Hepatic Acat2 overexpression promotes systemic cholesterol metabolism and adipose lipid metabolism in mice

Aims/hypothesis Acetyl coenzyme A acetyltransferase (ACAT), also known as acetoacetyl-CoA thiolase, catalyses the formation of acetoacetyl-CoA from acetyl-CoA and forms part of the isoprenoid biosynthesis pathway. Thus, ACAT plays a central role in cholesterol metabolism in a variety of cells. Here, we aimed to assess the effect of hepatic Acat2 overexpression on cholesterol metabolism and systemic energy metabolism. Methods We generated liver-targeted adeno-associated virus 9 (AAV9) to achieve hepatic Acat2 overexpression in mice. Mice were injected with AAV9 through the tail vein and subjected to morphological, physiological (body composition, indirect calorimetry, treadmill, GTT, blood biochemistry, cardiac ultrasonography and ECG), histochemical, gene expression and metabolomic analysis under normal diet or feeding with high-fat diet to investigate the role of ACAT2 in the liver. Results Hepatic Acat2 overexpression reduced body weight and total fat mass, elevated the metabolic rate, improved glucose tolerance and lowered the serum cholesterol level of mice. In addition, the overexpression of Acat2 inhibited fatty acid, glucose and ketone metabolic pathways but promoted cholesterol metabolism and changed the bile acid pool and composition of the liver. Hepatic Acat2 overexpression also decreased the size of white adipocytes and promoted lipid metabolism in white adipose tissue. Furthermore, hepatic Acat2 overexpression protected mice from high-fat-diet-induced weight gain and metabolic defects Conclusions/interpretation Our study identifies an essential role for ACAT2 in cholesterol metabolism and systemic energy expenditure and provides key insights into the metabolic benefits of hepatic Acat2 overexpression. Thus, adenoviral Acat2 overexpression in the liver may be a potential therapeutic tool in the treatment of obesity and hypercholesterolaemia. Graphical abstract Supplementary Information The online version contains peer-reviewed and unedited supplementary material available at 10.1007/s00125-022-05829-9.


Indirect calorimetry and body composition measurement
The Oxygen consumption (VO2) and carbon dioxide production (VCO2) of the mice were measured by using an indirect calorimetry system (Oxymax, Columbus Instruments). The system was kept in a stable environmental temperature (24 °C) that also had a 12-h light (8 AM-8 PM), 12-h dark cycle (8 PM-8 AM). Mice were individually placed in each chamber with free access to food and water. Mice were adapted to the chamber for 24 h before the measurements. The data were presented as uncorrected energy expenditure levels. Average energy expenditure of day (8 AM-8 PM) and night (8 PM-8 AM) values were the average mean value of all points measured during the 12h period.
Total body fat and lean mass in live animals without anesthesia were measured by using a Minispec LF50 body composition analyzer located in Small Animal Facility of CAM-SU.
Animals were placed in a specially sized, clear plastic holder without sedation or anesthesia. The holder was then inserted into a tubular space in the side of the Minispec LF50 system. The animals were forced to not move in the holder to ganrantee the accuracy of result. Each scan took about 2 minutes.

Treadmill
Mice were firstly trained started 5 days before testing at a speed of 5m/min for 5 min to adapted to the treadmill. Mice were forced to run with an electric shock setting at constant 0.7mA on a 15% incline. Then on the day of experiment,run the indirect calorimetry program and the treadmill program at the same time. Mice were allowed to run at a constant speed (5m/min) for 5 min, before increasing the speed at a rate of 2.5 m/min every 2 min, then the mice were running at 25 m/min for the next 4min. After 25 minutes, stop the treadmill program and the indirect calorimetry program. Then remove the mice and clean the treadmill with 75% alcohol.

Cardiac ultrasonography and electrocardiogram
Cardiac ultrasonography was visualized using an ultrasound platform incorporated with a probe for mice (VINNO 6, VINNO). Mice were anesthetized in the induction chamber before sedated onto the operating board. Then a nose cone was applied to ensure anesthesia and hair removal cream was utilized to remove fur in the chest area. Wipe with wet gauze to ensure all hair was removed. Move the animal to the imaging platform with a heatpad to maintain body temperature at 36-37 °C. During imaging, reduce anesthesia to maintain proper heart rate. If the animal shows signs of being awake, use a higher concentration of anesthetic. Then gel was applied on to the imaging area and probe was made contacted to the gel until it was fully covered. The image of the heart was then taken in the short-axis mode with papillary muscles being the point of reference. Once the imaging was complete, removed animal from the platform and allowed them to recover on a heating pad. Complete results were showed in Table S3.
For electrocardiogram (ECG), mice were gently removed from their own cages and transferred into a ECGenie™ recording system (Mouse Specifics, Inc., USA) that sized comfortably to accommodate adult mice. A pair of ECG electrodes (silver-chloride) were embedded in the floor of the enclosure and spaced to provide contact between the electrodes and animals' paws. Since even modest handling of mice may induce alterations in heart rate, each mouse was permitted to acclimatize for ∼10 min prior to collection of data. The signals were digitized at a sampling rate of 2000 samples/s. When mice were positioned such that a forepaw and hind paw were not uniquely in contact with one of the electrodes, the output from the amplifier was discarded. Only data from continuous recordings were used in the analyses. Each signal was analyzed using e-MOUSE™, which incorporates Fourier analyses and linear time-invariant digital filtering of frequencies below 3 Hz and above 100 Hz to minimize environmental signal disturbances.

Blood biochemistry
Blood biochemistry was performed by using a clinical chemistry analyzer (Hitachi 7100).
Appropriate volume of blood that required (160-200μl of plasma) for test was collected from each mouse, and transfer to gel tube containing lithium Heparin. Centrifuge for 15 minutes at 5000 rpm in a refrigerated centrifuge set at 4°C. If plasma samples cannot be analyzed immediately, keep them in -80°C before analysis. Use plasma samples undiluted or diluted to a ratio of 1:2 with deionized water if the volume was insufficient.

H&E staining
Adipose tissues and liver from the control and AAV9-Acat2 mice were fixed in 4% paraformaldehyde (PFA , wt/vol)for 24 h at room temperature. Then the tissues were embedded in paraffin, blocked and cut at 6 mm. For H&E staining, the sections were deparaffinised, rehydrated and the nuclei stained with haematoxylin for 15 min. Sections were then rinsed in running tap water for 3 min before being stained with eosin for 3 min, then dehydrated and mounted. Images were captured using a Leica DM 6000B fluorescent microscope. (Leica, Germany).

Total RNA extraction and real-time PCR
Total RNA was extracted from cells or tissues by using Trizol Reagent according to the manufacturer's instructions. The purity and concentration of the extracted RNA were measured by a spectrophotometer (Nanodrop 3000, Thermo Fisher) at 260 and 280 nm.
Ratios of absorption (260/280 nm) of all samples were made sure to be ~2.0. 3 μg of RNA were reversed transcribed using random primers and M-MLV reverse transcriptase to make cDNA. Real-time PCR was carried out with a Roche Lightcycler 480 PCR System using SYBR Green Master Mix and gene-specific primers. Primer sequences were retrieved from PrimerBank and listed below in Primer Table. The 2 −ΔΔCT method was used to analyze the relative changes in gene expression normalized against mouse β-Actin as internal control.
Primer Table   Primer Sequence (

Transcriptome sequencing
Total RNA was extracted from liver after 3-month of AAV9 injection, and subjected to RNA-seq analysis performed by Azenta Life Sciences. Briefly, RNA quality analysis was checked by Agarose Gel Electrophoresis and Agilent 2100. A complementary DNA library was then constructed using mRNA enriched by anti-polyA beads, and sequencing was performed according to the Illumina HiSeq standard protocol. Raw reads from RNA-seq libraries are filtered to remove reads containing adapters or reads of low quality. After filtering, statistics analysis of data production and quality was performed to confirm the sequencing quality. Reference genome and gene annotation files were downloaded from a genome website browser (NCBI/UCSC/Ensembl). TopHat2 was used for mapping the filtered reads to the reference genome. For the quantification of gene expression level, HTSeq V0.6.1 was used to analyze the read numbers mapped for each gene. The FPKM of each gene was calculated based on the gene read counts mapped to genes or exons.
A differential expression analysis was performed using the DESeq R package (1.10.1) with the threshold of significance set as p< 0.05. Heatmap was made by an online tool (http://heatmapper.ca/) based on the Log2FPKM. Gene Ontology annotation was done by using the DAVID Bioinformatics Resources (https://david.ncifcrf.gov/).

Non-targeted metabolomics
The non-targeted metabolic profiling analysis was performed by using an ultra-high performance liquid chromatography (Vanquish Flex UHPLC system, Thermo Scientific, Bremen, Germany) system coupled wth high-resolution mass spectrometry (Q Exactive Focus, Thermo Scientific, Bremen, Germany (cum) = 1) values while their prediction performance was measured by Q2 (cumulative) (perfect model: Q2 (cum) = 1) and a permutation test. The permuted model should not be able to predict classes: R2 and Q2 values at the Y-axis intercept must be lower than those of Q2 and the R2 of the non-permuted model. OPLS-DA allowed the determination of discriminating metabolites using the variable importance on projection (VIP). The P value, Variable importance projection (VIP) produced by OPLS-DA, fold change (FC) was applied to discover the contributable-variable for classification. Finally, P value < 0.05 and VIP values > 1 were considered to be statistically significant metabolites.
Differential metabolites were subjected to pathway analysis by MetaboAnalyst, which combines results from powerful pathway enrichment analysis with the pathway topology analysis. The identified metabolites in metabolomics were then mapped to the KEGG pathway for biological interpretation of higher-level systemic functions. The metabolites and corresponding pathways were visualized using KEGG Mapper tool.