GENETICS Gene Expression Profiles of Two B-Complex Disparate
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FK506-Binding Protein 12.6/1B, a Negative Regulator of [Ca2+], Rescues Memory and Restores Genomic Regulation in the Hippocampus of Aging Rats
This Accepted Manuscript has not been copyedited and formatted. The final version may differ from this version. A link to any extended data will be provided when the final version is posted online. Research Articles: Neurobiology of Disease FK506-Binding Protein 12.6/1b, a negative regulator of [Ca2+], rescues memory and restores genomic regulation in the hippocampus of aging rats John C. Gant1, Eric M. Blalock1, Kuey-Chu Chen1, Inga Kadish2, Olivier Thibault1, Nada M. Porter1 and Philip W. Landfield1 1Department of Pharmacology & Nutritional Sciences, University of Kentucky, Lexington, KY 40536 2Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL 35294 DOI: 10.1523/JNEUROSCI.2234-17.2017 Received: 7 August 2017 Revised: 10 October 2017 Accepted: 24 November 2017 Published: 18 December 2017 Author contributions: J.C.G. and P.W.L. designed research; J.C.G., E.M.B., K.-c.C., and I.K. performed research; J.C.G., E.M.B., K.-c.C., I.K., and P.W.L. analyzed data; J.C.G., E.M.B., O.T., N.M.P., and P.W.L. wrote the paper. Conflict of Interest: The authors declare no competing financial interests. NIH grants AG004542, AG033649, AG052050, AG037868 and McAlpine Foundation for Neuroscience Research Corresponding author: Philip W. Landfield, [email protected], Department of Pharmacology & Nutritional Sciences, University of Kentucky, 800 Rose Street, UKMC MS 307, Lexington, KY 40536 Cite as: J. Neurosci ; 10.1523/JNEUROSCI.2234-17.2017 Alerts: Sign up at www.jneurosci.org/cgi/alerts to receive customized email alerts when the fully formatted version of this article is published. -
SPATA33 Localizes Calcineurin to the Mitochondria and Regulates Sperm Motility in Mice
SPATA33 localizes calcineurin to the mitochondria and regulates sperm motility in mice Haruhiko Miyataa, Seiya Ouraa,b, Akane Morohoshia,c, Keisuke Shimadaa, Daisuke Mashikoa,1, Yuki Oyamaa,b, Yuki Kanedaa,b, Takafumi Matsumuraa,2, Ferheen Abbasia,3, and Masahito Ikawaa,b,c,d,4 aResearch Institute for Microbial Diseases, Osaka University, Osaka 5650871, Japan; bGraduate School of Pharmaceutical Sciences, Osaka University, Osaka 5650871, Japan; cGraduate School of Medicine, Osaka University, Osaka 5650871, Japan; and dThe Institute of Medical Science, The University of Tokyo, Tokyo 1088639, Japan Edited by Mariana F. Wolfner, Cornell University, Ithaca, NY, and approved July 27, 2021 (received for review April 8, 2021) Calcineurin is a calcium-dependent phosphatase that plays roles in calcineurin can be a target for reversible and rapidly acting male a variety of biological processes including immune responses. In sper- contraceptives (5). However, it is challenging to develop molecules matozoa, there is a testis-enriched calcineurin composed of PPP3CC and that specifically inhibit sperm calcineurin and not somatic calci- PPP3R2 (sperm calcineurin) that is essential for sperm motility and male neurin because of sequence similarities (82% amino acid identity fertility. Because sperm calcineurin has been proposed as a target for between human PPP3CA and PPP3CC and 85% amino acid reversible male contraceptives, identifying proteins that interact with identity between human PPP3R1 and PPP3R2). Therefore, identi- sperm calcineurin widens the choice for developing specific inhibitors. fying proteins that interact with sperm calcineurin widens the choice Here, by screening the calcineurin-interacting PxIxIT consensus motif of inhibitors that target the sperm calcineurin pathway. in silico and analyzing the function of candidate proteins through the The PxIxIT motif is a conserved sequence found in generation of gene-modified mice, we discovered that SPATA33 inter- calcineurin-binding proteins (8, 9). -
SARS-Cov-2 Entry Protein TMPRSS2 and Its Homologue, TMPRSS4
bioRxiv preprint doi: https://doi.org/10.1101/2021.04.26.441280; this version posted April 26, 2021. 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-ND 4.0 International license. 1 SARS-CoV-2 Entry Protein TMPRSS2 and Its 2 Homologue, TMPRSS4 Adopts Structural Fold Similar 3 to Blood Coagulation and Complement Pathway 4 Related Proteins ∗,a ∗∗,b b 5 Vijaykumar Yogesh Muley , Amit Singh , Karl Gruber , Alfredo ∗,a 6 Varela-Echavarría a 7 Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, México b 8 Institute of Molecular Biosciences, University of Graz, Graz, Austria 9 Abstract The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) utilizes TMPRSS2 receptor to enter target human cells and subsequently causes coron- avirus disease 19 (COVID-19). TMPRSS2 belongs to the type II serine proteases of subfamily TMPRSS, which is characterized by the presence of the serine- protease domain. TMPRSS4 is another TMPRSS member, which has a domain architecture similar to TMPRSS2. TMPRSS2 and TMPRSS4 have been shown to be involved in SARS-CoV-2 infection. However, their normal physiological roles have not been explored in detail. In this study, we analyzed the amino acid sequences and predicted 3D structures of TMPRSS2 and TMPRSS4 to under- stand their functional aspects at the protein domain level. Our results suggest that these proteins are likely to have common functions based on their conserved domain organization. -
Novel Driver Strength Index Highlights Important Cancer Genes in TCGA Pancanatlas Patients
medRxiv preprint doi: https://doi.org/10.1101/2021.08.01.21261447; this version posted August 5, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients Aleksey V. Belikov*, Danila V. Otnyukov, Alexey D. Vyatkin and Sergey V. Leonov Laboratory of Innovative Medicine, School of Biological and Medical Physics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Moscow Region, Russia *Corresponding author: [email protected] NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. 1 medRxiv preprint doi: https://doi.org/10.1101/2021.08.01.21261447; this version posted August 5, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Abstract Elucidating crucial driver genes is paramount for understanding the cancer origins and mechanisms of progression, as well as selecting targets for molecular therapy. Cancer genes are usually ranked by the frequency of mutation, which, however, does not necessarily reflect their driver strength. Here we hypothesize that driver strength is higher for genes that are preferentially mutated in patients with few driver mutations overall, because these few mutations should be strong enough to initiate cancer. -
Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A. -
CD29 Identifies IFN-Γ–Producing Human CD8+ T Cells with an Increased Cytotoxic Potential
+ CD29 identifies IFN-γ–producing human CD8 T cells with an increased cytotoxic potential Benoît P. Nicoleta,b, Aurélie Guislaina,b, Floris P. J. van Alphenc, Raquel Gomez-Eerlandd, Ton N. M. Schumacherd, Maartje van den Biggelaarc,e, and Monika C. Wolkersa,b,1 aDepartment of Hematopoiesis, Sanquin Research, 1066 CX Amsterdam, The Netherlands; bLandsteiner Laboratory, Oncode Institute, Amsterdam University Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; cDepartment of Research Facilities, Sanquin Research, 1066 CX Amsterdam, The Netherlands; dDivision of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands; and eDepartment of Molecular and Cellular Haemostasis, Sanquin Research, 1066 CX Amsterdam, The Netherlands Edited by Anjana Rao, La Jolla Institute for Allergy and Immunology, La Jolla, CA, and approved February 12, 2020 (received for review August 12, 2019) Cytotoxic CD8+ T cells can effectively kill target cells by producing therefore developed a protocol that allowed for efficient iso- cytokines, chemokines, and granzymes. Expression of these effector lation of RNA and protein from fluorescence-activated cell molecules is however highly divergent, and tools that identify and sorting (FACS)-sorted fixed T cells after intracellular cytokine + preselect CD8 T cells with a cytotoxic expression profile are lacking. staining. With this top-down approach, we performed an un- + Human CD8 T cells can be divided into IFN-γ– and IL-2–producing biased RNA-sequencing (RNA-seq) and mass spectrometry cells. Unbiased transcriptomics and proteomics analysis on cytokine- γ– – + + (MS) analyses on IFN- and IL-2 producing primary human producing fixed CD8 T cells revealed that IL-2 cells produce helper + + + CD8 Tcells. -
The Proteomic Landscape of Resting and Activated CD4+ T Cells Reveal Insights Into Cell Differentiation and Function
International Journal of Molecular Sciences Article The Proteomic Landscape of Resting and Activated CD4+ T Cells Reveal Insights into Cell Differentiation and Function Yashwanth Subbannayya 1 , Markus Haug 1, Sneha M. Pinto 1, Varshasnata Mohanty 2, Hany Zakaria Meås 1, Trude Helen Flo 1, T.S. Keshava Prasad 2 and Richard K. Kandasamy 1,* 1 Centre of Molecular Inflammation Research (CEMIR), Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology, 7491 Trondheim, Norway; [email protected] (Y.S.); [email protected] (M.H.); [email protected] (S.M.P.); [email protected] (H.Z.M.); trude.fl[email protected] (T.H.F.) 2 Center for Systems Biology and Molecular Medicine, Yenepoya (Deemed to be University), Mangalore 575018, India; [email protected] (V.M.); [email protected] (T.S.K.P.) * Correspondence: [email protected] Abstract: CD4+ T cells (T helper cells) are cytokine-producing adaptive immune cells that activate or regulate the responses of various immune cells. The activation and functional status of CD4+ T cells is important for adequate responses to pathogen infections but has also been associated with auto-immune disorders and survival in several cancers. In the current study, we carried out a label-free high-resolution FTMS-based proteomic profiling of resting and T cell receptor-activated (72 h) primary human CD4+ T cells from peripheral blood of healthy donors as well as SUP-T1 cells. We identified 5237 proteins, of which significant alterations in the levels of 1119 proteins were observed between resting and activated CD4+ T cells. -
ID AKI Vs Control Fold Change P Value Symbol Entrez Gene Name *In
ID AKI vs control P value Symbol Entrez Gene Name *In case of multiple probesets per gene, one with the highest fold change was selected. Fold Change 208083_s_at 7.88 0.000932 ITGB6 integrin, beta 6 202376_at 6.12 0.000518 SERPINA3 serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 3 1553575_at 5.62 0.0033 MT-ND6 NADH dehydrogenase, subunit 6 (complex I) 212768_s_at 5.50 0.000896 OLFM4 olfactomedin 4 206157_at 5.26 0.00177 PTX3 pentraxin 3, long 212531_at 4.26 0.00405 LCN2 lipocalin 2 215646_s_at 4.13 0.00408 VCAN versican 202018_s_at 4.12 0.0318 LTF lactotransferrin 203021_at 4.05 0.0129 SLPI secretory leukocyte peptidase inhibitor 222486_s_at 4.03 0.000329 ADAMTS1 ADAM metallopeptidase with thrombospondin type 1 motif, 1 1552439_s_at 3.82 0.000714 MEGF11 multiple EGF-like-domains 11 210602_s_at 3.74 0.000408 CDH6 cadherin 6, type 2, K-cadherin (fetal kidney) 229947_at 3.62 0.00843 PI15 peptidase inhibitor 15 204006_s_at 3.39 0.00241 FCGR3A Fc fragment of IgG, low affinity IIIa, receptor (CD16a) 202238_s_at 3.29 0.00492 NNMT nicotinamide N-methyltransferase 202917_s_at 3.20 0.00369 S100A8 S100 calcium binding protein A8 215223_s_at 3.17 0.000516 SOD2 superoxide dismutase 2, mitochondrial 204627_s_at 3.04 0.00619 ITGB3 integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61) 223217_s_at 2.99 0.00397 NFKBIZ nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, zeta 231067_s_at 2.97 0.00681 AKAP12 A kinase (PRKA) anchor protein 12 224917_at 2.94 0.00256 VMP1/ mir-21likely ortholog -
Gene Expression Profiling in Acute Allograft Rejection
Spivey et al. Journal of Translational Medicine 2011, 9:174 http://www.translational-medicine.com/content/9/1/174 REVIEW Open Access Gene expression profiling in acute allograft rejection: challenging the immunologic constant of rejection hypothesis Tara L Spivey1,2,3, Lorenzo Uccellini1,4, Maria Libera Ascierto1,5,6, Gabriele Zoppoli5,7, Valeria De Giorgi1, Lucia Gemma Delogu8, Alyson M Engle1, Jaime M Thomas1, Ena Wang1, Francesco M Marincola1* and Davide Bedognetti1,5,9* Abstract In humans, the role and relationship between molecular pathways that lead to tissue destruction during acute allograft rejection are not fully understood. Based on studies conducted in humans, we recently hypothesized that different immune-mediated tissue destruction processes (i.e. cancer, infection, autoimmunity) share common convergent final mechanisms. We called this phenomenon the “Immunologic Constant of Rejection (ICR).” The elements of the ICR include molecular pathways that are consistently described through different immune- mediated tissue destruction processes and demonstrate the activation of interferon-stimulated genes (ISGs), the recruitment of cytotoxic immune cells (primarily through CXCR3/CCR5 ligand pathways), and the activation of immune effector function genes (IEF genes; granzymes A/B, perforin, etc.). Here, we challenge the ICR hypothesis by using a meta-analytical approach and systematically reviewing microarray studies evaluating gene expression on tissue biopsies during acute allograft rejection. We found the pillars of the ICR consistently present among the studies reviewed, despite implicit heterogeneity. Additionally, we provide a descriptive mechanistic overview of acute allograft rejection by describing those molecular pathways most frequently encountered and thereby thought to be most significant. The biological role of the following molecular pathways is described: IFN-g, CXCR3/CCR5 ligand, IEF genes, TNF-a, IL-10, IRF-1/STAT-1, and complement pathways. -
A Genomic Approach to Delineating the Occurrence of Scoliosis in Arthrogryposis Multiplex Congenita
G C A T T A C G G C A T genes Article A Genomic Approach to Delineating the Occurrence of Scoliosis in Arthrogryposis Multiplex Congenita Xenia Latypova 1, Stefan Giovanni Creadore 2, Noémi Dahan-Oliel 3,4, Anxhela Gjyshi Gustafson 2, Steven Wei-Hung Hwang 5, Tanya Bedard 6, Kamran Shazand 2, Harold J. P. van Bosse 5 , Philip F. Giampietro 7,* and Klaus Dieterich 8,* 1 Grenoble Institut Neurosciences, Université Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, 38000 Grenoble, France; [email protected] 2 Shriners Hospitals for Children Headquarters, Tampa, FL 33607, USA; [email protected] (S.G.C.); [email protected] (A.G.G.); [email protected] (K.S.) 3 Shriners Hospitals for Children, Montreal, QC H4A 0A9, Canada; [email protected] 4 School of Physical & Occupational Therapy, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC H3G 2M1, Canada 5 Shriners Hospitals for Children, Philadelphia, PA 19140, USA; [email protected] (S.W.-H.H.); [email protected] (H.J.P.v.B.) 6 Alberta Congenital Anomalies Surveillance System, Alberta Health Services, Edmonton, AB T5J 3E4, Canada; [email protected] 7 Department of Pediatrics, University of Illinois-Chicago, Chicago, IL 60607, USA 8 Institut of Advanced Biosciences, Université Grenoble Alpes, Inserm, U1209, CHU Grenoble Alpes, 38000 Grenoble, France * Correspondence: [email protected] (P.F.G.); [email protected] (K.D.) Citation: Latypova, X.; Creadore, S.G.; Dahan-Oliel, N.; Gustafson, Abstract: Arthrogryposis multiplex congenita (AMC) describes a group of conditions characterized A.G.; Wei-Hung Hwang, S.; Bedard, by the presence of non-progressive congenital contractures in multiple body areas. -
Single-Cell Transcriptome Analysis Reveals Gene Signatures Associated with T-Cell Persistence
Author Manuscript Published OnlineFirst on September 4, 2019; DOI: 10.1158/2326-6066.CIR-19-0299 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Single-cell transcriptome analysis reveals gene signatures associated with T-cell persistence following adoptive cell therapy Yong-Chen Lu1, Li Jia1, Zhili Zheng1, Eric Tran1,2, Paul F. Robbins1, Steven A. Rosenberg1 1Surgery Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. 2Current address: Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR 97213, USA Corresponding author: Yong-Chen Lu, Surgery Branch, National Cancer Institute, National Institutes of Health, Building 10-CRC, Rm 3-5930, 10 Center Dr, Bethesda, MD, 20892. Phone: 240-858-3818, E-mail: [email protected] Corresponding author: Steven A. Rosenberg, Surgery Branch, National Cancer Institute, National Institutes of Health, Building 10-CRC, Rm 3-3940, 10 Center Dr, Bethesda, MD, 20892. Phone: 240-858-3080, E-mail: [email protected] Running title: Gene signatures associated with T-cell persistence Financial Support: This work was supported by the Intramural Research Program of National Cancer Institute. Disclosure of Potential Conflicts of Interest: No potential conflicts of interest were disclosed. Keywords: single-cell, adoptive cell therapy, persistence, tumor-infiltrating lymphocytes, cancer immunotherapy 1 Downloaded from cancerimmunolres.aacrjournals.org on September 27, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on September 4, 2019; DOI: 10.1158/2326-6066.CIR-19-0299 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. -
Supplemental Table 2
Supplemental Table 2 probeID geneSymbol geneTitle cox.tt 223518_at DFFA DNA fragmentation factor, 45kDa, alpha polypeptide 4.86807974 212370_x_at FAM21A /// FAM21B /// FAM21C family with sequence similarity 21, member A /// family with sequence similarity 21, member B /// family with sequence similarity 21, member C -4.039911936 228937_at C13orf31 chromosome 13 open reading frame 31 -3.965652752 224217_s_at FAF1 Fas (TNFRSF6) associated factor 1 3.818692986 219066_at PPCDC phosphopantothenoylcysteine decarboxylase 3.812463041 200994_at IPO7 importin 7 3.722967297 203853_s_at GAB2 GRB2-associated binding protein 2 -3.576924648 218118_s_at TIMM23 translocase of inner mitochondrial membrane 23 homolog (yeast) -3.525051556 238662_at ATPBD4 ATP binding domain 4 3.522716621 212905_at CSTF2T cleavage stimulation factor, 3' pre-RNA, subunit 2, 64kDa, tau variant -3.458703211 225075_at PDRG1 p53 and DNA-damage regulated 1 3.437886045 211430_s_at IGH@ /// IGHG1 /// IGHG2 /// IGHM /// IGHV4-31 /// LOC100294459 immunoglobulin heavy locus /// immunoglobulin heavy constant gamma 1 (G1m marker) /// immunoglobulin heavy constant gamma 2 (G2m marker) /// immunoglobulin heavy constant mu /// immunoglobulin heavy variable 4-31 /// similar to immunoglobulin lambda-like polypeptide 1 -3.404297079 244181_at 244181_at --- -3.363118329 202231_at EIF3M eukaryotic translation initiation factor 3, subunit M 3.345657723 201160_s_at CSDA cold shock domain protein A 3.337512802 214946_x_at FAM21A /// FAM21B /// FAM21C /// FAM21D family with sequence similarity 21,