👤 Christina E Baer

Chemically modified small interfering RNAs (siRNAs) are a promising drug class that silences disease-causing genes via mRNA degradation. Both siRNA-specific features (e.g. sequence, modification pattern, and structure) and target mRNA-specific factors contribute to observed efficacy. Systematically defining the relative contributions of siRNA sequence, structure, and modification pattern versus the native context of the target mRNA is necessary to inform design considerations and facilitate the widespread application of this therapeutic platform. To address this, we synthesized a panel of ∼1260 differentially modified siRNAs and evaluated their silencing efficiency against therapeutically relevant mRNAs (APP, BACE1, MAPT, and SNCA) using both reporter-based and native expression assays. Our results demonstrate that the siRNA modification pattern (e.g. level of 2'-O-methyl content) significantly impacts efficacy, while structural features (e.g. symmetric versus asymmetric configurations) do not. Furthermore, we observed substantial differences in the number of effective siRNAs identified per target. These target-specific differences in hit rates are largely mitigated when efficacy is tested in the context of a reporter assay, confirming that native mRNA-specific features influence siRNA performance. Key target-specific factors, including exon usage, polyadenylation site selection, and ribosomal occupancy, partially explained efficacy variability. These insights led to a proposed framework of parameters for optimizing therapeutic siRNA design. Show less

The number of nutrigenetic studies dedicated to the identification of single nucleotide polymorphisms (SNPs) modulating blood lipid profiles in response to dietary interventions has increased considerably over the last decade. However, the robustness of the evidence-based science supporting the area remains to be evaluated. The objective of this review was to present recent findings concerning the effects of interactions between SNPs in genes involved in cholesterol metabolism and transport, and dietary intakes or interventions on circulating cholesterol concentrations, which are causally involved in cardiovascular diseases and established biomarkers of cardiovascular health. We identified recent studies (2014-2020) that reported significant SNP-diet interactions in 14 cholesterol-related genes ( Show less

Cholesteryl ester transfer protein (CETP) mediates the transfer of HDL cholesteryl esters for triglyceride (TG) in VLDL/LDL. CETP inhibition, with anacetrapib, increases HDL-cholesterol, reduces LDL-cholesterol, and lowers TG levels. This study describes the mechanisms responsible for TG lowering by examining the kinetics of VLDL-TG, apoC-II, apoC-III, and apoE. Mildly hypercholesterolemic subjects were randomized to either placebo (N = 10) or atorvastatin 20 mg/qd (N = 29) for 4 weeks (period 1) followed by 8 weeks of anacetrapib, 100 mg/qd (period 2). Following each period, subjects underwent stable isotope metabolic studies to determine the fractional catabolic rates (FCRs) and production rates (PRs) of VLDL-TG and plasma apoC-II, apoC-III, and apoE. Anacetrapib reduced the VLDL-TG pool on a statin background due to an increased VLDL-TG FCR (29%; Show less

In a marker-trait association study we estimated the statistical significance of 65 single nucleotide polymorphisms (SNP) in 23 candidate genes on HDL levels of two independent Caucasian populations. Each population consisted of men and women and their HDL levels were adjusted for gender and body weight. We used a linear regression model. Selected genes corresponded to folate metabolism, vitamins B-12, A, and E, and cholesterol pathways or lipid metabolism. Extracted DNA from both the Sacramento and Beltsville populations was analyzed using an allele discrimination assay with a MALDI-TOF mass spectrometry platform. The adjusted phenotype, y, was HDL levels adjusted for gender and body weight only statistical analyses were performed using the genotype association and regression modules from the SNP Variation Suite v7. Statistically significant SNP (where P values were adjusted for false discovery rate) included: CETP (rs7499892 and rs5882); SLC46A1 (rs37514694; rs739439); SLC19A1 (rs3788199); CD36 (rs3211956); BCMO1 (rs6564851), APOA5 (rs662799), and ABCA1 (rs4149267). Many prior association trends of the SNP with HDL were replicated in our cross-validation study. Significantly, the association of SNP in folate transporters (SLC46A1 rs37514694 and rs739439; SLC19A1 rs3788199) with HDL was identified in our study. Given recent literature on the role of niacin in the biogenesis of HDL, focus on status and metabolism of B-vitamins and metabolites of eccentric cleavage of β-carotene with lipid metabolism is exciting for future study. Show less

Imprinted genes are monoallelically expressed from either the maternal or paternal genome. Because cancer develops through genetic and epigenetic alterations, imprinted genes affect tumorigenesis depending on which parental allele undergoes alteration. We have shown previously in a mouse model of neurofibromatosis type 1 (NF1) that inheriting mutant alleles of Nf1 and Trp53 on chromosome 11 from the mother or father dramatically changes the tumor spectrum of mutant progeny, likely due to alteration in an imprinted gene(s) linked to Nf1 and Trp53. In order to identify imprinted genes on chromosome 11 that are responsible for differences in susceptibility, we tested candidate imprinted genes predicted by a bioinformatics approach and an experimental approach. We have tested 30 candidate genes (Havcr2, Camk2b, Ccdc85a, Cntnap1, Ikzf1, 5730522E02Rik, Gria1, Zfp39, Sgcd, Jup, Nxph3, Spnb2, Asb3, Rasd1, Map2k3, Map2k4, Trp53, Serpinf1, Crk, Rasl10b, Itga3, Hoxb5, Cbx1, Pparbp, Igfbp4, Smarce1, Stat3, Atp6v0a1, Nbr1 and Meox1), two known imprinted genes (Grb10 and Impact) and Nf1, which has not been previously identified as an imprinted gene. Although we confirmed the imprinting of Grb10 and Impact, we found no other genes imprinted in the brain. We did, however, find strain-biased expression of Camk2b, 5730522E02Rik, Havcr2, Map2k3, Serpinf1, Rasl10b, Itga3, Asb3, Trp53, Nf1, Smarce1, Stat3, Cbx1, Pparbp and Cntnap1. These results suggest that the prediction of imprinted genes is complicated and must be individually validated. This manuscript includes supplementary data listing primer sequences for Taqman assays and Ct values for Taqman PCR. Show less