Deep Proteome Profiling of White Adipose Tissue Reveals Marked Conservation and Distinct Features Between Different Anatomical Depots
2023; Elsevier BV; Volume: 22; Issue: 3 Linguagem: Inglês
10.1016/j.mcpro.2023.100508
ISSN1535-9484
AutoresSøren Madsen, Marin E. Nelson, Vinita Deshpande, Sean J. Humphrey, Kristen C. Cooke, Anna Howell, Alexis Díaz‐Vegas, James G. Burchfield, Jacqueline Stöckli, David E. James,
Tópico(s)Diet and metabolism studies
Resumo•Adipocyte proteomes were highly conserved between white depots in lean mice.•Sustained obesogenic environment caused mitochondrial stress in visceral adipocytes.•Subcutaneous adipose tissue adaptations could not be detected at the adipocyte level.•3T3-L1 total proteome was a good representation of white adipocytes from lean mice. White adipose tissue is deposited mainly as subcutaneous adipose tissue (SAT), often associated with metabolic protection, and abdominal/visceral adipose tissue, which contributes to metabolic disease. To investigate the molecular underpinnings of these differences, we conducted comprehensive proteomics profiling of whole tissue and isolated adipocytes from these two depots across two diets from C57Bl/6J mice. The adipocyte proteomes from lean mice were highly conserved between depots, with the major depot-specific differences encoded by just 3% of the proteome. Adipocytes from SAT (SAdi) were enriched in pathways related to mitochondrial complex I and beiging, whereas visceral adipocytes (VAdi) were enriched in structural proteins and positive regulators of mTOR presumably to promote nutrient storage and cellular expansion. This indicates that SAdi are geared toward higher catabolic activity, while VAdi are more suited for lipid storage. By comparing adipocytes from mice fed chow or Western diet (WD), we define a core adaptive proteomics signature consisting of increased extracellular matrix proteins and decreased fatty acid metabolism and mitochondrial Coenzyme Q biosynthesis. Relative to SAdi, VAdi displayed greater changes with WD including a pronounced decrease in mitochondrial proteins concomitant with upregulation of apoptotic signaling and decreased mitophagy, indicating pervasive mitochondrial stress. Furthermore, WD caused a reduction in lipid handling and glucose uptake pathways particularly in VAdi, consistent with adipocyte de-differentiation. By overlaying the proteomics changes with diet in whole adipose tissue and isolated adipocytes, we uncovered concordance between adipocytes and tissue only in the visceral adipose tissue, indicating a unique tissue-specific adaptation to sustained WD in SAT. Finally, an in-depth comparison of isolated adipocytes and 3T3-L1 proteomes revealed a high degree of overlap, supporting the utility of the 3T3-L1 adipocyte model. These deep proteomes provide an invaluable resource highlighting differences between white adipose depots that may fine-tune their unique functions and adaptation to an obesogenic environment. White adipose tissue is deposited mainly as subcutaneous adipose tissue (SAT), often associated with metabolic protection, and abdominal/visceral adipose tissue, which contributes to metabolic disease. To investigate the molecular underpinnings of these differences, we conducted comprehensive proteomics profiling of whole tissue and isolated adipocytes from these two depots across two diets from C57Bl/6J mice. The adipocyte proteomes from lean mice were highly conserved between depots, with the major depot-specific differences encoded by just 3% of the proteome. Adipocytes from SAT (SAdi) were enriched in pathways related to mitochondrial complex I and beiging, whereas visceral adipocytes (VAdi) were enriched in structural proteins and positive regulators of mTOR presumably to promote nutrient storage and cellular expansion. This indicates that SAdi are geared toward higher catabolic activity, while VAdi are more suited for lipid storage. By comparing adipocytes from mice fed chow or Western diet (WD), we define a core adaptive proteomics signature consisting of increased extracellular matrix proteins and decreased fatty acid metabolism and mitochondrial Coenzyme Q biosynthesis. Relative to SAdi, VAdi displayed greater changes with WD including a pronounced decrease in mitochondrial proteins concomitant with upregulation of apoptotic signaling and decreased mitophagy, indicating pervasive mitochondrial stress. Furthermore, WD caused a reduction in lipid handling and glucose uptake pathways particularly in VAdi, consistent with adipocyte de-differentiation. By overlaying the proteomics changes with diet in whole adipose tissue and isolated adipocytes, we uncovered concordance between adipocytes and tissue only in the visceral adipose tissue, indicating a unique tissue-specific adaptation to sustained WD in SAT. Finally, an in-depth comparison of isolated adipocytes and 3T3-L1 proteomes revealed a high degree of overlap, supporting the utility of the 3T3-L1 adipocyte model. These deep proteomes provide an invaluable resource highlighting differences between white adipose depots that may fine-tune their unique functions and adaptation to an obesogenic environment. White adipose tissue is one of the most adaptive tissues in mammals and can expand to account for greater than 70% of total body mass in extreme cases of sustained positive energy balance. Adipose tissue expandability is crucial to accommodate the storage of excess lipids in times of plenty and mobilize nutrients for use by tissues throughout the body in times of limited food availability. However, in the case of sustained positive energy balance, adipose tissue stores can be overwhelmed, resulting in spill over and accumulation of harmful ectopic lipids in other tissues such as cardiovascular tissue, skeletal muscle, liver, and the pancreas. Intriguingly, in humans, there is a strong relationship between visceral adiposity and metabolic disease risk (1Fox C.S. Heard-Costa N. Cupples L.A. 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Metab. 2022; 107: 755-775Crossref PubMed Scopus (5) Google Scholar, 18Raajendiran A. Krisp C. Souza D.P.D. Ooi G. Burton P.R. Taylor R.A. et al.Proteome analysis of human adipocytes identifies depot-specific heterogeneity at metabolic control points.Am. J. Physiol. Endocrinol. Metab. 2021; 320: E1068-E1084Crossref PubMed Google Scholar). To our knowledge, our study is the first to examine adipose tissue and adipocytes from distinct depots in both the lean and obese states in a pair-wise manner. It is unclear which aspects of the proteome define adipocytes from different depots in a lean, healthy context, or how different adipocyte proteomes adapt to obesogenic conditions. One possibility is that the proteomes from adipocytes from different depots are highly conserved and functional differences may be conferred by interactions with the microenvironment established by the surrounding stromal vascular cells. Since adipose tissue is a milieu of many cell types including fibroblasts and immune cells, it is crucial to compare both the adipocyte and whole tissue proteomes to link molecular differences between depots to physiologic consequences. Here we report a deep proteomics analysis of different depots using both whole tissue and isolated adipocytes from lean and diet-induced obese mice by Western diet (WD) feeding. In lean mice, we discovered that only 3% of the adipocyte proteome differs between the two depots, and we revealed that adipocytes from subcutaneous adipose tissue (SAdis) had enhanced capacity for catabolic processes, while adipocytes isolated from visceral adipose tissue (VAdis) were equipped for lipid storage and cell expansion. WD caused a greater divergence in the adipocyte proteomes, with the greatest changes occurring in visceral adipocytes and tissue compared to subcutaneous, including immune cell infiltration, downregulation of 'adipocyte' processes such as glucose metabolism, and upregulation of 'fibroblastic' processes including collagen deposition. Furthermore, we uncovered a pro-apoptotic proteomics signal in the VAdi after WD feeding pointing to severe mitochondrial dysfunction in these adipocytes. These data provide an invaluable adipose-centric resource for the metabolic research community by highlighting both similarities and key differences that emerge between the biology of each adipose depot, and how each depot adapts to overnutrition. C57BL/6J male mice were obtained from Australian BioResources (Moss Vale). Mice were maintained at 23 °C on a 12-h light-dark cycle and ad libitum access to food and water. From weaning, mice were fed standard laboratory chow [containing 12% calories from fat, 65% calories from carbohydrate, 23% calories from protein ('Irradiated Rat and Mouse Diet', Specialty Feeds)]. For WD studies, mice were fed a diet made in-house containing 45% fat, 35% carbohydrate, and 20% protein as we have described (19Nelson M.E. Madsen S. Cooke K.C. Fritzen A.M. Thorius I.H. Masson S.W.C. et al.Systems-level analysis of insulin action in mouse strains provides insight into tissue- and pathway-specific interactions that drive insulin resistance.Cell Metab. 2022; 34: 227-239.e6Abstract Full Text Full Text PDF PubMed Scopus (11) Google Scholar) for 9 months from 11 to 14 weeks of age. Mice were weighed once per week. Body composition was analyzed at 43 weeks of age using quantitative magnetic resonance technology (EchoMRI Body Composition Analyser, EchoMRI). For glucose tolerance tests, 43-week-old mice were fasted for 6 h and received an oral bolus of glucose (1 g/kg lean mass). Blood was sampled from the tail vein at indicated time points using an Accu-Check II glucometer (Roche Diagnostics). For insulin measurements, whole blood was collected from the tail vein at basal and 15 min after oral glucose into wells of commercially available ELISA kit (Crystal Chem) containing sample buffer, then the assay was carried out according to the manufacturer's specifications. Results were multiplied by a factor of 2 to estimate the concentration of insulin in the plasma. For histological assessment of white adipose tissue, 8- to 10-week-old C57BL/6J mice were fed either chow or WD for 8 weeks. Experiments were performed in accordance with NHMRC (Australia) guidelines approved by The University of Sydney Animal Ethics Committee (#2014/694 ethics protocol covered animal for proteomics and #2017/1274 for animals used for histology). Mice were sacrificed by cervical dislocation. The epididymal fat pad was taken as 'visceral' adipose and was excised carefully to avoid the testes. The inguinal fat pad was taken as 'subcutaneous' adipose, from which lymph nodes were removed following excision. Fat pads were rinsed in PBS and either snap frozen in liquid nitrogen or stored in fresh Hepes buffer (120 mM NaCl, 30 mM Hepes, 10 mM NaHCO3, 5 mM glucose, 4.7 mM KCl, 2 mM CaCl2, 1.18 mM KH2PO4, 1.17 mM MgSO4.7H2O, 1% BSA, pH 7.4) for immediate adipocyte isolation. The two diet groups were subdivided into three groups each and pooled for the isolation of primary adipocytes. Each group of mice [chow 1 (n = 4), chow 2 (n = 4), chow 3 (n = 3); WD 1 (n = 5), WD 2 (n = 5), WD 3 (n = 5)] represents one pooled data point for proteomics analysis. Adipose tissues were minced in HEPES buffer until pieces were approximately <1 mm2 in size. Collagenase (Type I, Worthington) was added at 0.5 mg/ml for visceral and 1 mg/ml for subcutaneous adipose tissue and digested for 1 h at 37 °C. Samples were passed through a 250 μm (chow adipocytes) or 300 μm (WD adipocytes) nylon mesh (Spectrum Labs) and washed three times with HES buffer (250 mM sucrose, 20 mM Hepes, 1 mM EDTA, pH 7.4). Between washes, adipocytes were left to float. Lysis buffer [2% sodium deoxycholate (Sigma), 200 mM Tris HCl, pH 8.5] was added and stored at −80 °C until further processing. 3T3-L1 fibroblasts (a gift from Howard Green, Harvard Medical School) were grown in Dulbecco's modified Eagle's medium (DMEM) containing 10% fetal bovine serum (FBS) (Sigma) and 2 mM GlutaMAX (Gibco) in 10% CO2 at 37 °C. For differentiation into adipocytes, cells were re-seeded and rapidly grown to confluence within 24 h, then treated with DMEM/FBS containing 4 μg/ml insulin, 0.25 mM dexamethasone, 0.5 mM 3-isobutyl-1-methylxanthine, and 100 ng/ml d-biotin. After 72 h, the differentiation medium was replaced with fresh FBS/DMEM containing 4 μg/ml insulin and 100 ng/ml d-biotin for a further 3 days, then replaced with fresh FBS/DMEM. Adipocytes were re-fed with FBS/DMEM every 48 h and used for experiments 10 days after initiation of differentiation. Cells were routinely tested for mycoplasma. Prior to harvesting, 3T3-L1 adipocyte cell monolayers were washed 5× with ice-cold PBS. Adipocytes were processed according to the in-StageTip protocol (20Kulak N.A. Pichler G. Paron I. Nagaraj N. Mann M. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells.Nat. Met. 2014; 11: 319-324Crossref PubMed Scopus (1065) Google Scholar). Briefly, samples were lysed in an equal volume of SDC lysis buffer (2% sodium deoxycholate (Sigma), 200 mM Tris HCl, pH 8.5) with boiling at 95 °C for 5 min with mixing at 1000 rpm on a ThermoMixer (Eppendorf), cooled on ice, sonicated using a tip probe sonicator (1 × 20 s, 90% output), then spun at 21,000g for 15 min at 4 °C. For tissue samples, 100 to 600 mg tissue was added to 1.5 ml lysis buffer, lysed using a tip probe sonicator (4-5 × 20 s, 90% output), and spun at 21,000g for 30 min at 4 °C. After centrifugation, fat layers were carefully removed and discarded, and an aliquot was taken from which protein quantification performed using the Pierce BCA Protein Assay (Thermo Fisher Scientific). 60 μg of protein was extracted into clean tubes and samples topped with lysis buffer to obtain equal volumes. Proteins were reduced and alkylated with the addition of TCEP (Thermo Fisher Scientific) and CAA (Sigma) to final concentrations of 10 mM and 40 mM respectively at 95 °C for 5 min at 1000 rpm on a ThermoMixer. Trypsin (Sigma) and LysC (Wako) were added in ratio of 1 μg enzyme to 50 μg protein, and samples digested at 37 °C overnight for 18 h with mixing at 2000 rpm on a ThermoMixer. Digested peptides were diluted 1:1 with water and then desalted on SDBRPS StageTips as follows. Samples were diluted 50% with 99% EA (ethyl acetate)/1% TFA (trifluoracetic acid), vortexed thoroughly, and loaded onto StageTips packed with 2× disks SDBRPS material (3M Empore). StageTips were washed 1× with 100 μl 99% ethyl acetate/1% TFA, and 2× with 100 μl 0.2% TFA, then eluted with 5% ammonia/80% ACN (acetonitrile) and dried in a vacuum concentrator (Eppendorf) prior to fractionation. Peptides derived from mouse adipocyte and adipose tissue samples were separated into three fractions using StageTip-based SCX fractionation (21Ishihama Y. Rappsilber J. Mann M. Modular stop and go extraction tips with stacked disks for parallel and multidimensional Peptide fractionation in proteomics.J. Proteome Res. 2006; 5: 988-994Crossref PubMed Scopus (235) Google Scholar). Briefly, approximately 30 μg of peptides were resuspended in 1% TFA and loaded onto StageTips packed with 6× disks of SCX material (3M Empore). Peptides were eluted and collected separately with increasing concentrations of ammonium acetate (150 mM and 300 mM) in 20% ACN, followed by 5% ammonia/80% ACN. Collected peptide fractions were dried in a vacuum concentrator (Eppendorf) and resuspended in MS loading buffer (2% ACN/0.3% TFA). Peptides derived from 3T3-L1 adipocytes were separated into 24 fractions using concatenated high pH reverse phase fractionation, as previously described (22Yau B. Naghiloo S. Diaz-Vegas A. Carr A.V. Van Gerwen J. Needham E.J. et al.Proteomic pathways to metabolic disease and type 2 diabetes in the pancreatic islet.iScience. 2021; 24103099Abstract Full Text Full Text PDF Scopus (5) Google Scholar). Briefly, peptides were fractionated using an UltiMate 3000 HPLC (Dionex, Thermo) with a XBridge Peptide BEH C18 column, (130A°, 3.5 mm 2.1 3250 mm, Waters). 30 μg of peptides were resuspended in buffer A and loaded onto the column that was maintained at 50 °C using a column oven. Buffer A comprised 10 mM ammonium formate/2% ACN and buffer B 10 mM ammonium formate/80% ACN, and buffers were adjusted to pH 9.0 with ammonium hydroxide. Peptides were separated by a gradient of 10 to 40% buffer B over 4.4 min, followed by 40 to 100% buffer B over 1 min. Peptides were collected for a total duration of 6.4 min, with 72 fractions concatenated directly into 24 wells of a 96-well deep-well plate (three concatenated fractions per well) using an automated fraction collector (Dionex, Thermo) maintained at 4 °C. After fraction, samples were dried down directly in the deep-well plate and resuspended in MS loading buffer (2% ACN/0.3% TFA) prior to LC-MS analysis. For the mouse adipocyte and adipose tissue proteomes, peptides were analyzed by mass spectrometry using a Dionex Ultimate 3000 UHPLC coupled to a Q Exactive Plus benchtop Orbitrap Mass Spectrometer (Thermo Fisher Scientific). Peptides were loaded onto an in-house packed 75 μm ID x 50 cm column packed with 1.9 μm C18 material (Dr Maisch, ReproSil Pur C18-AQ) and separated with a gradient of 5 to 30% ACN containing 0.1% FA over 95 min at 300 nl/min, and column temperature was maintained at 60 °C with a column oven (Sonation). MS1 scans were acquired from 300 to 1650 m/z (35,000 resolution, 3e6 fill target, 20 ms maximum fill time), followed by MS/MS data-dependent acquisition of the top 15 ions using high-energy dissociation, with MS2 fragment ions read out in the Orbitrap (17,500 resolution, 1e5 AGC, 25 ms maximum fill time, 25 NCE, 1.4 m/z isolation width). For the 3T3-L1 proteome samples, peptides were analyzed by mass spectrometry using a Dionex Ultimate 3000 coupled to a Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific). Peptides were loaded onto an in-house packed 75 μm ID x 50 cm column packed with 1.9 μm C18 material (DrMaisch, ReproSil Pur C18-AQ) maintained at 60 °C with a column oven (Sonation). Peptides were eluted with a gradient of 5 to 30% buffer B (80% ACN/0.1% formic acid) over 40 min at a flow rate of 300 nl/min and analyzed by data-dependent acquisition with one full scan (350–1400 m/z; R = 60,000 at 200 m/z) at a target of 3e6 ions, followed by up to 20 data-dependent MS2 scans using high-energy dissociation (target 1e5 ions; max. IT 28 ms; isolation window 1.4 m/z; NCE 27%; min. AGC target 1e4), detected in the Orbitrap mass analyzer (R = 15,000 at 200 m/z). Dynamic exclusion (15 s) was switched on. Raw data were processed using MaxQuant (version 1.5.9.1) (23Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9787) Google Scholar). The built-in Andromeda search engine scored MS2 spectra against fragment masses of tryptic peptides that were derived from a mouse reference proteome containing 58,268 entries including isoforms (UniProt January 2016/01 release) and a list of 245 potential contaminant proteins. The search space was restricted to peptides with a minimum length of seven amino acids, a maximum peptide mass of 4600 Da, and two missed cleavage sites. Carbamidomethylation of cysteine was specified as a fixed modification, and methionine oxidation and acetylation at protein N termini as variable modifications. MaxQuant uses individual peptide mass tolerances, while initial maximum precursor mass tolerances were 20 ppm in the first search, and fragment ion mass tolerances were set to 20 ppm. False discovery rate was controlled using a target-decoy approach at <1% for peptide spectrum matches and <1% for protein group identifications. Match between runs was enabled to facilitate the transfer of MS/MS identifications between equivalent and adjacent fraction measurements. The MaxLFQ algorithm was employed for label-free protein quantification as described (24Cox J. Hein M.Y. Luber C.A. Paron I. Nagaraj N. Mann M. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ.Mol. Cell. Proteomics. 2014; 13: 2513-2526Abstract Full Text Full Text PDF PubMed Scopus (2938) Google Scholar) and MS/MS was required for all LFQ comparisons. Intensity values were normalized using total reporter area sum normalization. Data were filtered to remove contaminants and proteins that were not quantified in any sample (supplemental Tables S1 and S2). Protein intensities were median normalized to account for differences between protein loading of tissue and cell-based samples (supplemental Fig. S1A). To find associations between lipolysis, lipids synthesis, and glucose uptake, STRING: Pubmed query in Cytoscape (v3.8.2) was used to identify the top 50 proteins within each term with a confidence score of 0.7 or greater. These three networks were then merged and filtered for differentially regulated protein for both VAdi and SAdi. The R package BisqueRNA (25Jew B. Alvarez M. Rahmani E. Miao Z. Ko A. Garske K.M. et al.Accurate estimation of cell composition in bulk expression through robust integration of single-cell information.Nat. Commun. 2020; 11: 1971Crossref PubMed Scopus (115) Google Scholar) was used for reference-based decomposition of the whole tissue proteomics data with 'marker = NULL' and 'use.overlap = FALSE'. As reference, we utilized murine single RNA sequencing data (26Emont M.P. Jacobs C. Essene A.L. Pant D. Tenen D. Colleluori G. et al.A single-cell atlas of human and mouse white adipose tissue.Nature. 2022; 603: 926-933Crossref PubMed Scopus (129) Google Scholar). Seurat formatted data were downloaded (https://gitlab.com/rosen-lab/white-adipose-atlas), and data from all male mice were selected as input for cell type estimation. Gene set enrichment was performed with the web-based GEne SeT AnaLysis Toolkit (27Liao Y. Wang J. Jaehnig E.J. Shi Z. Zhang B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs.Nucl. Acids Res. 2019; 47: W199-W205Crossref PubMed Scopus (1433) Google Scholar) with the minimum number of genes in a pathway specified as 15 and maximal as 200 within the Gene Ontology Resource or using the Reactome database (reactome.org). Pathways with p < 0.05 false discovery rate were considered to be significantly overrepresented. The aim of this study was to examine adipose depot adaptations to a sustained obesogenic challenge. To this end, the duration of feeding was selected based on prior time course data in C57Bl6/J fed similar diets (28Burchfield J.G. Kebede M.A. Meoli C.C. Stöckli J. Whitworth P.T. Wright A.L. et al.High dietary fat and sucrose results in an extensive and time-dependent deterioration in health of multiple physiological systems in mice.J. Biol. Chem. 2018; 293: 5731-5745Abstract Full Text Full Text PDF PubMed Scopus (53) Google Scholar). Adiposity reached a plateau at approximately 40 weeks (10 months) of WD feeding without changes to lean mass, indicating the time at which adipose storage capacity is exceeded. Therefore, we selected 9 months of WD feeding for this study to maximize WD expansion but before complete exhaustion of adipose fat storage capacity. To achieve measurement of the adipocyte proteome, the biological replicates analyzed comprised pooled isolated adipocytes from three to five mice. Pooling was necessary to ensure sufficient protein yields for proteomics analysis. "Enrichment" was considered at a fold change of ±2 and p < 0.05 unless otherwise stated. For histology, each group contained five animals, except for visceral adipose tissue (VAT) from chow fed animals, which contained three animals. Bioinformatics, statistical analyses, and plot generation were performed within the R statistical environment. Differential expression analysis was performed using the LIMMA package (29Ritchie M.E. Phipson B. Wu D. Hu Y. Law C.W. Shi W. et al.Limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucl. Acids Res. 2015; 43: e47Crossref PubMed Scopus (17708) Google Scholar). Two-way ANOVAs were performed using a standard linear model function. All p values were adjusted for multiple testing using the Benjamini & Hochberg or Tukeys HSD method. Formalin-fixed epididymal adipose tissue was paraffin embedded, sectioned, mounted on coverslips, and stained with H&E. Coverslips were scanned to digital images using an Aperio ScanScope. Adipocyte cell area was then analyzed in ImageJ version 1.51 as described in (19Nelson M
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