Page 1 of 72 Diabetes

1 Full title

2 PRMT1 is required for the maintenance of mature β cell identity

3

4 Short running title

5 Essential role of PRMT1 in β cell identity

6

7 Authors

8 Hyunki Kim1, #, young-Ha Yoon2, 3, #, Chang-Myung Oh4, #, Joonyub Lee1, #, Kanghoon Lee1, Heein

9 Song1, Eunha Kim5, Kijong Yi1, Mi-Young Kim6, Hyeongseok Kim1, Yong Kyung Kim7, Eun-Hye

10 Seo2, 3, Haejeong Heo2, 3, Hee-Jin Kim2, Junguee Lee8, Jae Myoung Suh1, Seung-Hoi Koo9, Je Kyung

11 Seong6,10, Seyun Kim5, Young Seok Ju1, Minho Shong7, Mirang Kim2, 3, * and Hail Kim1, 11, *

12

13 Affiliations

14 1Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and

15 Technology, Daejeon 34141, Republic of Korea

16 2Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and

17 Biotechnology, Daejeon 34141, Republic of Korea

18 3Department of Functional Genomics, University of Science and Technology, Daejeon 34113,

19 Republic of Korea

20 4Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology,

21 Gwangju 61005, Republic of Korea

22 5Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon

23 34141, Republic of Korea

24 6Laboratory of Developmental Biology and Genomics, Research Institute for Veterinary Science,

25 BK21 PLUS Program for Creative Veterinary Science Research, College of Veterinary Medicine,

1

Diabetes Publish Ahead of Print, published online December 17, 2019 Diabetes Page 2 of 72

26 Seoul National University, and Korea Mouse Phenotyping Center (KMPC), Seoul 08826, Republic

27 of Korea

28 7Research Center for Endocrine and Metabolic Diseases, Chungnam National University School of

29 Medicine, 282 Munhwaro, Daejeon 35015, Republic of Korea

30 8Department of Pathology, Daejeon St. Mary's Hospital, College of Medicine, The Catholic

31 University of Korea, 64 Daeheung-ro, Jung-gu, Daejeon 34943, Republic of Korea

32 9Division of Life Sciences, Korea University, 145 Anam-Ro, Seongbuk-Gu, Seoul 02841, Republic

33 of Korea

34 10 Interdisciplinary Program for Bioinformatics, Program for Cancer Biology and BIO-MAX/N-Bio

35 Institute, Seoul National University, Seoul 08826, Republic of Korea

36 11KAIST Institute for the BioCentury, Korea Advanced Institute of Science and Technology,

37 Daejeon 34141, Republic of Korea

38 #These authors contributed equally to this work

39

40 * Corresponding authors

41 Hail Kim, M.D., Ph.D.

42 Graduate School of Medical Science and Engineering

43 Korea Advanced Institute of Science and Technology

44 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea

45 Phone: +82-42-350-4243

46 e-mail: [email protected]

47

48 Mirang Kim, Ph.D.

49 Personalized Genomic Medicine Research Center

50 Korea Research Institute of Bioscience and Biotechnology

2 Page 3 of 72 Diabetes

51 125 Gwahak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea

52 Phone: +82-42-879-8113

53 e-mail: [email protected]

54

55 Word count: 4,444

56

57 Number of figures: 6

3 Diabetes Page 4 of 72

58 Abstract

59 Loss of functional β cell mass is an essential feature of type 2 diabetes, and maintaining mature

60  cell identity is important for preserving a functional β cell mass. However, it is unclear how β cells

61 achieve and maintain their mature identity. Here, we demonstrate a novel function of PRMT1 in

62 maintaining mature β cell identity. Prmt1 knockout in fetal and adult β cells induced diabetes, which

63 was aggravated by high fat diet-induced metabolic stress. Deletion of Prmt1 in adult β cells resulted

64 in the immediate loss of histone H4 arginine 3 asymmetric di-methylation (H4R3me2a) and the

65 subsequent loss of β cell identity. The expression levels of genes involved in mature β cell function

66 and identity were robustly downregulated as soon as Prmt1 deletion was induced in adult β cells. ChIP-

67 seq and ATAC-seq analyses revealed that PRMT1-dependent H4R3me2a increases chromatin

68 accessibility at the binding sites for CTCF and β cell transcription factors. In addition, PRMT1-

69 dependent open chromatin regions may show an association with the risk of diabetes in humans.

70 Together, our results indicate that PRMT1 plays an essential role in maintaining β cell identity by

71 regulating chromatin accessibility.

4 Page 5 of 72 Diabetes

72 Introduction

73 Maintaining the functional β cell mass is crucial for preventing diabetes, which develops when

74 β cells fail to meet the insulin demand (1,2). Although β cell death is thought to be the major

75 mechanism of β cell failure (3), recent studies indicate that β cell dedifferentiation can decrease the

76 functional β cell mass and thereby deteriorate systemic glucose homeostasis (4,5). Maintaining mature

77  cell identity is also important for maintaining  cell function (6,7). A hierarchy of transcription factor

78 (TF) cascades directs β cell differentiation, and β cells require continuous activation of these TFs to

79 maintain their function and identity (8–10). The genetic identity of a differentiated cell is generally

80 controlled by the chromatin state, which is overall stable and has a limited epigenomic flexibility

81 (11,12). Likewise, epigenetic regulation plays an essential role in the postnatal maturation of β cells

82 and the maintenance of mature β cell identity (13–16).

83 Histone arginine methylation, which is regulated by protein arginine methyltransferase

84 (PRMT), can affect chromatin structures to facilitate the recruitment of protein complexes that regulate

85 gene transcription (17,18). PRMT4-dependent histone H3 arginine 17 asymmetric di-methylation

86 (H3R17me2a) in β cells has been reported to regulate glucose-stimulated insulin secretion (GSIS) (19).

87 However, the role of PRMT-induced histone arginine methylation in regulating β cell identity has not

88 yet been elucidated. Among the nine members of the PRMT family, PRMT1 predominates in

89 mammalian cells (20). It appears to be associated with diabetes, as its catalytic activity is decreased in

90 the liver and pancreas of diabetic Goto-Kakizaki rats (21). PRMT1 has also been shown to specifically

91 induce the active histone code, histone H4 arginine 3 asymmetric di-methylation (H4R3me2a), which

92 potentiates subsequent histone acetylation and contributes to establishing euchromatin structure

93 (22,23). Based on these previous findings, we herein explored the role of PRMT1-dependent

94 H4R3me2a in mature β cells.

5 Diabetes Page 6 of 72

95 Research Design and Methods

96 Animals

97 Prmt1 floxed (Prmt1fl/fl) [MGI: 4432476] mice were crossed with Rip2-Cre [MGI: 2387567] and

98 Pdx1-CreERT2 [MGI: 2684321] mice to generate Prmt1 βKO and Prmt1 βiKO mice, respectively.

99 R26-eYFP [MGI: 2449038] mice were crossed for lineage-tracing experiments and β cell sorting. All

100 mice were backcrossed and maintained on a C57BL/6J background. Cre recombination for CreERT2

101 was induced by a total of five intraperitoneal injections of corn oil-dissolved tamoxifen (75 mg/kg)

102 over 2 weeks. Mice were housed in climate-controlled, specific pathogen-free barrier facilities under

103 a 12-hour light/dark cycle, and chow and water were provided ad libitum. Mice were fed either a

104 standard chow diet or high-fat diet (HFD; 60% kcal fat). The animal experiment protocols for this

105 study were approved by the Institutional Animal Care and Use Committee at the Korea Advanced

106 Institute of Science and Technology. All experiments were performed in accordance with the

107 relevant guidelines and regulations.

108

109 Metabolic assays

110 Body weight and random blood glucose levels were measured in the afternoon of the daytime. The

111 glucose tolerance test and the insulin tolerance test were performed as previously described (24).

112

113 Histological analyses

114 For histological analyses, formalin-fixed paraffin-embedded pancreatic slides were prepared, stained

115 and analyzed as described in Supplementary Materials.

116

117 Pancreatic insulin content

118 Pancreatic tissues were dissected, placed in acid-ethanol (1.5% HCl in 70% ethanol), homogenized

119 and incubated at 4°C for 16 hours. The aqueous phase of pancreatic insulin extract was neutralized

6 Page 7 of 72 Diabetes

120 with an equal amount of 1 M Tris-Cl buffer (pH 7.5). The pancreatic insulin content was calculated by

121 dividing the total pancreatic insulin by the weight of the pancreas.

122

123 Glucose-stimulated insulin secretion (GSIS)

124 For the in vivo GSIS assay, mice were fasted for 16 hours and then given an intraperitoneal injection

125 of D-glucose in PBS (2 g/kg). For the ex vivo islet GSIS assay, pancreatic islets were isolated from

126 mice as described previously (25), and the assay was performed as described in the Supplementary

127 Materials.

128

129 Oxygen consumption rate (OCR)

130 Pancreatic islets were isolated from mice as described previously (25), and the OCR assay was

131 performed as described in the Supplementary Materials.

132

133 Quantitative reverse transcription PCR (qRT-PCR)

134 Total RNA was extracted from mouse tissues and qRT-PCR was performed as described in the

135 Supplementary Materials. The sequences of the utilized primers are listed in Supplementary Table 1.

136

137 ChIP-seq, RNA-seq and ATAC-seq analyses

138 ChIP experiments were performed in MIN6 cells as previously described (26) with modifications.

139 RNA-seq experiments were performed using WT and Prmt1-null islets. ATAC experiments were

140 performed as previously described (27), using MIN6 cells and FACS-sorted WT and Prmt1-null β

141 cells. ChIP-seq, RNA-seq and ATAC-seq analyses were performed as described in the Supplementary

142 Materials.

143

144 Chromatin conformation capture PCR (3C-PCR)

7 Diabetes Page 8 of 72

145 3C experiments were performed in MIN6 cells as previously described (28) with modifications. The

146 data were normalized with respect to those obtained using internal primers that recognized sequences

147 within the Gapdh gene. At least three independent biological replicates were included for each 3C-

148 PCR assay. The sequences of the utilized primers are listed in Supplementary Table 1.

149

150 Statistics

151 All values are expressed as the mean ± standard error of mean (SEM). The two-tailed Student’s t test

152 or one-way analysis of variance (ANOVA) followed by post-hoc Tukey’s test were used to compare

153 groups. P values below 0.05 were considered statistically significant. The levels of significance

154 indicated in the graphs are *; P < 0.05, **; P < 0.01 and ***; P < 0.001.

155

156 Data and resource availability

157 The ChIP-seq, RNA-seq and ATAC-seq data that support the findings of this study have been

158 deposited in NCBI GEO under accession code GSE117100. All data that support the findings of this

159 study are available from the authors on reasonable request. No applicable resources were generated or

160 analyzed during the current study.

161

162 Results

163 Prmt1 βKO mice develop progressive glucose intolerance

164 We first checked the gene expression of the Prmt family genes in pancreatic islets and

165 confirmed that Prmt1 exhibited the highest expression level among them (Supplementary Fig. 1A, B).

166 The expression level of Prmt1 was higher in pancreatic islets than in liver or brain, and PRMT1 and

167 H4R3me2a were enriched in pancreatic islets of both mice and humans (Supplementary Fig. 1C, D).

168 To test the possible role of H4R3me2a in β cells, we performed chromatin immunoprecipitation

169 sequencing (ChIP-seq) for H4R3me2a and assay for transposase accessible chromatin sequencing

8 Page 9 of 72 Diabetes

170 (ATAC-seq) in MIN6 cells. Motif analysis of the H4R3me2a ChIP-seq data suggested that H4R3me2a

171 was significantly associated with the β cell TFs; MAFA, NEUROD1 and FOXA1 (Supplementary Fig.

172 1E). Intriguingly, the most significantly associated TF was CCCTC-binding factor (CTCF), which is

173 known to play a crucial role in regulating the chromatin architecture (29,30). In order to assess the

174 association of H4R3me2a with CTCF and β cell TF, we performed ChIP-seq for CTCF in MIN6 cells.

175 Analyses of ChIP-seq data obtained for H4R3me2a and CTCF, combined with publicly available

176 ChIP-seq data for MAFA, indicated that CTCF and MAFA bind near H4R3me2a-occupied chromatin

177 regions (Supplementary Fig. 1F). The association of H4R3me2a with CTCF and MAFA, together with

178 the enrichment of PRMT1 and H4R3me2a in adult β cells, suggests that PRMT1 and H4R3me2a may

179 play roles in β cells.

180 To further investigate the physiological role of PRMT1 and H4R3me2a in β cells, we

181 generated β cell-specific Prmt1 knockout (Rip2-Cre; Prmt1 fl/fl, herein called Prmt1 βKO) mice.

182 Immunofluorescence staining confirmed the deletion of PRMT1 at postnatal day 7 (P7) and the

183 subsequent removal of H4R3me2a in the β cells of these mice around weaning at 3 weeks of age

184 (Supplementary Fig. 2). In wild-type (WT) control mice, the fluorescence signals of both PRMT1 and

185 H4R3me2a became enriched in pancreatic islets after weaning at 3 weeks of age, when the β cells

186 become mature. In Prmt1 KO mice, PRMT1 was nearly undetectable at postnatal day 7 (P7) whereas

187 H4R3me2a remained detectable in substantial number of  cells until 3 weeks of age, suggesting the

188 more important role of PRMT1-dependent H4R3me2a in mature β cells. Indeed, the pancreatic islets

189 of Prmt1 KO mice developed normally and did not show any abnormality in the markers of β cell

190 development (Supplementary Fig. 3A, B). Prmt1 βKO mice grew normally and showed normal

191 glucose tolerance until they developed glucose intolerance at 12 weeks of age (Fig. 1A, B and

192 Supplementary Fig. 3C). Despite this glucose intolerance, Prmt1 βKO mice showed no defects in

193 insulin sensitivity and insulin production (Supplementary Fig. 4A-C). Instead, GSIS was impaired in

194 Prmt1 βKO islets (Fig. 1C). Basal insulin secretion was increased when Prmt1 βKO islets were treated

9 Diabetes Page 10 of 72

195 with 2.8 mM glucose, and those treated with 20 mM glucose failed to show any further increase of

196 insulin secretion. The impairment of GSIS was further confirmed by analyses of plasma insulin levels

197 and mitochondrial oxygen consumption rates in the isolated islets (Fig. 1D and E). To investigate

198 whether a transcriptional change was responsible for the defect of GSIS in Prmt1 βKO mice, we

199 performed RNA-seq analysis in the islets of Prmt1 βKO mice at 12 weeks of age (Supplementary Fig.

200 5A). Although there was no robust change in global gene expression, Prmt1 βKO islets exhibited

201 downregulation of mature β cell genes that are involved in GSIS and misexpression of genes that are

202 disallowed to be expressed in mature β cells (31–33) (Supplementary Fig. 5B). These gene expression

203 changes were further confirmed by quantitative reverse transcription PCR (qRT-PCR) analysis

204 (Supplementary Fig. 5C-F). Pathway analysis showed that most of the genes downregulated in Prmt1

205 βKO islets were involved in pancreas development and maturity onset diabetes of the young (MODY)

206 (Supplementary Fig. 5G). This notion was further supported by electron microscopic analysis (Fig.

207 1F), which showed that Prmt1-null β cells exhibited ultrastructural changes resembling those found in

208 the β cells of type 2 diabetes patients (34,35). The volume and density of insulin granules were reduced,

209 the endoplasmic reticulum was dilated, and the mitochondria appeared round and swollen. Condensed

210 chromatin was observed in the nuclei of Prmt1-null β cell (Fig. 1F), but apoptosis was not observed in

211 the islets of Prmt1 KO mice (Supplementary Fig. 5H). In addition, islet hormones and β cell TFs

212 were normally expressed in these mice (Supplementary Fig. 5I, J). These data indicate that PRMT1 is

213 needed to maintain the function, not the survival, of β cells.

214 As the phenotypes of Prmt1 βKO mice resembled the early features of type 2 diabetes (34–

215 36), we fed Prmt1 βKO mice with a high fat diet (HFD) from 8 weeks of age to test how these mice

216 respond to metabolic stress (Supplementary Fig. 6A). HFD exacerbated the glucose intolerance in

217 Prmt1 βKO mice without perturbing compensatory  cell expansion (Fig. 1G and Supplementary Fig.

218 6B). Interestingly, the islets of HFD-fed Prmt1 KO mice contained polyhormonal cells that co-

219 expressed insulin and glucagon (Fig. 1H). A lineage-tracing analysis revealed that these polyhormonal

10 Page 11 of 72 Diabetes

220 cells were originated from insulin-producing β cells (GCG: ~0.4 %, SST: ~2.4 %, PPY: ~0.19 %),

221 suggesting that the β cells of HFD-fed Prmt1 βKO mice had undergone some changes in their

222 differentiated states (Fig. 1I-M). Immunofluorescence staining of mature β cell markers further

223 confirmed the loss of mature β cell identity in HFD-fed Prmt1 KO mice; such cells showed loss of

224 MAFA and SLC2A2, cytoplasmic localization of NKX6.1 and blockage of HFD-induced FOXO1

225 nuclear translocation (Fig. 1N, O) (37–39). These phenotypes of HFD-fed Prmt1 KO mice suggest

226 that PRMT1 plays an essential role in maintaining the mature β cell identity.

227

228 PRMT1 is required for the maintenance of β cell identity

229 Although Prmt1 βKO mice presented the features of loss of β cell identity, these phenotypes

230 were weak. This could reflect the presence of metabolic compensation, which often comes into play

231 in genetic KO models. To minimize the involvement of any compensatory mechanism and further

232 confirm the role of PRMT1 in mature β cells, we generated an inducible β cell-specific Prmt1 KO

233 mouse model by crossing Prmt1 fl/fl mice with Pdx1 promoter-driven CreERT2 (Pdx1-CreERT2) mice

234 (herein called Prmt1 βiKO). Prmt1 KO was induced in adult β cells by intraperitoneally injecting the

235 mice with tamoxifen (75 mg/kg) five times over 2 weeks, beginning at 6 weeks of age (Supplementary

236 Fig. 7A). At 8 weeks of age, these mice exhibited deletion of PRMT1 in β cells and subsequent removal

237 of H4R3me2a, but maintained normoglycemia (Fig. 2A and Supplementary Fig. 7B, C). At 12 weeks

238 of age, Prmt1 iKO mice developed glucose intolerance and exhibited elevated random glucose levels

239 due to impaired GSIS (Supplementary Fig. 7C-E). Thus, acute loss of PRMT1 in adult β cells is

240 sufficient to induce the loss of mature β cell function.

241 Further immunofluorescence staining revealed more severe defects in the β cells of Prmt1

242 βiKO mice (Fig. 2A). At 8 weeks of age, insulin expression was robustly reduced in the β cells of

243 Prmt1 βiKO mice and substantial numbers of INS-/PDX1+, INS-/NKX6.1+ and INS-/MAFA+ cells

244 were observed in the islets. At 12 weeks of age, the insulin signals were slightly recovered in Prmt1

11 Diabetes Page 12 of 72

245 βiKO mice, but their intensities were still low and most of the β cells had lost MAFA and SLC2A2. A

246 lineage-tracing analysis showed that a number of β cells lost their identity and became INS- cells (~

247 30 %), UCN3- cells (~ 33 %), polyhormonal cells or other endocrine cells (GCG: ~ 1.5 %, SST: ~ 3.5

248 %, PPY: ~ 0.24 %) after induction of Prmt1 KO (Fig. 2B-G and Supplementary Fig. 8A, B). Electron

249 microscopic analyses showed similar ultrastructural changes in Prmt1 βiKO and Prmt1 βKO mice (Fig.

250 1F and Supplementary Fig. 8C).

251 Furthermore, HFD aggravated the metabolic phenotypes of Prmt1 βiKO mice (Supplementary

252 Fig. 9A-D). Random blood glucose levels were continuously elevated and glucose intolerance became

253 more severe in HFD-fed Prmt1 βiKO mice, but the insulin sensitivity and β cell mass were comparable

254 to those of the WT mice (Supplementary Fig. 9C-F). Consistent with these findings, β cells lost their

255 identity in HFD-fed Prmt1 βiKO mice; β cells losing insulin (~ 30 %) and switching to other endocrine

256 cell types (GCG: ~ 1.7 %, SST: ~ 3.8 %, PPY: ~ 0.37 %), presence of INS-/PDX1+ cells, cytoplasmic

257 localization of NKX6.1 and loss of MAFA (Fig. 3A-F). Also, the HFD-induced nuclear translocation

258 of FOXO1 was blocked in Prmt1 βiKO mice (Fig. 3G). These data indicate that β cells require PRMT1

259 to maintain their mature identity, and that the loss of PRMT1 leads to the aberrant reprogramming of

260 β cells to express other hormones.

261

262 PRMT1 regulates the transcriptomic program of mature β cell

263 In Prmt1 iKO mice, β cells lost their identity soon after Prmt1 was ablated and before the

264 glucose homeostasis deteriorated. To examine the molecular mechanism underlying the observed loss

265 of mature β cell identity, we explored the global gene expression pattern in islets of Prmt1 βiKO mice

266 at two different stages: the early stage at 8 weeks of age and the late stage at 12 weeks of age (Fig.

267 4A). RNA-seq analysis revealed robust changes in gene expression at both stages (Supplementary Fig.

268 10A, B). In particular, genes involved in cellular energy production processes, such as oxidation-

269 reduction and electron transport chain, were commonly down regulated at both stages (Fig. 4B). Our

12 Page 13 of 72 Diabetes

270 stage-specific gene expression analysis showed that the gene expression patterns differed between the

271 early and late stages (Fig. 4C and Supplementary Table 2). The mature β cell genes showed various

272 changes at the early stage, whereas most of these genes were downregulated at the late stage. In

273 particular, the expression levels of Ins1, Ins2, Ucn3 and Pdx1 were decreased in Prmt1 βiKO mice at

274 both stages, whereas those of Mafa and Slc2a2 were downregulated only at the late stage. These results

275 were further confirmed by qRT-PCR analysis (Fig. 4D-I). The notable novel features in the β cells of

276 Prmt1 βiKO mice included the robust downregulation of Ins1 at the early stage and its recovery at the

277 late stage, and the downregulations of Ins2 and Ucn3 at the early stage (Fig. 4D-F). These findings

278 correlated with our immunofluorescence staining observations in Prmt1 βiKO mice (Fig. 2B).

279 Meanwhile, RNA-seq and qRT-PCR analyses revealed that most of the genes related to oxidative

280 phosphorylation (OXPHOS) were downregulated in Prmt1 βiKO mice at both stages, suggesting that

281 mitochondrial dysfunction could be a feature of the loss of mature β cell identity (Fig. 4C and

282 Supplementary Fig. 10C). Stage-specific gene ontology and pathway analyses revealed that most of

283 the genes downregulated at the early stage were linked with the electron transport chain and MODY,

284 whereas genes related to GSIS function (e.g., those related to vesicle transport and intracellular protein

285 trafficking) were downregulated at the late stage (Fig. 4J, K). Taken together, these data indicate that

286 PRMT1 is required to maintain the transcriptional program of mature β cells.

287

288 PRMT1-dependent H4R3me2a regulates chromatin accessibility in mature β cells

289 Cell type-specific chromatin state is essential for maintaining the transcriptional program and

290 identity of a differentiated cell (40). Given the extensive gene expression changes in Prmt1 βiKO islets

291 and the association of H4R3me2a with CTCF and β cell TFs in MIN6 cells, we speculated that

292 PRMT1-dependent H4R3me2a could regulate gene transcription through the actions on the chromatin

293 status of mature β cells. To test our hypothesis, we performed ATAC-seq with β cells purified from

294 Prmt1 βiKO and WT mice at 8 weeks of age (Fig. 5A). Unlike MIN6 cells which showed clear and

13 Diabetes Page 14 of 72

295 distinctive ATAC-seq peaks, ATAC-seq with purified  cells showed some background noise in the

296 peaks. Therefore, we analyzed ATAC-seq data from WT and Prmt1-null β cells along with ATAC-

297 seq data from MIN6 cells and identified 5,044 peaks corresponding to the PRMT1-dependent open

298 chromatin regions in mature β cells (Fig. 5B). The average ChIP-seq peak intensities of H4R3me2a

299 and H3K27ac (active enhancer marks) were higher in PRMT1-dependent open chromatin regions

300 compared to PRMT1-independent open chromatin regions (Fig. 5C, D). However, the average ChIP-

301 seq peak intensity of H3K4me1 (a poised enhancer mark) was similar in PRMT1-dependent and -

302 independent open chromatin regions (Fig. 5E). These data indicate that H4R3me2a is responsible for

303 the PRMT1-dependent chromatin openings in mature β cells. The PRMT1-dependent open chromatin

304 regions were also correlated with the binding sites of CTCF and β cell TFs, including NKX6.1,

305 NKX2.2, MAFA, NEUROD1 and PDX1 (Fig. 5F). Further motif analysis showed that the DNA-

306 binding motifs of CTCF and β cell TFs were highly associated with PRMT1-dependent open chromatin

307 regions (Fig. 5G and Supplementary Fig. 11). Intriguingly, genes near PRMT1-dependent open

308 chromatin regions were significantly associated with mature β cell function and identity (Fig. 5H, I).

309 These data indicate that PRMT1-dependent H4R3me2a is needed to maintain the unique chromatin

310 architecture of mature β cells, and that the loss of mature β cell identity in Prmt1 βiKO mice is

311 associated with widespread alterations of the chromatin landscape.

312 To delineate how PRMT1-dependent chromatin openings relate to the transcriptional changes

313 observed in Prmt1-null β cells at the early stage, we closely examined the regulatory regions of genes

314 downregulated in the islets of Prmt1 βiKO mice at 8 weeks of age in parallel with RNA-seq and ChIP-

315 seq data of β cell TFs. Indeed, the PRMT1-dependent open chromatin regions included multiple

316 promoter or enhancer regions of β cell and OXPHOS genes that were downregulated in the islets of

317 Prmt1 βiKO mice. A comparative analysis of data from RNA-seq and ATAC-seq showed a correlation

318 between the gene expression changes of β cell genes and OXPHOS genes, and the chromatin

319 accessibility in the promoters of these genes (Fig. 5J, K and Supplementary Table 3). Moreover, the

14 Page 15 of 72 Diabetes

320 average ATAC-seq peak intensities of Prmt1-null β cells were decreased in the promoters of β cell

321 genes and OXPHOS genes, indicating that the decreased expression of these genes at the early stage

322 could be attributed to the loss of PRMT1-dependent H4R3me2a (Supplementary Fig. 12A, B).

323 We also identified PRMT1-dependent open chromatin regions at 12 kb upstream of the

324 transcription start site (TSS) for the Ins1 gene and at 40 kb (E1) and 60 kb (E2) upstream of the TSS

325 for the Ucn3 gene (Fig. 5L, N). These regions contained binding sites for β cell TFs, including PDX1,

326 MAFA, NKX2.2 and NKX6.1. Although the expression levels of Ins1 and Ucn3 were robustly and

327 rapidly downregulated in Prmt1 βiKO mice at the early stage, the ATAC-seq peaks at the promoters

328 of both genes were not significantly reduced at this point. Instead, chromosome conformation capture

329 (3C)-PCR experiments showed the long-range interactions between the upstream enhancer elements

330 and the promoters of the Ins1 and Ucn3 genes, indicating that the loss of chromatin accessibility for

331 the β cell TFs at the upstream enhancer elements had reduced the promoter activities of these genes

332 (Fig. 5M, O). We also identified a PRMT1-dependent open chromatin region at 5 kb upstream of the

333 TSS for the Pdx1 gene (Fig. 5P); this region, which is called area IV, was recently reported to play an

334 essential role in β cell maturation during the weaning period (41). PDX1 has also been shown to

335 directly regulate the gene expression of numerous mitochondrial genes that were downregulated in

336 Prmt1 null β cells (42–45). These data suggest that PRMT1-dependent H4R3me2a plays a critical role

337 in maintaining the unique chromatin architecture of mature β cells, and that the alteration of this

338 chromatin architecture can result in the loss of mature β cell identity.

339 In an effort to test the possible implication of PRMT1-dependent H4R3me2a in human

340 diabetes, we performed sequence alignment analysis of PRMT1-dependent open chromatin regions in

341 the human genome and searched for conserved regulatory elements in these regions. The E2 element

342 of the mouse Ucn3 gene and area IV of the mouse Pdx1 gene were highly conserved in the human

343 UCN3 and PDX1 genes, which are found at similar genomic locations (Fig. 6A, B). A type 2 diabetes-

344 associated locus was found near area IV of PDX1, and mice lacking endogenous area IV showed the

15 Diabetes Page 16 of 72

345 impairment of β cell maturation (41,46). We also found a highly conserved PRMT1-dependent open

346 chromatin region in the human SLC30A8 gene, which is strongly associated with type 2 diabetes (46–

347 49) (Fig. 6C). These findings prompted us to examine the association of human orthologous sequences

348 of PRMT1-dependent open chromatin regions with diabetes GWAS SNPs. Interestingly, the human

349 diabetes-associated loci were more closely localized with PRMT1-dependent open chromatin regions

350 than with PRMT1-independent open chromatin regions (Fig. 6D). This suggests that there may be a

351 link between PRMT1-dependent H4R3me2a and the susceptibility to type 2 diabetes in humans.

352

353 Discussion

354 Epigenetic regulation is crucial for β cell maturation and the maintenance of mature β cell

355 identity (13–16). As one of the major mechanisms of epigenetic regulation, histone arginine

356 methylation plays important roles in transcriptional regulation (17,18). However, its role in β cells has

357 not yet been explored. Here, we demonstrate a novel function of PRMT1-dependent H4R3me2a in

358 maintaining mature β cell identity. Both Prmt1 βKO and Prmt1 βiKO mice developed diabetes, which

359 was aggravated by HFD-induced metabolic stress (Supplementary Fig. 13). Deletion of Prmt1 in adult

360 β cells resulted in the immediate loss of H4R3me2a, which induced robust changes in the transcriptions

361 of genes necessary for the maintenance of mature β cell function and identity. PRMT1-dependent

362 H4R3me2a worked as an active histone code that increased chromatin accessibility at the binding sites

363 for CTCF and β cell TFs, including NKX6.1, MAFA, PDX1 and NEUROD1 (Fig. 6E). Furthermore,

364 PRMT1-dependent open chromatin regions appear to be associated with genes that have been

365 associated with diabetes susceptibility in humans.

366 GWAS-based studies have indicated that most diabetes-susceptibility genes are related to β

367 cells, and thus β cell failure is thought to be an essential feature of diabetes (50,51). The dysfunction

368 of β cells occurs long before hyperglycemia develops in humans (52), suggesting that β cell

369 dysfunction is an early feature of diabetic β cell failure. However, due to the lack of an appropriate

16 Page 17 of 72 Diabetes

370 animal model, researchers have been limited in their ability to study how β cell dysfunction begins in

371 response to metabolic stress, and how β cell failure progresses before the apoptosis or dedifferentiation

372 of β cell is observed. In this regard, Prmt1 βiKO mice provide the following useful features: initial

373 events of loss of β cell identity, which has not previously been described in an animal model; loss of

374 INS and UCN3 prior to loss of other mature β cell TFs, decreased expression of β cell genes, decreased

375 expression of mitochondrial genes and subsequent mitochondrial dysfunction. Despite these extensive

376 changes of gene expression in the β cells of Prmt1 βiKO mice, their metabolic phenotype was

377 unexpectedly mild and became severe upon an HFD-feeding. This discrepancy prompted us to propose

378 the following explanations: (1) Since β cells are highly dedicated to insulin production and secretion,

379 even though the β cells of Prmt1 iKO mice are not fully functional, they can maintain glycemic

380 control as long as mice are insulin sensitive. (2) There may be compensatory mechanisms to maintain

381 glycemic control in Prmt1 βiKO mice. The restoration of Ins1 expression in the late stage of Prmt1

382 βiKO mice (12 weeks of age) supports the existence of these compensatory mechanisms.

383 Since the phenotype of Prmt1 βiKO mice resembles the natural history of type 2 diabetes, this

384 mouse model may be useful for studying how β cells behave in response to metabolic stress during the

385 development of type 2 diabetes. Prmt1 βiKO mice showed the following features in the progression

386 of β cell failure: loss of β cell identity - aberrant expression of β cell TFs - cell type change of β cell

387 to other endocrine cells. We speculate that Prmt1 deletion resulted in the loss of H4R3me2a, and that

388 this causes β cells to lose their cell-specific chromatin architecture and gene expression program, and

389 thereby lose their identity. These β cells that lose their identity, then undergo different changes based

390 on their genetic heterogeneity in response to metabolic stress. However, further study will be needed

391 to elucidate the precise mechanisms underlying the severe β cell phenotypes of HFD-fed Prmt1 βKO

392 and Prmt1 βiKO mice. The chromatin changes driven by HFD-feeding together with the losses of

393 H4R3me2a and arginine methylation in non-histone substrates of PRMT1 may have affected these

394 phenotypes. FOXO1 and HNF4 are PRMT1’s non-histone targets that are also known to play roles

17 Diabetes Page 18 of 72

395 in β cell function and identity (38,53–55). However, the phenotypes of β cell-specific Foxo1 or Hnf4

396 KO mice differed from those of Prmt1 KO mice (38,55). In this regard, we think that the phenotypes

397 of Prmt1 βKO and Prmt1 βiKO mice may be largely attributed to the loss of H4R3me2a in β cells.

398 Here, we provide novel insight into the importance of epigenetic control of PRMT1-dependent

399 H4R3me2a in maintaining mature β cell identity. Taken together with the associations seen among

400 CTCF, β cell TFs and H4R3me2a, our work reveals previously unknown functions of PRMT1-

401 dependent open chromatin regions that govern mature β cell identity. Thus, our phenotypic,

402 transcriptomic and epigenomic analyses of stage-specific Prmt1 KO in β cells provide a new

403 mechanistic insight into the regulation of mature β cell identity.

18 Page 19 of 72 Diabetes

404 Acknowledgements

405 We thank Hee-Saeng Jung, Jueun Kim and Hanna Jung for their technical support; Dr. Dahee Choi

406 and Hyun Jung Hong for the technical advice and support; Drs. Yong-Ho Ahn and Joo-Man Park for

407 the help with electron microscopic analysis; Jeong-Hwan Kim for the technical assistance with NGS

408 library preparation; Drs. Kyong Soo Park, Kun-Ho Yoon, Soo Heon Kwak and Kyoung-Jae Won for

409 the helpful discussions; Drs. Mark O. Huising and Paul E. Sawchenko for the gift of the UCN3

410 antibody.

411

412 Funding

413 This work was supported by grants from the National Research Foundation (NRF) funded by the

414 Ministry of Science, ICT & Future planning, Republic of Korea [Grant numbers: NRF-

415 2017M3C9A5028693 to M.K., NRF-2014M3A9D5A01073546, NRF-2018R1A2A3074646 and

416 NRF-2015M3A9B3028218 to Hail K., NRF-2013M3A9D5072550 to J.K.S.], the KRIBB Research

417 Initiative [to M.K.] and the KAIST Institute for the BioCentury [Grant number: N10180027 to Hail

418 K.].

419

420 Competing interests

421 The authors declare no competing interests.

422

423 Author Contributions

424 Hyunki K., B.-H.Y., C.-M.O., Joonyub L., M.K. and Hail K. generated the hypothesis, designed the

425 experiments and analyzed the results. Hyunki K., C.-M.O., Joonyub L., H.S., Y.K.K., K.L., M.-Y. K.,

426 J.K.S. and Hyeongseok K. performed the animal experiments. Hyunki K., C.-M.O., E.K., E.-H.S.,

427 H.H., H.-J.K., Junguee L., J.M.S., S.-H.K., S.K. and M.S. performed the cell and in vitro experiments.

428 Hyunki K., B.-H.Y., C.-M.O., K.Y., Y.S.J., M.K. and Hail K. analyzed the NGS data. Hyunki K., B.-

19 Diabetes Page 20 of 72

429 H.Y., C.-M.O., Joonyub L., M.K. and Hail K. wrote the manuscript. M.K. and Hail K. supervised the

430 research. M.K. and Hail K. are the guarantors of this work and, as such, had full access to all the data

431 in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

432

433 Prior Presentation

434 Parts of this study were presented in abstract form at the conference: Drivers of Type 2 Diabetes: From

435 Genes to Environment (S1) of the Keystone Symposia, Seoul, South Korea, 7–11 October 2018. Third

436 Joint EASD Islet Study Group and Beta-Cell Workshop, Oxford, UK, 1-3 April 2019.

20 Page 21 of 72 Diabetes

437 References

438 1. Halban PA, Polonsky KS, Bowden DW, Hawkins MA, Ling C, Mather KJ, et al. β-Cell 439 Failure in Type 2 Diabetes: Postulated Mechanisms and Prospects for Prevention and 440 Treatment. Diabetes Care [Internet]. 2014 Jun 1;37(6):1751 LP – 1758. Available from: 441 http://care.diabetesjournals.org/content/37/6/1751.abstract 442 2. Prentki M, Nolan CJ. Islet β cell failure in type 2 diabetes. J Clin Invest [Internet]. 2006 Jul 443 3;116(7):1802–12. Available from: https://doi.org/10.1172/JCI29103 444 3. Donath MY, Ehses JA, Maedler K, Schumann DM, Ellingsgaard H, Eppler E, et al. 445 Mechanisms of β-Cell Death in Type 2 Diabetes. Diabetes [Internet]. 2005 Dec 1;54(suppl 446 2):S108 LP-S113. Available from: 447 http://diabetes.diabetesjournals.org/content/54/suppl_2/S108.abstract 448 4. Talchai C, Xuan S, Lin H V, Sussel L, Accili D. Pancreatic β-Cell Dedifferentiation As 449 Mechanism Of Diabetic β-Cell Failure. Cell [Internet]. 2012 Sep 14;150(6):1223–34. 450 Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3445031/ 451 5. Cigliola V, Thorel F, Chera S, Herrera PL. Stress-Induced Adaptive Islet Cell Identity 452 Changes. Diabetes Obes Metab [Internet]. 2016 Sep;18(Suppl 1):87–96. Available from: 453 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5021189/ 454 6. Nishimura W, Takahashi S, Yasuda K. MafA is critical for maintenance of the mature beta 455 cell phenotype in mice. Diabetologia [Internet]. 2015 Mar;58(3):566–74. Available from: 456 https://doi.org/10.1007/s00125-014-3464-9 457 7. Gu C, Stein GH, Pan N, Goebbels S, Hörnberg H, Nave K-A, et al. Pancreatic β Cells Require 458 NeuroD to Achieve and Maintain Functional Maturity. Cell Metab [Internet]. 2010;11(4):298– 459 310. Available from: http://www.sciencedirect.com/science/article/pii/S1550413110000768 460 8. Swisa A, Avrahami D, Eden N, Zhang J, Feleke E, Dahan T, et al. PAX6 maintains β cell 461 identity by repressing genes of alternative islet cell types. J Clin Invest [Internet]. 2017 Jan 462 3;127(1):230–43. Available from: https://doi.org/10.1172/JCI88015 463 9. Ediger BN, Lim H-W, Juliana C, Groff DN, Williams LT, Dominguez G, et al. LIM domain– 464 binding 1 maintains the terminally differentiated state of pancreatic β cells. J Clin Invest 465 [Internet]. 2017 Jan 3;127(1):215–29. Available from: https://doi.org/10.1172/JCI88016 466 10. Gutiérrez GD, Bender AS, Cirulli V, Mastracci TL, Kelly SM, Tsirigos A, et al. Pancreatic β 467 cell identity requires continual repression of non–β cell programs. J Clin Invest [Internet]. 468 2017 Jan 3;127(1):244–59. Available from: https://doi.org/10.1172/JCI88017 469 11. Dixon JR, Jung I, Selvaraj S, Shen Y, Antosiewicz-Bourget JE, Lee AY, et al. Chromatin 470 architecture reorganization during stem cell differentiation. Nature [Internet]. 2015 Feb 471 18;518:331. Available from: http://dx.doi.org/10.1038/nature14222 472 12. Ernst J, Kheradpour P, Mikkelsen TS, Shoresh N, Ward LD, Epstein CB, et al. Systematic 473 analysis of chromatin state dynamics in nine human cell types. Nature [Internet]. 2011 May 474 5;473(7345):43–9. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3088773/ 475 13. Campbell SA, Hoffman BG. Chromatin Regulators in Pancreas Development and Diabetes. 476 Vol. 27, Trends in Endocrinology and Metabolism. 2016. p. 142–52. 477 14. Avrahami D, Li C, Zhang J, Schug J, Avrahami R, Rao S, et al. Aging-Dependent 478 Demethylation of Regulatory Elements Correlates with Chromatin State and Improved β Cell 479 Function. Cell Metab [Internet]. 2015 Oct 6;22(4):619–32. Available from: 480 http://dx.doi.org/10.1016/j.cmet.2015.07.025 481 15. Arda HE, Li L, Tsai J, Torre EA, Rosli Y, Peiris H, et al. Age-dependent pancreatic gene 482 regulation reveals mechanisms governing human β cell function. Cell Metab [Internet]. 483 2016;23(5):909–20. Available from: http://dx.doi.org/10.1016/j.cmet.2016.04.002 484 16. Lu TT-H, Heyne S, Dror E, Casas E, Leonhardt L, Boenke T, et al. The Polycomb-Dependent 485 Epigenome Controls β Cell Dysfunction, Dedifferentiation, and Diabetes. Cell Metab

21 Diabetes Page 22 of 72

486 [Internet]. 2018 Jun 5;27(6):1294-1308.e7. Available from: 487 https://doi.org/10.1016/j.cmet.2018.04.013 488 17. Lorenzo A Di, Bedford MT. Histone Arginine Methylation. FEBS Lett [Internet]. 2011 Jul 489 7;585(13):2024–31. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3409563/ 490 18. Blanc RS, Richard S. Arginine Methylation: The Coming of Age. Vol. 65, Molecular Cell. 491 2017. p. 8–24. 492 19. Kim JK, Lim Y, Lee JO, Lee YS, Won NH, Kim H, et al. PRMT4 is involved in insulin 493 secretion via methylation of histone H3 in pancreatic β-cells. J Mol Endocrinol [Internet]. 494 2015 Apr 27; Available from: http://jme.endocrinology- 495 journals.org/content/early/2015/04/17/JME-14-0325.abstract 496 20. Tang J, Frankel A, Cook RJ, Kim S, Paik WK, Williams KR, et al. PRMT1 is the predominant 497 type I protein arginine methyltransferase in mammalian cells. J Biol Chem. 498 2000;275(11):7723–30. 499 21. Iwasaki H. Impaired PRMT1 activity in the liver and pancreas of type 2 diabetic Goto- 500 Kakizaki rats. Life Sci [Internet]. 2009;85(3–4):161–6. Available from: 501 http://dx.doi.org/10.1016/j.lfs.2009.05.007 502 22. Wang H, Huang ZQ, Xia L, Feng Q, Erdjument-Bromage H, Strahl BD, et al. Methylation of 503 histone H4 at arginine 3 facilitating transcriptional activation by nuclear hormone receptor. 504 Science. 2001;293(5531):853–7. 505 23. Huang S, Litt M, Felsenfeld G. Methylation of histone H4 by arginine methyltransferase 506 PRMT1 is essential in vivo for many subsequent histone modifications. Genes Dev. 507 2005;19(16):1885–93. 508 24. Kim K, Oh CM, Ohara-Imaizumi M, Park S, Namkung J, Yadav VK, et al. Functional role of 509 serotonin in insulin secretion in a diet-induced insulin-resistant state. Endocrinology. 510 2015;156(2):444–52. 511 25. Szot GL, Koudria P, Bluestone JA. Murine Pancreatic Islet Isolation. J Vis Exp [Internet]. 512 2007 Aug 22;(7):255. Available from: 513 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2565847/ 514 26. Baek S-J, Kim M, Bae D-H, Kim J-H, Kim H-J, Han M-E, et al. Integrated epigenomic 515 analyses of enhancer as well as promoter regions in gastric cancer. Oncotarget [Internet]. 516 2016;7(18). Available from: http://www.ncbi.nlm.nih.gov/pubmed/27016420 517 27. Buenrostro JD, Wu B, Chang HY, Greenleaf WJ. ATAC-seq: A method for assaying 518 chromatin accessibility genome-wide. Curr Protoc Mol Biol. 2015;2015:21.29.1-21.29.9. 519 28. Hagège H, Klous P, Braem C, Splinter E, Dekker J, Cathala G, et al. Quantitative analysis of 520 chromosome conformation capture assays (3C-qPCR). Nat Protoc [Internet]. 2007 Jul 521 5;2:1722. Available from: http://dx.doi.org/10.1038/nprot.2007.243 522 29. Tang Z, Luo OJ, Li X, Zheng M, Zhu JJ, Szalaj P, et al. CTCF-Mediated Human 3D Genome 523 Architecture Reveals Chromatin Topology for Transcription. Cell. 2015;163(7):1611–27. 524 30. Ong C-T, Corces VG. CTCF: an architectural protein bridging genome topology and function. 525 Nat Rev Genet [Internet]. 2014;15(4):234–46. Available from: 526 http://www.nature.com/doifinder/10.1038/nrg3663 527 31. Dhawan S, Tschen S-I, Zeng C, Guo T, Hebrok M, Matveyenko A, et al. DNA methylation 528 directs functional maturation of pancreatic β cells. J Clin Invest [Internet]. 2015 Jul 529 1;125(7):2851–60. Available from: https://doi.org/10.1172/JCI79956 530 32. Yoshihara E, Wei Z, Lin CS, Fang S, Ahmadian M, Kida Y, et al. ERRγ is required for the 531 metabolic maturation of therapeutically functional glucose-responsive β cells. Cell Metab 532 [Internet]. 2016 Apr 12;23(4):622–34. Available from: 533 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4832237/ 534 33. J. PT, A. RG. When less is more: the forbidden fruits of gene repression in the adult β-cell. 535 Diabetes, Obes Metab [Internet]. 2012 Nov 1;15(6):503–12. Available from:

22 Page 23 of 72 Diabetes

536 https://doi.org/10.1111/dom.12029 537 34. Marchetti P, Bugliani M, Lupi R, Marselli L, Masini M, Boggi U, et al. The endoplasmic 538 reticulum in pancreatic beta cells of type 2 diabetes patients. Diabetologia [Internet]. 539 2007;50(12):2486–94. Available from: https://doi.org/10.1007/s00125-007-0816-8 540 35. Matilde M, Luisa M, Lorella M, Marco B, Ugo B, Franco F, et al. Ultrastructural alterations of 541 pancreatic beta cells in human diabetes mellitus. Diabetes Metab Res Rev [Internet]. 2017 542 Mar 16;33(6):e2894. Available from: https://doi.org/10.1002/dmrr.2894 543 36. Weir GC, Bonner-Weir S. Five Stages of Evolving Beta-Cell Dysfunction During Progression 544 to Diabetes. Diabetes [Internet]. 2004 Dec 1;53(suppl 3):S16 LP-S21. Available from: 545 http://diabetes.diabetesjournals.org/content/53/suppl_3/S16.abstract 546 37. Guo S, Dai C, Guo M, Taylor B, Harmon JS, Sander M, et al. Inactivation of specific β cell 547 transcription factors in type 2 diabetes. J Clin Invest [Internet]. 2013 Aug 1;123(8):3305–16. 548 Available from: https://doi.org/10.1172/JCI65390 549 38. Talchai C, Xuan S, Lin H V., Sussel L, Accili D. Pancreatic β cell dedifferentiation as a 550 mechanism of diabetic β cell failure. Cell. 2012;150(6):1223–34. 551 39. Cinti F, Bouchi R, Kim-Muller JY, Ohmura Y, Sandoval PR, Masini M, et al. Evidence of β- 552 Cell Dedifferentiation in Human Type 2 Diabetes. J Clin Endocrinol Metab [Internet]. 2016 553 Mar 1;101(3):1044–54. Available from: http://dx.doi.org/10.1210/jc.2015-2860 554 40. Dowen JM, Fan ZP, Hnisz D, Ren G, Abraham BJ, Zhang LN, et al. Control of Cell Identity 555 Genes Occurs in Insulated Neighborhoods in Mammalian Chromosomes. Cell [Internet]. 556 2014;159(2):374–87. Available from: 557 http://www.sciencedirect.com/science/article/pii/S0092867414011799 558 41. Spaeth JM, Gupte M, Perelis M, Yang Y-P, Cyphert H, Guo S, et al. Defining a Novel Role 559 for the Pdx1 Transcription Factor in Islet β-Cell Maturation and Proliferation During 560 Weaning. Diabetes [Internet]. 2017 Nov 1;66(11):2830 LP – 2839. Available from: 561 http://diabetes.diabetesjournals.org/content/66/11/2830.abstract 562 42. Brissova M, Shiota M, Nicholson WE, Gannon M, Knobel SM, Piston DW, et al. Reduction 563 in Pancreatic Transcription Factor PDX-1 Impairs Glucose-stimulated Insulin Secretion. J 564 Biol Chem [Internet]. 2002 Mar 29;277(13):11225–32. Available from: 565 http://www.jbc.org/content/277/13/11225.abstract 566 43. Gauthier BR, Wiederkehr A, Baquié M, Dai C, Powers AC, Kerr-Conte J, et al. PDX1 567 Deficiency Causes Mitochondrial Dysfunction and Defective Insulin Secretion through TFAM 568 Suppression. Cell Metab [Internet]. 2009 Aug 6;10(2):110–8. Available from: 569 https://doi.org/10.1016/j.cmet.2009.07.002 570 44. Fujimoto K, Chen Y, Polonsky KS, Dorn GW. Targeting cyclophilin D and the mitochondrial 571 permeability transition enhances β-cell survival and prevents diabetes in Pdx1 deficiency. 572 Proc Natl Acad Sci [Internet]. 2010 Jun 1;107(22):10214 LP – 10219. Available from: 573 http://www.pnas.org/content/107/22/10214.abstract 574 45. Soleimanpour SA, Ferrari AM, Raum JC, Groff DN, Yang J, Kaufman BA, et al. Diabetes 575 Susceptibility Genes <em>Pdx1</em> and <em>Clec16a</em> 576 Function in a Pathway Regulating Mitophagy in β-Cells. Diabetes [Internet]. 2015 Oct 577 1;64(10):3475 LP – 3484. Available from: 578 http://diabetes.diabetesjournals.org/content/64/10/3475.abstract 579 46. Wheeler E, Leong A, Liu C-T, Hivert M-F, Strawbridge RJ, Podmore C, et al. Impact of 580 common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in 581 ancestrally diverse populations: A transethnic genome-wide meta-analysis. PLOS Med 582 [Internet]. 2017 Sep 12;14(9):e1002383. Available from: 583 https://doi.org/10.1371/journal.pmed.1002383 584 47. Rutter GA, Chimienti F. SLC30A8 mutations in type 2 diabetes. Vol. 58, Diabetologia. 2015. 585 p. 31–6.

23 Diabetes Page 24 of 72

586 48. Zhao W, Rasheed A, Tikkanen E, Lee J-J, Butterworth AS, Howson JMM, et al. Identification 587 of new susceptibility loci for type 2 diabetes and shared etiological pathways with coronary 588 heart disease. Nat Genet [Internet]. 2017 Sep 4;49:1450. Available from: 589 http://dx.doi.org/10.1038/ng.3943 590 49. Consortium DiaGRAM (DIAGRAM), Consortium AGENT 2 D (AGEN-T, Consortium SAT 591 2 D (SAT2D), Consortium MAT 2 D (MAT2D), Consortium T 2 DGE by N sequencing in 592 multi-ES (T2D-G, Mahajan A, et al. Genome-wide trans-ancestry meta-analysis provides 593 insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet [Internet]. 594 2014 Feb 9;46:234. Available from: http://dx.doi.org/10.1038/ng.2897 595 50. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A Genome-Wide 596 Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants. 597 Science (80- ) [Internet]. 2007 Jun 1;316(5829):1341 LP – 1345. Available from: 598 http://science.sciencemag.org/content/316/5829/1341.abstract 599 51. Tsai F-J, Yang C-F, Chen C-C, Chuang L-M, Lu C-H, Chang C-T, et al. A Genome-Wide 600 Association Study Identifies Susceptibility Variants for Type 2 Diabetes in Han Chinese. 601 PLOS Genet [Internet]. 2010 Feb 19;6(2):e1000847. Available from: 602 https://doi.org/10.1371/journal.pgen.1000847 603 52. BRUNZELL JD, ROBERTSON RP, LERNER RL, HAZZARD WR, ENSINCK JW, 604 BIERMAN EL, et al. Relationships Between Fasting Plasma Glucose Levels and Insulin 605 Secretion During Intravenous Glucose Tolerance Tests. J Clin Endocrinol Metab [Internet]. 606 1976 Feb 1;42(2):222–9. Available from: http://dx.doi.org/10.1210/jcem-42-2-222 607 53. Yamagata K, Daitoku H, Takahashi Y, Namiki K, Hisatake K, Kako K, et al. Arginine 608 Methylation of FOXO Transcription Factors Inhibits Their Phosphorylation by Akt. Mol Cell 609 [Internet]. 2008;32(2):221–31. Available from: 610 http://www.sciencedirect.com/science/article/pii/S1097276508006588 611 54. Barrero MJ, Malik S. Two functional modes of a nuclear receptor-recruited arginine 612 methyltransferase in transcriptional activation. Mol Cell [Internet]. 2006 Oct 20;24(2):233–43. 613 Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1647399/ 614 55. Miura A, Yamagata K, Kakei M, Hatakeyama H, Takahashi N, Fukui K, et al. Hepatocyte 615 nuclear factor-4α is essential for glucose-stimulated insulin secretion by pancreatic β-cells. J 616 Biol Chem. 2006;281(8):5246–57.

24 Page 25 of 72 Diabetes

618 Figure Legends

619 Figure 1. Prmt1 βKO mice develop a progressive diabetes phenotype.

620 (A) Intraperitoneal glucose tolerance test (IPGTT) of 6wks male control and Prmt1 βKO mice after a

621 16-hour fasting; n = 4 per group. (B-F) Male control and Prmt1 βKO mice of 12-13wks were fed

622 standard chow diet (SCD) and used for experiments. (B) IPGTT after a 16-hour fasting; n = 5 per

623 group. (C) Ex vivo islet glucose-stimulated insulin secretion (GSIS) assay; n = 4 per group. (D) In vivo

624 GSIS assay after a 16-hour fasting; n = 3 per group. (E) Oxygen consumption rate (OCR) analysis of

625 isolated islets; n = 6 per group. Olig.; Oligomycin, Rot.; Rotenone. (F) Representative β cell images

626 obtained by transmission electron microscopy (TEM). Arrows indicate immature insulin granules

627 (blue), dilated endoplasmic reticulum (green) and dysmorphic mitochondria (red); n = 3 per group.

628 (G-O) Male control and Prmt1 βKO mice (8wks) were fed high-fat diet (HFD) for 18 weeks and used

629 for experiments. (G) IPGTT after a 16-hour fasting; n = 5 per group. (H) Representative islet images

630 obtained by IF staining of INS (green), glucagon (GCG, red) and DAPI (blue) from HFD-fed 26wks

631 Prmt1 βKO mice; n = 3 per group. (I) Representative islet images obtained by IF staining of eYFP

632 (green), INS (blue) and GCG, somatostatin (SST) or pancreatic polypeptide (PPY) (red) from HFD-

633 fed 26wks R26-eYFP; Rip2-Cre (control) and R26-eYFP; Prmt1 βKO mice. (J-M) Quantification

634 analysis of eYFP co-positive cells expressing (J) INS, (K) GCG, (L) SST and (M) PPY in the islets

635 of HFD-fed 26wks R26-eYFP; Rip2-Cre (WT) and R26-eYFP; Prmt1 βKO (KO) mice; n = 3 per group.

636 (N) Representative islet images obtained by IF staining of INS (green) and PDX1, NKX6.1, MAFA

637 or SLC2A2 (red); n = 3 per group. (O) Representative islet images obtained by IF staining of INS

638 (green), FOXO1 (red) and DAPI (blue); n = 3 per group. White scale bars, 50 μm (H, I, N, O). Yellow

639 scale bars, 2.5 μm (F). Prmt1fl/fl or Rip2-Cre mice were used as controls (A-G, N, O). Data are

640 expressed as the means ± SEM. *P<0.05, **P<0.01 and ***P<0.001, by Student’s t-test (A, B, E, G,

641 J-M) or one-way ANOVA with post-hoc Tukey’s test (C, D).

25 Diabetes Page 26 of 72

642 Figure 2. PRMT1 is required for the maintenance of mature β cell identity.

643 (A) Representative islet images obtained by IF staining of INS (green) and PRMT1, H4R3me2a, PDX1,

644 NKX6.1, MAFA or SLC2A2 (red) in 8 and 12wks control and Prmt1 βiKO mice; n = 3 per group. (B)

645 Representative islet images obtained by IF staining of eYFP (green), INS (blue) and GCG, SST, PPY

646 and urocortin3 (UCN3) (red) from 8 and 12wks R26-eYFP; Pdx1-CreERT2 (control) and R26-eYFP;

647 Prmt1 βiKO mice; n = 3 per group. (C-G) Quantification analysis of eYFP co-positive cells expressing

648 (C) GCG, (D) SST, (E) PPY, (F) INS and (G) UCN3 in the islets of 8 and 12wks R26-eYFP; Pdx1-

649 CreERT2 (WT) and R26-eYFP; Prmt1 βiKO (KO) mice; n = 3 per group. White scale bars, 50 μm (A,

650 B). Tamoxifen (TAM)-injected Prmt1fl/fl or Pdx1-CreERT2 mice were used as controls (A-G). Data are

651 expressed as the means ± SEM. *P<0.05, **P<0.01 and ***P<0.001, by Student’s t-test (C-G).

26 Page 27 of 72 Diabetes

652 Figure 3. HFD exacerbates mature β cell identity in Prmt1 βiKO mice.

653 (A-G) Male control and Prmt1 βiKO mice (12wks) were fed HFD for 14 weeks and used for

654 experiments. (A) Representative islet images obtained by IF staining of eYFP (green), INS (blue) and

655 GCG, SST and PPY (red) from HFD-fed 26wks R26-eYFP; Pdx1-CreERT2 (control) and R26-eYFP;

656 Prmt1 βiKO mice; n = 3 per group. (B-E) Quantification analysis of eYFP co-positive cells expressing

657 (B) INS, (C) GCG, (D) SST and (E) PPY in the islets of HFD-fed 26wks R26-eYFP; Pdx1-CreERT2

658 (WT) and R26-eYFP; Prmt1 βiKO (KO) mice; n = 3 per group. (F) Representative islet images

659 obtained by IF staining of INS (green) and PDX1, NKX6.1 or MAFA (red); n = 3 per group. (G)

660 Representative islet images obtained by IF staining of INS (green), FOXO1 (red) and DAPI (blue); n

661 = 3 per group. White scale bars, 50 μm (A, F, G). TAM-injected Prmt1fl/fl or Pdx1-CreERT2 mice were

662 used as controls (A-G). Data are expressed as the means ± SEM. *P<0.05, **P<0.01 and ***P<0.001,

663 by Student’s t-test (B-E).

27 Diabetes Page 28 of 72

664 Figure 4. Deletion of Prmt1 from mature β cells induces robust transcriptomic changes.

665 (A-K) RNA-seq analysis of islets from Prmt1 βiKO mice at the early (8wks) and late (12wks) stages

666 of loss of mature β cell identity. Age- and sex (male)-matched, TAM-injected littermates (Prmt1fl/fl)

667 were used as controls at the two different stages; n = 2 per group. Differentially expressed genes (DEGs)

668 were identified using the following parameters: log2(Fold-change of counts per million (CPM) mapped

669 reads; FC) ≤-1.5 or ≥1.5, False discovery rate (FDR) <0.05. (A) Schematic representation of time

670 points at which RNA-seq sampling was performed for islets of control and Prmt1 βiKO mice. (B) Top

671 ranked gene ontology of commonly downregulated (n = 260) and upregulated (n = 310) DEGs. (C)

672 Expression heat-maps of gene subsets relative to the functional categories identified in our islet RNA-

673 seq analysis; n = 2 per group. Gene lists for the heat-maps are presented in Supplementary Table 2.

674 (D-I) RNA-seq and qRT-PCR analyses of islets from Prmt1 βiKO mice at the early (8wks) and late

675 (12wks) stages of loss of mature β cell identity. n = 2 per group for RNA-seq, n = 4 per group for qRT-

676 PCR. Line (RNA-seq) and bar (qRT-PCR) graphs showing relative expressions of the representative

677 mature β cell genes, (D) Ins1, (E) Ins2, (F) Ucn3, (G) Pdx1, (H) Mafa and (I) Slc2a2, at the two

678 different stages of loss of mature β cell identity. Fold-changes of CPMs are plotted and FDR values

679 are indicated in line graphs for each stage. Expression levels of genes were normalized to Actb in each

680 sample in qRT-PCR analysis. (J, K) Heat-maps of (J) gene ontology and (K) KEGG pathway analyses

681 of the stage-specific downregulated and upregulated DEGs. Data are expressed as the means ± SEM.

682 *P<0.05, **P<0.01 and ***P<0.001, by one-way ANOVA with post-hoc Tukey’s test (D-I).

28 Page 29 of 72 Diabetes

683 Figure 5. PRMT1-dependent H4R3me2a regulates the chromatin accessibility of mature β cell.

684 (A-L, N, P) ATAC-seq analysis performed on β cells purified from 8wks R26-eYFP; Pdx1-CreERT2

685 (WT) and R26-eYFP; Prmt1 βiKO (KO) mice; n = 12 per group were used for one ATAC-seq replicate.

686 ATAC-seq peaks of WT and KO β cells; n = 2 were used for replicates. (A) Schematic representation

687 of the ATAC-seq analysis (B) Volcano plot showing differential ATAC-seq peaks in WT and KO β

688 cells. Differentially changed ATAC-seq peaks were identified using the following parameters:

689 log2(Fold-change; WT/KO) ≤-1 or ≥1., P-value <0.01. (C-E) Average ChIP-seq peak intensities for

690 (C) H4R3me2a, (D) H3K27ac and (E) H3K4me1 in PRMT1-independent and -dependent open

691 chromatin regions. (F) Heat-maps of normalized ATAC-seq and ChIP-seq signals at PRMT1-

692 dependent open chromatin regions. Each row represents a peak. Normalized ChIP-seq signals of

693 histone marks, CTCF and β cell TFs are shown. (G) Known TF-binding motifs returned by HOMER

694 analysis for PRMT1-dependent open chromatin regions. (H, I) Gene ontology and pathway enrichment

695 analyses of the genes nearby (H) promoters and (I) enhancers of PRMT1-dependent open chromatin

696 regions. (J, K) Scatter plots showing correlation between gene expression and open chromatin changes

697 in WT and KO β cells for (J) β cell genes and (K) OXPHOS genes. The x-axis represents log2(Fold-

698 change; KO/WT) of RNA-seq data from 8wks Prmt1 βiKO islets. The y-axis is a change of the highest

699 ATAC-seq peak from same gene promoters (TSS± 2kb). R, Pearson’s r correlation coefficient. (L, N,

700 P) Integrative maps of ChIP-seq (histone marks and β cell TFs), ATAC-seq (MIN6, WT and KO β

701 cells) and RNA-seq (WT and KO islets) data obtained for the (L) Ins1, (N) Ucn3 and (P) Pdx1 genes.

702 P; Promoter, E; Enhancer. Red boxes indicate the regions where the ATAC-seq signals of KO β cells

703 are decreased. Asterisks (red) indicate PRMT1-dependent open chromatin regions. (M, O) Chromatin

704 conformation capture PCR (3C-PCR) assays were performed in MIN6 cells for (M) Ins1 and (O) Ucn3

705 genes. Arrows indicate 3C-PCR primers. ATAC-seq peaks of MIN6 cells; n = 3 were used for

706 replicates (L, N, P). H4R3me2a ChIP-seq peaks of MIN6 cells; n = 3 were used for replicates (C, F,

707 L, N, P). CTCF ChIP-seq peaks of MIN6 cells; n = 2 were used for replicates (F).

29 Diabetes Page 30 of 72

708 Figure 6. PRMT1-dependent open chromatin regions are conserved in the human genome.

709 (A-C) Human genome alignment of PRMT1-dependent open chromatin regions in mouse β cells for

710 the (A) Ucn3, (B) Pdx1 and (C) Slc30a8 genes. Asterisks (red) indicate PRMT1-dependent open

711 chromatin regions. Red-colored bars and numbers indicate genomic locations and percentages of

712 sequence identity, respectively. Type 2 diabetes susceptibility loci are indicated in green. Multiple

713 alignments were performed for the genomes of 100 vertebrate species, which were captured from the

714 UCSC genome browser (https://genome.ucsc.edu/). (D) Cumulative plot of human conserved ATAC-

715 seq peaks with the distances to the closest diabetes susceptibility locus. Human conserved PRMT1-

716 dependent peaks tended to be closer to the diabetes susceptibility loci compared to the PRMT1-

717 independent peaks; P = 0.0003739 by paired Student’s t-test, with matched sampling. (E) Schematic

718 representation describing the physiological role of PRMT1-dependent H4R3me2a in mature β cell.

30 Page 31 of 72 Diabetes Figure 1

A B C D E * 20mM 6wks 12wks N.S. Olig. CCCP Rot. *** Glc 500 500 5 1.0 600 WT WT * *** ** WT 400 400 KO 4 **** KO * ** * KO N.S. 400 ** * 3 * 300 300 ** 0.5 ** 200 200 ** 2 200 ** Insulin release 1 100 100 of Basal) (% OCR (% of total content) Plasma insulin (ng/ml) Blood glucose (mg/dl) Blood glucose (mg/dl) 0 0 0 0.0 0 0 30 60 90 120 0 30 60 90 120 Glc 2.8 20 2.8 20 Time 0 15 0 15 0 50 100 150 200 Time (min) Time (min) (mM) WT KO (min) WT KO Time (minutes)

F WT 12wks KO 12wks-1 KO 12wks-2 G H

KO 26wks (HFD) 600 ** * * ** ** 500 400 300

200** WT (HFD) 100 KO (HFD) Blood glucose (mg/dl) 0 0 30 60 90 120 150 INS GCG DAPI Time (min)

I J K eYFP INS GCG eYFP INS SST eYFP INS PPY WT KO

100 2.0 *

75 1.5 Control Control 50 1.0 eYFP; eYFP;

- 25 0.5 INS+ / eYFP+ (%) 26wks (HFD) 26wks GCG+ / eYFP+ (%)

R26 0 0.0 WT KO WT KO L M

KO 4 0.3 b ** * 3 0.2 2 0.1 1 eYFP; Prmt1 eYFP; (HFD) 26wks SST+ / eYFP+ (%) PPY+ / eYFP+ (%) - 0 0.0

R26 WT KO WT KO

N O INS PDX1 INS NKX6.1 INS MAFA INS SLC2A2 FOXO1 INS FOXO1 DAPI WT 26wksWT (HFD) WT 26wksWT (HFD) KO 26wks (HFD) 26wksKO KO 26wks (HFD) 26wksKO Diabetes Page 32 of 72 Figure 2

A INS PRMT1 INS H4R3me2a INS PDX1 INS NKX6.1 INS MAFA INS SLC2A2 8wks WT 8wks KO KO 12wks KO KO

B R26-eYFP; Control 8wks R26-eYFP; Prmt1 biKO 8wks R26-eYFP; Prmt1 biKO 12wks

C WT KO

2.5 * *

GCG 2.0

1.5 INS 1.0

eYFP 0.5 GCG+ / eYFP+ (%) 0.0 8wks 12wks D 5 *** **

SST 4

3 INS 2

1 eYFP SST+ / eYFP+ (%) 0 8wks 12wks E 0.4 * * PPY 0.3

INS 0.2

0.1 eYFP PPY+ / eYFP+ (%) 0.0 8wks 12wks F G 100 100

*** 75 *** 75 *** *** UCN3

50 50 INS

25 25 INS+ / eYFP+ (%) UCN3+ / eYFP+ (%) eYFP 0 0 8wks 12wks 8wks 12wks Page 33 of 72 Diabetes Figure 3

A R26-eYFP; Control R26-eYFP; Prmt1 biKO 26wks (HFD) 26wks (HFD)

B WT KO C 100 2.0 **

GCG 75 *** 1.5

INS 50 1.0

25 0.5 INS+ / eYFP+ (%) eYFP GCG+ / eYFP+ (%) 0 0.0 WT KO WT KO D 5 ***

SST 4

3 INS 2

1 eYFP SST+ / eYFP+ (%) 0 WT KO E

0.6 * PPY 0.4 INS

0.2 PPY+ / eYFP+ (%) eYFP 0.0 WT KO

F G INS PDX1 INS NKX6.1 INS MAFA FOXO1 INS FOXO1 DAPI WT 26wksWT (HFD) WT 26wksWT (HFD) KO 26wks (HFD) 26wksKO KO 26wks (HFD) 26wksKO Diabetes Page 34 of 72 Figure 4

A B Common down in Prmt1 βiKO Common up in Prmt1 βiKO Oxidation reduction 5X TAM Early Late Electron transport chain Generation of precursor metabolites and energy Intracellular transport

Phosphorylation Phosphorus metabolic process 6wks 8wks 12wks Protein amino acid phosphorylation Microtubule cytoskeleton organization

6 4 2 0 2 4 6 -log (P-value) C 10

D E F Ins1 Ins2 Ucn3

1.50 1.5 WT KO 1.50 1.5 WT KO 1.50 2.0 WT KO 1.25 1.25 1.25 1.5 1.00 1.0 *** 1.00 1.0 1.00 3.3E-07 ** *** 0.75 0.75 3.0E-12 0.75 4.1E-12 1.0 ** *** *** 0.50 0.5 0.50 0.5 0.50 Fold change 6.9E-21 Fold change 4.9E-18 Fold change 0.5 0.25 0.25 0.25 1.5E-21 Fold change (KO/WT) change Fold Fold change (KO/WT) change Fold Fold change (KO/WT) change Fold 0.00 0.0 0.00 0.0 0.00 0.0 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks G H I Pdx1 Mafa Slc2a2

WT KO WT KO WT KO 1.50 2.0 1.50 2.5 1.50 N.S. 3.0 * ** 1.25 1.25 2.0 1.25 2.5 1.5 N.S. 1.00 1.00 * 1.00 2.0 1.5 0.75 6.4E-07 1.0 * ** 0.75 0.75 1.5 1.0 0.50 0.50 0.50 1.0 *** 6.2E-09 Fold change 0.5 Fold change Fold change 0.25 0.25 9.7E-07 0.5 0.25 5.2E-38 0.5 Fold change (KO/WT) change Fold Fold change (KO/WT) change Fold Fold change (KO/WT) change Fold 0.00 0.0 0.00 0.0 0.00 0.0 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks

J K GO term 8w down 12w down 8w up 12w up Pathway 8w down 12w down 8w up 12w up Enrichment DNA metabolism Enrichment Apoptosis 0 0 Cell cycle MAPK signaling pathway Nucleic acid metabolism NOD-like receptor signaling pathway ( P -value) ( P -value) 10

Cell structure and motility 10 Toll-like receptor signaling pathway -log Intracellular signaling cascade -log RIG-I-like receptor signaling pathway 25 27 Protein phosphorylation Aminoacyl-tRNA biosynthesis NF-kappaB cascade Notch signaling pathway Electron transport Phosphatidylinositol signaling system Calcium mediated signaling Spliceosome Oxidative phosphorylation N-Glycan biosynthesis Protein folding Citrate cycle (TCA cycle) General vesicle transport Pentose phosphate pathway Intracellular protein traffic Lysosome Carbohydrate metabolism Valine, leucine and isoleucine degradation Protein glycosylation Maturity onset diabetes of the young Protein modification Purine metabolism Oxidative phosphorylation Pyrimidine metabolism Page 35 of 72 Diabetes Figure 5

A C D E H4R3me2a H3K27ac H3K4me1

Islet isolation PRMT1-indep. open PRMT1-indep. open PRMT1-indep. open 5.2 2.8 1.2 PRMT1-dep. open PRMT1-dep. open PRMT1-dep. open WT: R26-eYFP; Prmt1+/+; Pdx1-CreERT2 KO: R26-eYFP; Prmt1fl/fl; Pdx1-CreERT2 0.8 TAM Trypsinization 2.6 1.6 Single-cell dispersion P0 6wks 8wks 12wks 0.4 H4R3me2a peak intensity H3K27ac peak intensity H3K4me1 peak intensity 0 0.4

FACS sorting ATAC-sequening -5 0 +5 -5 0 +5 -5 0 +5 eYFP+ β cells Distance from β cell ATAC-seq peak center (kb) Distance from β cell ATAC-seq peak center (kb) Distance from β cell ATAC-seq peak center (kb) B F 10 15 20 ( P -value) 10 -log 0 5

−3 −2 −1 0 1 2 3

log2(WT/KO) G H I

Mouse/mm10 (Chr19) 25kb J K L Ins1 M b cell genes OXPHOS genes

H3K4me1 H3K27ac gDNA 3C H4R3me2a ATAC_MIN6 Ins1 ATAC_WT (E+P) 200 bp ATAC_KO * Gapdh 200 bp MAFA (internal) NEUROD1 NKX2.2 NKX6.1 PDX1 RNA-seq_WT RNA-seq_KO

E P

N Mouse/mm10 (Chr13) 66kb O P Mouse/mm10 (Chr5) 26kb Ucn3 Pdx1

H3K4me1 H3K4me1 H3K27ac H3K27ac H4R3me2a gDNA 3C H4R3me2a ATAC_MIN6 Ucn3 ATAC_MIN6 ATAC_WT (P+E1) 200 bp ATAC_WT ATAC_KO * ATAC_KO * * MAFA Ucn3 300 bp MAFA NEUROD1 (P+E2) NEUROD1 NKX2.2 Gapdh 200 bp NKX2.2 NKX6.1 (internal) NKX6.1 PDX1 PDX1 RNA-seq_WT RNA-seq_WT RNA-seq_KO RNA-seq_KO

P E1 E2 Diabetes Page 36 of 72 Page 37 of 72 Diabetes

1 Supplementary data

2

3 Full title

4 PRMT1 is required for the maintenance of mature β cell identity

5

6 Authors

7 Hyunki Kim1, #, Byoung-Ha Yoon2, 3, #, Chang-Myung Oh4, #, Joonyub Lee1, #, Kanghoon Lee1, Heein

8 Song1, Eunha Kim5, Mi-Young Kim6, Hyeongseok Kim1, Yong Kyung Kim7, Kijong Yi1, Eun-Hye

9 Seo2, 3, Haejeong Heo2, 3, Hee-Jin Kim2, Junguee Lee8, Jae Myoung Suh1, Seung-Hoi Koo9, Je Kyung

10 Seong6,10, Seyun Kim5, Young Seok Ju1, Minho Shong7, Mirang Kim2, 3, * and Hail Kim1, 11, *

11

1 Diabetes Page 38 of 72

Supplementary Figure 1

A B D 0.10 INS PRMT1 DAPI INS H4R3me2a DAPI 0.08 Actb ) 0.06

0.04

0.02 (Normalized to

Relative mRNA expression Relative mRNA 0.00 Human Prmt1Prmt2Prmt3Prmt4Prmt5Prmt6Prmt7Prmt9 C F

4.5 ** CTCF 25 * MAFA 20 Actb )

15 2.5

10 Prmt1 expression Mouse 5 peak intensity Average (Normalized to 0.5

Relative 0 Liver Brain Islet -5 0 +5 Distance from H4R3me2a peak center (kb)

E HOMER known motif results (H4R3me2a ChIP-seq) - Top 15 of total 84

HOMER de novo motif results (H4R3me2a ChIP-seq) – Top 15 of total 51

12

13

2 Page 39 of 72 Diabetes

14 Supplementary Figure 1. H4R3me2a occupies the regulatory regions of β cell.

15 (A) The relative mRNA expression levels of the Prmt genes were assessed by quantitative reverse

16 transcription PCR (qRT-PCR) of islets collected from C57BL/6J mice at 12 weeks of age (wks); n =

17 3 per group. (B) CPM values of the Prmt genes from RNA-seq data of 12wks WT (Prmt1fl/fl) mice

18 islets; n = 2 per group. (C) Relative Prmt1 expression levels were assessed by qRT-PCR of liver,

19 brain and islets from 12wks C57BL/6J mice; n = 3 per group. (D) Representative islet images were

20 obtained using immunofluorescent (IF) staining of insulin (INS, green), PRMT1 or H4R3me2a (red)

21 and DAPI (blue) in samples from 12wks C57BL/6J mice and adult human donors; n = 3 per group.

22 White scale bars, 50 µm. (E) Complete output of known and de novo TF-binding motifs returned by

23 the HOMER software for the H4R3me2a ChIP-seq peaks of MIN6 cells. Top 15 of total 84 (known)

24 and top 15 of 51 results (de novo) are shown. CTCF and β cell TFs are marked with red boxes. (F)

25 Average ChIP-seq peak intensities for CTCF and MAFA centered at the H4R3me2a ChIP-seq peaks

26 of MIN6 cells. H4R3me2a ChIP-seq peaks of MIN6 cells; n = 3 were used for replicates (E, F).

27 CTCF ChIP-seq peaks of MIN6 cells; n = 2 were used for replicates (F). ChIP-seq peaks for MAFA

28 was obtained from NCBI’s GEO database; GSE30298 (F). Data are expressed as the means ±

29 standard error of the mean (SEM). *P<0.05 and **P<0.01, by one-way ANOVA with post-hoc

30 Tukey’s test (C).

31

3 Diabetes Page 40 of 72

Supplementary Figure 2 Control Prmt1 bKO INS PRMT1 INS H4R3me2a INS PRMT1 INS H4R3me2a

P0

P7

2wks

3wks

4wks

6wks

12wks

32

33

34

4 Page 41 of 72 Diabetes

35 Supplementary Figure 2. Detection of PRMT1 and H4R3me2a in Prmt1 βKO mice islets.

36 Representative islet images obtained by IF staining of INS (green) and PRMT1 or H4R3me2a (red)

37 in control and Prmt1 βKO mice of the indicated ages. Prmt1fl/fl or Rip2-Cre mice were used as

38 controls; n = 3 per group. White scale bars, 50 µm.

5 Diabetes Page 42 of 72

Supplementary Figure 3

A P0 P7 3wks 6wks

C

Control WT KO 30

20

10 KO Body weight (g) b 0 3wks 6wks 12wks Age (weeks) Prmt1

INS GCG/SST/PPY

B P7 3wks 6wks Control Prmt1 bKO Control Prmt1 bKO Control Prmt1 bKO PDX1 INS NKX6.1 INS MAFA INS 39

40 Supplementary Figure 3. Normal development of Prmt1 βKO mice islets.

41 (A, B) Representative islet images obtained by IF staining of INS (green) and (A) glucagon

42 (GCG)/somatostatin (SST)/pancreatic polypeptide (PPY) (cocktail, red), and (B) PDX1, NKX6.1 or

43 MAFA (red) in control and Prmt1 βKO mice of the indicated ages; n = 3 per group. (C) Body

44 weights of 3-, 6- and 12wks control and Prmt1 βKO mice; n = 5 per group. Prmt1fl/fl or Rip2-Cre

45 mice were used as controls (A-C). Data are expressed as the means ± SEM (C). White scale bars, 50

46 µm.

6 Page 43 of 72 Diabetes

Supplementary Figure 4

A B C 13wks N.S. N.S. 120 1.0 WT 100 100 KO 0.8 80 80 0.6 60 60

cell area 0.4 40

40 β Blood glucose 0.2 Insulin content 20 (ng/mg pancreas) 20 (% of total pancreas) (Percentage of initial) of (Percentage 0 0.0 0 0 15 30 45 60 WT KO WT KO 47 Time (min)

48 Supplementary Figure 4. Metabolic phenotype of Prmt1 βKO mice.

49 (A-C) Male control and Prmt1 βKO mice (12-13wks) were fed standard chow diet (SCD) and used

50 for experiments. (A) Intraperitoneal insulin tolerance test (IPITT) after a 5-hour fasting; n = 4 per

51 group. (B) Pancreatic β cell area measurement; n = 3 per group. (C) Pancreatic insulin content

52 measurement; n = 4 per group. Prmt1fl/fl or Rip2-Cre mice were used as controls (A-C). Data are

53 expressed as the means ± SEM (A-C).

7 Diabetes Page 44 of 72

Supplementary Figure 5

A WT KO B C

Hormones Down DEGs:168

Gene category Gene symbol (Fold-change) 2.0 WT KO Ins1(0.89), Ins2(0.86), Hormones Gcg(1.4), Sst(1.27), Ppy(0.98), 1.5

Ghr(1.05), Pyy(1.18) Actb ) Z-score Pdx1(0.67), Nkx6.1(0.55), MafA(0.80), cell TFs 1 Insm1(0.64), Rfx6(0.77), Neurod1(0.69) 1.0 * 0 Slc2a2(0.60), Gck(0.84), Glp1r(1.18), cell function Gipr(0.79), ChgA(1.18), Ucn3(0.73) 0.5 -1 (Normalized to Kcnj11(1.02), Abcc8(0.87), Slc30a8(0.65), Up DEGs:194 *** Atp2a2(0.87), Atp2a3(0.88) expression Relative mRNA Hk1 (1.45), Hk2 (N/A), AldoB(4.08), 0.0 cell disallowed Arx(0.77), Hhex(1.17), Ldha(1.56) Ins1 Ins2 Gcg Sst Ppy Ghr Pyy Prmt1

1 2 1 2 D E F b cell TFs b cell function b cell disallowed

2.0 2.0 10 WT KO WT KO WT KO **

8 1.5 1.5 Actb ) Actb ) Actb ) 6

1.0 * 1.0 * ** * * 4 * ** ** * 0.5 0.5 (Normalized to (Normalized to (Normalized to 2 Relative mRNA expression Relative mRNA Relative mRNA expression Relative mRNA Relative mRNA expression Relative mRNA

0.0 0.0 0

Arx Rfx6 Gck Gipr Hk1 Hk2 Pdx1 MafA Insm1 Glp1r ChgA Ucn3 AldoB Hhex Ldha Nkx6.1 Slc2a2 Kcnj11 Abcc8 Atp2a2Atp2a3 NeuroD1 Slc30a8

G H Lymph node WT 12wks KO 12wks Down-regulated DEGs Gene Ontology P-value Pancreas development 6.19E-03 3 Endocrine pancreas development 1.12E-02 Intracellular signaling cascade 1.13E-02 Negative regulation of multicellular organismal process 1.89E-02 Cilium morphogenesis 2.84E-02

Down-regulated DEGs KEGG pathway P-value Maturity onset diabetes of the young 1.86E-04

MAPK signaling pathway 9.56E-03 Cleavedcaspase-

I J

INS GCG INS SST INS PPY INS PDX1 INS NKX6.1 INS MAFA WT 12wksWT 12wksWT KO 12wksKO KO 12wksKO 54

55 Supplementary Figure 5. Analysis of islets from Prmt1 βKO mice.

56 (A-J) Male control and Prmt1 βKO mice (12-13wks) were fed SCD and used for experiments. (A)

57 Expression heat-map of differentially expressed genes (DEGs) identified by our RNA-seq analysis; n

58 = 2 per group. The parameters used to define DEGs were: log2(Fold-change of CPM; FC) ≤-0.05 or

59 ≥0.05, P<0.05. (B) Expression fold-changes of gene subsets and their functional categories, as

8 Page 45 of 72 Diabetes

60 identified from the islet RNA-seq analysis. The fold-change of CPM is indicated beside each gene.

61 Significantly (P<0.05) downregulated and upregulated genes are indicated in green and red,

62 respectively; n = 2 per group. (C-F) The relative mRNA expressions of (C) hormone genes, (D) β

63 cell TF genes, (E) β cell function genes and (F) genes disallowed to express in mature β cells were

64 assessed by quantitative reverse transcription PCR (qRT-PCR) of islets; n = 4 per group. (G) Gene

65 ontology and KEGG pathway analyses of downregulated DEGs in islets of Prmt1 βKO mice; n = 2

66 per group. (H) Representative islet images obtained by immunohistochemical (IHC) staining of

67 cleaved caspase-3 (DAB, brown); n = 3 per group. Lymph node was used as a positive control.

68 Slides were counterstained with hematoxylin. (I, J) Representative islet images obtained by

69 immunofluorescent (IF) staining of INS (green) and (I) GCG, SST, PPY (red), (j) PDX1, NKX6.1 or

70 MAFA (red); n = 3 per group. White and black scale bars, 50 µm (H-J). Prmt1fl/fl (A-J) or Rip2-Cre

71 (H-J) mice were used as controls. Data are expressed as the means ± SEM. *P<0.05, **P<0.01 and

72 ***P<0.001, by Student’s t-test (C-F).

9 Diabetes Page 46 of 72

Supplementary Figure 6

A B 60 1.5 N.S. 50 40 1.0 30 cell area

20 WT (HFD) β 0.5

Body weight (g) 10 KO (HFD) (% of total pancreas) 0 0.0 8 10 12 14 16 18 20 22 24 WT KO 73 Age (wks)

74 Supplementary Figure 6. Metabolic phenotype of HFD-fed Prmt1 βKO mice.

75 (A, B) Male control and Prmt1 βKO mice (8wks) were fed high-fat diet (HFD) for 18 weeks and

76 used for experiments. (A) Body weight curve; n = 7-9 per group. (B) Pancreatic β cell area

77 measurement; n = 3 per group. Prmt1fl/fl or Rip2-Cre mice were used as controls (A, B). Data are

78 expressed as the means ± SEM (A, B).

10 Page 47 of 72 Diabetes

Supplementary Figure 7

A B 8wks 500 WT 5X TAM 400 KO

300

200

6wks 8wks 12wks 100 Blood glucose (mg/dl) 0 0 30 60 90 120 Time (min)

C D E 12wks ** 250 500 8 *** *** WT N.S. 200 400 KO 6

150 300 * 4 ** 100 200 *

Insulin release 2 50 100 (% of total content) Blood glucose (mg/dl) Blood glucose (mg/dl) 0 0 0 8wks 12wks 0 30 60 90 120 Glc 2.8 20 2.8 20 Time (min) (mM) 79 Age (weeks) WT KO

80 Supplementary Figure 7. Metabolic phenotype of Prmt1 βiKO mice.

81 (A) Schematic representation of the generation of Prmt1 βiKO mice and the experimental design.

82 (B) Intraperitoneal glucose tolerance test (IPGTT) of 8wks male control and Prmt1 βiKO mice after

83 a 16-hour fasting; n = 5 per group. (C) Random blood glucose levels of 8 and 12wks male control

84 and Prmt1 βiKO mice; n = 5 per group. (D) IPGTT of 12wks male control and Prmt1 βiKO mice

85 after a 16-hour fasting; n = 5 per group. (E) Ex vivo islet GSIS assay of 12wks control and Prmt1

86 βiKO mice; n = 4 per group. Tamoxifen (TAM)-injected Prmt1fl/fl or Pdx1-CreERT2 mice were used as

87 controls (B-E). Data are expressed as the means ± SEM. *P<0.05, **P<0.01 and ***P<0.001, by

88 Student’s t-test (B-D) or one-way ANOVA with post-hoc Tukey’s test (E).

11 Diabetes Page 48 of 72

Supplementary Figure 8

A B R26-eYFP; Control 8wks R26-eYFP; Prmt1 biKO 8wks R26-eYFP; Prmt1 biKO 12wks WT KO

100

INS 75

50 CHGA 25 CHGA+ / eYFP+ (%)

eYFP 0 8wks 12wks

C WT 12wks KO 12wks-1 KO 12wks-2

89

90 Supplementary Figure 8. Analysis of islets from Prmt1 βiKO mice.

91 (A) Representative islet images obtained by IF staining of eYFP (green), chromogranin A (CHGA,

92 blue) and INS (red) from 8 and 12wks R26-eYFP; Pdx1-CreERT2 (control) and R26-eYFP; Prmt1

93 βiKO mice; n = 3 per group. (B) Quantification analysis of eYFP co-positive cells expressing CHGA

94 in the islets of 8 and 12wks R26-eYFP; Pdx1-CreERT2 (control) and R26-eYFP; Prmt1 βiKO mice; n

95 = 3 per group. (C) Representative β cell images obtained by TEM of 12wks control and Prmt1 βiKO

96 mice. Arrows indicate immature insulin granules (blue), dilated endoplasmic reticulum (green) and

97 dysmorphic mitochondria (red); n = 3 per group. White scale bars, 50 µm (A). Yellow scale bars, 2.5

98 µm (C). Data are expressed as the means ± SEM (B).

12 Page 49 of 72 Diabetes

Supplementary Figure 9

A B C

60 300 50 250 * * ** *** 5X TAM *** *** *** 40 200 30 150 HFD 20 WT (HFD) 100 WT (HFD) 6wks 8wks 12wks 26wks Body weight (g) 10 KO (HFD) 50 KO (HFD) Blood glucose (mg/dl) 0 0 12 14 16 18 20 22 24 12 14 16 18 20 22 24 Age (wks) Age (wks)

D E F

** 600 * * 120 1.0 N.S. * 500 * 100 0.8 400 80 0.6 300 60

cell area 0.4

200** 40 β WT (HFD) WT (HFD) Blood glucose 100 20 KO (HFD) 0.2

KO (HFD) (% of total pancreas) (Percentage of initial) of (Percentage Blood glucose (mg/dl) 0 0 0.0 0 30 60 90 120 150 0 15 30 45 60 WT KO 99 Time (min) Time (min)

100 Supplementary Figure 9. Metabolic phenotype of HFD-fed Prmt1 βiKO mice.

101 (A-F) Male control and Prmt1 βiKO mice (12wks) were fed HFD for 14 weeks and used for

102 experiments. (A) Schematic representation of the experimental design. (B) Body weight curve; n =

103 5-6 per group. (C) Random blood glucose level; n = 5-6 per group. (D) IPGTT after a 16-hour

104 fasting; n = 6 per group. (E) IPITT after a 5-hour fasting; n = 5 per group. (F) Pancreatic β cell area

105 measurement; n = 3 per group. TAM-injected Prmt1fl/fl and Pdx1-CreERT2 mice were used as controls

106 (A-F). Data are expressed as the means ± SEM. *P<0.05, **P<0.01 and ***P<0.001, by Student’s t-

107 test (B-F).

13 Diabetes Page 50 of 72

Supplementary Figure 10

A B

C

Ndufs8 Cox8a Sdhd Uqcrfs1

1.50 1.5 WT KO 1.50 1.5 WT KO 1.50 1.5 WT KO 1.50 2.5 WT KO 1.25 1.25 1.25 1.25 2.0 * 1.00 1.0 1.00 1.0 ** 1.00 1.0 * 1.00 4.4E-03 ** 1.4E-04 6.2E-03 1.5 0.75 0.75 0.75 *** 0.75 1.0E-04 1.5E-07 1.0 0.50 0.5 0.50 0.5 0.50 0.5 0.50 *** 7.1E-07 Fold change Fold change Fold change Fold change 1.2E-16 6.6E-13 0.25 0.25 0.25 0.25 0.5 Fold change (KO/WT) change Fold Fold change (KO/WT) change Fold Fold change (KO/WT) change Fold Fold change (KO/WT) change Fold 0.00 0.0 0.00 0.0 0.00 0.0 0.00 0.0 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks

Uqcr11 Ndufa6 Cox7a2 Atp6v0e2

1.50 1.5 WT KO 1.50 1.5 WT KO 1.50 1.5 WT KO 1.50 1.5 WT KO 1.25 1.25 1.25 1.25 1.00 1.0 * 1.00 1.0 1.00 1.0 1.00 1.0 ** * ** * 0.75 1.6E-05 0.75 1.8E-04 0.75 *** 0.75 4.4E-06 8.4E-13 *** *** 0.50 0.5 0.50 0.5 0.50 0.5 0.50 0.5 Fold change 3.0E-07 Fold change Fold change Fold change 5.3E-12 0.25 0.25 0.25 1.2E-16 0.25 1.6E-25 Fold change (KO/WT) change Fold Fold change (KO/WT) change Fold Fold change (KO/WT) change Fold Fold change (KO/WT) change Fold 0.00 0.0 0.00 0.0 0.00 0.0 0.00 0.0 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks

Atp6v1h Uqcrc1 Sdhc Uqcrb

WT KO 1.50 3.0 1.50 1.5 1.50 1.5 1.50 1.5 * WT KO WT KO WT KO 1.25 N.S. 2.5 1.25 1.25 1.25 2.0E-02 1.00 2.0 1.00 1.0 ** 1.00 1.0 1.00 1.0 ** ** *** *** 0.75 1.5 0.75 0.75 4.0E-06 0.75 2.1E-07 0.50 1.0 * 0.50 0.5 0.50 0.5 0.50 0.5 Fold change *** 5.0E-09 Fold change Fold change Fold change 5.8E-13 5.7E-14 0.25 1.6E-23 0.5 0.25 0.25 0.25 Fold change (KO/WT) change Fold Fold change (KO/WT) change Fold Fold change (KO/WT) change Fold Fold change (KO/WT) change Fold 0.00 0.0 0.00 0.0 0.00 0.0 0.00 0.0 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks 8wks12wks

Cox6a1 Atp5g3

1.50 1.5 WT KO 1.50 1.5 WT KO 1.25 1.25 ** ** 1.00 1.0 ** 1.00 1.0E-03 1.0 ** 0.75 0.75 1.4E-08 5.7E-05 0.50 0.5 0.50 0.5 5.0E-10 Fold change Fold change 0.25 0.25 Fold change (KO/WT) change Fold Fold change (KO/WT) change Fold 0.00 0.0 0.00 0.0 108 8wks12wks 8wks12wks 8wks12wks 8wks12wks

109 Supplementary Figure 10. RNA-seq and qRT-PCR analyses of islets from Prmt1 βiKO mice.

110 (A-C) RNA-seq analysis of islets from Prmt1 βiKO mice at the early (8wks) and late (12wks) stages

111 of β cell dedifferentiation. Age- and sex (male)- matched, TAM-injected littermates (Prmt1fl/fl) were

112 used as controls for both stages; n = 2 per group. Differentially expressed genes (DEGs) were

113 identified using the parameters: log2(Fold-change of CPM; FC) ≤-1.5 or ≥1.5, FDR<0.05. (A, B)

114 Venn diagrams showing the numbers of (A) downregulated and (B) upregulated DEGs in islets from

115 Prmt1 βiKO mice at the indicated stages. (C) RNA-seq and qRT-PCR analyses of islets from Prmt1

116 βiKO mice at the early (8wks) and late (12wks) stages of β cell dedifferentiation. n = 2 per group for

14 Page 51 of 72 Diabetes

117 RNA-seq, n = 4 per group for qRT-PCR. Line (RNA-seq) and bar (qRT-PCR) graphs showing

118 relative expressions of the representative OXPHOS genes at the two different stages of β cell

119 dedifferentiation. Fold-changes of CPMs are plotted and FDR values are indicated in line graphs for

120 each stage. Expression levels of genes were normalized to Actb in each sample in qRT-PCR analysis.

121 qRT-PCR data are expressed as the means ± SEM. *P<0.05, **P<0.01 and ***P<0.001, by one-way

122 ANOVA with post-hoc Tukey’s test.

15 Diabetes Page 52 of 72

Supplementary Figure 11

HOMER known motif results (PRMT1-dependent open chromatin regions) - Top 55 of total 213

123

124

16 Page 53 of 72 Diabetes

125 Supplementary Figure 11. Motif analysis for the PRMT1-dependent open chromatin regions of

126 β cells.

127 Complete output of known TF-binding motifs returned by the HOMER software for the PRMT1-

128 dependent open chromatin regions of β cells. Top 55 of total 213 results are shown. TFs shown in

129 Fig. 5G are marked with red boxes.

17 Diabetes Page 54 of 72

Supplementary Figure 12

A b cell gene promoters B OXPHOS gene promoters

WT WT 2.2 3.4 KO KO

1.2 2.0

0.2 0.6 Average ATAC-seq peak intensity ATAC-seq Average Average ATAC-seq peak intensity ATAC-seq Average

-1 0 +1 -1 0 +1 130 Distance from TSS (kb) Distance from TSS (kb)

131 Supplementary Figure 12. ATAC-seq analysis of β cells from Prmt1 βiKO mice.

132 (A, B) Average ATAC-seq peak intensities for WT and Prmt1-null (KO) β cells near the

133 transcription start site (TSS) of (A) β cell genes and (B) OXPHOS genes.

18 Page 55 of 72 Diabetes

Supplementary Figure 13

134

135 Supplementary Figure 13. Phenotype summary of Prmt1 bKO and Prmt1 biKO mice.

136 Summarized table of metabolic phenotype, b cell histology/IF staining and islet gene expression of

137 Prmt1 bKO and Prmt1 biKO mice.

19 Diabetes Page 56 of 72

138 Supplementary Materials

139 Human pancreatic tissues

140 Formalin-fixed paraffin-embedded pancreatic tissues of adult human donors were obtained from

141 Daejeon St. Mary's Hospital of the Catholic University of Korea (IRB approval number;

142 DC17SESI0077).

143

144 Cell culture

145 MIN6 cells (1) were incubated in Dulbecco’s Modified Eagle’s Medium (DMEM) containing 25 mM

146 glucose, supplemented with 15% fetal bovine serum (FBS), 100 U/ml penicillin, 0.1 mg/ml

147 streptomycin and 71.5 µM 2-mercaptoethanol. Cells were grown in a humidified atmosphere

148 containing 5% CO2 at 37°C.

149

150 Immunofluorescence staining

151 Mice pancreata were perfused with PBS, fixed in 10% neutral buffered formalin (HT501128, Sigma-

152 Aldrich) for 4 hours at room temperature and washed for 1 hour with deionized water. The tissues

153 were processed with an automatic tissue processor (TP1020, Leica Biosystems) and embedded in

154 molten paraffin wax. Paraffin-embedded tissue sections were sliced at 4 µm thickness and mounted on

155 adhesive glass slides (081000, Marienfeld). The slides were deparaffinized and rehydrated. Antigen

156 retrieval was performed by incubating the slides in sodium citrate buffer (10 mM sodium citrate, pH

157 6.0) for 15 minutes at 95°C. The slides were then cooled for 10 minutes at room temperature and

158 washed in PBS for 10 minutes, and the samples were blocked with 4% normal donkey serum (017-

159 000-121, Jackson ImmunoResearch) in PBS for 1 hour at room temperature. The samples were then

160 incubated for 18 hours at 4°C with these primary antibodies against following: INS (A0564, Dako,

161 1:1000), PRMT1 (84361, Abcam, 1:1000), H4R3me2a (194683, Abcam, 1:1000), GFP (eYFP) (13970,

162 Abcam, 1:1000), GCG (G2654, Sigma-Aldrich, 1:1000), SST (A0566, Dako, 1:1000), PPY (113694,

20 Page 57 of 72 Diabetes

163 Abcam, 1:1000), PDX1 (F6A11, DSHB, 1:500), NKX6.1 (F65A2, DSHB, 1:500), MAFA (IHC-00352,

164 Bethyl, 1:100), SLC2A2 (07-1402-I, Merck Millipore, 1:1000), CHGA (15160, Abcam, 1:1000),

165 UCN3 (2) (7218, gifted by Drs. Mark O. Huising and Paul E. Sawchenko, 1:1000) and FOXO1 (4130,

166 Merck Millipore, 1:1000). The samples were washed in PBS for 10 minutes and incubated for 2 hours

167 at room temperature with following secondary antibodies: Alexa Fluor 647 conjugated anti-chicken

168 IgY (703-605-155, Jackson ImmunoResearch, 1:1000), Alexa Fluor 488 conjugated anti-guinea pig

169 IgG (706-545-148, Jackson ImmunoResearch, 1:1000), Alexa Fluor 594 conjugated anti-rabbit IgG

170 (711-585-152, Jackson ImmunoResearch, 1:1000), or Alexa Fluor 594 conjugated anti-mouse IgG

171 (715-585-151, Jackson ImmunoResearch, 1:1000). The samples were washed in PBS for 10 minutes,

172 incubated for 5 minutes with DAPI (D9542, Sigma-Aldrich, 1 µg/ml) at room temperature, and then

173 mounted with fluorescence mounting medium (S3023, Dako). Images were acquired using a

174 fluorescence microscope (DS-Ri2 camera, Nikon) and a confocal microscope (LSM 780, Carl Zeiss).

175 Imaging analyses were performed using the NIS-Elements BR (Nikon) and ZEN (Carl Zeiss) software

176 packages.

177

178 Quantification analysis for lineage-tracing

179 Pancreata of TAM-injected controls (R26-eYFP; Prmt1+/+; Pdx1-CreERT2) and R26-eYFP; Prmt1 βiKO

180 mice (R26-eYFP; Prmt1fl/fl; Pdx1-CreERT2) were whole-sectioned at 4 µm thickness. Pancreatic

181 sections (10 per mouse, taken at least 100 µm apart) were subjected to immunofluorescent staining.

182 Cell counting was performed using the ImageJ (NIH) program.

183

184 Immunohistochemical (IHC) staining

185 Formalin-fixed paraffin-embedded pancreatic slides were deparaffinized and rehydrated. Antigen

186 retrieval was performed as described for immunofluorescent staining. The slides were cooled and

187 washed in PBS for 10 minutes and endogenous peroxidases were blocked with BLOXALL solution

21 Diabetes Page 58 of 72

188 (SP-6000, Vector Laboratories) for 10 minutes at room temperature. The samples were blocked with

189 2% normal goat serum (S-1000, Vector Laboratories) in PBS for 1 hour at room temperature, and then

190 incubated for 18 hours at 4°C with these primary antibodies against INS (A0564, Dako, 1:1000) or

191 cleaved caspase-3 (9661, Cell Signaling Technology, 1:200). The samples were washed in PBS for 10

192 minutes and stained using an ABC-HRP kits (PK-4001/4007, Vector Laboratories) as directed. 3,3’-

193 Diaminobenzidine (DAB, SK-4100, Vector Laboratories) was used as a substrate for the HRP enzyme.

194 The samples were counterstained with hematoxylin and mounted. Images were acquired using a bright-

195 field microscope (DS-Ri2 camera, Nikon) and analyses were performed with the NIS-Elements BR

196 (Nikon) software.

197

198 Transmission electron microscopy

199 Pancreatic tissues were fixed for 12 hours in 2% glutaraldehyde - paraformaldehyde in 0.1 M

200 phosphate buffer (pH 7.4) and then washed in 0.1 M phosphate buffer. They were post-fixed with 1%

201 OsO4 dissolved in 0.1 M phosphate buffer for 2 hours and dehydrated using an ascending gradual

202 series (50 ~ 100%) of ethanol, infiltrated with propylene oxide, and embedded using a Poly/Bed 812

203 kit (Polysciences). The samples were then incubated for 24 hours at 65°C electron microscopic oven

204 (TD-700, DOSAKA). Sections of about 200~250 nm thick were cut and stained with toluidine blue

205 (T3260, Sigma-Aldrich) for light microscopy. In addition, 70-nm thin sections were double stained

206 with 6% uranyl acetate (EMS, 22400) for 20 minutes and then with lead citrate (Thermo Fisher

207 Scientific, for contrast staining) for 10 minutes. The sections were cut using a LEICA EM UC-7 (Leica

208 Microsystems) with a diamond knife (Diatome) and transferred to copper and nickel grids. All thin

209 sections were observed by transmission electron microscopy (JEM-1011, JEOL) at an acceleration

210 voltage of 80 kV.

211

212

22 Page 59 of 72 Diabetes

213 Pancreatic β cell area

214 Pancreatic sections on slides (10 per mouse, taken at least 100 µm apart) were subjected to insulin IHC

215 staining and hematoxylin counterstaining as described for our IHC staining. Whole-pancreas images

216 were acquired using a JuLI Stage recorder (NanoEnTek). The insulin-positive area and the pancreatic

217 area were measured using the ImageJ (NIH) program. The β cell area was calculated by dividing the

218 insulin-positive area by the pancreatic area.

219

220 Glucose-stimulated insulin secretion (GSIS)

221 For the in vivo GSIS assay, mice were fasted for 16 hours and then given an intraperitoneal injection

222 of D-glucose in PBS (2 g/kg). Blood samples were then obtained from the retro-orbital area at 0 and

223 15 minutes after injection using heparinized capillary tubes (2501, Kimble Chase). The samples were

224 centrifuged for 10 minutes at 1500 ×g at 4°C, and plasma insulin levels were measured using a

225 mouse ultrasensitive insulin ELISA kit (80-INSMSU-E01, ALPCO). For the ex vivo islet GSIS

226 assay, pancreatic islets were isolated from mice as described previously (3) and incubated overnight

227 in Roswell Park Memorial Institute (RPMI) 1640 medium containing 10% fetal bovine serum (FBS),

228 100 U/ml penicillin and 0.1 mg/ml streptomycin at 37°C in a humidified atmosphere of 5% CO2. The

229 islets were transferred to Krebs-Ringer HEPES (KRH) buffer containing 5 mM glucose, incubated

230 for 2 hours at 37°C, transferred to KRH buffer containing 2.8 mM glucose and incubated for 1 hour.

231 The islets were then divided into two groups (n=10 each); one group was incubated for 30 minutes in

232 KRH buffer containing 2.8 mM glucose, and the other group was incubated for the same duration in

233 KRH buffer containing 20 mM glucose. Supernatants were collected from each group and stored at -

234 80°C. Next, the islets were sonicated and incubated in acid-ethanol (1.5% HCl in 100 ml of 70%

235 ethanol) for 18 hours at 4°C for intracellular insulin extraction. The same volume of 1 M Tris-Cl

236 buffer (pH 8.0) was added for neutralization. The insulin concentrations present in equal amounts of

237 supernatant and extracted insulin were measured using a mouse ultrasensitive insulin ELISA kit (80-

23 Diabetes Page 60 of 72

238 INSMSU-E01, ALPCO). Insulin secretion was calculated by dividing the secreted insulin by the

239 insulin content extracted from the islets.

240

241 Oxygen consumption rate (OCR)

242 Pancreatic islets were isolated, washed with assay medium (102365-100, Agilent Technologies),

243 split into groups (n = 50 islets per group) and plated to XF24 islet-capture microplates (101122-100,

244 Agilent Technologies). The islets were pre-incubated for 1 hour at 37°C without CO2 in assay

245 medium supplemented with 10 mM sodium pyruvate (S8636, Sigma-Aldrich) and 2 mM glutamine

246 (35050061, Gibco) to optimize the islet respiration conditions. Then, 20 mM D-glucose (Glc, D9434,

247 Sigma-Aldrich) was added to stimulate cellular oxygen consumption and 5 mM oligomycin (Olig.,

248 75351, Sigma-Aldrich) was injected to inhibit ATP synthase. Carbonyl cyanide 3-

249 chlorophenylhydrazone (CCCP, 1 µM; C2759, Sigma-Aldrich) was injected to determine the

250 maximal electron transport capacity, and 5 µM rotenone (Rot., R8875, Sigma-Aldrich) was added to

251 block electron transport. The OCR was measured using a Seahorse XFe24 analyzer (Agilent

252 Technologies). The measured OCR was normalized to the initial rates obtained under basal

253 conditions and analyzed using the Seahorse Wave 2.4 software (Agilent Technologies).

254

255 Quantitative reverse transcription PCR (qRT-PCR)

256 Total RNA was extracted using TRIzol (15596026, Invitrogen) according to the manufacturer’s

257 protocol. Genomic DNA was removed using a TURBO DNA-free kit (AM1907, Invitrogen) and 1 µg

258 of total RNA was used to generate complementary DNA (cDNA) with High-Capacity cDNA Reverse

259 Transcription kit (4368813, Applied Biosystems). qRT-PCR was performed with Fast SYBR Green

260 Master Mix (4385614, Applied Biosystems) and a Viia 7 Real-time PCR System (Applied Biosystems)

261 according to the manufacturer’s instructions. Relative gene expression analysis was performed using

262 the delta Ct (threshold cycle) method (4), with the beta actin detected as a reference gene.

24 Page 61 of 72 Diabetes

263 Fluorescence-activated cell sorting (FACS)

264 Isolated islets were incubated for 2 hours in RPMI 1640 medium containing 10% FBS, 100 U/ml

265 penicillin and 0.1 mg/ml streptomycin at 37°C in a humidified atmosphere of 5% CO2. After being

266 washed with Dulbecco's phosphate-buffered saline (DPBS; 14190250, Gibco), the islets were gently

267 rotated with 0.05% trypsin and 3 mM EDTA in DPBS at 37°C. Single cells were dissociated with

268 gentle pipetting, washed twice with cold RPMI 1640 medium containing 10% FBS, 100 U/ml

269 penicillin and 0.1 mg/ml streptomycin, and immediately processed on the fluorescence activated cell

270 sorter (MoFlo Astrios EQ, Beckman Coulter). eYFP-expressing cells were collected according to the

271 manufacturer’s instructions.

272

273 Chromatin immunoprecipitation sequencing (ChIP-seq) analysis

274 ChIP experiments were performed as previously described (5). MIN6 cells were fixed with 1%

275 formaldehyde, lysed and sonicated using a Bioruptor (Diagenode). Immunoprecipitation was

276 performed with anti-H4R3me2a (39705, Active Motif) or anti-CTCF (07-729, Merck Millipore)

277 antibodies. Genomic libraries (250- to 400-bp) were generated from the input and immunoprecipitated

278 DNAs and sequenced using Illumina NextSeq500 to generate 100-bp paired-end reads. The sequenced

279 reads were subjected to quality control using cut-adapt (v1.1) (6) and the following parameters: read

280 quality > 30 (Phred score), read length > 20 bp and replicated level < 40%. The reads were trimmed

281 and mapped by Bowtie2 (v2.2.2) (7) using default parameters (m=1), and duplicate reads were

282 removed using the Picard MarkDuplicates function (https://broadinstitute.github.io/picard/). Unique

283 reads were subjected to peak annotation using MACS2 (v2.1.120160309) (8) and the parameters, -g

284 mm --B -extsize 200 -nomodel -q 0.05. H4R3me2a ChIP-seq peaks were called by HOMER software

285 (v.4.10) (9). Read intensities of normalized ChIP-seq peaks were allocated in 50 bp resolution

286 windows. Windows with potential outlier peaks (outlier windows) were identified by following criteria:

287 (1) Below -1.5 x interquartile range (IQR) or above 1.5 x IQR. (2) Below 5th percentile or above 95th

25 Diabetes Page 62 of 72

288 percentile. (3) Detection by lofactor function of the R statistical package (version 3.3.1) (10). Peaks

289 that contain consecutive outlier windows within 1 kb of peak center were filtered. Peaks with P values

290 above 0.0001 were considered outlier peaks and excluded from the analysis. Public ChIP-seq data for

291 histone modifications and β cell TFs in mouse β cells or islets were obtained from NCBI’s Gene

292 Expression Omnibus (GEO) database. Data were obtained for H3K4me1, H3K27ac and H3K27me3

293 (GSE68618), MAFA (GSE30298), NKX6.1 (GSE40975), PDX1 (GSE70960), NKX2.2 (GSE79725)

294 and NEUROD1 (GSE30298). Transcription motif analysis was performed by using the

295 findMotifsGenome.pl command of the HOMER (v.4.8.2) software (9). The Integrative Genomics

296 Viewer (IGV) software (11) and UCSC genome browser (https://genome.ucsc.edu/) (12) were used to

297 visualize the sequencing data.

298

299 RNA sequencing (RNA-seq) analysis

300 Total RNA was isolated using a RNeasy Plus Mini kit (QIAGEN) according to the manufacturer’s

301 instructions. The integrity of the total RNA was assessed using an Agilent 2100 Bioanalyzer System

302 (Agilent Technologies) and an Agilent RNA 6000 Nano Kit (Agilent Technologies). Samples with an

303 RNA Integrity Number (RIN) value > 8 were selected for use. Libraries were constructed using 1 µg

304 total RNA. The RNA sequencing library was prepared using a TruSeq RNA Sample Prep kit (Illumina)

305 and sequencing was performed using an Illumina NextSeq500 to generate 100-bp paired-end reads.

306 FastQC (FastQC v0.11.3) (13) and cut-adapt (v1.1) (6) were used to filter out sequencing reads of low

307 quality, and the retained reads were mapped to mouse genome build mm10 using TopHat (TopHat

308 v2.0.11) (14) with default parameters. HTseq (v0.9.1) (15) was used to generate raw read counts.

309 DESeq2 (DESeq2 v3.1) (16) was used to select differentially expressed genes (DEGs). Hierarchical

310 clustering was performed to characterize the overall expression patterns of the DEGs using the

311 Euclidean distance and the complete linkage applications of the R statistical package (version 3.3.1)

26 Page 63 of 72 Diabetes

312 (10). Gene ontology and pathway enrichment analyses were performed using DAVID

313 (https://david.ncifcrf.gov/) (17).

314

315 Assay for transposase accessible chromatin sequencing (ATAC-seq) analysis

316 ATAC experiments were performed as previously described (18), using MIN6 cells and FACS-sorted

317 WT and Prmt1-null β cells. Libraries were purified with AMPure XP beads (Beckman Coulter) to

318 remove contaminating primer dimers. Library quality was assessed using an Agilent Bioanalyzer

319 High-Sensitivity DNA kit (Agilent Technologies). All libraries were sequenced as 100-bp paired-end

320 reads using an Illumina NextSeq 500. The ATAC-seq peaks were called as described for our ChIP-seq

321 analysis. To identify peak regions that were differentially enriched between WT and Prmt1-null β cells,

322 we used the getdifferentialPeaks.pl command of the HOMER (v.4.8.2) software (9). We counted the

323 number of reads in each enriched peak region and calculated the normalized read counts. Then, it was

324 returned in the output file along with the differential statistics (Fold-change > 0.5, cumulative Poisson

325 p-value < 0.001). Statistical testing was performed with DESeq2 (DESeq2 v3.1) (16). Gene ontology

326 and pathway enrichment analyses for PRMT1-dependent open chromatin regions were performed

327 using Enrichr (19).

328

329 Human genome alignment of mouse β cell ATAC-seq peaks

330 Each MIN6 ATAC-seq peaks was annotated for its closest transcription start site (TSS), and the

331 matched human ortholog, using the Ensembl database (dataset version Genes 91, GRCm38.p2 for

332 mouse and GRCh37.p13 for human). When one mouse gene mapped to multiple human genes, we

333 randomly selected one of the human genes. Each peak was aligned to the relevant human genome

334 sequence confined by the topologically associating domain (TAD) boundaries, which contained the

335 human ortholog of the nearest mouse gene, using BLASTn (-word_size 11 num_descriptions 26

336 num_alignments 25 dust yes). More specifically, each peak was annotated for the closest TSS and its

27 Diabetes Page 64 of 72

337 orthologous human gene. The human gene was annotated for its TAD boundaries, which were

338 retrieved from Panc1 pancreas epithelial cell line data (ENCODE database, accession code:

339 ENCSR440CTR). The alignment results were then filtered by e-value ≤ 0.0005, and the MIN6 peak

340 was assessed for overlap with regions conserved among 21 Euarchontoglires species, using the

341 phastCons algorithm (20) (retrieved from the UCSC genome browser database). Through this process,

342 we obtained the 19,215 conserved MIN6 ATAC-seq peaks and human genome coordinates based on

343 sequence alignments and the orthology of adjacent genes.

344

345 GWAS SNPs association analysis

346 Diabetes-associated SNPs were retrieved from the EBML-EBI GWAS catalog using the query term

347 “diabetes” (n = 2,135; date, 4-11-2018). The distances between the midpoints of each human

348 conserved MIN6 ATAC-seq peak and the nearest diabetes-associated locus were plotted. (PRMT1-

349 dependent peaks, n = 1,488; PRMT1-independent peaks, n = 17,727). The mean distances were

350 compared by even sampling of PRMT1-independent peaks for paired sample t test.

28 Page 65 of 72 Diabetes

351 References

352 1. Ishihara H, Asano T, Tsukuda K, Katagiri H, Inukai K, Anai M, et al. Pancreatic beta cell line 353 MIN6 exhibits characteristics of glucose metabolism and glucose-stimulated insulin secretion 354 similar to those of normal islets. Diabetologia [Internet]. 1993;36(11):1139–45. Available 355 from: https://doi.org/10.1007/BF00401058 356 2. van der Meulen T, Mawla AM, DiGruccio MR, Adams MW, Nies V, Dólleman S, et al. 357 Virgin Beta Cells Persist throughout Life at a Neogenic Niche within Pancreatic Islets. Cell 358 Metab [Internet]. 2017;25(4):911-926.e6. Available from: 359 http://www.sciencedirect.com/science/article/pii/S1550413117301699 360 3. Szot GL, Koudria P, Bluestone JA. Murine Pancreatic Islet Isolation. J Vis Exp [Internet]. 361 2007 Aug 22;(7):255. Available from: 362 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2565847/ 363 4. Livak KJ, Schmittgen TD. Analysis of Relative Gene Expression Data Using Real-Time 364 Quantitative PCR and the 2−ΔΔCT Method. Methods [Internet]. 2001;25(4):402–8. Available 365 from: http://www.sciencedirect.com/science/article/pii/S1046202301912629 366 5. Baek S-J, Kim M, Bae D-H, Kim J-H, Kim H-J, Han M-E, et al. Integrated epigenomic 367 analyses of enhancer as well as promoter regions in gastric cancer. Oncotarget [Internet]. 368 2016;7(18). Available from: http://www.ncbi.nlm.nih.gov/pubmed/27016420 369 6. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. 370 EMBnet.journal; Vol 17, No 1 Next Gener Seq Data Anal [Internet]. 2011; Available from: 371 http://journal.embnet.org/index.php/embnetjournal/article/view/200 372 7. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 373 [Internet]. 2012;9(4):357–9. Available from: 374 http://www.ncbi.nlm.nih.gov/pubmed/22388286%0Ahttp://www.pubmedcentral.nih.gov/articl 375 erender.fcgi?artid=PMC3322381 376 8. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based 377 Analysis of ChIP-Seq (MACS). Genome Biol [Internet]. 2008 Sep 17;9(9):R137–R137. 378 Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2592715/ 379 9. Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple Combinations of 380 Lineage-Determining Transcription Factors Prime cis-Regulatory Elements Required for 381 Macrophage and B Cell Identities. Mol Cell. 2010;38(4):576–89. 382 10. R Core Team RA language and environment for statistical computing. R: A language and 383 environment for statistical computing. R Foundation for Statistical Computing. 2013. p. 409. 384 11. Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, et al. 385 Integrative genomics viewer. Nat Biotechnol [Internet]. 2011 Jan 10;29:24. Available from: 386 http://dx.doi.org/10.1038/nbt.1754 387 12. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The Human 388 Genome Browser at UCSC. Genome Res [Internet]. 2002 Jun 19;12(6):996–1006. Available 389 from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC186604/ 390 13. Andrews S. FastQC A Quality Control tool for High Throughput Sequence Data. 391 http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. 2014. 392 14. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. 393 Bioinformatics [Internet]. 2009 May 1;25(9):1105–11. Available from: 394 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2672628/ 395 15. Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput 396 sequencing data. Bioinformatics [Internet]. 2015 Jan 15;31(2):166–9. Available from: 397 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4287950/ 398 16. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol 399 [Internet]. 2010 Oct 27;11(10):R106–R106. Available from:

29 Diabetes Page 66 of 72

400 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3218662/ 401 17. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene 402 lists using DAVID bioinformatics resources. Nat Protoc [Internet]. 2008 Dec 18;4:44. 403 Available from: http://dx.doi.org/10.1038/nprot.2008.211 404 18. Buenrostro JD, Wu B, Chang HY, Greenleaf WJ. ATAC-seq: A method for assaying 405 chromatin accessibility genome-wide. Curr Protoc Mol Biol. 2015;2015:21.29.1-21.29.9. 406 19. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, et al. Enrichr: interactive and 407 collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics [Internet]. 408 2013;14(1):128. Available from: https://doi.org/10.1186/1471-2105-14-128 409 20. Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, et al. Evolutionarily 410 conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res [Internet]. 411 2005 Aug 19;15(8):1034–50. Available from: 412 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1182216/ 413

30 Page 67 of 72 Diabetes

Primers used for qRT-PCR Gene Fw (5' to 3') Rv (5' to 3') Actb GGT ACC ACC ATG TAC CCA GG GAA AGG GTG TAA AAC GCA GC Prmt1 CGC AAG GTT ATT GGG ATT GAG TG CCT CCA CCT TGC CCT TGA TGA T Prmt2 CCT AGA ATT CAG CGG AGA AAC G GGG GTC TTC CTC CAG AAC CG Prmt3 GCA AAA GGG GGA TCG GTC TA CAA CTT CCA CAA CAG CTT CCG Prmt4 GCT GTG GCT GGA ATG CCT AC CAA TGC CCG TGC TCA TTA TGG Prmt5 CCG CGT TTC AAG AGG GAG TT ACA TAG CCG CTT CAG AGT TCC Prmt6 GGT GCC GGT GGA ACA AGA TA CTC CCA CTT TGT AGC GCA GA Prmt7 CTT CCC ACA GCG GGC ATT AT GCA CAG AAA CGT GAG GCT TG Prmt9 AGA AAC TGT CGA TGC AGG TGT ACC AAC CCT ATG ATG CCT TCG Ins1 GAC CAG CTA TAA TCA GAG ACC ATC GTA GGA AGT GCA CCA ACA GG Ins2 GGC TTC TTC TAC ACA CCC AT CCA AGG TCT GAA GGT CAC CT Pdx1 CTT AAC CTA GGC GTC GCA CAA GAA GCT CAG GGC TGT TTT TCC Nkx6.1 CTT CTG GCC CGG AGT GAT G GGG TCT GGT GTG TTT TCT CTT C MafA GAG GAG GTC ATC CGA CTG AAA GCA CTT CTC GCT CTC CAG AAT Ucn3 GCT GTG CCC CTC GAC CT TGG GCA TCA GCA TCG CT NeuroD1 GCC CAG CTT AAT GCC ATC TTT CAA AAG GGC TGC CTT CTG TAA Slc2a2 TTC CAG TTC GGC TAT GAC ATC G CTG GTG TGA CTG TAA GTG GGG Gck TAT GAA GAC CGC CAA TGT GA TTT CCG CCA ATG ATC TTT TC Hk1 GTG GAC GGG ACG CTC TAC TTC ACT GTT TGG TGC ATG ATT Hk2 CAA CTC CGG ATG GGA CAG CAC ACG GAA GTT GGT TCC TC Ldha GGC ACT GAC GCA GAC AAG TGA TCA CCT CGT AGG CAC TG AldoB CAC ACA GCT TCT GAT ACC TTG G TGA GCC ATG ATG ACA GGT ACA Gcg ACA TCT CGT GCC AGT CAC TT CGT TGG GTT ACA CAA TGC T Sst ATG CTG TCC TGC CGT CTC CA CTA ACA GGA TGT GAA TGT CTT CCA G Ppy GGC CCA ACA CTC ACT AGC TC CCA GGA AGT CCA CCT GTG TT Ghr GAA GCC ACC AGC TAA ACT GC CGG ATG TGA GTT CTT GCT CA Pyy CGG CAG CGG TAT GGA AAA AG CGA GAC CTG AAG GGG AGG TT Insm1 TGT CTG TAG CGT ACG GGT TG AAG CCA GAC TCC AGC AGT TC Rfx6 TGC CAG TGC ATA CTC GAC AAT AAC AGG ATT TTC AAG CAG GGG Glp1r ACG GTG TCC CTC TCA GAG AC ATC AAA GGT CCG GTT GCA GAA Gipr GAG TTG GTT CTG CCT TGG GA CTC AGA GTC TGT CTC CGC CC ChgA GAG GAG GAA GAG GAG GCT GT TGT CCT CCC ATT CTC TGG AC Kcnj11 AAG GGC ATT ATC CCT GAG GAA TTG CCT TTC TTG GAC ACG AAG Abcc8 TCA ACT TGT CTG GTG GTC AGC GAG CTG AGA AAG GGT CAT CCA Slc30a8 CAG AGA ACT TCG ACA GAA GCC CTT GCT TGC TCG ACC TGT T Atp2a2 TCT ACG TGG AAC CTT TGC CG GCT GCA CAC ACT CTT TAC CG Atp2a3 ATG ACT GCA GCC GGT TTG TA CTC TGG TCT CGG TGG GTC TA Arx CAG CAT TTG GCA GGC TCT AGG ATG TTG AGC TGC GTG AG Hhex GCC ATT TCA GAG CAC TTG GC CCT GTA GGG ACT GCG TCA TC Ndufs8 CGC AGC ACT TCA AGA TGT ATC G CTG CAT TGT CAG TTG CGG AC Cox8a AGG GAG CAG TCT TCC CTC AT CCC ACC AAG CAG AGC CAA TA Sdhd GAT CCC TGC TGG GTA CTT GA AAG TAG CAA AGC CCA GCA AA Uqcrfs1 TGG TCT CCC AGT TTG TTT CC GCA GCT TCC TGG TCA ATC TC Uqcr11 TGC TGA GCA GGT TTC TAG GC TCC TTC TTA AAC TTG CCG TTG Diabetes Page 68 of 72

Ndufa6 AGT ATG GAA GCA GCG GAC AC ATG CAC CTT CCC ATC AGG TG Cox7a2 GGG TAA CAA CCG AGC CAA GA CCC CGC CTT TCA GAT GAA CT Atp6v0e2 GAT ACC CCA CCT GAG CAT CG CAC AGC AGG GCA TAA GAG GT Atp6v1h CCT TCG TTC TGC TCT GGG TC TCC ACA GCA CCT CGA ATG TC Uqcrc1 GCC TGG TCT CAC TCC ATG TC TTG GAG GGT CAC GTT GTC TG Sdhc TGA GAC ATG TCA GCC GTC AC GGG AGA CAG AGG ACG GTT TG Uqcrb TCA AGC AAG TGG CTG GAT GG CAT AGT CAG GTC CAG GGC TC Cox6a1 TAG TTC CAT GAT GGC GTC GG TTC CAC ATC CGA GCT GAA CC Atp5g3 CGC CTG TCA CCT AGA TCC AC AGC CCT CTC CAG TCC TAG TC

Primers used for 3C-PCR Gene Primer 1 (5' to 3') Primer 2 (5' to 3') Gapdh TCT CCA TGG TGG TGA AG ACA ACT CCA CTC ACG GCA AAT TC Ins1 TTG AGG GAC AAA GGG CAT AA CCC TCC TGA CTC TTG TCC TG Ucn3 (E1) AAG TAT GGG CCA GAC CTA ACT TC AAA GGA TTA GAT GCA CAG AAC AAT TT Ucn3 (E2) TCA ATT GGC AAA TGC CTT AGT GCC TCC CAA GGA CCT ACT TC Page 69 of 72 Diabetes

Gene lists of heat-maps Mature b cell OXPHOS Pcsk1 Ndufc2 Insm1 Ndufs8 Kcnj11 Ndufb10 Mafa Ndufa8 Gjd2 Ndufb3 Glp1r Ndufab1 Chga Ndufa3 Slc2a2 Atp5j Ins2 Cox7a2l Pcsk2 Cox8a Ucn3 Lhpp Ins1 Ppa2 Pdx1 Sdhd Nkx6−1 Atp6v1g1 G6pc2 Uqcrfs1 Cox5a Uqcr11 Cox6c Uqcrh Atp5j2 Ndufb9 Cox7b Cox7c Ndufs4 Ndufa6 Atp5g1 Cox7a2 Atp5b Atp5f1 Atp6v0e2 Atp5a1 Atp6ap1 Atp6v1h Atp6v1e1 Uqcrc1 Atp6v1a Sdha Uqcrc2 Ndufs1 Ndufa1 Sdhc Cox6b1 Ndufb8 Atp5g2 Diabetes Page 70 of 72

Uqcrb Ppa1 Atp6v1f Cox6a1 Ndufa7 Ndufa4 Ndufb6 Atp5g3 Ndufa9 Page 71 of 72 Diabetes

Correlation analysis_beta cell gene list Correlation analysis_OXPHOS gene list PeakID log2FC_ATACseqGenes dataset log2FC_RNAseq PeakID ./_peak_27404-2.5406 Ins1 beta -3.0037 ./_peak_44563 ./_peak_24812-2.7629 Egr1 beta -2.6106 ./_peak_22677 ./_peak_24813-2.2977 Egr1 beta -2.6106 ./_peak_52167 ./_peak_11414-1.9052 Fos beta -2.4716 ./_peak_27199 ./_peak_11413-2.0464 Fos beta -2.4716 ./_peak_5966 ./_peak_30879-2.6271 Id1 beta -2.4671 ./_peak_54171 ./_peak_32722-2.0302 P2ry1 beta -2.4247 ./_peak_45210 ./_peak_38905-2.4677 Mnx1 beta -2.3762 ./_peak_5577 ./_peak_38906-1.9902 Mnx1 beta -2.3762 ./_peak_41769 ./_peak_48993-2.9055 Rab3a beta -2.2395 ./_peak_47128 ./_peak_4113 -2.28 Cited2 beta -2.1577 ./_peak_53641 ./_peak_4111-2.063 Cited2 beta -2.1577 ./_peak_20550 ./_peak_4112-1.6581 Cited2 beta -2.1577 ./_peak_42021 ./_peak_29096-2.0645 G6pc2 beta -2.1563 ./_peak_6988 ./_peak_29095-1.7221 G6pc2 beta -2.1563 ./_peak_8894 ./_peak_1257-1.3979 Fev beta -2.0782 ./_peak_26152 ./_peak_30775-1.3813 Foxa2 beta -2.0522 ./_peak_51394 ./_peak_30633-1.6459 Pcsk2 beta -1.9361 ./_peak_13661 ./_peak_47653-1.3454 Ins2 beta -1.8427 ./_peak_40623 ./_peak_37718-1.6669 Id3 beta -1.8124 ./_peak_3087 ./_peak_26296-1.141 Men1 beta -1.6978 ./_peak_28164 ./_peak_45231-1.6253 Lsr beta -1.6039 ./_peak_13803 ./_peak_53343-1.423 Syp beta -1.4271 ./_peak_48938 ./_peak_41452-2.3553 Pdx1 beta -1.4225 ./_peak_28473 ./_peak_44078-1.0609 Ccnd2 beta -1.3941 ./_peak_21410 ./_peak_18318-1.5067 Rad21 beta -1.3709 ./_peak_44071 ./_peak_13768-1.0511 Irx1 beta -1.3545 ./_peak_9499 ./_peak_20319-1.477 Hes1 beta -1.3458 ./_peak_9498 ./_peak_20318-1.0367 Hes1 beta -1.3458 ./_peak_23435 ./_peak_49468-0.9944 Rbl2 beta -1.3389 ./_peak_19095 ./_peak_44225-1.3553 Cdkn1b beta -1.3365 ./_peak_32159 ./_peak_39864-1.3962 Hopx beta -1.2711 ./_peak_42345 ./_peak_39865-2.3217 Hopx beta -1.2711 ./_peak_21621 ./_peak_39862-1.0752 Hopx beta -1.2711 ./_peak_21622 ./_peak_52024-1.6079 Rab27a beta -1.2638 ./_peak_6815 ./_peak_52025-1.1423 Rab27a beta -1.2638 ./_peak_12983 ./_peak_40969-1.6344 Gusb beta -1.218 ./_peak_29220 ./_peak_11838-0.9905 Chga beta -1.0951 ./_peak_53559 ./_peak_11101-1.5287 Six4 beta -1.0696 ./_peak_26329 ./_peak_11102-2.4029 Six4 beta -1.0696 ./_peak_50188 ./_peak_40255-1.3956 Nkx6-1 beta -0.9886 ./_peak_25840 Diabetes Page 72 of 72

Correlation analysis_OXPHOS gene list log2FC_ATACseqGenes dataset log2FC_RNAseq -2.4153 Ndufa3 oxphos -2.7013 -3.0354 Ndufa7 oxphos -2.4329 -1.9938 Cox7a2 oxphos -2.4002 -1.8287 Ndufb8 oxphos -2.3126 -1.8639 Ndufa12 oxphos -2.2376 -1.8507 Cox7b oxphos -2.224 -1.0833 Cox6b1 oxphos -2.2151 -1.2678 Uqcr11 oxphos -2.14 -1.2922 Ndufa4 oxphos -2.135 -1.5298 Ndufab1 oxphos -2.0789 -1.276 Ndufa1 oxphos -2.0155 -1.1705 Cox17 oxphos -1.9063 -1.263 Ndufa5 oxphos -1.8791 -1.2558 Uqcr10 oxphos -1.84 -1.4293 Atp5g1 oxphos -1.8015 -1.4216 Ndufs8 oxphos -1.7781 -1.0845 Atp5l oxphos -1.7721 -1.048 Uqcrb oxphos -1.76 -1.5073 Cox6a1 oxphos -1.7438 -1.3924 Sdhc oxphos -1.7316 -1.274 Surf1 oxphos -1.72 -1.3547 Ndufs6 oxphos -1.6907 -1.4039 Ndufa13 oxphos -1.6857 -1.2458 Ndufa8 oxphos -1.68 -1.1276 Atp5j oxphos -1.6711 -0.9169 Ndufa9 oxphos -1.604 -1.4189 Atp5h oxphos -1.5972 -1.6369 Atp5h oxphos -1.5972 -1.5775 Ndufv2 oxphos -1.5968 -1.517 Ndufa6 oxphos -1.5843 -1.0217 Ndufb5 oxphos -1.566 -1.5361 Ndufb2 oxphos -1.5324 -1.2871 Atp5o oxphos -1.5083 -1.2242 Atp5o oxphos -1.5083 -1.3002 Atp5b oxphos -1.4436 -1.1386 Uqcrfs1 oxphos -1.3915 -1.0441 Atp5g3 oxphos -1.3411 -1.4238 Ndufb11 oxphos -1.3358 -1.3293 Cox8a oxphos -1.2682 -1.3598 Cox4i1 oxphos -1.2229 -1.5559 Atp5a1 oxphos -1.0476