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. 2023 Feb 24;15(3):622.
doi: 10.3390/v15030622.

SIV Infection Regulates Compartmentalization of Circulating Blood Plasma miRNAs within Extracellular Vesicles (EVs) and Extracellular Condensates (ECs) and Decreases EV-Associated miRNA-128

Affiliations

SIV Infection Regulates Compartmentalization of Circulating Blood Plasma miRNAs within Extracellular Vesicles (EVs) and Extracellular Condensates (ECs) and Decreases EV-Associated miRNA-128

Steven Kopcho et al. Viruses. .

Abstract

Background: This is Manuscript 1 of a two-part Manuscript of the same series. Here, we present findings from our first set of studies on the abundance and compartmentalization of blood plasma extracellular microRNAs (exmiRNAs) into extracellular particles, including blood plasma extracellular vesicles (EVs) and extracellular condensates (ECs) in the setting of untreated HIV/SIV infection. The goals of the study presented in this Manuscript 1 are to (i) assess the abundance and compartmentalization of exmiRNAs in EVs versus ECs in the healthy uninfected state, and (ii) evaluate how SIV infection may affect exmiRNA abundance and compartmentalization in these particles. Considerable effort has been devoted to studying the epigenetic control of viral infection, particularly in understanding the role of exmiRNAs as key regulators of viral pathogenesis. MicroRNA (miRNAs) are small (~20-22 nts) non-coding RNAs that regulate cellular processes through targeted mRNA degradation and/or repression of protein translation. Originally associated with the cellular microenvironment, circulating miRNAs are now known to be present in various extracellular environments, including blood serum and plasma. While in circulation, miRNAs are protected from degradation by ribonucleases through their association with lipid and protein carriers, such as lipoproteins and other extracellular particles-EVs and ECs. Functionally, miRNAs play important roles in diverse biological processes and diseases (cell proliferation, differentiation, apoptosis, stress responses, inflammation, cardiovascular diseases, cancer, aging, neurological diseases, and HIV/SIV pathogenesis). While lipoproteins and EV-associated exmiRNAs have been characterized and linked to various disease processes, the association of exmiRNAs with ECs is yet to be made. Likewise, the effect of SIV infection on the abundance and compartmentalization of exmiRNAs within extracellular particles is unclear. Literature in the EV field has suggested that most circulating miRNAs may not be associated with EVs. However, a systematic analysis of the carriers of exmiRNAs has not been conducted due to the inefficient separation of EVs from other extracellular particles, including ECs. Methods: Paired EVs and ECs were separated from EDTA blood plasma of SIV-uninfected male Indian rhesus macaques (RMs, n = 15). Additionally, paired EVs and ECs were isolated from EDTA blood plasma of combination anti-retroviral therapy (cART) naïve SIV-infected (SIV+, n = 3) RMs at two time points (1- and 5-months post infection, 1 MPI and 5 MPI). Separation of EVs and ECs was achieved with PPLC, a state-of-the-art, innovative technology equipped with gradient agarose bead sizes and a fast fraction collector that allows high-resolution separation and retrieval of preparative quantities of sub-populations of extracellular particles. Global miRNA profiles of the paired EVs and ECs were determined with RealSeq Biosciences (Santa Cruz, CA) custom sequencing platform by conducting small RNA (sRNA)-seq. The sRNA-seq data were analyzed using various bioinformatic tools. Validation of key exmiRNAs was performed using specific TaqMan microRNA stem-loop RT-qPCR assays. Results: We showed that exmiRNAs in blood plasma are not restricted to any type of extracellular particles but are associated with lipid-based carriers-EVs and non-lipid-based carriers-ECs, with a significant (~30%) proportion of the exmiRNAs being associated with ECs. In the blood plasma of uninfected RMs, a total of 315 miRNAs were associated with EVs, while 410 miRNAs were associated with ECs. A comparison of detectable miRNAs within paired EVs and ECs revealed 19 and 114 common miRNAs, respectively, detected in all 15 RMs. Let-7a-5p, Let-7c-5p, miR-26a-5p, miR-191-5p, and let-7f-5p were among the top 5 detectable miRNAs associated with EVs in that order. In ECs, miR-16-5p, miR-451, miR-191-5p, miR-27a-3p, and miR-27b-3p, in that order, were the top detectable miRNAs in ECs. miRNA-target enrichment analysis of the top 10 detected common EV and EC miRNAs identified MYC and TNPO1 as top target genes, respectively. Functional enrichment analysis of top EV- and EC-associated miRNAs identified common and distinct gene-network signatures associated with various biological and disease processes. Top EV-associated miRNAs were implicated in cytokine-cytokine receptor interactions, Th17 cell differentiation, IL-17 signaling, inflammatory bowel disease, and glioma. On the other hand, top EC-associated miRNAs were implicated in lipid and atherosclerosis, Th1 and Th2 cell differentiation, Th17 cell differentiation, and glioma. Interestingly, infection of RMs with SIV revealed that the brain-enriched miR-128-3p was longitudinally and significantly downregulated in EVs, but not ECs. This SIV-mediated decrease in miR-128-3p counts was validated by specific TaqMan microRNA stem-loop RT-qPCR assay. Remarkably, the observed SIV-mediated decrease in miR-128-3p levels in EVs from RMs agrees with publicly available EV miRNAome data by Kaddour et al., 2021, which showed that miR-128-3p levels were significantly lower in semen-derived EVs from HIV-infected men who used or did not use cocaine compared to HIV-uninfected individuals. These findings confirmed our previously reported finding and suggested that miR-128 may be a target of HIV/SIV. Conclusions: In the present study, we used sRNA sequencing to provide a holistic understanding of the repertoire of circulating exmiRNAs and their association with extracellular particles, such as EVs and ECs. Our data also showed that SIV infection altered the profile of the miRNAome of EVs and revealed that miR-128-3p may be a potential target of HIV/SIV. The significant decrease in miR-128-3p in HIV-infected humans and in SIV-infected RMs may indicate disease progression. Our study has important implications for the development of biomarker approaches for various types of cancer, cardiovascular diseases, organ injury, and HIV based on the capture and analysis of circulating exmiRNAs.

Keywords: SIV; extracellular condensates; extracellular vesicles; miRNA; miRNA-128.

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Conflict of interest statement

The authors report no biomedical financial interests or potential conflicts of interest.

Figures

Figure 1
Figure 1
Study workflow, EV and EC isolation and characterization. (A) Description of experimental model; 15 male Indian Rhesus Macaques were randomly assigned to 5 groups of 3. Pre-infection and pre-treatment blood plasma samples were collected and processed. (B) Methodological workflow for isolation of EVs and ECs and their characterization. (C) Representative PPLC spectra of EVs and ECs. Blue box: indicates EV-containing fraction. Green box: indicates EC-containing fraction. (D) Representative negative-stain TEM images of purified EVs and ECs from pooled (n = 15) RMs. Blue arrows indicate gold-labeled CD9 on the surface of EVs. Green arrows indicate ECs. Scale bars: 200 nm for EVs and ECs (Top), 50 nm EVs (bottom), and 100 nm ECs (bottom). (EG) Nanoparticle tracking analysis (NTA) measurements of different BEV properties, including (E) mean EV size, (F) mean EV concentration, (G) mean EV zeta-potential.
Figure 2
Figure 2
Identification of common BEV and BEC miRNAs. (A) Number of miRNAs detected (miRNA distribution count ≥1) for each RM (n = 15), for both EVs and ECs. (B) Venn diagram comparing total detectable miRNAs for EVs and ECs (n = 15). To be included in the list, miRNA count needed to be ≥1 at least 1 RM. (C,D) Venn diagram showing common and unique miRNAs among the 5 groups for (C) EVs and (D) ECs. Dotted red circle indicates miRNAs detected in monkeys (n = 15) for EVs (19) and ECs (114). (E,F) Top 10 detected commonly expressed miRNAs as measured by miRNA distribution counts for (E) EVs and (F) ECs. Unpaired T-test with Welch’s correction was used to assess statistical differences between EVs and ECs in panel (A). Error bars represent S.E.M. ****, p < 0.0001.
Figure 3
Figure 3
The top 10 miRNAs identified in EVs and ECs regulate distinctive pathways. (A,B) miRNA-target enrichment analysis showing top target genes by number of interactions for A) EV-associated miRNAs and (B) EC-associated miRNAs. The color of the bars represents adjusted p-values (FDR). (C,D) Visualization of miRNA-target interaction network for (C) EV-associated miRNAs and (D) EC-associated miRNAs. Blue circles indicate miRNAs, yellow circles indicate their target genes. (E,F) Dot plot of functional enrichment analysis for target genes of top 10 miRNAs resulting from miRNA-target enrichment analysis for (E) EV-associated miRNAs and (F) EC-associated miRNAs. Color of dots represents adjusted p-values (FDR), and size of dots represents gene ratio (number of miRNA targets found enriched in each category/number of total genes associated with that category). (G) Venn diagram comparing differences and similarities in KEGG pathways of EV- and EC-associated miRNAs.
Figure 4
Figure 4
Identification and pathway analysis of common and unique miRNAs associated with EVs and ECs. (A) Venn diagram showing common and unique miRNAs among the common EV and EC miRNAs (n = 15). (B) miRNA distribution counts of EV-associated unique miRNAs (1) for n = 15 RMs. (C) miRNA distribution counts of top 10 EC-associated miRNAs. (D) miRNA-target enrichment analysis showing top target genes by number of interactions for the 1 unique EV-associated miRNA. (E) Visualization of miRNA-target interaction network for the 1 unique EV-associated miRNA. (F) miRNA-target enrichment analysis showing top target genes by number of interactions for the top 10 unique EC-associated miRNAs. (G) Visualization of miRNA-target interaction network for the top 10 unique EC-associated miRNAs. (H) Dot plot of functional enrichment analysis for the top 10 unique EC-associated miRNAs. Color of dots represents adjusted p-values (FDR), and size of dots represents gene ratio (number of miRNA targets found enriched in each category/number of total genes associated with that category). (I) PCA plot of the 18 (arrow from panel (A)) common EV and EC miRNAs. Unit variance scaling is applied to rows; SVD with imputation is used to calculate principal components. X and Y axis show principal component 1 and principal component 2, which explain 74.4% and 19.1% of the total variance, respectively. Predication ellipses are such that with a probability of 0.95, a new observation from the same group will fall inside the ellipse. N = 15 data points. (J) Hierarchical clustering heatmap of the 18 common EV and EC miRNAs. Rows are centered; unit variance scaling is applied to rows. Both rows and columns are clustered using correlation distance and average linkage. (K) miRNA-target enrichment analysis showing top target genes by number of interactions for the 18 common EV- and EC-associated miRNAs. (L) Visualization of miRNA-target interaction network for 18 common EV- and EC-associated miRNAs. (M) Dot plot of functional enrichment analysis for target genes of 18 common EV- and EC-associated miRNAs. Color of dots represents adjusted p-values (FDR), and size of dots represents gene ratio (number of miRNA targets found enriched in each category/number of total genes associated with that category.
Figure 5
Figure 5
SIV infection of RMs longitudinally downregulates EV-associated miR-128-3p. (A) Schematic of SIV infection of RMs; 12 male Indian RMs were infected with SIV. One month post-infection (1 MPI), blood plasma was collected from n = 12 RMS. Five months post-infection (5 MPI), blood plasma was collected from n = 3 RMS. (B) Number of miRNAs detected (miRNA distribution count ≥ 1) for each RM, for both EVs and ECs. Pre (n = 15), SIV 1 MPI (n = 12), SIV 5 MPI (n = 3). (CF) Volcano plots showing down-regulated (blue) and up-regulated (red) miRNAs in (C) EVs 1 MPI, (D) ECs 1 MPI, (E) EVs 5 MPI, and (F) BCs 5 MPI compared to healthy uninfected RMs (Pre). (GI) miRNA-target enrichment analysis (G), visualization of miRNA-target interaction network (H), and dot plot of functional enrichment analysis (I) for the longitudinally downregulated EV-associated miRNAs (miR-206, miR-99a-5p, miR-128-3p). Color of dots in panel (I) represents adjusted p-values (FDR), and size of dots represents gene ratio (number of miRNA targets found enriched in each category/number of total genes associated with that category. (J) TaqMan PCR validation using 128a-3p specific assays. Statistical differences were assessed by ordinary one-way ANOVA test with Tukey’s correction (n = 3). *, p < 0.05. (K) miRNA-target enrichment analysis showing top target genes by number of interactions for miR-128-3p. (L) Visualization of miRNA-target interaction network for miR-128-3p. (M,N) Dot plots of functional enrichment analysis (M) KEGG and (N) disease Ontology for target genes of miR-128-3p. Color of dots represents adjusted p-values (FDR), and size of dots represents gene ratio (number of miRNA targets found enriched in each category/number of total genes associated with that category). Unpaired T-test with Welch’s correction was used to assess statistical differences between EVs and ECs in panels (B) and (J) (left). Error bars represent S.E.M. *, p < 0.05; ****, p < 0.0001; ns, not significant. In Panel J, Ordinary One-way ANOVA multiple comparison test (Tukey’s test) was used to assess statistical differences, with ns denoting non-significant.
Figure 6
Figure 6
Circulating blood plasma miRNAs and their association with EVs and ECs in uninfected and SIV-infected rhesus macaques. Part of this illustration was created with BioRender.com.

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