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MMP1 bimodal expression and differential response to inflammatory mediators is linked to promoter polymorphisms.

Affara M, Dunmore BJ, Sanders DA, Johnson N, Print CG, Charnock-Jones DS - BMC Genomics (2011)

Bottom Line: Identifying the functional importance of the millions of single nucleotide polymorphisms (SNPs) in the human genome is a difficult challenge.In this study, we used a novel but straightforward method based on agglomerative hierarchical clustering to identify bimodally expressed transcripts in human umbilical vein endothelial cell (HUVEC) microarray data from 15 individuals.We describe a simple method to identify putative bimodally expressed RNAs from transcriptome data that is effective yet easy for non-statisticians to understand and use.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK.

ABSTRACT

Background: Identifying the functional importance of the millions of single nucleotide polymorphisms (SNPs) in the human genome is a difficult challenge. Therefore, a reverse strategy, which identifies functionally important SNPs by virtue of the bimodal abundance across the human population of the SNP-related mRNAs will be useful. Those mRNA transcripts that are expressed at two distinct abundances in proportion to SNP allele frequency may warrant further study. Matrix metalloproteinase 1 (MMP1) is important in both normal development and in numerous pathologies. Although much research has been conducted to investigate the expression of MMP1 in many different cell types and conditions, the regulation of its expression is still not fully understood.

Results: In this study, we used a novel but straightforward method based on agglomerative hierarchical clustering to identify bimodally expressed transcripts in human umbilical vein endothelial cell (HUVEC) microarray data from 15 individuals. We found that MMP1 mRNA abundance was bimodally distributed in un-treated HUVECs and showed a bimodal response to inflammatory mediator treatment. RT-PCR and MMP1 activity assays confirmed the bimodal regulation and DNA sequencing of 69 individuals identified an MMP1 gene promoter polymorphism that segregated precisely with the MMP1 bimodal expression. Chromatin immunoprecipitation (ChIP) experiments indicated that the transcription factors (TFs) ETS1, ETS2 and GATA3, bind to the MMP1 promoter in the region of this polymorphism and may contribute to the bimodal expression.

Conclusions: We describe a simple method to identify putative bimodally expressed RNAs from transcriptome data that is effective yet easy for non-statisticians to understand and use. This method identified bimodal endothelial cell expression of MMP1, which appears to be biologically significant with implications for inflammatory disease. (271 Words).

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Microarray data of approximately 16,000 transcripts in un-treated HUVECs from 15 individuals and IM-treated HUVECs from 9 individuals were analysed separately using the clustering method described in this paper. The maximum branch height (max height, an estimate of the degree of bimodal expression) for each transcript was plotted against the -log2 empirical p-value (an estimate of the frequency of that transcript appearing to be bimodally expressed due to chance alone) for the un-treated data set (A) and the IM-treated data set (B). The 21 RNA transcripts for which the empirical p-values were ≤ 0.1 in both the un-treated and IM-treated data sets were identified and are listed alphabetically in panel (C).
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Figure 2: Microarray data of approximately 16,000 transcripts in un-treated HUVECs from 15 individuals and IM-treated HUVECs from 9 individuals were analysed separately using the clustering method described in this paper. The maximum branch height (max height, an estimate of the degree of bimodal expression) for each transcript was plotted against the -log2 empirical p-value (an estimate of the frequency of that transcript appearing to be bimodally expressed due to chance alone) for the un-treated data set (A) and the IM-treated data set (B). The 21 RNA transcripts for which the empirical p-values were ≤ 0.1 in both the un-treated and IM-treated data sets were identified and are listed alphabetically in panel (C).

Mentions: This algorithm was applied to two RNA microarray data sets: (i) data from HUVECs from 15 different human individuals cultured to passage 4 in standard conditions (the untreated data set; UT) and (ii) data from passage 4 HUVECs from nine different human individuals cultured with a cocktail of 10 ng/ml TNF-α, IL-1β and IL-8 for 24 hours (the IM-treated data set; IM). The bimodally expressed RNAs found in the UT and IM HUVECs are listed in Additional File 2. The relationship between the maximum branch height (an estimate of the degree of bimodal expression) and the -log2 transformed empirical p-value (an estimate of the frequency of a transcript appearing to be bimodally expressed due to chance alone) is shown in Figure 2A-B. In each of the un-treated and IM-treated data sets, a relatively small group of transcripts with high maximum branch height and high -log2 p were identified. We decided to accept an estimated type I error rate of 10% for each of these data sets, and found there were 21 RNA transcripts for which the empirical p-values were ≤ 0.1 in both the un-treated and IM-treated data sets (Figure 2C). A table of features for each of the 21 shortlisted RNA transcripts is given in Additional file 3.


MMP1 bimodal expression and differential response to inflammatory mediators is linked to promoter polymorphisms.

Affara M, Dunmore BJ, Sanders DA, Johnson N, Print CG, Charnock-Jones DS - BMC Genomics (2011)

Microarray data of approximately 16,000 transcripts in un-treated HUVECs from 15 individuals and IM-treated HUVECs from 9 individuals were analysed separately using the clustering method described in this paper. The maximum branch height (max height, an estimate of the degree of bimodal expression) for each transcript was plotted against the -log2 empirical p-value (an estimate of the frequency of that transcript appearing to be bimodally expressed due to chance alone) for the un-treated data set (A) and the IM-treated data set (B). The 21 RNA transcripts for which the empirical p-values were ≤ 0.1 in both the un-treated and IM-treated data sets were identified and are listed alphabetically in panel (C).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3040839&req=5

Figure 2: Microarray data of approximately 16,000 transcripts in un-treated HUVECs from 15 individuals and IM-treated HUVECs from 9 individuals were analysed separately using the clustering method described in this paper. The maximum branch height (max height, an estimate of the degree of bimodal expression) for each transcript was plotted against the -log2 empirical p-value (an estimate of the frequency of that transcript appearing to be bimodally expressed due to chance alone) for the un-treated data set (A) and the IM-treated data set (B). The 21 RNA transcripts for which the empirical p-values were ≤ 0.1 in both the un-treated and IM-treated data sets were identified and are listed alphabetically in panel (C).
Mentions: This algorithm was applied to two RNA microarray data sets: (i) data from HUVECs from 15 different human individuals cultured to passage 4 in standard conditions (the untreated data set; UT) and (ii) data from passage 4 HUVECs from nine different human individuals cultured with a cocktail of 10 ng/ml TNF-α, IL-1β and IL-8 for 24 hours (the IM-treated data set; IM). The bimodally expressed RNAs found in the UT and IM HUVECs are listed in Additional File 2. The relationship between the maximum branch height (an estimate of the degree of bimodal expression) and the -log2 transformed empirical p-value (an estimate of the frequency of a transcript appearing to be bimodally expressed due to chance alone) is shown in Figure 2A-B. In each of the un-treated and IM-treated data sets, a relatively small group of transcripts with high maximum branch height and high -log2 p were identified. We decided to accept an estimated type I error rate of 10% for each of these data sets, and found there were 21 RNA transcripts for which the empirical p-values were ≤ 0.1 in both the un-treated and IM-treated data sets (Figure 2C). A table of features for each of the 21 shortlisted RNA transcripts is given in Additional file 3.

Bottom Line: Identifying the functional importance of the millions of single nucleotide polymorphisms (SNPs) in the human genome is a difficult challenge.In this study, we used a novel but straightforward method based on agglomerative hierarchical clustering to identify bimodally expressed transcripts in human umbilical vein endothelial cell (HUVEC) microarray data from 15 individuals.We describe a simple method to identify putative bimodally expressed RNAs from transcriptome data that is effective yet easy for non-statisticians to understand and use.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK.

ABSTRACT

Background: Identifying the functional importance of the millions of single nucleotide polymorphisms (SNPs) in the human genome is a difficult challenge. Therefore, a reverse strategy, which identifies functionally important SNPs by virtue of the bimodal abundance across the human population of the SNP-related mRNAs will be useful. Those mRNA transcripts that are expressed at two distinct abundances in proportion to SNP allele frequency may warrant further study. Matrix metalloproteinase 1 (MMP1) is important in both normal development and in numerous pathologies. Although much research has been conducted to investigate the expression of MMP1 in many different cell types and conditions, the regulation of its expression is still not fully understood.

Results: In this study, we used a novel but straightforward method based on agglomerative hierarchical clustering to identify bimodally expressed transcripts in human umbilical vein endothelial cell (HUVEC) microarray data from 15 individuals. We found that MMP1 mRNA abundance was bimodally distributed in un-treated HUVECs and showed a bimodal response to inflammatory mediator treatment. RT-PCR and MMP1 activity assays confirmed the bimodal regulation and DNA sequencing of 69 individuals identified an MMP1 gene promoter polymorphism that segregated precisely with the MMP1 bimodal expression. Chromatin immunoprecipitation (ChIP) experiments indicated that the transcription factors (TFs) ETS1, ETS2 and GATA3, bind to the MMP1 promoter in the region of this polymorphism and may contribute to the bimodal expression.

Conclusions: We describe a simple method to identify putative bimodally expressed RNAs from transcriptome data that is effective yet easy for non-statisticians to understand and use. This method identified bimodal endothelial cell expression of MMP1, which appears to be biologically significant with implications for inflammatory disease. (271 Words).

Show MeSH
Related in: MedlinePlus