| Literature DB >> 35857195 |
Silvia Corvera1,2, Javier Solivan-Rivera3, Zinger Yang Loureiro3.
Abstract
While most tissues exhibit their greatest growth during development, adipose tissue is capable of additional massive expansion in adults. Adipose tissue expandability is advantageous when temporarily storing fuel for use during fasting, but becomes pathological upon continuous food intake, leading to obesity and its many comorbidities. The dense vasculature of adipose tissue provides necessary oxygen and nutrients, and supports delivery of fuel to and from adipocytes under fed or fasting conditions. Moreover, the vasculature of adipose tissue comprises a major niche for multipotent progenitor cells, which give rise to new adipocytes and are necessary for tissue repair. Given the multiple, pivotal roles of the adipose tissue vasculature, impairments in angiogenic capacity may underlie obesity-associated diseases such as diabetes and cardiometabolic disease. Exciting new studies on the single-cell and single-nuclei composition of adipose tissues in mouse and humans are providing new insights into mechanisms of adipose tissue angiogenesis. Moreover, new modes of intercellular communication involving micro vesicle and exosome transfer of proteins, nucleic acids and organelles are also being recognized to play key roles. This review focuses on new insights on the cellular and signaling mechanisms underlying adipose tissue angiogenesis, and on their impact on obesity and its pathophysiological consequences.Entities:
Keywords: ADSC; Adipocyte; Collagen; Diabetes; Endothelial; Extracellular matrix; Hypoxia
Mesh:
Substances:
Year: 2022 PMID: 35857195 PMCID: PMC9519636 DOI: 10.1007/s10456-022-09848-3
Source DB: PubMed Journal: Angiogenesis ISSN: 0969-6970 Impact factor: 10.658
Fig. 1Modes of adipose tissue expansion. Adipose tissue expansion involves numerous cell types, including endothelial cells of the vasculature, adipocytes and multipotent progenitor cells. Under some conditions, angiogenesis, progenitor cell proliferation and adipocyte differentiation lead to hyperplastic expansion, forming tissue containing multiple small adipocytes. Under other conditions, angiogenesis fails, leading to capillary rarefaction, impaired multipotent progenitor cells proliferation and adipocyte hypertrophy. Hypertrophic expansion is strongly associated with metabolic disease risk
Fig. 2Predicted interactions between adipocytes, endothelial cells and multipotent progenitor cells in human subcutaneous adipose tissue, not including collagens. Cell type interaction analysis was performed using expression data from Emont et al. [93]. The human adipose tissue single-cell/single-nuclei Seurat object (human_all.rds) was retrieved and used to identify the top 5000 most variable features across the dataset using the Seurat FindVariableFeature function. The gene symbol of the top 5000 variable features were translated into Ensembl gene ID using biomaRt (Ensembl Genes 106, hsapiens_gene_ensembl dataset). Normalized count tables for the top features with cells from human SqAT of donors with BMI ≤ 30 or BMI > 40 were exported into tab delimited files for CellPhoneDB analyses. Interaction analysis with CellPhoneDB v3.0.0 was executed in python 3.8.3 environment using the statistical_analysis method and default settings (database release v2.0.0). Gene symbols are depicted in yellow, green or red as a function of their enrichment in adipocytes, multipotent progenitor cells or endothelial cells, respectively. The direction of the interaction (ligand → receptor) is depicted by the arrows
Genes affected by obesity across depots and species
| Gene | Mouse | Human | ||||||
|---|---|---|---|---|---|---|---|---|
| Subcutaneous | Visceral | Subcutaneous | Visceral | |||||
| Normal diet | High fat diet | Normal diet | High fat diet | BMI < 30 | BMI > 40 | BMI < 30 | BMI > 40 | |
| ADIPOQ | 4.18 | 1.35 | 4.66 | 1.62 | 9.81 | 4.93 | 13.71 | 5.19 |
| VEGFA | 6.11 | 4.55 | 6.01 | 3.42 | 1.54 | 1.15 | 1.43 | 0.80 |
| IGF1 | 5.67 | 4.13 | 7.44 | 2.63 | 4.56 | 1.21 | 5.02 | 0.86 |
| FGFR1 | 6.60 | 3.63 | 5.25 | 3.23 | 1.58 | 1.00 | 0.99 | 0.76 |
| MET | 1.74 | 1.02 | 1.91 | 1.06 | 0.18 | 0.10 | 0.32 | 0.14 |
| NAMPT | 1.22 | 0.88 | 1.09 | 0.73 | 1.76 | 0.54 | 0.74 | 0.30 |
| SCTR | 2.02 | 0.31 | 1.35 | 0.68 | 0.42 | 0.27 | 0.44 | 0.29 |
| PRLR | 0.35 | 0.15 | 3.68 | 0.93 | 0.02 | 0.01 | 0.04 | 0.01 |
Values were extracted from Supplementary Table 5 from Emont et al., representing average expression of genes in adipocyte clusters from mouse or human depots under different dietary conditions (mouse) or from individuals with different BMIs (human) [93]. Genes shown are all decreased by obesity. No genes were found to be increased by obesity across both depots in both species
Predicted interactions between multipotent progenitor cells and endothelial cells in human subcutaneous and visceral adipose tissues
| Progenitor cells to endothelial cells | Endothelial cells to progenitor cells | ||||||
|---|---|---|---|---|---|---|---|
| SAT | VAT | SAT | VAT | ||||
| COL1A2_a1b1 | 1.079 | COL1A2_a1b1 | 0.568 | COL4A2_a11b1 | 0.434 | COL15A1_a11b1 | 0.181 |
| COL3A1_a1b1 | 0.881 | COL6A3_a1b1 | 0.537 | COL4A1_a11b1 | 0.415 | COL15A1_a1b1 | 0.127 |
| COL6A3_a1b1 | 0.685 | COL5A2_a1b1 | 0.458 | COL15A1_a11b1 | 0.277 | FGF2_CD44 | 0.181 |
| COL1A1_a1b1 | 0.68 | COL4A2_a1b1 | 0.445 | COL8A1_a11b1 | 0.243 | FN1_a11b1 | 0.222 |
| COL6A2_a1b1 | 0.585 | COL6A2_a1b1 | 0.443 | COL21A1_a11b1 | 0.188 | KDR_VEGFC | 0.203 |
| COL5A2_a1b1 | 0.491 | COL3A1_a1b1 | 0.428 | CXCL2_DPP4 | 0.147 | TEK_ANGPT1 | 0.288 |
| COL4A2_a1b1 | 0.414 | COL4A1_a1b1 | 0.396 | FGF2_CD44 | 0.358 | TNFRSF1B_GRN | 0.148 |
| COL4A1_a1b1 | 0.413 | COL12A1_a1b1 | 0.348 | FN1_a11b1 | 0.34 | ||
| COL14A1_a1b1 | 0.347 | COL1A1_a1b1 | 0.32 | IL6_HRH1 | 0.166 | ||
| COL12A1_a1b1 | 0.346 | COL15A1_a1b1 | 0.276 | NRP2_SEMA3C | 0.348 | ||
| COL15A1_a1b1 | 0.29 | COL14A1_a1b1 | 0.217 | TNFRSF1B_GRN | 0.196 | ||
| COL6A6_a1b1 | 0.27 | COL21A1_a1b1 | 0.213 | ||||
| CSF1_SLC7A1 | 0.303 | COL4A5_a1b1 | 0.213 | ||||
| EFNA5_EPHA4 | 0.261 | COL6A6_a1b1 | 0.211 | ||||
| JAG1_NOTCH4 | 0.118 | COL5A3_a1b1 | 0.19 | ||||
| JAG1_NOTCH4 | 0.25 | ||||||
| ACKR3_CXCL12 | 0.132 | ||||||
Cell type interaction analysis was performed using expression data from Emont et al. [93]. The human adipose tissue single-cell/single-nuclei Seurat object (human_all.rds) was retrieved and used to identify the top 5000 most variable features across the dataset using the Seurat FindVariableFeature function. The gene symbol of the top 5000 variable features were translated into Ensembl gene ID using biomaRt (Ensembl Genes 106, hsapiens_gene_ensembl dataset). Normalized count tables for the top features were exported into tab delimited files for CellPhoneDB analyses. Interaction analysis with CellPhoneDB v3.0.0 was executed in python 3.8.3 environment using the statistical_analysis method and default settings (database release v2.0.0). Values refer to the total mean of the individual partner average expression values in the interacting pair of cell types. Significant interactions (P < 0.05) are distinguished on the basis of the value distribution of all predicted ligand-receptor pairs after random permutation of the cluster labels in all cell types analyzed [99]. a1b1 = Integrin α1β1 complex; a11b1 = Integrin α11β1 complex
Predicted interactions between adipocytes and endothelial cells in human subcutaneous adipose tissues
| Adipocytes to endothelial cells | Endothelial cells to adipocytes | ||||
|---|---|---|---|---|---|
| Interacting_pair | BMI < 30 | BMI > 40 | Interacting_pair | BMI < 30 | BMI > 40 |
| COL4A2_a1b1 | 0.852 | 0.888 | COL4A2_a1b1 | 0.499 | 0.66 |
| COL4A1_a1b1 | 0.789 | 0.774 | COL4A1_a1b1 | 0.48 | 0.625 |
| VEGFA_FLT1 | 0.587 | 0.472 | TEK_ANGPT1 | 0.403 | 0.771 |
| EFNA5_EPHA4 | 0.571 | 0.726 | NRG2_ERBB4 | 0.387 | 0.668 |
| COL5A2_a1b1 | 0.45 | 0.671 | COL15A1_a1b1 | 0.342 | 0.63 |
| COL8A1_a1b1 | 0.374 | 0.396 | COL8A1_a1b1 | 0.308 | 0.571 |
| COL12A1_a1b1 | 0.3 | 0.437 | EFNB2_EPHB1 | 0.259 | 0.309 |
| COL15A1_a1b1 | 0.276 | 0.405 | COL21A1_a1b1 | 0.253 | 0.517 |
| 0.272 | NRP2_SEMA3C | 0.25 | 0.793 | ||
| COL5A3_a1b1 | 0.26 | 0.331 | FLT1 | 0.21 | 0.243 |
| COL24A1_a1b1 | 0.256 | 0.35 | NRP2_VEGFA | 0.207 | 0.249 |
| LEP_LEPR | 0.226 | 0.279 | CADM1_CADM1 | 0.121 | 0.197 |
| VEGFA_KDR | 0.21 | 0.243 | |||
| CADM1_CADM1 | 0.121 | 0.197 | |||
| 0.119 | |||||
In bold are interactions that are predicted to occur only in the obese state
Cell type interaction analysis was performed using data from Emont et al. [93]. The human adipose tissue single-cell/single-nuclei Seurat object (human_all.rds) was retrieved and used to identify the top 5000 most variable features across the dataset using the Seurat FindVariableFeature function. The gene symbol of the top 5000 variable features were translated into Ensembl gene ID using biomaRt (Ensembl Genes 106, hsapiens_gene_ensembl dataset). Normalized count tables for the top features were exported into tab delimited files for CellPhoneDB analyses. Interaction analysis with CellPhoneDB v3.0.0 was executed in python 3.8.3 environment using the statistical_analysis method and default settings (database release v2.0.0). Values refer to the total mean of the individual partner average expression values in the interacting pair of cell types. In bold are interactions that are predicted to occur only in the obese state
Summary of reports on adipose tissue angiogenesis and metabolic outcomes
| Authors | Approach | Model | Outcome |
|---|---|---|---|
| Brakenhielm et al. [ | Pharmacological, TNP-450 (MetAP2 inhibitor) | Ob/Ob mouse | Decreased weight gain, decreased food intake |
| White et al. [ | Pharmacological, TNP-450 (MetAP2 inhibitor) | Ob/Ob mouse | Decreased weight gain, decreased food intake, impaired glucose tolerance |
| Park et al. [ | Pharmacological, ALS-L1023 | Diet-induced obese mouse | Decreased weight gain |
| Siddik et al. [ | Pharmacological, BL6 (MetAP2 inhibitor) | Cultured adipocytes | Decreased glucose uptake, decreased lipid uptake |
| Pottorf et al. [ | Pharmacological, ZGN-1258 (MetAP2 inhibitor) | Bardett–Beidle mouse model | Decreased weight gain, decreased food intake |
| Wang et al. [ | Pharmacological, AARP (CTT peptide-endostatin mimic-kringle 5) | Diet-induced obese mouse | Decreased weigh gain, increased locomotor activity, increased thermogenesis |
| Robciuc et al. [ | Genetic, AAV-mediated VEGFB transduction | Diet-induced obese mouse | Improved glucose metabolism, improved adipose tissue vascularization, increased thermogenic adipose tissue |
| Park et al. [ | Genetic, increased VEGFA expression through Adiponectin-Cre | Doxicicline inducible, diet-induced obese mouse | Decreased weight gain, increased thermogenic adipose tissue |
| Jin et al. [ | Genetic, repressed VEGF expression through AP2-cre | Doxicicline inducible | Decreased weight gain, improved glucose tolerance, improved insulin sensitivity, increased thermogenic adipose tissue |
| Seki et al. [ | Genetic, increased VEGFA bioavailabillity in adipose tissue | Anti-VEGFR1 neutralizing antibodies; genetic deletion of VEGFR1 | Decreased weight gain, increased thermogenic adipose tissue |