Supplemental Figs, Tables, Methods for Complete

Total Page:16

File Type:pdf, Size:1020Kb

Supplemental Figs, Tables, Methods for Complete Apple us22617 Supplemental Information An Ensemble of Regulatory Elements Controls Runx3 Spatiotemporal Expression in Subsets of Dorsal Root Ganglia Proprioceptive Neurons Supplemental _Fig _ S1 is related to Fig. 1 Supplemental _Fig _ S2 is related to Fig. 1 Supplemental _Fig _ S3 is related to Fig. 1 Supplemental _Fig _ S4 is related to Fig. 2 Supplemental _Fig _ S5 is related to Figs. 3, 5 Supplemental _Fig _ S6 is related to Figs. 3, 5 Supplemental _Fig _ S7 is related to Fig. 3 Supplemental _Fig _ S8 is related to Fig. 6 Supplemental _Fig _ S9 is related to Fig. 7 Supplemental _ Table_ S1 is related to Fig. 1 Supplemental _ Table_ S2 is related to Fig. 1 Supplemental _ Table_ S3 is related to Fig. 1 Supplemental _ Table_ S4 is related to Fig. 1 Supplemental _ Table_ S5 is related to Material and methods Supplemental _ Table_ S6 is related to Fig. 7 Supplemental _ Table_ S7 is related to Fig. 7 Supplemental_ Movie_ S1.mp4 is related to Fig. 3 Supplemental_ Methods.pdf 1 Supplemental Figures 2 Supplemental Fig. S1. The overlapping BACs –C, -E and -A encompass the genomic region required for the established Runx3 expression Sagittal sections of E14.5 BAC transgenic and Runx3LacZ/LacZ knock-in embryos depicting LacZ expression. BAC-B, -D and -F, completely lacked expression in hematopoietic tissues and in DRG and exhibited faint expression at all other sites (Supplemental Table S1). Despite the fact that BAC-B contains both P1 and P2, and BACs -D and -F contain only P2, the expression pattern of the three BACs was highly similar. BACs -A, -C and -E that extend 5’upstream of Runx3 exhibited a more intense LacZ expression in the same organs as BACs -B, -D and -F (Supplemental Table S1), indicating that the region upstream of P1 contains REs that intensify Runx3 expression in these tissues. These BACs also exhibited LacZ expression in hematopoietic organs, with BAC-A conferring the highest reporter expressing in the liver and thymus. Upper panels show embryos outer side and lower panels show inner side. AFP GFP Supplemental Fig. S2. Schematic representation of the Runx3 gene with P1 and P2 specific KI alleles 3 Runx3 gene (upper line) was modified to generate the two KI alleles; P1-driven AFP KI allele (P1AFP, middle line) and P2-driven GFP KI allele (P2GFP, lower line). 4 Supplemental Fig. S3. Strategy for generation of compound heterozygote mutant mice (A) CRISPR-mediated deletion of R1,2,3 REs. Blue arrows underneath the line indicate position of sgRNAs while the shears mark Cas9 cutting sites (see also Supplemental Table S4). (B) Diagnostic PCR for detection of R1,2,3 REs homozygous deletion. PCR primers were designed to detect WT internal R1 and R3 fragments (lanes 1 and 3) and their absence in the mutant (lanes 2 and 4). R3/R1 PCR reaction detecting the deleted allele is seen in mutant (lane 7) but not in WT (lane 6). Lane 5 shows MW markers (see PCR oligos in Supplemental Table S5). (C) Schematic representation of compound heterozygote mice carrying one Runx3 allele bearing CRISPR-deleted REs (CRΔR1,2,3) and a second P2GFP Runx3 null allele. 5 Supplemental Fig. S4. Differential REs Activity in BAC transgenics at E12.5 (A) GFP expression in TrkC/Runx3 neurons: shown are intact BAC-C and various BAC-C RE-deleted brachial DRG. (B) Histograms showing percentage of TrkC/Runx3 neurons expressing BAC-derived GFP. BAC-C-GFP (Ct) and its indicated deleted versions. *p<0.005, **p<0.0001. 6 Supplemental Fig. S5. Histograms showing the results of counting TrkC neurons in serial sections of E15.5 DRG (A) Total number of Runx3 expressing TrkC/GFP cells in C5-T1 DRGs of: WT/P2GFP (marked as Ct), ΔR1= (CRΔR1/P2GFP), ΔR2= (CRΔR2/P2GFP), ΔR3= (CRΔR3/P2GFP), R1= (CRΔR2,3/P2GFP), R2= (CRΔR1,3/P2GFP), R3= (CRΔR1,2/P2GFP), Data were obtained by counting the total number of TrkC/GFP neurons in serial sections of an entire ganglion (n=2-4 embryos of either control or each mutant). *p<0.05, **p<0.001, ***p<0.0002. (B) Total number of TrkC/GFP cells in C5-T1 DRGs acquired by counting the same set of embryos as indicated in (A). 7 Supplemental Fig. S6. Histograms showing the proportion of TrkC neurons in various RE-deleted brachial DRGs (A) Percentage of Runx3 expressing TrkC/GFP cells present in RE-mutant compared to WT/P2GFP embryos. Data was deduced from the cell counting presented in Supplemental Fig. S5A (average mutant cell number/average WT/P2GFPx100). *p<0.05, **p<0.001, ***p<0.0002. (B) Percentage of TrkC/GFP cells present in mutant compared to WT/P2GFP embryos. Data was deduced from the cell counting presented in Supplemental Fig. S5B. 8 Supplemental Fig. S7. The DRG REs also regulate Runx3 expression in the trigeminal ganglion Expression of endogenous Runx3, TrkC and GFP in trigeminal ganglion at E14.5. Panels (from left to right): CRΔR1,2,3/P2GFP, CRΔR1,3/P2GFP, CRΔR1/P2GFP. 9 Supplemental Fig. S8. Deletion of R2 evokes GFP expression in TrkB neurons of BAC transgenics (A) Expression of Runx3, TrkB and GFP in BAC-C and (B) in BAC-C-ΔR2. Panels in (A) or (B) from left to right: transgenic embryos at E11, E11.5, E12.5, and E14.5. (C) Histograms showing percentage of Runx3 expressing TrkB neurons in E11-E14.5 WT embryos. (D) Histograms showing percentage of GFP expressing TrkB neurons in E11-E14.5 BAC-C-ΔR2 transgenic embryos. *p< 0.0001 compared to WT (C) at E11.5, E12.5 and E14.5. (E) Expression of endogenous Runx3, TrkB and GFP in CRΔR2/P2GFP E12.5 embryos. 10 Supplemental Fig. S9. Gene expression analysis discovers TrkC-neuron specific Runx3-responsive genes that were previously presumed to be Brn3a-targets RNA-seq was conducted on TrkC neurons of E11.5 P2GFP/+ and P2GFP/GFP embryos (see Supplemental Materials and methods). This early developmental stage was selected because at this stage the number of TrkC neurons is relatively high. FACS-sorted P2GFP/+ neurons express significantly higher level of Runx3 compared to Runx1 or Runx2 as well as a higher level of Ntrk3 compared to Ntrk1 and Ntrk2 (Supplemental Table S7), thereby providing strong indication for the TrkC identity of the neurons used for RNA-seq analysis. Data analysis revealed pronounced gene expression changes between P2GFP/+ and P2GFP/GFP neurons (Supplemental Table S7), underscoring the central role of Runx3 in TrkC neuron homeostasis. Interestingly, Ingenuity upstream regulator analysis revealed that several previously reported Brn3a (Pou4f1) regulated genes behave as Runx3-responsive genes. Namely, similarly to their response in Brn3a-mutant those genes were either upregulated (marked in pink) or downregulated (marked in green) in P2GFP/GFP neurons (lacking Runx3) compared to P2GFP/+ neurons. These findings are consistent with the observation that in TrkC neurons, Brn3a is an upstream regulator of Runx3. 11 Supplemental Table S1 Expression pattern of BACs surrounding Runx3 The detailed expression pattern of the six BACs: BAC-A, -B, -C, -D, -E, -F, listed in the Table according to their position in the murine genome starting from the 5’-end, and a Runx3 LacZ knock-in line (Runx3 KI). Original BAC names are indicated in brackets (see UCSC browser; mm9). The intensity of expression was evaluated qualitatively and is marked by + to +++. No expression is marked by a minus (-). The numbers in brackets indicate the number of positively-stained embryos out of the total number of embryos analyzed for each BAC. BAC E BAC A BAC B BAC F BAC D Runx3 BAC C Organ BAC (RP23- (RP24- (RP24- (RP24- (RP23- KI (RP23-394B) 307D6) 118B14) 252E9) 180O8) 57P18) Thymus ++ + (2/9) ++ (1/7) ++ (6/12) - (9/9) - (5/5) - (8/8) Liver ++ +/- (3/9) + (1/7) + (6/12) - (9/9) ++ (1/5) - (8/8) Eyelid mesenchyme + ++ (8/9) ++ (7/7) +++ (12/12) ++ (3/9) + (4/5) + (5/8) Tongue – mesenchymal element ++ ++ (9/9) ++ (7/7) +++ (11/12) + (7/9) + (5/5) + (8/8) of filiform papillae DRG Cervical ++ ++ (8/9) ++ (4/7) + (1/12) - (9/9) - (5/5) +/- (1/8) Brachial ++ ++ (8/9) +/- (5/7) +/- (4/12) - (9/9) - (5/5) +/- (1/8) Thoracic + ++ (8/9) +/- (5/7) +/- (8/12) - (9/9) - (5/5) +/- (2/8) Lumbar ++ ++ (7/9) + (5/7) +/- (6/12) - (9/9) - (5/5) +/- (1/8) Coccygeal (Tail) ++ ++ (9/9) ++ (5/7) + (7/12) - (9/9) - (5/5) +/- (1/8) Hair follicles + ++ (7/9) ++ (7/7) ++ (11/12) + (2/9) + (3/5) +/- (3/8) Whiskers upper ++ + (8/9) ++ (7/7) +++ 10/12) + (2/9) +/- (4/5) - (8/8) Whiskers lower + +++ (9/9) +++ (7/7) +++ 12/12) ++ (6/9) ++ (5/5) ++ (8/8) Inner Epithelium of the ear + + (1/9) - (7/7) + (1/12) + (2/9) +/- (3/5) + (2/8) Teeth mesenchyme + + (4/9) + (6/7) + (9/12) - (9/9) ++ (1/5) +/- (1/8) Nails upper limbs ++ +++ (8/9) +++ (7/7) +++ (11/12) + (3/9) + (4/4) - (8/8) Nails lower limbs ++ +++ (8/9) ++ (7/7) +++ (11/12) + (2/9) + (2/4) - (8/8) Nose mesenchyme ++ +++ (9/9) +++ (7/7) +++ (12/12) ++ (8/9) ++ (5/5) ++ (7/8) Penis ++ +++ (9/9) +++ (7/7) +++ (10/10) + (4/5) ++ (4/5) + (3/5) Upper limb cartilage Scapula ++ +++ (9/9) +++ (7/7) +++ (11/12) + (6/9) ++ (5/5) ++ (8/8) Humerus ++ +++ (9/9) +++ (7/7) +++ (11/12) + (6/9) ++ (5/5) ++ (8/8) Radius ++ +++ (9/9) +++ (7/7) +++ (12/12) + (6/9) ++ (5/5) ++ (8/8) Ulna ++ +++ (9/9) +++ (7/7) +++ (12/12) + (6/9) ++ (5/5) ++ (8/8) Carpus - - (9/9) +/- (1/7) - (12/12) - (9/9) - (5/5) - (8/8) Metacarpus + +++ (7/9) +++ (6/7) +++ (12/12) +/- (4/9) ++ (4/5) ++ (8/8) Digits P1 + +++ (4/9) +++ (5/7) +++ (12/12) +/- (1/9) + (4/5) ++ (8/8) Digits P2 - +++ (3/9) ++ (4/7) +++ (9/12) - (9/9) +/- (3/5) ++ (6/8) Lower limb cartilage Femur ++ +++ (9/9) +++ (7/7) +++ (12/12) + (6/9) ++ (5/5) ++ (8/8) Tibia ++ +++ (9/9) +++ (7/7) +++ (12/12) + (6/9) ++ (5/5) ++ (8/8) Fibula ++ +++ (9/9) +++ (7/7) +++ (12/12) + (6/9) ++ (5/5) ++ (8/8) Tarsus - +/- (2/9) +/- (5/7) + (11/12) - (9/9) + (4/5) + (2/8) Metatarsus + +++ (7/9) +++ (6/7) +++ (12/12) +/- (3/9) ++ (5/5) ++ (8/8) Digits P1 + +++ (3/9) +++ (5/7) +++ (12/12) - (9/9) ++ (4/5) ++ (7/8) Digits P2 - - (9/9) + (2/7) +++ (9/12) - (9/9) - (5/5) +++ (4/8) Ribs ++ +++ (9/9) +++ (7/7) +++ (11/12) + (5/9) ++ (5/5) + (8/8) Vertebra cartilage ++ +++ (9/9) +++ (7/7) +++ (11/12) + (5/9) ++ (5/5) + (8/8) 12 Supplemental Table S2 A list of candidate cis-regulatory elements in the murine Runx3 locus (chr4:134,953,991- 135,208,237; mm10).
Recommended publications
  • THE ROLE of HOMEODOMAIN TRANSCRIPTION FACTOR IRX5 in CARDIAC CONTRACTILITY and HYPERTROPHIC RESPONSE by © COPYRIGHT by KYOUNG H
    THE ROLE OF HOMEODOMAIN TRANSCRIPTION FACTOR IRX5 IN CARDIAC CONTRACTILITY AND HYPERTROPHIC RESPONSE By KYOUNG HAN KIM A THESIS SUBMITTED IN CONFORMITY WITH THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY GRADUATE DEPARTMENT OF PHYSIOLOGY UNIVERSITY OF TORONTO © COPYRIGHT BY KYOUNG HAN KIM (2011) THE ROLE OF HOMEODOMAIN TRANSCRIPTION FACTOR IRX5 IN CARDIAC CONTRACTILITY AND HYPERTROPHIC RESPONSE KYOUNG HAN KIM DOCTOR OF PHILOSOPHY GRADUATE DEPARTMENT OF PHYSIOLOGY UNIVERSITY OF TORONTO 2011 ABSTRACT Irx5 is a homeodomain transcription factor that negatively regulates cardiac fast transient + outward K currents (Ito,f) via the KV4.2 gene and is thereby a major determinant of the transmural repolarization gradient. While Ito,f is invariably reduced in heart disease and changes in Ito,f can modulate both cardiac contractility and hypertrophy, less is known about a functional role of Irx5, and its relationship with Ito,f, in the normal and diseased heart. Here I show that Irx5 plays crucial roles in the regulation of cardiac contractility and proper adaptive hypertrophy. Specifically, Irx5-deficient (Irx5-/-) hearts had reduced cardiac contractility and lacked the normal regional difference in excitation-contraction with decreased action potential duration, Ca2+ transients and myocyte shortening in sub-endocardial, but not sub-epicardial, myocytes. In addition, Irx5-/- mice showed less cardiac hypertrophy, but increased interstitial fibrosis and greater contractility impairment following pressure overload. A defect in hypertrophic responses in Irx5-/- myocardium was confirmed in cultured neonatal mouse ventricular myocytes, exposed to norepinephrine while being restored with Irx5 replacement. Interestingly, studies using mice ii -/- virtually lacking Ito,f (i.e. KV4.2-deficient) showed that reduced contractility in Irx5 mice was completely restored by loss of KV4.2, whereas hypertrophic responses to pressure-overload in hearts remained impaired when both Irx5 and Ito,f were absent.
    [Show full text]
  • Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse
    Welcome to More Choice CD Marker Handbook For more information, please visit: Human bdbiosciences.com/eu/go/humancdmarkers Mouse bdbiosciences.com/eu/go/mousecdmarkers Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse CD3 CD3 CD (cluster of differentiation) molecules are cell surface markers T Cell CD4 CD4 useful for the identification and characterization of leukocytes. The CD CD8 CD8 nomenclature was developed and is maintained through the HLDA (Human Leukocyte Differentiation Antigens) workshop started in 1982. CD45R/B220 CD19 CD19 The goal is to provide standardization of monoclonal antibodies to B Cell CD20 CD22 (B cell activation marker) human antigens across laboratories. To characterize or “workshop” the antibodies, multiple laboratories carry out blind analyses of antibodies. These results independently validate antibody specificity. CD11c CD11c Dendritic Cell CD123 CD123 While the CD nomenclature has been developed for use with human antigens, it is applied to corresponding mouse antigens as well as antigens from other species. However, the mouse and other species NK Cell CD56 CD335 (NKp46) antibodies are not tested by HLDA. Human CD markers were reviewed by the HLDA. New CD markers Stem Cell/ CD34 CD34 were established at the HLDA9 meeting held in Barcelona in 2010. For Precursor hematopoetic stem cell only hematopoetic stem cell only additional information and CD markers please visit www.hcdm.org. Macrophage/ CD14 CD11b/ Mac-1 Monocyte CD33 Ly-71 (F4/80) CD66b Granulocyte CD66b Gr-1/Ly6G Ly6C CD41 CD41 CD61 (Integrin b3) CD61 Platelet CD9 CD62 CD62P (activated platelets) CD235a CD235a Erythrocyte Ter-119 CD146 MECA-32 CD106 CD146 Endothelial Cell CD31 CD62E (activated endothelial cells) Epithelial Cell CD236 CD326 (EPCAM1) For Research Use Only.
    [Show full text]
  • Viewed Under 23 (B) Or 203 (C) fi M M Male Cko Mice, and Largely Unaffected Magni Cation; Scale Bars, 500 M (B) and 50 M (C)
    BRIEF COMMUNICATION www.jasn.org Renal Fanconi Syndrome and Hypophosphatemic Rickets in the Absence of Xenotropic and Polytropic Retroviral Receptor in the Nephron Camille Ansermet,* Matthias B. Moor,* Gabriel Centeno,* Muriel Auberson,* † † ‡ Dorothy Zhang Hu, Roland Baron, Svetlana Nikolaeva,* Barbara Haenzi,* | Natalya Katanaeva,* Ivan Gautschi,* Vladimir Katanaev,*§ Samuel Rotman, Robert Koesters,¶ †† Laurent Schild,* Sylvain Pradervand,** Olivier Bonny,* and Dmitri Firsov* BRIEF COMMUNICATION *Department of Pharmacology and Toxicology and **Genomic Technologies Facility, University of Lausanne, Lausanne, Switzerland; †Department of Oral Medicine, Infection, and Immunity, Harvard School of Dental Medicine, Boston, Massachusetts; ‡Institute of Evolutionary Physiology and Biochemistry, St. Petersburg, Russia; §School of Biomedicine, Far Eastern Federal University, Vladivostok, Russia; |Services of Pathology and ††Nephrology, Department of Medicine, University Hospital of Lausanne, Lausanne, Switzerland; and ¶Université Pierre et Marie Curie, Paris, France ABSTRACT Tight control of extracellular and intracellular inorganic phosphate (Pi) levels is crit- leaves.4 Most recently, Legati et al. have ical to most biochemical and physiologic processes. Urinary Pi is freely filtered at the shown an association between genetic kidney glomerulus and is reabsorbed in the renal tubule by the action of the apical polymorphisms in Xpr1 and primary fa- sodium-dependent phosphate transporters, NaPi-IIa/NaPi-IIc/Pit2. However, the milial brain calcification disorder.5 How- molecular identity of the protein(s) participating in the basolateral Pi efflux remains ever, the role of XPR1 in the maintenance unknown. Evidence has suggested that xenotropic and polytropic retroviral recep- of Pi homeostasis remains unknown. Here, tor 1 (XPR1) might be involved in this process. Here, we show that conditional in- we addressed this issue in mice deficient for activation of Xpr1 in the renal tubule in mice resulted in impaired renal Pi Xpr1 in the nephron.
    [Show full text]
  • Tools for Cell Therapy and Immunoregulation
    RnDSy-lu-2945 Tools for Cell Therapy and Immunoregulation Target Cell TIM-4 SLAM/CD150 BTNL8 PD-L2/B7-DC B7-H1/PD-L1 (Human) Unknown PD-1 B7-1/CD80 TIM-1 SLAM/CD150 Receptor TIM Family SLAM Family Butyrophilins B7/CD28 Families T Cell Multiple Co-Signaling Molecules Co-stimulatory Co-inhibitory Ig Superfamily Regulate T Cell Activation Target Cell T Cell Target Cell T Cell B7-1/CD80 B7-H1/PD-L1 T cell activation requires two signals: 1) recognition of the antigenic peptide/ B7-1/CD80 B7-2/CD86 CTLA-4 major histocompatibility complex (MHC) by the T cell receptor (TCR) and 2) CD28 antigen-independent co-stimulation induced by interactions between B7-2/CD86 B7-H1/PD-L1 B7-1/CD80 co-signaling molecules expressed on target cells, such as antigen-presenting PD-L2/B7-DC PD-1 ICOS cells (APCs), and their T cell-expressed receptors. Engagement of the TCR in B7-H2/ICOS L 2Ig B7-H3 (Mouse) the absence of this second co-stimulatory signal typically results in T cell B7-H1/PD-L1 B7/CD28 Families 4Ig B7-H3 (Human) anergy or apoptosis. In addition, T cell activation can be negatively regulated Unknown Receptors by co-inhibitory molecules present on APCs. Therefore, integration of the 2Ig B7-H3 Unknown B7-H4 (Mouse) Receptors signals transduced by co-stimulatory and co-inhibitory molecules following TCR B7-H5 4Ig B7-H3 engagement directs the outcome and magnitude of a T cell response Unknown Ligand (Human) B7-H5 including the enhancement or suppression of T cell proliferation, B7-H7 Unknown Receptor differentiation, and/or cytokine secretion.
    [Show full text]
  • In Silico Prediction of High-Resolution Hi-C Interaction Matrices
    ARTICLE https://doi.org/10.1038/s41467-019-13423-8 OPEN In silico prediction of high-resolution Hi-C interaction matrices Shilu Zhang1, Deborah Chasman 1, Sara Knaack1 & Sushmita Roy1,2* The three-dimensional (3D) organization of the genome plays an important role in gene regulation bringing distal sequence elements in 3D proximity to genes hundreds of kilobases away. Hi-C is a powerful genome-wide technique to study 3D genome organization. Owing to 1234567890():,; experimental costs, high resolution Hi-C datasets are limited to a few cell lines. Computa- tional prediction of Hi-C counts can offer a scalable and inexpensive approach to examine 3D genome organization across multiple cellular contexts. Here we present HiC-Reg, an approach to predict contact counts from one-dimensional regulatory signals. HiC-Reg pre- dictions identify topologically associating domains and significant interactions that are enri- ched for CCCTC-binding factor (CTCF) bidirectional motifs and interactions identified from complementary sources. CTCF and chromatin marks, especially repressive and elongation marks, are most important for HiC-Reg’s predictive performance. Taken together, HiC-Reg provides a powerful framework to generate high-resolution profiles of contact counts that can be used to study individual locus level interactions and higher-order organizational units of the genome. 1 Wisconsin Institute for Discovery, 330 North Orchard Street, Madison, WI 53715, USA. 2 Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, USA. *email: [email protected] NATURE COMMUNICATIONS | (2019) 10:5449 | https://doi.org/10.1038/s41467-019-13423-8 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13423-8 he three-dimensional (3D) organization of the genome has Results Temerged as an important component of the gene regulation HiC-Reg for predicting contact count using Random Forests.
    [Show full text]
  • Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
    Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897
    [Show full text]
  • Table 2. Significant
    Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S.
    [Show full text]
  • 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.
    [Show full text]
  • A Clinicopathological and Molecular Genetic Analysis of Low-Grade Glioma in Adults
    A CLINICOPATHOLOGICAL AND MOLECULAR GENETIC ANALYSIS OF LOW-GRADE GLIOMA IN ADULTS Presented by ANUSHREE SINGH MSc A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy Brain Tumour Research Centre Research Institute in Healthcare Sciences Faculty of Science and Engineering University of Wolverhampton November 2014 i DECLARATION This work or any part thereof has not previously been presented in any form to the University or to any other body whether for the purposes of assessment, publication or for any other purpose (unless otherwise indicated). Save for any express acknowledgments, references and/or bibliographies cited in the work, I confirm that the intellectual content of the work is the result of my own efforts and of no other person. The right of Anushree Singh to be identified as author of this work is asserted in accordance with ss.77 and 78 of the Copyright, Designs and Patents Act 1988. At this date copyright is owned by the author. Signature: Anushree Date: 30th November 2014 ii ABSTRACT The aim of the study was to identify molecular markers that can determine progression of low grade glioma. This was done using various approaches such as IDH1 and IDH2 mutation analysis, MGMT methylation analysis, copy number analysis using array comparative genomic hybridisation and identification of differentially expressed miRNAs using miRNA microarray analysis. IDH1 mutation was present at a frequency of 71% in low grade glioma and was identified as an independent marker for improved OS in a multivariate analysis, which confirms the previous findings in low grade glioma studies.
    [Show full text]
  • Supplementary Table 1: Adhesion Genes Data Set
    Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like,
    [Show full text]
  • Understanding Chronic Kidney Disease: Genetic and Epigenetic Approaches
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2017 Understanding Chronic Kidney Disease: Genetic And Epigenetic Approaches Yi-An Ko Ko University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Bioinformatics Commons, Genetics Commons, and the Systems Biology Commons Recommended Citation Ko, Yi-An Ko, "Understanding Chronic Kidney Disease: Genetic And Epigenetic Approaches" (2017). Publicly Accessible Penn Dissertations. 2404. https://repository.upenn.edu/edissertations/2404 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2404 For more information, please contact [email protected]. Understanding Chronic Kidney Disease: Genetic And Epigenetic Approaches Abstract The work described in this dissertation aimed to better understand the genetic and epigenetic factors influencing chronic kidney disease (CKD) development. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) significantly associated with chronic kidney disease. However, these studies have not effectively identified target genes for the CKD variants. Most of the identified variants are localized to non-coding genomic regions, and how they associate with CKD development is not well-understood. As GWAS studies only explain a small fraction of heritability, we hypothesized that epigenetic changes could explain part of this missing heritability. To identify potential gene targets of the genetic variants, we performed expression quantitative loci (eQTL) analysis, using genotyping arrays and RNA sequencing from human kidney samples. To identify the target genes of CKD-associated SNPs, we integrated the GWAS-identified SNPs with the eQTL results using a Bayesian colocalization method, coloc. This resulted in a short list of target genes, including PGAP3 and CASP9, two genes that have been shown to present with kidney phenotypes in knockout mice.
    [Show full text]
  • Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses
    bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract The high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a proteinprotein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes.
    [Show full text]