DOCSLIB.ORG

Supplementary Information Supplementary Methods Evaluation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Supplementary Information Supplementary Methods Evaluation Supplementary Information Supplementary Methods Evaluation of the Affymetrix Rhesus Macaque GeneChip for use on Sooty Mangabey Samples Gene expression in rhesus macaques and sooty mangabeys was monitored using the Affymetrix Rhesus Genome Array which contains probesets for ~47, 000 transcripts (1) and has been tested rigorously by independent primate centers (2) These arrays were designed using rhesus cDNA and genomic rhesus sequences from multiple sources (1). Prior to the availability of rhesus macaque arrays, several groups, including our own, have published microarray studies hybridizing samples from various non-human primates on arrays with probes specific for human sequences, however a central concern has been cross-species specificity of these arrays. At the onset of this study, we were concerned with the ability of the Rhesus Genome Array to accurately hybridize cRNA derived from SMs, and whether data from the arrays would allow meaningful comparison between RM and SM samples. The design of probesets for this array employed an algorithm in which macaque subsequences most orthologous between human and macaque were utilized; to further reduce the effect of polymorphisms, 16 probes per probeset (rather than the customary 11) were used for ~15,000 probesets (1). A recent study comparing 1,180 transcripts between rhesus macaques and humans have estimated the average degree of nucleotide identity to be 97.791.78% in the coding region, and 95.104.15% in the 3’ UTR (3), with less than 1% of genes having lower than 88% homology. To minimize the effect of individual probes with sequence mismatches due to interpecies variation, we 1 used the Robust Multi-Array analysis for normalization and probeset summarization of the dataset. We feel that RMA normalization is the most appropriate method for interspecies comparisons for two reasons: (i) the global background adjustment (as opposed to background correction at the probe level, ie. Perfect Match – Mismatch correction) will reduce the effect of mismatches on expression measurements at the probe level, and (ii) the use of median polish to summarize individual probes into probeset measurements is robust and the effect of individual probes with sequence variation due to interspecies differences is minimized. Lastly, as described in detail below, we limited our analysis to include only genes for which (i) a two-fold change in expression relative to uninfected samples could be detected, and (ii) the gene expression change was determined to be statistically significant. Including the requirement of a two-fold ratio cut-off in our analysis, rather than direct comparison of probeset intensities, would further reduce false-negative comparisons between the species. Based on the high degree of cDNA transcript identity between SMs and RMs, the probe design of the Rhesus GeneChip towards orthologous sequences, and our data analysis approach, we reasoned that the Rhesus GeneChip would be able to accurately detect changes in sooty mangabey transcript levels, and that our analysis would be focused on genes that could be meaningfully compared between species. Nevertheless, to confirm the utililty of the array for our analysis, we employed in silico analyses prior to hybridization and post-analysis validation experiments of our results from the main study. To predict the overall binding ability of the Rhesus GeneChip with SM cDNAs, we performed an in silico analysis of perfect and mismatch binding based on SM sequences in GenBank and individual probes on the array. Due to the variation in 2 annotation of probesets on the Affymetrix RM Array, probesets were matched to genes based on the presence of at least one perfect match within the gene sequence. In some cases multiple genes matched with a single probeset due to the presence of multiple alleles or orthologs of particular genes within the set of sequences. The resultant probesets were then aligned using Clustal to the available sequence and probes within each set were binned based on the number of mismatches and/or indels between the probe and the sequence. Probes with >5 mismatches or containing indels are likely not contained within the query sequence. As detailed in Supplementary Table S2, 54 of the 100 sequences were successfully aligned with probesets on the Affymetrix RM array. These 54 sequences represent 42 unique genes. In almost all cases where probesets aligned with their intended gene (e.g. not a close paralog), the number of perfect match probes outnumbered the probes containing mismatches. Because of the median polish summarization employed by the RMA algorithm, and our estimate that most probesets have a majority of probes that match perfectly with SM sequences, we felt any effect due to interspecies sequence variation on the arrays would be minimal. An independent analysis by (C. Davies, Affymetrix, Inc., pers. communication) verified our findings. To verify that hybridization efficiency was similar between RMs and SMs, we calculated the global statistics of the log10 intensity on all 94 arrays described in this study. The array data was pre-filtered to exclude all probesets representing control sequences, viral genomes or reporter genes. As summarized in Supplementary Table S1, the means, medians, ranges and standard deviations on arrays hybridized with total RNA from SMs (33 arrays) or RMs (61 arrays) were nearly identical, and were very similar to the cumulative statistics from all 94 arrays. The distribution of log10 intensities on each 3 individual array is depicted in Supplementary Figure S1. Similar to the global statistics for each species, the RM arrays have a slightly larger range of expression values compared to those hybridized with SM RNA, and arrays hybridized with SM samples have a moderately higher median intensity; however no gross differences were apparent between species hybridization statistics. Collectively, these data validate our in silico model of hybridization of SM RNA on the Rhesus GeneChips. More importantly, these data indicate that gene expression measurements obtained from hybridizing SM RNA samples to the Rhesus GeneChips is directly comparable to data obtained from RM samples. Microarray data analysis – normalization, principal components analysis, hierarchical clustering Arrays were scanned using the Affymetrix GeneChip Scanner 3000 7G, and visual inspection and evaluation of overall intensity was used to judge the quality of the hybridization; 2 of 96 total arrays were judged to be of low quality and removed from the analysis. .CEL files from the remaining Rhesus GeneChip arrays were imported into Partek Genomics Suite v.6.4 (Partek, Inc. St. Louis, MO) and RMA pre-processing was used to perform global background correction, quantile normalization and median polish probeset summarization. Probeset annotation was based on NetAffx Annotation Release 27 (November 2008, Affymetrix, Inc.) which utilizes the most update-to-date build of the rhesus macaque genome (Unigene Macaca mulatta Build #13, September, 2008). After generation of smaller gene lists (ie. One-way ANOVA to define SIV-inducible genes in SMs), probeset annotations were further updated using the DAVID annotation tool 4 (http://david.abcc.ncifcrf.gov/) (3-4). For probesets with no Unigene rhesus macaque identifier, the Gene Symbol for the human orthologue was used. Similarity between samples was estimated using principal component analysis was used to reduce the log10 intensities on individual arrays and using a covariance dispersion matrix and normalized eigenvector scaling. PC#1 (37.4%), PC#2 (11.7%) and PC#3 (7.8%) accounted for 56.9% of the variance in the dataset. Hierarchical clusters between samples or between genes were assembled using Euclidean metric and average linkage to determine distance between datasets and clusters, respectively. Identification of genes induced by SIV infection and genes with differential SIV regulation between species One-way ANOVA was used to test for differences in the log10 intensities of individual probesets in the sooty mangabey group (33 arrays) after infection, and the P-value was corrected for multiple hypothesis testing using the Benjamini-Hochberg False Discovery Rate method. This corrected P-value was 0.0008342 was used in combination with a second criteria, a two-fold change up or down, in the average gene expression relative to pre-infected samples (log10 time X – Log10 time 0 > 0.301 or < -0.301) to define differentially expressed genes. The same algorithm was used to identify differentially expressed genes in the SIVmac239-infected RM group (36 arrays, corrected P-value = 0.00746). Although fairly stringent by current standards, this approach allowed us to have a high degree of confidence in probesets/genes defined as differential. The utility of our approach in identifying genes with SIV-inducible expression in SMs and RMs was 5 supported by the high degree of overlap between our SM/RM dataset and concurrent, independent, studies detailing SIV infection in AGMs (5) and previously published observations from our group in SHIV89.6P infected cynomolgus macaques (6). Because of the differences in sample sizes between the SIVsmm SM (33 arrays), SIVsmm RM (25 arrays) and SIVmac239 RM (36 arrays) groups, a direct Boolean analysis to determine SIV-regulated genes that differed between species would not be appropriate; exclusion of a gene from one group and not another may be due to differing P-values rather than underlying biology. Instead, we employed a two-layered approach. In the first step, we used a two-way ANOVA to define genes with expression that differed (i) between the three infection groups, and (ii) over time after infection. This gene list was further reduced by a post-hoc filter to include only genes that also exhibited significantly different expression values between SMs and both RM groups. This initial gene list was large (>20,000 probesets) and we reasoned that it included genes with differential expression due to (i) species-specific differences in constitutive expression, (ii) differing hybridization efficiency of genes with sequence disparity, or (iii) distinct patterns of transcription in response to SIV infection.
Load more

Recommended publications
  • The C9orf72-Interacting Protein Smcr8 Is a Negative Regulator of Autoimmunity and Lysosomal Exocytosis
    Downloaded from genesdev.cshlp.org on October 5, 2021 - Published by Cold Spring Harbor Laboratory Press The C9orf72-interacting protein Smcr8 is a negative regulator of autoimmunity and lysosomal exocytosis Yingying Zhang,1,2,3 Aaron Burberry,1,2,3 Jin-Yuan Wang,1,2,3 Jackson Sandoe,1,2,3 Sulagna Ghosh,1,2,3 Namrata D. Udeshi,4 Tanya Svinkina,4 Daniel A. Mordes,1,2,3,5 Joanie Mok,1,2,3 Maura Charlton,1,2,3 Quan-Zhen Li,6,7 Steven A. Carr,4 and Kevin Eggan1,2,3 1Department of Stem Cell and Regenerative Biology, 2Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts 02138, USA; 3Stanley Center for Psychiatric Research, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts 02142, USA; 4Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA; 5Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts 02114, USA; 6Department of Immunology, 7Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA While a mutation in C9ORF72 is the most common genetic contributor to amyotrophic lateral sclerosis (ALS), much remains to be learned concerning the function of the protein normally encoded at this locus. To elaborate further on functions for C9ORF72, we used quantitative mass spectrometry-based proteomics to identify interacting proteins in motor neurons and found that its long isoform complexes with and stabilizes SMCR8, which further enables interaction with WDR41. To study the organismal and cellular functions for this tripartite complex, we generated Smcr8 loss-of-function mutant mice and found that they developed phenotypes also observed in C9orf72 loss-of- function animals, including autoimmunity.
    [Show full text]
  • 4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
    Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4).
    [Show full text]
  • UNIVERSITY of CALIFORNIA RIVERSIDE Investigations Into The
    UNIVERSITY OF CALIFORNIA RIVERSIDE Investigations into the Role of TAF1-mediated Phosphorylation in Gene Regulation A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Cell, Molecular and Developmental Biology by Brian James Gadd December 2012 Dissertation Committee: Dr. Xuan Liu, Chairperson Dr. Frank Sauer Dr. Frances M. Sladek Copyright by Brian James Gadd 2012 The Dissertation of Brian James Gadd is approved Committee Chairperson University of California, Riverside Acknowledgments I am thankful to Dr. Liu for her patience and support over the last eight years. I am deeply indebted to my committee members, Dr. Frank Sauer and Dr. Frances Sladek for the insightful comments on my research and this dissertation. Thanks goes out to CMDB, especially Dr. Bachant, Dr. Springer and Kathy Redd for their support. Thanks to all the members of the Liu lab both past and present. A very special thanks to the members of the Sauer lab, including Silvia, Stephane, David, Matt, Stephen, Ninuo, Toby, Josh, Alice, Alex and Flora. You have made all the years here fly by and made them so enjoyable. From the Sladek lab I want to thank Eugene, John, Linh and Karthi. Special thanks go out to all the friends I’ve made over the years here. Chris, Amber, Stephane and David, thank you so much for feeding me, encouraging me and keeping me sane. Thanks to the brothers for all your encouragement and prayers. To any I haven’t mentioned by name, I promise I haven’t forgotten all you’ve done for me during my graduate years.
    [Show full text]
  • TITLE PAGE Oxidative Stress and Response to Thymidylate Synthase
    Downloaded from molpharm.aspetjournals.org at ASPET Journals on October 2, 2021 -Targeted -Targeted 1 , University of of , University SC K.W.B., South Columbia, (U.O., Carolina, This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted.
    [Show full text]
  • Porcine Interferon Complex and Co-Evolution with Increasing Viral Pressure After Domestication
    viruses Review Porcine Interferon Complex and Co-Evolution with Increasing Viral Pressure after Domestication Jordan Jennings and Yongming Sang * Department of Agricultural and Environmental Sciences, College of Agriculture, Tennessee State University, Nashville, TN 37209, USA; [email protected] * Correspondence: [email protected]; Tel.: +615-963-5183 Received: 17 May 2019; Accepted: 13 June 2019; Published: 15 June 2019 Abstract: Consisting of nearly 60 functional genes, porcine interferon (IFN)-complex represents an evolutionary surge of IFN evolution in domestic ungulate species. To compare with humans and mice, each of these species contains about 20 IFN functional genes, which are better characterized using the conventional IFN-α/β subtypes as examples. Porcine IFN-complex thus represents an optimal model for studying IFN evolution that resulted from increasing viral pressure during domestication and industrialization. We hypothesize and justify that porcine IFN-complex may extend its functionality in antiviral and immunomodulatory activity due to its superior molecular diversity. Furthermore, these unconventional IFNs could even confer some functional and signaling novelty beyond that of the well-studied IFN-α/β subtypes. Investigations into porcine IFN-complex will further our understanding of IFN biology and promote IFN-based therapeutic designs to confront swine viral diseases. Keywords: interferon; immune evolution; antiviral; porcine model 1. Introduction Interferons (IFNs) are a group of cytokines that have evolved in jawed vertebrates and bear a pivotal role in antiviral regulation as well as other biological functions [1–4]. Three types of IFNs, namely Type I, II, and III IFNs, have been defined based on their molecular signatures, interacting receptors, and signaling propensities in immune regulation [3,4].
    [Show full text]
  • 2018 Award Recipients
    AWARD RECIPIENTS 1 CREATING A BRIGHTER FUTURE FOR RHEUMATOLOGY The Rheumatology Research Foundation is committed to improving care for the more than 54 million Americans affected by arthritis or other forms of rheumatic disease. The Foundation’s extensive awards program helps patients by increasing the number of rheumatology health professionals while also funding research advancements that lead to new treatments and cures. For more than two decades, the Foundation has supported high-quality clinical and translational research as well as education and training programs. In the coming fiscal year (July 1, 2018 – June 30, 2019), the Foundation has committed to fund more than $9.4 million to rheumatology research and training. About half of those awards will support efforts to recruit and train the next generation of rheumatology professionals, which decreases patient wait times and increases access to rheumatology care. The remaining funds will be awarded to advance research projects that lead to breakthroughs in treating people with rheumatic diseases. In all, the Foundation has committed more than $161 million to fund more than 3,400 awards since 1985, making it the largest private funding source of rheumatology research and training in the United States. Congratulations to the Foundation’s latest award recipients. Their work is vital to creating a brighter future for the field of rheumatology and for the people impacted by rheumatic disease. BRYCE A. BINSTADT, MD, PHD CHAIR, SCIENTIFIC ADVISORY COUNCIL ASSOCIATE PROFESSOR OF PEDIATRICS AND A DISTINGUISHED UNIVERSITY TEACHING PROFESSOR, DIVISION OF PEDIATRIC RHEUMATOLOGY, UNIVERSITY OF MINNESOTA MEDICAL SCHOOL 2 TABLE OF CONTENTS 04 INNOVATIVE RESEARCH AWARDS 13 CAREER DEVELOPMENT RESEARCH AWARDS 14 CAREER DEVELOPMENT BRIDGE FUNDING AWARD: K BRIDGE 15 CAREER DEVELOPMENT BRIDGE FUNDING AWARD: K SUPPLEMENT 16 CAREER DEVELOPMENT BRIDGE FUNDING AWARD: R BRIDGE 18 INVESTIGATOR AWARD 21 SCIENTIST DEVELOPMENT AWARD 29 TOBÉ AND STEPHEN E.
    [Show full text]
  • Antibody List
    產品編號 產品名稱 PA569955 1110059E24Rik Polyclonal Antibody PA569956 1110059E24Rik Polyclonal Antibody PA570131 1190002N15Rik Polyclonal Antibody 01-1234-42 123count eBeads Counting Beads MA512242 14.3.3 Pan Monoclonal Antibody (CG15) LFMA0074 14-3-3 beta Monoclonal Antibody (60C10) LFPA0077 14-3-3 beta Polyclonal Antibody PA137002 14-3-3 beta Polyclonal Antibody PA14647 14-3-3 beta Polyclonal Antibody PA515477 14-3-3 beta Polyclonal Antibody PA517425 14-3-3 beta Polyclonal Antibody PA522264 14-3-3 beta Polyclonal Antibody PA529689 14-3-3 beta Polyclonal Antibody MA134561 14-3-3 beta/epsilon/zeta Monoclonal Antibody (3C8) MA125492 14-3-3 beta/zeta Monoclonal Antibody (22-IID8B) MA125665 14-3-3 beta/zeta Monoclonal Antibody (4E2) 702477 14-3-3 delta/zeta Antibody (1H9L19), ABfinity Rabbit Monoclonal 711507 14-3-3 delta/zeta Antibody (1HCLC), ABfinity Rabbit Oligoclonal 702241 14-3-3 epsilon Antibody (5H10L5), ABfinity Rabbit Monoclonal 711273 14-3-3 epsilon Antibody (5HCLC), ABfinity Rabbit Oligoclonal PA517104 14-3-3 epsilon Polyclonal Antibody PA528937 14-3-3 epsilon Polyclonal Antibody PA529773 14-3-3 epsilon Polyclonal Antibody PA575298 14-3-3 eta (Lys81) Polyclonal Antibody MA524792 14-3-3 eta Monoclonal Antibody PA528113 14-3-3 eta Polyclonal Antibody PA529774 14-3-3 eta Polyclonal Antibody PA546811 14-3-3 eta Polyclonal Antibody MA116588 14-3-3 gamma Monoclonal Antibody (HS23) MA116587 14-3-3 gamma Monoclonal Antibody (KC21) PA529690 14-3-3 gamma Polyclonal Antibody PA578233 14-3-3 gamma Polyclonal Antibody 510700 14-3-3 Pan Polyclonal
    [Show full text]
  • WO2019226953A1.Pdf
    ) ( 2 (51) International Patent Classification: Street, Brookline, MA 02446 (US). WILSON, Christo¬ C12N 9/22 (2006.01) pher, Gerard; 696 Main Street, Apartment 311, Waltham, MA 0245 1(US). DOMAN, Jordan, Leigh; 25 Avon Street, (21) International Application Number: Somverville, MA 02143 (US). PCT/US20 19/033 848 (74) Agent: HEBERT, Alan, M. et al. ;Wolf, Greenfield, Sacks, (22) International Filing Date: P.C., 600 Atlanitc Avenue, Boston, MA 02210-2206 (US). 23 May 2019 (23.05.2019) (81) Designated States (unless otherwise indicated, for every (25) Filing Language: English kind of national protection av ailable) . AE, AG, AL, AM, (26) Publication Language: English AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, (30) Priority Data: DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, 62/675,726 23 May 2018 (23.05.2018) US HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, 62/677,658 29 May 2018 (29.05.2018) US KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (71) Applicants: THE BROAD INSTITUTE, INC. [US/US]; MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, 415 Main Street, Cambridge, MA 02142 (US). PRESI¬ OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, DENT AND FELLOWS OF HARVARD COLLEGE SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, [US/US]; 17 Quincy Street, Cambridge, MA 02138 (US).
    [Show full text]
  • Interactions Between APOBEC3 and Murine Retroviruses: Mechanisms of Restriction and Drug Resistance
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2013 Interactions Between APOBEC3 and Murine Retroviruses: Mechanisms of Restriction and Drug Resistance Alyssa Lea MacMillan University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Virology Commons Recommended Citation MacMillan, Alyssa Lea, "Interactions Between APOBEC3 and Murine Retroviruses: Mechanisms of Restriction and Drug Resistance" (2013). Publicly Accessible Penn Dissertations. 894. https://repository.upenn.edu/edissertations/894 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/894 For more information, please contact [email protected]. Interactions Between APOBEC3 and Murine Retroviruses: Mechanisms of Restriction and Drug Resistance Abstract APOBEC3 proteins are important for antiretroviral defense in mammals. The activity of these factors has been well characterized in vitro, identifying cytidine deamination as an active source of viral restriction leading to hypermutation of viral DNA synthesized during reverse transcription. These mutations can result in viral lethality via disruption of critical genes, but in some cases is insufficiento t completely obstruct viral replication. This sublethal level of mutagenesis could aid in viral evolution. A cytidine deaminase-independent mechanism of restriction has also been identified, as catalytically inactive proteins are still able to inhibit infection in vitro. Murine retroviruses do not exhibit characteristics of hypermutation by mouse APOBEC3 in vivo. However, human APOBEC3G protein expressed in transgenic mice maintains antiviral restriction and actively deaminates viral genomes. The mechanism by which endogenous APOBEC3 proteins function is unclear. The mouse provides a system amenable to studying the interaction of APOBEC3 and retroviral targets in vivo.
    [Show full text]
  • IFNK (NM 020124) Human Tagged ORF Clone Product Data
    OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for RC221055L4 IFNK (NM_020124) Human Tagged ORF Clone Product data: Product Type: Expression Plasmids Product Name: IFNK (NM_020124) Human Tagged ORF Clone Tag: mGFP Symbol: IFNK Synonyms: IFNT1; INFE1 Vector: pLenti-C-mGFP-P2A-Puro (PS100093) E. coli Selection: Chloramphenicol (34 ug/mL) Cell Selection: Puromycin ORF Nucleotide The ORF insert of this clone is exactly the same as(RC221055). Sequence: Restriction Sites: SgfI-MluI Cloning Scheme: ACCN: NM_020124 ORF Size: 621 bp This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 2 IFNK (NM_020124) Human Tagged ORF Clone – RC221055L4 OTI Disclaimer: The molecular sequence of this clone aligns with the gene accession number as a point of reference only. However, individual transcript sequences of the same gene can differ through naturally occurring variations (e.g. polymorphisms), each with its own valid existence. This clone is substantially in agreement with the reference, but a complete review of all prevailing variants is recommended prior to use. More info OTI Annotation: This clone was engineered to express the complete ORF with an expression tag. Expression varies depending on the nature of the gene. RefSeq: NM_020124.1 RefSeq Size: 1164 bp RefSeq ORF: 624 bp Locus ID: 56832 UniProt ID: Q9P0W0 Protein Families: Druggable Genome, Secreted Protein, Transmembrane Protein Pathways: Cytokine-cytokine receptor interaction, Jak-STAT signaling pathway, RIG-I-like receptor signaling pathway MW: 22.1 kDa Gene Summary: This gene encodes a member of the type I interferon family.
    [Show full text]
  • Supplemental Table S1. Primers for Sybrgreen Quantitative RT-PCR Assays
    Supplemental Table S1. Primers for SYBRGreen quantitative RT-PCR assays. Gene Accession Primer Sequence Length Start Stop Tm GC% GAPDH NM_002046.3 GAPDH F TCCTGTTCGACAGTCAGCCGCA 22 39 60 60.43 59.09 GAPDH R GCGCCCAATACGACCAAATCCGT 23 150 128 60.12 56.52 Exon junction 131/132 (reverse primer) on template NM_002046.3 DNAH6 NM_001370.1 DNAH6 F GGGCCTGGTGCTGCTTTGATGA 22 4690 4711 59.66 59.09% DNAH6 R TAGAGAGCTTTGCCGCTTTGGCG 23 4797 4775 60.06 56.52% Exon junction 4790/4791 (reverse primer) on template NM_001370.1 DNAH7 NM_018897.2 DNAH7 F TGCTGCATGAGCGGGCGATTA 21 9973 9993 59.25 57.14% DNAH7 R AGGAAGCCATGTACAAAGGTTGGCA 25 10073 10049 58.85 48.00% Exon junction 9989/9990 (forward primer) on template NM_018897.2 DNAI1 NM_012144.2 DNAI1 F AACAGATGTGCCTGCAGCTGGG 22 673 694 59.67 59.09 DNAI1 R TCTCGATCCCGGACAGGGTTGT 22 822 801 59.07 59.09 Exon junction 814/815 (reverse primer) on template NM_012144.2 RPGRIP1L NM_015272.2 RPGRIP1L F TCCCAAGGTTTCACAAGAAGGCAGT 25 3118 3142 58.5 48.00% RPGRIP1L R TGCCAAGCTTTGTTCTGCAAGCTGA 25 3238 3214 60.06 48.00% Exon junction 3124/3125 (forward primer) on template NM_015272.2 Supplemental Table S2. Transcripts that differentiate IPF/UIP from controls at 5%FDR Fold- p-value Change Transcript Gene p-value p-value p-value (IPF/UIP (IPF/UIP Cluster ID RefSeq Symbol gene_assignment (Age) (Gender) (Smoking) vs. C) vs. C) NM_001178008 // CBS // cystathionine-beta- 8070632 NM_001178008 CBS synthase // 21q22.3 // 875 /// NM_0000 0.456642 0.314761 0.418564 4.83E-36 -2.23 NM_003013 // SFRP2 // secreted frizzled- 8103254 NM_003013
    [Show full text]
  • Genome-Scale Activation Screen Identifies a Lncrna Locus Regulating a Gene Neighbourhood Julia Joung1,2,3,4, Jesse M
    LETTER doi:10.1038/nature23451 Genome-scale activation screen identifies a lncRNA locus regulating a gene neighbourhood Julia Joung1,2,3,4, Jesse M. Engreitz2, Silvana Konermann2,3,4†, Omar O. Abudayyeh2,3,4,5, Vanessa K. Verdine2,3, Francois Aguet2, Jonathan S. Gootenberg2,3,4,6, Neville E. Sanjana2,3,4†, Jason B. Wright2,3,4, Charles P. Fulco2,6, Yuen-Yi Tseng2, Charles H. Yoon7, Jesse S. Boehm2, Eric S. Lander2,6,8 & Feng Zhang1,2,3,4 Mammalian genomes contain thousands of loci that transcribe long genes, one of which confers the resistance phenotype. Our screening noncoding RNAs (lncRNAs)1,2, some of which are known to carry and characterization approach provides a CRISPR toolkit with out critical roles in diverse cellular processes through a variety of which to systematically discover the functions of noncoding loci and mechanisms3–8. Although some lncRNA loci encode RNAs that elucidate their diverse roles in gene regulation and cellular function. act non-locally (in trans)5, there is emerging evidence that many We have previously used the Cas9 synergistic activation mediator lncRNA loci act locally (in cis) to regulate the expression of nearby (SAM) to screen for protein-coding genes that confer resistance to genes—for example, through functions of the lncRNA promoter, the BRAF inhibitor vemurafenib in melanoma cells9, making this an transcription, or transcript itself3,6–8. Despite their potentially ideal phenotype for screening lncRNA loci (Supplementary Note 1). important roles, it remains challenging to identify functional We designed a genome-scale single guide RNA (sgRNA) library lncRNA loci and distinguish among these and other mechanisms.
    [Show full text]