RHOA is a modulator of the cholesterol-lowering effects of statin

Participant-derived cell line transcriptomic analyses and mouse studies reveal a role for ZNF335 in plasma cholesterol statin response

BACKGROUND

Statins lower circulating low-density lipoprotein cholesterol (LDLC) levels and reduce cardiovascular disease risk. Though highly efficacious in general, there is considerable inter-individual variation in statin efficacy that remains largely unexplained.

METHODS

To identify novel genes that may modulate statin-induced LDLC lowering, we used RNA-sequencing data from 426 control- and 2 µM simvastatin-treated lymphoblastoid cell lines (LCLs) derived from European and African American ancestry participants of the Cholesterol and Pharmacogenetics (CAP) 40 mg/day 6-week simvastatin clinical trial (ClinicalTrials.gov Identifier: NCT00451828). We correlated statin-induced changes in LCL gene expression with plasma LDLC statin response in the corresponding CAP participants. For the most correlated gene identified (ZNF335), we followed up in vivo by comparing plasma cholesterol levels, lipoprotein profiles, and lipid statin response between wild-type mice and carriers of a hypomorphic (partial loss of function) missense mutation in Zfp335 (the mouse homolog of ZNF335).

RESULTS

The statin-induced expression changes of 147 human LCL genes were significantly correlated to the plasma LDLC statin responses of the corresponding CAP participants in vivo (FDR = 5%). The two genes with the strongest correlations were zinc finger protein 335 (ZNF335 aka NIF-1, rho = 0.237, FDR-adj p = 0.0085) and CCR4-NOT transcription complex subunit 3 (CNOT3, rho = 0.233, FDR-adj p = 0.0085). Chow-fed mice carrying a hypomorphic missense (R1092W; aka bloto) mutation in Zfp335 had significantly lower non-HDL cholesterol levels than wild-type C57BL/6J mice in a sex combined model (p = 0.04). Furthermore, male (but not female) mice carrying the Zfp335R1092W allele had significantly lower total and HDL cholesterol levels than wild-type mice. In a separate experiment, wild-type mice fed a control diet for 4 weeks and a matched simvastatin diet for an additional 4 weeks had significant statin-induced reductions in non-HDLC (-43 ± 18% and -23 ± 19% for males and females, respectively). Wild-type male (but not female) mice experienced significant reductions in plasma LDL particle concentrations, while male mice carrying Zfp335R1092W allele(s) exhibited a significantly blunted LDL statin response.

CONCLUSIONS

Our in vitro and in vivo studies identified ZNF335 as a novel modulator of plasma cholesterol levels and statin response, suggesting that variation in ZNF335 activity could contribute to inter-individual differences in statin clinical efficacy.

Geranylgeranyl isoprenoids and hepatic Rap1a regulate basal and statin-induced expression of PCSK9

LDL-C lowering is the main goal of atherosclerotic cardiovascular disease prevention, and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition is now a validated therapeutic strategy that lowers serum LDL-C and reduces coronary events. Ironically, the most widely used medicine to lower cholesterol, statins, has been shown to increase circulating PCSK9 levels, which limits their efficacy. Here, we show that geranylgeranyl isoprenoids and hepatic Rap1a regulate both basal and statin-induced expression of PCSK9 and contribute to LDL-C homeostasis. Rap1a prenylation and activity is inhibited upon statin treatment, and statin-mediated PCSK9 induction is dependent on geranylgeranyl synthesis and hepatic Rap1a. Accordingly, treatment of mice with a small-molecule activator of Rap1a lowered PCSK9 protein and plasma cholesterol and inhibited statin-mediated PCSK9 induction in hepatocytes. The mechanism involves inhibition of the downstream RhoA-ROCK pathway and regulation of PCSK9 at the post-transcriptional level. These data further identify Rap1a as a novel regulator of PCSK9 protein and show that blocking Rap1a prenylation through lowering geranylgeranyl levels contributes to statin-mediated induction of PCSK9.

Geranylgeranyl Isoprenoids and Hepatic Rap1a Regulate Basal and Statin-Induced Expression of PCSK9

Low-density lipoprotein cholesterol (LDL-C) lowering is the main goal of atherosclerotic cardiovascular disease prevention, and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition is now a validated therapeutic strategy that lowers serum LDL-C and reduces coronary events. Ironically, the most widely used medicine to lower cholesterol, statins, has been shown to increase circulating PCSK9 levels, which limits their efficacy. Here, we show that geranylgeranyl isoprenoids and hepatic Rap1a regulate both basal and statin induced expression of PCSK9 and contribute to LDL-C homeostasis. Rap1a prenylation and activity is inhibited upon statin treatment, and statin mediated PCSK9 induction is dependent on geranylgeranyl synthesis and hepatic Rap1a. Accordingly, treatment of mice with a small molecule activator of Rap1a lowered PCSK9 protein and plasma cholesterol and inhibited statin mediated PCSK9 induction in hepatocytes. The mechanism involves inhibition of the downstream RhoA-ROCK pathway and regulation of PCSK9 at the post transcriptional level. These data further identify Rap1a as a novel regulator of PCSK9 protein and show that blocking Rap1a prenylation through lowering geranylgeranyl levels contributes to statin-mediated induction of PCSK9.

Participant-derived cell line transcriptomic analyses and mouse studies reveal a role for ZNF335 in plasma cholesterol statin response

Background

Statins lower circulating low-density lipoprotein cholesterol (LDLC) levels and reduce cardiovascular disease risk. Though highly efficacious in general, there is considerable inter-individual variation in statin efficacy that remains largely unexplained.

Methods

To identify novel genes that may modulate statin-induced LDLC lowering, we used RNA-sequencing data from 426 control- and 2 μM simvastatin-treated lymphoblastoid cell lines (LCLs) derived from European and African American ancestry participants of the Cholesterol and Pharmacogenetics (CAP) 40 mg/day 6-week simvastatin clinical trial (ClinicalTrials.gov Identifier: NCT00451828). We correlated statin-induced changes in LCL gene expression with plasma LDLC statin response in the corresponding CAP participants. For the most correlated gene identified (ZNF335), we followed up in vivo by comparing plasma cholesterol levels, lipoprotein profiles, and lipid statin response between wild-type mice and carriers of a hypomorphic (partial loss of function) missense mutation in Zfp335 (the mouse homolog of ZNF335).

Results

The statin-induced expression changes of 147 human LCL genes were significantly correlated to the plasma LDLC statin responses of the corresponding CAP participants in vivo (FDR=5%). The two genes with the strongest correlations were zinc finger protein 335 (ZNF335 aka NIF-1, rho=0.237, FDR-adj p=0.0085) and CCR4-NOT transcription complex subunit 3 (CNOT3, rho=0.233, FDR-adj p=0.0085). Chow-fed mice carrying a hypomorphic missense (R1092W; aka bloto) mutation in Zfp335 had significantly lower non-HDL cholesterol levels than wild type C57BL/6J mice in a sex combined model (p=0.04). Furthermore, male (but not female) mice carrying the Zfp335R1092W allele had significantly lower total and HDL cholesterol levels than wild-type mice. In a separate experiment, wild-type mice fed a control diet for 4 weeks and a matched simvastatin diet for an additional 4 weeks had significant statin-induced reductions in non-HDLC (-43±18% and -23±19% for males and females, respectively). Wild-type male (but not female) mice experienced significant reductions in plasma LDL particle concentrations, while male mice carrying Zfp335R1092W allele(s) exhibited a significantly blunted LDL statin response.

Conclusions

Our in vitro and in vivo studies identified ZNF335 as a novel modulator of plasma cholesterol levels and statin response, suggesting that variation in ZNF335 activity could contribute to inter-individual differences in statin clinical efficacy.

Analysis of Pathogenic Pseudoexons Reveals Novel Mechanisms Driving Cryptic Splicing

Understanding pre-mRNA splicing is crucial to accurately diagnosing and treating genetic diseases. However, mutations that alter splicing can exert highly diverse effects. Of all the known types of splicing mutations, perhaps the rarest and most difficult to predict are those that activate pseudoexons, sometimes also called cryptic exons. Unlike other splicing mutations that either destroy or redirect existing splice events, pseudoexon mutations appear to create entirely new exons within introns. Since exon definition in vertebrates requires coordinated arrangements of numerous RNA motifs, one might expect that pseudoexons would only arise when rearrangements of intronic DNA create novel exons by chance. Surprisingly, although such mutations do occur, a far more common cause of pseudoexons is deep-intronic single nucleotide variants, raising the question of why these latent exon-like tracts near the mutation sites have not already been purged from the genome by the evolutionary advantage of more efficient splicing. Possible answers may lie in deep intronic splicing processes such as recursive splicing or poison exon splicing. Because these processes utilize intronic motifs that benignly engage with the spliceosome, the regions involved may be more susceptible to exonization than other intronic regions would be. We speculated that a comprehensive study of reported pseudoexons might detect alignments with known deep intronic splice sites and could also permit the characterisation of novel pseudoexon categories. In this report, we present and analyse a catalogue of over 400 published pseudoexon splice events. In addition to confirming prior observations of the most common pseudoexon mutation types, the size of this catalogue also enabled us to suggest new categories for some of the rarer types of pseudoexon mutation. By comparing our catalogue against published datasets of non-canonical splice events, we also found that 15.7% of pseudoexons exhibit some splicing activity at one or both of their splice sites in non-mutant cells. Importantly, this included seven examples of experimentally confirmed recursive splice sites, confirming for the first time a long-suspected link between these two splicing phenomena. These findings have the potential to improve the fidelity of genetic diagnostics and reveal new targets for splice-modulating therapies.

A spotter's guide to SNPtic exons: The common splice variants underlying some SNP-phenotype correlations

BACKGROUND

Cryptic exons are typically characterised as deleterious splicing aberrations caused by deep intronic mutations. However, low-level splicing of cryptic exons is sometimes observed in the absence of any pathogenic mutation. Five recent reports have described how low-level splicing of cryptic exons can be modulated by common single-nucleotide polymorphisms (SNPs), resulting in phenotypic differences amongst different genotypes.

METHODS

We sought to investigate whether additional 'SNPtic' exons may exist, and whether these could provide an explanatory mechanism for some of the genotype-phenotype correlations revealed by genome-wide association studies. We thoroughly searched the literature for reported cryptic exons, cross-referenced their genomic coordinates against the dbSNP database of common SNPs, then screened out SNPs with no reported phenotype associations.

RESULTS

This method discovered five probable SNPtic exons in the genes APC, FGB, GHRL, MYPBC3 and OTC. For four of these five exons, we observed that the phenotype associated with the SNP was compatible with the predicted splicing effect of the nucleotide change, whilst the fifth (in GHRL) likely had a more complex splice-switching effect.

CONCLUSION

Application of our search methods could augment the knowledge value of future cryptic exon reports and aid in generating better hypotheses for genome-wide association studies.

Identifying genetic modulators of statin response using subject-derived lymphoblastoid cell lines

Although statins (3-hydroxy-3-methylglutaryl-CoA reductase inhibitors) have proven effective in reducing plasma low-density lipoprotein levels and risk of cardiovascular disease, their lipid lowering efficacy is highly variable among individuals. Furthermore, statin treatment carries a small but significant risk of adverse effects, most notably myopathy and new onset diabetes. Hence, identification of biomarkers for predicting patients who would most likely benefit from statin treatment without incurring increased risk of adverse effects can have a significant public health impact. In this review, we discuss the rationale for the use of subject-derived lymphoblastoid cell lines in studies of statin pharmacogenomics and describe a variety of approaches we have employed to identify novel genetic markers associated with interindividual variation in statin response.

Regulation of endoplasmic reticulum stress and trophectoderm lineage specification by the mevalonate pathway in the mouse preimplantation embryo

Early embryos are vulnerable to environmental insults, such as medications taken by the mother. Due to increasing prevalence of hypercholesterolemia, more women of childbearing potential are taking cholesterol-lowering medications called statins. Previously, we showed that inhibition of the mevalonate pathway by statins impaired mouse preimplantation development, by modulating HIPPO signaling, a key regulator for trophectoderm (TE) lineage specification. Here, we further evaluated molecular events that are altered by mevalonate pathway inhibition during the timeframe of morphogenesis and cell lineage specification. Whole transcriptome analysis revealed that statin treatment dysregulated gene expression underlying multiple processes, including cholesterol biosynthesis, HIPPO signaling, cell lineage specification and endoplasmic reticulum (ER) stress response. We explored mechanisms that link the mevalonate pathway to ER stress, because of its potential impact on embryonic health and development. Upregulation of ER stress-responsive genes was inhibited when statin-treated embryos were supplemented with the mevalonate pathway product, geranylgeranyl pyrophosphate (GGPP). Inhibition of geranylgeranylation was sufficient to upregulate ER stress-responsive genes. However, ER stress-responsive genes were not upregulated by inhibition of ras homolog family member A (RHOA), a geranylgeranylation target, although it interfered with TE specification and blastocyst cavity formation. In contrast, inhibition of Rac family small GTPase 1 (RAC1), another geranylgeranylation target, upregulated ER stress-responsive genes, while it did not impair TE specification or cavity formation. Thus, our study suggests that the mevalonate pathway regulates cellular homeostasis (ER stress repression) and differentiation (TE lineage specification) in preimplantation embryos through GGPP-dependent activation of two distinct small GTPases, RAC1 and RHOA, respectively. Translation of the findings to human embryos and clinical settings requires further investigations.

Pleiotropic use of Statins as non-lipid-lowering drugs

Statins, known as HMG-CoA reductase (HMGCR) inhibitors, have primarily been utilized for metabolic and angiographic medical applications because of their cholesterol-lowering effects. Similar to other drugs, statins may also induce a series of potential side effects. Statins inhibit the HMGCR (rate-limiting enzyme) activity in early stages of mevalonate pathway and then indirectly affect a number of intermediate products, including non-sterol isoprenoids (coenzyme Q10, dolichol etc.), which can result in impaired functions of body organs. Recently, scores of studies have uncovered additional functional mechanisms of statins in other diseases, such as diabetes mellitus, nervous system diseases, coronary heart disease, inflammation and cancers. This review aims to summarize the positive and adverse mechanisms of statin therapy. Statin care should be taken in the treatment of many diseases including cancers. Since the underlying mechanisms are not fully elucidated, future studies should spend more time and efforts on basic research to explore the mechanisms of statins.

Effect of statins on toll-like receptors: a new insight to pleiotropic effects

The toll-like receptors (TLRs) are a class of transmembrane-spanning receptors that are sentinels of both innate and adaptive immunity. Statins (3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors) are the most commonly prescribed therapeutic agents for treating hypercholesterolemia globally. However, statin therapy appears to have pleiotropic effects including attenuation of chronic low-grade inflammation and modulation of TLR activity. Statins through abolition of TLR4 expression and regulation of the TLR4/Myd88/NF-κB signaling pathway may slow the progression of atherosclerosis and other inflammatory diseases. In this review, we have focused on the impact and mechanism of action of statins on cardiovascular and non-cardiovascular diseases.

ZNF542P is a pseudogene associated with LDL response to simvastatin treatment

Statins are the most commonly prescribed cardiovascular disease drug, but their inter-individual efficacy varies considerably. Genetic factors uncovered to date have only explained a small proportion of variation in low-density lipoprotein cholesterol (LDLC) lowering. To identify novel markers and determinants of statin response, we used whole transcriptome sequence data collected from simvastatin and control incubated lymphoblastoid cell lines (LCLs) established from participants of the Cholesterol and Pharmacogenetics (CAP) simvastatin clinical trial. We looked for genes whose statin-induced expression changes were most different between LCLs derived from individuals with high versus low plasma LDLC statin response during the CAP trial. We created a classification model of 82 “signature” gene expression changes that distinguished high versus low LDLC statin response. One of the most differentially changing genes was zinc finger protein 542 pseudogene (ZNF542P), the signature gene with changes most correlated with statin-induced change in cellular cholesterol ester, an in vitro marker of statin response. ZNF542P knock-down in a human hepatoma cell line increased intracellular cholesterol ester levels upon simvastatin treatment. Together, these findings imply a role for ZNF542P in LDLC response to simvastatin and, importantly, highlight the potential significance of noncoding RNAs as a contributing factor to variation in drug response.

Systems Pharmacology Dissection of Cholesterol Regulation Reveals Determinants of Large Pharmacodynamic Variability between Cell Lines

In individuals, heterogeneous drug-response phenotypes result from a complex interplay of dose, drug specificity, genetic background, and environmental factors, thus challenging our understanding of the underlying processes and optimal use of drugs in the clinical setting. Here, we use mass-spectrometry-based quantification of molecular response phenotypes and logic modeling to explain drug-response differences in a panel of cell lines. We apply this approach to cellular cholesterol regulation, a biological process with high clinical relevance. From the quantified molecular phenotypes elicited by various targeted pharmacologic or genetic treatments, we generated cell-line-specific models that quantified the processes beneath the idiotypic intracellular drug responses. The models revealed that, in addition to drug uptake and metabolism, further cellular processes displayed significant pharmacodynamic response variability between the cell lines, resulting in cell-line-specific drug-response phenotypes. This study demonstrates the importance of integrating different types of quantitative systems-level molecular measurements with modeling to understand the effect of pharmacological perturbations on complex biological processes.

SUGP1 is a novel regulator of cholesterol metabolism

A large haplotype on chromosome 19p13.11 tagged by rs10401969 in intron 8 of SURP and G patch domain containing 1 (SUGP1) is associated with coronary artery disease (CAD), plasma LDL cholesterol levels, and other energy metabolism phenotypes. Recent studies have suggested that TM6SF2 is the causal gene within the locus, but we postulated that this locus could harbor additional CAD risk genes, including the putative splicing factor SUGP1. Indeed, we found that rs10401969 regulates SUGP1 exon 8 skipping, causing non-sense-mediated mRNA decay. Hepatic Sugp1 overexpression in CD1 male mice increased plasma cholesterol levels 20–50%. In human hepatoma cell lines, SUGP1 knockdown stimulated 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) alternative splicing and decreased HMGCR transcript stability, thus reducing cholesterol synthesis and increasing LDL uptake. Our results strongly support a role for SUGP1 as a novel regulator of cholesterol metabolism and suggest that it contributes to the relationship between rs10401969 and plasma cholesterol.

Inhibition of cholesterol metabolism underlies synergy between mTOR pathway inhibition and chloroquine in bladder cancer cells

Mutations to fibroblast growth factor receptor 3 (FGFR3) and phosphatase and tensin homologue (PTEN) signalling pathway components (for example, PTEN loss, PIK3CA, AKT1, TSC1/2) are common in bladder cancer, yet small-molecule inhibitors of these nodes (FGFR/PTENi) show only modest activity in preclinical models. As activation of autophagy is proposed to promote survival under FGFR/PTENi, we have investigated this relationship in a panel of 18 genetically diverse bladder cell lines. We found that autophagy inhibition does not sensitise bladder cell lines to FGFR/PTENi, but newly identify an autophagy-independent cell death synergy in FGFR3-mutant cell lines between mTOR (mammalian target of rapamycin) pathway inhibitors and chloroquine (CQ)—an anti-malarial drug used as a cancer therapy adjuvant in over 30 clinical trials. The mechanism of synergy is consistent with lysosomal cell death (LCD), including cathepsin-driven caspase activation, and correlates with suppression of cSREBP1 and cholesterol biosynthesis in sensitive cell lines. Remarkably, loss of viability can be rescued by saturating cellular membranes with cholesterol or recapitulated by statin-mediated inhibition, or small interfering RNA knockdown, of enzymes regulating cholesterol metabolism. Modulation of CQ-induced cell death by atorvastatin and cholesterol is reproduced across numerous cell lines, confirming a novel and fundamental role for cholesterol biosynthesis in regulating LCD. Thus, we have catalogued the molecular events underlying cell death induced by CQ in combination with an anticancer therapeutic. Moreover, by revealing a hitherto unknown aspect of lysosomal biology under stress, we propose that suppression of cholesterol metabolism in cancer cells should elicit synergy with CQ and define a novel approach to future cancer treatments.

Seventeen years of statin pharmacogenetics: a systematic review

AIM

We evaluated the evidence of pharmacogenetic associations with statins in a systematic review.

METHODS

Two separate outcomes were considered of interest: modification of low-density lipoprotein cholesterol (LDL-C) response and modification of risk for cardiovascular events.

RESULTS

In candidate gene studies, 141 loci were claimed to be associated with LDL-C response. Only 5% of these associations were positively replicated. In addition, six genome-wide association studies of LDL-C response identified common SNPs in APOE, LPA, SLCO1B1, SORT1 and ABCG2 at genome-wide significance. None of the investigated SNPs consistently affected the risk reduction for cardiovascular events.

CONCLUSION

Only five genetic loci were consistently associated with LDL-C response. However, as effect sizes are modest, there is no evidence for the value of genetic testing in clinical practice.

Virtual pathway explorer (viPEr) and pathway enrichment analysis tool (PEANuT): creating and analyzing focus networks to identify cross-talk between molecules and pathways

Background

Interpreting large-scale studies from microarrays or next-generation sequencing for further experimental testing remains one of the major challenges in quantitative biology. Combining expression with physical or genetic interaction data has already been successfully applied to enhance knowledge from all types of high-throughput studies. Yet, toolboxes for navigating and understanding even small gene or protein networks are poorly developed.

Results

We introduce two Cytoscape plug-ins, which support the generation and interpretation of experiment-based interaction networks. The virtual pathway explorer viPEr creates so-called focus networks by joining a list of experimentally determined genes with the interactome of a specific organism. viPEr calculates all paths between two or more user-selected nodes, or explores the neighborhood of a single selected node. Numerical values from expression studies assigned to the nodes serve to score identified paths. The pathway enrichment analysis tool PEANuT annotates networks with pathway information from various sources and calculates enriched pathways between a focus and a background network. Using time series expression data of atorvastatin treated primary hepatocytes from six patients, we demonstrate the handling and applicability of viPEr and PEANuT. Based on our investigations using viPEr and PEANuT, we suggest a role of the FoxA1/A2/A3 transcriptional network in the cellular response to atorvastatin treatment. Moreover, we find an enrichment of metabolic and cancer pathways in the Fox transcriptional network and demonstrate a patient-specific reaction to the drug.

Conclusions

The Cytoscape plug-in viPEr integrates –omics data with interactome data. It supports the interpretation and navigation of large-scale datasets by creating focus networks, facilitating mechanistic predictions from –omics studies. PEANuT provides an up-front method to identify underlying biological principles by calculating enriched pathways in focus networks.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2017-z) contains supplementary material, which is available to authorized users.

Xanthine-based KMUP-1 improves HDL via PPARγ/SR-B1, LDL via LDLRs, and HSL via PKA/PKG for hepatic fat loss

The phosphodiesterase inhibitor (PDEI)/eNOS enhancer KMUP-1, targeting G-protein coupled receptors (GPCRs), improves dyslipidemia. We compared its lipid-lowering effects with simvastatin and explored hormone-sensitive lipase (HSL) translocation in hepatic fat loss. KMUP-1 HCl (1, 2.5, and 5 mg/kg/day) and simvastatin (5 mg/kg/day) were administered in C57BL/6J male mice fed a high-fat diet (HFD) by gavage for 8 weeks. KMUP-1 inhibited HFD-induced plasma/liver TG, total cholesterol, and LDL; increased HDL/3-hydroxy-3-methylglutaryl-CoA reductase (HMGR)/Rho kinase II (ROCK II)/PPARγ/ABCA1; and decreased liver and body weight. KMUP-1 HCl in drinking water (2.5 mg/200 ml tap water) for 1–14 or 8–14 weeks decreased HFD-induced liver and body weight and scavenger receptor class B type I expression and increased protein kinase A (PKA)/PKG/LDLRs/HSL expression and immunoreactivity. In HepG2 cells incubated with serum or exogenous mevalonate, KMUP-1 (10⁻⁷∼10⁻⁵ M) reversed HMGR expression by feedback regulation, colocalized expression of ABCA1/apolipoprotein A-I/LXRα/PPARγ, and reduced exogenous geranylgeranyl pyrophosphate/farnesyl pyrophosphate (FPP)-induced RhoA/ROCK II expression. A guanosine 3′,5′-cyclic monophosphate (cGMP) antagonist reversed KMUP-1-induced ROCK II reduction, indicating cGMP/eNOS involvement. KMUP-1 inceased PKG and LDLRs surrounded by LDL and restored oxidized LDL-induced PKA expresion. Unlike simvastatin, KMUP-1 could not inhibit ¹⁴C mevalonate formation. KMUP-1 could, but simvastatin could not, decrease ROCK II expression by exogenous FPP/CGPP. KMUP-1 improves HDL via PPARγ/LXRα/ABCA1/Apo-I expression and increases LDLRs/PKA/PKG/HSL expression and immunoreactivity, leading to TG hydrolysis to lower hepatic fat and body weight.

Resistance and intolerance to statins

BACKGROUND AND AIMS

Many patients treated with statins are considered statin-resistant because they fail to achieve adequate reduction of low density lipoprotein cholesterol (LDL-C) levels. Some patients are statin-intolerant because they are unable to tolerate statin therapy at all or to tolerate a full therapeutic statin dose because of adverse effects, particularly myopathy and increased activity of liver enzymes.

RESULTS

The resistance to statins has been associated with polymorphisms in the 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMG-CoA-R), P-glycoprotein (Pg-P/ABCB1), breast cancer resistance protein (BCRP/ABCG2), multidrug resistance-associated proteins (MRP1/ABCC1 and MRP2/ABCC2), organic anion transporting polypeptides (OATP), RHOA, Nieman-Pick C1-like1 protein (NPC1L1), farnesoid X receptor (FXR), cholesterol 7alpha-hydroxylase (CYP7A1), Apolipoprotein E (ApoE), proprotein convertase subtilisin/kexin type 9 (PCSK9), low density lipoprotein receptor (LDLR), lipoprotein (a) (LPA), cholesteryl ester transfer protein (CETP), and tumor necrosis factor α (TNF-α) genes. However, currently, there is still not enough evidence to advocate pharmacogenetic testing before initiating statin therapy. Patients with inflammatory states and HIV infection also have diminished LDL-C lowering as a response to statin treatment. Pseudo-resistance due to nonadherence or non-persistence in real-life circumstances is probably the main cause of insufficient LDL-C response to statin treatment.

CONCLUSIONS

If a patient is really statin-resistant or statin-intolerant, several other treatment possibilities are nowadays available: ezetimibe alone or in combination with bile acid sequestrants, and possibly in the near future mipomersen, lomitapide, or monoclonal antibodies against PCSK9.

Prediction of LDL cholesterol response to statin using transcriptomic and genetic variation

Background

Statins are widely prescribed for lowering LDL-cholesterol (LDLC) levels and risk of cardiovascular disease. There is, however, substantial inter-individual variation in the magnitude of statin-induced LDLC reduction. To date, analysis of individual DNA sequence variants has explained only a small proportion of this variability. The present study was aimed at assessing whether transcriptomic analyses could be used to identify additional genetic contributions to inter-individual differences in statin efficacy.

Results

Using expression array data from immortalized lymphoblastoid cell lines derived from 372 participants of the Cholesterol and Pharmacogenetics clinical trial, we identify 100 signature genes differentiating high versus low statin responders. A radial-basis support vector machine prediction model of these signature genes explains 12.3% of the variance in statin-mediated LDLC change. Addition of SNPs either associated with expression levels of the signature genes (eQTLs) or previously reported to be associated with statin response in genome-wide association studies results in a combined model that predicts 15.0% of the variance. Notably, a model of the signature gene associated eQTLs alone explains up to 17.2% of the variance in the tails of a separate subset of the Cholesterol and Pharmacogenetics population. Furthermore, using a support vector machine classification model, we classify the most extreme 15% of high and low responders with high accuracy.

Conclusions

These results demonstrate that transcriptomic information can explain a substantial proportion of the variance in LDLC response to statin treatment, and suggest that this may provide a framework for identifying novel pathways that influence cholesterol metabolism.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0460-9) contains supplementary material, which is available to authorized users.

RNA-sequencing analysis of HepG2 cells treated with atorvastatin

The cholesterol-lowering drug atorvastatin is among the most prescribed drug in the world. Alternative splicing in a number of genes has been reported to be associated with variable statin response. RNA-seq has proven to be a powerful technique for genome-wide splice variant analysis. In the present study, we sought to investigate atorvastatin responsive splice variants in HepG2 cells using RNA-seq analysis to identify novel candidate genes implicated in cholesterol homeostasis and in the statin response. HepG2 cells were treated with 10 µM atorvastatin for 24 hours. RNA-seq and exon array analyses were performed. The validation of selected genes was performed using Taqman gene expression assays. RNA-seq analysis identified 121 genes and 98 specific splice variants, of which four were minor splice variants to be differentially expressed, 11 were genes with potential changes in their splicing patterns (SYCP3, ZNF195, ZNF674, MYD88, WHSC1, KIF16B, ZNF92, AGER, FCHO1, SLC6A12 and AKAP9), and one was a gene (RAP1GAP) with differential promoter usage. The IL21R transcript was detected to be differentially expressed via RNA-seq and RT-qPCR, but not in the exon array. In conclusion, several novel candidate genes that are affected by atorvastatin treatment were identified in this study. Further studies are needed to determine the biological significance of the atorvastatin responsive splice variants that have been uniquely identified using RNA-seq.

Transmembrane protein 55B is a novel regulator of cellular cholesterol metabolism

OBJECTIVE

Interindividual variation in pathways affecting cellular cholesterol metabolism can influence levels of plasma cholesterol, a well-established risk factor for cardiovascular disease. Inherent variation among immortalized lymphoblastoid cell lines from different donors can be leveraged to discover novel genes that modulate cellular cholesterol metabolism. The objective of this study was to identify novel genes that regulate cholesterol metabolism by testing for evidence of correlated gene expression with cellular levels of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) mRNA, a marker for cellular cholesterol homeostasis, in a large panel of lymphoblastoid cell lines.

APPROACH AND RESULTS

Expression array profiling was performed on 480 lymphoblastoid cell lines established from participants of the Cholesterol and Pharmacogenetics (CAP) statin clinical trial, and transcripts were tested for evidence of correlated expression with HMGCR as a marker of intracellular cholesterol homeostasis. Of these, transmembrane protein 55b (TMEM55B) showed the strongest correlation (r=0.29; P=4.0E-08) of all genes not previously implicated in cholesterol metabolism and was found to be sterol regulated. TMEM55B knockdown in human hepatoma cell lines promoted the decay rate of the low-density lipoprotein receptor, reduced cell surface low-density lipoprotein receptor protein, impaired low-density lipoprotein uptake, and reduced intracellular cholesterol.

CONCLUSIONS

Here, we report identification of TMEM55B as a novel regulator of cellular cholesterol metabolism through the combination of gene expression profiling and functional studies. The findings highlight the value of an integrated genomic approach for identifying genes that influence cholesterol homeostasis.

Poly-omic prediction of complex traits: OmicKriging

High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. We provide an R package to implement OmicKriging (http://www.scandb.org/newinterface/tools/OmicKriging.html).

The pharmacology of statins

Statins, inhibitors of the hydroxymethylglutaryl-CoA (HMG-CoA) reductase enzyme, are molecules of fungal origin. By inhibiting a key step in the sterol biosynthetic pathway statins are powerful cholesterol lowering medications and have provided outstanding contributions to the prevention of cardiovascular disease. Their detection in mycetes traces back to close to 40 years ago: there were, originally, widely opposing views on their therapeutic potential. From then on, intensive pharmaceutical development has led to the final availability in the clinic of seven statin molecules, characterized by differences in bioavailability, lipo/hydrophilicity, cytochrome P-450 mediated metabolism and cellular transport mechanisms. These differences are reflected in their relative power (mg LDL-cholesterol reduction per mg dose) and possibly in parenchymal or muscular toxicities. The impact of the antagonism of statins on a crucial step of intermediary metabolism leads, in fact, both to a reduction of cholesterol biosynthesis as well as to additional pharmacodynamic (so called "pleiotropic") effects. In the face of an extraordinary clinical success, the emergence of some side effects, e.g. raised incidence of diabetes and cataracts as well as frequent muscular side effects, have led to increasing concern by physicians. However, also in view of the present relatively low cost of these drugs, their impact on daily therapy of vascular patients is unlikely to change.

Systems genetics approaches to understand complex traits

Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It uses a range of experimental and statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein or metabolite levels, in populations that vary for traits of interest. Systems genetics studies have provided the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases. Given the urgent need to understand how the thousands of loci that have been identified in genome-wide association studies contribute to disease susceptibility, systems genetics is likely to become an increasingly important approach to understanding both biology and disease.

Statin-induced changes in gene expression in EBV-transformed and native B-cells

Human lymphoblastoid cell lines (LCLs), generated through Epstein-Barr Virus (EBV) transformation of B-lymphocytes (B-cells), are a commonly used model system for identifying genetic influences on human diseases and on drug responses. We have previously used LCLs to examine the cellular effects of genetic variants that modulate the efficacy of statins, the most prescribed class of cholesterol-lowering drugs used for the prevention and treatment of cardiovascular disease. However, statin-induced gene expression differences observed in LCLs may be influenced by their transformation, and thus differ from those observed in native B-cells. To assess this possibility, we prepared LCLs and purified B-cells from the same donors, and compared mRNA profiles after 24 h incubation with simvastatin (2 µm) or sham buffer. Genes involved in cholesterol metabolism were similarly regulated between the two cell types under both the statin and sham-treated conditions, and the statin-induced changes were significantly correlated. Genes whose expression differed between the native and transformed cells were primarily implicated in cell cycle, apoptosis and alternative splicing. We found that ChIP-seq signals for MYC and EBNA2 (an EBV transcriptional co-activator) were significantly enriched in the promoters of genes up-regulated in the LCLs compared with the B-cells, and could be involved in the regulation of cell cycle and alternative splicing. Taken together, the results support the use of LCLs for the study of statin effects on cholesterol metabolism, but suggest that drug effects on cell cycle, apoptosis and alternative splicing may be affected by EBV transformation. This dataset is now uploaded to GEO at the accession number GSE51444.

Statins and lipid metabolism: an update

PURPOSE OF REVIEW

The reduction in cardiovascular disease risk by statins is well established. This risk reduction has mostly been attributed to decreases in plasma LDL cholesterol and other pleiotropic effects of statins. Emerging evidence indicates that statins exert multiple effects on lipoprotein metabolism, including chylomicrons and HDLs.

RECENT FINDINGS

Kinetic and in-vitro studies have documented that the effects of statins on the metabolism of different lipoproteins are for the most part the direct consequence of cholesterol biosynthesis inhibition and the subsequent change in transcription factors and cell signaling, regulating different aspects of lipoprotein metabolism. Differences in pharmacokinetics and pharmacodynamics among statins lead to diverse biological outcomes.

SUMMARY

The current review summarizes recent experimental evidence highlighting the different effects of statins on cellular pathways regulating gene expression. Understanding the basic mechanisms by which different statins regulate lipoprotein metabolism will lead to improved strategies for the prevention and treatment of specific lipoprotein disorders.