Hypercholesterolemia is a strong predictor of cardiovascular diseases. 3-Hydroxy-3-methylglutaryl-coenzyme A reductase gene (Hmgcr) coding for the rate-limiting enzyme in the cholesterol biosynthesis pathway is a crucial regulator of plasma cholesterol levels. However, the post-transcriptional regulation of Hmgcr remains poorly understood. The main objective of this study was to explore the role of miRNAs in the regulation of Hmgcr expression.Systematic in silico predictions and experimental analyses reveal that miR-27a specifically interacts with the Hmgcr 3'-untranslated region in murine and human hepatocytes. Moreover, our data shows that Hmgcr expression is inversely correlated with miR-27a levels in various cultured cell lines, human and rodent tissues. Actinomycin D chase assays and relevant experiments demonstrate that miR-27a regulates Hmgcr by translational attenuation followed by mRNA degradation. Early Growth Response 1 (Egr1) regulates miR-27a expression under basal and cholesterol-modulated conditions. miR-27a augmentation via tail-vein injection of miR-27a mimic in high cholesterol diet-fed Apoe -/mice shows down-regulation of hepatic Hmgcr and plasma cholesterol levels. Pathway and gene expression analyses show that miR-27a also targets several other genes (apart from Hmgcr) in cholesterol biosynthesis pathway. Taken together, miR-27a emerges as a key regulator of cholesterol biosynthesis and has therapeutic potential for clinical management of hypercholesterolemia.Sterol-regulatory element-binding protein 1; PVDF, polyvinylidene difluoride; ERAD, ER-associated protein degradation; SCAP, SREBP cleavage activated protein.the respective LOD scores. We also compared mouse and rat Hmgcr gene sequences (GenBank accession no: NM_008255 and NM_013134.2, respectively) using mVISTA browser.In silico predictions of potential miRNA binding sites in Hmgcr-3′UTR and putative miRNA targets in the cholesterol biosynthesis pathway.Putative miRNA binding sites in mouse Hmgcr (Hmgcr)-3'UTR sequence (NCBI reference number: NM_008255.2) were predicted using various bioinformatic algorithms [viz. miRWalk, miRanda, TargetScan, PITA, RNA22 and RNAhybrid (Table S2)]. Since, a large number of miRNAs were predicted by these online tools, we selected only those miRNAs that were predicted by at least five algorithms. Further, differences in hybridization free energy indicating the stability of the microRNA-mRNA interaction was determined computationally by two online tools called PITA and RNAhybrid. The lower or more negative ∆∆G value predicted by PITA indicates stronger binding of the microRNA to the given site; as a rule of thumb, sites having ∆∆G values below -10 are likely to be functional and selected for experimental validation. RNAhybrid calculates the minimum free energy (∆G) of hybridization between target mRNA and miRNA. An RNAhybrid ∆G score of less than -20 kcal/mol is a strong indicator for interactions of a miRNA with target mRNA. In each case, the minimum number of nucleotides in seed sequence was selected as ...