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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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Related in: MedlinePlus

Multiparametric analysis and clustering of functional effector genes.(A) Functional consequences upon knockdown of each candidate gene (using 3–5 different siRNAs/gene) were quantified from microscopic images with regard to seven phenotypic parameters: total cellular LDL-signal; LDL concentration and number of cellular structures; total free cholesterol (FC) signal; and FC concentration, area and number of cellular structures. Shown are heatmaps for 37 out of 55 most pronounced functional effector genes that according to parameter “total cellular intensity” (“total”) of the two strongest effector siRNAs/gene were clustered into five distinct functional groups (B–F) (see Figure S2 and Table S4 for comprehensive datasets). Phenotypes (red, increasing; blue, decreasing) meeting statistical criteria as described in Materials and Methods are framed in orange.
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pgen-1003338-g002: Multiparametric analysis and clustering of functional effector genes.(A) Functional consequences upon knockdown of each candidate gene (using 3–5 different siRNAs/gene) were quantified from microscopic images with regard to seven phenotypic parameters: total cellular LDL-signal; LDL concentration and number of cellular structures; total free cholesterol (FC) signal; and FC concentration, area and number of cellular structures. Shown are heatmaps for 37 out of 55 most pronounced functional effector genes that according to parameter “total cellular intensity” (“total”) of the two strongest effector siRNAs/gene were clustered into five distinct functional groups (B–F) (see Figure S2 and Table S4 for comprehensive datasets). Phenotypes (red, increasing; blue, decreasing) meeting statistical criteria as described in Materials and Methods are framed in orange.

Mentions: (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.


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)

Multiparametric analysis and clustering of functional effector genes.(A) Functional consequences upon knockdown of each candidate gene (using 3–5 different siRNAs/gene) were quantified from microscopic images with regard to seven phenotypic parameters: total cellular LDL-signal; LDL concentration and number of cellular structures; total free cholesterol (FC) signal; and FC concentration, area and number of cellular structures. Shown are heatmaps for 37 out of 55 most pronounced functional effector genes that according to parameter “total cellular intensity” (“total”) of the two strongest effector siRNAs/gene were clustered into five distinct functional groups (B–F) (see Figure S2 and Table S4 for comprehensive datasets). Phenotypes (red, increasing; blue, decreasing) meeting statistical criteria as described in Materials and Methods are framed in orange.
© Copyright Policy
Related In: Results  -  Collection

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

pgen-1003338-g002: Multiparametric analysis and clustering of functional effector genes.(A) Functional consequences upon knockdown of each candidate gene (using 3–5 different siRNAs/gene) were quantified from microscopic images with regard to seven phenotypic parameters: total cellular LDL-signal; LDL concentration and number of cellular structures; total free cholesterol (FC) signal; and FC concentration, area and number of cellular structures. Shown are heatmaps for 37 out of 55 most pronounced functional effector genes that according to parameter “total cellular intensity” (“total”) of the two strongest effector siRNAs/gene were clustered into five distinct functional groups (B–F) (see Figure S2 and Table S4 for comprehensive datasets). Phenotypes (red, increasing; blue, decreasing) meeting statistical criteria as described in Materials and Methods are framed in orange.
Mentions: (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.

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