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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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Impact on FC levels and subcellular localization of GFP–tagged candidate genes.(A) cDNAs encoding for indicated candidate genes linked to GFP were transiently expressed in Hela-Kyoto cells and impact on cellular FC levels was analyzed (see Materials and Methods, Figure S6 and Table S8 for comprehensive datasets). Arrows denote “transfected”, arrowheads “untransfected” cells. See Materials and Methods for definition of threshholds (dashed lines in graphs).Graphs depict total segmental filipin signal plotted against total cellular intensities in the GFP-channel. Each dot reflects one individual cell, trend lines are given in red. Numbers indicate mean ratios of FC in GFP-positive relative to non-expressing cells within the identical dish (n = 3–4 experiments). (B) Maximal projections of confocal stacks showing representative GFP-cDNA expressing cells under control and sterol-depleted conditions (see Materials and Methods). Arrows denote cellular compartments with increased signals upon sterol-depletion. Bars = 10 µm.
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pgen-1003338-g004: Impact on FC levels and subcellular localization of GFP–tagged candidate genes.(A) cDNAs encoding for indicated candidate genes linked to GFP were transiently expressed in Hela-Kyoto cells and impact on cellular FC levels was analyzed (see Materials and Methods, Figure S6 and Table S8 for comprehensive datasets). Arrows denote “transfected”, arrowheads “untransfected” cells. See Materials and Methods for definition of threshholds (dashed lines in graphs).Graphs depict total segmental filipin signal plotted against total cellular intensities in the GFP-channel. Each dot reflects one individual cell, trend lines are given in red. Numbers indicate mean ratios of FC in GFP-positive relative to non-expressing cells within the identical dish (n = 3–4 experiments). (B) Maximal projections of confocal stacks showing representative GFP-cDNA expressing cells under control and sterol-depleted conditions (see Materials and Methods). Arrows denote cellular compartments with increased signals upon sterol-depletion. Bars = 10 µm.

Mentions: In order to gain initial insight into the mechanisms how newly identified effectors could functionally contribute to cholesterol homeostasis, we validated data from our RNAi-screens with a set of secondary assays (Table 2; Figure 4; Figures S4, S5, S6, S7; Tables S6, S7, S8; and Materials and Methods). For instance, an enzymatic assay (see Materials and Methods) applied under screening conditions showed a considerably lower dynamic range to detect changes in cellular cholesterol levels than our image-based approach using filipin. However, results from both approaches correlated well (R2 = 0.48, p<10−6), and biochemical analyses corroborated six candidate genes (C12orf43, GATAD2A, SEZ6L, SORT1, TOMM40, TSSK6) as cholesterol regulators. Likewise, 18 out of 70 effector-siRNAs tested (26%) were also above thresholds when FC was measured from HuH7 liver cells and two candidate genes (BAZ1B, HAVCR1) could be validated with two independent siRNAs also in this cell model (Figure S4, Table S7). Interestingly, despite similar knockdown efficiencies at the protein level (Figure S4C), phenotypic changes upon knockdown of individual effectors were in general less pronounced in HuH7 compared to Hela cells (Figure S4D). One explanation why only a low number of candidate genes could be confirmed in HuH7 cells could be a reduced sensitivity of the filipin assay to monitor changes in free cholesterol in this cell line. Alternatively, liver cells could have mechanisms that compensate in parts the knockdown of specific candidate genes tested that are absent or less effective in Hela cells. One way to identify such compensatory mechanisms in the future could be double knockdown experiments where the candidate genes identified in this work in Hela cells would be knocked-down in combination with putative genes accounting for the compensatory mechanisms in HuH7 cells. We further assessed whether knockdown of effector genes affected mRNA and protein levels of LDLR, a major determinant of blood LDL-levels [38]. Remarkably, for 19 out of 35 effector genes tested at least one siRNA also affected LDLR expression, either on the mRNA level (3 siRNAs), protein level (15 siRNAs) or both (CETP). In several instances LDLR levels and phenotypic effects on LDL-uptake and/or FC positively correlated. For instance, impaired LDL-uptake upon knockdown of B4GALT4 and PAFAH1B1 could be directly caused by a lack of LDLR, as siRNAs targeting these genes also reduced LDLR-protein levels. Perturbed levels of LDLR-mRNA or protein on the other hand are consistent with increased or decreased FC upon knockdown of CXCL12, TSSK6 or WDR12. These results support a role for these effectors on LDLR and suggest a mechanism how variants affecting expression and/or function of these genes may impact on lipid traits and/or CAD/MI risk. No direct correlation between LDLR levels and cellular phenotype was observed for most other effectors. Such results may be explained by compensatory cellular mechanisms that tightly control LDLR at the transcriptional and post-transcriptional level [39], [40]. Alternatively, they could as well hint at yet unknown mechanisms and pathways that contribute to control blood lipid levels independent of LDLR, which await clarification in further studies.


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)

Impact on FC levels and subcellular localization of GFP–tagged candidate genes.(A) cDNAs encoding for indicated candidate genes linked to GFP were transiently expressed in Hela-Kyoto cells and impact on cellular FC levels was analyzed (see Materials and Methods, Figure S6 and Table S8 for comprehensive datasets). Arrows denote “transfected”, arrowheads “untransfected” cells. See Materials and Methods for definition of threshholds (dashed lines in graphs).Graphs depict total segmental filipin signal plotted against total cellular intensities in the GFP-channel. Each dot reflects one individual cell, trend lines are given in red. Numbers indicate mean ratios of FC in GFP-positive relative to non-expressing cells within the identical dish (n = 3–4 experiments). (B) Maximal projections of confocal stacks showing representative GFP-cDNA expressing cells under control and sterol-depleted conditions (see Materials and Methods). Arrows denote cellular compartments with increased signals upon sterol-depletion. Bars = 10 µm.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3585126&req=5

pgen-1003338-g004: Impact on FC levels and subcellular localization of GFP–tagged candidate genes.(A) cDNAs encoding for indicated candidate genes linked to GFP were transiently expressed in Hela-Kyoto cells and impact on cellular FC levels was analyzed (see Materials and Methods, Figure S6 and Table S8 for comprehensive datasets). Arrows denote “transfected”, arrowheads “untransfected” cells. See Materials and Methods for definition of threshholds (dashed lines in graphs).Graphs depict total segmental filipin signal plotted against total cellular intensities in the GFP-channel. Each dot reflects one individual cell, trend lines are given in red. Numbers indicate mean ratios of FC in GFP-positive relative to non-expressing cells within the identical dish (n = 3–4 experiments). (B) Maximal projections of confocal stacks showing representative GFP-cDNA expressing cells under control and sterol-depleted conditions (see Materials and Methods). Arrows denote cellular compartments with increased signals upon sterol-depletion. Bars = 10 µm.
Mentions: In order to gain initial insight into the mechanisms how newly identified effectors could functionally contribute to cholesterol homeostasis, we validated data from our RNAi-screens with a set of secondary assays (Table 2; Figure 4; Figures S4, S5, S6, S7; Tables S6, S7, S8; and Materials and Methods). For instance, an enzymatic assay (see Materials and Methods) applied under screening conditions showed a considerably lower dynamic range to detect changes in cellular cholesterol levels than our image-based approach using filipin. However, results from both approaches correlated well (R2 = 0.48, p<10−6), and biochemical analyses corroborated six candidate genes (C12orf43, GATAD2A, SEZ6L, SORT1, TOMM40, TSSK6) as cholesterol regulators. Likewise, 18 out of 70 effector-siRNAs tested (26%) were also above thresholds when FC was measured from HuH7 liver cells and two candidate genes (BAZ1B, HAVCR1) could be validated with two independent siRNAs also in this cell model (Figure S4, Table S7). Interestingly, despite similar knockdown efficiencies at the protein level (Figure S4C), phenotypic changes upon knockdown of individual effectors were in general less pronounced in HuH7 compared to Hela cells (Figure S4D). One explanation why only a low number of candidate genes could be confirmed in HuH7 cells could be a reduced sensitivity of the filipin assay to monitor changes in free cholesterol in this cell line. Alternatively, liver cells could have mechanisms that compensate in parts the knockdown of specific candidate genes tested that are absent or less effective in Hela cells. One way to identify such compensatory mechanisms in the future could be double knockdown experiments where the candidate genes identified in this work in Hela cells would be knocked-down in combination with putative genes accounting for the compensatory mechanisms in HuH7 cells. We further assessed whether knockdown of effector genes affected mRNA and protein levels of LDLR, a major determinant of blood LDL-levels [38]. Remarkably, for 19 out of 35 effector genes tested at least one siRNA also affected LDLR expression, either on the mRNA level (3 siRNAs), protein level (15 siRNAs) or both (CETP). In several instances LDLR levels and phenotypic effects on LDL-uptake and/or FC positively correlated. For instance, impaired LDL-uptake upon knockdown of B4GALT4 and PAFAH1B1 could be directly caused by a lack of LDLR, as siRNAs targeting these genes also reduced LDLR-protein levels. Perturbed levels of LDLR-mRNA or protein on the other hand are consistent with increased or decreased FC upon knockdown of CXCL12, TSSK6 or WDR12. These results support a role for these effectors on LDLR and suggest a mechanism how variants affecting expression and/or function of these genes may impact on lipid traits and/or CAD/MI risk. No direct correlation between LDLR levels and cellular phenotype was observed for most other effectors. Such results may be explained by compensatory cellular mechanisms that tightly control LDLR at the transcriptional and post-transcriptional level [39], [40]. Alternatively, they could as well hint at yet unknown mechanisms and pathways that contribute to control blood lipid levels independent of LDLR, which await clarification in further studies.

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