bioRxivBioinformaticsPreprint
Identification of an allele-specific transcription factor binding interaction that regulates PLA2G2A gene expression
Aki HaraiD, Eric LuiD, Laurel M. JohnstoneiD, Michelle Wei, Shudong Sun, Brian HallmarkiD, Joseph C. WatkinsiD, Hao Helen ZhangiD, Guang YaoiD, Floyd H. ChiltoniD
Abstract
The secreted phospholipase A2 (sPLA2) isoform, sPLA2-IIA, has been implicated in a variety of diseases and conditions, including bacteremia, cardiovascular disease, COVID-19, sepsis, adult respiratory distress syndrome, and certain cancers. Given its significant role in these conditions, understanding the regulatory mechanisms impacting its levels is crucial. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs), including rs11573156, that are associated with circulating levels of sPLA2-IIA. Through Genotype-Tissue Expression (GTEx), 234 expression quantitative trait loci (eQTLs) were identified for the gene that encodes for sPLA2-IIA, PLA2G2A. SNP2TFBS (https://ccg.epfl.ch/snp2tfbs/) was utilized to ascertain the binding affinities between transcription factors (TFs) to both the reference and alternative alleles of identified SNPs. Subsequently, ChIP-seq peaks highlighted the TF combinations that specifically bind to the SNP, rs11573156. SP1 emerged as a significant TF/SNP pair in liver cells, with rs11573156/SP1 interaction being most prominent in liver, prostate, ovary, and adipose tissues. Further analysis revealed that the upregulation of PLA2G2A transcript levels through the rs11573156 variant was affected by tissue SP1 protein levels. By leveraging an ordinary differential equation, structured upon Michaelis-Menten enzyme kinetics assumptions, we modeled the PLA2G2A transcriptions dependence on SP1 protein levels, incorporating the SNPs influence. Collectively, these data strongly suggest that the binding affinity differences of SP1 for the different rs11573156 alleles can influence PLA2G2A expression. This, in turn, can modulate sPLA2-IIA levels, impacting a wide range of human diseases.
- organism: human
- assay: DNA methylation
- assay: ChIP-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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