Gene Therapy in a Humanized Mouse Model of Familial Hypercholesterolemia Leads to Marked Regression of Atherosclerosis

Familial hypercholesterolemia (FH) is an autosomal codominant disorder caused by mutations in the low-density lipoprotein receptor (LDLR) gene. Homozygous FH patients (hoFH) have severe hypercholesterolemia leading to life threatening atherosclerosis in childhood and adolescence. Mice with germ line interruptions in the Ldlr and Apobec1 genes (Ldlr(-/-)Apobec1(-/-)) simulate metabolic and clinical aspects of hoFH, including atherogenesis on a chow diet.In this study, vectors based on adeno-associated virus 8 (AAV8) were used to deliver the gene for mouse Ldlr (mLDLR) to the livers of Ldlr(-/-)Apobec1(-/-) mice. A single intravenous injection of AAV8.mLDLR was found to significantly reduce plasma cholesterol and non-HDL cholesterol levels in chow-fed animals at doses as low as 3×10(9) genome copies/mouse. Whereas Ldlr(-/-)Apobec1(-/-) mice fed a western-type diet and injected with a control AAV8.null vector experienced a further 65% progression in atherosclerosis over 2 months compared with baseline mice, Ldlr(-/-)Apobec1(-/-) mice treated with AAV8.mLDLR realized an 87% regression of atherosclerotic lesions after 3 months compared to baseline mice. Immunohistochemical analyses revealed a substantial remodeling of atherosclerotic lesions.Collectively, the results presented herein suggest that AAV8-based gene therapy for FH may be feasible and support further development of this approach. The pre-clinical data from these studies will enable for the effective translation of gene therapy into the clinic for treatment of FH

Farnesoid X Receptor Induces Murine Scavenger Receptor Class B Type I via Intron Binding

Farnesoid X receptor (FXR) is a nuclear receptor and a key regulator of liver cholesterol and triglyceride homeostasis. Scavenger receptor class B type I (SR-BI) is critical for reverse cholesterol transport (RCT) by transporting high-density lipoprotein (HDL) into liver. FXR induces SR-BI, however, the underlying molecular mechanism of this induction is not known. The current study confirmed induction of SR-BI mRNA by activated FXR in mouse livers, a human hepatoma cell line, and primary human hepatocytes. Genome-wide FXR binding analysis in mouse livers identified 4 putative FXR response elements in the form of inverse repeat separated by one nucleotide (IR1) at the first intron and 1 IR1 at the downstream of the mouse Sr-bi gene. ChIP-qPCR analysis revealed FXR binding to only the intronic IR1s, but not the downstream one. Luciferase assays and site-directed mutagenesis further showed that 3 out of 4 IR1s were able to activate gene transcription. A 16-week high-fat diet (HFD) feeding in mice increased hepatic Sr-bi gene expression in a FXR-dependent manner. In addition, FXR bound to the 3 bona fide IR1s in vivo, which was increased following HFD feeding. Serum total and HDL cholesterol levels were increased in FXR knockout mice fed the HFD, compared to wild-type mice. In conclusion, the Sr-bi/SR-BI gene is confirmed as a FXR target gene in both mice and humans, and at least in mice, induction of Sr-bi by FXR is via binding to intronic IR1s. This study suggests that FXR may serve as a promising molecular target for increasing reverse cholesterol transport

Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk

The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.The work was partially supported by the Fondo de Investigacion Sanitaria, Instituto de Salud Carlos III (G03/174, 00/0745, PI051436, PI061614, PI09-02102, G03/174 and Sara Borrell fellowship to ELM) and Ministry of Science and Innovation (MTM2008-06747-C02-02 and FPU fellowship award to VU), Spain; AGAUR-Generalitat de Catalunya (Grant 2009SGR-581); Fundaciola Maratode TV3; Red Tematica de Investigacion Cooperativa en Cancer (RTICC); Asociacion Espanola Contra el Cancer (AECC); EU-FP7-201663; and RO1-CA089715 and CA34627; the Spanish National Institute for Bioinformatics (www.inab.org); and by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, USA. MD Anderson support for this project included U01 CA 127615 (XW); R01 CA 74880 (XW); P50 CA 91846 (XW, CPD); Betty B. Marcus Chair fund in Cancer Prevention (XW); UT Research Trust fund (XW) and R01 CA 131335 (JG)