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A B 12 *** *** *** *** *** 12 10 10 r=0.62 expression (log2) expression p<0.0001 8 n=1063 MEN1 MEN1 expression (log2) expression MEN1 8 -2 0 2 4 MEN1 Copy Number ((log2)-1) like (n = 8) - -like (n = 89) Normal (n = 61) enriched (n = 54) Basal - Normal Luminal A (nLuminal = 221) B (n = 122) HER2 C D (%) (%) (%) (%) ERa+ ERa- 100 ERa+ ERa- 100 100 100 75 75 p<0.01 75 p=n.s. p<0.0001 75 p=n.s. 50 50 50 50 CN>2 (n=81) CN>2 (n=21) 25 High (n=207) 25 High (n=74) 25 25 CN=2 (n=830) CN=2 (n=309) Low (n=207) Low (n=74) 0 0 CN<2 (n=162) 0 CN<2 (n=49) Disease free probabilityDisease free 0 0 100 200 300 0 100 200 300 probabilityDiseasefree 0 100 200 300 0 100 200 300 Months Months Months Months Figure S1. Increased MEN1 expression and copy number are related to poor prognosis of ERa+ breast cancer patients. (A) MEN1 expression in different breast cancer PAM50 subtypes in TCGA breast cancer cohort. Significant levels were based on Duncan’s multiple range tests (***P < 0.001). (B) Scatter plot and linear regression of MEN1 expression and copy number in TCGA breast cancer cohort. Significant levels were based on Pearson correlation coefficient. (C) and (D) Kaplan-Meier survival analyses and significant levels were based on logrank tests from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort based on MEN1 expression (c) or copy number (D). A rtTA Doxycycline BirA Menin Biotin MAPs B T47D EIF2B5 KMT2D CBX4 AFF4 NOC2L GATA3 NOLC1 EP400 LIG3 GTF TLE3 ECPAS 3C1 WAPL KDM3B SF1 MED12 NUP153 MKI67 SART1 Menin ZFP36 SNRPD3 L2 SMC HD1 MSH2 KMT2A SRSF5 EMSY POLR BPTF 2A PURB STAG1 TP53 I11 LENG8 ZC3H18 FOXK1 KPNA6 FRG1 C Glycolytic enzyme 7 ALDOA 7 TPI1 3 ENO2 Menin 0 0 0 7 7 3 KMT2A 0 0 0 chr16 ⎮30071000 ⎮30076000 ⎮30081000 ⎮ chr12 ⎮6971000 ⎮6975000 ⎮6979000 chr12 ⎮7015000 ⎮7024000 ⎮7033000 ⎮ D OXPHOS complex I NDUFA11 NDUFB5 7 7 7 NDUFB6 Menin 0 0 0 7 7 7 KMT2A 0 0 0 chr19 ⎮5885000 ⎮5894000 ⎮5903000 ⎮5912000 chr3 ⎮179319000 ⎮179325000 ⎮179331000 ⎮ chr9 ⎮32561000 ⎮32567000 ⎮325730000 ⎮ NDUFS6 NDUFS7 7 7 Menin 0 0 7 7 KMT2A 0 0 chr5 ⎮1799000 ⎮1803000 ⎮1807000 chr19 ⎮1381000 ⎮1385000 ⎮13889000 OXPHOS complex II OXPHOS complex III OXPHOS complex IV SDHA UQCRB COX6B1 7 7 7 Menin 0 0 0 7 7 7 KMT2A 0 0 0 chr5 ⎮214000 ⎮220000 ⎮226000 ⎮ chr8 ⎮97240000 ⎮97245000 ⎮97250000 ⎮ chr19 ⎮36137000 ⎮36141000 ⎮36145000 OXPHOS complex V COX14 ATP5F1D ATP5PB 7 14 7 Menin 0 0 0 7 14 7 KMT2A 0 0 0 chr12 ⎮50502000 ⎮50507000 ⎮50512000 ⎮ chr19 ⎮1240500 ⎮1242100 ⎮1243700 ⎮124530 chr1 ⎮111984000 ⎮111991000 ⎮11199000 ⎮ Figure S2. The BioID system, network analysis of 35 MAPs in T47D and in silico analysis of anti-menin and anti-KMT2A ChIP-seq. (A) Schematic illustration of biotin labeling of MAPs using BioID proximity-dependent biotinylation with a doxycycline-inducible biotin ligase BirA-MEN1 fused gene. (B) Network analysis of 35 MAPs in T47D cells. The distance between menin and MAPs represented the protein quantitative ratio of each MAP and menin. MAPs marked in blue were further validated by WES. (C) In silico analysis of anti-menin and anti-KMT2A ChIP-seq data indicates that anti-Menin immunoprecipitation enriched the promoter and gene body DNA sequence of the representative glycolytic genes and (D) OXPHOS genes in MCF-7 cells. A B SNRPD3 MEN1 ZC3H18 NOC2L SART1 FRG1 EIF2B5 SRSF5 LENG8 TLE3 CBX4 GTF3C1 TP53I11 FOXK1 SF1 ZFP36L2 NOLC1 MKI67 MSH2 NUP153 SMCHD1 STAG1 MED12 KPNA6 PURB BPTF EMSY AFF4 KDM3B KMT2D EP400 LIG3 POLR2A ECPAS GATA3 KMT2A WAPL MEN1 CBX4 ZC3H18 EIF2B5 SNRPD3 SART1 NOC2L FRG1 ECPAS MSH2 MKI67 NOLC1 SF1 ZFP36L2 SRSF5 LENG8 TLE3 LIG3 GTF3C1 TP53I11 FOXK1 KPNA6 PURB MED12 POLR2A NUP153 SMCHD1 STAG1 EMSY BPTF AFF4 KDM3B KMT2D EP400 GATA3 KMT2A WAPL Normal Normal Glycolysis (n=77) Glycolysis OXPHOS (n=87) OXPHOS Tumor Tumor Glycolysis (n=77) Glycolysis OXPHOS (n=87) OXPHOS -0.7 0 0.7 corr. coef. C 13 12 Group 4: Low-High Group 2: High-High 11 expression 10 MEN1 9 Group 3: Low-Low Group 1: High-Low 8 8 9 10 11 12 KMT2A expression Figure S3. Expression correlation relationship of MAPs and OXPHOS/glycolytic genes. (A) and (B), Heatmaps of gene expression correlations of 37 MAPs (including MEN1) genes with OXPHOS genes (A) or glycolytic genes (B) in normal and tumor samples from TCGA breast cancer cohort. (C) Scatter plot of MEN1 and KMT2A expression in TCGA breast tumors. The tumors are grouped into 4 groups (1-4) as in Figure 3E according to median values of both genes’ expression. Similar groupings are also applied based on the median expression of MEN1 and the other three MAP genes (MED12, WAPL and GATA3). A T47D MCF-7 T47D MCF-7 T47D MCF-7 T47D MCF-7 WAPL WAPL KMT2A KMT2A MED12 GATA3 GATA3 MED12 sh sh sh Vehicle sh Vehicle Vehiclesh Vehiclesh Vehicle Vehicle Vehiclesh Vehiclesh KMT2A MED12 WAPL GATA3-FL GATA3-SP b-Actin b-Actin b-Actin b-Actin B T47D MCF-7 C T47D MCF-7 KMT2A GATA3 KMT2A GATA3 KMT2A KMT2A GATA3 Vehiclesh Vehiclesh Vehiclesh Vehiclesh Vehiclesh VehicleshGATA3 Vehiclesh Vehiclesh NDUFA13 NDUFA13 ALDOA ALDOA NDUFS7 NDUFS7 ALDOC ALDOC NDUFS8 NDUFS8 ENO1 ENO1 NDUFB7 NDUFB7 PFKL PFKL NDUFV1 NDUFV1 PFKP PFKP SDHA SDHA PGK1 PGK1 SDHB SDHB TPI1 TPI1 SDHC SDHC KMT2A KMT2A 0 1 2 SDHD SDHD GATA3 GATA3 Relative expression D Oligo FCCP Rot+AA E 20 T47D Vehicle T47D Vehicle T47D shMED12 T47D Vehicle 20 15 15 1515 T47D shMEN1 T47D shKMT2A T47D shWAPL T47D shGATA3 1515 Spare 10 Capacity 1010 10 1010 /min/1000 cells) /min/1000 cells) ATP Maximal 55 5 5 55 Basal mpH Proton Leak mpH Non-Mitochondrial Respiration 0 00 00 0 OCR ( 00 2020 40 60 80 OCR ( 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 Time (min) Time (min) Time (min) Time (min) F MCF-7 Vehicle MCF-7 Vehicle MCF-7 shMED12 MCF-7 Vehicle MCF-7 Vehicle MCF-7 shMEN1 MCF-7 shKMT2A MCF-7 shWAPL MCF-7 shGATA3 15 15 15 20 15 10 10 10 10 /min/1000 cells) 5 5 5 5 mpH 0 0 0 0 OCR ( 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 G Time (min) H Time (min) Time (min) Time (min) 4 4 Glucose Oligo 2-DG 4 T47D Vehicle T47D Vehicle T47D shMED12 4 T47D Vehicle T47D shMEN1 T47D shKMT2A T47D shWAPL T47D shGATA3 3 3 3 3 Glycolytic 2 Reserve 2 2 2 Glycolysis /min/1000 cells) /min/1000 cells) Glycolytic 1 1 1 Capacity mpH 1 mpH Non-Glycolytic Acidification 0 0 0 0 0 20 40 60 80 ECAR ( 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 ECAR ( I Time (min) Time (min) Time (min) Time (min) MCF-7 Vehicle MCF-7 Vehicle MCF-7 shMED12 MCF-7 Vehicle MCF-7 Vehicle 4 4 MCF-7 shMEN1 4 MCF-7 shKMT2A MCF-7 shWAPL 4 MCF-7 shGATA3 3 3 3 3 2 2 2 2 /min/1000 cells) 1 mpH 1 1 1 0 0 0 0 ECAR ( 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 Time (min) Time (min) Time (min) Time (min) Figure S4. Gene and protein expressions and extracellular-flux analysis in MAP shRNA knockdown cells. (A) WES of total lysate from T47D or MCF-7 cells subject to vehicle or MAP shRNA lentivirus infection. (B) and (C) Heat maps of RT-qPCR of OXPHOS genes (B) or glycolytic genes (C) in T47D or MCF-7 cells. The expression in vehicle control is normalized as 1 (n = 3). (D) Seahorse mitochondrial stress test profile. (E) and (F) Line charts of OCR for mitochondrial functions in T47D (E) and MCF-7 (F) cells. (G) Seahorse glycolytic stress test profile. (H) and (I) Line charts of ECAR for glycolytic functions in T47D (H) and MCF-7 (I) cells. Data are presented as mean±S.D. (n = 15-20 technical-replicate wells). A C T47D MCF-7 D T47D MCF-7 1.21.2 T47D MCF-7 *** 0.80.8 503 0 h503 1 h503 3 h503 6 h -503 0 h503 1 h503 3 h503 6 h503 72 h 503 0 h503 1 h503 3 h503 6 h503 72 h 503 0 h503 1 h503 3 h503 6 h503 72 h ** - - - - -503 72 h - - - - - - - - - - - - - - ** MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI MI 0.40.4 NDUFS7 ALDOA NDUFA11 Cell viability (%) viability Cell ALDOC 0.00.0 SHDA ENO1 0 0.5 1 1.5 2 2.5 SDHB PFKL MI-503 (µM) CYC1 ctrl PFKP B MI(1) MI(2) COX6C PGK1 T47D UQCR11 TPI1 ATP5D 0 1 2 Relative expression Input IP:Menin IP:IgG WAPL E T47D MI-503 0h 15MCF-7 MI-503 0 h MCF-7 MI-503 3 h MCF-7 DMSO 72 h T47D MI-503 1h MCF-7 MI-503 1 h MCF-7 MI-503 6 h MCF-7 MI-503 72 h T47D MI-503 3 h T47D DMSO 72 h Oligo FCCP Rot+AA T47D MI-503 6 h T47D MI-503 72 h Oligo FCCP Rot+AA 10 10 10 10 Oligo FCCP Rot+AA Oligo FCCP Rot+AA 5 5 5 5 /min/1000 cells) mpH OCR ( 0 0 0 0 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 Time (min) Time (min) Time (min) Time (min) F T47D MI-503 0 h T47D DMSO 72 h MCF-7 MI-503 0 h MCF-7 MI-503 3 h MCF-7 DMSO 72 h T47D MI-503 1h T47D MI-503 72 h MCF-7 MI-503 1 h MCF-7 MI-503 6 h MCF-7 MI-503 72 h T47D MI-503 3 h Glucose Oligo 2-DG Glucose Oligo 2-DG T47D MI-503 6 h 4 4 4 4 Glucose Oligo 2-DG Glucose Oligo 2-DG 3 3 3 3 2 2 2 2 /min/1000 cells) mpH 1 1 1 1 0 0 ECAR ( 0 0 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 Time (min) Time (min) Time (min) Time (min) Figure S5.
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  • RNA Epigenetics: Fine-Tuning Chromatin Plasticity and Transcriptional Regulation, and the Implications in Human Diseases

    RNA Epigenetics: Fine-Tuning Chromatin Plasticity and Transcriptional Regulation, and the Implications in Human Diseases

    G C A T T A C G G C A T genes Review RNA Epigenetics: Fine-Tuning Chromatin Plasticity and Transcriptional Regulation, and the Implications in Human Diseases Amber Willbanks, Shaun Wood and Jason X. Cheng * Department of Pathology, Hematopathology Section, University of Chicago, Chicago, IL 60637, USA; [email protected] (A.W.); [email protected] (S.W.) * Correspondence: [email protected] Abstract: Chromatin structure plays an essential role in eukaryotic gene expression and cell identity. Traditionally, DNA and histone modifications have been the focus of chromatin regulation; however, recent molecular and imaging studies have revealed an intimate connection between RNA epigenetics and chromatin structure. Accumulating evidence suggests that RNA serves as the interplay between chromatin and the transcription and splicing machineries within the cell. Additionally, epigenetic modifications of nascent RNAs fine-tune these interactions to regulate gene expression at the co- and post-transcriptional levels in normal cell development and human diseases. This review will provide an overview of recent advances in the emerging field of RNA epigenetics, specifically the role of RNA modifications and RNA modifying proteins in chromatin remodeling, transcription activation and RNA processing, as well as translational implications in human diseases. Keywords: 5’ cap (5’ cap); 7-methylguanosine (m7G); R-loops; N6-methyladenosine (m6A); RNA editing; A-to-I; C-to-U; 2’-O-methylation (Nm); 5-methylcytosine (m5C); NOL1/NOP2/sun domain Citation: Willbanks, A.; Wood, S.; (NSUN); MYC Cheng, J.X. RNA Epigenetics: Fine-Tuning Chromatin Plasticity and Transcriptional Regulation, and the Implications in Human Diseases. Genes 2021, 12, 627.
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.