Epigenetic Interplay Between Mouse Endogenous
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
WNT16 Is a New Marker of Senescence
Table S1. A. Complete list of 177 genes overexpressed in replicative senescence Value Gene Description UniGene RefSeq 2.440 WNT16 wingless-type MMTV integration site family, member 16 (WNT16), transcript variant 2, mRNA. Hs.272375 NM_016087 2.355 MMP10 matrix metallopeptidase 10 (stromelysin 2) (MMP10), mRNA. Hs.2258 NM_002425 2.344 MMP3 matrix metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3), mRNA. Hs.375129 NM_002422 2.300 HIST1H2AC Histone cluster 1, H2ac Hs.484950 2.134 CLDN1 claudin 1 (CLDN1), mRNA. Hs.439060 NM_021101 2.119 TSPAN13 tetraspanin 13 (TSPAN13), mRNA. Hs.364544 NM_014399 2.112 HIST2H2BE histone cluster 2, H2be (HIST2H2BE), mRNA. Hs.2178 NM_003528 2.070 HIST2H2BE histone cluster 2, H2be (HIST2H2BE), mRNA. Hs.2178 NM_003528 2.026 DCBLD2 discoidin, CUB and LCCL domain containing 2 (DCBLD2), mRNA. Hs.203691 NM_080927 2.007 SERPINB2 serpin peptidase inhibitor, clade B (ovalbumin), member 2 (SERPINB2), mRNA. Hs.594481 NM_002575 2.004 HIST2H2BE histone cluster 2, H2be (HIST2H2BE), mRNA. Hs.2178 NM_003528 1.989 OBFC2A Oligonucleotide/oligosaccharide-binding fold containing 2A Hs.591610 1.962 HIST2H2BE histone cluster 2, H2be (HIST2H2BE), mRNA. Hs.2178 NM_003528 1.947 PLCB4 phospholipase C, beta 4 (PLCB4), transcript variant 2, mRNA. Hs.472101 NM_182797 1.934 PLCB4 phospholipase C, beta 4 (PLCB4), transcript variant 1, mRNA. Hs.472101 NM_000933 1.933 KRTAP1-5 keratin associated protein 1-5 (KRTAP1-5), mRNA. Hs.534499 NM_031957 1.894 HIST2H2BE histone cluster 2, H2be (HIST2H2BE), mRNA. Hs.2178 NM_003528 1.884 CYTL1 cytokine-like 1 (CYTL1), mRNA. Hs.13872 NM_018659 tumor necrosis factor receptor superfamily, member 10d, decoy with truncated death domain (TNFRSF10D), 1.848 TNFRSF10D Hs.213467 NM_003840 mRNA. -
Supplementary Material Contents
Supplementary Material Contents Immune modulating proteins identified from exosomal samples.....................................................................2 Figure S1: Overlap between exosomal and soluble proteomes.................................................................................... 4 Bacterial strains:..............................................................................................................................................4 Figure S2: Variability between subjects of effects of exosomes on BL21-lux growth.................................................... 5 Figure S3: Early effects of exosomes on growth of BL21 E. coli .................................................................................... 5 Figure S4: Exosomal Lysis............................................................................................................................................ 6 Figure S5: Effect of pH on exosomal action.................................................................................................................. 7 Figure S6: Effect of exosomes on growth of UPEC (pH = 6.5) suspended in exosome-depleted urine supernatant ....... 8 Effective exosomal concentration....................................................................................................................8 Figure S7: Sample constitution for luminometry experiments..................................................................................... 8 Figure S8: Determining effective concentration ......................................................................................................... -
Mouse Cyb5r1 Conditional Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Cyb5r1 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Cyb5r1 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Cyb5r1 gene (NCBI Reference Sequence: NM_028057 ; Ensembl: ENSMUSG00000026456 ) is located on Mouse chromosome 1. 9 exons are identified, with the ATG start codon in exon 1 and the TGA stop codon in exon 9 (Transcript: ENSMUST00000027726). Exon 6~7 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Cyb5r1 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-260A4 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 6 starts from about 52.02% of the coding region. The knockout of Exon 6~7 will result in frameshift of the gene. The size of intron 5 for 5'-loxP site insertion: 700 bp, and the size of intron 7 for 3'-loxP site insertion: 806 bp. The size of effective cKO region: ~1783 bp. The cKO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 2 3 4 5 6 7 8 9 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Cyb5r1 Homology arm cKO region loxP site Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats. -
Downloaded Per Proteome Cohort Via the Web- Site Links of Table 1, Also Providing Information on the Deposited Spectral Datasets
www.nature.com/scientificreports OPEN Assessment of a complete and classifed platelet proteome from genome‑wide transcripts of human platelets and megakaryocytes covering platelet functions Jingnan Huang1,2*, Frauke Swieringa1,2,9, Fiorella A. Solari2,9, Isabella Provenzale1, Luigi Grassi3, Ilaria De Simone1, Constance C. F. M. J. Baaten1,4, Rachel Cavill5, Albert Sickmann2,6,7,9, Mattia Frontini3,8,9 & Johan W. M. Heemskerk1,9* Novel platelet and megakaryocyte transcriptome analysis allows prediction of the full or theoretical proteome of a representative human platelet. Here, we integrated the established platelet proteomes from six cohorts of healthy subjects, encompassing 5.2 k proteins, with two novel genome‑wide transcriptomes (57.8 k mRNAs). For 14.8 k protein‑coding transcripts, we assigned the proteins to 21 UniProt‑based classes, based on their preferential intracellular localization and presumed function. This classifed transcriptome‑proteome profle of platelets revealed: (i) Absence of 37.2 k genome‑ wide transcripts. (ii) High quantitative similarity of platelet and megakaryocyte transcriptomes (R = 0.75) for 14.8 k protein‑coding genes, but not for 3.8 k RNA genes or 1.9 k pseudogenes (R = 0.43–0.54), suggesting redistribution of mRNAs upon platelet shedding from megakaryocytes. (iii) Copy numbers of 3.5 k proteins that were restricted in size by the corresponding transcript levels (iv) Near complete coverage of identifed proteins in the relevant transcriptome (log2fpkm > 0.20) except for plasma‑derived secretory proteins, pointing to adhesion and uptake of such proteins. (v) Underrepresentation in the identifed proteome of nuclear‑related, membrane and signaling proteins, as well proteins with low‑level transcripts. -
Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations Fall 2010 Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress Renuka Nayak University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Computational Biology Commons, and the Genomics Commons Recommended Citation Nayak, Renuka, "Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress" (2010). Publicly Accessible Penn Dissertations. 1559. https://repository.upenn.edu/edissertations/1559 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/1559 For more information, please contact [email protected]. Coexpression Networks Based on Natural Variation in Human Gene Expression at Baseline and Under Stress Abstract Genes interact in networks to orchestrate cellular processes. Here, we used coexpression networks based on natural variation in gene expression to study the functions and interactions of human genes. We asked how these networks change in response to stress. First, we studied human coexpression networks at baseline. We constructed networks by identifying correlations in expression levels of 8.9 million gene pairs in immortalized B cells from 295 individuals comprising three independent samples. The resulting networks allowed us to infer interactions between biological processes. We used the network to predict the functions of poorly-characterized human genes, and provided some experimental support. Examining genes implicated in disease, we found that IFIH1, a diabetes susceptibility gene, interacts with YES1, which affects glucose transport. Genes predisposing to the same diseases are clustered non-randomly in the network, suggesting that the network may be used to identify candidate genes that influence disease susceptibility. -
Role of Maternal Sin3a in Reprogramming Gene Expression During Mouse Preimplantation Development
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2016 Role Of Maternal Sin3a In Reprogramming Gene Expression During Mouse Preimplantation Development Richard A. Jimenez University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Developmental Biology Commons, and the Genetics Commons Recommended Citation Jimenez, Richard A., "Role Of Maternal Sin3a In Reprogramming Gene Expression During Mouse Preimplantation Development" (2016). Publicly Accessible Penn Dissertations. 2366. https://repository.upenn.edu/edissertations/2366 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2366 For more information, please contact [email protected]. Role Of Maternal Sin3a In Reprogramming Gene Expression During Mouse Preimplantation Development Abstract In mouse, the maternal-to-zygotic transition entails a dramatic reprogramming of gene expression during the course of zygotic genome activation, which is essential for continued development beyond the 2-cell stage. Superimposed on zygotic genome activation and reprogramming of gene expression is formation of a chromatin-mediated transcriptionally repressive state that promotes repression of genes at the 2-cell stage. Experimentally inducing global histone hyperacetylation relieves this repression and histone deacetylase 1 (HDAC1) is the major HDAC involved in the development of this transcriptionally repressive state. Because SIN3A is essential -
Anamika Tripathi
WEIGHTED GENE CO-EXPRESSION NETWORK ANALYSIS OF COLORECTAL PATIENTS TO IDENTIFY RIGHT DRUG-RIGHT TARGET FOR POTENT EFFICACY OF TARGETED THERAPY Anamika Tripathi Submitted to the faculty of the School of Informatics in partial fulfillment of the requirements for the degree of Master of Science in Bioinformatics, Indiana University 12/2017 I TABLE OF CONTENTS Contents WEIGHTED GENE CO-EXPRESSION NETWORK ANALYSIS OF COLORECTAL PATIENTS TO IDENTIFY RIGHT DRUG- RIGHT TARGET FOR POTENT EFFICACY OF TARGETED THERAPY ................................................................................ I ANAMIKA TRIPATHI.................................................................................................................................................... I TABLE OF CONTENTS ..................................................................................................................................... II LIST OF FIGURES ............................................................................................................................................. III ABSTRACT .............................................................................................................................................................. V WEIGHTED GENE CO-EXPRESSION NETWORK ANALYSIS OF COLORECTAL PATIENTS TO IDENTIFY RIGHT DRUG- RIGHT TARGET FOR POTENT EFFICACY OF TARGETED THERAPY .............................................................................. V CHAPTER ONE: INTRODUCTION & BACKGROUND ................................................................................................. -
Kaposi's Sarcoma-Associated Herpesvirus Replication and Transcription Activator Regulates Extracellular Matrix Signal Pathway
Kaposi’s sarcoma-associated herpesvirus replication and transcription activator regulates extracellular matrix signal pathway Item Type Thesis Authors Pfalmer, Daniel Download date 05/10/2021 09:12:57 Link to Item http://hdl.handle.net/11122/6851 KAPOSI’S SARCOMA-ASSOCIATED HERPESVIRUS REPLICATION AND TRANSCRIPTION ACTIVATOR REGULATES EXTRACELLULAR MATRIX SIGNAL PATHWAY By Daniel Pfalmer RECOMMENDED: Dr. Karsten Hueff£r Advisory Committee Membs fmfiiill^Meinfrer SrTJiguo “Jack” Chen Advisory Committee Chair k — Dr. Diane Wagner Chair, Department of Biology and Wildlife APPROVED: Dr. Paul Layer DearOCollege of cs Dr. John Eichelberger Dean of the Graduate School KAPOSI'S SARCOMA-ASSOCIATED HERPESVIRUS REPLICATION AND TRANSCRIPTION ACTIVATOR REGULATES EXTRACELLULAR MATRIX SIGNAL PATHWAY A THESIS Presented to the Faculty of the University of Alaska Fairbanks in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE By Daniel Pfalmer, B.S. Fairbanks, AK August 2016 Abstract Kaposi's Sarcoma (KS) is a malignancy caused by infection with Kaposi's Sarcoma-associated Herpesvirus [KSHV; also known as Human Herpesvirus 8 (HHV8)] in which tumor cells show a characteristic 'spindle-like' morphology. The transcription factor RTA (Replication and Transcription Activator) is the viral protein responsible for reactivating KSHV from its latent state. Production of RTA in latently infected cells causes a number of viral proteins to be produced and leads to a cascade of gene expression changes in both viral and host genes. Previous work in our lab showed that RTA was capable of reprogramming cells in vitro to display a spindle-like morphology. In this study we aimed to identify the host gene expression changes caused directly by RTA which could be responsible for that reprogramming. -
Placental DNA Methylation Signatures of Maternal Smoking During Pregnancy and Potential Impacts on Fetal Growth ✉ Todd M
ARTICLE https://doi.org/10.1038/s41467-021-24558-y OPEN Placental DNA methylation signatures of maternal smoking during pregnancy and potential impacts on fetal growth ✉ Todd M. Everson 1,38 , Marta Vives-Usano2,3,4,38, Emie Seyve5,38, Andres Cardenas6,7, Marina Lacasaña 4,8,9, Jeffrey M. Craig10,11,12, Corina Lesseur 13, Emily R. Baker14, Nora Fernandez-Jimenez 15,16,17, Barbara Heude 18, Patrice Perron19, Beatriz Gónzalez-Alzaga8,9, Jane Halliday20,11, Maya A. Deyssenroth13, Margaret R. Karagas21, Carmen Íñiguez4,22,23, Luigi Bouchard24, Pedro Carmona-Sáez 25,26, Yuk J. Loke10,11, Ke Hao 27, Thalia Belmonte 28, Marie A. Charles18, 1234567890():,; Jordi Martorell-Marugán 25,29, Evelyne Muggli 11,20, Jia Chen13, Mariana F. Fernández 4,9,30, Jorg Tost 31, Antonio Gómez-Martín 32, Stephanie J. London 33, Jordi Sunyer3,4,34,35, ✉ Carmen J. Marsit 1,36, Johanna Lepeule 5,38, Marie-France Hivert6,37,38 & Mariona Bustamante 2,3,4,34,38 Maternal smoking during pregnancy (MSDP) contributes to poor birth outcomes, in part through disrupted placental functions, which may be reflected in the placental epigenome. Here we present a meta-analysis of the associations between MSDP and placental DNA methylation (DNAm) and between DNAm and birth outcomes within the Pregnancy And Childhood Epigenetics (PACE) consortium (N = 1700, 344 with MSDP). We identify 443 CpGs that are associated with MSDP, of which 142 associated with birth outcomes, 40 associated with gene expression, and 13 CpGs are associated with all three. Only two CpGs have consistent associations from a prior meta-analysis of cord blood DNAm, demonstrating substantial tissue-specific responses to MSDP. -
Proteogenomic Single Cell Analysis of Skeletal Muscle Myocytes 1 2 Katherine M. Fomchenko1,4, Rohan X. Verma1,4, Suraj Kannan2
bioRxiv preprint doi: https://doi.org/10.1101/2020.01.23.916791; this version posted January 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. 1 Proteogenomic single cell analysis of skeletal muscle myocytes 2 3 Katherine M. Fomchenko1,4, Rohan X. Verma1,4, Suraj Kannan2, Brian L. Lin2, Xiaoping Yang1, 4 Tim O. Nieuwenhuis1, Arun H. Patil1, Karen Fox-Talbot1, Matthew N. McCall3, Chulan Kwon2, 5 David A. Kass2, Avi Z. Rosenberg1, Marc K. Halushka1* 6 7 1 Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA 8 2 Division of Cardiology, Department of Medicine, Johns Hopkins University School of 9 Medicine, Baltimore, MD, USA 10 3 Department of Biostatistics and Computational Biology, University of Rochester Medical 11 Center, Rochester, NY, USA 12 4 These authors contributed equally to this project 13 14 Email Addresses: 15 [email protected] 16 [email protected] 17 [email protected] 18 [email protected] 19 [email protected] 20 [email protected] 21 [email protected] 22 [email protected] 23 [email protected] 24 [email protected] 25 [email protected] 26 [email protected] 27 [email protected] 28 29 * Correspondence and address for reprints to: 30 Marc K. Halushka, M.D., Ph.D. 31 Johns Hopkins University School of Medicine 32 Ross Bldg. Rm 632B 33 720 Rutland Avenue 34 Baltimore, MD 21205 35 410-614-8138 (ph) 36 410-502-5862 (fax) 37 [email protected] 38 bioRxiv preprint doi: https://doi.org/10.1101/2020.01.23.916791; this version posted January 24, 2020. -
Analysis of Breast Cancer Subtypes by AP-ISA Biclustering Liying Yang1*, Yunyan Shen1, Xiguo Yuan1, Junying Zhang1 and Jianhua Wei2*
Yang et al. BMC Bioinformatics (2017) 18:481 DOI 10.1186/s12859-017-1926-z RESEARCHARTICLE Open Access Analysis of breast cancer subtypes by AP-ISA biclustering Liying Yang1*, Yunyan Shen1, Xiguo Yuan1, Junying Zhang1 and Jianhua Wei2* Abstract Background: Gene expression profiling has led to the definition of breast cancer molecular subtypes: Basal-like, HER2-enriched, LuminalA, LuminalB and Normal-like. Different subtypes exhibit diverse responses to treatment. In the past years, several traditional clustering algorithms have been applied to analyze gene expression profiling. However, accurate identification of breast cancer subtypes, especially within highly variable LuminalA subtype, remains a challenge. Furthermore, the relationship between DNA methylation and expression level in different breast cancer subtypes is not clear. Results: In this study, a modified ISA biclustering algorithm, termed AP-ISA, was proposed to identify breast cancer subtypes. Comparing with ISA, AP-ISA provides the optimized strategy to select seeds and thresholds in the circumstance that prior knowledge is absent. Experimental results on 574 breast cancer samples were evaluated using clinical ER/PR information, PAM50 subtypes and the results of five peer to peer methods. One remarkable point in the experiment is that, AP-ISA divided the expression profiles of the luminal samples into four distinct classes. Enrichment analysis and methylation analysis showed obvious distinction among the four subgroups. Tumor variability within the Luminal subtype is observed in the experiments, which could contribute to the development of novel directed therapies. Conclusions: Aiming at breast cancer subtype classification, a novel biclustering algorithm AP-ISA is proposed in this paper. AP-ISA classifies breast cancer into seven subtypes and we argue that there are four subtypes in luminal samples.