Familial Cortical Myoclonus Caused by Mutation in NOL3 by Jonathan Foster Rnsseil DISSERTATION Submitted in Partial Satisfaction
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Dissertation Philip Böhler
Three Tales of Death: New Pathways in the Induction, Inhibition and Execution of Apoptosis Inaugural-Dissertation zur Erlangung des Doktorgrades der Mathematisch-Naturwissenschaftlichen Fakultät der Heinrich-Heine-Universität Düsseldorf vorgelegt von Philip Böhler aus Bonn Düsseldorf, Juni 2019 aus dem Institut für Molekulare Medizin I der Heinrich-Heine-Universität Düsseldorf Gedruckt mit der Genehmigung der Mathematisch-Naturwissenschaftlichen Fakultät der Heinrich-Heine-Universität Düsseldorf Berichterstatter: 1. Prof. Dr. Sebastian Wesselborg 2. Prof. Dr. Henrike Heise Tag der mündlichen Prüfung: 29. Oktober 2019 “Where the first primal cell was, there was I also. Where man is, there am I. When the last life crawls under freezing stars, there will I be.” — DEATH, in: Mort, by Terry Pratchett “Right away I found out something about biology: it was very easy to find a question that was very interesting, and that nobody knew the answer to.” — Richard Feynman, in: Surely You're Joking, Mr. Feynman! Acknowledgements (Danksagung) Acknowledgements (Danksagung) Viele Menschen haben zum Gelingen meiner Forschungsarbeit und dieser Dissertation beigetragen, und nicht alle können hier namentlich erwähnt werden. Dennoch möchte ich einige besonders hervorheben. An erster Stelle möchte ich Professor Sebastian Wesselborg danken, der diese Dissertation als Erstgutachter betreut hat und der mir die Möglichkeit gab, die dazugehörigen experimentellen Arbeiten am Institut für Molekulare Medizin durchzuführen. Er und Professor Björn Stork, dem ich für die herzliche Aufnahme in seine Arbeitsgruppe danke, legten durch die richtige Mischung aus aktiver Förderung und dem Freiraum zur Umsetzung eigener wissenschaftlicher Ideen die ideale Grundlage für die Forschungsprojekte, aus denen diese Dissertation entstand. Professorin Henrike Heise, die sich freundlicherweise zur Betreuung dieser Dissertation als Zweitgutachterin bereiterklärt hat, gilt ebenfalls mein herzlicher Dank. -
Dynamic Molecular Linkers of the Genome: the First Decade of SMC Proteins
Downloaded from genesdev.cshlp.org on October 8, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW Dynamic molecular linkers of the genome: the first decade of SMC proteins Ana Losada1 and Tatsuya Hirano2,3 1Spanish National Cancer Center (CNIO), Madrid E-28029, Spain; 2Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA Structural maintenance of chromosomes (SMC) proteins in eukaryotes. The proposed actions of cohesin and con- are chromosomal ATPases, highly conserved from bac- densins offer a plausible, if not complete, explanation for teria to humans, that play fundamental roles in many the sudden appearance of thread-like “substances” (the aspects of higher-order chromosome organization and chromosomes) and their longitudinal splitting during dynamics. In eukaryotes, SMC1 and SMC3 act as the mitosis, first described by Walther Flemming (1882). core of the cohesin complexes that mediate sister chro- Remarkably, SMC proteins are conserved among the matid cohesion, whereas SMC2 and SMC4 function as three phyla of life, indicating that the basic strategy of the core of the condensin complexes that are essential chromosome organization may be evolutionarily con- for chromosome assembly and segregation. Another served from bacteria to humans. An emerging theme is complex containing SMC5 and SMC6 is implicated in that SMC proteins are dynamic molecular linkers of the DNA repair and checkpoint responses. The SMC com- genome that actively fold, tether, and manipulate DNA plexes form unique ring- or V-shaped structures with strands. Their diverse functions range far beyond chro- long coiled-coil arms, and function as ATP-modulated, mosome segregation, and involve nearly all aspects of dynamic molecular linkers of the genome. -
Epigenetic Silencing of TGFBI Confers Resistance to Trastuzumab in Human Breast Cancer
Palomeras et al. Breast Cancer Research (2019) 21:79 https://doi.org/10.1186/s13058-019-1160-x RESEARCH ARTICLE Open Access Epigenetic silencing of TGFBI confers resistance to trastuzumab in human breast cancer Sònia Palomeras1†, Ángel Diaz-Lagares2,3†, Gemma Viñas1,4,5, Fernando Setien2, Humberto J. Ferreira2, Glòria Oliveras1,6, Ana B. Crujeiras7,8, Alejandro Hernández4, David H. Lum9, Alana L. Welm9, Manel Esteller2,10,11,12,13* and Teresa Puig1* Abstract Background: Acquired resistance to trastuzumab is a major clinical problem in the treatment of HER2-positive (HER2+) breast cancer patients. The selection of trastuzumab-resistant patients is a great challenge of precision oncology. The aim of this study was to identify novel epigenetic biomarkers associated to trastuzumab resistance in HER2+ BC patients. Methods: We performed a genome-wide DNA methylation (450K array) and a transcriptomic analysis (RNA-Seq) comparing trastuzumab-sensitive (SK) and trastuzumab-resistant (SKTR) HER2+ human breast cancer cell models. The methylation and expression levels of candidate genes were validated by bisulfite pyrosequencing and qRT-PCR, respectively. Functional assays were conducted in the SK and SKTR models by gene silencing and overexpression. Methylation analysis in 24 HER2+ human BC samples with complete response or non-response to trastuzumab- based treatment was conducted by bisulfite pyrosequencing. Results: Epigenomic and transcriptomic analysis revealed the consistent hypermethylation and downregulation of TGFBI, CXCL2, and SLC38A1 genes in association with trastuzumab resistance. The DNA methylation and expression levels of these genes were validated in both sensitive and resistant models analyzed. Of the genes, TGFBI presented the highest hypermethylation-associated silencing both at the transcriptional and protein level. -
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). -
Supplementary A
Genomic Analysis of the Immune Gene Repertoire of Amphioxus Reveals Extraordinary Innate Complexity and Diversity Supplementary A Content 1 TLR system....................................................................................................................................2 2 NLR system ...................................................................................................................................4 3 LRRIG genes .................................................................................................................................5 4 Other LRR-containing models.......................................................................................................6 5 Domain combinations in amphioxus C-type lectins ......................................................................8 References.........................................................................................................................................9 Table S1. Cross-species comparison of the immune-related protein domains................................10 Table S2. Information of 927 amphioxus CTL gene models containing single CTLD domain. ....11 Table S3. Grouping of the amphioxus DFD gene models based on their architectures..................12 Figure S1. Two structural types of TLR. ........................................................................................13 Figure S2. Phylogenetic analysis of amphioxus P-TLRs and all vertebrate TLR families.............14 Figure S3. Phylogenetic analysis of amphioxus TLRs -
Investigation of Candidate Genes and Mechanisms Underlying Obesity
Prashanth et al. BMC Endocrine Disorders (2021) 21:80 https://doi.org/10.1186/s12902-021-00718-5 RESEARCH ARTICLE Open Access Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules G. Prashanth1 , Basavaraj Vastrad2 , Anandkumar Tengli3 , Chanabasayya Vastrad4* and Iranna Kotturshetti5 Abstract Background: Obesity associated type 2 diabetes mellitus is a metabolic disorder ; however, the etiology of obesity associated type 2 diabetes mellitus remains largely unknown. There is an urgent need to further broaden the understanding of the molecular mechanism associated in obesity associated type 2 diabetes mellitus. Methods: To screen the differentially expressed genes (DEGs) that might play essential roles in obesity associated type 2 diabetes mellitus, the publicly available expression profiling by high throughput sequencing data (GSE143319) was downloaded and screened for DEGs. Then, Gene Ontology (GO) and REACTOME pathway enrichment analysis were performed. The protein - protein interaction network, miRNA - target genes regulatory network and TF-target gene regulatory network were constructed and analyzed for identification of hub and target genes. The hub genes were validated by receiver operating characteristic (ROC) curve analysis and RT- PCR analysis. Finally, a molecular docking study was performed on over expressed proteins to predict the target small drug molecules. Results: A total of 820 DEGs were identified between -
2020 Program Book
PROGRAM BOOK Note that TAGC was cancelled and held online with a different schedule and program. This document serves as a record of the original program designed for the in-person meeting. April 22–26, 2020 Gaylord National Resort & Convention Center Metro Washington, DC TABLE OF CONTENTS About the GSA ........................................................................................................................................................ 3 Conference Organizers ...........................................................................................................................................4 General Information ...............................................................................................................................................7 Mobile App ....................................................................................................................................................7 Registration, Badges, and Pre-ordered T-shirts .............................................................................................7 Oral Presenters: Speaker Ready Room - Camellia 4.......................................................................................7 Poster Sessions and Exhibits - Prince George’s Exhibition Hall ......................................................................7 GSA Central - Booth 520 ................................................................................................................................8 Internet Access ..............................................................................................................................................8 -
Prognostic Significance of Autophagy-Relevant Gene Markers in Colorectal Cancer
ORIGINAL RESEARCH published: 15 April 2021 doi: 10.3389/fonc.2021.566539 Prognostic Significance of Autophagy-Relevant Gene Markers in Colorectal Cancer Qinglian He 1, Ziqi Li 1, Jinbao Yin 1, Yuling Li 2, Yuting Yin 1, Xue Lei 1 and Wei Zhu 1* 1 Department of Pathology, Guangdong Medical University, Dongguan, China, 2 Department of Pathology, Dongguan People’s Hospital, Southern Medical University, Dongguan, China Background: Colorectal cancer (CRC) is a common malignant solid tumor with an extremely low survival rate after relapse. Previous investigations have shown that autophagy possesses a crucial function in tumors. However, there is no consensus on the value of autophagy-associated genes in predicting the prognosis of CRC patients. Edited by: This work screens autophagy-related markers and signaling pathways that may Fenglin Liu, Fudan University, China participate in the development of CRC, and establishes a prognostic model of CRC Reviewed by: based on autophagy-associated genes. Brian M. Olson, Emory University, United States Methods: Gene transcripts from the TCGA database and autophagy-associated gene Zhengzhi Zou, data from the GeneCards database were used to obtain expression levels of autophagy- South China Normal University, China associated genes, followed by Wilcox tests to screen for autophagy-related differentially Faqing Tian, Longgang District People's expressed genes. Then, 11 key autophagy-associated genes were identified through Hospital of Shenzhen, China univariate and multivariate Cox proportional hazard regression analysis and used to Yibing Chen, Zhengzhou University, China establish prognostic models. Additionally, immunohistochemical and CRC cell line data Jian Tu, were used to evaluate the results of our three autophagy-associated genes EPHB2, University of South China, China NOL3, and SNAI1 in TCGA. -
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 -
Bioinformatic Identification of Genes Suppressing Genome Instability
Bioinformatic identification of genes suppressing PNAS PLUS genome instability Christopher D. Putnama,b, Stephanie R. Allen-Solteroa,c, Sandra L. Martineza, Jason E. Chana,b, Tikvah K. Hayesa,1, and Richard D. Kolodnera,b,c,d,e,2 aLudwig Institute for Cancer Research, Departments of bMedicine and cCellular and Molecular Medicine, dMoores-University of California at San Diego Cancer Center, and eInstitute of Genomic Medicine, University of California School of Medicine at San Diego, La Jolla, CA 92093 Contributed by Richard D. Kolodner, September 28, 2012 (sent for review August 25, 2011) Unbiased forward genetic screens for mutations causing increased in concert to prevent genome rearrangements (reviewed in 12). gross chromosomal rearrangement (GCR) rates in Saccharomyces Modifications of the original GCR assay demonstrated that sup- cerevisiae are hampered by the difficulty in reliably using qualitative pression of GCRs mediated by segmental duplications and Ty GCR assays to detect mutants with small but significantly increased elements involves additional genes and pathways that do not GCR rates. We therefore developed a bioinformatic procedure using suppress single-copy sequence-mediated GCRs (13–15). Inter- genome-wide functional genomics screens to identify and prioritize estingly, homologs of some GCR-suppressing genes and pathways candidate GCR-suppressing genes on the basis of the shared drug suppress the development of cancer in mammals (16). Most of the sensitivity suppression and similar genetic interactions as known genes that suppress GCRs have been identified through a candi- GCR suppressors. The number of known suppressors was increased date gene approach. Some studies have screened collections of from 75 to 110 by testing 87 predicted genes, which identified un- arrayed S. -
NLRP3) Inflammasome Activity Is Regulated by and Potentially Targetable Through Bruton Tyrosine Kinase
Human NACHT, LRR, and PYD domain-containing protein 3 (NLRP3) inflammasome activity is regulated by and potentially targetable through Bruton tyrosine kinase Thesis submitted as requirement to fulfill the degree „Doctor of Philosophy“ (Ph.D.) at the Faculty of Medicine Eberhard Karls University Tübingen by Xiao Liu (刘晓) from Shandong, China 2018 1 Dean: Professor Dr. I. B. Autenrieth 1. Reviewer: Professor A. Weber 2. Reviewer: Professor S. Beer-Hammer 2 Content Content Figures ..................................................................................................................................................... iv Tables ....................................................................................................................................................... vi Abbreviations ........................................................................................................................................ vii 1 Introduction ....................................................................................................................................... 1 1.1 The human immune system .................................................................................................... 1 1.1.1 Innate immune response ................................................................................................................... 1 1.1.2 Adaptive immune response ............................................................................................................. 2 1.2 Inflammasomes are a group of