DOCSLIB.ORG

VITA Peter M. Govey

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
VITA Peter M. Govey The Pennsylvania State University The Graduate School Department of Bioengineering MULTISCALE ANABOLIC BONE RESPONSES TO FLUID FLOW AND POST-IRRADITION LOADING A Dissertation in Bioengineering by Peter M. Govey © 2015 Peter M. Govey Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy May 2015 The dissertation of Peter M. Govey was reviewed and approved* by the following: Henry J. Donahue Michael and Myrtle Baker Professor of Orthopaedics and Rehabilitation, Cellular and Molecular Physiology, and Bioengineering Dissertation Advisor Chair of Committee Justin Brown Assistant Professor of Bioengineering Gregory Lewis Assistant Professor of Orthopaedics and Rehabilitation Christopher Niyibizi Associate Professor of Orthopaedics and Rehabilitation Christopher Siedlecki Professor of Surgery and Bioengineering Cheng Dong Distinguished Professor of Bioengineering Head of the Department of Bioengineering *Signatures are on file in the Graduate School. ii ABSTRACT Bone responds to its mechanical environment, strengthening in response to daily use and wasting during disuse. Examples of disuse include bed rest, immobility, and microgravity experienced in space. Bone resorption may be exacerbated by aging, radiation exposure, sex hormone reduction, and various other pathologies, leading to osteoporosis. Resulting insufficiency fractures represent a tremendous toll on our population’s health, and current treatments such as anti-resorptive bisphosphonates are stop-gap measures with accompanying complications. Improved treatment and preventative strategies require an understanding of the body’s innate anabolic response to loading. This work examines bone’s anabolic response on multiple scales—first examining the cellular signaling response to mechanical stimulation, and extending this to an animal model of loading. A literature review reveals that bone adaptation to its mechanical environment, from embryonic through adult life, is thought to be the product of increased osteoblastic differentiation from putative mesenchymal stem cells (MSCs). These heterogeneous populations of multipotent stem cells are subject to a variety of biophysical cues within their native microenvironments. Bone marrow-derived MSCs—the most broadly studied source of osteoblastic progenitors—undergo osteoblastic differentiation in vitro in response to biophysical signals, including hydrostatic pressure, fluid flow and accompanying shear stress, substrate strain and stiffness, substrate topography, and electromagnetic fields. Additionally, stem cells may be subject to indirect regulation by mechano-sensing osteocytes positioned to more readily detect these same loading- iii induced signals within the bone matrix. Such paracrine and juxtacrine regulation of differentiation by osteocytes occurs in vitro. However, the osteocytic signaling molecules responsible for mediating this response remain unknown. We addressed this gap in knowledge by mapping a time course of broad in vitro signaling responses in fluid flowed osteocytic MLO-Y4 cells to physiological magnitude fluid shear stress. We hypothesized that high-throughput analyses of gene transcripts and proteins using DNA microarrays and mass-spectrometry, respectively, would reveal mechano-sensitive shifts in response to fluid flow. Osteocytic MLO-Y4 cells were exposed to 2 hours of 10 dyn/cm2 oscillating fluid shear stress and post-incubated for 0, 2, 8, and 24 hours. Fluid flow-regulated gene transcripts included known and unknown mechano-sensitive genes. qPCR analysis confirmed novel up-regulation of two of the most highly-upregulated gene transcripts by microarray: Cxcl1 and Cxcl2. Proteomic analysis identified notable up-regulation of ATP-producing enzyme NDK, calcium- binding Calcyclin, and G protein-coupled receptor kinase 6. Integrative analysis of transcriptomic and proteomic data revealed additional signaling nodes, including transcription factors c-Myc, c-Jun, and RelA/NF-κB. Finally, gene ontology analysis identified associations with various signaling pathways and functions, including inflammatory pathways, immune cell trafficking, and hematological system function. We followed up with a second analysis of these fluid flowed MLO-Y4 RNA samples using next-generation RNA-Seq. We sought to confirm previous results from DNA microarray, compare these analytical techniques, and identify additional novel signaling transcripts, particularly small RNA. Though a similar number of gene transcripts were significantly regulated, many more of these transcripts met a >1.5 fold- iv change cutoff, and the majority were newly identified. Once again, gene ontology analysis associated an inflammatory response, immune cell trafficking, and hematological system development. Cxcl1, Cxcl2, Cxcl5, which encode proteins known to stimulate migration of bone marrow MSCs and hematopoietic stem cells (HSCs), were once again highly up-regulated. We proposed that inflammatory chemokine up-regulation might be a novel paracrine means by which osteocytic cells exposed to fluid flow increase local bone formation via osteoblastic recruitment of progenitor cells. We next explored this in mice, hypothesizing that mechanical loading on a whole tissue scale up-regulates engraftment and bone formation by osteoprogenitor cells. We first utilized in vivo cantilever loading, hypothesizing that adaptation to a maximum 1,800 μϵ for 100 cycles, 3 times/week for 3 weeks could be quantified by microCT. Previous analyses had used histomorphometry alone, which lacks the detailed trabecular characterization of microCT. Though mice all lost some cortical bone mass, we identified a statistically significant preservation of cortical minimum moment of inertia, and non- significant trends towards preserved cortical thickness and polar moment of inertia. However, there was a significant reduction in relative trabecular thickness, the first microCT evidence that cantilever loading is not well-suited for trabecular bone formation. We progressed to a model of bone formation known to induce marked trabecular and cortical adaptation: compression loading. Immediately prior to 3 weeks of loading, we conditioned these mice to engraft syngeneic donor bone marrow stem cells via lethal irradiation (600+475 cGy) and intravenous transplantation of 20 million eGFP+ whole bone marrow cells. Irradiation primed bones for cellular engraftment and prevented v immune rejection of trackable donor cells. This was also the first study of mechanical loading in irradiated or bone marrow transplanted animals, both clinically relevant scenarios. Irradiation is known to rapidly induce bone loss and increase fracture risk, in part due to depletion of bone marrow cells. Right limbs were cyclically loaded to physiological strains of ~1,640 μϵ (10 N) 5 days per week for 3 weeks. Examining donor cell presence in flushed marrows by flow cytometry and in bone by relative DNA, we observed no clear loading-mediated differences in donor cell fractions in trabecular and cortical regions. However, there were significantly fewer cells of both donor and host origin in trabecular marrow regions of loaded bones. We associate this with robust attenuation of trabecular bone loss in loaded limbs compared to non-loaded internal and sham controls (-16% BV/TV relative to baseline in loaded vs. -48% BV/TV in non- loaded, p<0.001). Loss of trabecular thickness was significantly reversed (p<0.0001), increasing 26% above baseline. Loss of cortical area was attenuated, and cortical total area increased with loading. Mineral density of both trabecular and cortical bone increased. Cxcl1 and Cxcl2 were not detected in loaded or control tibias by qPCR using current protocols. Importantly, this is the first evidence that mechanical loading attenuates bone loss in irradiated subjects. This may be informative to the care of patients receiving focal or whole body irradiation for treatment of malignancies or hematological disorders. Moreover, new bone formation points to osteoblastogenesis from either conserved endogenous cells or donor cells. Future work will examine donor cell dependency and relative contribution vs. host cells. These studies add to our knowledge of the signaling mechanisms and contexts within which bone adapts to mechanical loads. vi TABLE OF CONTENTS LIST OF FIGURES ............................................................................................................ x LIST OF TABLES ........................................................................................................... xiii ABBREVIATIONS ......................................................................................................... xiv PREFACE ........................................................................................................................ xvi ACKNOWLEDGEMENTS ............................................................................................ xvii Chapter 1. BIOPHYSICAL MECHANISMS OF STEM CELL DIFFERENTIATION 1 1.1 Mechanical loading as a modulator of bone homeostasis ....................................... 1 1.2 Direct regulation of stem cells ................................................................................ 3 1.2.1 Mechano-transduction....................................................................................... 3 1.2.2 Hydrostatic pressure and fluid flow .................................................................. 4 1.2.3 Substrate strain and stiffness ............................................................................
Load more

Recommended publications
  • Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
    bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 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. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 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 Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis.
    [Show full text]
  • Caracterització De L'efecte De Compostos Naturals En Models In
    Caracterització de l’efecte de compostos naturals en models in vitro i in vivo de càncer de còlon Susana Sánchez Tena ADVERTIMENT. La consulta d’aquesta tesi queda condicionada a l’acceptació de les següents condicions d'ús: La difusió d’aquesta tesi per mitjà del servei TDX (www.tdx.cat) i a través del Dipòsit Digital de la UB (diposit.ub.edu) ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d’investigació i docència. No s’autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposició des d’un lloc aliè al servei TDX ni al Dipòsit Digital de la UB. No s’autoritza la presentació del seu contingut en una finestra o marc aliè a TDX o al Dipòsit Digital de la UB (framing). Aquesta reserva de drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita de parts de la tesi és obligat indicar el nom de la persona autora. ADVERTENCIA. La consulta de esta tesis queda condicionada a la aceptación de las siguientes condiciones de uso: La difusión de esta tesis por medio del servicio TDR (www.tdx.cat) y a través del Repositorio Digital de la UB (diposit.ub.edu) ha sido autorizada por los titulares de los derechos de propiedad intelectual únicamente para usos privados enmarcados en actividades de investigación y docencia. No se autoriza su reproducción con finalidades de lucro ni su difusión y puesta a disposición desde un sitio ajeno al servicio TDR o al Repositorio Digital de la UB.
    [Show full text]
  • Supplementary Data
    Supplementary Fig. 1 A B Responder_Xenograft_ Responder_Xenograft_ NON- NON- Lu7336, Vehicle vs Lu7466, Vehicle vs Responder_Xenograft_ Responder_Xenograft_ Sagopilone, Welch- Sagopilone, Welch- Lu7187, Vehicle vs Lu7406, Vehicle vs Test: 638 Test: 600 Sagopilone, Welch- Sagopilone, Welch- Test: 468 Test: 482 Responder_Xenograft_ NON- Lu7860, Vehicle vs Responder_Xenograft_ Sagopilone, Welch - Lu7558, Vehicle vs Test: 605 Sagopilone, Welch- Test: 333 Supplementary Fig. 2 Supplementary Fig. 3 Supplementary Figure S1. Venn diagrams comparing probe sets regulated by Sagopilone treatment (10mg/kg for 24h) between individual models (Welsh Test ellipse p-value<0.001 or 5-fold change). A Sagopilone responder models, B Sagopilone non-responder models. Supplementary Figure S2. Pathway analysis of genes regulated by Sagopilone treatment in responder xenograft models 24h after Sagopilone treatment by GeneGo Metacore; the most significant pathway map representing cell cycle/spindle assembly and chromosome separation is shown, genes upregulated by Sagopilone treatment are marked with red thermometers. Supplementary Figure S3. GeneGo Metacore pathway analysis of genes differentially expressed between Sagopilone Responder and Non-Responder models displaying –log(p-Values) of most significant pathway maps. Supplementary Tables Supplementary Table 1. Response and activity in 22 non-small-cell lung cancer (NSCLC) xenograft models after treatment with Sagopilone and other cytotoxic agents commonly used in the management of NSCLC Tumor Model Response type
    [Show full text]
  • Supplementary Materials and Tables a and B
    SUPPLEMENTARY MATERIAL 1 Table A. Main characteristics of the subset of 23 AML patients studied by high-density arrays (subset A) WBC BM blasts MYST3- MLL Age/Gender WHO / FAB subtype Karyotype FLT3-ITD NPM status (x109/L) (%) CREBBP status 1 51 / F M4 NA 21 78 + - G A 2 28 / M M4 t(8;16)(p11;p13) 8 92 + - G G 3 53 / F M4 t(8;16)(p11;p13) 27 96 + NA G NA 4 24 / M PML-RARα / M3 t(15;17) 5 90 - - G G 5 52 / M PML-RARα / M3 t(15;17) 1.5 75 - - G G 6 31 / F PML-RARα / M3 t(15;17) 3.2 89 - - G G 7 23 / M RUNX1-RUNX1T1 / M2 t(8;21) 38 34 - + ND G 8 52 / M RUNX1-RUNX1T1 / M2 t(8;21) 8 68 - - ND G 9 40 / M RUNX1-RUNX1T1 / M2 t(8;21) 5.1 54 - - ND G 10 63 / M CBFβ-MYH11 / M4 inv(16) 297 80 - - ND G 11 63 / M CBFβ-MYH11 / M4 inv(16) 7 74 - - ND G 12 59 / M CBFβ-MYH11 / M0 t(16;16) 108 94 - - ND G 13 41 / F MLLT3-MLL / M5 t(9;11) 51 90 - + G R 14 38 / F M5 46, XX 36 79 - + G G 15 76 / M M4 46 XY, der(10) 21 90 - - G NA 16 59 / M M4 NA 29 59 - - M G 17 26 / M M5 46, XY 295 92 - + G G 18 62 / F M5 NA 67 88 - + M A 19 47 / F M5 del(11q23) 17 78 - + M G 20 50 / F M5 46, XX 61 59 - + M G 21 28 / F M5 46, XX 132 90 - + G G 22 30 / F AML-MD / M5 46, XX 6 79 - + M G 23 64 / M AML-MD / M1 46, XY 17 83 - + M G WBC: white blood cell.
    [Show full text]
  • Supplementary Table 1
    Supplementary Table 1. 492 genes are unique to 0 h post-heat timepoint. The name, p-value, fold change, location and family of each gene are indicated. Genes were filtered for an absolute value log2 ration 1.5 and a significance value of p ≤ 0.05. Symbol p-value Log Gene Name Location Family Ratio ABCA13 1.87E-02 3.292 ATP-binding cassette, sub-family unknown transporter A (ABC1), member 13 ABCB1 1.93E-02 −1.819 ATP-binding cassette, sub-family Plasma transporter B (MDR/TAP), member 1 Membrane ABCC3 2.83E-02 2.016 ATP-binding cassette, sub-family Plasma transporter C (CFTR/MRP), member 3 Membrane ABHD6 7.79E-03 −2.717 abhydrolase domain containing 6 Cytoplasm enzyme ACAT1 4.10E-02 3.009 acetyl-CoA acetyltransferase 1 Cytoplasm enzyme ACBD4 2.66E-03 1.722 acyl-CoA binding domain unknown other containing 4 ACSL5 1.86E-02 −2.876 acyl-CoA synthetase long-chain Cytoplasm enzyme family member 5 ADAM23 3.33E-02 −3.008 ADAM metallopeptidase domain Plasma peptidase 23 Membrane ADAM29 5.58E-03 3.463 ADAM metallopeptidase domain Plasma peptidase 29 Membrane ADAMTS17 2.67E-04 3.051 ADAM metallopeptidase with Extracellular other thrombospondin type 1 motif, 17 Space ADCYAP1R1 1.20E-02 1.848 adenylate cyclase activating Plasma G-protein polypeptide 1 (pituitary) receptor Membrane coupled type I receptor ADH6 (includes 4.02E-02 −1.845 alcohol dehydrogenase 6 (class Cytoplasm enzyme EG:130) V) AHSA2 1.54E-04 −1.6 AHA1, activator of heat shock unknown other 90kDa protein ATPase homolog 2 (yeast) AK5 3.32E-02 1.658 adenylate kinase 5 Cytoplasm kinase AK7
    [Show full text]
  • Supplemental Material 1
    Supplemental Figure 1 A CD8+ è Tet+ è B CD8+ è Tet+ è Gang strategy for defining subpopulaons of CD8+ T cells in terms of expression of combinaons of transcrip#on factors, T-bet (TB) and Eomesodermin (EO), and differen#aon markers, CCR7 (R7), CD27 (27), and CD45RA (RA). A. Sample in AIM, labeled with pMHC1 tetramers to detect EBV-specific CD8+ T cells with specificity for HLA-A*0201 restricted immunodominant epitope in BMRF1 (TLD) is shown. Top row, leY to right shows gates used to define CD8+ T cells. Middle row, leY to right shows gates used to define populaons of CD8+ T cells expressing each marker, and tetramer- posi#ve cells. Bo[om row, leY to right shows marker gates applied to the tetramer- posi#ve populaon. All marker and tetramer gates were checked against a “fluorescence minus one” control. B. Sample in CONV that is paired with sample represented in top panel of this figure with same gang strategy applied. Supplemental Figure 2 A p < 0.05 p < 0.05 B p < 0.05 p < 0.05 p < 0.05 100 8000 80 6000 60 4000 40 (median; IQR) 2000 20 Relative Percentage 0 Relative Signal Intensity 0 CD27 IL7R CCR7 CD57 CD27 IL7R CCR7 CD57 Surface Marker Gene Expressed C D Tcm/naive: CD27+ CD127+ CCR7+ CD57-CD127 Tem/early:Phenotype CD27+ CD127+ or CCR7+CD27 CD57- CCR7 CD57 Tem/int_late: CD27+ CD127- CCR7- CD57+/-(IL-7R) Teff: CD27- CD127- CCR7- CD57+/- Tcm/naïve (central memory or naïve) + + + - Tem/early (effector memory, early differen#aon) + +/- +/- - Tem/late (effector memory, late differen#aon) + - - +/- Teff (effector) - - - +/- A.
    [Show full text]
  • Forms of Supplemental Selenium in Vitamin-Mineral
    University of Kentucky UKnowledge Theses and Dissertations--Animal and Food Sciences Animal and Food Sciences 2019 FORMS OF SUPPLEMENTAL SELENIUM IN VITAMIN-MINERAL MIXES DIFFERENTIALLY AFFECT SEROLOGICAL AND HEPATIC PARAMETERS OF GROWING BEEF STEERS GRAZING ENDOPHYTE-INFECTED TALL FESCUE Yang Jia University of Kentucky, [email protected] Digital Object Identifier: https://doi.org/10.13023/etd.2019.028 Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Jia, Yang, "FORMS OF SUPPLEMENTAL SELENIUM IN VITAMIN-MINERAL MIXES DIFFERENTIALLY AFFECT SEROLOGICAL AND HEPATIC PARAMETERS OF GROWING BEEF STEERS GRAZING ENDOPHYTE- INFECTED TALL FESCUE" (2019). Theses and Dissertations--Animal and Food Sciences. 97. https://uknowledge.uky.edu/animalsci_etds/97 This Doctoral Dissertation is brought to you for free and open access by the Animal and Food Sciences at UKnowledge. It has been accepted for inclusion in Theses and Dissertations--Animal and Food Sciences by an authorized administrator of UKnowledge. For more information, please contact [email protected]. STUDENT AGREEMENT: I represent that my thesis or dissertation and abstract are my original work. Proper attribution has been given to all outside sources. I understand that I am solely responsible for obtaining any needed copyright permissions. I have obtained needed written permission statement(s) from the owner(s) of each third-party copyrighted matter to be included in my work, allowing electronic distribution (if such use is not permitted by the fair use doctrine) which will be submitted to UKnowledge as Additional File. I hereby grant to The University of Kentucky and its agents the irrevocable, non-exclusive, and royalty-free license to archive and make accessible my work in whole or in part in all forms of media, now or hereafter known.
    [Show full text]
  • 14 SI D. Chauss Et Al. Table S3 Detected EQ Gene-Specific
    Table S3 Detected EQ gene‐specific transcripts statistically decreased in expression during EQ to FP transition. Gene Description log2(Fold Change) p‐value* CC2D2A coiled‐coil and C2 domain containing 2A ‐2.0 1.2E‐03 INSIG2 insulin induced gene 2 ‐2.0 1.2E‐03 ODZ2 teneurin transmembrane protein 2 ‐2.0 1.2E‐03 SEPHS1 selenophosphate synthetase 1 ‐2.0 1.2E‐03 B4GALT6 UDP‐Gal:betaGlcNAc beta 1,4‐ galactosyltransferase, ‐2.0 1.2E‐03 polypeptide 6 CDC42SE2 CDC42 small effector 2 ‐2.0 1.2E‐03 SLIT3 slit homolog 3 (Drosophila) ‐2.1 1.2E‐03 FKBP9 FK506 binding protein 9, 63 kDa ‐2.1 1.2E‐03 ATAD2 ATPase family, AAA domain containing 2 ‐2.1 1.2E‐03 PURH 5‐aminoimidazole‐4‐carboxamide ribonucleotide ‐2.1 1.2E‐03 formyltransferase/IMP cyclohydrolase PLXNA2 plexin A2 ‐2.1 1.2E‐03 CSRNP1 cysteine‐serine‐rich nuclear protein 1 ‐2.1 1.2E‐03 PER2 period circadian clock 2 ‐2.1 1.2E‐03 CERK ceramide kinase ‐2.1 1.2E‐03 NRSN1 neurensin 1 ‐2.1 1.2E‐03 C1H21orf33 ES1 protein homolog, mitochondrial ‐2.1 1.2E‐03 REPS2 RALBP1 associated Eps domain containing 2 ‐2.2 1.2E‐03 TPX2 TPX2, microtubule‐associated, homolog (Xenopus laevis) ‐2.2 1.2E‐03 PPIC peptidylprolyl isomerase C (cyclophilin C) ‐2.2 1.2E‐03 GNG10 guanine nucleotide binding protein (G protein), gamma 10 ‐2.2 1.2E‐03 PHF16 PHD finger protein 16 ‐2.2 1.2E‐03 TMEM108 transmembrane protein 108 ‐2.2 1.2E‐03 MCAM melanoma cell adhesion molecule ‐2.2 1.2E‐03 TLL1 tolloid‐like 1 ‐2.2 1.2E‐03 TMEM194B transmembrane protein 194B ‐2.2 1.2E‐03 PIWIL1 piwi‐like RNA‐mediated gene silencing 1 ‐2.2 1.2E‐03 SORCS1
    [Show full text]
  • Supplemental Table 1
    Supplemental Data Supplemental Table 1. Genes differentially regulated by Ad-KLF2 vs. Ad-GFP infected EC. Three independent genome-wide transcriptional profiling experiments were performed, and significantly regulated genes were identified. Color-coding scheme: Up, p < 1e-15 Up, 1e-15 < p < 5e-10 Up, 5e-10 < p < 5e-5 Up, 5e-5 < p <.05 Down, p < 1e-15 As determined by Zpool Down, 1e-15 < p < 5e-10 Down, 5e-10 < p < 5e-5 Down, 5e-5 < p <.05 p<.05 as determined by Iterative Standard Deviation Algorithm as described in Supplemental Methods Ratio RefSeq Number Gene Name 1,058.52 KRT13 - keratin 13 565.72 NM_007117.1 TRH - thyrotropin-releasing hormone 244.04 NM_001878.2 CRABP2 - cellular retinoic acid binding protein 2 118.90 NM_013279.1 C11orf9 - chromosome 11 open reading frame 9 109.68 NM_000517.3 HBA2;HBA1 - hemoglobin, alpha 2;hemoglobin, alpha 1 102.04 NM_001823.3 CKB - creatine kinase, brain 96.23 LYNX1 95.53 NM_002514.2 NOV - nephroblastoma overexpressed gene 75.82 CeleraFN113625 FLJ45224;PTGDS - FLJ45224 protein;prostaglandin D2 synthase 21kDa 74.73 NM_000954.5 (brain) 68.53 NM_205545.1 UNQ430 - RGTR430 66.89 NM_005980.2 S100P - S100 calcium binding protein P 64.39 NM_153370.1 PI16 - protease inhibitor 16 58.24 NM_031918.1 KLF16 - Kruppel-like factor 16 46.45 NM_024409.1 NPPC - natriuretic peptide precursor C 45.48 NM_032470.2 TNXB - tenascin XB 34.92 NM_001264.2 CDSN - corneodesmosin 33.86 NM_017671.3 C20orf42 - chromosome 20 open reading frame 42 33.76 NM_024829.3 FLJ22662 - hypothetical protein FLJ22662 32.10 NM_003283.3 TNNT1 - troponin T1, skeletal, slow LOC388888 (LOC388888), mRNA according to UniGene - potential 31.45 AK095686.1 CONFLICT - LOC388888 (na) according to LocusLink.
    [Show full text]
  • Table 4. 391 Probe Sets Still Rhythmic After Sleep Deprivation
    Table 4. 391 probe sets still rhythmic after sleep deprivation Affymetrix ID Gene Symbol Description Accession time_sin time_cos adj.P.Val 1438211_s_at Dbp D site albumin promoter binding protein BB550183 -0.013 -0.877 1.44E-13 1418174_at Dbp D site albumin promoter binding protein BC018323 -0.036 -0.880 1.74E-13 1425099_a_at Arntl aryl hydrocarbon receptor nuclear translocator-like BC011080 0.135 0.418 1.02E-11 1416958_at Nr1d2 nuclear receptor subfamily 1, group D, member 2 NM_011584 -0.035 -0.397 7.53E-11 1421087_at Per3 period homolog 3 (Drosophila) NM_011067 -0.119 -0.477 3.46E-10 1450779_at Fabp7 fatty acid binding protein 7, brain NM_021272 0.433 0.404 3.82E-10 1424175_at Tef thyrotroph embryonic factor BC017689 -0.113 -0.279 1.39E-09 1435188_at Gm129 gene model 129, (NCBI) BB407125 -0.097 -0.667 4.17E-09 1417602_at Per2 period homolog 2 (Drosophila) AF035830 -0.460 -0.408 5.97E-09 1425560_a_at S100a16 S100 calcium binding protein A16 BC020031 0.228 0.194 5.97E-09 1435459_at Fmo2 flavin containing monooxygenase 2 BM936480 -0.255 -0.426 6.23E-09 1457350_at Per2 period homolog 2 (Drosophila) BG298986 -0.312 -0.485 7.81E-09 1445892_at Per2 Period homolog 2 (Drosophila) BM238318 -0.300 -0.432 1.50E-08 1448383_at Mmp14 matrix metallopeptidase 14 (membrane-inserted) NM_008608 0.082 0.474 3.74E-08 1456046_at Cd93 CD93 antigen AV319144 0.357 0.320 4.89E-08 1429286_at 1190003M12Rik RIKEN cDNA 1190003M12 gene AK004474 -0.511 -0.245 5.08E-08 similar to Putative RNA-binding protein 3 (RNA- 1422660_at LOC671237 AY052560 -0.269 -0.318 5.40E-08
    [Show full text]
  • Blood-Based Gene-Expression Biomarkers of Post-Traumatic Stress Disorder Among Deployed Marines: a Pilot Study 3
    +Model PNEC-2813; No. of Pages 23 ARTICLE IN PRESS Psychoneuroendocrinology (2014) xxx, xxx—xxx Available online at www.sciencedirect.com ScienceDirect journa l homepage: www.elsevier.com/locate/psyneuen Blood-based gene-expression biomarkers of post-traumatic stress disorder among deployed marines: A pilot study a b Daniel S. Tylee , Sharon D. Chandler , c,d,e b,i b Caroline M. Nievergelt , Xiaohua Liu , Joel Pazol , d,f c,d,e Christopher H. Woelk , James B. Lohr , b,c,e c,d,e William S. Kremen , Dewleen G. Baker , a,∗ b,c,d,e,g,h Stephen J. Glatt , Ming T. Tsuang , 1 Marine Resiliency Study Investigators a Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab), Departments of Psychiatry and Behavioral Sciences & Neuroscience and Physiology, Medical Genetics Research Center, SUNY Upstate Medical University, Syracuse, NY, USA b Center for Behavioral Genomics, Department of Psychiatry, University of California, La Jolla, San Diego, CA, USA c VA Center of Excellence for Stress and Mental Health, San Diego, CA, USA d Veterans Affairs San Diego Healthcare System, San Diego, CA, USA e Department of Psychiatry, University of California, La Jolla, San Diego, CA, USA f Department of Medicine, University of California, La Jolla, San Diego, CA, USA g Institute of Genomic Medicine, University of California, La Jolla, San Diego, CA, USA h Harvard Institute of Psychiatric Epidemiology and Genetics, Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA i Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China Received 14 April 2014; received in revised form 20 August 2014; accepted 22 September 2014 ∗ Corresponding author.
    [Show full text]
  • Hypoxia and Hypoxia-Inducible Factor 1Α Modulate the Immune Response of Human Dendritic Cells Against Aspergillus Fumigatus
    Hypoxia and hypoxia-inducible factor 1α modulate the immune response of human dendritic cells against Aspergillus fumigatus Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Julius-Maximilians-Universität Würzburg vorgelegt von Mirjam Fließer Geboren in Nürnberg Würzburg 2015 Eingereicht am: 11. August 2015 Mitglieder der Promotionskommission: Vorsitzender: Herr Prof. Dr. Thomas Dandekar Gutachter : Herr Prof. Dr. Jürgen Löffler Gutachter: Herr Prof. Dr. Thomas Rudel Tag des Promotionskolloquiums: 14. Oktober 2015 Doktorurkunde ausgehändigt am: Contents Summary .................................................................................................... 1 Zusammenfassung .................................................................................. 3 1 Introduction........................................................................................ 5 1.1 Aspergillus fumigatus as a human pathogen ..................................... 5 1.1.1 Clinical relevance of A. fumigatus infections .............................................................. 5 1.1.2 Biology and life-cycle of A. fumigatus ........................................................................... 6 1.1.3 A. fumigatus associated diseases .................................................................................... 7 1.2 Host immune responses against A. fumigatus ............................... 11 1.2.1 Components of the anti-fungal immune response................................................. 11 1.2.2 Pattern recognition
    [Show full text]