Oligosyndactylism Mice Have an Inversion of Chromosome 8
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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. -
Disruption of the Anaphase-Promoting Complex Confers Resistance to TTK Inhibitors in Triple-Negative Breast Cancer
Disruption of the anaphase-promoting complex confers resistance to TTK inhibitors in triple-negative breast cancer K. L. Thua,b, J. Silvestera,b, M. J. Elliotta,b, W. Ba-alawib,c, M. H. Duncana,b, A. C. Eliaa,b, A. S. Merb, P. Smirnovb,c, Z. Safikhanib, B. Haibe-Kainsb,c,d,e, T. W. Maka,b,c,1, and D. W. Cescona,b,f,1 aCampbell Family Institute for Breast Cancer Research, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada M5G 1L7; bPrincess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada M5G 1L7; cDepartment of Medical Biophysics, University of Toronto, Toronto, ON, Canada M5G 1L7; dDepartment of Computer Science, University of Toronto, Toronto, ON, Canada M5G 1L7; eOntario Institute for Cancer Research, Toronto, ON, Canada M5G 0A3; and fDepartment of Medicine, University of Toronto, Toronto, ON, Canada M5G 1L7 Contributed by T. W. Mak, December 27, 2017 (sent for review November 9, 2017; reviewed by Mark E. Burkard and Sabine Elowe) TTK protein kinase (TTK), also known as Monopolar spindle 1 (MPS1), ator of the spindle assembly checkpoint (SAC), which delays is a key regulator of the spindle assembly checkpoint (SAC), which anaphase until all chromosomes are properly attached to the functions to maintain genomic integrity. TTK has emerged as a mitotic spindle, TTK has an integral role in maintaining genomic promising therapeutic target in human cancers, including triple- integrity (6). Because most cancer cells are aneuploid, they are negative breast cancer (TNBC). Several TTK inhibitors (TTKis) are heavily reliant on the SAC to adequately segregate their abnormal being evaluated in clinical trials, and an understanding of karyotypes during mitosis. -
Variation in Protein Coding Genes Identifies Information Flow
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. 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-ND 4.0 International license. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. 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-ND 4.0 International license. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
Gene-Gene Interaction Mapping of Human Cytomegalic Virus Through System Biology Approach
tems: ys Op l S e a n A ic c g Vijaylaxmi Saxena et al., Biol syst Open Access c o l e s o i s 2015, 4:2 B Biological Systems: Open Access DOI: 10.4172/2329-6577.1000141 ISSN: 2329-6577 Research Article Open Access Gene-Gene Interaction Mapping Of Human Cytomegalic Virus through System Biology Approach Vijaylaxmi Saxena, Supriya Dixit* and Alfisha Ashraf Coordinator, Bioinformatics Infrastructure Facility, Centre of DBT (Govt. of India), D.G. (P.G.) College, Kanpur (U.P), India *Corresponding author: Supriya Dixit, Bioinformatics Infrastructure Facility, Centre of DBT (Govt. India), Dayanand Girl’s P.G. College, Kanpur (U.P), India, Tel: 09415125252, E-mail: [email protected] Received date: Jul 14, 2015; Accepted date: Aug 28, 2015; Published date: Sep 05, 2015 Copyright: © 2015 Vijaylaxmi Saxena et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract Systems biology is concerned with the study of biological systems, by investigating the components of cellular networks and their interactions. The objective of present study is to build gene-gene interaction network of human cytomegalovirus genes with human genes and other influenza causing genes which helps to identify pathways, recognize gene function and find potential drug targets for cytomegalovirus visualized through cytoscape and its plugin. So, genetic interaction is logical interaction between two genes and more than that affects any organism phenotypically. Human cytomegalovirus has many strategies to survive the attack of the host. -
Pathview: Pathway Based Data Integration and Visualization
Pathview: pathway based data integration and visualization Weijun Luo (luo weijun AT yahoo.com) May 19, 2021 Abstract In this vignette, we demonstrate the pathview package as a tool set for pathway based data integration and visualization. It maps and renders user data on relevant pathway graphs. All users need is to supply their gene or compound data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and renders pathway graph with the mapped data. Although built as a stand-alone program, pathview may seamlessly integrate with pathway and gene set (enrichment) analysis tools for a large-scale and fully automated analysis pipeline. In this vignette, we introduce common and advanced uses of pathview. We also cover package installation, data preparation, other useful features and common application errors. In gage package, vignette "RNA-Seq Data Pathway and Gene-set Analysis Workflows" demonstrates GAGE/Pathview workflows on RNA-seq (and microarray) pathway analysis. 1 Cite our work Please cite the Pathview paper formally if you use this package. This will help to support the maintenance and growth of the open source project. Weijun Luo and Cory Brouwer. Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics, 29(14):1830-1831, 2013. doi: 10.1093/bioinformatics/btt285. 2 Quick start with demo data This is the most basic use of pathview, please check the full description below for details. Here we assume that the input data are already read in R as in the demo examples. -
Whole Genome Analyses of a Well-Differentiated Liposarcoma Reveals Novel SYT1 and DDR2 Rearrangements
Whole Genome Analyses of a Well-Differentiated Liposarcoma Reveals Novel SYT1 and DDR2 Rearrangements Jan B. Egan1, Michael T. Barrett2, Mia D. Champion3,4, Sumit Middha5, Elizabeth Lenkiewicz2, Lisa Evers2, Princy Francis 6 Jessica Schmidt 6 Chang-Xin , Shi 6 , Scott Van Wier, 6 Sandra, Badar 6 , Gregory Ahmann 6 K., Martin Kortuem 7 , Nicole J. Boczek8 , Rafael Fonseca 1 , 9, David W. Craig10, John D. Carpten11, Mitesh J. Borad1,9, A. Keith Stewart1,9* 1 Comprehensive Cancer Center, Mayo Clinic, Scottsdale, Arizona, United States of America, 2 Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona, United States of America, 3 Department of Biomedical Statistics and Informatics, Mayo Clinic, Scottsdale, Arizona, United States of America, 4 Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America, 5 Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America, 6 Research, Mayo Clinic, Scottsdale, Arizona, United States of America, 7 Hematology, Mayo Clinic, Scottsdale, Arizona, United States of America, 8 Mayo Graduate School, Mayo Clinic, Rochester, Minnesota, United States of America, 9 Division of Hematology/Oncology Mayo Clinic, Scottsdale, Arizona, United States of America, 10 Neurogenomics Division, Translational Genomics Research Institute, Phoenix, Arizona, United States of America, 11 Integrated Cancer Genomics Division, Translational Genomics Research Institute, Phoenix, Arizona, United States of America Abstract Liposarcoma is the most common soft tissue sarcoma, but little is known about the genomic basis of this disease. Given the low cell content of this tumor type, we utilized flow cytometry to isolate the diploid normal and aneuploid tumor populations from a well-differentiated liposarcoma prior to array comparative genomic hybridization and whole genome sequencing. -
PRC1 Prevents Replication Stress During Chondrogenic Transit Amplification
Article Supplementary Materials: PRC1 Prevents Replication Stress during Chondrogenic Transit Amplification Figure S1. BMI1 is required for chondrogenic differentiation. (A) Array expression analysis of PRC1 genes during chondrogenesis; Bmi1, Mel18, Phc (polyhomeotic homologs 1 and 2), Ring1 (Rnf2, Ring1), Epigenomes 2017, 1, 22; doi: 10.3390/epigenomes1030022 www.mdpi.com/journal/epigenomes Epigenomes 2017, 1, 22 2 of 23 Rybp (RING1 and YY1 binding protein), Cbx (chromobox homologs 4 and 6) and of PRC2 genes Ezh2, Eed and Suz12; values in all panels represent mean of triplicates ± s.d. (B) Expression analysis of Sox9 and Runx2 mRNA in shcon and shBmi1 cells (qRT‐PCR; triplicates). (C) Confirmation of murine BMI1 mRNA (mBmi1) overexpression in ATDC5 cells; empty vector was used as control (con; qRT‐PCR; triplicates). (D) Proliferation curves of ATDC5 cells overexpressing murine BMI1 (mBmi1) vs control cells (con). (E) Acan and Col10A1 expression during differentiation in ATDC5 cells overexpressing murine Bmi1 (mBmi1) cDNA versus control (con) cells (qRT‐PCR; triplicates); asterisks (*; C‐E): p<0.05. Figure S2. IntraS‐phase accumulation during TA in the absence of PRC1. (A) Representative IF images showing co‐staining for BdrU‐incorporation and H3S10 phosphorylation (H3S10ph) in shcon and Epigenomes 2017, 1, 22 3 of 23 shBmi1 ATDC5 cells at t=3 days pid; 19 of 100 shcon cells were positive for H3S10ph, of which 7 were brightly stained (G2/M); less than 3% of shBmi1 cells was weakly positive (late S/G2). BrdU pulse: 45 min. (B) Cell cycle distribution of shcon and shBmi1 ATDC5 cells throughout differentiation (left panel). SubG1 fractions (t=6 days pid) shcon: 0.47% ±0.035, shBmi1: 0.43% ±0.041; asterisks (*): p<0.05; representative cell cycle profiles (of triplicates) of ATDC5 shcon and shBmi1 at 6 days pid (right panels); DNA content was measured by propidium‐iodide (PI) staining; values represent percentages S‐phase cells of total cells analysed. -
Identification of Germ Cell-Specific Genes in Mammalian Meiotic Prophase Yunfei Li1, Debjit Ray1,2 and Ping Ye1,3*
Li et al. BMC Bioinformatics 2013, 14:72 http://www.biomedcentral.com/1471-2105/14/72 RESEARCH ARTICLE Open Access Identification of germ cell-specific genes in mammalian meiotic prophase Yunfei Li1, Debjit Ray1,2 and Ping Ye1,3* Abstract Background: Mammalian germ cells undergo meiosis to produce sperm or eggs, haploid cells that are primed to meet and propagate life. Meiosis is initiated by retinoic acid and meiotic prophase is the first and most complex stage of meiosis when homologous chromosomes pair to exchange genetic information. Errors in meiosis can lead to infertility and birth defects. However, despite the importance of this process, germ cell-specific gene expression patterns during meiosis remain undefined due to difficulty in obtaining pure germ cell samples, especially in females, where prophase occurs in the embryonic ovary. Indeed, mixed signals from both germ cells and somatic cells complicate gonadal transcriptome studies. Results: We developed a machine-learning method for identifying germ cell-specific patterns of gene expression in microarray data from mammalian gonads, specifically during meiotic initiation and prophase. At 10% recall, the method detected spermatocyte genes and oocyte genes with 90% and 94% precision, respectively. Our method outperformed gonadal expression levels and gonadal expression correlations in predicting germ cell-specific expression. Top-predicted spermatocyte and oocyte genes were both preferentially localized to the X chromosome and significantly enriched for essential genes. Also identified were transcription factors and microRNAs that might regulate germ cell-specific expression. Finally, we experimentally validated Rps6ka3, a top-predicted X-linked spermatocyte gene. Protein localization studies in the mouse testis revealed germ cell-specific expression of RPS6KA3, mainly detected in the cytoplasm of spermatogonia and prophase spermatocytes. -
Downloaded from Here
bioRxiv preprint doi: https://doi.org/10.1101/017566; this version posted April 6, 2015. 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-ND 4.0 International license. 1 1 Testing for ancient selection using cross-population allele 2 frequency differentiation 1;∗ 3 Fernando Racimo 4 1 Department of Integrative Biology, University of California, Berkeley, CA, USA 5 ∗ E-mail: [email protected] 6 1 Abstract 7 A powerful way to detect selection in a population is by modeling local allele frequency changes in a 8 particular region of the genome under scenarios of selection and neutrality, and finding which model is 9 most compatible with the data. Chen et al. [1] developed a composite likelihood method called XP-CLR 10 that uses an outgroup population to detect departures from neutrality which could be compatible with 11 hard or soft sweeps, at linked sites near a beneficial allele. However, this method is most sensitive to recent 12 selection and may miss selective events that happened a long time ago. To overcome this, we developed 13 an extension of XP-CLR that jointly models the behavior of a selected allele in a three-population three. 14 Our method - called 3P-CLR - outperforms XP-CLR when testing for selection that occurred before two 15 populations split from each other, and can distinguish between those events and events that occurred 16 specifically in each of the populations after the split. -
DNA Methylation Biomarkers for Hepatocellular Carcinoma Guorun Fan1†, Yaqin Tu1†, Cai Chen3, Haiying Sun1, Chidan Wan2* and Xiong Cai2*
Fan et al. Cancer Cell Int (2018) 18:140 https://doi.org/10.1186/s12935-018-0629-5 Cancer Cell International PRIMARY RESEARCH Open Access DNA methylation biomarkers for hepatocellular carcinoma Guorun Fan1†, Yaqin Tu1†, Cai Chen3, Haiying Sun1, Chidan Wan2* and Xiong Cai2* Abstract Background: Aberrant methylation of DNA is a key driver of hepatocellular carcinoma (HCC). In this study, we sought to integrate four cohorts profle datasets to identify such abnormally methylated genes and pathways associated with HCC. Methods: To this end, we downloaded microarray datasets examining gene expression (GSE84402, GSE46408) and gene methylation (GSE73003, GSE57956) from the GEO database. Abnormally methylated diferentially expressed genes (DEGs) were sorted and pathways were analyzed. The String database was then used to perform enrichment and functional analysis of identifed pathways and genes. Cytoscape software was used to create a protein–protein interaction network, and MCODE was used for module analysis. Finally, overall survival analysis of hub genes was performed by the OncoLnc online tool. Results: In total, we identifed 19 hypomethylated highly expressed genes and 14 hypermethylated lowly expressed genes at the screening step, and fnally found six mostly changed hub genes including MAD2L1, CDC20, CCNB1, CCND1, AR and ESR1. Pathway analysis showed that aberrantly methylated-DEGs mainly associated with the cell cycle process, p53 signaling, and MAPK signaling in HCC. After validation in TCGA database, the methylation and expres- sion status of hub genes was signifcantly altered and same with our results. Patients with high expression of MAD2L1, CDC20 and CCNB1 and low expression of CCND1, AR, and ESR1 was associated with shorter overall survival. -
APC/C Dysfunction Limits Excessive Cancer Chromosomal Instability
Published OnlineFirst January 9, 2017; DOI: 10.1158/2159-8290.CD-16-0645 RESEARCH ARTICLE APC/C Dysfunction Limits Excessive Cancer Chromosomal Instability Laurent Sansregret1, James O. Patterson1, Sally Dewhurst1, Carlos López-García1, André Koch2, Nicholas McGranahan1,3, William Chong Hang Chao1, David J. Barry1, Andrew Rowan1, Rachael Instrell1, Stuart Horswell1, Michael Way1, Michael Howell1, Martin R. Singleton1, René H. Medema2, Paul Nurse1, Mark Petronczki1,4, and Charles Swanton1,3 Downloaded from cancerdiscovery.aacrjournals.org on September 26, 2021. © 2017 American Association for Cancer Research. Published OnlineFirst January 9, 2017; DOI: 10.1158/2159-8290.CD-16-0645 ABSTRACT Intercellular heterogeneity, exacerbated by chromosomal instability (CIN), fosters tumor heterogeneity and drug resistance. However, extreme CIN correlates with improved cancer outcome, suggesting that karyotypic diversity required to adapt to selection pressures might be balanced in tumors against the risk of excessive instability. Here, we used a functional genomics screen, genome editing, and pharmacologic approaches to identify CIN-survival factors in diploid cells. We find partial anaphase-promoting complex/cyclosome (APC/C) dysfunction lengthens mitosis, suppresses pharmacologically induced chromosome segregation errors, and reduces naturally occurring lagging chromosomes in cancer cell lines or following tetraploidization. APC/C impairment caused adaptation to MPS1 inhibitors, revealing a likely resistance mechanism to therapies targeting the spindle assembly checkpoint. Finally, CRISPR-mediated introduction of cancer somatic mutations in the APC/C subunit cancer driver gene CDC27 reduces chromosome segregation errors, whereas reversal of an APC/C subu- nit nonsense mutation increases CIN. Subtle variations in mitotic duration, determined by APC/C activity, influence the extent of CIN, allowing cancer cells to dynamically optimize fitness during tumor evolution. -
Stable DNMT3L Overexpression in SH-SY5Y Neurons Recreates a Facet of the Genome-Wide Down Syndrome DNA Methylation Signature
Laufer et al. Epigenetics & Chromatin (2021) 14:13 https://doi.org/10.1186/s13072-021-00387-7 Epigenetics & Chromatin RESEARCH Open Access Stable DNMT3L overexpression in SH-SY5Y neurons recreates a facet of the genome-wide Down syndrome DNA methylation signature Benjamin I. Laufer1,2,3†, J. Antonio Gomez1,2,3†, Julia M. Jianu1,2,3 and Janine M. LaSalle1,2,3* Abstract Background: Down syndrome (DS) is characterized by a genome-wide profle of diferential DNA methylation that is skewed towards hypermethylation in most tissues, including brain, and includes pan-tissue diferential methylation. The molecular mechanisms involve the overexpression of genes related to DNA methylation on chromosome 21. Here, we stably overexpressed the chromosome 21 gene DNA methyltransferase 3L (DNMT3L) in the human SH-SY5Y neuroblastoma cell line and assayed DNA methylation at over 26 million CpGs by whole genome bisulfte sequencing (WGBS) at three diferent developmental phases (undiferentiated, diferentiating, and diferentiated). Results: DNMT3L overexpression resulted in global CpG and CpG island hypermethylation as well as thousands of diferentially methylated regions (DMRs). The DNMT3L DMRs were skewed towards hypermethylation and mapped to genes involved in neurodevelopment, cellular signaling, and gene regulation. Consensus DNMT3L DMRs showed that cell lines clustered by genotype and then diferentiation phase, demonstrating sets of common genes afected across neuronal diferentiation. The hypermethylated DNMT3L DMRs from all pairwise comparisons were enriched for regions of bivalent chromatin marked by H3K4me3 as well as diferentially methylated sites from previous DS studies of diverse tissues. In contrast, the hypomethylated DNMT3L DMRs from all pairwise comparisons displayed a tissue- specifc profle enriched for regions of heterochromatin marked by H3K9me3 during embryonic development.