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. 2025 Sep 10;5(9):100925.
doi: 10.1016/j.xgen.2025.100925. Epub 2025 Jul 15.

The extracellular vesicle transcriptome provides tissue-specific functional genomic annotation relevant to disease susceptibility in obesity

Affiliations

The extracellular vesicle transcriptome provides tissue-specific functional genomic annotation relevant to disease susceptibility in obesity

Emeli Chatterjee et al. Cell Genom. .

Abstract

We characterized circulating extracellular vesicles (EVs) in obese and lean humans, identifying transcriptional cargo differentially expressed in obesity (277 unique genes; false discovery rate < 10%). Since circulating EVs may have broad origin, we compared this obesity EV transcriptome with expression from human visceral-adipose-tissue-derived EVs from freshly collected and cultured biopsies from the same obese individuals, observing high concordance. Using a comprehensive set of adipose-specific epigenomic and chromatin conformation assays, we found that the differentially expressed transcripts from the EVs were those regulated in adipose by body mass index-associated SNPs (p < 5 × 10-8) from a large-scale genome-wide association study (GWAS). Using a phenome-wide association study of the regulatory SNPs for the EV-derived transcripts, we identified a substantial enrichment for inflammatory phenotypes, including type 2 diabetes. Collectively, these findings represent the convergence of the GWAS (genetics), epigenomics (transcript regulation), and EV (liquid biopsy) fields, enabling powerful future genomic studies of complex diseases.

Keywords: BMI; GWAS; PheWAS; RNA; extracellular vesicles; obesity; plasma; visceral adipose tissue.

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Conflict of interest statement

Declaration of interests R.S. is supported by grants from the National Institutes of Health. R.S. has equity ownership in and is a consultant for Thryv Therapeutics. R.S. is a co-inventor on pending patents or disclosures on molecular biomarkers of fitness, lung disease, cardiovascular diseases and phenotypes, and metabolic health; use of RNAs as therapeutics and diagnostic biomarkers in disease; and methods in metabolomics. E.R.G. is a consultant for Thryv. He is a co-inventor on disclosures or patents on cardiovascular diseases and phenotypes and metabolic health, use of RNAs as therapeutics and diagnostic biomarkers in disease, and methods in metabolomics. S.D. is a cofounder and consultant and has equity in Thryv Therapeutics and Switch Therapeutics, neither of which played a role in this study.

Figures

None
Graphical abstract
Figure 1
Figure 1
Adipose EVs and plasma EVs share transcriptome during obesity (A) Histogram showing the frequency distribution of EVs size in lean plasma, obese plasma, and VAT groups isolated by size-exclusion chromatography (SEC). The x axis represents particle diameter (nm), and the y axis represents the concentration (particles/mL/nm). n = 3, data are presented as mean ± SD of three independent experiments. (B) Representative western blot of the expression of CD63, CD81, TSG101, Alix, PLIN1, adiponectin, GLUT4, ApoB, ApoE, and 58K Golgi protein, as determined in the pooled EV samples from lean plasma, obese plasma, and VAT groups isolated by SEC. (C) Representative transmission electron microscopy images of isolated EVs (scale bar, 500 nm). (D) Volcano plots showing the significantly differentially expressed genes from the EV bulk RNA-seq analysis in lean versus obese groups. The y axis shows log10p value and the x axis displays the log2-fold change value. The red dots represent the differentially expressed genes with adjusted p ≤ 0.1 and absolute fold change of ≥1.5, while green dots represent non-significantly modulated genes. Positive log2-fold change means the gene is highly expressed in obese samples. (E and F) (E) Hierarchical clustering and (F) PCA of differentially expressed genes by group. n = 6 for lean plasma; n = 9 for obese plasma and VAT groups. See also Figures S1–S3 and Table S1.
Figure 2
Figure 2
Adipose tissue transcripts are prevalent in circulation Dot plot analysis showing differentially expressed genes (obese versus lean plasma) that are present in the serum of obese patients are expressed across different cell types of adipose tissue with diverse expression (single-nuclear RNA-seq [snRNA-seq]) derived from the matched adipose tissue samples. Source of snRNA-seq described in text. n = 6 for lean plasma; n = 9 for obese plasma; n = 8 for serum. See also Figure S4.
Figure 3
Figure 3
Adipose-specific regulatory elements of differentially expressed EV genes are enriched for GWAS SNPs associated with BMI and T2D (A) BMI GWAS (N = 419,163) associations for adipose-specific regulatory SNPs of the 282 differentially expressed EV transcripts. The red horizontal line denotes genome-wide significance (p < 5 × 10−8). The top SNP associated with each gene is labeled. (B) T2D GWAS (N = 2,535,601) associations for adipose-specific regulatory SNPs of the 282 differentially expressed EV transcripts. (C) Proportions of differentially expressed EV genes in contact (Hi-C) with an adipose regulatory element versus all remaining genes that can be linked to a BMI- or T2D-associated SNP. (D) SNP rs9321878, a variant within an intron of HIVEP2 at locus 6q24.2. Hi-C contacts suggest that this SNP is in a regulatory element in contact with neighboring gene PEX3. See also Figure S5 and Table S2.
Figure 4
Figure 4
Phenotypic associations for SNPs overlapping an adipose-specific regulatory element in contact with one of the 282 differentially expressed EV transcripts Among the top 50 traits associated with the regulatory SNPs for the EV-implicated genes, 17 were inflammatory phenotypes, with prominent enrichment for T2D and BMI. Traits labeled in red are inflammatory, while those in blue are non-inflammatory. Each phenotype shown here is implicated by a SNP association (p < 1.59 × 10−9) for a SNP in an adipose-specific regulatory element for an EV-implicated gene.
Figure 5
Figure 5
LocusZoom plots of BMI-associated SNPs linked to differentially expressed EV genes with mouse validation (A) SNP rs3785354, a regulatory SNP of SGF29, at locus 16p11.2. This SNP is upstream of its target gene. SNP LD patterns are based on a set of European ancestry reference individuals. (B) SNP rs17055384, a regulatory SNP of FNDC3A, at locus 13q21.1. This SNP is in an intergenic region 9 Mb downstream of FNDC3A, suggesting that it is a distal regulatory variant. (C) SNP rs11643872, a regulatory SNP of ADCY9, at locus 16p11.2. This SNP is in an intron of SETD1A and is located over 26 Mb downstream of ADCY9, suggesting that it is a distal regulatory variant. (D) SNP rs754611720, a regulatory SNP of ADCY9, at locus 16p13.3. This SNP is located directly downstream of ADCY9. (E) SNP rs9321878, a regulatory SNP of PEX3, at locus 6q24.2. This SNP is within an intron of HIVEP2, a neighboring gene of PEX3. See also Figure S6.
Figure 6
Figure 6
Chromatin contacts around BMI-associated SNPs linked to differentially expressed EV genes with mouse validation (A) SNP rs3785354 is variant that is immediately upstream of NUPR1 at locus 16p11.2. It overlaps with an adipose regulatory element that is in contact with SGF29. Contacts are derived from Hi-C in RUES2. (B) SNP rs17055384 (grouped with the depicted SNP rs9569942) is an intergenic SNP at locus 13q21.1 that is ∼9 Mb downstream of FNDC3A. However, Hi-C contacts indicate that this SNP overlaps an adipose regulatory that is in contact with FNDC3A. (C) SNP rs11643872, a regulatory SNP of ADCY9 at locus 16p11.2. Despite being over 28 Mb downstream of ADCY9, Hi-C contacts suggest that rs11643872 overlaps an adipose regulatory element in contact with this gene. (D) SNP rs754611720, a regulatory SNP downstream of ADCY9, at locus 16p13.3. (E) SNP rs9321878, a variant within an intron of HIVEP2 at locus 6q24.2. Hi-C contacts suggest that this SNP is in a regulatory element in contact with neighboring gene PEX3. See also Figure S6.

Update of

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