes.ConclusionsWe develop an algorithm, EpiTracer, to find epicenters in condition-specific biological networks, given the PPI network and gene expression levels. EpiTracer includes programs which can extract the immediate influence zone of epicenters and provide a summary of dysregulated genes, facilitating quick biological analysis. We demonstrate its efficacy on two datasets with differing characteristics, highlighting its general applicability. We also show that EpiTracer is not sensitive to minor changes in the network. The source code for EpiTracer is provided at Github (https://github.com/narmada26/EpiTracer).

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks

2016

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“…Oleaga et al have reported that human FGF21 might be a Sp1 target gene by functional genomics [17]. However, experiments in their study were performed in human Hela cells and its aim was to find novel Sp1 targets involved in proliferation and cancer, and the specific Sp1 binding sites in mouse FGF21 promoter and its transcriptional role in liver and in adipose tissues, two predominant tissues of FGF21 expression, remained unclear.…”

Section: Discussionmentioning

confidence: 99%

“…A previous study identified that FGF21 might be a target gene of Sp1 by functional genomics [17]. The experiment was performed in human Hela cells and the study focused on finding novel Sp1 targets involved in proliferation and cancer.…”

Section: Introductionmentioning

confidence: 99%

Identification of Sp1 as a Transcription Activator to Regulate Fibroblast Growth Factor 21 Gene Expression

Chen

Zhang

et al. 2017

BioMed Research International

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Fibroblast growth factor 21 (FGF21) is a metabolic hormone with multiple beneficial effects on lipid and glucose homeostasis. Previous study demonstrated that FGF21 might be one of the Sp1 target genes. However, the transcriptional role of Sp1 on FGF21 in adipose tissue and liver has not been reported. In this study, we found that the proximal promoter of mouse FGF21 is located between −63 and −20 containing two putative Sp1-binding sites. Sp1 is a mammalian transcription factor involved in the regulation of many genes during physiological and pathological processes. Our study showed that overexpression of Sp1 or suppressing Sp1 expression resulted in increased or reduced FGF21 promoter activity, respectively. Mutation analysis demonstrated that the Sp1-binding site located between −46 and −38 plays a primary role in transcription of FGF21. Electrophoretic mobility shift assay and chromatin immunoprecipitation analysis indicated that Sp1 specifically bound to this region. Furthermore, the binding activity of Sp1 was significantly increased in adipose tissues of HFD-induced obese mouse and liver of DEN-treated mouse. Thus, our results demonstrate that Sp1 positively regulates the basal transcription of FGF21 in the liver and adipose tissue and contributes to the obesity-induced FGF21 upregulation in mouse adipose tissue and hepatic FGF21 upregulation in hepatocarcinogenesis.

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Heparanase: Cloning, Function and Regulation

Gaskin

Costa

Hulett

2020

Advances in Experimental Medicine and Biology

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Identification of novel Sp1 targets involved in proliferation and cancer by functional genomics

Cited by 29 publications

References 49 publications

EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks

EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks

Identification of Sp1 as a Transcription Activator to Regulate Fibroblast Growth Factor 21 Gene Expression

Heparanase: Cloning, Function and Regulation

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