Rapid Microevolution During Recent Range Expansion to Harsh Environments Yiyong Chen1,2, Noa Shenkar3,4, Ping Ni1,2, Yaping Lin1,5, Shiguo Li1,2 and Aibin Zhan1,2*
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Genome-Wide Analysis of 5-Hmc in the Peripheral Blood of Systemic Lupus Erythematosus Patients Using an Hmedip-Chip
INTERNATIONAL JOURNAL OF MOLECULAR MEDICINE 35: 1467-1479, 2015 Genome-wide analysis of 5-hmC in the peripheral blood of systemic lupus erythematosus patients using an hMeDIP-chip WEIGUO SUI1*, QIUPEI TAN1*, MING YANG1, QIANG YAN1, HUA LIN1, MINGLIN OU1, WEN XUE1, JIEJING CHEN1, TONGXIANG ZOU1, HUANYUN JING1, LI GUO1, CUIHUI CAO1, YUFENG SUN1, ZHENZHEN CUI1 and YONG DAI2 1Guangxi Key Laboratory of Metabolic Diseases Research, Central Laboratory of Guilin 181st Hospital, Guilin, Guangxi 541002; 2Clinical Medical Research Center, the Second Clinical Medical College of Jinan University (Shenzhen People's Hospital), Shenzhen, Guangdong 518020, P.R. China Received July 9, 2014; Accepted February 27, 2015 DOI: 10.3892/ijmm.2015.2149 Abstract. Systemic lupus erythematosus (SLE) is a chronic, Introduction potentially fatal systemic autoimmune disease characterized by the production of autoantibodies against a wide range Systemic lupus erythematosus (SLE) is a typical systemic auto- of self-antigens. To investigate the role of the 5-hmC DNA immune disease, involving diffuse connective tissues (1) and modification with regard to the onset of SLE, we compared is characterized by immune inflammation. SLE has a complex the levels 5-hmC between SLE patients and normal controls. pathogenesis (2), involving genetic, immunologic and envi- Whole blood was obtained from patients, and genomic DNA ronmental factors. Thus, it may result in damage to multiple was extracted. Using the hMeDIP-chip analysis and valida- tissues and organs, especially the kidneys (3). SLE arises from tion by quantitative RT-PCR (RT-qPCR), we identified the a combination of heritable and environmental influences. differentially hydroxymethylated regions that are associated Epigenetics, the study of changes in gene expression with SLE. -
A Genetic Screen Identifies the Triple T Complex Required for DNA Damage Signaling and ATM and ATR Stability
Downloaded from genesdev.cshlp.org on September 27, 2021 - Published by Cold Spring Harbor Laboratory Press A genetic screen identifies the Triple T complex required for DNA damage signaling and ATM and ATR stability Kristen E. Hurov, Cecilia Cotta-Ramusino, and Stephen J. Elledge1 Howard Hughes Medical Institute and Department of Genetics, Harvard Medical School, Division of Genetics, Brigham and Women’s Hospital, Boston, Massachusetts 02115, USA In response to DNA damage, cells activate a complex signal transduction network called the DNA damage response (DDR). To enhance our current understanding of the DDR network, we performed a genome-wide RNAi screen to identify genes required for resistance to ionizing radiation (IR). Along with a number of known DDR genes, we discovered a large set of novel genes whose depletion leads to cellular sensitivity to IR. Here we describe TTI1 (Tel two-interacting protein 1) and TTI2 as highly conserved regulators of the DDR in mammals. TTI1 and TTI2 protect cells from spontaneous DNA damage, and are required for the establishment of the intra-S and G2/M checkpoints. TTI1 and TTI2 exist in multiple complexes, including a 2-MDa complex with TEL2 (telomere maintenance 2), called the Triple T complex, and phosphoinositide-3-kinase-related protein kinases (PIKKs) such as ataxia telangiectasia-mutated (ATM). The components of the TTT complex are mutually dependent on each other, and act as critical regulators of PIKK abundance and checkpoint signaling. [Keywords: TTI1; TEL2; TTI2; PIKK; TTT complex; IR sensitivity] Supplemental material is available at http://www.genesdev.org. Received April 5, 2010; revised version accepted July 22, 2010. -
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. -
Supplementary Materials
1 Supplementary Materials: Supplemental Figure 1. Gene expression profiles of kidneys in the Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice. (A) A heat map of microarray data show the genes that significantly changed up to 2 fold compared between Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice (N=4 mice per group; p<0.05). Data show in log2 (sample/wild-type). 2 Supplemental Figure 2. Sting signaling is essential for immuno-phenotypes of the Fcgr2b-/-lupus mice. (A-C) Flow cytometry analysis of splenocytes isolated from wild-type, Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice at the age of 6-7 months (N= 13-14 per group). Data shown in the percentage of (A) CD4+ ICOS+ cells, (B) B220+ I-Ab+ cells and (C) CD138+ cells. Data show as mean ± SEM (*p < 0.05, **p<0.01 and ***p<0.001). 3 Supplemental Figure 3. Phenotypes of Sting activated dendritic cells. (A) Representative of western blot analysis from immunoprecipitation with Sting of Fcgr2b-/- mice (N= 4). The band was shown in STING protein of activated BMDC with DMXAA at 0, 3 and 6 hr. and phosphorylation of STING at Ser357. (B) Mass spectra of phosphorylation of STING at Ser357 of activated BMDC from Fcgr2b-/- mice after stimulated with DMXAA for 3 hour and followed by immunoprecipitation with STING. (C) Sting-activated BMDC were co-cultured with LYN inhibitor PP2 and analyzed by flow cytometry, which showed the mean fluorescence intensity (MFI) of IAb expressing DC (N = 3 mice per group). 4 Supplemental Table 1. Lists of up and down of regulated proteins Accession No. -
Microevolution and the Genetics of Populations Microevolution Refers to Varieties Within a Given Type
Chapter 8: Evolution Lesson 8.3: Microevolution and the Genetics of Populations Microevolution refers to varieties within a given type. Change happens within a group, but the descendant is clearly of the same type as the ancestor. This might better be called variation, or adaptation, but the changes are "horizontal" in effect, not "vertical." Such changes might be accomplished by "natural selection," in which a trait within the present variety is selected as the best for a given set of conditions, or accomplished by "artificial selection," such as when dog breeders produce a new breed of dog. Lesson Objectives ● Distinguish what is microevolution and how it affects changes in populations. ● Define gene pool, and explain how to calculate allele frequencies. ● State the Hardy-Weinberg theorem ● Identify the five forces of evolution. Vocabulary ● adaptive radiation ● gene pool ● migration ● allele frequency ● genetic drift ● mutation ● artificial selection ● Hardy-Weinberg theorem ● natural selection ● directional selection ● macroevolution ● population genetics ● disruptive selection ● microevolution ● stabilizing selection ● gene flow Introduction Darwin knew that heritable variations are needed for evolution to occur. However, he knew nothing about Mendel’s laws of genetics. Mendel’s laws were rediscovered in the early 1900s. Only then could scientists fully understand the process of evolution. Microevolution is how individual traits within a population change over time. In order for a population to change, some things must be assumed to be true. In other words, there must be some sort of process happening that causes microevolution. The five ways alleles within a population change over time are natural selection, migration (gene flow), mating, mutations, or genetic drift. -
Chromosomal Rearrangements Are Commonly Post-Transcriptionally Attenuated in Cancer
bioRxiv preprint doi: https://doi.org/10.1101/093369; this version posted February 1, 2017. 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 4.0 International license. Chromosomal rearrangements are commonly post-transcriptionally attenuated in cancer 1 3 1 3, 4, 5 Emanuel Gonçalves , Athanassios Fragoulis , Luz Garcia-Alonso , Thorsten Cramer , 1,2# 1# Julio Saez-Rodriguez , Pedro Beltrao 1 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK 2 RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen 52057, Germany 3 Molecular Tumor Biology, Department of General, Visceral and Transplantation Surgery, RWTH University Hospital, Pauwelsstraße 30, 52074 Aachen, Germany 4 NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands 5 ESCAM – European Surgery Center Aachen Maastricht, Germany and The Netherlands # co-last authors: [email protected]; [email protected] Running title: Chromosomal rearrangement attenuation in cancer Keywords: Cancer; Gene dosage; Proteomics; Copy-number variation; Protein complexes 1 bioRxiv preprint doi: https://doi.org/10.1101/093369; this version posted February 1, 2017. 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 4.0 International license. Abstract Chromosomal rearrangements, despite being detrimental, are ubiquitous in cancer and often act as driver events. -
Phenotypic Plasticity Vs. Microevolution in Relation to Climate Change Noticeable Impacts of Climate Change Phenotypic Plasticit
6/6/14 Phenotypic Plasticity vs. Microevolution in Relation to Climate Change By Elizabeth Berry, Alex Lefort, Andy Tran, and Maya Vrba (EPA, 2013) Noticeable Impacts of Climate Change Phenotypic Plasticity vs Microevolution !! Canadian Squirrel: earlier breeding !! Phenotypic Plasticity: The ability of a genotype to produce different phenotypes in different environments (Charmantier & Gienapp 2013) !! American Mosquito: changes in dormancy !! Microevolution: Evolution in a small scale-within a single population (UC Museum of Paleontology 2008) !! Field Mustard plant: early blooming times !! Distinction: Phenotypic Plasticity acts on individuals, Microevolution acts on populations. !! Drosophila melanogaster: changes in gene flow !! Norm of Reaction: The range of phenotypic variation available to a given genotype that can change based on the environment. University of California Museum of Paleontology, 2008 European Great Tit: Parus major European Blackcap: Sylvia atricapilla !! Breeding times are evolving earlier in females to account for !! ADCYAP1: gene that controls the Climate Change. expression of migratory behavior !! Phenotypic Plasticity evident in (Mueller et al., 2011) laying times. !! Migratory activity is heritable and population-specific (Berthold & !! Some females having more flexible laying dates. Pulido 1994) ! Climate change causes evolving !! Success of offspring dependent ! on breeding times and caterpillar migratory patterns (Berthold & biomass coinciding, Pulido 1994) Jerry Nicholls and BBC, 2014 University of California -
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 -
•How Does Microevolution Add up to Macroevolution? •What Are Species
Microevolution and Macroevolution • How does Microevolution add up to macroevolution? • What are species? • How are species created? • What are anagenesis and cladogenesis? 1 Sunday, March 6, 2011 Species Concepts • Biological species concept: Defines species as interbreeding populations reproductively isolated from other such populations. • Evolutionary species concept: Defines species as evolutionary lineages with their own unique identity. • Ecological species concept: Defines species based on the uniqueness of their ecological niche. • Recognition species concept: Defines species based on unique traits or behaviors that allow members of one species to identify each other for mating. 2 Sunday, March 6, 2011 Reproductive Isolating Mechanisms • Premating RIMs Habitat isolation Temporal isolation Behavioral isolation Mechanical incompatibility • Postmating RIMs Sperm-egg incompatibility Zygote inviability Embryonic or fetal inviability 3 Sunday, March 6, 2011 Modes of Evolutionary Change 4 Sunday, March 6, 2011 Cladogenesis 5 Sunday, March 6, 2011 6 Sunday, March 6, 2011 7 Sunday, March 6, 2011 Evolution is “the simple way by which species (populations) become exquisitely adapted to various ends” 8 Sunday, March 6, 2011 All characteristics are due to the four forces • Mutation creates new alleles - new variation • Genetic drift moves these around by chance • Gene flow moves these from one population to the next creating clines • Natural selection increases and decreases them in frequency through adaptation 9 Sunday, March 6, 2011 Clines -
DEB Virtual Office Hour
Division of Environmental Biology (DEB) Virtual Office Hour Welcome to the DEB Virtual Office Hour. We will begin soon. Please submit questions via the Q&A box available to you on WebEx. Please set notification to ‘All Panelists’ Division of Environmental Biology (DEB) Virtual Office Hour – Welcome! Program Directors in attendance today • Matt Olson – Evolutionary Processes ([email protected]) • Kendra McLauchlan – Ecosystem Sciences ([email protected]) • Ford Ballantyne – Ecosystem Sciences ([email protected]) • Doug Levey – Population and Community Ecology ([email protected]) • Leslie Rissler – Evolutionary Processes ([email protected]) • Sam Scheiner – Evolutionary Processes ([email protected]) • Chris Schneider – Systematics and Biodiversity Sciences ([email protected]) Facilitators – Christina Washington, Alina Dallmeier, and Megan Lewis DEB Virtual Office Hour Questions: • Submit your questions via the Q&A box on your screen and set to “All Panelists” • For recently asked questions and future office hour topics, see the DEB blog (https://debblog.nsfbio.com/) • For specific questions about your project, please contact a Program Director DEB Virtual Office Hour • DEB Office Hours: second Monday of each month, 1-2 pm EST Upcoming Topics: Feb 10: Rules of Life/Understanding the Rules of Life Mar 9: RAPID/EAGER/Workshops Apr 13: OPUS May 11: CAREERs June 8: BIO Postdoc Program DEB Virtual Office Hour Today’s Topics: • Bridging Ecology and Evolution Track in DEB Core Solicitation • Demystifying the Co-Review Process • Open question and answer -
Current State of Mtor Targeting in Human Breast Cancer UMAR WAZIR 1,2 , ALI WAZIR 3, ZUBAIR S
CANCER GENOMICS & PROTEOMICS 11 : 167-174 (2014) Review Current State of mTOR Targeting in Human Breast Cancer UMAR WAZIR 1,2 , ALI WAZIR 3, ZUBAIR S. KHANZADA 4, WEN G. JIANG 4, ANUP K. SHARMA 2 and KEFAH MOKBEL 1,2 1The London Breast Institute, Princess Grace Hospital, London, U.K.; 2Department of Breast Surgery, St. George’s Hospital and Medical School, University of London, London, U.K.; 3Aga Khan University, Karachi, Pakistan; 4Cardiff University-Peking University Cancer Institute (CUPUCI), University Department of Surgery, Cardiff University School of Medicine, Cardiff University, Cardiff, Wales, U.K. Abstract. Background/Aim: The mammalian, or Subsequently, it was found to have immunosuppressive mechanistic, target of rapamycin (mTOR) pathway has been properties, and is currently used therapeutically in renal implicated in several models of human oncogenesis. transplant patients. Like tacrolimus and cyclosporine, it Research in the role of mTOR in human oncogenesis affects the actions of interlekin-2 (IL-2). Unlike tacrolimus, remains a field of intense activity. In this mini-review, we which reduces IL-2 transcription by T-cells, sirolimus intend to recount our current understanding of the mTOR inhibits the proliferative effects of IL-2 on T-cells (3). In pathway, its interactions, and its role in human mammalian models, rapamycin was known to form a carcinogenesis in general, and breast cancer in particular. complex with FK506 binding protein 1A, 12 kDa Materials and Methods: We herein outline the discrete (FKBP12), which interacts with the mammalian, or components of the two complexes of mTOR, and attempt to mechanistic, target of rapamycin (mTOR) subunit of define their distinct roles and interactions. -
Supplementary Table 2
Supplementary Table 2. Differentially Expressed Genes following Sham treatment relative to Untreated Controls Fold Change Accession Name Symbol 3 h 12 h NM_013121 CD28 antigen Cd28 12.82 BG665360 FMS-like tyrosine kinase 1 Flt1 9.63 NM_012701 Adrenergic receptor, beta 1 Adrb1 8.24 0.46 U20796 Nuclear receptor subfamily 1, group D, member 2 Nr1d2 7.22 NM_017116 Calpain 2 Capn2 6.41 BE097282 Guanine nucleotide binding protein, alpha 12 Gna12 6.21 NM_053328 Basic helix-loop-helix domain containing, class B2 Bhlhb2 5.79 NM_053831 Guanylate cyclase 2f Gucy2f 5.71 AW251703 Tumor necrosis factor receptor superfamily, member 12a Tnfrsf12a 5.57 NM_021691 Twist homolog 2 (Drosophila) Twist2 5.42 NM_133550 Fc receptor, IgE, low affinity II, alpha polypeptide Fcer2a 4.93 NM_031120 Signal sequence receptor, gamma Ssr3 4.84 NM_053544 Secreted frizzled-related protein 4 Sfrp4 4.73 NM_053910 Pleckstrin homology, Sec7 and coiled/coil domains 1 Pscd1 4.69 BE113233 Suppressor of cytokine signaling 2 Socs2 4.68 NM_053949 Potassium voltage-gated channel, subfamily H (eag- Kcnh2 4.60 related), member 2 NM_017305 Glutamate cysteine ligase, modifier subunit Gclm 4.59 NM_017309 Protein phospatase 3, regulatory subunit B, alpha Ppp3r1 4.54 isoform,type 1 NM_012765 5-hydroxytryptamine (serotonin) receptor 2C Htr2c 4.46 NM_017218 V-erb-b2 erythroblastic leukemia viral oncogene homolog Erbb3 4.42 3 (avian) AW918369 Zinc finger protein 191 Zfp191 4.38 NM_031034 Guanine nucleotide binding protein, alpha 12 Gna12 4.38 NM_017020 Interleukin 6 receptor Il6r 4.37 AJ002942