Methamphetamine use alters human plasma extracellular vesicles and their microRNA cargo: An exploratory study

Abstract

Methamphetamine (MA) is the largest drug threat across the globe, with health effects including neurotoxicity and cardiovascular disease. Recent studies have begun to link microRNAs (miRNAs) to the processes related to MA use and addiction. Our studies are the first to analyse plasma EVs and their miRNA cargo in humans actively using MA (MA-ACT) and control participants (CTL). In this cohort we also assessed the effects of tobacco use on plasma EVs. We used vesicle flow cytometry to show that the MA-ACT group had an increased abundance of EV tetraspanin markers (CD9, CD63, CD81), but not pro-coagulant, platelet-, and red blood cell-derived EVs. We also found that of the 169 plasma EV miRNAs, eight were of interest in MA-ACT based on multiple statistical criteria. In smokers, we identified 15 miRNAs of interest, two that overlapped with the eight MA-ACT miRNAs. Three of the MA-ACT miRNAs significantly correlated with clinical features of MA use and target prediction with these miRNAs identified pathways implicated in MA use, including cardiovascular disease and neuroinflammation. Together our findings indicate that MA use regulates EVs and their miRNA cargo, and support that further studies are warranted to investigate their mechanistic role in addiction, recovery, and recidivism.

Keywords: addiction; extracellular vesicle; methamphetamine; microRNA; plasma; tobacco; vesicle flow cytometry.

FIGURE 1

TS+ EVs are increased in the plasma of MA‐ACT, but not smokers. a) Estimated diameter (nm) of all particles, and (b) representative violet side scatter (VSSC) profile of plasma samples stained with vFRed and measured by vesicle flow cytometry (vFC). c) Corresponding diameter versus VSSC distributions of vFRed show three populations of particles differentiated by high, medium and low light scatter. d‐f) Plasma samples stained with vFRed, CFSE FITC and antibodies against CD9, CD63 and CD81 (TS PE mix). Representative diameter versus fluorescence distributions of CFSE FITC (d) or TS PE (e). f) Staining events backgated onto diameter vs VSSC for CFSE FITC (green) and TS PE (orange). G‐L) Analysis of size histograms show the concentration (particles/ml) of all membrane particles (A) and CFSE+ EVs (C) is not significantly different in MA‐ACT (♦, n = 10) versus CTL (○, n = 10). Concentration of TS+ EVs (E) is significantly increased at 105 nm in MA‐ACT (♦, n = 10) versus CTL (○, n = 10). (1B,D,F) Samples re‐categorized for smoking status (non‐smokers, □, n = 10 versus smokers,▼, n = 10) show no significant difference in total particles (B), CFSE+ (D) or TS+ (F) EVs in non‐smokers versus smokers (B). Data are shown as mean ± SEM and analysed by two‐way repeated measures ANOVA followed by Sidak's multiple comparisons post hoc analysis when appropriate; P < 0.01.

FIGURE 2

Characterization of plasma EVs isolated by size exclusion chromatography (SEC). a) NTA of plasma SEC fractions (Fx): Fx 1–6 pooled (column void volume), Fx 7, Fx 8, Fx 9, Fx 10. Note that Fxs 7–10 contain particles of the size for expected EVs (∼40–200 nm). Fx 8 has the peak concentration (4 × 10⁹ particles/ml), while Fx 1–6 has a negligible concentration of particles. Representative transmission electron microscopy (TEM) images of SEC Fx 7 (b), Fx 8 (c), Fx 9 (d) and Fx 10 (e) show membrane bound vesicles at a size range of ∼40–200 nm, with most ∼100 nm. Wide field of view (scale bars = 200 nm) and close up views (scale bars = 100 nm) for each TEM image are shown. f) Representative wide field of view and close up CryoTEM image of Fx 9 shows multiple membrane bound vesicles. Scale bars: F = 50nm; F’ = 10 nm. g) Plasma SEC Fxs stained for total protein, and immunoblotted for CD9, CD63, CD81, flotillin, Alix, TSG101, Ago2, and Albumin. Figure represents four separate SEC isolations: SEC isolation 1 = protein stain, Alix, and Ago2; SEC isolation 2 = TSG101 and albumin, SEC isolation 3 = CD9 and flotillin, SEC isolation 4 = CD63 and CD81. h) Estimated diameter (nm) of all particles, and (i) representative violet side scatter (VSSC) profile of a pool of plasma SEC Fxs 7–10 stained with vFRed and measured by vesicle flow cytometry (vFC). j) Corresponding diameter versus VSSC distributions of vFRed show three populations of particles differentiated by high, medium, and low light scatter. K‐M) A pool of plasma SEC Fxs 7–10 stained with vFRed, CFSE FITC and antibodies against CD9, CD63 and CD81 (TS PE mix). Representative diameter versus fluorescence distributions of CFSE FITC (k) or TS PE (l). m) Staining events backgated onto diameter vs VSSC for CFSE FITC (green) and TS PE (orange).

FIGURE 3

Plasma EV miRNA expression is altered by MA and smoking. MiRNA expression in plasma EVs isolated by SEC was assayed by qPCR in MA‐ACT (n = 10) and CTL (n = 10). a) Manhattan plot shows 169 miRNAs expressed in MA‐ACT and/or CTL plasma EVs; miRNAs with a 1.2 fold increase or decrease in MA‐ACT relative to CTL are depicted in red. b) Recategorizing based on tobacco use shows 175 miRNAs expressed in smokers (n = 10) and/or non‐smokers (n = 10); miRNAs with a 1.2‐fold increase or decrease in smokers relative to non‐smokers are depicted in blue. Venn diagrams show miRNAs that are either 1.2‐fold increased (c) or decreased (d) in MA‐ACT or smokers, and the overlap between miRNAs altered in each group.

FIGURE 4

Plasma EV miRNAs differentially expressed by MA and smoking. Identification of miRNAs of interest that are differentially expressed in plasma EVs as a result of MA or smoking. a,b) Manhattan plots of the top 20% of ranked miRNAs based on corrected F‐statistic generated in multiple variable linear regression analysis of each participant's ΔCq, MA‐ACT and smoking status. MiRNAs are shown based on fold change of MA‐ACT versus CTL (a) or smokers versus non‐smokers (4b), with coloured bars representing at least a 1.2‐fold change in expression. c,d) Frequency distribution of the top 20% of ranked miRNAs based on Glass's Δ for MA‐ACT versus CTL (5c) or smokers versus non‐smokers (d). Coloured bars are miRNAs with a large effect size of at least 0.8. e,f) Manhattan plots of area under the curve (AUC) statistic for the top 20% of ranked miRNAs for MA‐ACT versus CTL (4e) or smokers versus non‐smokers (4f), with coloured bars representing an AUC of at least 0.75.

FIGURE 5

Linkage analysis with miRNA of interest show clustering consistent with MA or smoking. A,B) Manhattan plots of the top 20% of miRNAs in rank order and shown as fold change of MA‐ACT versus CTL (a) or Smokers versus Non‐smokers (b). Coloured bars are miRNAs of interest based on at least a 1.2‐fold change in expression, Glass's Δ of at least 0.8, and AUC of at least 0.75. c) Hierarchal clustering of each participant based on the ΔCq values for 10 miRNAs of interest in MA‐ACT. Participants are labelled based on their MA‐ACT (MA, n = 10, red font) or CTL (c, n = 10, black font). Bold indicates participants that use tobacco. d) Hierarchal clustering of each participant based on the ΔCq values for 16 miRNAs of interest in smoking. Participants are labelled based on their smoking (S, n = 10, blue font) or non‐smoking (NS, n = 10, black font). Bold indicates MA‐ACT participants. Analysis was done using one minus the Pearson correlation and average linkage. The numbers in parenthesis identify corresponding participants in panels c, d.

FIGURE 6

Expression levels of MA‐ACT miRNAs correlate with clinical features of MA use. Correlations of each subjects ΔCq values for miR‐301a‐3p, miR‐382‐5p, and miR‐628‐5p and the age at which the subject started to use MA (a‐c, age of onset), percent of the subjects lifetime spent using MA (d‐f, percent of lifetime), and frequency of MA use (g). All three miRNAs had significant correlations for age of onset and percent of lifetime. MiR‐382‐5p had significant correlation with frequency of use. Data were analysed using Pearson correlations and fitted with a simple linear regression curve.

FIGURE 7

Target and pathway prediction for miRNA effected by lifetime MA exposure. a) MiRNA target prediction work flow using the three miRNAs effected by lifetime MA exposure. b) Top 20 canonical pathways associated with the predicted mRNA targets. Top pathways are related to neuroinflammation, neurodegeneration, neuronal plasticity, cardiac‐, kidney‐ and liver disease.