Functional profiling of lipid-trait/CAD/MI associated genes by cell-based RNAi.

<p>(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 <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#pgen.1003338-Bartz1" target="_blank">[30]</a>). 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 <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#pgen-1003338-g002" target="_blank">Figure 2</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#s3" target="_blank">Materials and Methods</a> for details). Arrows denote selected compartments representative for respective heatmaps (see text). Bars = 20 µm.</p

RNAi–Based Functional Profiling of Loci from Blood Lipid Genome-Wide Association Studies Identifies Genes with Cholesterol-Regulatory Function

<div><p>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.</p> </div

Comparison of multiparametric datasets for neighboring genes within lipid-trait-associated loci.

<p>Shown are parameters “total cellular intensity” (“total”) of the two strongest effector siRNAs/gene and relative genomic position of lead SNPs (arrowheads) for seven (A–G) selected lipid-trait/CAD/MI loci in which multiple neighboring candidate genes (±50 kB up-/downstream of lead SNP) were functionally analyzed (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#pgen.1003338.s002" target="_blank">Figure S2</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#pgen.1003338.s011" target="_blank">Table S4</a> for comprehensive datasets). Phenotypes (red, increasing; blue, decreasing) meeting statistical criteria as described in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#s3" target="_blank">Materials and Methods</a> are framed in orange.</p

Multiparametric analysis and clustering of functional effector genes.

<p>(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 <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#pgen.1003338.s002" target="_blank">Figure S2</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#pgen.1003338.s011" target="_blank">Table S4</a> for comprehensive datasets). Phenotypes (red, increasing; blue, decreasing) meeting statistical criteria as described in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003338#s3" target="_blank">Materials and Methods</a> are framed in orange.</p

Betaine feeding improves hFVIII and hFIX secretion <i>in vivo</i>.

<p>(A–F) 24 hours post minicircle FVIII-BDD gene transfer the FVIII knockout mice received water without (group I) or with 2% betaine supplementation in the drinking water (group II, each n = 10). After 3 days plasma samples were collected and each group was monitored for human FVIII antigen (A and B) and related activity levels (D and E) and group treatment was switched. After another 3 days, plasma levels were tested again. (C and F) represent the calculated overall effect of betaine on FVIII antigen levels (C) or FVIII activity (D). square symbols indicate samples of the first measuring point, triangles the second one. (G–I) After reaching stable FIX expression levels following minicircle FIX gene transfer, FIX knockout mice were fed 2% Betaine-supplemented drinking water ad libitum in a crossover-study of two groups. After 3 and 17 days of treatment, retroorbitally collected plasma samples were monitored for human FIX antigen levels (G and H). (I) shows the overall change in FIX expression from both groups after 17 days of administration. All values are represented as mean ± SEM. Same symbols indicate samples of the same mouse at different time points; clear: tap water treatment (control), filled: betaine administration. Student’s t-test ((G) ANOVA). *<i>P</i><.05, **<i>P</i><.005, ***<i>P</i><.0005.</p

Rescue of mutant FVIII proteins <i>in vitro</i> and <i>in vivo</i>.

<p>(A–D) HepG2 cells expressing hFVIII muteins were incubated with CC for 72 h. Amount of hFVIII activity in cell supernatant of HepG2 cells expressing hFVIII-BDD Q305P (A and C) or hFVIII-BDD W2313A (B and C) was measured 72 h post betaine treatment. (A and B) show the supplementation of single CC and (C) betaine-ectoine combined treatment. (D) Post CC incubation HepG2 hFVIII-BDDQ305P cells were successively lysed in PBS/0.5% Triton X-100 and PBS/1% SDS. hFVIII antigen was determined in both fractions by indirect ELISA. (E–J) hFVIII-BDDQ305P injected Hem A mice were treated with 2% betaine ad libitum per os in a crossover-study of two groups (each n = 10). After 3 days of treatment hFVIII antigen and activity was measured and treatment was switched between mouse groups. 3 days later, plasma levels were tested again. (E and F) show hFVIII antigen levels, (H and I) the related hFVIII activity levels in plasma of group I or II. (G and J) represent the calculated overall effect of betaine on FVIII antigen levels (G) or FVIII activity (J). square symbols indicate samples of the first measuring point, triangles the second one; clear: tap water-administration (control), filled: 2% betaine administration. (K and L) Endogenous murine FIX levels in all injected FVIII knockout mice (K) and murine FVIII levels of all used FIX knockout mice (L) with and without betaine in the drinking water. Normal mouse levels were set to 100%. Values are presented as means ± SEM. (A–D) ANOVA; (E–H; K–L) Student’s t-test; (I–J) Wilcoxon signed rank test;*<i>P</i><.05, **<i>P</i><.005, ***<i>P</i><.0005.</p

Betaine increases solubility of intracellular FVIII.

<p>CHO cells expressing eGFP-tagged FVIII-BDD protein were incubated with and without betaine. (A,B) Flow cytometry analysis was used to determine the eGFP-signal in untreated cells versus cells treated with betaine or control substance BHA. The mean eGFP intensity (X mean GFP) in the range M1 was used as distinctive parameter. (A) representative histogram after 48 h of treatment and (B) values after 72 h presented as means ± SEM of 3 independent experiments. ANOVA **<i>P</i><.001. (C,D) After 72 h incubation cells were successively lysed in PBS/0.5% Triton X-100 and PBS/1% SDS. (C) FVIII antigen was determined in both fractions by indirect ELISA. (D) Triton X-100-soluble and insoluble fractions were separated on SDS-polyacrylamid gradient gels, and hFVIII light chains (lc), eGFP in hFVIII-single chain (sc) and GAPDH were detected by Western blot. Δ indicates lower band of hFVIII lc doublet.</p

CC improve secretion of FVIII-BDD, FVIII-BDD-eGFP and FVIII-FL <i>in vitro</i>.

<p>Heterologous CHO cells were incubated with CC at different concentrations. FVIII activity was determined in cell supernatants after 72 h by chromogenic assay. (A) Effect of the following CC on human (h)FVIII-BDD secretion: Betaine (100; 50; 25 mM), ectoine (150; 100; 50 mM), trehalose (150; 100; 50 mM), sorbitol (150; 100; 50 mM), taurine (150; 100; 50 mM), trimethylamine N-oxide (TMAO;50; 25; 12,5 mM) and sodium 4-phenylbutyrate (4-PBA; 2; 0,4 mM). Number of experiments, n = 2. (B) Effect of betaine, ectoine, and the endoplasmatic ATPase inhibitors curcumin and thapsigargin on FVIII-.BDD-eGFP secretion. Butylated hydroxyanisole (BHA) is added as treatment control. n = 3. The mean FVIII secretion level ± SD of untreated hFVIII-BDD-eGFP expressing cells was 19±12 IU per 10e6 cells per 72 h. (C) FVIII-BDD-eGFP secretion into cell supernatants over time at different betaine concentrations. n = 3. (D) Influence of betaine, ectoine, curcumin and thapsigargin on FVIII-FL secretion 72 hours following drug supplementation. n = 3. All values are presented as means ± SEM. ANOVA test * <i>P</i><.05; ** <i>P</i><.001.</p

Systematic Cell-Based Phenotyping of Missense Alleles Empowers Rare Variant Association Studies: A Case for <i>LDLR</i> and Myocardial Infarction

<div><p>A fundamental challenge to contemporary genetics is to distinguish rare missense alleles that disrupt protein functions from the majority of alleles neutral on protein activities. High-throughput experimental tools to securely discriminate between disruptive and non-disruptive missense alleles are currently missing. Here we establish a scalable cell-based strategy to profile the biological effects and likely disease relevance of rare missense variants <i>in vitro</i>. We apply this strategy to systematically characterize missense alleles in the low-density lipoprotein receptor (<i>LDLR</i>) gene identified through exome sequencing of 3,235 individuals and exome-chip profiling of 39,186 individuals. Our strategy reliably identifies disruptive missense alleles, and disruptive-allele carriers have higher plasma LDL-cholesterol (LDL-C). Importantly, considering experimental data refined the risk of rare <i>LDLR</i> allele carriers from 4.5- to 25.3-fold for high LDL-C, and from 2.1- to 20-fold for early-onset myocardial infarction. Our study generates proof-of-concept that systematic functional variant profiling may empower rare variant-association studies by orders of magnitude.</p></div

Functions and distribution of <i>LDLR</i> rare missense alleles identified through exome sequencing of 3,235 individuals.

<p><b>(A)</b> Plasma LDL-C (in mg/dl) in <i>LDLR</i> missense allele carriers (dots) from the ATVB cohort according to functional category (for classification, see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004855#sec004" target="_blank">Methods</a>). LoF, loss-of-function. Means are indicated by horizontal bars. p-value was determined by 2-sided, 2-tailed Student’s t-test. <b>(B, C)</b> Individual <i>LDLR</i> missense variants identified through exome sequencing of indicated number of individuals are depicted according to genomic position starting at the 5’end (top). The numbers next to each variant represent the number of times the respective variant was observed in cases and controls, respectively, with regard to plasma LDL-C levels (b) and early-onset myocardial infarction (MI; c). Colors in circles represent indicated functional classes as determined either by an overlap of four bioinformatic prediction tools (PolyPhen-2, SIFT, MutationAssessor and MutationTaster; see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004855#sec004" target="_blank">Methods</a>) (“prediction”) or cell-based experimental studies of LDL-uptake. Variants in bold have been observed in both, cases and controls. <b>(D)</b> Power calculations for the number of sequenced individuals needed to reach exome-wide significance (p<2.5×10^-6, reflected by power = 1) for association with MI-risk when the indicated classes of rare <i>LDLR</i> alleles are taken into account. For details, see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004855#sec004" target="_blank">Methods</a>.</p