Supplemental Material.Pdf

Total Page:16

File Type:pdf, Size:1020Kb

Supplemental Material.Pdf SUPPLEMENTAL MATERIAL FOR: Pax5 loss imposes a reversible differentiation block in B-progenitor acute lymphoblastic leukemia Grace J. Liu, Luisa Cimmino, Julian G. Jude, Yifang Hu, Matthew T. Witkowski, Mark D. McKenzie, Mutlu Kartal-Kaess, Sarah A. Best, Laura Tuohey, Yang Liao, Wei Shi, Charles G. Mullighan, Michael A. Farrar, Stephen L. Nutt, Gordon K. Smyth, Johannes Zuber, and Ross A. Dickins SUPPLEMENTAL FIGURE LEGENDS Figure S1. Restricted expression of Igh variable segments in STAT5-CA;Vav-tTA;TRE- GFP-shPax5 B-ALL. Expression (RNA-seq RPKM) of immunoglobulin heavy chain variable (Ighv) gene segments in leukemic cells sorted from untreated Rag1-/- mice (black) compared with normal pre-B cells (grey), showing dominant expression of Ighv2-5 in leukemia. Segments are arranged from 5’ to 3’. Mean ± SEM, n=3 mice for each group. Figure S2. Characterisation of the Pax5 restoration response of independent Stat5-CA; Vav-tTA; TRE-GFP-shPax5 leukemia A008. (A) Peripheral white blood cell (WBC) counts in Rag1–/– mice transplanted with leukemia cells from STAT5-CA;Vav-tTA;TRE-GFP-shPax5 mouse A008. Mean ± SEM, n=4 for each group prior to Dox treatment and following 14 days of Dox treatment as indicated. P < 0.0005, Student’s t test. (B) Flow cytometry of CD19 and IgM expression on mononuclear cells from the peripheral blood of a representative Rag1–/– mouse that was transplanted with leukemia A008 and subsequently Dox treated as indicated. (C) Flow cytometry of CD19 and IgD expression as shown in (B). (D) Leukemia burden (proportion of CD19+ cells in the blood) upon Dox treatment (mean ± SEM, n=4 mice). (E) Proportion of CD19+ cells co-expressing IgM upon Dox treatment (mean ± SEM, n=4 mice). (F) Proportion of IgM+ cells co-expressing IgD upon Dox treatment (mean ± SEM, n=4 mice). (G) Kaplan-Meier survival curve for Rag1–/– mice transplanted with the leukemia A008. Dox treatment of leukemic mice was initiated at day zero. n=3 untreated mice and 5 Dox treated mice, logrank test P < 0.005. (H) Immunophenotype of bone marrow (upper panels), lymph nodes (middle panels) and peripheral blood (lower panels) of representative Rag1–/– recipient mice when moribund following prolonged Dox treatment. Flow cytometry of CD45.1 and CD19 expression is shown on left, and IgM and IgD expression on CD45.1–CD19+ cells is shown on right. (I) Cell cycle 1 profiles of CD45.1–CD19+ leukemia cells freshly isolated from representative untreated and Dox-treated leukemic mice. Percentage of cells in G0/G1 and S/G2/M phases are indicated. (J) Proportion of CD45.1–CD19+ cells in S/G2/M phases in untreated and Dox-treated leukemic mice (mean ± SEM, n=3 mice). Bone marrow P = NS, Blood P < 0.05, Spleen P < 0.005, Lymph node P < 0.05. (K) Quantitative RT-PCR analysis of changes in Pax5, Myc and Rag1 expression in A008 leukemia cells harvested from a Dox-treated leukemic mouse. Results were normalized to housekeeping gene Gapdh and shown relative to levels in leukemia cells from an untreated mouse (mean ± SEM from 3 technical replicates). Figure S3. Moribund phenotype following long-term Dox treatment of mice transplanted with Stat5-CA; Vav-tTA; TRE-GFP-shPax5 leukemia A024. Immunophenotype of bone marrow (upper panels), blood (middle panels) and spleen (lower panels) of representative Rag1– /– mice bearing triple transgenic leukemia A024, which then became moribund following prolonged Dox treatment. Flow cytometry of CD45.1 (host cell marker) and CD19 expression is shown on left, and CD45.1 and Mac1 is shown on right. Figure S4. Gene expression changes upon Pax5 restoration specifically correlate with the large cycling to small resting pre-B transition. Gene set analysis barcode plots (left panels) comparing differential gene expression upon Pax5 restoration in STAT5-CA;Vav-tTA;TRE-GFP- shPax5 leukemia cells in vivo with sets of previously described genes that are induced (red bars) or repressed (blue bars) upon the transition from: (A) pre-BI to large pre-BII (pro-B to large cycling pre-B); (B) large pre-BII to small pre-BII (large cycling pre-B to small resting pre-B); (C) small resting pre-B to immature B; (D) immature to mature B (Hoffmann et al. 2002). Red/blue traces above/below the bars represent relative enrichment. Scatter plots (right panels) comparing the log2 fold changes upon Pax5 restoration with the log2 fold changes during consecutive stages of normal B lymphocyte development from (Hoffmann et al. 2002). Regression lines are shown in red. Figure S5. Coordinate upregulation of pre-BCR complex and signaling components following Pax5 restoration. (A) Schematic of the pre-BCR complex and associated signaling molecules. (B-D) Expression (RNA-seq RPKM) of Pax5 (B), selected pre-BCR complex components (C), and critical downstream pre-BCR signaling components (D) in B-ALL cells from untreated and Dox treated mice, compared with normal pre-B cells (mean ± SEM, n=3 mice for each group). (E) Expression (RNA-seq RPKM) of Stat5a and Stat5b as described above. 2 Figure S6. Pax5 restoration induces transcriptional changes associated with loss of Myc activity. Gene set analysis barcode plots comparing differential gene expression upon Pax5 restoration in STAT5-CA;Vav-tTA;TRE-GFP-shPax5 leukemia cells in vivo with sets of previously described genes that are induced (red bars) or repressed (blue bars) upon (A) activation of Myc (Zeller et al., 2003) or (B) or inactivation of Myc (Shachaf et al., 2004). Red/blue traces above/below the bar represent relative enrichment. Figure S7. Cell cycle analysis and additional control data for human B-ALL cell lines. (A) Representative cell cycle flow cytometry profiles of 697 cells, with BrdU and DAPI staining indicating the proportion of cells in S phase 3 days after PAX5-IRES-GFP induction relative to matched IRES-GFP control cells. Cells with less than 2N DNA content were excluded from analysis. (B) Quantitation of S phase cells (%) based on analysis shown in (A). For each cell line, the proportion GFP– (left) and GFP+ (right) cells (gated from within the same population) in S phase following Dox-mediated induction of PAX5-IRES-GFP (red) or control IRES-GFP (blue) is indicated. (C, D) Flow cytometric analysis of cell size/FSC (C) and cell surface markers (D) in GFP– (red line) and GFP+ (green line) cells in B-ALL cell lines showing no changes 3 days after Dox-dependent induction of the control IRES-GFP cassette. (E) Scatter plot of gene expression fold changes upon inducible PAX5 expression in the human B-ALL cell line REH versus Pax5 restoration in STAT5-CA;Vav-tTA;TRE-GFP-shPax5 leukemia A024. Dotted lines indicate 2- fold differential expression, and genes with > 2-fold upregulation in both experiments are shown in red. (F) Gene set analysis barcode plot. The RNA-seq differential gene expression dataset upon Pax5 restoration in STAT5-CA;Vav-tTA;TRE-GFP-shPax5 leukemia cells in vivo is shown as a horizontal bar as described in Figure 5F. Vertical lines indicate the expressed mouse homologs (106 genes) of genes upregulated > 2-fold upon inducible PAX5 expression in REH cells (203 genes). The curved line indicates relative enrichment. A significant positive correlation is observed with genes upregulated upon Pax5 restoration in mouse B-ALL (P < 0.0005, roast test). 3 SUPPLEMENTAL MATERIALS AND METHODS Transgenic mice TRE-GFP-shRNA transgenes were detected by PCR using forward primers specific for each shRNA (Pax5.437: TGTATTTGTCCGAATGATCCTGTTG; Ren.713: GTATAGATAAGCATTATAATTCC) and a common reverse primer (GAAAGAACAATCAAGGGTCC) yielding a 210 bp product. The Stat5b-CA transgene was detected using Stat5b-CA specific forward (TAGGAAGAAGCCTATATCCCAAAGG) and reverse (ACAGTCTCTCAAAGTCAGTGGGG) primers, yielding a 275 bp product. The CAGS-rtTA3 transgene was detected by CAGS specific forward (CTGCTGTCCATTCCTTATTC) and reverse (CGAAACTCTGGTTGACATG) primers, yielding a 200 bp product. The Vav-tTA transgene (Kim et al. 2007) was detected tTA specific forward (CCATACTCACTTTTGCCCTTTAG) and reverse (CAGCGCTGAGTGCATATAATGCA) primers, yielding a 221 bp product. All mice were on an inbred C57BL6/J background except for Vav-tTA mice, which were on an FVB/N background. Blood and flow cytometry analysis Antibodies recognising mouse CD19 (eBio1D3), IgM (II/41), IgD (11-26c) and CD93 (AA4.1) were from Affymetrix eBioscience (San Diego, CA). Anti-mouse CD25 (PC61), TCRβ (H57- 597) and Mac1 (M1/70) was from BD Pharmingen (San Jose, CA), while anti-cKit (2B8) was from BioLegend (San Diego, CA). Antibodies against mouse B220 (RA36B2), CD45.1 (A20.1), CD45.2 (S450) and cKit (ACK-4) were generated in-house. Cell preparations were co-stained with Fluoro-Gold to exclude dead cells (Sigma-Aldrich, St Louis, MO). qPCR Total RNA was extracted using RNAeasy Plus Mini Kit (QIAGEN, Valencia, CA) and cDNA was prepared using SuperScript III First-Strand Synthesis System (Life Technologies). qPCR was performed using SYBR Green-based detection in a LightCycler 480 (Roche) using the following primers: Pax5-F, 5'-CCACAGTCCTACCCTATTGTCA-3'; Pax5-R, 5'- GTAATAGTATGGGGAGCCAAGC-3'; Myc-F, 5'-AGAGCTCCTCGAGCTGTTTG -3'; Myc- R, 5'-AGGGCTGTACGGAGTCGTAG-3'; Rag1-F, 5'- CTGGGTTTACCATGAACTCAAA-3'; Rag1-R, 5'-GGTGCTAGGAGAAGACCTCACT-3'; Gapdh-F, 5'- ACCCAGAAGACTGTGGATGG-3'; Gapdh-R, 5'-CCCTGTTGCTGTAGCCGTAT-3'. 4 Human leukemia cell lines The human B-ALL cell lines BV173 (Pegoraro et al. 1983), NALM-6 (Hurwitz et al. 1979), REH (Rosenfeld et al. 1977), and 697 (Findley et al. 1982) were obtained from the Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ, Braunschweig, Germany) and cultured as previously described (Drexler 2010). PAX5 copy number data for these cell lines was derived from our previous Affymetrix Mapping 250k SNP array data available in the NCBI Gene Expression Omnibus under GEO series accession number GSE9112 (Mullighan et al. 2008). The NALM-6 cell PAX5 promoter deletion coordinates are 37.025-37.067 Mb (build hg17).
Recommended publications
  • (COMT) Gene As a Candidate for Psychiatric Phenotypes
    Molecular Psychiatry (2006) 11, 446–458 & 2006 Nature Publishing Group All rights reserved 1359-4184/06 $30.00 www.nature.com/mp FEATURE REVIEW The catechol-O-methyl transferase (COMT) gene as a candidate for psychiatric phenotypes: evidence and lessons N Craddock, MJ Owen and MC O’Donovan Department of Psychological Medicine, The Henry Wellcome Building for Biomedical Research in Wales, Cardiff University, School of Medicine, Heath Park, Cardiff, UK The enzyme catechol-O-methyl transferase (COMT), identified in the 1950s, is involved in catabolism of monoamines that are influenced by psychotropic medications, including neuroleptics and antidepressants. The COMT gene lies in a chromosomal region of interest for psychosis and bipolar spectrum disorder and a common polymorphism within the gene alters the activity of the enzyme. As a consequence, COMT has been one of the most studied genes for psychosis. On the basis of prior probabilities it would seem surprising if functional variation at COMT did not have some influence either on susceptibility to psychiatric phenotypes, modification of the course of illness or moderation of response to treatment. There is now robust evidence that variation at COMT influences frontal lobe function. However, despite considerable research effort, it has not proved straightforward to demonstrate and characterise a clear relationship between genetic variation at COMT and psychiatric phenotypes. It is of course, possible that COMT will turn out to be an unusually intractable case but it seems more likely that the experiences with this gene will provide a foretaste of the complexity of genotype–phenotype relationships that will be found for psychiatric traits.
    [Show full text]
  • (ALDH1A3) for the Maintenance of Non-Small Cell Lung Cancer Stem Cells Is Associated with the STAT3 Pathway
    Author Manuscript Published OnlineFirst on June 6, 2014; DOI: 10.1158/1078-0432.CCR-13-3292 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Essential role of aldehyde dehydrogenase 1A3 (ALDH1A3) for the maintenance of non-small cell lung cancer stem cells is associated with the STAT3 pathway Chunli Shao1,2, James P. Sullivan3, Luc Girard1,2, Alexander Augustyn1,2, Paul Yenerall1,2, Jaime Rodriguez4, Hui Liu4, Carmen Behrens4, Jerry W. Shay5, Ignacio I. Wistuba4, John D. Minna 1,2,6,7 1Hamon Center for Therapeutic Oncology Research, 2Simmons Comprehensive Cancer Center, 3Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114, 4Department of Translational Molecular Pathology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, 77054, 5Department of Cell Biology, 6Department of Pharmacology, 7Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA. Running Title: ALDH1A3 in non-small cell lung cancer stem cells Keywords: Lung cancer, cancer stem cells, ALDH1A3, STAT3, Stattic Financial Support This project was supported by CPRIT, NCI SPORE P50CA70907, UTSW Cancer Center Support Grant 5P30-CA142543, and the Gillson-Longenbaugh Foundation. Address Correspondence: John D. Minna, M.D. 6000 Harry Hines Blvd Dallas, TX 75390-8593 Hamon Center for Therapeutic Oncology Research UT Southwestern Medical Center Phone: 214-648-4900; Fax: 214-648-4940 [email protected] Disclosure of Potential Conflict of Interest The authors indicate no potential conflicts of interest. Word count: 4583 Total number of figures and tables: 6 figures Downloaded from clincancerres.aacrjournals.org on September 28, 2021. © 2014 American Association for Cancer Research.
    [Show full text]
  • Regulation and Dysregulation of Chromosome Structure in Cancer
    Regulation and Dysregulation of Chromosome Structure in Cancer The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Hnisz, Denes et al. “Regulation and Dysregulation of Chromosome Structure in Cancer.” Annual Review of Cancer Biology 2, 1 (March 2018): 21–40 © 2018 Annual Reviews As Published https://doi.org/10.1146/annurev-cancerbio-030617-050134 Version Author's final manuscript Citable link http://hdl.handle.net/1721.1/117286 Terms of Use Creative Commons Attribution-Noncommercial-Share Alike Detailed Terms http://creativecommons.org/licenses/by-nc-sa/4.0/ Regulation and dysregulation of chromosome structure in cancer Denes Hnisz1*, Jurian Schuijers1, Charles H. Li1,2, Richard A. Young1,2* 1 Whitehead Institute for Biomedical Research, 455 Main Street, Cambridge, MA 02142, USA 2 Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA * Corresponding authors Corresponding Authors: Denes Hnisz Whitehead Institute for Biomedical Research 455 Main Street Cambridge, MA 02142 Tel: (617) 258-7181 Fax: (617) 258-0376 [email protected] Richard A. Young Whitehead Institute for Biomedical Research 455 Main Street Cambridge, MA 02142 Tel: (617) 258-5218 Fax: (617) 258-0376 [email protected] 1 Summary Cancer arises from genetic alterations that produce dysregulated gene expression programs. Normal gene regulation occurs in the context of chromosome loop structures called insulated neighborhoods, and recent studies have shown that these structures are altered and can contribute to oncogene dysregulation in various cancer cells. We review here the types of genetic and epigenetic alterations that influence neighborhood structures and contribute to gene dysregulation in cancer, present models for insulated neighborhoods associated with the most prominent human oncogenes, and discuss how such models may lead to further advances in cancer diagnosis and therapy.
    [Show full text]
  • Novel Candidate Key Drivers in the Integrative Network of Genes, Micrornas, Methylations, and Copy Number Variations in Squamous Cell Lung Carcinoma
    Hindawi Publishing Corporation BioMed Research International Volume 2015, Article ID 358125, 11 pages http://dx.doi.org/10.1155/2015/358125 Research Article Novel Candidate Key Drivers in the Integrative Network of Genes, MicroRNAs, Methylations, and Copy Number Variations in Squamous Cell Lung Carcinoma Tao Huang,1,2 Jing Yang,2 and Yu-dong Cai1 1 CollegeofLifeScience,ShanghaiUniversity,Shanghai200444,China 2Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Shanghai Jiao Tong University School of Medicine, Shanghai 200031, China Correspondence should be addressed to Tao Huang; [email protected] and Yu-dong Cai; cai [email protected] Received 17 September 2014; Revised 6 January 2015; Accepted 22 January 2015 Academic Editor: Aparup Das Copyright © 2015 Tao Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The mechanisms of lung cancer are highly complex. Not only mRNA gene expression but also microRNAs, DNA methylation, and copy number variation (CNV) play roles in tumorigenesis. It is difficult to incorporate so much information into a single model that can comprehensively reflect all these lung cancer mechanisms. In this study, we analyzed the 129 TCGA (The Cancer Genome Atlas) squamous cell lung carcinoma samples with gene expression, microRNA expression, DNA methylation, and CNV data. First, we used variance inflation factor (VIF) regression to build the whole genome integrative network. Then, we isolated the lung cancer subnetwork by identifying the known lung cancer genes and their direct regulators.
    [Show full text]
  • Autophagy Modulation in Bladder Cancer Development and Treatment (Review)
    ONCOLOGY REPORTS 42: 1647-1655, 2019 Autophagy modulation in bladder cancer development and treatment (Review) FAPING LI, HUI GUO, YUXUAN YANG, MINGLIANG FENG, BIN LIU, XIANG REN and HONGLAN ZHOU Department of Urology, The First Hospital of Jilin University, Changchun, Jilin 130021, P.R. China Received April 24, 2019; Accepted August 1, 2019 DOI: 10.3892/or.2019.7286 Abstract. Bladder cancer (BC) is a potentially life-threatening 1. Introduction malignancy. Due to a high recurrence rate, frequent surveil- lance strategies and intravesical drug therapies, BC is Bladder cancer (BC) is a potentially life-threatening malig- considered one of the most expensive tumors to treat. As a nancy that is considered one of the most expensive tumors fundamental evolutionary catabolic process, autophagy plays in terms of treatment and medical care (1-3). After prostate an important role in the maintenance of cellular environ- cancer, it is the second most common type of urological cancer mental homeostasis by degrading and recycling damaged and ranks 10th among the most common types of cancer cytoplasmic components, including macromolecules and around the globe (4). It has been estimated that there were organelles. Scientific studies in the last two decades have 549,393 new cases of BC and 199,922 deaths resulting from shown that autophagy acts as a double‑edged sword with this disease worldwide in 2018, according to a report from regard to the treatment of cancer. On one hand, autophagy the International Agency for Research on Cancer (4). The inhibition is able to increase the sensitivity of cancer cells to primary histological subtype of human BC is transitional cell treatment, a process known as protective autophagy.
    [Show full text]
  • Number 2 February 2017 Atlas of Genetics and Cytogenetics in Oncology and Haematology
    Volume 1 - Number 1 May - September 1997 Volume 21 - Number 2 February 2017 Atlas of Genetics and Cytogenetics in Oncology and Haematology OPEN ACCESS JOURNAL INIST-CNRS Scope The Atlas of Genetics and Cytogenetics in Oncology and Haematology is a peer reviewed on-line journal in open access, devoted to genes, cytogenetics, and clinical entities in cancer, and cancer-prone diseases. It is made for and by: clinicians and researchers in cytogenetics, molecular biology, oncology, haematology, and pathology. One main scope of the Atlas is to conjugate the scientific information provided by cytogenetics/molecular genetics to the clinical setting (diagnostics, prognostics and therapeutic design), another is to provide an encyclopedic knowledge in cancer genetics. The Atlas deals with cancer research and genomics. It is at the crossroads of research, virtual medical university (university and post-university e-learning), and telemedicine. It contributes to "meta-medicine", this mediation, using information technology, between the increasing amount of knowledge and the individual, having to use the information. Towards a personalized medicine of cancer. It presents structured review articles ("cards") on: 1- Genes, 2- Leukemias, 3- Solid tumors, 4- Cancer-prone diseases, and also 5- "Deep insights": more traditional review articles on the above subjects and on surrounding topics. It also present 6- Case reports in hematology and 7- Educational items in the various related topics for students in Medicine and in Sciences. The Atlas of Genetics and Cytogenetics in Oncology and Haematology does not publish research articles. See also: http://documents.irevues.inist.fr/bitstream/handle/2042/56067/Scope.pdf Editorial correspondance Jean-Loup Huret, MD, PhD, Genetics, Department of Medical Information, University Hospital F-86021 Poitiers, France phone +33 5 49 44 45 46 [email protected] or [email protected] .
    [Show full text]
  • The N-Cadherin Interactome in Primary Cardiomyocytes As Defined Using Quantitative Proximity Proteomics Yang Li1,*, Chelsea D
    © 2019. Published by The Company of Biologists Ltd | Journal of Cell Science (2019) 132, jcs221606. doi:10.1242/jcs.221606 TOOLS AND RESOURCES The N-cadherin interactome in primary cardiomyocytes as defined using quantitative proximity proteomics Yang Li1,*, Chelsea D. Merkel1,*, Xuemei Zeng2, Jonathon A. Heier1, Pamela S. Cantrell2, Mai Sun2, Donna B. Stolz1, Simon C. Watkins1, Nathan A. Yates1,2,3 and Adam V. Kwiatkowski1,‡ ABSTRACT requires multiple adhesion, cytoskeletal and signaling proteins, The junctional complexes that couple cardiomyocytes must transmit and mutations in these proteins can cause cardiomyopathies (Ehler, the mechanical forces of contraction while maintaining adhesive 2018). However, the molecular composition of ICD junctional homeostasis. The adherens junction (AJ) connects the actomyosin complexes remains poorly defined. – networks of neighboring cardiomyocytes and is required for proper The core of the AJ is the cadherin catenin complex (Halbleib and heart function. Yet little is known about the molecular composition of the Nelson, 2006; Ratheesh and Yap, 2012). Classical cadherins are cardiomyocyte AJ or how it is organized to function under mechanical single-pass transmembrane proteins with an extracellular domain that load. Here, we define the architecture, dynamics and proteome of mediates calcium-dependent homotypic interactions. The adhesive the cardiomyocyte AJ. Mouse neonatal cardiomyocytes assemble properties of classical cadherins are driven by the recruitment of stable AJs along intercellular contacts with organizational and cytosolic catenin proteins to the cadherin tail, with p120-catenin β structural hallmarks similar to mature contacts. We combine (CTNND1) binding to the juxta-membrane domain and -catenin β quantitative mass spectrometry with proximity labeling to identify the (CTNNB1) binding to the distal part of the tail.
    [Show full text]
  • Ahnaks Are a Class of Giant Propeller-Like Proteins That Associate with Calcium Channel Proteins of Cardiomyocytes and Other Cells
    The AHNAKs are a class of giant propeller-like proteins that associate with calcium channel proteins of cardiomyocytes and other cells Akihiko Komuro*, Yutaka Masuda*, Koichi Kobayashi, Roger Babbitt, Murat Gunel, Richard A. Flavell, and Vincent T. Marchesi† Departments of Pathology and Immunobiology, Boyer Center for Molecular Medicine, Yale University School of Medicine, New Haven, CT 06510 Contributed by Vincent T. Marchesi, December 31, 2003 To explore the function of the giant AHNAK molecule, first de- mechanisms, one operating at the cell surface in collaboration with scribed in 1992 [Shtivelman, E., Cohen, F. E. & Bishop, J. M. (1992) calcium channels, and the second, PLC activation, which is a process Proc. Natl. Acad. Sci. USA 89, 5472–5476], we created AHNAK null that could potentially take place at multiple points throughout the mice by homologous recombination. Homozygous knockouts cell. showed no obvious phenotype, but revealed instead a second The arrangement of channel proteins at the cell surface is AHNAK-like molecule, provisionally designated AHNAK2. Like the believed to be controlled by multidomain polypeptides known as original AHNAK, AHNAK2 is a 600-kDa protein composed of a large scaffolding proteins that link together activated channels at specific number of highly conserved repeat segments. Structural predic- points on the membrane surface. Scaffolding proteins also coordi- tions suggest that the repeat segments of both AHNAKs may have nate the activities of multienzyme complexes by physically linking as their basic framework a series of linked, antiparallel ␤-strands them together, and as in the case with AHNAK, they are often similar to those found in ␤-propeller proteins.
    [Show full text]
  • 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.
    [Show full text]
  • Redefining the Specificity of Phosphoinositide-Binding by Human
    bioRxiv preprint doi: https://doi.org/10.1101/2020.06.20.163253; this version posted June 21, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Redefining the specificity of phosphoinositide-binding by human PH domain-containing proteins Nilmani Singh1†, Adriana Reyes-Ordoñez1†, Michael A. Compagnone1, Jesus F. Moreno Castillo1, Benjamin J. Leslie2, Taekjip Ha2,3,4,5, Jie Chen1* 1Department of Cell & Developmental Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801; 2Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD 21205; 3Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218; 4Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205; 5Howard Hughes Medical Institute, Baltimore, MD 21205, USA †These authors contributed equally to this work. *Correspondence: [email protected]. bioRxiv preprint doi: https://doi.org/10.1101/2020.06.20.163253; this version posted June 21, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. ABSTRACT Pleckstrin homology (PH) domains are presumed to bind phosphoinositides (PIPs), but specific interaction with and regulation by PIPs for most PH domain-containing proteins are unclear. Here we employed a single-molecule pulldown assay to study interactions of lipid vesicles with full-length proteins in mammalian whole cell lysates.
    [Show full text]
  • 1 Supporting Information for a Microrna Network Regulates
    Supporting Information for A microRNA Network Regulates Expression and Biosynthesis of CFTR and CFTR-ΔF508 Shyam Ramachandrana,b, Philip H. Karpc, Peng Jiangc, Lynda S. Ostedgaardc, Amy E. Walza, John T. Fishere, Shaf Keshavjeeh, Kim A. Lennoxi, Ashley M. Jacobii, Scott D. Rosei, Mark A. Behlkei, Michael J. Welshb,c,d,g, Yi Xingb,c,f, Paul B. McCray Jr.a,b,c Author Affiliations: Department of Pediatricsa, Interdisciplinary Program in Geneticsb, Departments of Internal Medicinec, Molecular Physiology and Biophysicsd, Anatomy and Cell Biologye, Biomedical Engineeringf, Howard Hughes Medical Instituteg, Carver College of Medicine, University of Iowa, Iowa City, IA-52242 Division of Thoracic Surgeryh, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada-M5G 2C4 Integrated DNA Technologiesi, Coralville, IA-52241 To whom correspondence should be addressed: Email: [email protected] (M.J.W.); yi- [email protected] (Y.X.); Email: [email protected] (P.B.M.) This PDF file includes: Materials and Methods References Fig. S1. miR-138 regulates SIN3A in a dose-dependent and site-specific manner. Fig. S2. miR-138 regulates endogenous SIN3A protein expression. Fig. S3. miR-138 regulates endogenous CFTR protein expression in Calu-3 cells. Fig. S4. miR-138 regulates endogenous CFTR protein expression in primary human airway epithelia. Fig. S5. miR-138 regulates CFTR expression in HeLa cells. Fig. S6. miR-138 regulates CFTR expression in HEK293T cells. Fig. S7. HeLa cells exhibit CFTR channel activity. Fig. S8. miR-138 improves CFTR processing. Fig. S9. miR-138 improves CFTR-ΔF508 processing. Fig. S10. SIN3A inhibition yields partial rescue of Cl- transport in CF epithelia.
    [Show full text]
  • Supplementary Table 1: Adhesion Genes Data Set
    Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like,
    [Show full text]