Dr. George Stoica. Texas A&M Universi
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Defining Functional Interactions During Biogenesis of Epithelial Junctions
ARTICLE Received 11 Dec 2015 | Accepted 13 Oct 2016 | Published 6 Dec 2016 | Updated 5 Jan 2017 DOI: 10.1038/ncomms13542 OPEN Defining functional interactions during biogenesis of epithelial junctions J.C. Erasmus1,*, S. Bruche1,*,w, L. Pizarro1,2,*, N. Maimari1,3,*, T. Poggioli1,w, C. Tomlinson4,J.Lees5, I. Zalivina1,w, A. Wheeler1,w, A. Alberts6, A. Russo2 & V.M.M. Braga1 In spite of extensive recent progress, a comprehensive understanding of how actin cytoskeleton remodelling supports stable junctions remains to be established. Here we design a platform that integrates actin functions with optimized phenotypic clustering and identify new cytoskeletal proteins, their functional hierarchy and pathways that modulate E-cadherin adhesion. Depletion of EEF1A, an actin bundling protein, increases E-cadherin levels at junctions without a corresponding reinforcement of cell–cell contacts. This unexpected result reflects a more dynamic and mobile junctional actin in EEF1A-depleted cells. A partner for EEF1A in cadherin contact maintenance is the formin DIAPH2, which interacts with EEF1A. In contrast, depletion of either the endocytic regulator TRIP10 or the Rho GTPase activator VAV2 reduces E-cadherin levels at junctions. TRIP10 binds to and requires VAV2 function for its junctional localization. Overall, we present new conceptual insights on junction stabilization, which integrate known and novel pathways with impact for epithelial morphogenesis, homeostasis and diseases. 1 National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. 2 Computing Department, Imperial College London, London SW7 2AZ, UK. 3 Bioengineering Department, Faculty of Engineering, Imperial College London, London SW7 2AZ, UK. 4 Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK. -
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. -
Supplemental Tables4.Pdf
Yano_Supplemental_Table_S4 Gene ontology – Biological process 1 of 9 Fold List Pop Pop GO Term Count % PValue Bonferroni Benjamini FDR Genes Total Hits Total Enrichment DLC1, CADM1, NELL2, CLSTN1, PCDHGA8, CTNNB1, NRCAM, APP, CNTNAP2, FERT2, RAPGEF1, PTPRM, MPDZ, SDK1, PCDH9, PTPRS, VEZT, NRXN1, MYH9, GO:0007155~cell CTNNA2, NCAM1, NCAM2, DDR1, LSAMP, CNTN1, 50 5.61 2.14E-08 510 311 7436 2.34 4.50E-05 4.50E-05 3.70E-05 adhesion ROR2, VCAN, DST, LIMS1, TNC, ASTN1, CTNND2, CTNND1, CDH2, NEO1, CDH4, CD24A, FAT3, PVRL3, TRO, TTYH1, MLLT4, LPP, NLGN1, PCDH19, LAMA1, ITGA9, CDH13, CDON, PSPC1 DLC1, CADM1, NELL2, CLSTN1, PCDHGA8, CTNNB1, NRCAM, APP, CNTNAP2, FERT2, RAPGEF1, PTPRM, MPDZ, SDK1, PCDH9, PTPRS, VEZT, NRXN1, MYH9, GO:0022610~biological CTNNA2, NCAM1, NCAM2, DDR1, LSAMP, CNTN1, 50 5.61 2.14E-08 510 311 7436 2.34 4.50E-05 4.50E-05 3.70E-05 adhesion ROR2, VCAN, DST, LIMS1, TNC, ASTN1, CTNND2, CTNND1, CDH2, NEO1, CDH4, CD24A, FAT3, PVRL3, TRO, TTYH1, MLLT4, LPP, NLGN1, PCDH19, LAMA1, ITGA9, CDH13, CDON, PSPC1 DCC, ENAH, PLXNA2, CAPZA2, ATP5B, ASTN1, PAX6, ZEB2, CDH2, CDH4, GLI3, CD24A, EPHB1, NRCAM, GO:0006928~cell CTTNBP2, EDNRB, APP, PTK2, ETV1, CLASP2, STRBP, 36 4.04 3.46E-07 510 205 7436 2.56 7.28E-04 3.64E-04 5.98E-04 motion NRG1, DCLK1, PLAT, SGPL1, TGFBR1, EVL, MYH9, YWHAE, NCKAP1, CTNNA2, SEMA6A, EPHA4, NDEL1, FYN, LRP6 PLXNA2, ADCY5, PAX6, GLI3, CTNNB1, LPHN2, EDNRB, LPHN3, APP, CSNK2A1, GPR45, NRG1, RAPGEF1, WWOX, SGPL1, TLE4, SPEN, NCAM1, DDR1, GRB10, GRM3, GNAQ, HIPK1, GNB1, HIPK2, PYGO1, GO:0007166~cell RNF138, ROR2, CNTN1, -
Genetic and Genomic Analysis of Hyperlipidemia, Obesity and Diabetes Using (C57BL/6J × TALLYHO/Jngj) F2 Mice
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Nutrition Publications and Other Works Nutrition 12-19-2010 Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P. Stewart Marshall University Hyoung Y. Kim University of Tennessee - Knoxville, [email protected] Arnold M. Saxton University of Tennessee - Knoxville, [email protected] Jung H. Kim Marshall University Follow this and additional works at: https://trace.tennessee.edu/utk_nutrpubs Part of the Animal Sciences Commons, and the Nutrition Commons Recommended Citation BMC Genomics 2010, 11:713 doi:10.1186/1471-2164-11-713 This Article is brought to you for free and open access by the Nutrition at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Nutrition Publications and Other Works by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Stewart et al. BMC Genomics 2010, 11:713 http://www.biomedcentral.com/1471-2164/11/713 RESEARCH ARTICLE Open Access Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P Stewart1, Hyoung Yon Kim2, Arnold M Saxton3, Jung Han Kim1* Abstract Background: Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/ JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia. -
Serum Albumin OS=Homo Sapiens
Protein Name Cluster of Glial fibrillary acidic protein OS=Homo sapiens GN=GFAP PE=1 SV=1 (P14136) Serum albumin OS=Homo sapiens GN=ALB PE=1 SV=2 Cluster of Isoform 3 of Plectin OS=Homo sapiens GN=PLEC (Q15149-3) Cluster of Hemoglobin subunit beta OS=Homo sapiens GN=HBB PE=1 SV=2 (P68871) Vimentin OS=Homo sapiens GN=VIM PE=1 SV=4 Cluster of Tubulin beta-3 chain OS=Homo sapiens GN=TUBB3 PE=1 SV=2 (Q13509) Cluster of Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 (P60709) Cluster of Tubulin alpha-1B chain OS=Homo sapiens GN=TUBA1B PE=1 SV=1 (P68363) Cluster of Isoform 2 of Spectrin alpha chain, non-erythrocytic 1 OS=Homo sapiens GN=SPTAN1 (Q13813-2) Hemoglobin subunit alpha OS=Homo sapiens GN=HBA1 PE=1 SV=2 Cluster of Spectrin beta chain, non-erythrocytic 1 OS=Homo sapiens GN=SPTBN1 PE=1 SV=2 (Q01082) Cluster of Pyruvate kinase isozymes M1/M2 OS=Homo sapiens GN=PKM PE=1 SV=4 (P14618) Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 Clathrin heavy chain 1 OS=Homo sapiens GN=CLTC PE=1 SV=5 Filamin-A OS=Homo sapiens GN=FLNA PE=1 SV=4 Cytoplasmic dynein 1 heavy chain 1 OS=Homo sapiens GN=DYNC1H1 PE=1 SV=5 Cluster of ATPase, Na+/K+ transporting, alpha 2 (+) polypeptide OS=Homo sapiens GN=ATP1A2 PE=3 SV=1 (B1AKY9) Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 Dihydropyrimidinase-related protein 2 OS=Homo sapiens GN=DPYSL2 PE=1 SV=1 Cluster of Alpha-actinin-1 OS=Homo sapiens GN=ACTN1 PE=1 SV=2 (P12814) 60 kDa heat shock protein, mitochondrial OS=Homo -
List of Down-Regulated Transcripts
Supplementary Table 1: List of down-regulated transcripts (Fold Change>1.7 and p<0.05). List of the transcipts downregulated by Tpr-Met in P27 cachectic gastrocnemius muscles. Shown are the transcripts gene annotation: Mus Musculus Entrez Gene ID, official symbol from NCBI, average (AVG) Log2 Ratio derived from Illumina microarrays (3 replicates/condition), fold change and p-value computations, Log2 ratio over AVG Log2 controls and full definition of the genes. All genes modulated with a fold change > 1.7 and p-value<0.05 are included. AVG Entrez Gene Fold Symbol Log2 T-test Ctrl Ctrl Ctrl TM TM TM Definition ID Change Ratio - - - - 26908 Eif2s3y 3.598 12.1 0.000 0.146 0.248 0.102 3.712 3.571 -3.510 eukaryotic translation initiation factor 2, subunit 3, structural gene Y-linked (Eif2s3y), mRNA. - - - 13628 Eef1a2 1.333 2.5 0.011 -0.444 0.261 0.183 1.503 0.950 -1.546 eukaryotic translation elongation factor 1 alpha 2 (Eef1a2), mRNA. - - - 13628 Eef1a2 1.298 2.5 0.031 -0.383 0.275 0.108 1.632 0.605 -1.656 eukaryotic translation elongation factor 1 alpha 2 (Eef1a2), mRNA. - - - 56222 Cited4 1.268 2.4 0.029 -0.466 0.035 0.431 1.109 0.889 -1.805 Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy-terminal domain, 4 (Cited4), mRNA. - - - 18810 Plec1 1.246 2.4 0.022 -0.440 0.327 0.113 1.303 0.779 -1.657 plectin 1 (Plec1), transcript variant 10, mRNA. - - - 20741 Spnb1 1.238 2.4 0.016 -0.384 0.314 0.071 1.129 0.902 -1.684 spectrin beta 1 (Spnb1), mRNA. -
MYO5A Rabbit Pab
Leader in Biomolecular Solutions for Life Science MYO5A Rabbit pAb Catalog No.: A9830 Basic Information Background Catalog No. This gene is one of three myosin V heavy-chain genes, belonging to the myosin gene A9830 superfamily. Myosin V is a class of actin-based motor proteins involved in cytoplasmic vesicle transport and anchorage, spindle-pole alignment and mRNA translocation. The Observed MW protein encoded by this gene is abundant in melanocytes and nerve cells. Mutations in 240kDa this gene cause Griscelli syndrome type-1 (GS1), Griscelli syndrome type-3 (GS3) and neuroectodermal melanolysosomal disease, or Elejalde disease. Multiple alternatively Calculated MW spliced transcript variants encoding different isoforms have been reported, but the full- 212kDa/215kDa/218kDa length nature of some variants has not been determined. Category Primary antibody Applications WB,IHC,IF Cross-Reactivity Human, Mouse, Rat Recommended Dilutions Immunogen Information WB 1:500 - 1:2000 Gene ID Swiss Prot 4644 Q9Y4I1 IHC 1:50 - 1:100 Immunogen 1:50 - 1:100 IF Recombinant fusion protein containing a sequence corresponding to amino acids 950-1150 of human MYO5A (NP_000250.3). Synonyms MYO5A;GS1;MYH12;MYO5;MYR12;myosin VA Contact Product Information www.abclonal.com Source Isotype Purification Rabbit IgG Affinity purification Storage Store at -20℃. Avoid freeze / thaw cycles. Buffer: PBS with 0.02% sodium azide,50% glycerol,pH7.3. Validation Data Western blot analysis of extracts of various cell lines, using MYO5A antibody (A9830) at 1:1000 dilution. Secondary antibody: HRP Goat Anti-Rabbit IgG (H+L) (AS014) at 1:10000 dilution. Lysates/proteins: 25ug per lane. Blocking buffer: 3% nonfat dry milk in TBST. -
Supplementary Data Vigneswaran Et Al Supplementary Data
Vigneswaran et al Supplementary Data Vigneswaran et al Supplementary Data Figure S1: Yki is required for EGFR-PI3K-driven glial neoplasia in Drosophila (A) Optical projections of whole brain-nerve cord complexes from 3rd instar larvae approximately 130 hrs old. Dorsal view; anterior up. CD8-GFP (green) labels glial cell bodies. Compared to repo>dEGFRλ;dp110CAAX, warts knockdown (repo>wartsdsRNA; dEGFRλ;dp110CAAX) increased neoplastic brain overgrowth and yki knockdown (repo>ykidsRNA;dEGFRλ;dp110CAAX) decreased neoplastic brain overgrowth. (B) 3 µm optical projections of brain hemispheres, age-matched 3rd instar larvae. Frontal sections; anterior up; midline to left. Repo (red) labels glial cell nuclei; CD8-GFP (green) labels glial cell bodies; anti-HRP (blue) counter-stains for neurons and neuropil. (middle) repo>dEGFRλ;dp110CAAX showed increased glial cell numbers (red nuclei) compared to (upper left) wild-type. Compared to repo>dEGFRλ;dp110CAAX, (right) warts knockdown increased neoplastic glial cell numbers (red nuclei), whereas (lower left) yki knockdown reduced neoplastic glial cell numbers (red nuclei). (C, D) Low levels of Yki protein (red) was observed in wild-type central brain glia (white arrows, left panel in C) compared to high levels of cytoplasmic and nuclear Yki protein in dEGFRλ;dp110CAAX neoplastic glia (white arrows, left panel in D); Repo (blue) labels glial cell nuclei; CD8-GFP (green) labels glial cell bodies. Vigneswaran et al Supplementary Data Figure S2: YAP/TAZ expression confined to RTK-amplified tuMor cells and Maintained in patient-derived xenografts (A) On the left, immunohistochemical (IHC) staining in representative normal brain parenchyma in the cortex where YAP expression and TAZ expression was limited to vascular cells and was not detectable in normal neuronal and glial cells. -
SUPPLEMENTARY APPENDIX Inflammation Regulates Long Non-Coding RNA-PTTG1-1:1 in Myeloid Leukemia
SUPPLEMENTARY APPENDIX Inflammation regulates long non-coding RNA-PTTG1-1:1 in myeloid leukemia Sébastien Chateauvieux, 1,2 Anthoula Gaigneaux, 1° Déborah Gérard, 1 Marion Orsini, 1 Franck Morceau, 1 Barbora Orlikova-Boyer, 1,2 Thomas Farge, 3,4 Christian Récher, 3,4,5 Jean-Emmanuel Sarry, 3,4 Mario Dicato 1 and Marc Diederich 2 °Current address: University of Luxembourg, Faculty of Science, Technology and Communication, Life Science Research Unit, Belvaux, Luxemburg. 1Laboratoire de Biologie Moléculaire et Cellulaire du Cancer, Hôpital Kirchberg, Luxembourg, Luxembourg; 2College of Pharmacy, Seoul National University, Gwanak-gu, Seoul, Korea; 3Cancer Research Center of Toulouse, UMR 1037 INSERM/ Université Toulouse III-Paul Sabatier, Toulouse, France; 4Université Toulouse III Paul Sabatier, Toulouse, France and 5Service d’Hématologie, Centre Hospitalier Universitaire de Toulouse, Institut Universitaire du Cancer de Toulouse Oncopôle, Toulouse, France Correspondence: MARC DIEDERICH - [email protected] doi:10.3324/haematol.2019.217281 Supplementary data Inflammation regulates long non-coding RNA-PTTG1-1:1 in myeloid leukemia Sébastien Chateauvieux1,2, Anthoula Gaigneaux1*, Déborah Gérard1, Marion Orsini1, Franck Morceau1, Barbora Orlikova-Boyer1,2, Thomas Farge3,4, Christian Récher3,4,5, Jean-Emmanuel Sarry3,4, Mario Dicato1 and Marc Diederich2 1 Laboratoire de Biologie Moléculaire et Cellulaire du Cancer, Hôpital Kirchberg, 9, rue Edward Steichen, 2540 Luxembourg, Luxemburg; 2 College of Pharmacy, Seoul National University, 1 Gwanak-ro, -
Bone Morphogenetic Protein Signaling Suppresses Wound-Induced Skin Repair by Inhibiting Keratinocyte Proliferation and Migration Christopher J
ORIGINAL ARTICLE Bone Morphogenetic Protein Signaling Suppresses Wound-Induced Skin Repair by Inhibiting Keratinocyte Proliferation and Migration Christopher J. Lewis1,2,AndreiN.Mardaryev1, Krzysztof Poterlowicz1, Tatyana Y. Sharova3, Ahmar Aziz3, David T. Sharpe2, Natalia V. Botchkareva1 and Andrey A. Sharov3 Bone morphogenetic protein (BMP) signaling plays a key role in the control of skin development and postnatal remodeling by regulating keratinocyte proliferation, differentiation, and apoptosis. To study the role of BMPs in wound-induced epidermal repair, we used transgenic mice overexpressing the BMP downstream component Smad1 under the control of a K14 promoter as an in vivo model, as well as ex vivo and in vitro assays. K14- caSmad1 mice (transgenic mice overexpressing a constitutively active form of Smad1 under K14 promoter) exhibited retarded wound healing associated with significant inhibition of proliferation and increased apoptosis in healing wound epithelium. Furthermore, microarray and quantitative real-time reverse-transcriptase–PCR (qRT– PCR) analyses revealed decreased expression of a number of cytoskeletal/cell motility–associated genes including wound-associated keratins (Krt16, Krt17)andMyosin VA (Myo5a), in the epidermis of K14-caSmad1 mice versus wild-type (WT) controls during wound healing. BMP treatment significantly inhibited keratinocyte migration ex vivo, and primary keratinocytes of K14-caSmad1 mice showed retarded migration compared with WT controls. Finally, small interfering RNA (siRNA)–mediated silencing of BMPR-1B in primary mouse keratinocytes accelerated cell migration and was associated with increased expression of Krt16, Krt17, and Myo5a compared with controls. Thus, this study demonstrates that BMPs inhibit keratinocyte proliferation, cytoskeletal organization, and migration in regenerating skin epithelium during wound healing, and raises a possibility for using BMP antagonists for the management of chronic wounds. -
Myth4-FERM Myosin Based Filopodia Initiation
MyTH4-FERM Myosin based filopodia initiation A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Ashley L. Arthur IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Margaret A. Titus, PhD ADVISOR July 2020 © Ashley L Arthur 2020 ACKNOWLEDGEMENTS I would first and foremost like to advisor my mentor, Dr. Margaret Titus, for her unassailable commitment to training, enthusiasm for science and her sup- port of my career. Meg has been superb advisor, has made me a better scientist and communicator and I genuinely enjoyed working for her. I am grateful for the long list of positive experiences and opportunities I gained while working in the Titus lab. I would like to thank all of my past and present lab mates. Thank you to Hilary Bauer, Sinzi Cornea and Zoe Henrot for welcoming me into the lab when I started and especially to Karl Petersen who share his imaging and analysis ex- pertise. I am so grateful for the help and from my PLA project teammate Livia Songster, you brought such great energy to the project and to the lab. Thanks Casey Eddington, Annika Schroeder for their support, encouragement and help reading and discussing many aspect of this work. Thanks to the University of MN undergraduate students who joined my on research projects over the years espe- cially to Himanshu Jain. I would like to thank Jordan Beach at Loyola, Guillermo Marques, Mark Sanders, for their help with imaging. I thank Ashim Rai for his as- sistance with motor purification and motility assays. -
Strong Correlation Between Air-Liquid Interface Cultures and in Vivo Transcriptomics of Nasal Brush Biopsy
Am J Physiol Lung Cell Mol Physiol 318: L1056–L1062, 2020. First published April 1, 2020; doi:10.1152/ajplung.00050.2020. RAPID REPORT Translational Physiology Strong correlation between air-liquid interface cultures and in vivo transcriptomics of nasal brush biopsy X Baishakhi Ghosh,1 Bongsoo Park,1 Debarshi Bhowmik,2 Kristine Nishida,3 Molly Lauver,3 Nirupama Putcha,3 Peisong Gao,4 Murugappan Ramanathan, Jr.,5 Nadia Hansel,3 Shyam Biswal,1 and Venkataramana K. Sidhaye1,3 1Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; 2Department of Biology, Johns Hopkins University, Baltimore, Maryland; 3Department of Pulmonary and Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland; 4Division of Allergy and Clinical Immunology, Johns Hopkins School of Medicine, Baltimore, Maryland; and 5Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins School of Medicine, Baltimore, Maryland Submitted 18 February 2020; accepted in final form 24 March 2020 Ghosh B, Park B, Bhowmik D, Nishida K, Lauver M, Putcha N, RNA sequencing to determine the correlation between ALI- Gao P, Ramanathan M Jr, Hansel N, Biswal S, Sidhaye VK. cultured epithelial cells and epithelial cells obtained from nasal Strong correlation between air-liquid interface cultures and in vivo brushing and identify differences that may arise as a result of transcriptomics of nasal brush biopsy. Am J Physiol Lung Cell Mol redifferentiation. Physiol 318: L1056–L1062, 2020. First published April 1, 2020; doi:10.1152/ajplung.00050.2020.—Air-liquid interface (ALI) cultures METHODS are ex vivo models that are used extensively to study the epithelium of patients with chronic respiratory diseases.