Abstract Background A high-fat diet can affect lipid metabolism and trigger cardiovascular diseases. A growing body of studies has revealed the HDL-bound miRNA profiles in familial hypercholesterolaemia; in sharp contrast, relevant studies on high-fat diet-induced dyslipidaemia are lacking. In the current study, HDL-bound miRNAs altered by a high-fat diet were explored to offer some clues for elucidating their effects on the pathogenesis of dyslipidaemia. Methods Six pigs were randomly divided into two groups of three pigs each, namely, the high-fat diet and the balanced diet groups, which were fed a high-fat diet and balanced diet separately for six months. HDL was separated from plasma, which was followed by dissociation of the miRNA bound to HDL. miRNA sequencing of the isolated miRNA was performed to identify the differential expression profiles between the two groups, which was validated by real-time PCR. TargetScan, miRDB, and miRWalk were used for the prediction of genes targeted by the differential miRNAs. Results Compared with the balanced diet group, the high-fat diet group had significantly higher levels of TG, TC, LDL-C and HDL-C at six months. miRNA sequencing revealed 6 upregulated and 14 downregulated HDL-bound miRNAs in the high-fat diet group compared to the balanced diet group, which was validated by real-time PCR. GO enrichment analysis showed that dysregulated miRNAs in the high-fat diet group were associated with the positive regulation of lipid metabolic processes, positive regulation of lipid biosynthetic processes, and positive regulation of Ras protein signal transduction. Insulin resistance and the Ras signalling pathway were enriched in the KEGG pathway enrichment analysis. Conclusions Twenty HDL-bound miRNAs are significantly dysregulated in high-fat diet-induced dyslipidaemia. This study presents an analysis of a new set of HDL-bound miRNAs that are altered by a high-fat diet and offers some valuable clues for novel mechanistic insights into high-fat diet-induced dyslipidaemia. Further functional verification study using a larger sample size will be required.
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