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. Author manuscript; available in PMC: 2022 Aug 1.
Published in final edited form as: Nanomedicine. 2021 Jun 24;36:102430. doi: 10.1016/j.nano.2021.102430

Circulating extracellular vesicles are a biomarker for NAFLD resolution and response to weight loss surgery’

Yasuhiko Nakao a,b, Pouya Amrollahi c,d, Gopanandan Parthasarathy a, Amy S Mauer a, Tejasav S Sehrawat a, Patrick Vanderboom e, K Sreekumaran Nair e, Kazuhiko Nakao b, Alina M Allen a, Tony Y Hu c,d,*, Harmeet Malhi a,**
PMCID: PMC8418232  NIHMSID: NIHMS1719753  PMID: 34174416

Abstract

There is increasing interest in the development of minimally invasive biomarkers for the diagnosis and prognosis of NAFLD via extracellular vesicles (EV). Plasma EVs were isolated by differential ultracentrifugation and quantified by nanoparticle tracking analysis from pre (n = 28) and post (n = 28) weight loss patients. In the pre weight loss group 22 had NAFLD. Nanoplasmon enhanced scattering (nPES) of gold nanoparticles conjugated to hepatocyte-specific antibodies was employed to identify hepatocyte-specific EVs. Complex lipid panel and targeted sphingolipids were performed. Logistic regression analysis was used to identify predictors of NAFLD. Plasma levels of EVs and hepatocyte-derived EVs are dynamic and decrease following NAFLD resolution due to weight loss surgery. Hepatocyte-derived EVs correlate with steatosis in NAFLD patients and steatosis and inflammation in NASH patients. Plasma levels of small EVs correlate with EV sphingolipids in patients with NASH. Hepatocyte-derived EVs measured by the nPES assay could serve as a point-of-care test for NAFLD.

Graphical Abstract

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Obesity is associated with high morbidity including cardiovascular disease, type 2 diabetes, and nonalcoholic fatty liver disease (NAFLD), which is the most prevalent chronic liver disease globally and has a significant economic burden.1,2 NAFLD is a heterogeneous illness with a wide spectrum of phenotypes, ranging from non-progressive steatosis, also termed nonalcoholic fatty liver (NAFL) to steatosis with injury, inflammation and fibrosis, known as nonalcoholic non-alcoholic steatohepatitis (NASH).3 Due to the large numbers of NAFLD patients, most of whom are asymptomatic in the early stages of NAFLD, and may have normal serum ALT values, a blood-based biomarker would be a significant advance as a means to help identify patients with NAFLD and to separate those with and without NASH.3,4 Furthermore, in those with NASH, progression is not linear, and patients followed over time can demonstrate worsening, stable, or improving inflammation and fibrosis.5 Therefore, if the test paralleled disease activity in NASH, it could potentially serve as a tool for risk stratification, aid in inclusion of patients in clinical trials, and help with prognostication.

Liver biopsy remains the gold standard for NAFLD evaluation. The histologic NAFLD activity score (NAS) established by the NASH Clinical Research Network6 is the benchmark for estimating NASH activity in liver biopsies. However, liver biopsy is invasive with inherent risk of complications, suffers from sampling error and lack of inter-observer agreement, and is not amenable to repeated testing over time.7,8 Here we examine the utility of using circulating extracellular vesicles (EVs) as a biomarker for risk stratification in patients with NAFLD.9-12 EVs are attractive biomarkers due to their abundance and stability in biofluids, the dynamic nature of their numbers and cargoes, both of which vary with the pathophysiological state of their cell of origin, the ability of specific EV cargoes to identify their cell of origin and the stimuli that induce their release, the relative ease of obtaining EVs from biofluids such as blood, and the opportunity for repeated testing over time.13,14

In addition to their biomarker potential, EVs are pathophysiologically important in NASH due to their known effects on immune cells and hepatic stellate cells.15-21 For example, we have reported that hepatocyte-derived sphingosine 1-phosphate (S1P)-containing EVs have a proinflammatory role in NASH.22,23 Thus, EVs may not just be a surrogate biomarker, but may indicate mechanisms leading to liver inflammation in NASH. Ceramide and S1P is essential for EVs biogenesis24,25 and dihydroceramide (DCER), ceramide precursors, likely serve as markers of increased fatty acid flux through the de novo ceramide pathway.26 We therefore measured the levels of sphingolipids and other complex lipids in EVs to understand their pathophysiological correlation with NAFLD and its resolution.

In this report we tested the hypothesis that EV numbers decrease with NAFLD resolution due to weight loss surgery, and that dynamic changes in EV lipidomics correlate with a reduction in liver inflammation. Using a novel nanoplasmon enhanced scattering (nPES) assay we measured the change in circulating hepatocyte-derived EVs before and after weight loss surgery. Lastly, we examined associations between invasive liver biopsy based NAS and EV sphingolipidomics data as potential non-invasive biomarkers of NAFLD.

Methods

Human samples

Banked, fasting plasma samples from adults who underwent weight loss surgery, and were prospectively enrolled in a clinical trial examining the diagnostic accuracy of 3D-MRE in the diagnosis of NASH (NCT0256544),27 were included in this study following written informed consent using materials approved by the Mayo Clinic's Institutional Review Board. Exclusion criteria included excessive alcohol consumption (>21 units/week for men and > 14 units/week for women), steatogenic medications, and the presence of liver diseases other than NAFLD. Liver biopsies were obtained as part of the clinical care of these patients and read by two pathologists.6 NAFLD severity was determined by NAS results, and NASH was histologically defined by the presence of steatosis with hepatocellular ballooning or lobular inflammation with fibrosis.28

Plasma extracellular vesicle isolation

EDTA plasma aliquots (1 mL) were thawed on ice and centrifuged at 3000 ×g for 15 min at room temperature to obtain clarified supernatants. A 25 μL aliquot was set aside for the nPES assay, and 900 μL supernatant was transferred to new tubes and diluted 1:10 with phosphate buffered saline (PBS, pH 7.4) and centrifuged at 2000 ×g for 30 min at room temperature. The supernatant was then centrifuged at 12,000 ×g for 45 min to pellet large EVs, which were resuspended in 100 μL PBS. The supernatant was then raised to 10 mL with PBS and centrifuged at 110,000×g for 120 min to pellet small EVs. The small EV pellet was resuspended in 10 mL PBS and centrifuged at 110,000 ×g for 120 min, and the final small EV pellet was resuspended in 100 μL PBS, of which 5 μL was reserved for nanoparticle tracking analysis (NTA), and the remainder was equally divided into two portions, one each for a complex lipid panel analysis (Metabolon) and for targeted sphingolipid measurement, at the Mayo Clinic Metabolomics Resource Core. Similarly isolated aliquots of EVs from paired plasma (900 μL plasma each) samples were also divided into two after reserving 5 μL for NTA analysis. One half was employed for measurement of protein content and western blotting and one half for electron microcopy (see supplementary methods for details). The SW 41 Ti swinging bucket rotor and Optima XPN-100 ultracentrifuge (Beckman Coulter) were employed for the 12,000 ×g and 110,000 ×g spins.

Far-field nano-plasmonic enhanced scattering (nPES) assay

Each well of a protein A/G functionalized slide (Arrayit Corporation) was incubated with capture antibodies specific for either ASGR2 or CYP2E1, PBS washed, and blocked with 1 μL/well SuperBlock™ (Thermo Scientific). Plasma samples were mixed and centrifuged at 500 ×g for 15 min to remove debris, and supernatants were diluted 1:1 with PBS and then incubated on assay slides for 4 h at 37 °C. For each sample 4 μL of plasma was applied to each well, in triplicate. After incubation, all sample wells were aspirated and supplemented with 1 μL of detection probe [neutravidin functionalized gold nanorods conjugated with biotinylated anti-human CD63 antibodies (MEM-259, Abcam)] and PBS washed, and then replicate wells in each column of the assay slide were analyzed for nPES signal by far-field dark-field microscopy (Eclipse Ti-S inverted microscope; Nikon Instruments Inc).

Targeted sphingolipid measurements

Targeted sphingolipid measurements were performed at the Mayo Clinic Metabolomics Resource Core, an NIH Regional Comprehensive Metabolomics Resource Core, using a previously published protocol for targeted EV sphingolipid measurement.29 Briefly, ceramides were extracted from EVs by sonication after the addition of internal standards,23 and measured against a standard curve on a Thermo TSQ Quantum Ultra mass spectrometer (Thermo Scientific, West Palm Beach, FL) coupled with a Waters Acquity UPLC system (Waters, Milford, MA), as previously described.30

Quantitative lipidomics analysis

EV samples were homogenized in deionized water and subjected to modified Bligh–Dyer extraction in methanol/water/dichloromethane in the presence of internal standards. Resulting EV lipid extracts were dried under nitrogen, reconstituted in a dichloromethane:methanol solution containing ammonium acetate, and transferred to vials for infusion-MS analysis using a Shimadzu LC with nano PEEK tubing and the SCIEX SelexIon-5500 QTRAP via both positive and negative mode electrospray. The 5500 QTRAP was operated in MRM mode with a total of more than 1100 MRMs. Individual lipid species were quantified by measuring the ratio of the signal intensity of a target compound to its assigned internal standard, then multiplying by the concentration of internal standard added to the sample. Lipid class concentrations were calculated from the sum of all molecular species within a class, and fatty acid compositions were determined by calculating the proportion of each class comprised by individual fatty acids. Lipid species concentrations were background-subtracted using the concentrations detected in process blanks (water extracts) and run-day normalized. The resulting background-subtracted, run-day normalized lipid species concentrations were used to calculate lipid class and fatty acid total concentrations, as well as the mol% composition values for lipid species, lipid classes, and fatty acids. Data for this matrix are expressed as total yield from EVs.

Statistical and data analysis

All analyses and graphical preparations were conducted in GraphPad Prism 8 (GraphPad Software Inc., La Jolla,CA). Figures were prepared with R-studio and the package ggplot2. For statistical modeling only lipids present in at least 50% of samples per group were used. If a lipid was present in at least 50% pre weight loss samples, it was also included for post weight loss samples, even if its occurrence rate was lower than 50%. Missing values were imputed with the median. Logistic regression analysis was performed to integrate the small EVs sphingolipids level with BMI and small EVs counts. Receiver operating characteristic (ROC) curve analysis and chi-square test were performed to assess the accuracy of biomarker models. Sensitivity and specificity were calculated based upon the optimal cut-off value according to Youden's index. Data were analyzed using JMP14.0 (SAS Institute Inc., Cary, NC, USA).

Results

Circulating EVs decrease following weight loss surgery

Fifty six plasma samples were included in this study. Forty of these were paired plasma samples from 20 patients collected before and after weight loss surgery (pre-group and post-group, respectively), with the remainder derived from patients with only pre- or post-weight loss surgery samples. Characteristics for these groups are listed in Table 1. Review of pre weight loss liver biopsies of all 28 patients demonstrated that 22 had NAFLD and 6 had normal liver histology, notably all 28 had normal ALT values. By expert pathologist review of liver biopsies, the pregroup contained 8 patients with NASH. NAS scores for all liver biopsies are in Supplementary Table 1. Large and small EVs were isolated sequentially from plasma. EVs isolated first at the 12,000 ×g for 45 min centrifugation were referred to as large EVs and EVs isolated next from centrifugation at 110,000 ×g for 120 min were referred to as small EVs in keeping with suggested nomenclature.31-33 Isolated large and small EVs were quantified by NTA (Figure 1, A and B) and qualitatively demonstrated the expected morphology and CD81 and CD63 expression, in large and small EVs respectively, by immuno-gold electron microscopy (Supplementary Figures 1 and 2). The expression of EV markers,34 Hsc70 and CD81 in large EVs and tumor susceptibility gene 101 (Tsg101) and CD81 in small EVs, was confirmed by western blotting; as expected, calnexin was absent, and apolipoprotein B100 (apoB100) [very-low-density-lipoprotein (VLDL)/low-density lipoprotein (LDL)]-specific apoprotein and apolipoprotein A1 (apoA1) [high density lipoprotein (HDL)]-specific apoprotein were not enriched on EVs and similarly distributed among pre- and post-bariatric samples (Supplementary Figure 3, A-F). Furthermore, the ratio of particles of EVs per μg protein demonstrated EV yields of high purity for small EVs (>3e10 particles/μg protein) and borderline high purity (> 2e10 particles/μg protein) for large EVs (Supplementary Figure 3, G).35,36

Table 1.

Clinical characteristics of the cohort.

Pre bariatric Post bariatric
Sample size (n) 28 28
Male (n) 5 8
Female (n) 23 20
Age (years), median (IQR) 46 (38-54) 48 (42-58)
BMI (kg/m2), median (IQR) 45.5 (42-58.3) 32.5 (30-38.8)
ALT (IU/L), median (IQR) 24.5 (18.8-30.3) –
HomaIR, median (IQR) 5.6 (2.4-10.8) –
Cholesterol (mM), median (IQR) 170 (137-204) –
LDL (mM), median (IQR) 95 (66.8-118.8) –
HDL (mM), median (IQR) 40 (37-49) –
Triglycerides (mM), median (IQR) 153 (103-268) –
NAFLD (n) 22 6
NASH (n) 8* 0
Steatosis grade, mean (95% CI) 0.89 (0.60-1.17) 0.07 (−0.03-0.17)
Inflammation grade, mean (95% CI) 0.46 (0.24-0.68) 0.17 (0.02-0.32)
Ballooning grade, mean (95% CI) 0.25 (0.04-0.45) 0
NAS score, mean (95% CI) 1.60 (1.09-2.11) 0.25 (0.04-0.45)
Fibrosis (n) 8 2
Fibrosis stage, mean (95% CI) 0.39 (0.10-0.67) 0.17(−0.08-0.43)
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*

by expert pathologist review.

Figure 1. Circulating EVs decrease following weight loss surgery.

Figure 1.

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(A) Small EV and (B) large EV concentrations in obese patients, N = 56 (pre 28, post 28). (C) Small EV and (D) large EV concentrations in NAFLD patients, N = 44 (pre 22 NAFLD vs post 22 no-NAFLD). (E) Small EV and (F) large EV concentrations in 14 paired pre- and post-weight loss NAFL patient samples. (G) Paired small EV and (H) large EV concentrations from 7 NASH patients. Two-tailed Student's t test was used for statistical analyses. P values of <0.05, 0.01, and 0.001 were denoted as *, **, and ***, respectively.

Plasma levels of both small and large EVs were decreased following weight loss (Figure 1, A and B). There was a significant reduction in small and large EVs in the 22 patients with NAFLD in the pre weight loss group versus the 22 patients in the post weight loss group that had normal liver histology, (Figure 1, C and D). In analysis of paired pre and post weight loss samples of NAFLD patients there was a significant reduction in small and large EVs (Figure 1, E and F). Small EVs levels also decreased after weight loss surgery in all patients with NASH who had paired plasma samples (Figure 1, G), while large EVs levels decreased in 6 of these 7 patients following weight loss surgery (Figure 1, H).

Hepatocyte-derived EVs decrease after bariatric surgery

To identify hepatocyte-specific EVs, we used a bioinformatics approach to identify transmembrane proteins which have an extracellular domain and are known to be enriched in hepatocytes. This method identified two candidates for hepatocyte-derived EVs: CYP2E1 and ASGR. CYP2E1 is a hepatocyte-enriched, predominantly membrane-bound protein found in the endoplasmic reticulum membrane37 and plasma membrane.38,39 ASGR is a type II transmembrane protein that is enriched in hepatocytes40 where it exists as a noncovalent heterooligomer of two subunits, ASGR1 and ASGR2. Both CYP2E1 and ASGR are reported constituents of the proteome of hepatocyte-derived EVs.41 We tested antibodies against the extracellular domain of CYP2E1 and ASGR1 and ASGR2 subunits (Supplementary Figure 4,A) by western blotting. Next we performed immune affinity capture of hepatocyte-derived EVs to confirm that these antibodies recognized EVs decorated with either CYP2E1 or ASGR1 or ASGR2 (Supplementary Figure 4, B). We detected EV markers including ALG-2-interacting protein X (ALIX), CD81, and Tsg101in hepatocyte-derived EVs pulled down with all three antibodies. Thus, based on EV capture in an immune affinity pull down assay we employed the CYP2E1 and ASGR2 antibodies in the nPES assay.

We next utilized these ASGR2 and CYP2E1 antibodies to investigate changes in hepatocyte-derived plasma EV levels in the pre- and post-weight loss groups by using them as capture antibodies for hepatocyte-derived EVs in a novel nPES assay (Figure 2, A). This assay was designed to quantify cell-specific EVs present in small volumes (4 μL/well) of plasma directly to avoid the need for EV isolation.42 We observed that nPES ASGR2-positive EV signal was significantly greater in plasma samples from the 22 pre weight loss NAFLD patients versus the 22 post weight loss patients with normal liver histology (Figure 2, B), as was CYP2E1-positive EV signal (Figure 2, C). Similarly, comparison of the nPES signals for the 14 NAFLD patients with paired samples (Figure 2, D and E) demonstrated that most patients exhibited marked reductions in both their plasma ASGR2- and CYP2E1-positive EV signal, indicative of a corresponding decrease in hepatocyte-derived EVs, following weight loss surgery. Subgroup analysis of subjects with NASH showed a similar reduction in hepatocyte-derived EVs following weight loss surgery (Figure 2, F and G). We next evaluated the correlation between plasma nPES ASGR2-EV signal and plasma CYP2E1-EV signal and small and large EV levels, isolated by differential ultracentrifugation from plasma from the same patients, and found a significant correlation between small EVs and ASGR2 and CYP2E1 signal in NASH patients, suggesting that hepatocyte-derived EVs detected in NASH patients are predominantly small EVs (Figure 3, A and B). Conversely, large EVs demonstrated inverse correlations with ASGR2-positive and CYP2E1-positive EV nPES signal in these NASH patients (Figure 3, C and D).

Figure 2. Hepatocyte-derived EVs decrease after bariatric surgery.

Figure 2.

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(A) A graphical representation of nanoplasmonic enhanced scattering (nPES) assay. (B) Optical response with ASGR2 antibody and (C) CYP2E1 antibody is lower in post bariatric surgery patients than in pre bariatric surgery patient, N = 44 (pre 22 NAFLD vs post 22 no-NAFLD). (D) Optical response with ASGR2 antibody and (E) CYP2E1 antibody is lower in post bariatric surgery patients than in pre bariatric surgery patients, N = 14 NAFLD paired samples. (F) Optical response with ASGR2 antibody and (G) CYP2E1 antibody is lower in post bariatric surgery patients than in pre bariatric surgery patients, n = 7 NASH patients. P values of <0.05 and 0.01 were denoted as * and **, respectively.

Figure 3. Small EVs are hepatocyte-derived EVs.

Figure 3.

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Graph showing linear regression between (A) small EV concentration and ASGR2 nPES signal; (B) small EV concentration and CYP2E1 nPES signal; (C) large EVs concentration and ASGR2 nPES signal; and (D) large EVs concentration and CYP2E1nPES signal. N = 8 NASH patients.

Targeted sphingolipid measurement of small EVs

As ceramides are mechanistically important in the biosynthesis of small EVs,22,23,26 we performed a targeted sphingolipid analysis of small EVs. We first examined the distribution of EV sphingolipids according to NAS category. All small EV samples (28 pre and 28 post weight loss) had matching liver biopsy data (Supplementary Table 1). Quantification of 10 sphingolipid species sphingosine (Sph), sphinganine (SPA), S1P, and 7 ceramide species (14:0, 16:0, 18:0, 20:0, 22:0, 24:0, and 24:1) demonstrated that most lipid differences were observed between NAS0 (N = 28) and NAS1 (N = 14), which had the most samples, with noted difference for several sphingolipids (Sph, SPA, S1P, CER(14:0, 16:0, 18:0, 20:0) (Figure 4, A) reflecting an increase in EVs sphingolipids with the development of steatosis or inflammation, the two histological features that accounted for an NAS1 categorization. Subgroup analysis of the NAFLD patients demonstrated a significant reduction in EV sphingolipids in 22 post weight loss NAFLD patients versus the 22 pre weight loss patients with normal liver histology (Figure 4, B). We next correlated EV sphingolipids with clinical and histological features of this patient population to find the most significant correlations (Figure 4, C). Small EV counts correlated strongly with C22:0 ceramide (Figure 4, D). Subgroup analysis of the NASH patients also demonstrated a significant reduction in EV sphingolipids after weight loss surgery (Figure 4, E), and several of these species revealed negative correlations with histological features in NASH patients (Figure 4, F). The most significant correlations were among C16:0 ceramide and ALT (R = 0.508) (Figure 4, G), and small EVs and steatosis (R = 0.638, data not shown).

Figure 4. Targeted sphingolipid measurement of small EVs.

Figure 4.

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(A) Sphingolipid concentration of small EVs, N = 56 (pre 28 vs post 28) according to NAS; two-tailed Student's t test was used for statistical analyses. P values of <0.05 were denoted as *. (B) Sphingolipid concentration of small EVs, N = 44 (pre 22 NAFLD vs post 22 no-NAFLD); two-tailed Student's t test was used for statistical analyses; P values of <0.05 and 0.01 were denoted as * and ** respectively. (C) Heatmap of correlations between small EV sphingolipids and clinical parameters (pre 22 NAFLD vs post 22 no-NAFLD). (D) Graph showing linear regression between small EV concentration and C22:0 CER (pre 22 NAFLD vs post 22 no-NAFLD). (E) Sphingolipid concentration of small EVs, N = 7 NASH paired patients (pre vs post); one-tailed Student's t test was used for statistical analyses. P values of <0.05 and 0.01 were denoted as # and * respectively. (F) Heatmap of correlations between EV sphingolipids and clinical parameters, N = 7 NASH paired patients (pre vs post). (G) Graph showing linear regression between ALT and C16:0 CER 7 NASH paired patients (pre vs post).

Comprehensive comparative lipidomics of small EVs

Our next objective was to identify lipids on small EVs which could serve as biomarkers in a blood-based test for NAFLD diagnosis and risk stratification. EV sample lipid composition was analyzed using a lipid panel covering 1100 lipid species across 14 lipid classes. Plasma and small EVs from the pre- and post-weight loss surgery groups were subjected to this lipid analysis to ensure that uniquely enriched candidate lipid biomarkers could be identified. We detected 784 and 186 lipid species in pre-weight loss plasma and small EVs, respectively, in 75% or more patient samples and 780 and 87 lipid species in plasma and small EVs, respectively, in the post-weight loss group (Table 2). The complex lipid panel detected changes in small EV sphingolipids which were complementary to the prior targeted sphingolipids analysis. For the sphingolipids of small EVs, ceramide (18:0, 20:0), DCER(14:0, 20:0, 24:1), lactosylceramide (22:0, 22:1, 24:0) and hexosylceramide (26:0) all exhibited greater than 1.5 fold changes between pre- and postsurgery (Figure 5, A).

Table 2.

Lipid species detected by complex lipid panel.

Class Plasma
 Small EVs
Pre Post Pre Post
Phosphatidylcholine (PC) 53 50 8 6
Phosphatidylethanolamine (PE) 42 40 1 0
Phosphatidylinositol (PI) 10 10 0 0
Lyso-PC 10 9 0 0
Lyso-PE 8 8 0 0
Dihydro-ceramid e(DCER) 10 6 4 1
Ceramide (CER) 12 12 7 7
Hexosyl-ceramide (HCER) 12 12 7 6
Lactosyl-ceramide (LCER) 12 12 7 7
Sphingomyelin (SM) 12 12 10 9
Triacylglycerol (TAG) 509 516 90 20
Diacylglycerol (DAG) 52 52 37 18
Monoacylglycerol (MAG) 16 15 8 7
Cholesteryl ester (CE) 26 26 7 6
Total species 784 780 186 87
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Only lipids present in 75% or more patient samples were counted.

Figure 5. The complex lipid panel analysis of small EVs.

Figure 5.

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(A) Sphingolipid concentration of small EVs. N = 56 (pre 28 vs post 28); two-tailed Student's t test was used for statistical analyses; P values of <0.05, 0.01, and 0.001 were denoted as *, **, and *** respectively. (B) Volcano plot of lipid changes in small EVs, N = 56, according to NAS score (0 vs 1, 1 vs 2, 2 vs ≧3, respectively); x axis is log2 fold change; y axis is −log10P values; labeled dot indicates upregulated or downregulated lipids (log2FC > 0.5 or log2FC < −0.5), which passed threshold for t test (P < 0.05); two-tailed Student's t test was used for statistical analyses.

We next determined the changes in total lipids after excluding neutral lipids in each NAS group (0 vs 1, 1 vs 2, 2 vs 3 or more). HCER was only high in group NAS0 group. SM, PC, and PE were found to be the highest in group NAS2 group (Figure 5, B). The observed sphingolipidomic changes suggest that in the obesity with healthy liver (NAS0), only HCER was high (Figure 6, A). In the early stage of steatosis (NAS1), small EVs were enriched for metabolites of the de novo ceramide synthesis pathways (SPA-DCER-CER-Sph-S1P) (Figure 4, A and Figure 6, B). In patients with NAS2, SM was also enriched, perhaps due to ongoing ceramide synthesis (Figure 5, B and Figure 6, C). In patients with NAS ≥ 3, DCER accumulated in small EVs, perhaps reflecting persistent hepatic increases in DCERs. Further analysis indicated that the ratio of DCER/CER on small EVs correlated best with the NAS ≥ 3 group (Figure 6, E). Based on these analyses, we applied a nonlinear regression model for all the lipids according to NAS score (Figure 7) to confirm the trend of specific lipid peaks at different NAS stages in small EVs. Notably, S1P and DCER (20:0) had a double peak at NAS1 and NAS4–5 suggesting a bimodal increase in these sphingolipids in NAFLD. The changes in EV lipidomics were not due to co-isolation of lipoprotein particles.

Figure 6. Metabolic pathways reflected in small EV sphingolipid enrichment.

Figure 6.

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The schematic representation of selected sphingolipid metabolic reactions from Kyoto Encyclopedia of Genes and Genomes (KEGG) with indications of quantified average lipid classes and acyl chains. (A) High in healthy liver (NAS =0), (B) high in NAS = 1, (C) high in NAS =2, and (D) high in NAS ≥ 3. (E) Change of small EV sphingolipid content expressed as the ratio of each sphingolipid normalized to mean of the total ceramide.

Figure 7. Changes in EV sphingolipids with each NAS score.

Figure 7.

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Nonlinear regression model was applied to depict lipid parameters according to their respective evolution with NAS. The 95% confidence interval is included in the display. All data were normalized by mean of each lipids class (NAS = 0).

Multivariable ROC analysis validated small EV sphingolipids as a NAFLD biomarker

Finally, we evaluated the performance of small EV counts and abundance of sphingolipid species to discriminate between patients with and without NAFLD using ROC curve analysis. First, in univariate analysis, small EVs had an AUC = 0.724, and small EVs Sph had the highest AUC = 0.761 for the diagnosis of NAFLD (Supplementary Table 2). Based on univariate analysis, we next performed multivariable logistic regression, followed by ROC analysis, to assess the performance of a model including small EV counts, BMI, Sph, and DCER (20:0) levels (Table 3). The discriminatory utility of Sph and DCER (20:0) was improved when combined with BMI and small EV counts. Indeed, a model combining small EV counts, BMI, Sph, and DCER (20:0) had an AUC of 0.801, indicating very good diagnostic accuracy based on AUC interpretation criteria in the literature.43

Table 3.

Multivariable logistic regression analysis of EVs for the identification of NAFLD.

NAFLD vs.
no-NAFLD		 AUC (95% CI) Sensitivity
(%)		 Specificity
(%)		 P
value		 Cut
off
BMI + small EVs 0.764 (0.610 to 0.870) 85.71 75.00 0.0008 0.388
Small EVs + DCER (20:0) 0.776 (0.631 to 0.875) 89.29 60.71 0.0013 0.500
BMI + Sph 0.7908 (0.639 to 0.889) 89.29 67.86 0.0006 0.571
BMI + Small EVs + Sph 0.797 (0.653 to 0.891) 89.29 60.71 0.001 0.500
BMI + Small EVs + DCER (20:0) 0.803 (0.659 to 0.896) 89.29 64.29 0.0008 0.535
BMI + Small Sph + DCER EVs + (20:0) 0.801 (0.656 to 0.894) 82.14 75.00 0.0013 0.571
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N = 56, Logistic regression analysis.

Discussion

In this study we examined the performance of circulating EVs as a biomarker for NAFLD. Our objective was to study the kinetics of circulating EVs, hepatocyte-derived EVs, and EV-derived sphingolipids, and to identify changes in EV lipids in a bariatric cohort with NAFLD, before and after weight loss. Our findings demonstrate that: i) total circulating EVs are elevated in NAFLD and decrease following NAFLD resolution due to weight loss surgery, ii) hepatocyte-derived EVs are elevated in NAFLD and decrease with NAFLD resolution, iii) hepatocyte-derived EVs correlate with steatosis and inflammation in NASH patients and steatosis in NAFLD patients, and iv) EV sphingolipids decrease with weight loss with significant reductions in ceramides, DCERs, and S1P. v) Multivariable regression modeling showed a model combining small EV counts, BMI, Sph, and DCER(20:0) had an AUC of 0.801 to discriminate between patients with and without NAFLD.

NAFLD is the most prevalent chronic liver disease globally with a significant economic burden. There is a need for the development of pathophysiologically informed blood-based biomarkers that can be employed to improve the diagnosis, prognosis, and assess treatment responses of these patients.2 EVs are interesting as a potential minimally invasive biomarker for NAFLD due to their biophysical properties, ease of isolation from body fluids such as blood, and unique disease-specific cargo signatures which may reflect underlying abnormalities such as lipotoxic hepatocellular stress or obesity-associated inflammation.22,23,44 EVs may be classified on the basis of their biogenesis into exosomes or microvesicles, but we followed the more practical classification of dividing them into large EVs and small EVs based on their isolation by differential ultracentrifugation,29 as endorsed by the International Society for Extracellular Vesicles.34 Plasma concentrations of small EVs and large EVs significantly decreased after weight loss surgery, and in the NASH subgroup analysis, there was strong correlation between small EV counts and hepatic steatosis (R2 = 0.63).

EVs derived from multiple cell types can be elevated in obese humans and mouse models of NASH40,44; we therefore demonstrated changes in hepatocyte-derived EVs following weight loss. We evaluated the hepatocyte-specificity of antibodies against two hepatocyte transmembrane protein, ASGR2 and CYP2E1, and employed these antibodies in an nPES assay designed to detect EVs expressing these proteins. Quantification of hepatocyte-derived EVs by this assay demonstrated a significant reduction in ASGR2- and CYP2E1-positive EVs with weight loss. These findings are similar to reported hepatocyte-derived EV reductions in a bariatric cohort measured using flowcytometry to detect EVs.45 In contrast to this study which did not employ dual markers to confirm that detected nanoparticles are EVs, all the ASGR2− positive and CYP2E1− positive EVs were also CD63 positive in our study, confirming that the ASGR2 positive and CYP2E1 positive nanoparticles detected by nPES are EVs. In our study we had a total of 56 patient samples (28 pre- and 28 post weight loss), a relatively small size but similar to other reported bariatric cohorts examining EVs.45 Additional studies are needed to determine whether these assays, nPES and flowcytometry, are sufficiently reproducible and scalable for development as clinically useful assays. We believe that the nPES assay approach offers several advantages that can facilitate clinical translation, including small sample volume and lack of sample pre-processing requirement, and use of low-cost dark field microscopy for assay readout.46

We found that nPES signal from ASGR2-positive EVs correlated with small EV levels better than nPES signal from CYP2E1-positive EVs, and that neither positively correlated with large EV concentrations. These data suggest that hepatocyte-derived EVs are predominantly small EVs and large EVs may arise from other cell types. Recent studies demonstrate an increase in circulating EVs in patients with cirrhosis, NASH, and alcoholic hepatitis18,19,29; thus, the development of disease-specific EV biomarkers would help improve the specificity of hepatocyte-derived EVs in liver diseases. To identify NAFLD-specific cargos we next examined the EV lipid composition.

NAFLD is an inherently lipotoxic disorder; therefore, we evaluated if lipid composition had pathophysiological specificity or provided insight into the roles of EVs in target organ injury. Targeted sphingolipid analysis of small EVs found a significant reduction in small EV sphingolipids following weight loss. In step-by-step analysis by NAS category, we found the most significant change in EV sphingolipids occurred between NAS0 and NAS1. Since patients with NAS1 had either hepatic steatosis or inflammation, this suggests that small EV sphingolipid levels are elevated in the earliest histologic lesions of NAFLD in patients with normal ALTs. The pattern of EV sphingolipid enrichment also suggested increased de novo ceramide synthesis in NAFLD, consistent with previous reports44 and the known role of ceramides in promoting EV formation.22,23

We next measured the comprehensive lipidomic signature in EVs from NAFLD patients and detected 784 and 186 lipid species in pre-weight loss plasma and small EVs. We detected comparable numbers of DAG, TAG and CE species in pre and post-weight loss plasma samples, but small EVs were significantly depleted in DAG, TAG, and CE.47 Chylomicrons are non-contributory as all samples were collected after an overnight fast; apoA1 and apoB100 were equally distributed across the pre- and post-bariatric small EVs and are known to be components of the proteome of hepatocyte-derived EVs.48 Though we cannot exclude minimal co-isolation of lipoprotein particles, the depletion of DAG, TAG, and CE, in small EVs and similar distribution of apoA1 and apoB100 across pre and post-bariatric samples suggests that the observed lipidomic changes are not due to contaminating lipoprotein particles in small EV isolates.

Changes in EV sphingolipids by the comprehensive lipidomic panel were similar to those detected with targeted sphingolipid analysis. We found an increase in long chain ceramides including Cer (26:1) and DCER (20:0, 20:1, 24:1) with increasing NAS, consistent with a previous report.44 Metabolic pathway modeling suggested increased lipid flux through the endogenous spingo-lipid synthesis pathways with increasing NAS. These findings are consistent with a role for these lipids in NAFLD pathogenesis.26 Lastly, we evaluated the diagnostic performance of an EV-based model for NAFLD versus no-NAFLD and determined that small EV counts, BMI, Sph, and DCER (20:0) yielded a high AUC for the diagnosis of NAFLD.

In conclusion, total circulating EVs and hepatocyte-derived EVs are elevated in NAFLD and decrease following NAFLD resolution due to weight loss surgery. Hepatocyte-derived EVs are primarily small EVs due to their correlation with ASGR2-positive EV signal. Our multivariate logistic regression data support a role for investigating the diagnostic performance of an EV-based test in larger sample sets. Furthermore, the decrease in hepatocyte-derived EVs with NAFLD resolution suggests that the nPES-based assay for hepatocyte-derived EVs may have utility as a point-of-care biomarker for assessment of response to NAFLD therapies. Given the relative advantages of a blood-based test over invasive measures such a liver biopsy, this would be significant advance in the care patients with NAFLD.

Supplementary Material

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Funding sources:

This work is supported by NIH grant DK111378 (to H.M.), the Mayo Foundation (to H.M.), the Ferring Research Foundation (to H.M.), the Clinical Core of the Mayo Clinic Center for Cell Signaling in Gastroenterology (P30DK084567), Mayo Clinic Metabolomics Resource Core (U24DK100469 and UL1TR000135), NIH grant DK115594 (to A.M.A.), and Kanae Foundation Foreign Study Grant (to Y.N.).

Abbreviations:

ApoA1

apolipoprotein A1

ApoB100

apolipoprotein B100

ASGR2

asialoglycoprotein receptor 2

CER

ceramide

CE

cholesteryl ester

CYP2E1

cytochrome P450 2E1

DCER

dihydroceramide

EV

extracellular vesicles

HCER

hexosylceramide

LCER

lactosylceramide

nPES

nanoplasmon enhanced scattering

NAFLD

non-alcoholic fatty liver disease

NASH

non-alcoholic steatohepatitis

NTA

nano-particle tracking analysis

Sph

sphingosine

S1P

sphingosine 1-phosphate

Tsg101

tumor susceptibility gene 101

Footnotes

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.nano.2021.102430.

Conflict of interest: The authors declare that they have no conflict of interest.

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Supplementary Materials

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NIHMS1719753-supplement-1.pdf (1,004KB, pdf)
2
NIHMS1719753-supplement-2.xlsx (2.2MB, xlsx)
3
NIHMS1719753-supplement-3.xlsx (2.5MB, xlsx)
4
NIHMS1719753-supplement-4.xlsx (27.8KB, xlsx)

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