1
2
3 Preadipocytes from obese humans with type 2 diabetes are epigenetically reprogrammed
4 at genes controlling adipose tissue function
5
6 Emil Andersen1, Lars Roed Ingerslev1, Odile Fabre1, Ida Donkin1, Ali Altıntaş1,
7 Soetkin Versteyhe1, Thue Bisgaard2, Viggo B. Kristiansen2, David Simar3 and Romain Barrès1,*
8
9 1Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical
10 Sciences, University of Copenhagen, Copenhagen, Denmark.
11 2Department of Surgical Gastroenterology, Hvidovre Hospital, University of Copenhagen,
12 Denmark.
13 3Mechanisms of Disease and Translational Research, School of Medical Sciences, UNSW
14 Australia, Sydney, Australia.
15
16 *Correspondence:
17 Romain Barrès, Panum Institutet 7.7, Blegdamsvej 3B, 2200 Copenhagen N, Denmark.
18 Tel. +45 35 33 72 88
19 Fax. +45 35 33 71 01
20 Email: [email protected]
21
22
23 Word count: 3972
24
1 25 ABSTRACT
26 Background
27 Deterioration of the adipogenic potential of preadipocytes may contribute to adipose tissue
28 dysfunction in obesity and type 2 diabetes (T2D). Here, we hypothesised that extracellular factors in
29 obesity epigenetically reprogram the adipogenesis potential and metabolic function of
30 preadipocytes.
31 Methods
32 The transcriptomic profile of visceral adipose tissue preadipocytes collected from lean, obese and
33 obese with T2D was assessed throughout in vitro differentiation using RNA-sequencing. Reduced
34 Representation Bisulfite Sequencing was used to establish the genome-wide DNA methylation
35 profile of human preadipocytes and 3T3-L1 preadipocytes treated by inflammatory cytokine TNF-α
36 or palmitate.
37 Results
38 While preadipocytes from all obese subjects (Obese + Obese T2D), compared to those of Lean,
39 were transcriptionally different in response to differentiation in culture, preadipocytes from Obese
40 T2D showed impaired insulin signaling and a further transcriptomic shift towards altered adipocyte
41 function. Cultures with a lower expression magnitude of adipogenic genes throughout
42 differentiation (PLIN1, CIDEC, FABP4, ADIPOQ, LPL, PDK4, APOE, LIPE, FABP3, LEP, RBP4
43 and CD36) were associated with DNA methylation remodeling at genes controlling insulin
44 sensitivity and adipocytokine signaling pathways. Prior incubation of 3T3-L1 preadipocytes with
45 TNF-α or palmitate markedly altered insulin responsiveness and metabolic function in the
46 differentiated adipocytes, and remodeled DNA methylation and gene expression at specific genes,
47 notably related to PPAR signaling.
48 Conclusions
49 Our findings that preadipocytes retain the memory of the donor in culture and can be reprogrammed
50 by extracellular factors support a mechanism by which adipocyte precursors are epigenetically
2 51 reprogrammed in vivo. Epigenetic reprogramming of preadipocytes represents a mechanism by
52 which metabolic function of visceral adipose tissue may be affected in the long term by past
53 exposure to obesity- or T2D-specific factors.
54
55
56 KEYWORDS
57 Epigenetics, DNA methylation, Adipocytes, Adipose tissue, Adipogenesis, Humans.
3 58 ABBREVIATIONS
59 T2D: Type 2 Diabetes
60 DMEM: Dulbecco's Modified Eagle Medium
61 FBS: Fetal Bovine Serum
62 SVF: Stromal Vascular Fraction
63 T3: Tri-iodothyronine
64 TNF: Tumor Necrosis Factor
65 KRPH: Krebs-Ringer Phosphate
66 PBS: Phosphate-buffered saline
67 EDTA: Ethylenediaminetetraacetic
68 qRT-PCR: Quantitative reverse transcription polymerase chain reaction
69 SDS-PAGE: Sodium dodecyl sulfate-polyacrylamide gel electrophoresis
70 RRBS: Reduced representation bisulfite sequencing
71 BrdU: bromodeoxyuridine
72 PVDF: polyvinylidine fluoride
73 IBMX: 3-isobutyl-1-methylxanthine
74 DEG: Differentially expressed genes
75 PCA: principal component analysis
76 KEGG: Kyoto Encyclopaedia of Genes and Genomes
77 WEBGESTALT: Web-based GEne SeT AnaLysis Toolkit
4 78 INTRODUCTION
79 During the course of obesity, adipose tissue undergoes hyperplasia and hypertrophy to allow net
80 storage of energy excess into intracellular lipids. Preadipocytes contribute to adipose tissue growth
81 by differentiating into metabolically mature adipocytes that store energy through insulin-mediated
82 glucose uptake followed by biosynthesis of lipids. Extracellular factors like the inflammatory
83 cytokine Tumor Necrosis Factor (TNF)-α participate in the development of insulin resistance in
84 human adipose tissue 1. While mature adipocytes have been predominantly considered as primary
85 targets for these extracellular mediators of insulin resistance 2, preadipocytes in the adipose tissue
86 niche are also affected.
87
88 The hypothesis that extracellular factors can reprogram preadipocytes is supported by several
89 studies. Preadipocytes exposed to high-glucose medium can be primed to become more
90 inflammatory after undergoing adipogenesis 3. Moreover, preadipocytes isolated from obese
91 subjects have a decreased potential to take up lipids in response to an adipogenic cocktail 4–6.
92 Despite the fact that a role of genetic factors in the adipogenic memory of preadipocytes cannot be
93 ruled out in this particular study, the observation that adipogenic capacity of human preadipocytes
94 could be ameliorated by weight loss strongly suggests that epigenetic factors contribute to the
95 decreased adipogenic potential of preadipocytes in obesity 7.
96
97 Adipogenesis is orchestrated by epigenetic factors that regulate chromatin conformation and
98 accessibility of pro- and anti-adipogenic transcription factors to the DNA 8,9. We and other groups
99 have previously established that the epigenome of adipose precursor cells is amenable to
100 extracellular factors in vivo 3,10–13. A role of environmentally-driven epigenetic changes in cell
101 differentiation has been suggested in pluripotent stem cells where developmental commitment and
102 potential were influenced by the epigenetic state at key regulatory genes 14,15.
103
5 104 Here, we sought to investigate the role of the extracellular milieu on the epigenetic reprogramming
105 of the preadipocytes and the effect on their adipogenic capacity. We hypothesised that
106 preadipocytes from obese humans with or without type 2 diabetes (T2D) exhibit distinct
107 epigenomes and transcriptomic responses to differentiation in culture.
6 108 MATERIALS/SUBJECTS AND METHODS
109 Study participants
110 The study was approved by the Ethics Committee from the Capital Region of Denmark (reference
111 H-1-2011-077) and informed consent was obtained from all participants. This study included a total
112 of fifteen lean controls, fourteen obese subjects with T2D according to ICPC-2-DK, and fourteen
113 obese subjects with no history of diabetes. The participants were recruited from Surgical
114 Gastrointestinal Department, Hvidovre Hospital, Denmark. The lean controls were subjects
115 undergoing surgery for laparoscopic inguinal hernia repair. Individuals of both the Obese T2D and
116 Obese groups were subjects to laparoscopic gastric bypass operation. The Obese groups were
117 matched for weight. The cohorts were matched for age. Prior to surgery, all study participants were
118 measured and weighted. Exclusion criteria for all three groups were: alcohol consumption of more
119 than 14 units/week, smoking, daily intake of medicine and presence of chronic/acute diseases. Lean
120 men with diagnosed hypercholesterolemia, hypertension and/or diabetes were excluded.
121 Participants were fasted for at least 12 hrs and blood was drawn before undergoing anesthetics.
122 Blood was analyzed at the Clinical Biochemistry Department, Hvidovre Hospital. Visceral adipose
123 tissue was collected from the omental fat pat with laparoscopic surgery instruments under full
124 narcosis during surgery.
125
126 Isolation and culture of human preadipocytes
127 Isolation of preadipocytes was performed as described 16. Isolation and culture of human
128 preadipocytes is further detailed in the ESM file.
129
130 Quantitative real-time polymerase chain reaction
131 Quantitative real-time polymerase chain reaction (qPCR) was performed using a protocol already
132 described 17 and further detailed in the ESM file.
133
7 134 Transcriptomic analysis by RNA sequencing
135 RNA-sequencing libraries were prepared using the Illumina TruSeq Stranded Total RNA with
136 Ribo-Zero Gold protocol (Illumina) and performed as described 11. Libraries were sequenced on a
137 NextSeq500 instrument (Illumina) with 38-bp paired end. Reads were mapped to ENSEMBL 18
138 release 79 cDNA transcripts with transcript support level of 3 or less, using kallisto 19 with bias
139 correction, stranded mapping and 100 bootstrap samples. Differentially expressed genes were found
140 using sleuth 20 aggregating transcripts to gene level. All models included a term to model individual
141 variation. Genes that change throughout the differentiation were found by a likelihood ratio test
142 comparing a model with only group information (Lean, Obese or Obese T2D) with a model with
143 both group and differentiation state. Main effects of Obesity and T2D were detected by a Wald test
144 in a model with differentiation state and group effects. All other comparisons were Wald tests on a
145 model with differentiation state and groups nested within differentiation state.
146
147 Multiplexed reduced representation bisulfite sequencing (RRBS)
148 Cells undergoing RRBS were purified using magnetic activated cell sorting (MACS) to deplete the
149 population for endothelial cells and leukocytes as previously described 21. Quickly, cells were
150 pelleted and resuspended in MACS buffer (Miltenyi Biotec) and incubated with FcR blocking
151 reagent and mixed. Anti-CD31 (Miltenyi Biotec) and anti-CD45 (Miltenyi Biotec) antibodies were
152 added to the tube for 15 min incubation at 4°C. The suspension was washed once in MACS buffer
153 and put through magnetic LD columns (Miltenyi Biotec). The flow-through was then collected and
154 tested for purity using fluorescence-activated cell sorting (FACS).
155 Multiplexed RRBS was performed as described 22, with minor modifications. Briefly, genomic
156 DNA was incubated with Msp1 restriction enzyme overnight for fragmentation. Adenylation was
157 performed using dNTP (NEB) and Klenow fragment (NEB) followed by an AMpure Bead clean-up
158 (Beckman Coulter) and ligation to TruSeq adapters (Illumina). Twelve ligated samples were pooled,
159 and subjected to bisulfite conversion using EZ DNA Methylation Kit (Zymo Research). The library
8 160 pool was amplified by PCR (2 min 95°C; (30 s 95°C, 30 s 65°C; 45 s 72°C) x 20 cycles; 7 min
161 72°C). PCR products were purified using the AMPure Bead clean-up. Libraries were quality-
162 controlled by a Bioanalyzer instrument running the Agilent High Sensitivity DNA Kit. Sequencing
163 libraries were sequenced on a HiSeq 2500 (Illumina) at the Danish National High-Throughput DNA
164 Sequencing Centre, on 50 bp single-end sequencing mode. Fasta files were preprocessed with Trim
165 Galore using the --rrbs flag. Alignment and CpG coverage statistics were computed using Bismark
166 23. Differentially methylated regions (DMRs) were detected using BiSeq 24 using standard settings,
167 except for the parameters min.sites, which was set to 5, and perc.samples which was set to 0.5.
168 Precision was allowed to vary between conditions. DMRs were found with a FDR cut-off of 10%.
169
170 Lipid staining
171 Lipid accumulation was measured using Oil red O staining (Sigma-Aldrich) as previously described 4.
172 Cells were washed twice with PBS then fixed with 10% formaldehyde during 20 min at room
173 temperature. Oil Red O (0.5%) in isopropanol was diluted 3/2 with distilled water (v/v), filtered and
174 added to the fixed cells for 1 hr at room temperature. Lipid staining was assessed under a light
175 microscope. Stained cells were then rinsed with distilled water, eluted in 1 ml of ethanol and optical
176 density for Oil Red O was measured at 540 nm on a Hidex Sense Microplate Reader.
177
178 Statistics
179 Statistical analysis was performed using R, SigmaPlot or GraphPad Prism software. Data were
180 tested for normality using Sigmaplot. Statistical difference between the groups for clinical
181 parameters, Western blots, qPCR, cell count, BrdU, glucose uptake and Oil Red O staining were
182 analyzed by one-way or two-way ANOVA setup with Turkey’s multiple-corrections test depending
183 on experimental setup. A P-value < 0.05 was considered statistically significant.
9 184 RESULTS
185 Preadipocytes from type 2 diabetic subjects display altered adipogenic potential
186 To compare the adipogenic potential of preadipocytes from lean humans to those from obese
187 humans with various types of metabolic dysfunction, we isolated preadipocytes from visceral
188 adipose tissue of lean subjects and obese subjects with or without type 2 diabetes (T2D) (Fig. 1a).
189 Clinical characteristics of the study participants are presented in ESM Table 1. The study
190 participants were matched for age at inclusion to avoid age-related epigenetic profiles, as previously
191 reported 26. As expected, all obese subjects, regardless of T2D, showed increased circulating HbA1c
192 and C-peptide, with higher values in the T2D group. Conversely, total HDL was lower in the two
193 obese groups, and further decreased in T2D. Expectedly, the clinical characteristics point at a more
194 insulin-resistant phenotype in subjects with T2D compared to obese only group or compared to the
195 lean group.
196 Morphological inspection of cultured preadipocytes by phase contrast microscopy at day 15 of
197 differentiation showed marked inter-individual variation (ESM Fig. 1). While gene expression and
198 protein abundance of the master regulator of adipogenesis Peroxisome proliferator-activated
199 receptor gamma (PPARG) were the same in Obese and Obese T2D compared to Lean (Fig. 1b-d),
200 gene expression of Fatty acid binding protein 4 (FABP4), another marker of adipocyte
201 differentiation, was markedly lower in Obese T2D compared to the other groups (Fig. 1e). Although
202 AKT phosphorylation was similar across groups (Fig. 1f-g), we detected a marked impairment in
203 ERK-related insulin responsiveness in the obese groups, as showed by a failure of insulin to further
204 increase phosphorylation of ERK compared to the basal condition (Fig. 1h-i).
205
206 Altered transcriptomic response to adipocyte differentiation in obesity
207 To study potential differences at the transcriptomic level, we performed RNA-sequencing (RNA-
208 Seq) of the preadipocytes at the proliferative state, at confluence, and at 3 and 15 days of
209 differentiation (ESM Table 2, Fig. 1a). Gene expression profiling of each individual sample by
10 210 principal component analysis (PCA) and hierarchical clustering revealed a marked gene expression
211 shift at day 3 and day 15, as well as between conditions at specific time points (ESM Fig. 2a-b).
212 Across all subjects, the transcriptome was markedly remodelled throughout differentiation, with,
213 respectively, 1311, 4348 and 4307 differentially expressed genes (DEG) at confluence, day 3 and
214 day 15 of differentiation (Fig. 2a).
215 To investigate enrichment for functional pathways, we attributed each DEG to Kyoto Encyclopedia
216 of Genes and Genomes (KEGG) pathways using the online web-based GEne SeT AnaLysis Toolkit
217 (WEBGESTALT) (Fig. 2b). At confluence, we found pathways related to cell to cell interaction,
218 cell cycle inhibition and switching of metabolism (Fig. 2b). At day 3, we found pathways related to
219 the establishment of adipocyte metabolism being upregulated, whereas DEG with decreased
220 expression were associated with pathways related to cytoskeletal organization and cell division
221 (Fig. 2b). At day 15, we found similar pathways as compared to day 3 but including additional
222 pathways involved in metabolism (Fig. 2b). Analysis of regulatory regions at proximity of DEG at
223 day 3 returned binding sites for FOXO4, FREAC2, TATA, NFAT, LEF1, GFI1, CDC5, OCT1 and
224 RP58 for upregulated genes, and AP1, E12, TEF1, SRF, E2F1DP1, CEPB and HFH4 for
225 downregulated genes (ESM Table 3). At day 15, we found binding sites for FOXO2, SD1, ERR1
226 for upregulated genes, and TEF1, LEF1, E12, AP1, SRF, E2F, E2F1DP1 and NFAT for
227 downregulated genes. These analyses suggest that distinct transcription factors drive differentiation
228 at day 3 compared to day 15.
229 Gene expression profiles were different in cells from the obese groups compared to the lean group
230 (Fig. 3a, ESM Fig. 3a-b). Indeed, 172 genes were upregulated and 447 downregulated in both obese
231 groups, with only 2 genes with opposite regulation between Obese and Obese T2D (Fig. 3a). Gene
232 pathway analysis showed that genes downregulated and in common in the obese groups were
233 enriched for the Ribosome pathway, with analysis of transcription factor binding sites at promoter
234 regions enriched for MAZ, YY1 and SMAD. Interestingly, genes downregulated only in Obese
235 T2D were enriched for pathways related to citrate cycle, fatty acid metabolism, PPAR signaling
11 236 pathway, insulin signaling pathway and oxidative phosphorylation. Analysis of the transcription
237 factor binding sites at promoter region of the downregulated genes returned CEBPB, SF1, HNF4,
238 PPAR and OCT1, suggesting that a lower binding activity of these transcription factors could be
239 involved in the altered transcription that we identified in T2D cells (Fig. 3b).
240 When we analyzed differences between groups at each specific time point, we found 285, 230, 669
241 and 345 genes being differentially expressed at the proliferative state, confluence, day 3 and day 15,
242 respectively, with a total of 886 unique genes for Obese subjects (without T2D). In cells from
243 Obese T2D, we found 361, 848, 960 and 592 genes, respectively, with a total of 1399 unique genes
244 (ESM Fig. 3a-b). KEGG pathway analysis of DEG at each time point returned more enriched terms
245 by comparing Obese T2D to Lean than the Obese vs. Lean comparison (ESM Fig. 3c-d). Notably,
246 we found an enrichment of pathways related to adipocyte function in Obese T2D, suggesting a more
247 pronounced phenotype in obese humans with T2D (ESM Fig. 3d). Consistently with the KEGG
248 pathway analysis, at day 15, cells from Obese T2D showed lower expression of key adipogenic
249 genes such as PLIN1, LPIN3, ADIPOQ, FAS, AGPAT2, NR1H3, RORA, LIPE, SREBF1, STAT3 and
250 STAT5A, as well as genes involved in metabolic processes like, amino acid metabolism, electron
251 transport chain and fatty acid metabolism (Fig. 3c). Altogether, these results indicate that, while
252 preadipocytes from all obese subjects have a distinct transcriptomic profile compared to the lean
253 individuals, the specific transcriptomic response of preadipocytes from diabetic obese individuals to
254 differentiation is further affected towards an impaired metabolism and oxidative phosphorylation in
255 differentiated adipocytes.
256
257 DNA methylation is reprogramed in preadipocytes from obese and T2D subjects
258 Next, we analysed genome-wide DNA methylation in preadipocytes at the proliferative state from
259 the Lean and the Obese ± T2D groups at single nucleotide resolution using Reduced Representation
260 Bisulfite Sequencing (RRBS), at GC rich loci 22. Global and region-specific methylation levels did
261 not vary between groups and CpG location (ESM Fig. 4a-f). When comparing Lean and Obese, we
12 262 found 132 differentially methylated regions (DMRs, 86 had decreased and 46 increased methylation
263 compared to Lean) and 73 DMRs (37 had decreased and 36 increased methylation compared to
264 Lean) between the Lean and the Obese T2D group. When comparing Obese and Obese T2D we
265 found 100 DMRs (53 had decreased and 47 had increased DNA methylation in Obese T2D, ESM
266 Table 4, Fig. 4a-c). Thirteen genes with altered methylation were found in common in both obese
267 groups compared to the Lean group. Most importantly, we found several DMRs at proximity of
268 genes differentially expressed. In the Obese group, for example, NAT10, SHC1, PITX1, FRMPD4,
269 THY1, ZBTB33, PA2G4 and DDX39B showed a positive correlation between expression and
270 methylation, while SNRPE, FST, BCOR, KANSL1L and COL27A1 showed a negative association
271 (ESM Table 5). In the Obese T2D group, RPL6 RNPS1, DDX39B and HNRNPD exhibited a
272 positive association, and GTF3C3, PRSS12 and L1TD1, a negative association (ESM Table 5).
273 While we did not find any statistically significant enriched pathways for Obese vs. Lean, we noted a
274 trend for pathways related to insulin signaling pathway (Fig. 4c). For Obese T2D vs. Lean, the
275 cellular senescence pathway was enriched (Fig. 4d). Yet, these results point at a moderate
276 association between transcriptional levels and DNA methylation.
277
278 DNA methylation signature of preadipocytes predicts adipogenesis potential
279 To investigate if the DNA methylation footprint of preadipocytes could predict adipogenic
280 potential, we assigned an Adipogenic Score based on expression of the most highly expressed
281 adipocyte-specific genes at day 15 of differentiation, in all of three groups (PLIN1, CIDEC, FABP4,
282 ADIPOQ, LPL, PDK4, APOE, LIPE, FABP3, LEP, RBP4 and CD36). A Principal Component
283 analysis based on Adipogenic Score revealed a large dispersion of subjects only at day 15 of
284 differentiation, suggesting the Adipogenic Score is a good determinant for classifying terminal
285 differentiation (Fig. 5a). We then analyzed the RRBS dataset for DMRs associated with an
286 alteration in Adipogenic Score. Independently of the group, we found that 3906 DMRs were
287 associated to an increased Adipogenic Score, with 1901 DMRs with decreased methylation and
13 288 2005 regions with increased methylation (ESM Table 6, Fig. 5b-c). Notably, gene pathway analysis
289 of the nearest genes to the DMRs showed enrichment for pathways related to insulin resistance,
290 adipocytokine signaling, toll-like receptor signaling and signaling pathways regulating pluripotency
291 of stem cells (Fig. 5d). Of interest, we found a significant enrichment between the nearest genes to
292 the DMRs and differentially expressed genes at day 15 (p=7.6E-06). Since we did not find a
293 correlation between DNA methylation and gene expression changes (ESM Fig. 5), this indicates
294 that the directional effect of DNA methylation on gene expression is not uniform. When comparing
295 Lean and Obese, we found 100 DMRs associated to an increased Adipogenic Score, and 305 DMRs
296 between Lean and Obese T2D (Fig. 5b). Pathway analysis of genes located at close proximity of
297 these DMRs only showed significant difference for the Obese T2D group, notably the Vitamin
298 digestion and absorption pathway, which was also found significantly upregulated at day 15 of
299 differentiation (Fig. 5e-f). Altogether, our results indicate that specific genes that are epigenetically
300 reprogrammed in adipocytes from obese subjects are also differentially expressed, supporting
301 epigenetic reprogramming of preadipocytes in obesity plays a functional role.
302
303 Extracellular milieu reprograms preadipocytes and alters adipogenesis
304 To identify factors in the extracellular milieu that reprogram preadipocytes in obesity and T2D, we
305 tested the effect of short-term incubation of cultured 3T3-L1 preadipocytes with the inflammatory
306 cytokine TNF-α or the free fatty acid palmitate, on later adipocyte differentiation, compared to cells
307 grown in control medium (Fig. 6a). Preadipocytes in the proliferative state were incubated for 1 day
308 with TNF-α or palmitate, then washed and grown in regular media. Cell proliferation, as measured
309 by BrdU incorporation, was only transiently altered by palmitate, and cell number was similar
310 across conditions 2 days after treatment removal (Fig. 6b-c). After differentiation, lipid content was
311 reduced in TNF-α-, but not palmitate-treated cells (Fig. 6d). Both TNF-α- and palmitate-treated
312 cells showed a reduced capacity to take up glucose in response to insulin (Fig. 6e). These results
14 313 indicate that a short-term treatment with TNF-α or palmitate impairs the metabolic potential of
314 preadipocytes.
315 To determine if short-term incubation with TNF-α or palmitate reprograms the epigenome of
316 preadipocytes, we performed RRBS on the treated preadipocytes at confluence. Treatment with
317 TNF-α induced 70 DMRs whereas palmitate induced 62 DMRs (Fig. 6f). Interestingly, we observed
318 an important overlap of DMRs between treatments, with a total of 36 regions; 21 hypomethylated,
319 10 hypermethylated and 4 with opposite regulation (Fig. 6g). Enrichment analysis of genes at close
320 proximity to DMRs found gene pathways related to HIF-1 signaling, PPAR signaling and ribosome
321 pathways enriched in both groups (Fig. 6h-i). Differential expression of Nr2f1, Rxra, Ctnnb1 and
322 Terf1 showed negative correlation between DNA methylation and expression for Nr2f1 and positive
323 correlation between Ctnnb1, Rxra and Terf1, suggesting that altered DNA methylation at specific
324 regions is associated with a changed gene expression (ESM Fig. 6). These results support that
325 extracellular factors can reprogram the differentiation potential of preadipocytes and their metabolic
326 function at the mature adipocyte level, through remodeling of DNA methylation at specific genomic
327 regions.
328
15 329 DISCUSSION
330 In this study, we investigated the adipogenic potential of preadipocytes collected from obese
331 humans with or without T2D. Analysis of the transcriptomic response to an adipocyte
332 differentiation cocktail in culture allowed us to establish that preadipocytes from humans with
333 obesity have distinct transcriptome and epigenome and an impaired metabolic potential. We
334 identified a subset of candidate genes that undergo epigenetic reprogramming under TNF-α or
335 palmitate exposure and which could participate in the altered adipogenic potential of preadipocytes
336 from obese humans.
337
338 Our transcriptomic analysis provides resource information establishing the gene expression pattern
339 throughout differentiation of human visceral fat. We found 12 651 transcripts varying over the
340 course of adipogenesis. This magnitude is strikingly consistent with a previous study showing that
341 expression of 11 830 transcripts was changed after differentiation of preadipocytes from
342 subcutaneous adipose tissue 27. Comparing the two datasets, we found that 16/20 gene pathways for
343 increased gene expression, and 8/20 pathways for decreased expression at day 15 overlapped. These
344 data suggest high similarities in gene expression programming during differentiation of visceral and
345 subcutaneous adipocytes 25. Our results support previous findings showing that depot specific
346 differences of adipose tissue is partly driven by preadipocyte differences 28,29. The subset of genes
347 specifically activated during the differentiation of visceral adipocytes, (e.g. genes related to the non-
348 alcoholic fatty liver disease pathway) or the genes unique to subcutaneous adipocytes (e.g. related
349 to the biosynthesis of unsaturated fatty acid pathway) likely represent gene networks setting the
350 specific biology of each respective adipose tissue depot. Given the different contribution of each
351 depot in the whole-body glucose and lipid metabolism, activation or inhibition of depot-specific
352 genes could provide therapeutic entry points. When comparing the transcriptome of preadipocytes
353 from the Obese and Obese T2D groups, we noted that numerous pathways (10 out of 18) that are
354 upregulated at day 15 of differentiation were downregulated in Obese T2D. This is consistent with
16 355 previous functional analysis of preadipocytes from T2D subjects differentiated in vitro and strongly
356 indicates an altered adipogenic potential in preadipocytes from Obese T2D 4–6.
357
358 In the present study, we identified that insulin signaling is altered in preadipocytes from obese
359 subjects. This is in line with previous studies showing distinct metabolic signatures 30,
360 mitochondrial function and inflammation 31, adipogenesis 6,32, tissue remodeling 33 and osteogenic
361 capacity 34 in adipocytes from obese subjects with or without T2D. Taken collectively, these results
362 strongly indicate a functional reprogramming of preadipocytes from obese subjects. Our
363 transcriptomic results support that gene expression is reprogrammed during preadipocyte
364 differentiation and provide insight into the specific altered pathways that could be responsible of the
365 downstream impaired function of the differentiated adipocytes. Indeed, we report gene expression
366 remodeling in pathways related to metabolic and mitochondrial function, inflammation,
367 adipogenesis and osteogenesis, and tissue remodeling in obese individuals. It is interesting to note
368 that some of these pathways were found by comparing adipose tissue from monozygotic twins with
369 or without T2D 12. These data support that preadipocytes are transcriptionally reprogrammed at
370 specific gene pathways in obese humans.
371
372 In addition to the transcriptome, we found genome-wide alteration of the DNA methylation
373 signature of preadipocytes from lean and obese subjects. Differential methylation in omental
374 adipose tissue and adipocytes from obese humans before and after gastric bypass has been
375 previously reported 10. We did not find any overlap with our differentially methylated gene list.
376 Nevertheless, in a comparison with another study where 5 529 DMRs were reported in mature
377 adipocytes from obese women 35, we identified an overlap of 61 genes and 39 genes, in our Obese
378 and Obese T2D groups, respectively. These modest overlaps are likely to be caused by the nature of
379 the cells investigated (either full tissue, purified adipocytes or, in our study, preadipocytes). To our
380 knowledge, only few studies have investigated the DNA methylation pattern of preadipocytes in
17 381 obese and T2D subjects 36,37. When methylation differences were reported, we however did not find
382 a substantial overlap with our dataset. Given that we found differential methylation at gene
383 pathways very specific to adipocyte function, the lack of overlap across studies is likely due to
384 disparities in factors such as the DNA methylation assays, cell type composition and purity, sex, age
385 and clinical characteristics of the respective cohorts, rather than caused by unspecific or noise
386 signal.
387
388 To our knowledge, we are the first to show that the DNA methylation footprint of preadipocytes is
389 highly related to their differentiation capacity. The adipogenic potential of mesenchymal stem cells
390 from various depots is associated with different DNA methylation profiles in the same individual 38.
391 We have previously shown that TNF-α and palmitate can dynamically alter DNA methylation of
392 skeletal muscle 39. Here, we show that preadipocytes treated with TNF-α or palmitate retains an
393 epigenetic “memory” of the treatments, even after numerous cell divisions. These data support
394 previous findings that prior cellular events can be epigenetically memorized, and that epigenetic
395 reprogramming may play a role in the dysfunctional adipose tissue 3,40,41. Resetting the epigenome
396 of preadipocytes could be used as a therapeutic option to ameliorate adipose tissue biology in
397 obesity.
398
399 In conclusion, we have identified that preadipocytes from obese humans are epigenetically
400 reprogrammed. We established a link between DNA methylation changes and remodeling of gene
401 expression during adipogenesis, which was further altered in T2D. Given the effect of TNF-α or
402 palmitate on the epigenome of preadipocytes and the function of the differentiated adipocytes, we
403 propose etiological signals that are specific to obesity or T2D reprogram preadipocytes and alter
404 adipocyte differentiation, thereby participating in the metabolic dysfunction of adipose tissue in
405 obesity and T2D.
18 406 Acknowledgements
407 O.F. was recipient of a research grant from the Danish Diabetes Academy supported by the Novo
408 Nordisk Foundation. We would like to acknowledge The Danish National High-Throughput DNA
409 Sequencing Centre, University of Copenhagen, for sequencing the samples. The Novo Nordisk
410 Foundation Centre for Basic Metabolic Research is an independent research centre at the University
411 of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation.
412
413 Conflict of interest
414 The authors declare no conflict of interest.
415
416 Contribution statement
417 E.A. collected human samples, performed experiments, analysed the data and wrote the manuscript;
418 L.R.I. and A.A. performed bioinformatics analysis and generated figures; O.F. performed
419 experiments, analysed the data and edited the manuscript; I.D., S.V., T.B. and V.B.K. collected
420 human samples, contributed to study design and edited the manuscript; D.S. provided expert advice
421 and edited the manuscript; R.B. designed the study, analysed the data and wrote the manuscript. All
422 authors approved the final version of the manuscript.
19 423 REFERENCES
424 1 Hotamisligil GS, Arner P, Caro JF, Atkinson RL, Spiegelman BM. Increased Adipose Tissue
425 Expression of Tumor Necrosis Factor-a in Human Obesity and Insulin Resistance. J Clin
426 Invest 1995; 95: 2409–2415.
427 2 Hajer GR, Haeften TW Van, Visseren FLJ. Adipose tissue dysfunction in obesity, diabetes,
428 and vascular diseases. Eur Hear J 2008; 29: 2959–2971.
429 3 Rønningen T, Shah A, Reiner AH, Collas P, Moskaug JØ. Epigenetic priming of
430 inflammatory response genes by high glucose in adipose progenitor cells. Biochem Biophys
431 Res Commun 2015; 467: 979–986.
432 4 Isakson P, Hammarstedt A, Gustafson B, Smith U. Impaired preadipocyte differentiation in
433 human abdominal obesity: role of Wnt, tumor necrosis factor-alpha, and inflammation.
434 Diabetes 2009; 58: 1550–1557.
435 5 Permana PA, Nair S, Lee Y, Luczy-bachman G, Courten BV De, Tataranni PA et al.
436 Subcutaneous abdominal preadipocyte differentiation in vitro inversely correlates with
437 central obesity. Am J Physiol Endocrinol Metab 2004; 286: 958–962.
438 6 Lessard J, Laforest S, Pelletier M, Leboeuf M, Blackburn L, Tchernof A. Low abdominal
439 subcutaneous preadipocyte adipogenesis is associated with visceral obesity, visceral
440 adipocyte hypertrophy, and a dysmetabolic state. Adipocyte 2014; 3: 197–205.
441 7 Rossmeislová L, Mališová L, Kračmerová J, Tencerová M, Kováčová Z, Koc M et al.
442 Weight loss improves the adipogenic capacity of human preadipocytes and modulates their
443 secretory profile. Diabetes 2013; 62: 1990–1995.
444 8 Siersbæk MS, Loft A, Aagaard MM, Nielsen R, Schmidt SF, Petrovic N et al. Genome-wide
445 profiling of peroxisome proliferator-activated receptor γ in primary epididymal, inguinal, and
446 brown adipocytes reveals depot-selective binding correlated with gene expression. Mol Cell
447 Biol 2012; 32: 3452–63.
448 9 Mikkelsen TS, Xu Z, Zhang X, Wang L, Gimble JM, Lander ES et al. Comparative
20 449 epigenomic analysis of murine and human adipogenesis. Cell 2010; 143: 156–69.
450 10 Benton MC, Johnstone A, Eccles D, Harmon B, Hayes MT, Lea R a et al. An analysis of
451 DNA methylation in human adipose tissue reveals differential modification of obesity genes
452 before and after gastric bypass and weight loss. Genome Biol 2015; : 1–21.
453 11 Nylander V, Ingerslev LR, Andersen E, Fabre O, Garde C, Rasmussen M et al. Ionizing
454 Radiation Potentiates High-Fat Diet – Induced Insulin Resistance and Reprograms Skeletal
455 Muscle and Adipose Progenitor Cells. Diabetes 2016; 65: 3573–3584.
456 12 Nilsson E, Jansson PA, Perfilyev A, Volkov P, Pedersen M, Svensson MK et al. Altered
457 DNA Methylation and Differential Expression of Genes Influencing Metabolism and
458 Inflammation in Adipose Tissue From Subjects With Type 2 Diabetes. Diabetes 2014; 63:
459 2962–276.
460 13 Rönn T, Volkov P, Davegårdh C, Dayeh T, Hall E, Olsson AH et al. A six months exercise
461 intervention influences the genome-wide DNA methylation pattern in human adipose tissue.
462 PLoS Genet 2013; 9: e1003572.
463 14 Mikkelsen TS, Ku M, Jaffe DB, Issac B, Lieberman E, Giannoukos G et al. Genome-wide
464 maps of chromatin state in pluripotent and lineage-committed cells. Nature 2007; 448: 553–
465 560.
466 15 Reik W. Stability and flexibility of epigenetic gene regulation in mammalian development.
467 Nature 2007; 447: 425–432.
468 16 Arner P, Arner E, Hammarstedt A, Smith U. Genetic predisposition for type 2 diabetes, but
469 not for overweight/obesity, is associated with a restricted adipogenesis. PLoS One 2011; 6:
470 e18284.
471 17 Pattamaprapanont P, Garde C, Fabre O, Barrès R. Muscle Contraction Induces Acute
472 Hydroxymethylation of the Exercise-Responsive Gene Nr4a3. Front Endocrinol (Lausanne)
473 2016; 7: 1–9.
474 18 Yates A, Akanni W, Amode MR, Barrell D, Billis K, Carvalho-Silva D et al. Ensembl 2016.
21 475 Nucleic Acids Res 2016; 44: D710–D716.
476 19 Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq
477 quantification. Nat Biotechnol 2016; 34: 525–527.
478 20 Pimentel H, Bray NL, Puente S, Melsted P, Pachter L. Differential analysis of RNA-seq
479 incorporating quantification uncertainty. Nat Methods 2017; 14: 687–690.
480 21 Busser H, Najar M, Raicevic G, Pieters K, Velez Pombo R, Philippart P et al. Isolation and
481 Characterization of Human Mesenchymal Stromal Cell Subpopulations: Comparison of Bone
482 Marrow and Adipose Tissue. Stem Cells Dev 2015; 24: 2142–57.
483 22 Boyle P, Clement K, Gu H, Smith ZD, Ziller M, Fostel JL et al. Gel-free multiplexed
484 reduced representation bisulfite sequencing for large-scale DNA methylation profiling.
485 Genome Biol 2012; 13: R92.
486 23 Krueger F, Andrews SR. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq
487 applications. Bioinformatics 2011; 27: 1571–1572.
488 24 Hebestreit K, Dugas M, Klein H-U. Detection of significantly differentially methylated
489 regions in targeted bisulfite sequencing data. Bioinformatics 2013; 29: 1647–1653.
490 25 Van Harmelen V, Röhrig K, Hauner H. Comparison of Proliferation and Differentiation
491 Capacity of Human Adipocyte Precursor Cells from the Omental and Subcutaneous Adipose
492 Tissue Depot of Obese Subjects. Metabolism 2004; 53: 632–637.
493 26 Bjornsson HT, Sigurdsson MI, Fallin MD, Irizarry RA, Aspelund T, Cui H et al. Intra-
494 individual Change Over Time in DNA Methylation With Familial Clustering. JAMA 2008;
495 299: 2877–83.
496 27 Broholm C, Olsson AH, Perfilyev A, Gillberg L, Hansen NS, Ali A et al. Human
497 adipogenesis is associated with genome-wide DNA methylation and gene-expression
498 changes. Epigenomics 2016; 8: 1601–1617.
499 28 Pinnick KE, Nicholson G, Manolopoulos KN, McQuaid SE, Valet P, Frayn KN et al.
500 Distinct developmental profile of lower-body adipose tissue defines resistance against
22 501 obesity-associated metabolic complications. Diabetes 2014; 63: 3785–97.
502 29 Baglioni S, Cantini G, Poli G, Francalanci M, Squecco R, Franco A et al. Functional
503 differences in visceral and subcutaneous fat pads originate from differences in the adipose
504 stem cell. PLoS One 2012; 7: e36569.
505 30 Böhm A, Halama A, Meile T, Zdichavsky M, Lehmann R, Weigert C et al. Metabolic
506 signatures of cultured human adipocytes from metabolically healthy versus unhealthy obese
507 individuals. PLoS One 2014; 9: e93148.
508 31 Naukkarinen J, Heinonen S, Hakkarainen A, Lundbom J, Vuolteenaho K. Characterising
509 metabolically healthy obesity in weight-discordant monozygotic twins. Diabetologia 2014;
510 57: 167–176.
511 32 van Tienen FHJ, van der Kallen CJH, Lindsey PJ, Wanders RJ, van Greevenbroek MM,
512 Smeets HJM. Preadipocytes of type 2 diabetes subjects display an intrinsic gene expression
513 profile of decreased differentiation capacity. Int J Obes 2011; 35: 1154–1164.
514 33 Henninger AMJ, Eliasson B, Jenndahl LE, Hammarstedt A. Adipocyte Hypertrophy ,
515 Inflammation and Fibrosis Characterize Subcutaneous Adipose Tissue of Healthy , Non-
516 Obese Subjects Predisposed to Type 2 Diabetes. PLoS One 2014; 9: 1–8.
517 34 Oliva-olivera W, Gea AL, Lhamyani S, Coín-aragüez L, Torres JA, Bernal-lópez MR et al.
518 Differences in the Osteogenic Differentiation Capacity of Omental Adipose-Derived Stem
519 Cells in Syndrome. Endocrinology 2015; 156: 4492–4501.
520 35 Arner P, Sinha I, Thorell A, Rydén M, Dahlman-Wright K, Dahlman I. The epigenetic
521 signature of subcutaneous fat cells is linked to altered expression of genes implicated in lipid
522 metabolism in obese women. Clin Epigenetics 2015; 7: 1–13.
523 36 Broholm C, Olsson AH, Perfilyev A, Hansen NS, Schrölkamp M, Strasko KS et al.
524 Epigenetic programming of adipose-derived stem cells in low birthweight individuals.
525 Diabetologia 2016; 59: 2664–2673.
526 37 Guénard F, Tchernof A, Deshaies Y, Pérusse L, Biron S, Lescelleur O et al. Differential
23 527 methylation in visceral adipose tissue of obese men discordant for metabolic disturbances.
528 Physiol Genomics 2014; 46: 216–22.
529 38 Collas P. Programming differentiation potential in mesenchymal stem cells. Epigenetics
530 2010; 5: 476–482.
531 39 Barrès R, Osler ME, Yan J, Rune A, Fritz T, Caidahl K et al. Non-CpG Methylation of the
532 PGC-1α Promoter through DNMT3B Controls Mitochondrial Density. Cell Metab 2009; 10:
533 189–198.
534 40 Sharples AP, Polydorou I, Hughes DC, Owens DJ, Hughes TM, Stewart CE. Skeletal muscle
535 cells possess a ‘memory’ of acute early life TNF-a exposure: role of epigenetic adaptation.
536 Biogerontology 2016; 17: 603–617.
537 41 Asterholm IW, Tao C, Morley TS, Wang QA, Delgado-Lopez F, Wang Z V et al. Adipocyte
538 Inflammation Is Essential for Healthy Adipose Tissue Expansion and Remodeling. Cell
539 Metab 2014; 20: 103–118.
540
24 541 Legends for figures
542
543 Fig. 1. Preadipocytes from Obese and Obese T2D show lower responsiveness to insulin and
544 decrease expression of markers of adipogenesis. (a) Graphical illustration of the experimental
545 setup (b-c) Representative blot and quantification of the abundance of PPARG protein. (d-e)
546 Expression of Pparg and Fabp4 at confluence and at day 3 of differentiation. Quantification was
547 made relative to the ribosomal RNA 18S. (f-i) Insulin-induced phosphorylation of AKT and ERK as
548 represented relative to total AKT and ERK, respectively. Representative Western blot image are
549 shown. Data are shown as the mean +SEM. Time and group (Obese ± T2D) effects were evaluated
550 by two-way ANOVA. * indicates p value < 0.05.
551
552 Fig. 2. Preadipocytes from Obese and Obese T2D have an altered transcriptome of
553 throughout differentiation. (a) Heatmap representing transcriptional clusters during
554 differentiation. Groups (Lean, Obese and Obese T2D) and timepoints (At proliferation, confluence,
555 day 3 or day 15) are indicated in the top panel with the colour codes showed in the legends. (b)
556 KEGG pathway enrichment analysis at confluence, day 3 and day 15 for upregulated genes (red
557 bars) and downregulated genes (blue bars). Numbers after pathways indicate (Total genes in
558 pathway/Genes annotated to pathway).
559
560 Fig. 3. Differential gene expression in Obese and Obese T2D compared to the Lean group. (a)
561 Venn diagram showing overlapping genes that are differentially regulated in Obese vs. Lean and
562 Obese T2D vs. Lean. (b) KEGG pathway analysis and regulatory binding factors at proximity of
563 downregulated genes, specific for Obese T2D (upper panel) and common between Obese and Obese
564 T2D (lower panel). (c) Heatmap for genes related to adipogenesis that are differentially regulated in
565 Obese T2D vs. Lean (upper panel), compared to Obese vs. Lean. Gene ontologies are indicated at
25 566 the top of the map. Numbers after pathway name indicate: (Total genes found in pathway/Genes
567 annotated to pathway).
568
569 Fig. 4. Overview of the differentially methylated regions between the Obese, Obese T2D and
570 Lean groups. Venn diagram showing overlapping regions that are differentially methylated in
571 Obese vs. Lean, Obese T2D vs. Lean and Obese vs. Obese T2D (a) and a table of the direction of
572 methylation change for the differentially methylated regions (b). Table of top 10 regions with most
573 increased (dark colour) or decreased (light colour) methylation in Obese vs Lean (Blue) and Obese
574 T2D vs Lean (Red)(c). Enriched KEGG pathways in (d) Obese and (e) Obese T2D.
575
576 Fig. 5. Evidence for a link between DNA methylation and adipogenic potential. Principal
577 Component Analysis for adipogenic score genes throughout differentiation (a). Groups (Lean,
578 Obese and Obese T2D) and timepoints (At proliferation, confluence, day 3 or day 15) are indicated
579 in the top panel with the colour codes showed in the legends. Differentially methylated genes for
580 adipogenic score genes (SCORE), Obese vs. Lean and Obese T2D vs. Lean (b). Table of top 10
581 regions with most increased (dark colour) or decreased (light colour) methylation associated with an
582 increase in adipogenic score (c). Enriched KEGG pathways for SCORE (d) Obese (e) and Obese
583 T2D (f). p values and gene ratios (number of gene hits within the respective terms) are represented
584 by colour gradient indicated in the figure.
585
586 Fig. 6. Short-term TNF-α and palmitate treatment in preadipocytes reprograms adipocyte
587 function. (a) Experimental setup. (b) Growth curve for control, palmitate and TNF-α-treated cells.
588 (c) BrdU incorporation 1 day after removing all treatments. (d) Quantification of the Oil red O
589 staining at day 16 (day 8 of differentiation). (e) Insulin-induced glucose uptake at day 16. (f) DMRs
590 in common between TNF-α- and palmitate-treated groups. (g) Number and example of genes
591 differentially methylated in common between TNF-α and palmitate treatments. KEGG pathway
26 592 enrichment analysis for TNF-α- (h) and palmitate- (i) treated cells. Data are shown as the mean
593 +SEM. The time and group (Obese ± T2D) effects were evaluated by two-way ANOVA. * indicates
594 p value < 0.05.
27 Figure 1
a Visceral adipose tissue Preadipocyte Differentiation in culture Proliferative Confluence Day 3 Day 15
Lean Obese Obese T2D Adipocyte
b c Effect of Time: P < 0.0001 Lean Obese Obese T2D 20 )
l Lean e )
l Lean Time Pro Con Day 3 Pro Con Day 3 Pro Con Day 3 e o c r o c
n Obese t r
n 15 Obese t a n 15 a n Obese T2D d o Obese T2DM d PPARG o n c
n c u
o u b t
o 10
b t a 10
e a
e v G i v
t Beta- G i R t a l R 5 a A
l actin
e 5 A P e R P P ( R P ( 0 ro n 3 ro n 3 ro n 3 o on y 3 o on y 3 o on y 3 Pr Co ay Pr Co ay Pr Co ay P C Da P C Da P C Da d D D D e Effect of Time: P < 0.005 Effect of Time: P < 0.0001 Control Effect of Time: P < 0.0001 n 20 Effect of Time: P < 0.05 20 n * o 10 200 i Obese o ) ) i s
l Lean l ControlLean e e s s o o c Obese T2DM c s ) r r e )
n Obese n Obese t t r Obese 8 e 15 S 15 S r a a n n p 150 8
8 Obese T2D Obese T2D p d d x o o Obese T2DM 1 1 x n n c c e
e u u
6 o o
o o t A t b b t t
10 10
A
a a 100 N e d
e e N v e R i v v G G i i t 4 t R t t a m a R R l l a a
m l 5 l 5 A A e e 2 50 e e 4 P P R 2 R G R R ( ( P P P ( ( R B
A 0 0 0 A 0 P F P ro n 3 ro n 3 ro n 3 ro n 3 ro n 3 ro n 3 n o 3y n o 3 y n o3 y no y3 n o 3y n o 3 y P oC ya Po Cy a oP yC a P oC ay Po C y a oP Cy a C aD C a D C a D C Da C aD C a D D D D D D D f g Lean Obese Obese T2D Effect of Time: P < 0.0001 Insulin - + - + - + 20 Effect of Insulin: P < 0.0001 )
l Lean e ) 15 l o c r a
n Obese t 15 Control s a n pAKT T a ObeseObese T2D d o b K n c
Obese T2DM f u A 10 o
b o l t 10
P = 0,0668 Total a a
t e e v o g G i t AKT t / n R a T a l 5 A h e K P c A R Beta- P ( p d l actin o 0 f
( 0 o n 3 o n 3 o n 3 r l o y rl o y lr o y P aC lain Pa C lina Pa Clin a s u s u s u a sD a s D a s D B In B In B In h i Lean Obese Obese T2D Effect of Time: P < 0.0001 Insulin - + - + - + 20 Effect of Insulin: P < 0.01 Effect of Interaction: P < 0.0005 )
l Lean e 2.0 o ) c l r Control
n Obese t a 15
a pERK n s ObeseObese T2D d o a n c
b 1.5 K
Obese T2DM u f o R b t o 10 Total
a E
t e e /
v 1.0
g ERK G i K t n R a R a l 5 A h E e P c
p 0.5 Beta-
R P ( d l actin o 0 f ( 0.0 ro n 3 ro n 3 ro n 3 P l o y P l o y Pl o y aC lain a C lina a Clina s Du s uD s u D a s a s a s B In B In B In Figure 2
a Timepoint Condition
Condition Lean Obese Obese T2D Timepoint Proliferation Confluent Day 3 Day 15
b Confluence Day 3 Day 15 Glycosphingolipid biosynthesis (11/2) Renin secretion (44/15) Vitamin digestion and absorption (16/11) Osteoclast differentiation (85/7) Taurine and hypotaurine metabolism (5/4) Terpenoid backbone biosynthesis (20/13) Retinol metabolism (23/3) Complement and coagulation cascades (39/14) Butanoate metabolism (17/12) Chemical carcinogenesis (36/4) Terpenoid backbone biosynthesis (20/9) Glyoxylate and dicarboxylate metabolism (22/14) Proteoglycans in cancer (165/12) Vitamin digestion and absorption (16/8) Huntington's disease (158/53) Rheumatoid arthritis (48/5) Glutathione metabolism (40/15) Peroxisome (69/31) Arachidonic acid metabolism (32/4) Fatty acid biosynthesis (9/6) Alzheimer's disease (138/53) Calcium signaling pathway (108/9) Metabolism of xenobiotics by cytochrome P450 (32/13) Pyruvate metabolism (33/21) Renin secretion (44/5) Tyrosine metabolism (21/10) Steroid biosynthesis (16/14) Butanoate metabolism (17/3) Regulation of lipolysis in adipocytes (42/16) Fatty acid degradation (38/24) Vitamin digestion and absorption (16/3) Fatty acid metabolism (42/16) Non-alcoholic fatty liver disease (NAFLD) (129/55) Steroid hormone biosynthesis (22/4) Renin-angiotensin system (14/8) Fatty acid metabolism (42/28) Staphylococcus aureus infection (20/4) Fat digestion and absorption (17/9) Parkinson's disease (107/51) Cytokine-cytokine receptor interaction (123/13) Lysosome (113/33) PPAR signaling pathway (50/32) Renin-angiotensin system (14/4) Fatty acid degradation (38/16) Propanoate metabolism (30/24) Synthesis and degradation of ketone bodies (7/3) AMPK signaling pathway (102/32) Oxidative phosphorylation (102/51) Metabol.of xenobiotics by cytochrome P450 (32/6) Metabolic pathways (946/196) Citrate cycle (TCA cycle) (27/23) Complement and coagulation cascades (39/10) Drug metabolism - cytochrome P450 (32/15) Valine, leucine and isoleucine degradation (44/31) Steroid biosynthesis (16/7) Steroid biosynthesis (16/11) Carbon metabolism (89/50) Terpenoid backbone biosynthesis (20/8) PPAR signaling pathway (50/27) Metabolic pathways (946/289)
Fructose and mannose metabolism (28/6) PI3K-Akt signaling pathway (243/71) Bacterial invasion of epithelial cells (69/25) Thyroid hormone signaling pathway (98/14) Tight junction (95/33) Systemic lupus erythematosus (37/16) Oxytocin signaling pathway (116/16) Fc gamma R-mediated phagocytosis (76/28) Proteoglycans in cancer (165/50) Oocyte meiosis (94/14) Cell cycle (116/39) Cell adhesion molecules (CAMs) (69/26) Adherens junction (66/11) Central carbon metabolism in cancer (54/22) ECM-receptor interaction (57/23) Long-term potentiation (48/9) Hematopoietic cell lineage (37/17) AGE-RAGE in diabetic complications (88/32) Proteoglycans in cancer (165/22) Alcoholism (90/33) Salmonella infection (69/27) Tight junction (95/15) Cell adhesion molecules (CAMs) (69/27) Alcoholism (90/33) cGMP-PKG signaling pathway (120/18) Adherens junction (66/27) Hematopoietic cell lineage (37/18) Gastric acid secretion (42/9) HIF-1 signaling pathway (84/33) Adherens junction (66/27) Salmonella infection (69/13) Hippo signaling pathway (114/42) Cell cycle (116/41) Biosynthesis of amino acids (53/11) Oocyte meiosis (94/37) Cytokine-cytokine receptor interaction (123/43) Leukocyte transendothelial migration (74/14) Cardiomyopathy ARVC(54/25) Rap1 signaling pathway (161/53) Glycolysis / Gluconeogenesis (47/11) Focal adhesion (162/57) Leukocyte transendothelial migration (74/31) Central carbon metabolism in cancer (54/12) Rap1 signaling pathway (161/57) Pathogenic Escherichia coli infection (46/23) Focal adhesion (162/25) Pathways in cancer (315/98) Focal adhesion (162/57) HIF-1 signaling pathway (84/16) Salmonella infection (69/31) Hippo signaling pathway (114/45) Pathogenic Escherichia coli infection (46/14) Leukocyte transendothelial migration (74/35) Cardiomyopathy ARVC (54/27) Hippo signaling pathway (114/24) Pathogenic Escherichia coli infection (46/26) Pathways in cancer (315/100) Regulation of actin cytoskeleton (165/34) Regulation of actin cytoskeleton (165/70) Regulation of actin cytoskeleton (165/69) Figure 3 Transcription Factor a b KEGG Binding Alzheimer's disease (138/16) Fat digestion and absorption (17/5) Glycine, serine and threonine metabolism (24/6) Metabolic pathways (946/70) 2-Oxocarboxylic acid metabolism (16/5) Biosynthesis of amino acids (53/10) Oxidative phosphorylation (102/15) Obese T2D vs. Lean Obese T2D vs. Lean Insulin signaling pathway (113/16) OCT1_05 (166/18) Upregulated genes Downregulated genes PPAR signaling pathway (50/10) Carbon metabolism (89/14) PPAR_DR1_Q2 (189/20) Non-alcoholic fatty liver disease (129/18) CEBPB_02_TTGCWCAAY (50/9) Fatty acid metabolism (42/10) 296 547 HNF4_01 (187/20) SGCA, FAM21C Parkinson's disease (107/17) Huntington's disease (158/22) CEBPB_02 (183/22) 172 2 447 Citrate cycle (TCA cycle) (27/9) SF1_Q6 (199/24)
62 204
Obese vs. Lean Obese vs. Lean MAZ_Q6 (1636/92) Upregulated genes Downregulated genes YY1_Q6 (378/31) Ribosome (125/25) SMAD_Q6 (178/19) c
Fat digestion PPAR signaling Citrate cycle and absorption Fatty acid metabolism Insulin signaling pathway Oxidative phosphorylation pathway
Obese T2D vs. Lean
Obese vs. Lean PC DBI CBL HK2 LIPE AKT2 TSC2 CPT2 DLAT SOS1 ACLY FASN CYC1 HRAS ACO2 PECR SDHB IDH3A IDH3B PLPP2 ACSL1 PDE3B PHKA1 PHKA2 CPT1B EIF4E2 NR1H3 ACAA1 ACAA2 ABCA1 DGAT2 COX7B PDHA1 MKNK2 ACOX1 PPARG ACADM ATP5F1 ATP5A1 ELOVL5 ADIPOQ MAPK10 COX6A1 AGPAT2 NDUFA6 NDUFA7 NDUFS2 NDUFS5 NDUFV1 SUCLG1 UQCRC2 PL2G12A NDUFA10 NDUFB11 PRKAR1A PRKAR2B UQCRFS1 Figure 4
a b Obese vs. Obese T2D vs. Obese vs. Lean Lean Obese T2D (132 DMR) (73 DMR) (100 DMR)
Decreased 86 37 53 methylation
Increased 46 36 47 methylation
c Obese T2D vs Lean Obese Increased methylated Decreased methylated Obese Increased methylated Decreased methylated vs. T2D vs. Lean Gene Length ∆Meth Gene Length ∆Meth Lean Gene Length ∆Meth Gene Length ∆Meth
1 ARL9 14 0.389 LOC400548 51 -0.583 1 MAFG 15 0.372 L1TD1 1 -0.442
2 B4GALNT3 1 0.376 NKX2-5 196 -0.534 2 KCNC3 14 0.352 BLOC1S4 15 -0.425
3 ZNF730 1 0.365 FLRT1 1 -0.413 3 DNAAF5 1 0.346 LINC01558 7 -0.366
4 FGF22 28 0.353 PCDH10 107 -0.397 4 HDAC5 20 0.343 FAM53A 1 -0.365
5 DEFB114 1 0.353 SQRDL 1 -0.396 5 ANKS6 10 0.341 PP14571 2 -0.350
6 GNG11 46 0.344 ACTR3B 50 -0.376 6 STAB1 6 0.333 PCDH10 76 -0.346
7 FRMPD4 1 0.339 GATA3 75 -0.369 7 GTF3C3 1 0.327 ZFP36L1 2 -0.339
8 TNS3 23 0.335 ELOVL3 29 -0.361 8 LARP1B 43 0.315 PARD3 16 -0.336
9 PCF11 1 0.332 MCU 1 -0.358 9 AMZ1 1 0.310 LRRC41 24 -0.320
10 HES1 45 0.328 RPTOR 1 -0.350 10 TMEM17 24 0.306 FBXL19 34 -0.317
d KEGG e KEGG Obese vs Lean Obese T2D vs Lean Figure 5
a Lean Obese Obese T2D
Proliferation Confluent Day 3 Day 15
SCORE Obese vs. Lean Obese T2D vs. Lean b (3906 DMR) (100 DMR) (305 DMR)
Decreased methylation 1901 60 233
Increased methylation 2005 40 72
KEGG c Increased methylation Decreased methylation d Adipogenic SCORE Gene Length ∆Meth Gene Length ∆Meth
ARHGAP27 1 0.079 NEURL3 1 -0.080
LOC1019283 36 48 0.068 THRAP3 8 -0.077 LMF1-AS1 31 0.064 ERGIC1 1 -0.072 NGF 70 0.061 ARHGAP8 22 -0.072 RBMX2 1 0.056 ZNF85 9 -0.072 BAIAP2L1 1 0.056 CARS2 35 -0.068 AGAP1 40 0.052 FBP1 4 -0.068 EVI5L 62 0.052 SHANK3 1 -0.067 ZNF469 19 0.051 VAT1 50 -0.067 TMEM105 116 0.050 DLGAP2 67 -0.065
e KEGG f KEGG Obese vs Lean Obese T2D vs Lean Figure 6 a Day -1 Day 0 Day 3 Day 8 Day 12 Day 16
Control C MDIR IR IR IR
TNF-α T MDIR IR IR IR
Palmitate P MDIR IR IR IR
Plating Treatment Proliferation Confluence Differentiation (D4) Differentiation (D8)
Effect of time: P < 0.001 b Effect of Interaction: P < 0.001 d
) 1.5 14 ) O ) l Control 5 o d 0
12 r e t 1
Tnf-a r
n l x i o ( 10 1.0
Palmitate c r O
( e
8 o t b e ** c e m *
6 n v u i t a 0.5 n
a b l 4 l r l e o e s R
C 2 (
* b 0 * A 0.0 0 1 2 3 4 5 6 7 8 Control Tnf-a Palmitate
Day EffectEffect of Insulin:of Insulin: P < P 0.05 < 0.05 EffectEffect of Interaction:of Interaction: P < P0.05 < 0.05 c e ****
0.15 30003000 * * ) U n
i 2500 n d
i 2500 e r t e t B o
( 0.10 r o 2000 r p e
2000 p c
g n g u / a
u 1500 / b
M 1500 r 0.05 M P o s P D 1000 b
D 1000 A 0.00 Insulin - + - + - + Control Tnf-a Palmitate Insulin Control- + Tnf-- a + Palmitate- + 25 Control Tnf-a Palmitate
f g 20 Ctnnb1 Rxra
s Terf1
e 15 Nudt12 n
e Jund G
Cul4b .
o 10 Eif2b2 Tcf3 N Nr2f1 Nfasc 5 Carnmt1 Ndufa7 Otulin 0 Hyper Hypo Diff
KEGG KEGG h Tnf-α vs. Control i Palmitate vs. Control Supplementary figure 1
Subject 1 Subject 2 Subject 3 Subject 4 Lean Subject 5 Subject 6 Subject 7 Subject 8 Obese Subject 9 Subject 10 Subject 11 Subject 12 Obese T2D Obese Supplementary figure 2
a
Lean Obese Obese T2D
Proliferation Confluent Day 3 Day 15
b
Condition Timepoint Condition Lean Obese Obese T2D Timepoint Proliferation Confluent Day 3 Day 15 Supplementary figure 3
a Obese vs Lean b Obese T2D vs Lean
c KEGG d KEGG Obese vs Lean Obese T2D vs Lean Supplementary figure 4
a b Obese vs Lean Obese T2D vs Lean
c d Obese vs Lean Obese T2D vs Lean
e
f Supplementary figure 5
Obese vs Obese T2D Lean vs Lean
Adipogenic score Supplementary figure 6
Nr2f1 Ctnnb1
1.5 1.5 Effect of Time: P < 0.05 Control Effect of Interaction: P < 0.05 n n o o S S i i
8 Tnf-
8 a s s s s 1 1
1.0 1.0 e e r o ** r o t t Palmitate p p
x ** x e e ** e e v v
i i t t ** A A a a l 0.5 l 0.5 ** N N e e R R R R m m 0.0 0.0 Pro Con D3 Pro Con D3
Rxra Terf1 1.5 1.5 Effect of Time: P < 0.05 Effect of Time: P < 0.05 n n o o S S i i 8 8 s s s s 1 1
1.0 1.0 e e r o r o t t p p
* x x e e * e e v v
i i * t t A A a a l l 0.5 0.5 N N e e R R R R m m 0.0 0.0 Pro Con D3 Pro Con