Hepatitis C virus infection causes viral genotype-specific differences in cholesterol metabolism and fatty liver disease
Participants with chronic HCV infection were recruited from two centres: Newcastle-upon-Tyne and Imperial College London. All participants gave written informed consent and the study received ethics approval (Northumberland REC 07/H0902/45 and Fife and Forth Valley REC 07/S0501/21). The research was carried out in accordance with relevant guidelines/regulations established by the Northumberland and Fife and Forth Valley Research Ethics Boards, and was carried out in accordance with the 1975 Declaration of Helsinki.
All participants were ≥ 18 years old, HCV RNA positive for > 6 months, and had not taken a lipid modulating agent in the 3 months prior to the study. Patients with hepatitis B, hepatitis delta, HIV co-infection or alcohol dependence were excluded. All participants attended following an overnight fast for more than 8 hours for sample collection. The fasting cohort consisted of 112 fasting sera (39 G3, 73 G1); 25% had compensated cirrhosis demonstrated by Fibroscan > 12.5 kPa (Echosens, Paris, France). Basic clinical and demographic data are presented in Table 1.
Additionally, a second cohort of non-fasting serum samples was obtained from the HCV Research UK Clinical Database and Biobank (Glasgow, UK) and included 150 treatment-naïve chronic HCV patients. (75 HCV-G1, 75 HCV-G3), matched to the fasting cohort for age, sex, body mass index (BMI) and presence of cirrhosis. Another 100 samples (50 HCV-G1, 50 HCV-G3) were obtained from the HCV Research UK clinical database and biobank from individuals following a sustained virological response (SVR) after antiviral treatment successful (the SVR cohort).
Liver function tests and blood sugar measurements
Standard serum liver function test and blood glucose measurements were performed on serum samples from all participants. Aspartate aminotransferase (AST) and alanine aminotransferase (ALT) and blood glucose were measured by standard biochemical methodologies using British National Health Service (NHS) laboratory protocols (https://www.england.nhs. uk/wp-content/uploads/2021/09/B0960-optimization-blood-analysis-secondary-care.pdf).
Fasting lipid profiling
Fasting serum lipids were measured using standard enzymatic methods. Where applicable, LDL cholesterol was calculated using the Friedewald equation. Apolipoprotein B concentrations were measured by automated flow nephelometric methods (BNII, Dade Behring Ltd, Milton Keynes, Buckinghamshire, UK). Insulin was assayed by ELISA (Linco Research Inc, St Charles, Missouri, USA). Lathosterol, desmosterol, cholestanol, and sitosterol were measured by gas chromatography-mass spectrometry (GC-MS), exactly as previously described by Kelley19.
Phenotyping of body fat distribution
A subset of 13 consecutive fasting cohort participants (6 HCV-G1, 7 HCV-G3) at Imperial College London underwent further detailed clinical phenotyping by determining body fat distribution throughout the body using magnetic resonance spectroscopy (MRS) in vivo to quantify intrahepatocellular lipids (IHCL), intramyocellular lipids in tibial (T IMCL) and soleus muscle (S IMCL), and distribution adipose tissue fat (% visceral and non-visceral fat) using magnetic resonance imaging, as previously described in detail by Thomas and colleagues20.
Briefly, 1H MR spectra were acquired from the liver and left calf muscles using a surface coil on a 1.5 T Phillips Achieva scanner (Phillips, Best, The Netherlands). Pilot images were obtained to ensure precise positioning of the voxel (20 × 20 × 20 mm) in the liver (avoiding blood vessels, gallbladder and fatty tissue) and muscle, ensuring correct placement in muscle soleus and tibialis. A PRESS sequence (repetition time 1500 ms, echo time 135 ms) was used20. Spectra were analyzed using jMRUI, with IHCL measured against liver water and IMCL measured against total muscle creatine20. Visceral and non-visceral fat were measured during the same examination. Axial contiguous T1-weighted 10 mm thick MR images were obtained throughout the body which were analyzed using SliceOmatic (Tomovision, Montreal, Quebec, Canada).
Ultra-Performance Liquid Chromatography Mass Spectroscopy (UPLC-MS) Lipidomics
All samples were thawed at 4°C and prepared for UPLC-MS analysis by isopropanol protein precipitation by adding 150 µL of cold isopropanol to each 50 µL serum sample (3:1 ratio), exactly as previously described by Sarafian and colleagues in 201421. Quality control (QC) samples were prepared by pooling equal volumes of all samples and injecting them into the mass spectrometry system at regular intervals throughout the runs to determine system suitability, analytical stability and repeatability of the sample. UPLC-MS profiling of serum lipids was performed using an ACQUITY UPLC system (Waters Ltd., Elstree, UK), coupled to a Q-ToF Premier mass spectrometer (Waters MS Technologies Ltd, Manchester , UK) using an electrospray ion source (ESI). operated in positive and negative electrospray ionization modes (ESI+ and ESI-).
Liquid chromatography (LC) conditions were previously described by Eliasson and colleagues in 201222. The separation was performed in a Waters Acquity UPLC HSS CSH column (1.7 μm, 2.1 × 100 mm) maintained at 55°C. Mobile phases consisted of acetonitrile (ACN)/H2O (60:40) (A) and isopropyl alcohol (IPA)/ACN (90:10) (B), both containing 10 mM ammonium formate and 0.1 % (v/v ) formic acid. The flow rate was set at 0.4 mL/min. The injection volume was 5 µL and 15 µL for positive (ESI +ve) and negative (ESI –ve) modes, respectively.
The ESI conditions were as follows: capillary voltage for ESI- 2500 V, for ESI + ve 3000 V, conical voltage 25 V for ESI -ve and 30 V for ESI + ve, source temperature 120 °C, desolvation temperature 400 °C , gas flow cone 25L/h, desolvation gas 800L/h. Data was collected in centroid mode. For mass accuracy, leucine enkephalin (calculated monoisotopic molecular weight of 555.2692 Da) was used as locking mass. Mass-locked scans were collected every 30 s and averaged over 3 scans to perform mass correction. Instrument calibration was performed using sodium formate before each ESI mode.
To balance the system, ten conditioning QC samples were taken at the start of the acquisition. QC samples were run periodically after 10 sample injections to monitor instrument performance. Data dependent acquisition (DDA) and MSE analysis of the QC sample were performed to obtain MS/MS information for metabolite annotation. Candidate metabolites were annotated using m/z values, fragmentation patterns, retention times, and the METLIN database (https://metlin.scripps.edu/).
MS data preprocessing
Raw UPLC-MS data were acquired using MassLynx version 4.1 software (Waters, Manchester, UK) and converted to NetCDF files using Databridge; a module in the MassLynx 4.1 software. CDF files were preprocessed using the XCMS package in R statistical software version (Rx64 3.2.5) and in-house developed scripts.
When continuous data were normally distributed, two-sample t-tests were used to compare means between control groups. The Kruskal-Wallis test was used for comparison of nonparametric data. Pearson’s r-correlation coefficient was used to determine the relationships between continuous variables and Spearman’s rank analysis for the correlation between nonparametric variables. P
Multivariate statistical analysis
Supervised and unsupervised multivariate models were generated using SIMCA (version 14.1, Umetrics, Umeå, Sweden). Principal component analysis (PCA) and orthogonal projections to latent structure discriminant analysis (OPLS-DA) were performed on all spectral data after Pareto scaling and logarithmic transformation for pattern detection , trends and outliers; and construction of discriminative models were generated for classification and discovery of potential biomarkers respectively.
Ethical approval was obtained from the Northumberland Research Ethics Committee (REC 07/H0902/45 and the Fife and Forth Valley Research Ethics Committee (REC 07/S0501/21).
Consent to participate
Prior written and informed consent was obtained from each participant.