What Biologists Want from Their Chloride Reporters
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Supplemental Material Table of Contents
Supplemental material Table of Contents Detailed Materials and Methods ......................................................................................................... 2 Perioperative period ........................................................................................................................... 2 Ethical aspects ................................................................................................................................... 4 Evaluation of heart failure ................................................................................................................. 4 Sample preparation for ANP mRNA expression .................................................................................. 5 Sample preparation for validative qRT-PCR (Postn, Myh7, Gpx3, Tgm2) ............................................ 6 Tissue fibrosis .................................................................................................................................... 7 Ventricular remodeling and histological tissue preservation ................................................................ 8 Evaluation of the histological preservation of cardiac tissue ................................................................ 9 Sample preparation and quantitative label-free proteomics analyses .................................................. 10 Statistical methods ........................................................................................................................... 12 References ........................................................................................................................................ -
Tomo-Seq Identifies SOX9 As a Key Regulator of Cardiac Fibrosis During Ischemic Injury
myocardial myocardial Eva van ◼ osis fibr SOX9 transcription ◼ PhD* PhD PhD PhD MSc, PhD naarden, MSc, PhD naarden, PhD* ventricular remodeling Correspondence to: Correspondence Rooij, MSc, PhD, Hubrecht Department of Institute, KNAW University Medical Cardiology, Uppsalalaan 8, Center Utrecht, The Netherlands. 3584CT Utrecht, E-mail [email protected] of Funding, see page 1408 Sources Key Words: ischemia ◼ © 2017 American Heart Association, Inc. *Drs. Lacraz and Junker contributed equally. Grégory P.A. Lacraz, MSc, P.A. Grégory MSc, Jan Philipp Junker, Monika M. Gladka, MSc, MSc Bas Molenaar, Scholman, MSc Koen T. MSc Marta Vigil-Garcia, BS Danielle Versteeg, BS Hesther de Ruiter, MSc, Vermunt, Marit W. MSc, Creyghton, Menno P. Manon M.H. Huibers, Nicolaas de Jonge, MD Alexander van Oude- Eva van Rooij, MSc, PhD 2017;136:1396–1409. DOI: 10.1161/CIRCULATIONAHA.117.027832 DOI: 2017;136:1396–1409. Circulation. blunted the cardiac fibrotic fibrotic blunted the cardiac Sox9 ). Subsequent correlation analysis allowed). Subsequent correlation Serca2 Editorial, see p 1410 , and Nppa Based on the exact local expression cues, tomo-seq can Based on the exact local expression Cardiac ischemic injury induces a pathological remodeling ischemic injury induces a pathological remodeling Cardiac , Although genome-wide transcriptome analysis on diseased tissues Tracing transcriptional differences with a high spatial resolution with a high spatial resolution transcriptional differences Tracing Col1a2 October 10, 2017 October 10, 1396 CONCLUSIONS: novel genes and key transcription factors involved in specific serve to reveal able to unveil the Using tomo-seq, we were remodeling. aspects of cardiac pointing fibrosis, of cardiac of SOX9 as a key regulator unknown relevance fibrosis. -
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. -
Transcriptomic Analysis of Native Versus Cultured Human and Mouse Dorsal Root Ganglia Focused on Pharmacological Targets Short
bioRxiv preprint doi: https://doi.org/10.1101/766865; this version posted September 12, 2019. 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-ND 4.0 International license. Transcriptomic analysis of native versus cultured human and mouse dorsal root ganglia focused on pharmacological targets Short title: Comparative transcriptomics of acutely dissected versus cultured DRGs Andi Wangzhou1, Lisa A. McIlvried2, Candler Paige1, Paulino Barragan-Iglesias1, Carolyn A. Guzman1, Gregory Dussor1, Pradipta R. Ray1,#, Robert W. Gereau IV2, # and Theodore J. Price1, # 1The University of Texas at Dallas, School of Behavioral and Brain Sciences and Center for Advanced Pain Studies, 800 W Campbell Rd. Richardson, TX, 75080, USA 2Washington University Pain Center and Department of Anesthesiology, Washington University School of Medicine # corresponding authors [email protected], [email protected] and [email protected] Funding: NIH grants T32DA007261 (LM); NS065926 and NS102161 (TJP); NS106953 and NS042595 (RWG). The authors declare no conflicts of interest Author Contributions Conceived of the Project: PRR, RWG IV and TJP Performed Experiments: AW, LAM, CP, PB-I Supervised Experiments: GD, RWG IV, TJP Analyzed Data: AW, LAM, CP, CAG, PRR Supervised Bioinformatics Analysis: PRR Drew Figures: AW, PRR Wrote and Edited Manuscript: AW, LAM, CP, GD, PRR, RWG IV, TJP All authors approved the final version of the manuscript. 1 bioRxiv preprint doi: https://doi.org/10.1101/766865; this version posted September 12, 2019. 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. -
Expression Profiling of Ion Channel Genes Predicts Clinical Outcome in Breast Cancer
UCSF UC San Francisco Previously Published Works Title Expression profiling of ion channel genes predicts clinical outcome in breast cancer Permalink https://escholarship.org/uc/item/1zq9j4nw Journal Molecular Cancer, 12(1) ISSN 1476-4598 Authors Ko, Jae-Hong Ko, Eun A Gu, Wanjun et al. Publication Date 2013-09-22 DOI http://dx.doi.org/10.1186/1476-4598-12-106 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Ko et al. Molecular Cancer 2013, 12:106 http://www.molecular-cancer.com/content/12/1/106 RESEARCH Open Access Expression profiling of ion channel genes predicts clinical outcome in breast cancer Jae-Hong Ko1, Eun A Ko2, Wanjun Gu3, Inja Lim1, Hyoweon Bang1* and Tong Zhou4,5* Abstract Background: Ion channels play a critical role in a wide variety of biological processes, including the development of human cancer. However, the overall impact of ion channels on tumorigenicity in breast cancer remains controversial. Methods: We conduct microarray meta-analysis on 280 ion channel genes. We identify candidate ion channels that are implicated in breast cancer based on gene expression profiling. We test the relationship between the expression of ion channel genes and p53 mutation status, ER status, and histological tumor grade in the discovery cohort. A molecular signature consisting of ion channel genes (IC30) is identified by Spearman’s rank correlation test conducted between tumor grade and gene expression. A risk scoring system is developed based on IC30. We test the prognostic power of IC30 in the discovery and seven validation cohorts by both Cox proportional hazard regression and log-rank test. -
Macropinocytosis Requires Gal-3 in a Subset of Patient-Derived Glioblastoma Stem Cells
ARTICLE https://doi.org/10.1038/s42003-021-02258-z OPEN Macropinocytosis requires Gal-3 in a subset of patient-derived glioblastoma stem cells Laetitia Seguin1,8, Soline Odouard2,8, Francesca Corlazzoli 2,8, Sarah Al Haddad2, Laurine Moindrot2, Marta Calvo Tardón3, Mayra Yebra4, Alexey Koval5, Eliana Marinari2, Viviane Bes3, Alexandre Guérin 6, Mathilde Allard2, Sten Ilmjärv6, Vladimir L. Katanaev 5, Paul R. Walker3, Karl-Heinz Krause6, Valérie Dutoit2, ✉ Jann N. Sarkaria 7, Pierre-Yves Dietrich2 & Érika Cosset 2 Recently, we involved the carbohydrate-binding protein Galectin-3 (Gal-3) as a druggable target for KRAS-mutant-addicted lung and pancreatic cancers. Here, using glioblastoma patient-derived stem cells (GSCs), we identify and characterize a subset of Gal-3high glio- 1234567890():,; blastoma (GBM) tumors mainly within the mesenchymal subtype that are addicted to Gal-3- mediated macropinocytosis. Using both genetic and pharmacologic inhibition of Gal-3, we showed a significant decrease of GSC macropinocytosis activity, cell survival and invasion, in vitro and in vivo. Mechanistically, we demonstrate that Gal-3 binds to RAB10, a member of the RAS superfamily of small GTPases, and β1 integrin, which are both required for macro- pinocytosis activity and cell survival. Finally, by defining a Gal-3/macropinocytosis molecular signature, we could predict sensitivity to this dependency pathway and provide proof-of- principle for innovative therapeutic strategies to exploit this Achilles’ heel for a significant and unique subset of GBM patients. 1 University Côte d’Azur, CNRS UMR7284, INSERM U1081, Institute for Research on Cancer and Aging (IRCAN), Nice, France. 2 Laboratory of Tumor Immunology, Department of Oncology, Center for Translational Research in Onco-Hematology, Swiss Cancer Center Léman (SCCL), Geneva University Hospitals, University of Geneva, Geneva, Switzerland. -
Transmembrane Protein with Unknown Function 16A Overexpression Promotes Glioma Formation Through the Nuclear Factor‑Κb Signaling Pathway
1068 MOLECULAR MEDICINE REPORTS 9: 1068-1074, 2014 Transmembrane protein with unknown function 16A overexpression promotes glioma formation through the nuclear factor‑κB signaling pathway JUN LIU1, YU LIU2, YINGANG REN1, LI KANG1 and LIHUA ZHANG1 Departments of 1Geriatrics and 2Neurology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, P.R. China Received July 18, 2013; Accepted January 2, 2014 DOI: 10.3892/mmr.2014.1888 Abstract. Ion channels have been suggested to be important in Introduction the development and progression of tumors, however, chloride channels have rarely been analyzed in tumorigenesis. More In previous years, the association between ion channels and recently, transmembrane protein with unknown function 16A tumors has drawn particular attention. Increasing evidence has (TMEM16A), hypothesized to be a candidate calcium-acti- demonstrated that ion channels are involved in the regulation vated Cl- channel, has been found to be overexpressed in a of tumor progression, including potassium (1-3), calcium (4) number of tumor types. Although several studies have impli- and sodium channels (5,6). Therefore, understanding the cated the overexpression of TMEM16A in certain tumor types, underlying molecular mechanisms of ion channels in tumori- the exact role of TMEM16A in gliomas and the underlying genesis, and tumor progression and migration provides novel mechanisms in tumorigenesis, remain poorly understood. In insights into tumor pathogenesis, and also identifies potential the present study, the role of TMEM16A in gliomas and the targets for tumor prevention and treatment. potential underlying mechanisms were analyzed. TMEM16A Chloride channels are expressed ubiquitously and are was highly abundant in various grades of gliomas and important in various cellular processes, including the cell cycle cultured glioma cells. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Supplementary Material
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) J Neurol Neurosurg Psychiatry Page 1 / 45 SUPPLEMENTARY MATERIAL Appendix A1: Neuropsychological protocol. Appendix A2: Description of the four cases at the transitional stage. Table A1: Clinical status and center proportion in each batch. Table A2: Complete output from EdgeR. Table A3: List of the putative target genes. Table A4: Complete output from DIANA-miRPath v.3. Table A5: Comparison of studies investigating miRNAs from brain samples. Figure A1: Stratified nested cross-validation. Figure A2: Expression heatmap of miRNA signature. Figure A3: Bootstrapped ROC AUC scores. Figure A4: ROC AUC scores with 100 different fold splits. Figure A5: Presymptomatic subjects probability scores. Figure A6: Heatmap of the level of enrichment in KEGG pathways. Kmetzsch V, et al. J Neurol Neurosurg Psychiatry 2021; 92:485–493. doi: 10.1136/jnnp-2020-324647 BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) J Neurol Neurosurg Psychiatry Appendix A1. Neuropsychological protocol The PREV-DEMALS cognitive evaluation included standardized neuropsychological tests to investigate all cognitive domains, and in particular frontal lobe functions. The scores were provided previously (Bertrand et al., 2018). Briefly, global cognitive efficiency was evaluated by means of Mini-Mental State Examination (MMSE) and Mattis Dementia Rating Scale (MDRS). Frontal executive functions were assessed with Frontal Assessment Battery (FAB), forward and backward digit spans, Trail Making Test part A and B (TMT-A and TMT-B), Wisconsin Card Sorting Test (WCST), and Symbol-Digit Modalities test. -
Transcriptomic Uniqueness and Commonality of the Ion Channels and Transporters in the Four Heart Chambers Sanda Iacobas1, Bogdan Amuzescu2 & Dumitru A
www.nature.com/scientificreports OPEN Transcriptomic uniqueness and commonality of the ion channels and transporters in the four heart chambers Sanda Iacobas1, Bogdan Amuzescu2 & Dumitru A. Iacobas3,4* Myocardium transcriptomes of left and right atria and ventricles from four adult male C57Bl/6j mice were profled with Agilent microarrays to identify the diferences responsible for the distinct functional roles of the four heart chambers. Female mice were not investigated owing to their transcriptome dependence on the estrous cycle phase. Out of the quantifed 16,886 unigenes, 15.76% on the left side and 16.5% on the right side exhibited diferential expression between the atrium and the ventricle, while 5.8% of genes were diferently expressed between the two atria and only 1.2% between the two ventricles. The study revealed also chamber diferences in gene expression control and coordination. We analyzed ion channels and transporters, and genes within the cardiac muscle contraction, oxidative phosphorylation, glycolysis/gluconeogenesis, calcium and adrenergic signaling pathways. Interestingly, while expression of Ank2 oscillates in phase with all 27 quantifed binding partners in the left ventricle, the percentage of in-phase oscillating partners of Ank2 is 15% and 37% in the left and right atria and 74% in the right ventricle. The analysis indicated high interventricular synchrony of the ion channels expressions and the substantially lower synchrony between the two atria and between the atrium and the ventricle from the same side. Starting with crocodilians, the heart pumps the blood through the pulmonary circulation and the systemic cir- culation by the coordinated rhythmic contractions of its upper lef and right atria (LA, RA) and lower lef and right ventricles (LV, RV). -
Supplementary Data
Supplementary Fig. 1 A B Responder_Xenograft_ Responder_Xenograft_ NON- NON- Lu7336, Vehicle vs Lu7466, Vehicle vs Responder_Xenograft_ Responder_Xenograft_ Sagopilone, Welch- Sagopilone, Welch- Lu7187, Vehicle vs Lu7406, Vehicle vs Test: 638 Test: 600 Sagopilone, Welch- Sagopilone, Welch- Test: 468 Test: 482 Responder_Xenograft_ NON- Lu7860, Vehicle vs Responder_Xenograft_ Sagopilone, Welch - Lu7558, Vehicle vs Test: 605 Sagopilone, Welch- Test: 333 Supplementary Fig. 2 Supplementary Fig. 3 Supplementary Figure S1. Venn diagrams comparing probe sets regulated by Sagopilone treatment (10mg/kg for 24h) between individual models (Welsh Test ellipse p-value<0.001 or 5-fold change). A Sagopilone responder models, B Sagopilone non-responder models. Supplementary Figure S2. Pathway analysis of genes regulated by Sagopilone treatment in responder xenograft models 24h after Sagopilone treatment by GeneGo Metacore; the most significant pathway map representing cell cycle/spindle assembly and chromosome separation is shown, genes upregulated by Sagopilone treatment are marked with red thermometers. Supplementary Figure S3. GeneGo Metacore pathway analysis of genes differentially expressed between Sagopilone Responder and Non-Responder models displaying –log(p-Values) of most significant pathway maps. Supplementary Tables Supplementary Table 1. Response and activity in 22 non-small-cell lung cancer (NSCLC) xenograft models after treatment with Sagopilone and other cytotoxic agents commonly used in the management of NSCLC Tumor Model Response type -
Identification of Key Pathways and Genes in Dementia Via Integrated Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2021.04.18.440371; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Identification of Key Pathways and Genes in Dementia via Integrated Bioinformatics Analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karnataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2021.04.18.440371; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract To provide a better understanding of dementia at the molecular level, this study aimed to identify the genes and key pathways associated with dementia by using integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing dataset GSE153960 derived from the Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) between patients with dementia and healthy controls were identified. With DEGs, we performed a series of functional enrichment analyses. Then, a protein–protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network was constructed, analyzed and visualized, with which the hub genes miRNAs and TFs nodes were screened out. Finally, validation of hub genes was performed by using receiver operating characteristic curve (ROC) analysis.