Transcriptome and fatty-acid signatures of adipocyte hypertrophy and its non-invasive MR-based characterization in human adipose tissue

Abstract

Background: The adipocyte-hypertrophy associated remodeling of fat cell function is considered causal for the development of metabolic disorders. A better understanding of transcriptome and fatty acid (FA) related alterations with adipocyte hypertrophy combined with less-invasive strategies for the detection of the latter can help to increase the prognostic and diagnostic value of adipocyte size and FA composition as markers for metabolic disease.

Methods: To clarify adipocyte-hypertrophy associated transcriptomic alterations, fat cell size was related to RNA-Seq data from white adipose tissue and size-separated adipocytes. The relationship between adipocyte size and adipose tissue FA composition as measured by GC-MS was investigated. MR spectroscopy (MRS) methods for clinical scanning were developed to characterize adipocyte size and FA composition in a fast and non-invasive manner.

Findings: With enlarged adipocyte size, substantial transcriptomic alterations of genes involved in mitochondrial function and FA metabolism were observed. Investigations of these two mechanisms revealed a reciprocal relationship between adipocyte size and estimated thermogenic adipocyte content as well as depot-specific correlations of adipocyte size and FA composition. MRS on a clinical scanner was suitable for the in-parallel assessment of adipose morphology and FA composition.

Interpretation: The current study provides a comprehensive overview of the adipocyte-hypertrophy associated transcriptomic and FA landscape in both subcutaneous and visceral adipose tissue. MRS represents a promising technique to translate the observed mechanistic, structural and functional changes in WAT with adipocyte hypertrophy into a clinical context for an improved phenotyping of WAT in the context of metabolic diseases.

Funding: Competence network for obesity (FKZ 42201GI1128), ERC (No 677661, ProFatMRI; No 875488, FatVirtualBiopsy), Else Kröner-Fresenius-Foundation.

Keywords: Browning; Fatty acids; Magnetic resonance; Obesity; Transcriptomics; White adipose tissue.

Figure 1

SAT and VAT are characterized by different transcriptomic signatures (a) MDS plot displaying the first two dimensions of the data (n = 99). (b) Hierarchical clustering of SAT and VAT samples based on euclidean sample distances (n = 99). (c) Volcano plot showing differentially expressed genes between SAT and VAT (n = 99). (d) Dotplot displaying the top 20 enriched KEGG pathways with an adjusted p-value < 0.05 from gene set enrichment analysis, thereby comparing the expression of KEGG pathway associated genes from SAT and to VAT depots (n =99). Positive scores indicate enrichment of gene-sets in VAT while negative scores indicate enrichment in SAT. (e) Log₂ counts per million (CPM) of SAT and VAT lineage specific marker genes (n = 99).

Figure 2

Adipocyte area is related to gene expression in SAT (a) Volcano plot showing differential expression of genes between the bin_small (n = 20) and bin_X-Large (n = 28) in SAT. (b) Scatterplots displaying the results of the continuous model-based analysis that was applied to GTEx RNA-Seq data from SAT (n = 153). Log₂ counts per million (CPM) are plotted against adipocyte area for 153 individuals. FDR is given for each gene below the plot. The left half of the figure shows 5 of the top-ranked genes from the continuous model-based RNA-Seq analysis (SLC27A2, AMN, TTC36, PTPN3, TOX3). On the right half 5 exemplary genes that are already known to be related to WAT biology and fat cell size that were also significant (FDR < 0.05) in our continuous model are depicted (SLC2A4, TNF, INSR, CS, EGFL6). A regression line including the standard error in a lighter shade of blue was added to the plots to visualize the relationship between gene expression and adipocyte area. (c) Dotplot displaying the top 20 enriched KEGG pathways with an adjusted p-value < 0.05 from gene set enrichment analysis in SAT. The “Human Diseases” category of the KEGG pathway database was not displayed on the graph. Positive scores indicate enrichment of gene-sets in bin_X-Large while negative scores indicate enrichment in bin_X-Small. (d) KEGG Pathview graph depicting the different complexes (I-V) of the respiratory chain (hsa00190: Oxidative phosphorylation). Rectangular red coloring around the EC number of the respective respiratory chain complex depicts mitochondrial complexes that are underrepresented in SAT from the bin_X-Large group (n = 28) compared to the bin_small group (n = 20). In contrast, green coloring (+1) displays complexes from the electron transport chain that are overrepresented in SAT from the bin_X-large group in relation to the bin_small samples.

Figure 3

Adipocyte area is related to gene expression in VAT (a) Scatterplots displaying the results of the continuous model-based analysis that was applied to GTEx RNA-Seq data from VAT. Log₂ counts per million (CPM) are plotted against adipocyte area for 141 individuals. FDR is given for each gene below the plot. Displayed is the relationship between adipocyte area and gene expression for 10 exemplary genes that were ranked highest in the analysis according to their FDR. A regression line including the standard error in a lighter shade of red was added to the plots to visualize the relationship between gene expression and adipocyte area. (b) Dotplot displaying the top 20 enriched KEGG pathways with an adjusted p-value < 0.05 from gene set enrichment analysis in VAT (n = 141). The “Human Diseases” category of the KEGG PATHWAY database is not displayed on the graph. (c) Scatterplot and violin plot assessing the relationship between the expression of FASN, UCP1 and adipocyte area (n_scatter = 141, n_{bin small} = 41, n_{bin medium} = 49, n_{bin large} = 40, n_{bin xlarge} = 11). (d) KEGG Pathview graph depicting the different complexes and genes involved in the canonical thermogenesis pathway (hsa04714:Thermogenesis). Red (- 1) indicates thermogenesis related genes that are underrepresented in VAT from the bin_X-Large (n = 11) group compared to the bin_small (n = 40) group. In contrast, green (+1) displays thermogenesis genes that are overrepresented in VAT from the bin_X-large group in relation to the bin_small samples. Genes depicted in grey (0) did not show differences between the two groups.

Figure 4

BATLAS analysis indicates differences in brown adipocyte content with adipocyte hypertrophy (a) Boxplot comparing the BATLAS estimated brown adipocyte content of paired SAT and VAT biopsies (n = 99, paired t-test). (b) Scatterplot depicting the correlation between estimated brown adipocyte content and adipocyte area in SAT (n = 153, one-sided spearman correlation). Boxplot comparing the estimated brown adipocyte content between individuals with small (bin_small, n = 20) and large subcutaneous adipocytes (bin_X-large, n= 28) by means of a one-sided wilcoxon test. (c) Scatterplot depicting the correlation between estimated brown adipocyte content and adipocyte area in VAT (n = 141, one-sided spearman correlation). Boxplot comparing the estimated brown adipocyte content between individuals with small (bin_small, n = 41) and large visceral adipocytes (bin_X-large, n = 11) by means of a one-sided t-test. (d) Dotplot depicting all BATLAS brown adipocyte marker genes that were found to be differentially expressed with a FDR < 0.05 in SAT group-wise comparisons between the bin_small (n = 20) and bin_X-Large (n = 28) group. (e) Dotplot depicting all BATLAS brown adipocyte marker genes that were found to be differentially expressed with a FDR < 0.05 in VAT group-wise comparisons between the bin_small (n = 41) and bin_X-Large (n =11) group.

Figure 5

Predicted relative differences in cell-type abundances in relation to adipocyte area. (a) Dotplot displaying the spearman correlation between predicted relative differences in cell-type abundances and adipocyte area in SAT (n = 153). (b) Dotplot displaying the spearman correlation between predicted relative differences in cell-type abundances and adipocyte area in VAT (n=141). (c) Scatterplots displaying the spearman correlation between predicted relative differences of adipocytes, ASPCs, Macrophages and T cells with adipocyte area in SAT (n = 153). (d) Scatterplots displaying the spearman correlation between predicted relative differences of adipocytes, ASPCs, Macrophages and T cells with adipocyte area in VAT (n = 141).

Figure 6

The transcriptomic signatures of size-separated mature adipocytes. (a) Results from mature adipocyte size-separation experiments. Representative images of the total, small and large adipocyte fraction from one donor are shown in the top half of the graph. A tabular overview listing the mean adipocyte diameter ± SD per fraction across all four different donors is given in the bottom half. (b) Volcano plot depicting the DE of genes between the small and large adipocyte fraction (n = 4). (c) Dotplot displaying the top 20 enriched KEGG pathways with an adjusted p-value < 0.05 from gene set enrichment analysis comparing large and small fat cells that were size-separated based on buoyancy (n = 4). The “Human Diseases” category of the KEGG PATHWAY database is not displayed on the graph.

Figure 7

Differences in fatty acid composition with regard to adipose depot and fat cell size. (a) Boxplots showing FA species that were found to show different abundancies in paired, SAT, and VAT with p-values originating from paired t-tests (n = 7). SAT samples are colored in red, while VAT samples are shown in blue. Out of the 28 FA species that were measured all FAs with a p-value < 0.05 are displayed. P-values written in bold are the ones that remained significant after Bonferroni adjustment for multiple testing (p_bonf = 0.05/28 = 0.0018). (b) Dotplot showing the pearson correlation between adipocyte diameter and all 28 FA species assessed. SAT samples (n = 22) are displayed on the left, while VAT samples (n = n = 12) are shown on the right. (c) Scatterplots displaying the three FA species showing the strongest association with mean adipocyte diameter as assessed by pearson correlations in SAT (blue, n =22) and VAT (red, n =12).

Figure 8

MRS-based characterization of fatty acid composition and fat cell size in vitro in human adipose tissue samples. (a) Exemplary MRS spectra with matched histology for two female subjects with similar age (59 and 63 year) and BMI (36 and 34 kg/m²) but difference median fat cell size (49.8 vs.74.4 µm) in abdominal subcutaneous AT. MRS spectra display signal variations with TI (at TE = 10 ms) and TE (TI = 1133 ms), respectively. Fitted methylene T1 and T2 relaxation times are given in green and orange, respectively. (b) Linear regression plots (pearson correlation, n = 32) from the adipose tissue sample experiment: GC-MS-based vs. MRS-based quantification of the FA characteristics ndb, nmidb, CL. Sample origin is indicated by color. (c) Linear regression plots (pearson correlation, n = 32) from the adipose tissue sample experiment: GC-MS-based and MRS-based FA characteristics ndb, nmidb, CL vs. median fat cell size. Sample origin is indicated by color. (d) Linear regression plot (pearson correlation, n = 32) for the methylene T1 and T2 relaxation vs. median fat cell size. Sample ndb (binned) as measured by GC-MS is indicated by color encoding.