Integration of TGF-Β-Induced Smad Signaling in the Insulin- Induced Transcriptional Response in Endothelial Cells Erine H

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OPEN Integration of TGF-β-induced Smad signaling in the insulin- induced transcriptional response in endothelial cells Erine H. Budi1, Steven Hofman1, Shaojian Gao2, Ying E. Zhang 3 & Rik Derynck1*

Insulin signaling governs many processes including glucose homeostasis and metabolism, and is therapeutically used to treat hyperglycemia in diabetes. We demonstrated that insulin-induced Akt activation enhances the sensitivity to TGF-β by directing an increase in cell surface TGF-β receptors from a pool of intracellular TGF-β receptors. Consequently, increased autocrine TGF-β signaling in response to insulin participates in insulin-induced angiogenic responses of endothelial cells. With TGF-β signaling controlling many cell responses, including diferentiation and extracellular matrix deposition, and pathologically promoting fbrosis and cancer cell dissemination, we addressed to which extent autocrine TGF-β signaling participates in insulin-induced gene responses of human endothelial cells. Transcriptome analyses of the insulin response, in the absence or presence of a TGF-β receptor kinase inhibitor, revealed substantial positive and negative contributions of autocrine TGF-β signaling in insulin-responsive gene responses. Furthermore, insulin-induced responses of many genes depended on or resulted from autocrine TGF-β signaling. Our analyses also highlight extensive contributions of autocrine TGF-β signaling to basal gene expression in the absence of insulin, and identifed many novel TGF-β-responsive genes. This data resource may aid in the appreciation of the roles of autocrine TGF-β signaling in normal physiological responses to insulin, and implications of therapeutic insulin usage.

Cell responses to extracellular ligands ofen lead to changes in gene expression, resulting from receptor-mediated activation of signaling pathways. Tese then target genes through post-translational modifcations and changes in chromatin binding of DNA-binding transcription factors. Accordingly, extracellular ligands, such as growth factors, activate or repress sets of genes that are identifed as cognate target genes, responsive to the ligand. Te extent to which target genes are regulated ofen depends on signaling crosstalk or cooperation of the ligand-induced signaling pathway with other pathways, and are therefore thought of as positive or negative modifers of ligand-induced transcription responses. Similar to many growth factors, insulin, a hormone that is secreted by cells in the pancreas, controls the expression of a variety of insulin-responsive genes that in turn control many essential functions at the cellular level and regulate glucose uptake and metabolism1,2. Insulin action is initiated by its interaction with a cognate cell surface tyrosine kinase receptor complex, leading to activation of kinase pathways, most notably the PI3K-Akt-mTOR and Erk MAPK signaling pathways3. Downstream from ligand-receptor binding and induction of signaling are target genes that, in response to insulin, are activated or repressed, and therefore are seen as insulin-responsive4. A number of insulin target genes have been identifed and their mechanism of activation has been studied. On a larger scale, genome-wide studies provide better insight into the gene expression responses that are exerted in response to insulin, and these have been done in many diferent cells including hepatoma cells4–6, mouse fbroblasts7,8, osteoclasts precursors9, human skeletal muscle10, placenta11 and endothelial cells12,13. Although many studies have looked into the efect of insulin in these cells, most large scale analyses did not take

1Departments of Cell and Tissue Biology, and Anatomy, Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California at San Francisco, San Francisco, CA, 94143-0669, USA. 2Thoracic and Gastrointestinal Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892- 1906, USA. 3Laboratory of Cellular and Molecular Biology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892-4256, USA. *email: [email protected]

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into account the participation or integration of other signaling pathways that may contribute to insulin-induced transcriptional responses. We reported that Akt activation in response to insulin induces a rapid increase in cell surface TGF-β receptors in fbroblasts, epithelial cells, and endothelial cells, through mobilization of TGF-β receptors from an intracellular pool to the cell surface13,14. Increased cell surface presentation of TGF-β receptors confers increased sensitivity to TGF-β, thus enhancing autocrine TGF-β signaling responses, raising the possibility that the insulin-induced increase in autocrine TGF-β signaling participates in gene expression and cellular responses to insulin. Indeed, we showed that elevated autocrine TGF-β signaling in response to insulin contributed to insulin-induced angiogenic responses in cell culture and ex vivo13. Tese fndings invite questions regarding the extent to which TGF-β signaling participates and integrates in the response to insulin. For example, what biological processes, functions and pathways are controlled by insulin through enhanced TGF-β signaling or with contributions of TGF-β/Smad signaling? To address the extent to which insulin-induced upregulation of autocrine TGF-β signaling contributes to insulin-induced gene expression, we carried out genome-wide RNA-Seq analyses of human umbilical vein endothelial cells (HuVECs). Endothelial cells are a prime target of insulin15–17; they are exposed to insulin in the circulation, and mediate insulin transport from circulation into skeletal muscle1,18. Additionally, endothelial cells play central roles in the control of vascular homeostasis and integrity, infammatory responses, and pathogenesis of many diseases including cardiovascular diseases, diabetes, fbrosis, and cancer1,19–22. We performed paired RNA-Seq studies on HUVECs treated or not with insulin in the presence or absence of the TGF-β receptor kinase inhibitor, SB431542, that blocks TGF-β -induced Smad activation, thus the TGF-β /Smad-mediated transcription responses23,24. Our data revealed that both insulin and TGF-β signaling inhibition afect the expression of a large number of genes associated with various biological processes and signaling pathways. Venn analyses of the diferentially expressed genes identifed numerous genes that integrate TGF-β/Smad signaling into the insulin-induced response, and these genes associate with a variety of biological responses and signaling pathways. Consequently, the genome-wide transcription changes in response to insulin are substantially restricted when TGF-β signaling is prevented, indicating crosstalk and participation of TGF-β signaling in insulin transcriptional responses. Finally, we demonstrate that basal gene expression of endothelial cells in the absence of insulin depends on autocrine TGF-β signaling. Results Concentration-dependent, insulin-induced Smad2 and Smad3 activation in endothelial cells. We previously reported that insulin increases TGF-β responsiveness and autocrine TGF-β /Smad signaling by promoting the transport of TGF-β receptors to the cell surface, and that this stimulation of TGF-β receptor transport results from insulin-induced Akt activation14. We also reported that the increased autocrine TGF-β response contributes to the insulin-induced angiogenic response13. To better appreciate the extent to which increased autocrine TGF-β contributes to insulin-induced changes in gene expression, we performed a genome-wide survey of changes in gene expression in response to insulin in human umbilical cord endothelial cells HUVECs, and assessed the contribution of TGF-β-induced Smad activation to the transcriptomic response. Insulin-induced gene expression changes depend on the concentration and duration of insulin exposure4,12. We therefore frst evaluated the extent to which autocrine TGF-β signaling is activated in response to insulin. HUVECs were treated for 30 min with increasing concentrations of insulin. Te TGF-β activation were assessed by examining the Smad2 and Smad3 activation levels by immunoblotting for C-terminally phosphorylated Smad2 or Smad3. As shown in Fig. 1 (Supplemental Fig S1 for original blots), Smad2 and Smad3 activation levels increased, as we increased the insulin concentration. Te levels of phospho-Smad2 showed maximal increase at 100 nM insulin, while Smad3 activation increased in a concentration-dependent manner up to 500 nM insulin. Akt phosphorylation at Ser473 also increased in a concentration-dependent manner up to 500 nM of insulin. SB431542, an inhibitor of the type I TGF-β receptor TβRI, prevented activation of Smad2 and Smad3, but did not afect insulin-induced Akt activation. Similar to HUVECs, increasing concentration of insulin treatment of human microvascular lung endothelial cells (HMVEC-L) induced increased Smad2 and Smad3 activation and addition of SB431542 prevented the response (Supplemental Fig. S2 and S3 for original blots). Based on this result, we elected to perform our transcriptome analyses using cells stimulated by insulin at 100 nM, i.e. the concentration that was also used to evaluate the contribution of autocrine TGF-β signaling to insulin-induced angiogenic response of endothelial cells13.

Genome-wide transcription responses to insulin and autocrine TGF-β signaling. To defne the contribution of the insulin-induced increase in autocrine TGF-β/Smad signaling to the insulin-induced transcriptional response in endothelial cells, we treated cells with or without insulin for 90 min or 6 h in the absence or presence of the TβRI kinase inhibitor SB431542. Treatment of the cells with SB431542 in the absence of insulin allowed us to evaluate the contribution of autocrine TGF-β signaling to gene expression under basal conditions, and served as control for cells treated with insulin in the presence of SB431542 (Fig. 2A). RNA-Seq data were collected using paired-end sequencing of three samples each for insulin (ins), SB431542 (SB) and insulin + SB431542 (ins + SB), and two samples each for controls at 90 min and 6 h. Gene expression that was enhanced or repressed at two diferent time points was defned on the basis of absolute fold change of > (0.35 log2) and p ≤ 0.05 (Fig. 2B). Te RNA-Seq data obtained from insulin-, SB431542- and insulin + SB431542-treated cells were compared with data from control cells, and data from cells treated with insulin were compared with those from cells treated with insulin + SB431542 (Fig. 2B). Te full lists of genes that met the criteria are provided in Supplemental Table 1 for 90 min treatment, and Supplemental Table 2 for 6 h treatment. Based on our cut-of criteria, we identifed 442 and 545 insulin-responsive genes at 90 min or 6 h, respectively. Conversely, SB431542 treatment identifed 744 and 1262 genes whose expression levels under basal cell culture

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Figure 1. Dose response of insulin-induced Smad2 and Smad3 activation. HUVECs were treated with insulin (Ins) in the presence or absence of SB431542 for 30 min using the indicated concentrations. Smad2 and Smad3 activation were assessed by immunoblotting for C-terminally phosphorylated Smad2 (pSmad2) or Smad3 (pSmad3). Insulin-induced Akt activation was assessed by immunoblotting for phosphorylated Akt (pAktS473). GAPDH was used as loading control. Te samples from the same experiment were run on 2 gels that were processed in parallel.Original blots are presented in Supplementary Fig. 1.

conditions were enhanced or repressed by autocrine TGF-β signaling. Te majority of genes that were responsive to insulin or SB431542 responded by increasing their expression (Fig. 2B). Overall, the number of genes identifed as regulated genes was higher afer 6 h of treatment, when compared to 90 min treatment. Of the 275 genes that were upregulated afer 90 min insulin treatment, 64% (175/275) continued to be upregulated afer 6 h, while 24% (41/167) of the insulin-responsive genes that were downregulated at 90 min stayed repressed afer 6 h (Fig. 2C,D, Supplemental Table 3). Among those genes that were upregulated afer 90 min in response to SB431542, 60% (248/410) continued to be upregulated afer 6 h, while 48% (159/334) of the genes that were downregulated at 90 min stayed repressed afer 6 h (Fig. 2E,F, Supplemental Table 3). We functionally annotated the 175 genes whose expression was enhanced at both 90 min and 6 h in response to insulin using the DAVID bioinformatics tool. Our results revealed enrichment of pathways known to associate with insulin, such as Ras, PI3K-Akt, ABC transporter, pathways in cancer, and MAPK signaling, as well as pathways that are less known to associate with the insulin response, such as Rap1 signaling, cell migration, proliferation, adhesion, angiogenesis, blood vessel development, hematopoiesis, immune system development, and response to stimulus (Fig. 2C, Supplemental Table 4). Enrichment analysis on the 41 genes that were repressed in response to insulin at both 90 min and 6 h revealed association with lupus erythematosus and alcoholism, and cell cycle and chromatin remodeling processes (Fig. 2D, Supplemental Table 5). Enrichment analysis of the 248 genes that were continuously upregulated in response to SB431542, and therefore were repressed by autocrine TGF-β signaling, revealed many pathways including FoxO, osteoclast-related, aldosterone-related, Rap1, AMPK, cGMP-PKG, PI3K-Akt, calcium signaling, and MAPK signaling (Fig. 2E, Supplemental Table 6). 520 gene ontology (GO) terms linked to the 248 upregulated genes, with the top 40 GO terms including kidney fltration, regulation of heparin sulfate metabolic processes, repression of wound healing, interferon signaling, promotion of endothelial proliferation and autophagy, T cell activation, blood vessel sprouting, and placental development (Fig. 2E, Supplemental Table 6). Functional annotation of the159 genes that were continuously repressed in response to SB431542, and therefore were stimulated by autocrine TGF-β signaling, revealed TGF-β pathway signaling (as expected), transcriptional misregulation in cancer, and FoxO and Ras signaling, while gene ontology terms linked to blood vessel development, negative regulation of RNA, gene expression, cellular metabolic processes and cell cycle (Fig. 2F, Supplemental Table 7).

Increased autocrine TGF-β responsiveness contributes extensively to the insulin-induced transcriptome changes. To gain insight into the contribution of autocrine signaling to the genome-wide gene expression changes in response to insulin, we performed RNA-Seq-based Venn analyses of diferentially expressed genes at 90 min and at 6 hours of treatment (Fig. 3A–C).

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Figure 2. RNA sequencing (RNA-Seq) of HUVECs treated or not with insulin in the presence or absence of the TGF-β signaling inhibitor SB431542. (A) Scheme of the RNA-Seq experimental design. mRNA collected from HUVECs treated with 100 nM insulin in the presence or absence of 5 µM SB431542 for 90 min or 6 h were reverse transcribed and cDNA libraries were generated that were then subjected to sequencing and analysis. (B) Summary of RNA-Seq diferential expression analyses at 90 min and 6 h afer initiation of treatment. Gene expression changes with an absolute fold change ≥ 1.27 or log 0.35 and p < 0.05 were considered signifcant. (C–F) Venn analyses (top lef) showing the distribution of diferentially expressed genes that were upregulated or downregulated in response to insulin (C,D) or SB431542 (E, F) afer 90 min (t = 90 min) and 6 h (t = 6 h) of treatment. KEGG pathway analyses and top 40 gene ontology process enrichment groups associated with genes that showed increased or decreased expression at both 90 min and 6 h treatment, Number of genes and p-values are shown on the right side of the graph.

Among the 442 genes that were induced or repressed in response to insulin at 90 min, 239 genes or 54% had their expression afected by the presence of SB431542. Te 442 insulin-responsive genes also showed a 56% (249/442 genes) overlap with those that are enhanced or repressed in response to insulin + SB431542, while 60% (445/744) of the SB431542-responsive genes were shared with those whose expression is changed in response to insulin + SB431542 (Fig. 3A, Supplemental Table 8). Evaluating the diferentially expressed genes that are shared by the three comparison groups identifed 196 genes whose expression is afected by insulin, SB431542, as well as insulin + SB431542. We note that, of the 442 insulin-responsive genes, only 150 (34%) were not afected by SB431542, and, thus, did not have a contribution of autocrine TGF-β signaling (Fig. 3A, Supplemental Table 8). Similar analyses of regulated genes afer 6 h of treatment led to similar conclusions, but involved a larger number of diferentially regulated genes and larger amplitudes in their regulation (Fig. 3B). In these analyses, the expression of 545 genes was enhanced or repressed by insulin, and 253 (46%) of these were also controlled by autocrine TGF-β signaling, i.e. were afected by SB431542. 240 (44%) out of the 545 genes were not afected

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A t = 90 min Ctrl vs Ins Ctrl vs SB431542 196 Ctrl vs Ins 442 744 Ctrl vs SB Ctrl vs Ins+ SB 43 F2RL10.58124 -0.452231-0.47687 150 256 43 Ctrl vs Ins Ctrl vs SB C8orf4 0.99764-1.25712 196 RARA 0.403422 -0.59741 249 SNAI22.05818 -0.998176 53 53 Ctrl vs Ins Ctrl vs Ins+ SB 235 249 Ctrl vs SB Ctrl vs Ins+ SB Ctrl vs Ins+SB431542 733

B 210 Ctrl vs Ins t = 6 h Ctrl vs SB Ctrl vs Ins+ SB Ctrl vs SB431542 Ctrl vs Ins 1262 C8orf4 0.742131 -0.910055-1.00096 PRKAR2B-0.407443 0.5111810.546588 545 FAT1 0.618066 -0.562902-0.567318 SLC1A1 -0.6522840.71021 0.547662 F2RL10.533872-0.819099 -1.14 ADAMTS1-0.703238 1.863351.83 347 240 43 PTPRE0.465018-0.543267 -0.474093 EFNA10.451178-0.671745 -0.881592 NEDD90.414637-1.02624-1.12 210 MERTK0.386724-0.498384 -0.439458

SNAI21.14344 -1.30492 52 662 Ctrl vs Ins 43 GJA4 1.00616 -1.94798 Ctrl vs SB SLC16A50.821619-0.967259 FMNL30.392061-0.45131 PTGER4 -0.471051 0.434339 388 Ctrl vs Ins 52 C13orf15 0.744257 -0.537109 Ctrl vs Ins+ SB SIRPB2 0.599-0.656241 Ctrl vs Ins+SB431542 PDGFB0.42677 -0.417989

1312 662 Ctrl vs SB Ctrl vs Ins+ SB - 33 log 2 expression C t = 90 min t = 6 h Ctrl vs Ins Ctrl vs SB431542 Ctrl vs Ins Ctrl vs SB431542 545 1262 442 744 347 Ins vs Ins+SB431542 240 443 4343 150 256 Ins vs Ins+SB431542 631 967 210 196 183 22449 541 452 52 662 53 t = 90 min t = 6 h 261 370 89 94 89 370 597 235 388 21 68 302

Ctrl vs Ins+SB431542 Ctrl vs Ins+SB431542 733 1312

Figure 3. Venn and cluster analyses of diferentially expressed genes in HUVECs treated with insulin (Ins), SB431542 or insulin + SB431542 (Ins + SB431542), in comparison to control. (A,B) Venn diagram showing the distribution of diferentially expressed genes ( > 0.35 log2 in expression and p ≤ 0.05) at 90 min or 6 h, comparing Ins versus SB431542 and/or Ins + SB431542 treatments. Clustered heatmaps to the right show diferentially expressed genes that are shared between three or two comparison groups. Red color shows upregulated genes while blue color indicates downregulated gene expression compared to control. Values shown are log2-based. Diferentially expressed genes with opposing expression trends when comparing treatments are shown below or besides the heatmap. (C) Comparisons between 90 min and 6 h treatment groups. Te pools of shared genes among 541 diferentially expressed genes at 90 min (C, lef) and 967 genes afer 6 h (C, right) were compared with those that overlapped between Ins and Ins + SB431542 treatments at the times shown. Further comparison of genes that are shared with Ins versus Ins + SB431542 at the two time points are shown in purple in the center.

by autocrine TGF-β signaling, and 210 genes (38%) had their expression regulated by insulin, SB431542 and insulin + SB431542 (Fig. 3B, Supplemental Table 9). Together, the Venn analyses at 90 min and 6 h of treatment revealed an extensive overlap between genes that are responsive to insulin and those that are controlled by autocrine TGF-β signaling. Tese results reveal signifcant participation of autocrine TGF-β signaling in insulin-induced gene responses. Heat maps of the diferentially expressed genes afer 90 min and 6 h treatment provided further insight into integration of autocrine TGF-β signaling in insulin-induced gene expression changes. For most genes, blocking TGF-β signaling using SB431542 afected the amplitude of insulin-induced gene activation or repression, without fully reversing the response. However, several genes that were induced in response to insulin were repressed when TGF-β signaling was prevented by SB431542, and a few genes saw insulin-induced repression reverted to activation in response to SB431542 (Fig. 3A,B, Supplemental Tables 8 and 9). Notably, in the 196-gene cluster that is shared by the three comparison groups at 90 min of treatment, the F2RL1 gene that encodes the proteinase-activated receptor 2 (PAR2)25, and three of the 43 genes that are shared between insulin- and SB431542-regulated genes, i.e. c8orf4, RARA and SNAI2, had their basal and insulin-induced expression repressed by SB431542. SNAI2 gene encodes a master transcription factor that drives epithelial- and endothelial-to-mesenchymal transition26. c8orf4 encodes Tyroid Cancer Protein-1 (TCP-1), which functions as positive regulator in the Wnt/b-catenin signaling pathway27, and RARA encodes the retinoic acid receptor-α, which controls processes in development, diferentiation, apoptosis and granulopoiesis28,29 (Fig. 3A). Among the genes identifed at 6 h of treatment, additional genes showed reversal of their insulin-induced activation or repression when autocrine TGF-β signaling was blocked (Fig. 3B). Among those regulated by insulin, SB431542

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A C t = 90 min t = 6 h Ctrl vs Ins Ctrl vs SB431542 Ctrl vs Ins Ctrl vs SB431542 545 1262 347 442 744 240 443 4433 150 Ins vs Ins+SB431542 256 541 221010 Ins vs Ins+SB431542 967 631 196 183 52 662 224949 452 5977 5353 37370 94 261 89 388 235 Ctrl vs Ins+SB431542 1312 Ctrl vs Ins+SB431542 733 KEGG Pathways # genes / p-value hsa04330:Notchsignaling pathway 7/ 6.01E-04 KEGG Pathways # genes / p-value hsa04350:TGF-betasignaling pathway 9/ 4.97E-04 hsa05220:Chronicmyeloid leukemia 7/ 0.00496 8 / 1.63E-05 hsa04068:FoxO signalingpathway hsa04390:Hippo signalingpathway 14 /2.60E-05 4 / 0.01525 hsa04668:TNF signalingpathway 9/ 0.00243 hsa04350:TGF-β signalingpathway hsa05410:Hypertrophiccardiomyopathy 6/ 0.02865 hsa05202:Transcrip onal misregula on in cancer 4 / 0.08568 hsa05414:Dilated cardiomyopathy 6/ 0.03776 hsa04068:FoxOsignaling pathway 9/ 0.00943 0246810 hsa04015:Rap1 signaling pathway 11 /0.01761 Enrichment score hsa05200:Pathways in cancer 20 /9.90E-04 hsa05205:Proteoglycansincancer 10 /0.03269 hsa04014:Ras signalingpathway 11 /0.02769 B hsa04510:Focal adhesion 10 /0.03841 hsa05206:MicroRNAs in cancer 13 /0.02407 Gene Ontology Biological Process hsa04151:PI3K-Akt signaling pathway 15 /0.01994

# genes / p-value 0 2 4 6 8 GO:0048514~blood vessel morphogenesis 17 / 1.48E-9 D Enrichment score GO:0001568~blood vessel development 19 / 2.33E-10 GO:0001944~vasculature development 19 / 5.85E-10 GO:0072359~circulatorysystemdevelopment 25 / 9.71E-12 Gene Ontology Biological Process # genes / p-value GO:0072358~cardiovascular system development 25 / 9.71E-12 GO:0051270~regulation of cellular componentmovement 21 / 1.04E-9 GO:0003007~heart morphogenesis 24 /1.49E-10 GO:0071495~cellularresponsetoendogenousstimulus 26 / 1.73E-10 GO:0001501~skeletal system development 35 /1.48E-10 GO:0048646~anatomicalstructure formationinvolvedinmorphogenesis 25 / 5.68E-10 GO:0001568~bloodvesseldevelopment 40 /1.23E-11 GO:0042981~regulation of apoptoticprocess 30/ 5.45E-12 GO:0030334~regulationofcellmigration 47 /1.94E-13 GO:0043067~regulationofprogrammedcelldeath 30/ 6.85E-12 GO:2000145~regulationofcellmotility 49 /1.63E-13 GO:0016477~cell migration 25 / 1.00E-9 GO:0010941~regulationofcelldeath GO:0048729~tissuemorphogenesis 41 /3.23E-11 31 / 5.76E-12 GO:0001944~vasculaturedevelopment 40 /6.96E-11 GO:0009719~responsetoendogenousstimulus 31 / 1.10E-11 GO:0051674~localizationofcell 26/ 1.93E-9 GO:0051270~regulationofcellularcomponentmovement 52 /6.83E-14 GO:0048870~cellmotility 26/ 1.93E-9 GO:0040012~regulationoflocomotion 49 /7.74E-13 GO:0006915~apoptotic process 34/ 1.27E-12 GO:0009887~organ morphogenesis 58 /1.40E-13 GO:0012501~programmedcelldeath 34 / 6.41E-12 GO:0072358~cardiovascular system development 54 /2.95E-12 GO:0040011~locomotion 27 / 6.97E-9 GO:0072359~circulatorysystem development 54 /2.95E-12 GO:0008219~cell death 34 / 2.93E-11 GO:0016477~cell migration 65 /5.70E-14 GO:0006928~movement of cell or subcellularcomponent 29 / 9.87E-9 GO:0060429~epithelium development 57 /7.07E-12 GO:0009966~regulationofsignaltransduction 37 / 2.54E-9 GO:0035556~intracellular signal transduction 36/ 5.15E-9 GO:0022603~regulationofanatomical structuremorphogenesis 56 /1.63E-11 GO:0010646~regulationofcellcommunication 40 / 6.35E-10 GO:0051674~localizationofcell 69 /1.14E-13 GO:0010033~response to organicsubstance 37 / 6.47E-9 GO:0048870~cell motility 69 /1.14E-13

GO:0023051~regulationofsignaling 40 / 1.04E-19 GO:0040011~locomotion 70 /2.55E-11 0 GO:0006928~movement of cell or subcellularcomponent 75 /2.73E-10 1 2 3 45 6 7 8 GO:0071310~cellular response to organicsubstance 86 /1.65E-10 Enrichment score GO:0009966~regulationofsignaltransduction 107/ 2.86E-13 GO:0007166~cell surfacereceptorsignaling pathway 102 /7.04E-12 GO:0010646~regulation of cell communication 114 /4.05E-13 GO:0023051~regulation of signaling 115/ 5.06E-13 GO:0035556~intracellularsignal transduction 98 /1.74E-10

0 1 23 456 Enrichment score

Figure 4. Functional annotation of diferentially expressed genes afer 90 min and 6 h treatment with insulin (Ins), SB431542 or insulin + SB431542 (Ins + SB431542). Enrichment analysis of the 89 genes at 90 min (A,B) and 370 genes at 6 h (C,D) analyzed using the DAVID functional annotation program. KEGG pathway (A,C) and the top 25 GO biological process terms (B,D) associated with the shared diferentially expressed genes are shown. Te number of genes and p-values associated with each term are shown on the right side.

and insulin + SB431542, seven genes, including c8orf4 and F2RL1 that were already induced at 90 min, and FAT1, PTPRE, EFNA1, NEDD9, and MERTK were induced by insulin but inhibited by SB431542 or insulin + SB431542, and three genes were downregulated by insulin and upregulated by SB431542 and insulin + SB431542. Of the 43 genes that were shared by the insulin- and SB431542-reponsive gene groups at 6 h, fve showed reversal of the insulin response by SB431542. SNAI2, GJA4 (encoding a gap junction protein)30, SLC16A5 (encoding a mono- carbohydrate transporter)31 and FMNL3 (formin-like 3)32 were upregulated in response to insulin but repressed by SB431542, whereas the PTGER4 gene, encoding a prostaglandin E2 receptor33, was inhibited by insulin and induced by SB431542. Among the 52 diferentially expressed genes that were shared by the insulin and insulin + SB431542 groups, three showed opposing expression patterns. Tese were c13orf15, which encodes a cell cycle regulator34,35, SIRPB2 encoding signal regulatory protein β236 and PDGFB, which encodes a well-known growth factor and angiogenic mediator37,38 (Fig. 3B). As apparent from these analyses of heat maps, increased autocrine TGF-β signaling in response to insulin helps defne the insulin-induced activation or repression of a very substantial fraction of insulin-responsive genes, and fully mediates the insulin-induced response of a select set of genes. Finally, comparing cells treated with insulin with those treated with insulin + SB431542 identifed 183 and 631 genes with diferential expression at 90 min and 6 h, respectively (Fig. 2B, Supplemental Tables 1 and 2); thus, autocrine TGF-β signaling contributed to their insulin response at 90 min and 6 h afer adding insulin. As shown in Fig. 3C (lef) and Supplemental Table 10, 89 of these 183 genes identifed at 90 min, were among the overlapping genes identifed in Fig. 3A. Similarly, of the 631 genes identifed by comparing insulin and insulin + SB431542 treatments at 6 h, 370 were also among the overlapping genes identifed in Fig. 3B (Fig. 3C, right, Supplemental Table 10). Of these 89 and 370 genes, 68 were genes that were shared by those at the two time points (Fig. 3C, Supplemental Table 10).

Functional annotation of TGF-β-dependent insulin target genes. We next aimed to gain further insights into the functional roles of those genes that were up- or downregulated in response to insulin, dependent on autocrine TGF-β signaling. For this purpose we focused on both the 89 and 370 genes identifed afer 90 min or 6 hours insulin treatment (Fig. 4A–D, Supplemental Table 11), and performed Gene Ontology (GO) and pathway analysis based on the KEGG database. Functional pathway annotation using the DAVID bioinformatics tool of the common 89 diferentially genes at 90 min suggested three pathways, i.e. FoxO and TGF-β signaling, and transcription misregulation in cancer (Fig. 4A, Supplemental Table 12). Ontology analysis of these genes resulted in 772 categories related to various biological responses (Supplemental Table 12). Most notably,

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A t = 90 min t = 6 h Ctrl vs Ins Ctrl vs SB431542 Ctrl vs Ins Ctrl vs SB431542 545 1262 744 347 Ins vs Ins+SB431542 240 434 4343 150 256 631 967 541 Ins vs Ins+SB431542 210 442 196 183 249249 452 52 662662 53 261 89 94 370 597 235 388

t = 90 min t = 6 h Ctrl vs Ins+SB431542 Ctrl vs Ins+SB431542 89 370 733 1312 21 68 302

KEGG Pathways 45B 0 D F2 6 7 32 AD F6 UR AD AD X0 FSF1 K3 F2 S2 PL FB TN FG SM GD SM IR KL SM hsa04068:FoxO signaling pathway hsa04350:TGF-betasignalingpathway

Gene Ontology Biological Process 1 B 4 3 IP 0 1 S1 5 15 2 7 N TL A F4 32 K 1 1 2 1F A1 F1 4 1 D T1 1 D4 F PA1 D6 P5 B MT 3 4 R RP I2 2 C RE SF L1 1 F6 IT RT E AD UR F2 D S R4 A N1 X4 G K GP L2 3 S1 B1 AI GF XO O C1 F S F2 GS P BD FS K3 C1 R TE S2 AF CN RH 1 GS IK MA T NA N R AB AD TN AN C8 CI CT DD DL DU ED ESM ET FB GA F2 GD GP HE H G NR KL M ME I NRA RHO RUNX PD PL PM R PD PT PT RA S SA MS SK IL S SM S TH T SM SN S SO SPAT 1 1 12 12 12 1 12 12 12 1 12 1 1 1 GO:0048514~blood vessel morphogenesis 2 2 23 23 2 2 2 2 GO:0001568~blood vessel development 35 34 34 35 3 3 34 345 3 34 34 56 35 3 GO:0001944~vasculature development 4 45 4 45 45 49 47 4 45 GO:0048729~tissuemorphogenesis 56 5 5 56 5 56 56 5 56 5 56 GO:0072358~cardiovascular systemdevelopment 61 69 6 69 67 67 6 6 6 6 67 61 6 GO:0072359~circulatory system development 78 78 78 7 78 7 78 78 78 78 7 GO:2000145~regulation of cell motility 8 8 89 89 89 89 89 GO:0040012~regulation of locomotion 9 9 9 9 9 91 91 91 91 91 91 9 91 9 GO:0016477~cell migration 10 10 1 1 10 10 1 10 1 1 10 10 10 10 1 10 10 10 01 01 01 01 01 0 0 0 01 0 GO:0071495~cellularresponsetoendogenousstimulus 11 11 1 1 1 1 11 1 11 11 1 1 1 11 11 11 11 11 11 11 11 11 11 1 11 1 11 GO:0060429~epitheliumdevelopment 12 12 12 1 12 12 12 12 12 12 2 2 21 2 2 21 21 2 2 2 21 GO:0048646~anatomical structureformation in morphogenesis 1 1 13 1 13 1 1 13 13 13 13 1 1 31 31 31 3 31 31 31 3 3 3 3 31 31 31 3 3 GO:0048870~cell motility 1 14 1 1 1 1 1 14 1 1 14 1 1 41 41 4 41 41 41 4 4 4 4 41 42 4 41 41 4 41 41 4 GO:0051674~localizationofcell 1 15 15 1 15 1 1 15 15 1 15 15 15 15 15 1 1 5 5 51 52 5 51 5 5 5 5 5 51 51 51 51 GO:0009719~response to endogenous stimulus 16 16 16 1 1 16 1 1 16 16 16 1 1 16 6 61 6 6 61 61 61 6 6 61 6 GO:0010648~negativeregulation of cell communication 17 1 1 1 1 1 17 17 17 17 1 17 1 1 7 7 7 7 7 7 72 7 72 7 72 7 72 GO:0023057~negativeregulationofsignaling 18 1 1 1 18 18 18 1 1 18 1 18 18 18 1 18 8 8 8 8 8 8 81 8 81 8 8 8 GO:0042981~regulationof apoptotic process 19 1 19 1 1 19 19 1 1 1 1 19 1 19 1 19 1 1 1 9 9 9 9 9 9 9 9 9 9 9 92 9 9 GO:0043067~regulation of programmedcell death 2 20 2 20 20 20 20 20 2 20 20 20 2 0 0 0 0 0 0 0 0 0 0 0 GO:0048585~negative regulation of response to stimulus

Figure 5. KEGG pathway and gene ontology biological process enrichment analysis of 68 diferentially expressed genes that are shared between the 90 min and 6 h treatment groups. Te KEGG pathways and top 20 gene ontology biological process terms with associated genes are listed in the panel.

the top 25 functional biological process categories, based on the p-value, related to blood vessel morphogenesis and development, apoptosis, cell migration and movement, as well as regulation of signal transduction (Fig. 4B, Supplemental Table 12). Analyzing the 370 genes identified after 6 h treatment using DAVID bioinformatics software identified 22 enriched pathways based on the KEGG database, and 1270 gene ontology terms for biological responses (Fig. 4C,D, Supplemental Table 13). Tese included FoxO and TGF-β signaling that were already identifed at 90 min, and now again enriched at 6 h. As expected and reported for insulin39, also Ras signaling and PI3K-Akt pathway genes were enriched. Additional pathways included Notch, Hippo, TNF and Rap1 signaling, as well as cancer-associated regulation pathways (Fig. 4C, Supplemental Table 13). Te top 25 functional biological process categories, based on the p-value, continued to relate to morphogenesis of the heart and skeletal system, blood vessel and vasculature development, cell migration and movement, as well as regulation of signal transduction (Fig. 4D, Supplemental Table 13). Tese results were consistent with the annotated pathways for diferentially expressed, insulin-responsive genes at 90 min. DAVID functional gene annotation analysis based on the 68 genes that are shared by the 90 min and 6 h treatments highlighted the importance of the FoxO and TGF-β signaling pathways and revealed 657 gene ontology term categories for biological processes (Fig. 5, Supplemental Table 14). Te top 20 ontology processes (Fig. 5, bottom), were consistent with those at 90 min and 6 h (Fig. 4). Together, these analyses further support the notion that increased autocrine TGF-β signaling in response to insulin integrates into insulin-induced gene expression responses in blood vessel development, tissue morphogenesis, cardiovascular development, cell movement, apoptosis and programmed cell death, as well as cellular metabolic processes.

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Confrmation of RNA-Seq results using qRT-PCR. To validate the RNA-Seq results, we selected 20 diferentially regulated genes among the TGF-β-dependent, insulin-regulated genes (Fig. 6) and evaluated the efects of insulin in the presence or absence of autocrine TGF-β signaling using qRT-PCR analysis (Fig. 6A). Teir expression patterns correlated well to those seen using RNA-Seq, although quantitative diferences between qRT-PCRT and RNA-Seq results were apparent, especially with those that showed low RNA-Seq fold changes, as expected. For example, qRT-PCR analysis showed that insulin treatment induced expression of SNAI2, c8orf4, F2RL1, SPATA13, HIVEP1, BHLHE40, MERTK, and that blocking autocrine TGF-β signaling with SB431542 substantially reduced the expression of most genes, including SNAI2, c8orf4, F2RL1, SPATA13, HIVEP1, BHLHE40, DDIT4, MERTK, ESM, HRH1, NRARP, PDGFA, ABL2, PTGS2, SMAD7 and CTGF (Fig. 6A). In contrast, autocrine TGF-β signaling dampened the responses of some genes, e.g. PHLDA1, IRS2 and ESM1. Tese qRT-PCR data are mostly consistent with the RNA-Seq fndings, showing similar patterns for insulin-induced changes and efects of SB431542 inhibition (Fig. 6B, Supplemental Table 15). Blocking TGF-β signaling using the pan-TGF-β neutralizing 1D11 antibody, which prevents ligand binding to its receptors, confrmed the participation of autocrine TGF-β signaling in insulin-induced expression changes of select target genes, similar to addition of SB431542 (Supplemental Fig. S4). Among the 20 genes comparing insulin treatment with control, the direction of fold change of TNFSF10, PHLDA1 and ESM difered between RNA-Seq and the qRT-PCR. Increasing or decreasing the concentration of insulin or SB431542 showed dose-dependent changes in the mRNA levels (Supplemental Fig. S5). Te efects of insulin in HMVEC-L cells in the presence or absence of autocrine TGF-β signaling, i.e. using SB431542 or 1D11 antibody to neutralize the ligand (Supplemental Figs. S6 and S7), were similar to the efects of insulin and SB431542 on gene expression in HUVECs (Fig. 6).

Regulatory gene sequences of TGF-β-dependent insulin target genes. Our RNA-Seq results revealed a large variety of genes that respond to both TGF-β and insulin, and illustrated the integration of autocrine TGF-β signaling in the insulin transcriptomic responses. We therefore anticipated to locate cis-regulatory sequence motifs known to respond to insulin and TGF-β/Smad signaling. Insulin signaling has been linked to target gene activation or repression through binding of transcription factors on insulin response elements (IREs). At least eight distinct consensus insulin response sequences (IRSs) have been identifed40–45. Scanning the 5 kbp DNA proximal to the transcription start site of fve insulin- and TGF-β-responsive genes that were identifed using RNA-Seq, i.e. PHLDA1, F2RL1, TNFSF10, C8ORF4, and SNAI2, for published sequence motifs revealed known insulin response elements and Smad binding motifs (Fig. 7A). Accordingly, these fve genes showed, by qRT-PCR, to have insulin-responsive expression that is modulated by autocrine TGF-β signaling (Fig. 6). Specifcally, they all contained TGF-β -responsive Smad binding elements (SBEs), as well as serum response elements (SREs) and TTF2 binding sites, which were shown to confer insulin responsiveness. Te insulin-responsive PEPCK binding site was found in the PHLDA1, TNFSF10, and SNAI2 regulatory sequences but not in F2RL1 and C8ORF4, while a TPA-response element that also has been linked to insulin responsiveness was observed in F2RL1, TNFSF10 and SNAI2. Tus, each of the fve genes showed that at least two insulin response elements and one Smad binding sequence (Fig. 7B). We also tested the efects of TGF on a subset of the genes shown in Fig. 6 and saw the activation or inhibition efect of TGF-β (Suppl. S8). Tese analyses further support and may explain our fndings that these genes respond to insulin and integrate TGF-β signaling in their response.

RNA-Seq profling reveals novel early TGF-β target genes. In addition to insights in the contributions of TGF-β signaling to the insulin-induced genomic response, our RNAseq results at 90 min allowed us to identify novel early target genes of the TGF-β/Smad pathway, i.e. as opposed to secondary gene activation that may result from Smad-mediated expression of transcription factors. Indeed, comparing gene expression in the absence versus presence of SB431542 identifed many genes that are repressed or activated by SB431542 at 90 min (Fig. 2, Supplemental Table 1, and Suplemental Table 2). Among these, we focused on genes that were transcrip- tionally repressed by SB431542 at 90 min. We tested fve early Smad/TGF-β-dependent genes that were not known to be TGF-β responsive, i.e. MAP1S, EHD4, c20orf112, SPATA13 and NRARP (Fig. 8A), and compared their responses with SMAD7, an established direct TGF-β /Smad-responsive gene (Fig. 8A). MAP1S, originally identified as c19orf5, encodes a widely expressed microtubule-associated protein that promotes autophagy, and, at increased expression, associates with poor prognosis of ovarian cancer46–48. EHD4, which stands for Eps15 Homology Domain 4 encodes an endocytic regulatory protein that has not been studied much49,50. C20orf112 encodes nucleolar protein 4-like (NOL4L), a largely uncharacterized protein found in the cytoplasm and nucleus51. Te protein encoded by SPATA13 associates with cytoskeletal remodeling during migration, adhesion assembly and disassembly52,53. NRARP, which stands for Notch-regulated ankyrin repeat protein, acts downstream of Notch signaling, and functions in cancer progression, cancer stem cell self-renewal, angiogenesis, as well as in patterning, and development54–57. Since direct TGF-β/Smad target genes have regulatory DNA sequences that recruit Smad complexes, we performed an in silico search for Smad3-binding elements (SBE) motifs using binding profles from the JASPAR CORE database of experimentally defned transcription factor binding sites for Smad358. A score is calculated for the probed sequence that provides a measure of similarity to the Smad3 transcription factor consensus sequence. Supporting our RNAseq data and PCR results, we found consensus Smad3 transcription factor binding sites, i.e. the GTCTAGAC sequence, in the fanking regions of all fve early TGF-β target genes; their presence was experimentally confrmed through PCR analysis (Fig. 8B). Our in silico analysis of transcription factor binding sites for Smad3 is consistent with their direct regulation by TGF-β/Smad signaling.

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A t = 90 min t = 6 h Ctrl vs Ins Ctrl vs SB431542 Ctrl vs Ins Ctrl vs SB431542 545 1262 442 744 347 Ins vs Ins+SB431542 240 434 43 150 256 Ins vs Ins+SB431542 631 967 210 196 183 249249 541 452 52 662 5353 t = 90 min t = 6 h 261 370 370 89 94 89 597 Ctrl vs Ins+SB431542 235 388 21 68 302 Ctrl vs Ins+SB431542 733 1312 n n n SNAI2 90 m SNAI2 6 h C8orf4 90 m n C8orf4 6h TNFSF10 90 m TNFSF10 6h PHLDA1 90 m PHLDA1 6 h o io * io 0.06 ** 0.06 s

* s ** ** * * * 1.6 0.06 2.5 2 1.6 * e

** 1.6 * * * * 2 1.2 0.06 r * * ressio 1.6 1.4 * p 1.4 p 1.4 1.4 x xpress x 1 2 expression

expressi 1.2 expression expression 1.5 1.2 1.2 * 1.2 expression 1.5

Ae 1 ** 1 1 1 0.8 1.5 NA N

0.8 RN Ae 1 RNA 0.8 RNA RNA RN Ae

0.8 0.8 RNA 1 0.6 m m mR 1 m m mR 0.6 0.6 0.6 m 0.6 0.4 0.5 0.4 0.4 0.4 0.5 em 0.4 iv 0.5 0.2 0.2 0.2 t 0.2 0.2 0 0 0 0 0 0

0 0 Relative Relative Relative Relative Relative Rela Relative Ctrl Ins Ctrl Ins Ctrl Ins Ctrl Ins Relative Ctrl Ins Ctrl Ins Ctrl Ins Ctrl Ins n 6 h SPATA13 90 m SPATA13 6 h HIVEP1 90 m n HIVEP1 6 h

o F2RL1 6 h CITED2 90 m CITED2

F2RL1 90 m o * 0.06 i 0.06 s 2 2 * 1.6 sion 1.2 1.6 s 1.4 * * 0.06 1.6 0.06

1.4 e * ession 1.2 1.4 r 1.4 1.4 pres pr 1 pression p pression 1.2 pression 0.06 1.5 1.5 1.2 1.2 1.2 expressi ex ex expression ex ex 1 ex 1 ex 0.8 1 1 1 0.8

NA 0.8 NA 1 1 0.8 0.8 0.6 0.8 R 0.6 RNA 0.6 0.6 0.6

m 0.6 mRNA mRNA mRNA m mRNA mR mRNA 0.4 0.4 0.4 0.5 0.5 0.4 0.4 0.4 ve ve ve ve ve i i i i

i 0.2 t t 0.2 t 0.2 t 0.2 0.2 0.2 at la l la

e 0 0 0 0 0 e 0 0 0 R Re Relative Relative Rela R Relative Ctrl Ins Ctrl Ins Rela Ctrl Ins Ctrl Ins Ctrl Ins Ctrl Ins Ctrl Ins Ctrl Ins 90 m

n IRS2

IRS2 n 6 h BHLHE40 n BHLHE40 6 h DDIT4 90 m DDIT4 6 h 6 h 90 m n MERTK MERTK

n 90 m o o o i * 0.06 * io s 3.5 s * * ssio 1.5 * 3 0.06 s 1.4 1.5 1.6 1.4 1.5 s essi * * * e ** * * * r r re 3 re * * pression pressi 1.4 * 1.2 2.5 pression 1.2 *

* pression xp xp xp xp ex

ex 2.5 1.2 ex ** 1 * 2 1 1 1 ex 1 1 Ae Ae 2 Ae 0.8 N 0.8 N 1.5 0.8 N RN Ae R

1.5 R 0.6 R 0.6 mRNA mRNA

0.6 mRNA m m 1 1 0.5 0.5 mRNA 0.5 em 0.4 em 0.4 0.4 v ve ve 0.5 0.5 i t 0.2 0.2 ti 0.2 ativ a lati l a

0 0 e 0 0 el 0 0 el 0 0 Relative Relative R Re Relative R R Ctrl Ins Ctrl Ins Ctrl Ins Ctrl Ins Ctrl Ins Ctrl Ins Ctrl Ins Relative Ctrl Ins 90 m 6 h 6 h 90 m 6 h 6 h n NRARP 90 m NRARP PDGFA 90 m PDGFA ESM1 ESM1 HRH1 HRH1 n on i on on on

0.06 io i 0.06 i s s * 0.06 * ss 1.2 * * ss i 1.2 1.2 1.2 1.4 0.06 1.2 1.6 1.2 * * e * e * 0.06 * * * ress ressio r * *

pr 0.06

pres 1.4 pres pression p 1.2 p 1 1 1 pression * 1 x 1 1 x xp

* ex

ex 1.2 ex e ex 0.8 0.8 0.8 1 ex 0.8 Ae 0.8 * 0.8 A Ae 0.8 1 0.6 0.6 0.6 N 0.6 RN 0.6 0.8 0.6 R RN 0.6 mRNA mRNA mRNA m mRNA 0.4 0.4 0.4 0.4 mRNA 0.4 0.6 0.4 em 0.4 em

v 0.4 ve ve v i i ti i 0.2 0.2 0.2 t t 0.2 0.2 0.2 0.2 0.2 la at a 0 0 l el 0 0 0 0 0 0 Re Rela Relative Relative Re Relative Relative R Ctrl Ins Ctrl Ins Ctrl Ins Ctrl Ins Ctrl Ins Ctrl Ins Ctrl Ins Ctrl Ins

ABL2 90 m n ABL2 6 h PTGS2 90 m PTGS2 6 h SMAD7 90 m SMAD7 6 h CTGF 90 m CTGF 6 h o on n 0.06 n * 0.06 0.06 1.4 * 1.4 o ** 1.2 0.06 1.2 0.06 * * * 1.4 * * * * 1.2 0.06 1.4 * *

essi 1.4 r 1.2 1.2 * * 1.2 ressi * * ressio pressi p 1 1 1.2 1 pression pression 1.2 pression 1 1 * p ** xp ex ex 1 1 ex ex ex

0.8 0.8 ex 1 0.8

A 0.8 0.8

Ae 0.8 0.8 NA 0.6 0.6 0.8 N 0.6 0.6 RN 0.6 0.6 * 0.6 0.6 mR mRNA

0.4 0.4 mRNA

mRNA expression 0.4 mR 0.4 0.4 0.4 mRNA 0.4 -SB em e 0.4 e v 0.2 0.2 0.2 0.2

ti 0.2 0.2 iv 0.2 0.2 +SB at 0 0 0 0 l 0 elativ 0 0 0 Relative R Rela Relative mRNA Relative

Ctrl Ins Ctrl Ins Relative Ctrl Ins Ctrl Ins Relative Ctrl Ins Re Ctrl Ins Ctrl Ins Ctrl Ins

B t= 90 minfoldchangeexpressiont=6hfoldchangeexpression Ctrl vs InsvsIns Ctrl vs InsvsIns Gene Ctrl vs InsCtrlvsSB InsSB SB Ctrl vs InsCtrlvsSB InsSB SB SNAI2 4.16 0.50 0.95 0.23 2.21 0.40 0.73 0.33 c8orf8 2.00 0.42 0.85 0.39 1.67 0.53 0.50 0.30 TNFSF10 1.64 3.13 2.62 1.60 1.52 3.49 2.64 1.74 PHLDA1 1.42 2.54 2.51 1.77 1.37 2.51 2.43 1.78 F2RL1 1.50 0.73 0.72 0.48 1.45 0.57 0.45 0.31 CITED2 1.01 1.63 1.41 1.40 0.82 1.59 1.40 1.70 SPATA13 1.03 0.73 0.67 0.64 1.09 0.41 0.41 0.37 HIVEP1 0.97 0.74 0.59 0.62 1.11 0.65 0.62 0.56 IRS2 0.85 2.56 1.54 1.80 1.08 2.19 1.84 1.70 BHLHE40 1.14 0.36 0.46 0.40 1.30 0.58 0.51 0.39 DDIT4 1.08 0.54 0.75 0.69 0.93 0.34 0.42 0.45 MERTK 1.10 0.69 0.72 0.65 1.31 0.71 0.74 0.57 ESM1 0.92 0.70 0.60 0.65 1.15 0.62 0.51 0.44 HRH1 0.98 0.55 0.54 0.55 1.01 0.58 0.62 0.62 NRARP 1.02 0.44 0.50 0.49 1.01 0.44 0.46 0.46 PDGFA 1.03 0.48 0.55 0.53 1.02 0.59 0.64 0.63 ABL2 1.00 0.49 0.48 0.47 1.08 0.42 0.39 0.36 PTGS2 0.87 0.31 0.34 0.40 1.00 0.68 0.51 0.52 SMAD7 0.97 0.11 0.25 0.26 0.85 0.19 0.21 0.24 CTGF 0.85 0.53 0.46 0.54 0.99 0.63 0.61 0.61

Figure 6. Validation of RNA-Seq data by qRT-PCR. (A) Relative mRNA levels of selected genes that are shared between 90 min and 6 h groups are shown. HUVECs were treated with or without 100 nM insulin in the presence or absence of 5 μM SB431542 for 90 min or 6 h. mRNA expression of the indicated genes afer 90 min and 6 h treatment was measured using qRT-PCR, and values were normalized to RPL13 mRNA. Te statistical signifcance was determined by Wilcoxon test. Error bars indicate standard error of the means, based on three independent experiments. *p < 0.05, **p < 0.0083 (B) RNA-Seq data on the fold expression changes of genes compared to control or insulin treatment. Red color indicates an increase and blue color indicates a decrease in fold change gene expression compared to control.

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A t = 90 min t = 6 h Ctrl vs Ins Ctrl vs SB431542 Ctrl vs Ins Ctrl vs SB431542 545 1262 744 347 Ins vs Ins+SB431542 240 434 43 150 256 631 967 541 Ins vs Ins+SB431542 210 196 183 24249 452 52 662 53 261 89 94 370 597 235 388

t = 90 min t = 6 h Ctrl vs Ins+SB431542 Ctrl vs Ins+SB431542 89 370 733 1312 21 6868 302

bp DNA -5000 -4000 -3000 -2000 -1000 +1

PHLDA1 5’flanking UTR Intron TSSS

F2RL1 5’flanking Intron

TNFSF10 5’flanking Intron TRE TTF2 C8ORF4 5’flanking Intron SRE PEPCK SMAD SNAI2 5’flanking Intron S: Start codon TSS: Transcription start site

B Sterol IRE-A TPA TTF2 ETS-2Serum PEPCK- Smad response (gapdh, response (FoxE1) response like Binding element c-myc, element element responsive Elements etc) (TRE) (SRE) elements (SBEs)

Sequence ATCCACCCAC CCCGCCTC TGA(G/C)TCA AAACAACCGGACAGGAT T(G/A)TTTTGCAFAC(A/T) PHLDA1 -- -+-+++ F2RL1 -- ++-+-+ TNFSF10 -- ++-+++ C8ORF10 -- -+-+-+ SNAI2 -- ++-+++

Figure 7. Identifcation of insulin-responsive and Smad3-binding elements within the 5 kbp sequences proximal to the transcription start site. (A) Illustration showing predicted locations of cis-elements that confer insulin or TGF-β/Smad3 responsiveness. Colored triangles mark response elements known to be regulated by insulin or Smad3. (B) Summary table of insulin-responsive and Smad-binding elements. Plus marks indicate the presence of at least one element.

Discussion Tis study provides a genome-wide characterization of the efects of insulin on gene expression and, more specif- ically, the extensive contribution and integration of autocrine TGF-β signaling in the gene expression responses of human endothelial cells to insulin. Our results also highlight the largely unappreciated role of autocrine TGF-β signaling in basal gene expression in cell culture. We hope that our results will serve as a resource for further studies not only on the response to insulin, but also on the control of cell physiology by autocrine TGF-β signaling. To evaluate the role of autocrine TGF-β signaling in the insulin response, we treated cells with insulin for 90 min to examine early responses that are likely to not depend on new protein synthesis, and for 6 hours to attain more durable gene responses and incorporate somewhat later gene responses, which may or may not depend on new protein synthesis. We treated endothelial cells with insulin at 100 nM, which induces Smad2 and Smad3 activation as a result of increased autocrine TGF-β signaling (Fig. 1), and is commonly used to study efects of insulin in cell culture59–62. We note that at a 10-fold lower concentration, insulin also induces Smad2 and Smad3 activation, albeit at a lower level (Fig. 1). Our genome-wide RNA-Seq analysis identifed hundreds of insulin-responsive genes at 90 min and 6 hours of treatment. At 90 min, 62% (275 out of 442) genes were upregulated and 38% (167 out of 442) were downregulated in response to insulin, and similar percentages of upregulated and downregulated genes were seen afer 6 hours of insulin. Te higher proportion of genes that is upregulated is consistent with the anabolic role of

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A B 90 min MA0795.1 sequence SMAD7 MAP1S

n 4 2 C T T C 3.5 A A TA CA

ssio GT CTAG C e 3 1.5 pression pr 2.5 ex ex bp DNA 2 1 -5000 +1 NA 1.5 C19ORF5 5’flanking UTR Intron mR mRNA 1 0.5 TSS S 0.5 -4633 bp -4299 bp -1866 bp 0 0 aggctaggca tgtccagcct aggcgagaca Relative Relative -TGF-ß +TGF-ß -TGF-ß +TGF-ß NOL4L 5’flanking Intron EHD4 C20orf112 2 2.5 -1968 bp tgacaagaca 1.5 2

pression 5’flanking Intron

pression EHD4 ex

ex 1.5 1 -4582 bp -2099 bp -1248 bp 1 tgggtagaca tgtctggatg ggtctggact mRNA 0.5 mRNA 0.5 SPATA13 5’flanking Intron 0 0 Relative -TGF-ß - +TGF-ß Relative -TGF-ß +TGF-ß 4447 bp ggtccagacc

SPATA13 NRARP 5’flanking Intron 3 2.5 NRARP

2.5 2 -1783 bp

ression aggctagact

2 pression xp

ex 1.5 1.5 : SMAD 1 RN Ae 1

mRNA S: Start codon

em 0.5 ve

iv 0.5 - SB431542 TSS: Transcription start site at l 0 0 + SB431542 Re -TGF-ß +TGF-ß Relati -TGF-ß +TGF-ß

Figure 8. Analysis of potentially direct TGF-β/Smad3 target genes. (A) qRT-PCR of mRNA extracted from HUVECs treated with or without TGF-β in the presence or absence of SB431542 for 90 min, normalized against RPL13 mRNA. (B) Using the consensus sequence available from JASPAR database, the 5 kbp DNA sequences upstream from the transcription start of each gene were scanned for Smad3-binding motifs. Potential Smad binding sites are marked by triangles.

insulin, and a microarray study of gene expression changes in skeletal muscle afer a 3-hour hyperinsulenemic clamp63. Te insulin-induced gene expression changes in our study suggest regulation of various KEGG-based pathways, including Rap1, Ras, PI3K-Akt and MAPK signaling as well as ABC transporter function, consistent with a hepatoma and liver RNA-Seq study6,64 and other insulin studies3,65–67. Our GO annotation related mainly to endothelial cell migration, proliferation, angiogenesis, immune system as well as cardiovascular system development, consistent with the functional nature of endothelial cells. Insulin is known to promote carbohydrate and lipid metabolism17. Although our functional annotation showed enrichment for these metabolic processes, they were not among the top 20 hits in our insulin GO terms list. Further examination of the enrichment results comparing DEGs between the 90 min and the 6 h insulin treated cells indicated more genes associated with these carbohydrate and lipid-related metabolic processes afer 6 h of insulin treatment. Supporting our data, it has been reported in rat H4IIE hepatoma cells that insulin-induced efects of genes related to glycolysis, fatty acid oxida- tion and lipid metabolism are less distinct at 6 h when compared to 24 h insulin exposure5. Central in the current study is the integration of autocrine TGF-β signaling into insulin-induced gene expression changes, which was predicted from our observation that insulin-induced Akt activation promotes rapid mobilization of intracellular receptors to the cell surface, resulting in increased TGF-β responsiveness autocrine TGF-β signaling14. To assess this integration, endothelial cells were treated with insulin in the absence or presence of SB431542 to block TGF-β/Smad signaling. We found that about half of the insulin-responsive genes showed decreased or increased expression when autocrine TGF-β/Smad signaling was prevented. Tese results reveal that, in response to insulin, many genes are coregulated by insulin and TGF-β signaling and that TGF-β signaling extensively participates in the insulin response. Many of the genes that were coregulated at both 90 min and 6 h of insulin treatment associated with FoxO and TGF-β signaling pathways, and biological processes involved in blood vessel development, cardiovascular system, cell migration, apoptosis, and cell communication. Tese associations are consistent with our observations that elevated autocrine TGF-β signaling in response to insulin contributes to insulin-induced angiogenic responses13. Te regulation of insulin-responsive gene expression through cooperation of autocrine TGF-β/Smad signaling with insulin receptor signaling predicts the presence of both insulin-responsive and Smad3-binding sequences in regulatory promoter sequences of target genes. We performed in silico analyses of 5 kbp sequences preced- ing the transcription start sites of several coordinately regulated, insulin-responsive genes, i.e. PHLDA1, F2RL1, TNFSF10, C8orf4 and SNAI2. Previous research identifed eight insulin-responsive sequences in regulatory regions of insulin target genes40–45, whereas direct TGF-β/Smad responsiveness results from binding of Smad3/4

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complexes to CAGA-like Smad binding elements68,69. Each of the insulin-responsive, coregulated genes examined contained at least two insulin response elements, as well as Smad3 binding elements, thus providing a molecular basis for the coregulation. Te insulin-induced enhancement of autocrine TGF-β signaling also predicts that insulin induces TGF-β/ Smad target genes whose regulation does not directly emanate from the activated insulin receptor. Tis is indeed the case. Among the insulin-responsive genes in our analyses, we recognize genes that are known as direct TGF-β/Smad3 target genes. Additionally, insulin-induced activation or repression of a number of genes is fully prevented when TGF-β signaling is blocked, indicating that insulin induces these through enhanced autocrine TGF-β signaling. In the current study, we also present a genome-wide analysis of the efect of autocrine TGF-β signaling inhibition on gene expression in untreated, control endothelial cells. Various studies report on efects of TGF-β on genome-wide transcription, comparing results of maximal TGF-β stimulation against those of either untreated cells or cells treated to inhibit TGF-β signaling. Tese studies in diferent cell types revealed gene expression changes of relevance to a variety of cellular processes and functions, including cellular homeostasis, growth and proliferation, and cell and tissue diferentiation, including epithelial-mesenchymal transition of carcinoma cells70–77. Our current results complement and support these previous studies, but, more importantly, provide to our knowl- edge the frst genome-wide analysis of the role of autocrine TGF-β signaling in directing the basal transcription program of cells in endothelial cells. Our analyses comparing cells treated with SB431542 to inhibit TGF-β/ Smad-signaling with untreated control endothelial cells shows that basal autocrine TGF-β activity controls various KEGG pathways, including FoxO, Ras, Rap1, AMPK, cGMP-PKG, PI3K-Akt, calcium and MAPK signaling. Tese pathways overlap with the non-Smad TGF-β signaling response78, which can be activated through autocrine signaling79. Functional ontology analyses suggest roles of autocrine TGF-β signaling in many processes, including blood vessel development, gene transcription, cell metabolism, interferon signaling, blood function and activation, as well as basic cell functions such as cell migration, autophagy, and proliferation. Tese basic processes are involved in epithelial to mesenchymal transition, a diferentiation process in normal development, wound healing and pathogenesis of cancer and fbrosis80–82. Our fndings on the role of autocrine TGF-β signaling in insulin-induced angiogenic responses support the notion that basal TGF-β signaling also participates in various other processes. Finally, our data also reveal novel direct target genes for TGF-β/Smad signaling. While TGF-β regulate the expression of many genes, the number of early direct TGF-β/Smad target genes has been limited to only few, which imposes restrictions on reliable marker profles to evaluate efcacy of therapeutic inhibition of TGF-β in immuno-oncology and anti-fbrotic approaches. Among newly identifed TGF-β target genes, we identify MAP1S, EHD4, c20orf112, SPATA13 and NRARP as novel direct targets of TGF-β/Smad3 signaling. Tese genes are rapidly activated in response to TGF-β, and contain Smad3-binding sites upstream from their transcription initiation sites. Further exploration of our data bases may therefore not only serve those who are interested in the cellular response to insulin, but also be of great interest to those who aspire to identify TGF-β responsive genes. In summary, we show that the genome-wide changes in mRNA expression in response to insulin integrate substantial contributions of autocrine TGF-β signaling. Tis mode of signaling crosstalk needs to be highlighted not only when evaluating the cell responses to insulin, but also when trying to appreciate the full consequences of therapeutic use of insulin. Methods Cell culture. Human umbilical vein endothelial cells (HUVECs) were obtained from Gibco and cultured in Medium 200 (Invitrogen) with 10% fetal bovine serum (FBS) from HyClone, and low serum growth supple- ment (LSGS) from Invitrogen. Lung microvascular endothelial cells (HMVEC-L) were obtained from PromoCell. Cells were grown in endothelial cell growth medium MV2 (PromoCell) with supplements added. All cells were maintained in a humidifed 5% CO2 incubator at 37 °C, and the medium was replaced every 2 days until the cells reached 80–90% confuence. All experiments were carried out using cells between 3rd and 7th passage. Insulin from Sigma was used at 100 nM. Te TβRI kinase inhibitor SB431542 from Sigma was used at 5 μM and added 45 min before treatment. Te anti-TGF-β1, -β2, -β3 neutralizing antibody 1D11 (R&D Systems, MAB1835-SP) was used at 2 μg/ml. TGF-β from Humanzyme was used at 0.4 ng/ml.

Immunoblotting. Cell lysis, protein extraction, and immunoblotting were performed as described83. Protein concentrations were quantifed using Bradford assays (Bio-Rad) with bovine serum albumin (BSA) as standard, and a SpectraMax M5 microplate reader. Samples were resolved by SDS-PAGE on 4–12% gradient polyacryla- mide gel. Proteins were transferred to a nitrocellulose membrane and detected by immunoblotting. As primary antibodies, we used rabbit monoclonal antibodies to Smad2, phospho-Smad2 (Ser465/467), Smad3, Akt, and phospho-Akt (p-AktS473) from Cell Signaling. Antibody against phospho-Smad3 (Ser423/425) was from Abcam, and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was from Sigma. For immunoblotting, immunoreactive bands were visualized using Western Lighting Plus ECL (Perkin Elmer).

cDNA library preparation for RNA-Seq. HUVECs were seeded onto six well plates at a density of 2 × 105 per well and cultured as described above. At the second day, cells were starved overnight in Medium 200 without growth factors. Te cells were treated with 100 nM insulin for 90 min or 6 h, with or without 5 μM SB431542. Total RNA was extracted from 22 independent samples of HUVECs, that is control 90 min (2 samples), control 6 h (2 samples), insulin 90 min (3 samples), insulin 6 h (3 samples), SB431542 90 min (3 samples), SB431542 6 h (3 samples), insulin + SB431542 90 min (3 samples), and insulin + SB431542 90 min (3 samples). Te quality of total RNA afer extraction using Qiagen RNAeasy Plus mini kit was checked by NanoDrop spectrophotometric read- ings at 260/280/230 nm and with an Agilent 2100 bioanalyzer. cDNA libraries were made using Encore Complete

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RNA-seq Library Systems kit from NUGEN following the manufacturer protocol. Te quality of the library was checked using DNA1000. All 22 samples of barcoded library, four controls from the two time points and three of each of treated samples from the two time points, were sequenced in seven lanes using a paired-end 100 protocol on the Illumina HiSeq. 2000 at the UCSF Genomics Core Facilities.

RNA-Seq diferential expression analysis. Paired-end RNAseq sequencing reads were aligned with TopHat version 2.0.1384 (http://tophat.cbcb.umd.edu) against the reference human genome build hg19, with UCSC known gene transcripts as the gene model annotation. Transcripts were assembled using Cufinks version 2.2.1, and diferentially expressed genes were identifed with Cufdif version 2.19 using an adjusted P-value of 0.05 or less based on the Benjamini and Hochberg correction85,86 (http://cufinks.cbcb.umd.edu/). Te demultiplxed RNA-Seq data reported in this paper were deposited in ArrayExpress with accession number E-MTAB-8297.

Venn analyses. To visualize insections of genes that are diferentially expressed by diferent groups, we used a free available online tool Venny 2.187.

Functional annotation. Functional and pathway analyses were performed mainly using the Database for Annotation, Visualization, and Integrated Discovery (DAVID, available at: https://david.ncifcrf.gov/)88,89. To understand the signifcance of genes, we performed Gene Ontology (GO) classifcation, making use of BP_Fat (biological process). We also used the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis option in DAVID to detect the potential pathway of target genes.

Validation using qRT-PCR. Complementary DNA was synthesized using an iScript cDNA synthesis Kit (Bio-Rad), according to manufacturer’s protocol. Complementary DNA samples were subjected to real-time PCR using Bio-Rad PCR and the double-stranded-specifc dye SYBR green. For all primer pairs, specifc amplifcation of the PCR products was confrmed by melting curve analysis. Primer sequences were designed using Primer390,91 or taken from the PrimerBank database92,93, and are presented in Supplemental Table 16.

Analysis of Smad3 binding elements. Smad3 transcription factor binding sites were predicted by the sofware JASPAR58. http://jaspar.genereg.net/search?q=Homo%20sapiens&collection=CORE&tax_group=ver- tebrates with Matrix ID: MA0795.1 was used to analyze the Smad3 potential binding elements. Submitted sequences of 5 kbp nucleotides upstream of the 5′UTR were analyzed using a relative profle score default thresh- old setting of 80% to the “CORE Vertebrate”database. Searches of 5′ fanking sequences were performed using Ensambl human database94. Insulin responsive elements were analyzed manually.

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Tis research was sponsored by NIH grant RO1-CA136690 to R.D and a Juvenile Diabetes Research Fund (JDRF) advanced postdoctoral fellowship 3-APF-2017-392-A-N and grant 1-FAC-2019-813-A-N to E.H.B. S.G. and Y.E.Z. were supported by the intramural research program of the NIH, National Cancer Institute, Center for Cancer Research.

Scientific Reports | (2019)9:16992 | https://doi.org/10.1038/s41598-019-53490-x 15 www.nature.com/scientificreports/ www.nature.com/scientificreports

Author contributions Conceptualization, E.H.B. and R.D.; Methodology, E.H.B., R.D., and Y.E.Z.; Investigation, E.H.B., S.G. and S.H.; Data analysis, E.H.B., S.H., S.G.; Writing, original draf, E.H.B. and R.D.; Writing, review and editing, E.H.B., R.D., S.G. and Y.E.Z.; Funding, acquisition, E.H.B., R.D., and Y.E.Z.; Supervision, R.D. and Y.E.Z. Competing interests Te authors declare no competing interests. Additional information Supplementary information is available for this paper at https://doi.org/10.1038/s41598-019-53490-x. Correspondence and requests for materials should be addressed to R.D. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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