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RNAi-based functional profiling of loci from blood lipid genome-wide association studies identifies genes with cholesterol-regulatory function.

Blattmann P, Schuberth C, Pepperkok R, Runz H - PLoS Genet. (2013)

Bottom Line: Genome-wide association studies (GWAS) are powerful tools to unravel genomic loci associated with common traits and complex human disease.Our data further show that individual GWAS loci may contain more than one gene with cholesterol-regulatory functions.By providing strong evidence for disease-relevant functions of lipid trait-associated genes, our study demonstrates that quantitative, cell-based RNAi is a scalable strategy for a systematic, unbiased detection of functional effectors within GWAS loci.

View Article: PubMed Central - PubMed

Affiliation: Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany.

ABSTRACT
Genome-wide association studies (GWAS) are powerful tools to unravel genomic loci associated with common traits and complex human disease. However, GWAS only rarely reveal information on the exact genetic elements and pathogenic events underlying an association. In order to extract functional information from genomic data, strategies for systematic follow-up studies on a phenotypic level are required. Here we address these limitations by applying RNA interference (RNAi) to analyze 133 candidate genes within 56 loci identified by GWAS as associated with blood lipid levels, coronary artery disease, and/or myocardial infarction for a function in regulating cholesterol levels in cells. Knockdown of a surprisingly high number (41%) of trait-associated genes affected low-density lipoprotein (LDL) internalization and/or cellular levels of free cholesterol. Our data further show that individual GWAS loci may contain more than one gene with cholesterol-regulatory functions. Using a set of secondary assays we demonstrate for a number of genes without previously known lipid-regulatory roles (e.g. CXCL12, FAM174A, PAFAH1B1, SEZ6L, TBL2, WDR12) that knockdown correlates with altered LDL-receptor levels and/or that overexpression as GFP-tagged fusion proteins inversely modifies cellular cholesterol levels. By providing strong evidence for disease-relevant functions of lipid trait-associated genes, our study demonstrates that quantitative, cell-based RNAi is a scalable strategy for a systematic, unbiased detection of functional effectors within GWAS loci.

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Functional profiling of lipid-trait/CAD/MI associated genes by cell-based RNAi.(A) Workflow of this study. (B,C) Profiling of lipid-trait associated genes for a cholesterol-regulating function in cells was performed by monitoring LDL-uptake (upper panels) and free perinuclear cholesterol (FC; lower panels) in siRNA-knockdown cells (for details, see [30]). Shown are automatically acquired images of Hela-Kyoto cells cultured and reverse siRNA transfected on cell microarrays for 48 h with control siRNAs (B) or indicated siRNAs targeting selected candidate genes increasing (red) or decreasing (blue) typical cellular phenotypes (C; see Figure 2 and Materials and Methods for details). Arrows denote selected compartments representative for respective heatmaps (see text). Bars = 20 µm.
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pgen-1003338-g001: Functional profiling of lipid-trait/CAD/MI associated genes by cell-based RNAi.(A) Workflow of this study. (B,C) Profiling of lipid-trait associated genes for a cholesterol-regulating function in cells was performed by monitoring LDL-uptake (upper panels) and free perinuclear cholesterol (FC; lower panels) in siRNA-knockdown cells (for details, see [30]). Shown are automatically acquired images of Hela-Kyoto cells cultured and reverse siRNA transfected on cell microarrays for 48 h with control siRNAs (B) or indicated siRNAs targeting selected candidate genes increasing (red) or decreasing (blue) typical cellular phenotypes (C; see Figure 2 and Materials and Methods for details). Arrows denote selected compartments representative for respective heatmaps (see text). Bars = 20 µm.

Mentions: Here we applied this technology with the aim to identify candidate genes within trait-associated loci with a conserved lipid-regulatory function in cells. For this, we functionally analyzed 56 of the 64 genomic loci that were reported until 2009 as associated with lipid traits and/or CAD/MI for genes with a role in cellular cholesterol homeostasis (Figure 1A; Table S1; see Materials and Methods for details). For 38 of the 56 loci all protein-coding genes within ±50 kb of the respective lead SNPs were analyzed, with up to 16 genes at the 19p12 locus. The 18 remaining loci were represented by candidate genes close to the lead SNPs (Table S2). We followed a two-step screening-approach: First, a core gene set of 109 genes was analyzed (“GWAS1”). Promising loci from this gene set were then complemented by additional genes and experimentally re-evaluated (“GWAS2”) (see Materials and Methods). In total, we profiled 133 candidate genes out of which 93 genes had not previously been functionally linked to lipid metabolism (Table S3). Each gene was profiled with 3–5 independent siRNAs, resulting in a total of 534 gene-specific siRNAs tested (Table S4). Uptake of fluorescently-labeled LDL and free perinuclear cholesterol (FC) within siRNA-transfected cells was determined using high-content automated microscopy as described [30] (see Figure S1 and Materials and Methods for how specificity of filipin to reliably detect free cholesterol was assured). For siRNAs analyzed in both, GWAS1 and GWAS2 screens (n = 86), findings correlated well (e.g., Pearson's correlations for the parameter “total cellular intensity” were 0.81 for DiI-LDL uptake and 0.71 for FC) (Table S5), proposing that the results obtained are reproducible and specific. SiRNA-mediated knockdown of multiple known and novel regulators resulted in a consistent increase or reduction of LDL-uptake, FC, or an altered distribution of relevant sub-cellular organelles as signs of perturbed cellular lipid homeostasis (Figure 1B, 1C).


RNAi-based functional profiling of loci from blood lipid genome-wide association studies identifies genes with cholesterol-regulatory function.

Blattmann P, Schuberth C, Pepperkok R, Runz H - PLoS Genet. (2013)

Functional profiling of lipid-trait/CAD/MI associated genes by cell-based RNAi.(A) Workflow of this study. (B,C) Profiling of lipid-trait associated genes for a cholesterol-regulating function in cells was performed by monitoring LDL-uptake (upper panels) and free perinuclear cholesterol (FC; lower panels) in siRNA-knockdown cells (for details, see [30]). Shown are automatically acquired images of Hela-Kyoto cells cultured and reverse siRNA transfected on cell microarrays for 48 h with control siRNAs (B) or indicated siRNAs targeting selected candidate genes increasing (red) or decreasing (blue) typical cellular phenotypes (C; see Figure 2 and Materials and Methods for details). Arrows denote selected compartments representative for respective heatmaps (see text). Bars = 20 µm.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3585126&req=5

pgen-1003338-g001: Functional profiling of lipid-trait/CAD/MI associated genes by cell-based RNAi.(A) Workflow of this study. (B,C) Profiling of lipid-trait associated genes for a cholesterol-regulating function in cells was performed by monitoring LDL-uptake (upper panels) and free perinuclear cholesterol (FC; lower panels) in siRNA-knockdown cells (for details, see [30]). Shown are automatically acquired images of Hela-Kyoto cells cultured and reverse siRNA transfected on cell microarrays for 48 h with control siRNAs (B) or indicated siRNAs targeting selected candidate genes increasing (red) or decreasing (blue) typical cellular phenotypes (C; see Figure 2 and Materials and Methods for details). Arrows denote selected compartments representative for respective heatmaps (see text). Bars = 20 µm.
Mentions: Here we applied this technology with the aim to identify candidate genes within trait-associated loci with a conserved lipid-regulatory function in cells. For this, we functionally analyzed 56 of the 64 genomic loci that were reported until 2009 as associated with lipid traits and/or CAD/MI for genes with a role in cellular cholesterol homeostasis (Figure 1A; Table S1; see Materials and Methods for details). For 38 of the 56 loci all protein-coding genes within ±50 kb of the respective lead SNPs were analyzed, with up to 16 genes at the 19p12 locus. The 18 remaining loci were represented by candidate genes close to the lead SNPs (Table S2). We followed a two-step screening-approach: First, a core gene set of 109 genes was analyzed (“GWAS1”). Promising loci from this gene set were then complemented by additional genes and experimentally re-evaluated (“GWAS2”) (see Materials and Methods). In total, we profiled 133 candidate genes out of which 93 genes had not previously been functionally linked to lipid metabolism (Table S3). Each gene was profiled with 3–5 independent siRNAs, resulting in a total of 534 gene-specific siRNAs tested (Table S4). Uptake of fluorescently-labeled LDL and free perinuclear cholesterol (FC) within siRNA-transfected cells was determined using high-content automated microscopy as described [30] (see Figure S1 and Materials and Methods for how specificity of filipin to reliably detect free cholesterol was assured). For siRNAs analyzed in both, GWAS1 and GWAS2 screens (n = 86), findings correlated well (e.g., Pearson's correlations for the parameter “total cellular intensity” were 0.81 for DiI-LDL uptake and 0.71 for FC) (Table S5), proposing that the results obtained are reproducible and specific. SiRNA-mediated knockdown of multiple known and novel regulators resulted in a consistent increase or reduction of LDL-uptake, FC, or an altered distribution of relevant sub-cellular organelles as signs of perturbed cellular lipid homeostasis (Figure 1B, 1C).

Bottom Line: Genome-wide association studies (GWAS) are powerful tools to unravel genomic loci associated with common traits and complex human disease.Our data further show that individual GWAS loci may contain more than one gene with cholesterol-regulatory functions.By providing strong evidence for disease-relevant functions of lipid trait-associated genes, our study demonstrates that quantitative, cell-based RNAi is a scalable strategy for a systematic, unbiased detection of functional effectors within GWAS loci.

View Article: PubMed Central - PubMed

Affiliation: Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany.

ABSTRACT
Genome-wide association studies (GWAS) are powerful tools to unravel genomic loci associated with common traits and complex human disease. However, GWAS only rarely reveal information on the exact genetic elements and pathogenic events underlying an association. In order to extract functional information from genomic data, strategies for systematic follow-up studies on a phenotypic level are required. Here we address these limitations by applying RNA interference (RNAi) to analyze 133 candidate genes within 56 loci identified by GWAS as associated with blood lipid levels, coronary artery disease, and/or myocardial infarction for a function in regulating cholesterol levels in cells. Knockdown of a surprisingly high number (41%) of trait-associated genes affected low-density lipoprotein (LDL) internalization and/or cellular levels of free cholesterol. Our data further show that individual GWAS loci may contain more than one gene with cholesterol-regulatory functions. Using a set of secondary assays we demonstrate for a number of genes without previously known lipid-regulatory roles (e.g. CXCL12, FAM174A, PAFAH1B1, SEZ6L, TBL2, WDR12) that knockdown correlates with altered LDL-receptor levels and/or that overexpression as GFP-tagged fusion proteins inversely modifies cellular cholesterol levels. By providing strong evidence for disease-relevant functions of lipid trait-associated genes, our study demonstrates that quantitative, cell-based RNAi is a scalable strategy for a systematic, unbiased detection of functional effectors within GWAS loci.

Show MeSH
Related in: MedlinePlus