Doxazosin Induces Activation of GADD153 and Cleavage of Focal
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Mutations in EDA and EDAR Genes in a Large Mexican Hispanic Cohort with Hypohidrotic Ectodermal Dysplasia
Letter to the Editor http://dx.doi.org/10.5021/ad.2015.27.4.474 Mutations in EDA and EDAR Genes in a Large Mexican Hispanic Cohort with Hypohidrotic Ectodermal Dysplasia Julio C Salas-Alanis1,2, Eva Wozniak3, Charles A Mein3, Carola C Duran Mckinster4, Jorge Ocampo-Candiani1, David P Kelsell5, Rong Hua6, Maria L Garza-Rodriguez7, Keith A Choate6, Hugo A Barrera Saldaña7,8 1Dermatology Department, Hospital Universitario, Universidad Autonoma de Nuevo Leon, 2Basic Science Department, Medicine School, Universidad de Monterrey, Monterrey, NL, Mexico, 3Barts and the London Genome Centre, John Vane Science Centre, Barts and the London School of Medicine and Dentistry, University of London, London, United Kingdom, 4Instituto Nacional de Pediatria, Coyoacan, CP, Mexico, 5Centre for Cutaneous Research, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom, 6Departments of Dermatology, Genetics, and Pathology, Yale University School of Medicine, New Haven, CT, USA, 7Facultad de Medicina, Departamento de Bioquímica y Medicina Molecular, Universidad Autonoma de Nuevo Leon,8Vitagenesis, Monterrey, NL, Mexico Dear Editor: cludes dryness of the skin, eyes, airways, and mucous Ectodermal dysplasias (ED) encompass nearly 200 differ- membranes, as well as other ectodermal defects and, in ent genetic conditions identified by the lack, or dysgenesis, some cases, fever, seizures, and rarely, death. of at least two ectodermal derivatives, such as hair, nails, XL-HED is caused by mutations in the EDA gene, located teeth, and sweat glands. Hypohidrotic/anhidrotic ED (HED) on chromosome Xq12-q13.1, which encodes a signaling is the most frequent form of ED and it can be inherited as molecule of the tumor necrosis factor (TNF) superfamily. -
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. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
NICU Gene List Generator.Xlsx
Neonatal Crisis Sequencing Panel Gene List Genes: A2ML1 - B3GLCT A2ML1 ADAMTS9 ALG1 ARHGEF15 AAAS ADAMTSL2 ALG11 ARHGEF9 AARS1 ADAR ALG12 ARID1A AARS2 ADARB1 ALG13 ARID1B ABAT ADCY6 ALG14 ARID2 ABCA12 ADD3 ALG2 ARL13B ABCA3 ADGRG1 ALG3 ARL6 ABCA4 ADGRV1 ALG6 ARMC9 ABCB11 ADK ALG8 ARPC1B ABCB4 ADNP ALG9 ARSA ABCC6 ADPRS ALK ARSL ABCC8 ADSL ALMS1 ARX ABCC9 AEBP1 ALOX12B ASAH1 ABCD1 AFF3 ALOXE3 ASCC1 ABCD3 AFF4 ALPK3 ASH1L ABCD4 AFG3L2 ALPL ASL ABHD5 AGA ALS2 ASNS ACAD8 AGK ALX3 ASPA ACAD9 AGL ALX4 ASPM ACADM AGPS AMELX ASS1 ACADS AGRN AMER1 ASXL1 ACADSB AGT AMH ASXL3 ACADVL AGTPBP1 AMHR2 ATAD1 ACAN AGTR1 AMN ATL1 ACAT1 AGXT AMPD2 ATM ACE AHCY AMT ATP1A1 ACO2 AHDC1 ANK1 ATP1A2 ACOX1 AHI1 ANK2 ATP1A3 ACP5 AIFM1 ANKH ATP2A1 ACSF3 AIMP1 ANKLE2 ATP5F1A ACTA1 AIMP2 ANKRD11 ATP5F1D ACTA2 AIRE ANKRD26 ATP5F1E ACTB AKAP9 ANTXR2 ATP6V0A2 ACTC1 AKR1D1 AP1S2 ATP6V1B1 ACTG1 AKT2 AP2S1 ATP7A ACTG2 AKT3 AP3B1 ATP8A2 ACTL6B ALAS2 AP3B2 ATP8B1 ACTN1 ALB AP4B1 ATPAF2 ACTN2 ALDH18A1 AP4M1 ATR ACTN4 ALDH1A3 AP4S1 ATRX ACVR1 ALDH3A2 APC AUH ACVRL1 ALDH4A1 APTX AVPR2 ACY1 ALDH5A1 AR B3GALNT2 ADA ALDH6A1 ARFGEF2 B3GALT6 ADAMTS13 ALDH7A1 ARG1 B3GAT3 ADAMTS2 ALDOB ARHGAP31 B3GLCT Updated: 03/15/2021; v.3.6 1 Neonatal Crisis Sequencing Panel Gene List Genes: B4GALT1 - COL11A2 B4GALT1 C1QBP CD3G CHKB B4GALT7 C3 CD40LG CHMP1A B4GAT1 CA2 CD59 CHRNA1 B9D1 CA5A CD70 CHRNB1 B9D2 CACNA1A CD96 CHRND BAAT CACNA1C CDAN1 CHRNE BBIP1 CACNA1D CDC42 CHRNG BBS1 CACNA1E CDH1 CHST14 BBS10 CACNA1F CDH2 CHST3 BBS12 CACNA1G CDK10 CHUK BBS2 CACNA2D2 CDK13 CILK1 BBS4 CACNB2 CDK5RAP2 -
Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. 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. Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses 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, Karanataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. 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 The high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a proteinprotein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes. -
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 -
4 Understanding the Role of GNA13 Deregulation in Lymphomagenesis
Integrative Genomics Reveals a Role for GNA13 in Lymphomagenesis by Adrienne Greenough University Program in Genetics and Genomics Duke University Approved: ___________________________ Sandeep Dave, Supervisor ___________________________ Fred Dietrich ___________________________ Jack Keene ___________________________ Yuan Zhuang Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the University Program in Genetics and Genomics in the Graduate School of Duke University 2014 i v ABSTRACT Integrative Genomics Reveals a Role for GNA13 in Lymphomagenesis by Adrienne Greenough University Program in Genetics and Genomics Duke University Approved: ___________________________ Sandeep Dave, Supervisor ___________________________ Fred Dietrich ___________________________ Jack Keene ___________________________ Yuan Zhuang An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the University Program in Genetics and Genomics in the Graduate School of Duke University 2014 Copyright by Adrienne Greenough 2014 Abstract Lymphomas comprise a diverse group of malignancies derived from immune cells. High throughput sequencing has recently emerged as a powerful and versatile method for analysis of the cancer genome and transcriptome. As these data continue to emerge, the crucial work lies in sorting through the wealth of information to hone in on the critical aspects that will give us a better understanding of biology and new insight for how to treat disease. Finding the important signals within these large data sets is one of the major challenges of next generation sequencing. In this dissertation, I have developed several complementary strategies to describe the genetic underpinnings of lymphomas. I begin with developing a better method for RNA sequencing that enables strand-specific total RNA sequencing and alternative splicing profiling in the same analysis. -
Multi-Functionality of Proteins Involved in GPCR and G Protein Signaling: Making Sense of Structure–Function Continuum with In
Cellular and Molecular Life Sciences (2019) 76:4461–4492 https://doi.org/10.1007/s00018-019-03276-1 Cellular andMolecular Life Sciences REVIEW Multi‑functionality of proteins involved in GPCR and G protein signaling: making sense of structure–function continuum with intrinsic disorder‑based proteoforms Alexander V. Fonin1 · April L. Darling2 · Irina M. Kuznetsova1 · Konstantin K. Turoverov1,3 · Vladimir N. Uversky2,4 Received: 5 August 2019 / Revised: 5 August 2019 / Accepted: 12 August 2019 / Published online: 19 August 2019 © Springer Nature Switzerland AG 2019 Abstract GPCR–G protein signaling system recognizes a multitude of extracellular ligands and triggers a variety of intracellular signal- ing cascades in response. In humans, this system includes more than 800 various GPCRs and a large set of heterotrimeric G proteins. Complexity of this system goes far beyond a multitude of pair-wise ligand–GPCR and GPCR–G protein interactions. In fact, one GPCR can recognize more than one extracellular signal and interact with more than one G protein. Furthermore, one ligand can activate more than one GPCR, and multiple GPCRs can couple to the same G protein. This defnes an intricate multifunctionality of this important signaling system. Here, we show that the multifunctionality of GPCR–G protein system represents an illustrative example of the protein structure–function continuum, where structures of the involved proteins represent a complex mosaic of diferently folded regions (foldons, non-foldons, unfoldons, semi-foldons, and inducible foldons). The functionality of resulting highly dynamic conformational ensembles is fne-tuned by various post-translational modifcations and alternative splicing, and such ensembles can undergo dramatic changes at interaction with their specifc partners. -
Gαo (GNAO1) Encephalopathies: Plasma Membrane Vs. Golgi Functions
www.oncotarget.com Oncotarget, 2018, Vol. 9, (No. 35), pp: 23846-23847 Editorial Gαo (GNAO1) encephalopathies: plasma membrane vs. Golgi functions Gonzalo P. Solis and Vladimir L. Katanaev Heterotrimeric G proteins are key signaling of Ca2+ currents by norepinephrine [5]. Intriguingly, molecules best recognized as the immediate transducers mutations in other five residues did not compromise (or of GPCRs (G protein coupled receptors). A heterotrimeric only slightly affected) α2A adrenergic receptor signaling G protein complex consists of three subunits, α, β, [4]. Thus, it is highly plausible that Gαo functions beyond and γ, of which the Gα is responsible for binding to its canonical role as GPCR-transducer also contribute to guanine nucleotides and to the cognate GPCR. Ligand- the development of Gαo encephalopathies. activated GPCRs catalyze the GDP-GTP exchange on The functional localization of heterotrimeric G Gα inducing the dissociation of Gα-GTP from Gβγ, both proteins at the plasma membrane (PM) is widely accepted. being competent to engage downstream effectors. GTP However, an increasing amount of data indicate that they hydrolysis returns the Gα to its GDP-bound state allowing also work at different cellular compartments, especially the formation of the heterotrimeric G protein, which binds at the Golgi apparatus [6]. Recently, we identified >250 the cognate GPCR for a subsequent activation cycle. proteins as potential Gαo interacting partners, and During the last five years, whole-exome sequencing subsequently uncovered a non-canonical Gαo signaling of patients with severe infantile encephalopathies resulted involved in the regulation of vesicular trafficking at the in an avalanche of de novo heterozygous mutations Golgi [7]. -
Methylation of Leukocyte DNA and Ovarian Cancer
Fridley et al. BMC Medical Genomics 2014, 7:21 http://www.biomedcentral.com/1755-8794/7/21 RESEARCH ARTICLE Open Access Methylation of leukocyte DNA and ovarian cancer: relationships with disease status and outcome Brooke L Fridley1*, Sebastian M Armasu2, Mine S Cicek2, Melissa C Larson2, Chen Wang2, Stacey J Winham2, Kimberly R Kalli3, Devin C Koestler1,4, David N Rider2, Viji Shridhar5, Janet E Olson2, Julie M Cunningham5 and Ellen L Goode2 Abstract Background: Genome-wide interrogation of DNA methylation (DNAm) in blood-derived leukocytes has become feasible with the advent of CpG genotyping arrays. In epithelial ovarian cancer (EOC), one report found substantial DNAm differences between cases and controls; however, many of these disease-associated CpGs were attributed to differences in white blood cell type distributions. Methods: We examined blood-based DNAm in 336 EOC cases and 398 controls; we included only high-quality CpG loci that did not show evidence of association with white blood cell type distributions to evaluate association with case status and overall survival. Results: Of 13,816 CpGs, no significant associations were observed with survival, although eight CpGs associated with survival at p < 10−3, including methylation within a CpG island located in the promoter region of GABRE (p = 5.38 x 10−5, HR = 0.95). In contrast, 53 CpG methylation sites were significantly associated with EOC risk (p <5 x10−6). The top association was observed for the methylation probe cg04834572 located approximately 315 kb upstream of DUSP13 (p = 1.6 x10−14). Other disease-associated CpGs included those near or within HHIP (cg14580567; p =5.6x10−11), HDAC3 (cg10414058; p = 6.3x10−12), and SCR (cg05498681; p = 4.8x10−7). -
Generation of the Primary Hair Follicle Pattern
Generation of the primary hair follicle pattern Chunyan Mou*, Ben Jackson*, Pascal Schneider†, Paul A. Overbeek‡, and Denis J. Headon*§ *Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, United Kingdom; †Department of Biochemistry, BIL Biomedical Research Center, University of Lausanne, CH-1066 Epalinges, Switzerland; and ‡Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030 Edited by Kathryn V. Anderson, Sloan–Kettering Institute, New York, NY, and approved May 8, 2006 (received for review January 31, 2006) Hair follicles are spaced apart from one another at regular intervals between signaling pathways involved in this process are largely through the skin. Although follicles are predominantly epidermal unknown. This is a particularly important problem because it is structures, classical tissue recombination experiments indicated the interactions between molecules, rather than the intrinsic that the underlying dermis defines their location during develop- function of any individual gene product, that is responsible for ment. Although many molecules involved in hair follicle formation orchestrating pattern formation. One such signaling pathway, have been identified, the molecular interactions that determine the composed of the extracellular ligand ectodysplasin (Eda), its emergent property of pattern formation have remained elusive. receptor Edar, and its cytoplasmic signaling adapter Edar- We have used embryonic skin cultures to dissect signaling re- associated death domain (Edaradd), is required for development sponses and patterning outcomes as the skin spatially organizes of a specific subset of hair follicles. Mutation of any of these three itself. We find that ectodysplasin receptor (Edar)–bone morpho- genes, all of which are specifically expressed in the epidermis, genetic protein (BMP) signaling and transcriptional interactions are causes identical ectodermal dysplasia phenotypes in mouse and central to generation of the primary hair follicle pattern, with human (9–12). -
Diallyl Disulfide Inhibits the Proliferation of HT-29 Human Colon Cancer Cells by Inducing Differentially Expressed Genes
MOLECULAR MEDICINE REPORTS 4: 553-559, 2011 Diallyl disulfide inhibits the proliferation of HT-29 human colon cancer cells by inducing differentially expressed genes YOU-SHENG HUANG1,2, NA XIE1,2, QI SU1, JIAN SU1, CHEN HUANG1 and QIAN-JIN LIAO1 1Cancer Research Institute, University of South China, Hengyang, Hunan 421001; 2Department of Pathology, Hainan Medical University, Haikou, Hainan 571101, P.R. China Received November 22, 2010; Accepted February 28, 2011 DOI: 10.3892/mmr.2011.453 Abstract. Diallyl disulfide (DADS), a sulfur compound Introduction derived from garlic, has been shown to have protective effects against colon carcinogenesis in several studies performed in Colon cancer is one of the major causes of cancer death rodent models. However, its molecular mechanism of action worldwide (1). An understanding of the mechanisms involved remains unclear. This study was designed to confirm the anti- in the occurrence and development of colon cancer would aid proliferative activity of DADS and to screen for differentially in its therapy. Epidemiological investigations have provided expressed genes induced by DADS in human colon cancer cells compelling evidence that environmental factors are modifiers with the aim of exploring its possible anticancer mechanisms. in colon cancer (1-3); diet has also been shown to be an impor- The anti-proliferative capability of DADS in the HT-29 human tant determinant of cancer risk and tumor behavior (3-5). colon cancer cells was analyzed by MTT assays and flow Garlic consumption is very popular all over the world. cytometry. The differences in gene expression between DADS- Epidemiological studies have shown an inverse correlation treated (experimental group) and untreated (control group) between the consumption of garlic and colon cancer in certain HT-29 cells were identified using two-directional suppression areas (6).