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Dynamic Molecular Linkers of the Genome: the First Decade of SMC Proteins
Downloaded from genesdev.cshlp.org on October 8, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW Dynamic molecular linkers of the genome: the first decade of SMC proteins Ana Losada1 and Tatsuya Hirano2,3 1Spanish National Cancer Center (CNIO), Madrid E-28029, Spain; 2Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA Structural maintenance of chromosomes (SMC) proteins in eukaryotes. The proposed actions of cohesin and con- are chromosomal ATPases, highly conserved from bac- densins offer a plausible, if not complete, explanation for teria to humans, that play fundamental roles in many the sudden appearance of thread-like “substances” (the aspects of higher-order chromosome organization and chromosomes) and their longitudinal splitting during dynamics. In eukaryotes, SMC1 and SMC3 act as the mitosis, first described by Walther Flemming (1882). core of the cohesin complexes that mediate sister chro- Remarkably, SMC proteins are conserved among the matid cohesion, whereas SMC2 and SMC4 function as three phyla of life, indicating that the basic strategy of the core of the condensin complexes that are essential chromosome organization may be evolutionarily con- for chromosome assembly and segregation. Another served from bacteria to humans. An emerging theme is complex containing SMC5 and SMC6 is implicated in that SMC proteins are dynamic molecular linkers of the DNA repair and checkpoint responses. The SMC com- genome that actively fold, tether, and manipulate DNA plexes form unique ring- or V-shaped structures with strands. Their diverse functions range far beyond chro- long coiled-coil arms, and function as ATP-modulated, mosome segregation, and involve nearly all aspects of dynamic molecular linkers of the genome. -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
Gene Expression Studies: from Case-Control to Multiple-Population-Based Studies
From the Institute of Human Genetics, Helmholtz Zentrum Munchen,¨ Deutsches Forschungszentrum fur¨ Gesundheit und Umwelt (GmbH) Head: Prof. Dr. Thomas Meitinger Gene expression studies: From case-control to multiple-population-based studies Thesis Submitted for a Doctoral Degree in Natural Sciences at the Faculty of Medicine, Ludwig-Maximilians-Universitat¨ Munchen¨ Katharina Schramm Dachau, Germany 2016 With approval of the Faculty of Medicine Ludwig-Maximilians-Universit¨atM ¨unchen Supervisor/Examiner: Prof. Dr. Thomas Illig Co-Examiners: Prof. Dr. Roland Kappler Dean: Prof. Dr. med. dent. Reinhard Hickel Date of oral examination: 22.12.2016 II Dedicated to my family. III Abstract Recent technological developments allow genome-wide scans of gene expression levels. The reduction of costs and increasing parallelization of processing enable the quantification of 47,000 transcripts in up to twelve samples on a single microarray. Thereby the data collec- tion of large population-based studies was improved. During my PhD, I first developed a workflow for the statistical analyses of case-control stu- dies of up to 50 samples. With large population-based data sets generated I established a pipeline for quality control, data preprocessing and correction for confounders, which re- sulted in substantially improved data. In total, I processed more than 3,000 genome-wide expression profiles using the generated pipeline. With 993 whole blood samples from the population-based KORA (Cooperative Health Research in the Region of Augsburg) study we established one of the largest population-based resource. Using this data set we contributed to a number of transcriptome-wide association studies within national (MetaXpress) and international (CHARGE) consortia. -
Supplementary Information Integrative Analyses of Splicing in the Aging Brain: Role in Susceptibility to Alzheimer’S Disease
Supplementary Information Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s Disease Contents 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project 1.2. Mount Sinai Brain Bank Alzheimer’s Disease 1.3. CommonMind Consortium 1.4. Data Availability 2. Supplementary Tables 3. Supplementary Figures Note: Supplementary Tables are provided as separate Excel files. 1. Supplementary Notes 1.1. Religious Orders Study and Memory and Aging Project Gene expression data1. Gene expression data were generated using RNA- sequencing from Dorsolateral Prefrontal Cortex (DLPFC) of 540 individuals, at an average sequence depth of 90M reads. Detailed description of data generation and processing was previously described2 (Mostafavi, Gaiteri et al., under review). Samples were submitted to the Broad Institute’s Genomics Platform for transcriptome analysis following the dUTP protocol with Poly(A) selection developed by Levin and colleagues3. All samples were chosen to pass two initial quality filters: RNA integrity (RIN) score >5 and quantity threshold of 5 ug (and were selected from a larger set of 724 samples). Sequencing was performed on the Illumina HiSeq with 101bp paired-end reads and achieved coverage of 150M reads of the first 12 samples. These 12 samples will serve as a deep coverage reference and included 2 males and 2 females of nonimpaired, mild cognitive impaired, and Alzheimer's cases. The remaining samples were sequenced with target coverage of 50M reads; the mean coverage for the samples passing QC is 95 million reads (median 90 million reads). The libraries were constructed and pooled according to the RIN scores such that similar RIN scores would be pooled together. -
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. -
Location Analysis of Estrogen Receptor Target Promoters Reveals That
Location analysis of estrogen receptor ␣ target promoters reveals that FOXA1 defines a domain of the estrogen response Jose´ e Laganie` re*†, Genevie` ve Deblois*, Ce´ line Lefebvre*, Alain R. Bataille‡, Franc¸ois Robert‡, and Vincent Gigue` re*†§ *Molecular Oncology Group, Departments of Medicine and Oncology, McGill University Health Centre, Montreal, QC, Canada H3A 1A1; †Department of Biochemistry, McGill University, Montreal, QC, Canada H3G 1Y6; and ‡Laboratory of Chromatin and Genomic Expression, Institut de Recherches Cliniques de Montre´al, Montreal, QC, Canada H2W 1R7 Communicated by Ronald M. Evans, The Salk Institute for Biological Studies, La Jolla, CA, July 1, 2005 (received for review June 3, 2005) Nuclear receptors can activate diverse biological pathways within general absence of large scale functional data linking these putative a target cell in response to their cognate ligands, but how this binding sites with gene expression in specific cell types. compartmentalization is achieved at the level of gene regulation is Recently, chromatin immunoprecipitation (ChIP) has been used poorly understood. We used a genome-wide analysis of promoter in combination with promoter or genomic DNA microarrays to occupancy by the estrogen receptor ␣ (ER␣) in MCF-7 cells to identify loci recognized by transcription factors in a genome-wide investigate the molecular mechanisms underlying the action of manner in mammalian cells (20–24). This technology, termed 17-estradiol (E2) in controlling the growth of breast cancer cells. ChIP-on-chip or location analysis, can therefore be used to deter- We identified 153 promoters bound by ER␣ in the presence of E2. mine the global gene expression program that characterize the Motif-finding algorithms demonstrated that the estrogen re- action of a nuclear receptor in response to its natural ligand. -
Supplementary Table 3 Complete List of RNA-Sequencing Analysis of Gene Expression Changed by ≥ Tenfold Between Xenograft and Cells Cultured in 10%O2
Supplementary Table 3 Complete list of RNA-Sequencing analysis of gene expression changed by ≥ tenfold between xenograft and cells cultured in 10%O2 Expr Log2 Ratio Symbol Entrez Gene Name (culture/xenograft) -7.182 PGM5 phosphoglucomutase 5 -6.883 GPBAR1 G protein-coupled bile acid receptor 1 -6.683 CPVL carboxypeptidase, vitellogenic like -6.398 MTMR9LP myotubularin related protein 9-like, pseudogene -6.131 SCN7A sodium voltage-gated channel alpha subunit 7 -6.115 POPDC2 popeye domain containing 2 -6.014 LGI1 leucine rich glioma inactivated 1 -5.86 SCN1A sodium voltage-gated channel alpha subunit 1 -5.713 C6 complement C6 -5.365 ANGPTL1 angiopoietin like 1 -5.327 TNN tenascin N -5.228 DHRS2 dehydrogenase/reductase 2 leucine rich repeat and fibronectin type III domain -5.115 LRFN2 containing 2 -5.076 FOXO6 forkhead box O6 -5.035 ETNPPL ethanolamine-phosphate phospho-lyase -4.993 MYO15A myosin XVA -4.972 IGF1 insulin like growth factor 1 -4.956 DLG2 discs large MAGUK scaffold protein 2 -4.86 SCML4 sex comb on midleg like 4 (Drosophila) Src homology 2 domain containing transforming -4.816 SHD protein D -4.764 PLP1 proteolipid protein 1 -4.764 TSPAN32 tetraspanin 32 -4.713 N4BP3 NEDD4 binding protein 3 -4.705 MYOC myocilin -4.646 CLEC3B C-type lectin domain family 3 member B -4.646 C7 complement C7 -4.62 TGM2 transglutaminase 2 -4.562 COL9A1 collagen type IX alpha 1 chain -4.55 SOSTDC1 sclerostin domain containing 1 -4.55 OGN osteoglycin -4.505 DAPL1 death associated protein like 1 -4.491 C10orf105 chromosome 10 open reading frame 105 -4.491 -
TMEM9 CRISPR/Cas9 KO Plasmid (M): Sc-425907
SANTA CRUZ BIOTECHNOLOGY, INC. TMEM9 CRISPR/Cas9 KO Plasmid (m): sc-425907 BACKGROUND APPLICATIONS The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and TMEM9 CRISPR/Cas9 KO Plasmid (m) is recommended for the disruption of CRISPR-associated protein (Cas9) system is an adaptive immune response gene expression in mouse cells. defense mechanism used by archea and bacteria for the degradation of foreign genetic material (4,6). This mechanism can be repurposed for other 20 nt non-coding RNA sequence: guides Cas9 functions, including genomic engineering for mammalian systems, such as to a specific target location in the genomic DNA gene knockout (KO) (1,2,3,5). CRISPR/Cas9 KO Plasmid products enable the U6 promoter: drives gRNA scaffold: helps Cas9 identification and cleavage of specific genes by utilizing guide RNA (gRNA) expression of gRNA bind to target DNA sequences derived from the Genome-scale CRISPR Knock-Out (GeCKO) v2 library developed in the Zhang Laboratory at the Broad Institute (3,5). Termination signal Green Fluorescent Protein: to visually REFERENCES verify transfection CRISPR/Cas9 Knockout Plasmid CBh (chicken β-Actin 1. Cong, L., et al. 2013. Multiplex genome engineering using CRISPR/Cas hybrid) promoter: drives systems. Science 339: 819-823. 2A peptide: expression of Cas9 allows production of both Cas9 and GFP from the 2. Mali, P., et al. 2013. RNA-guided human genome engineering via Cas9. same CBh promoter Science 339: 823-826. Nuclear localization signal 3. Ran, F.A., et al. 2013. Genome engineering using the CRISPR-Cas9 system. Nuclear localization signal SpCas9 ribonuclease Nat. Protoc. 8: 2281-2308. -
S41467-020-16223-7.Pdf
ARTICLE https://doi.org/10.1038/s41467-020-16223-7 OPEN Trefoil factors share a lectin activity that defines their role in mucus Michael A. Järvå 1,2, James P. Lingford1,2, Alan John1,2, Niccolay Madiedo Soler1,2, Nichollas E. Scott 3 & ✉ Ethan D. Goddard-Borger 1,2 The mucosal epithelium secretes a host of protective disulfide-rich peptides, including the trefoil factors (TFFs). The TFFs increase the viscoelasticity of the mucosa and promote cell 1234567890():,; migration, though the molecular mechanisms underlying these functions have remained poorly defined. Here, we demonstrate that all TFFs are divalent lectins that recognise the GlcNAc-α-1,4-Gal disaccharide, which terminates some mucin-like O-glycans. Degradation of this disaccharide by a glycoside hydrolase abrogates TFF binding to mucins. Structural, mutagenic and biophysical data provide insights into how the TFFs recognise this dis- accharide and rationalise their ability to modulate the physical properties of mucus across different pH ranges. These data reveal that TFF activity is dependent on the glycosylation state of mucosal glycoproteins and alludes to a lectin function for trefoil domains in other human proteins. 1 The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia. 2 Department of Medical Biology, University of Melbourne, Parkville, VIC 3010, Australia. 3 Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, ✉ Parkville, VIC 3010, Australia. email: [email protected] NATURE COMMUNICATIONS | (2020) 11:2265 | https://doi.org/10.1038/s41467-020-16223-7 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-16223-7 he three human TFFs (TFF1, TFF2, and TFF3)1 are ubi- considering the glycosylation state of mucosal proteins when Tquitous in mucosal environments. -
Supporting Information
Supporting Information Muro et al. 10.1073/pnas.0807813105 SI Text (Fig. S1) showed for 10 of the PAS variants a marked tendency 1. Algorithm for Automated Recognition of EST End Clusters. To to lie 10–30 nt upstream of EST ends, whereas no strong trend determine the coordinates of genomic EST alignments we used was seen for 3 additional variants. Based on this simple analysis the UCSC genome annotation (1), a regularly updated database we chose to accept only 10 of the variants as functional PAS which includes mouse and human EST sequences and their (Table S1), and only EST ends validated by a PAS 10–30 nt positions in the genome. The UCSC Golden Path version was upstream. Furthermore, we considered all EST ends validated by mm6 for mouse (equivalent to NCBI build 34, March 2005) and the same PAS and ending within 20 nt of one another to hg18 for human (NCBI Build 36.1, March 2006). The UCSC represent the same transcript end and to be clustered together, mapping database is based on alignments of ESTs to the as others have also proposed (6). corresponding genome using the BLAT program (2). Because of Next, we studied the distribution of individual EST ends the relatively low accuracy of EST sequencing and recent around our predicted rough ends (Fig. S2). The graph indicates genomic duplications (3), some ESTs can be aligned to more that in a range of Ϫ200 to ϩ150 nt around these rough ends the than 1 genomic position. To avoid possible misidentifications, frequency of EST ends is distinguishable from the surrounding ambiguously aligning ESTs were excluded from our analysis. -
Trefoil Factor 3 Overexpression in Prostatic Carcinoma
Trefoil Factor 3 Overexpression in Prostatic Carcinoma: Prognostic Importance Using Tissue Microarrays Dennis A Faith, William B Isaacs, James D Morgan, Helen L Fedor, Jessica L Hicks, Leslie A Mangold, Patrick C Walsh, Alan W Partin, Elizabeth A. Platz, Jun Luo, and Angelo M De Marzo Brady Urological Institute [D.A.F., J.L.,L.M., P.C.W., A.W.P., W.B.I, A.M.D..], Department of Pathology [J.D.M., H.F., J.H., A.M.D.], The Sidney Kimmel Comprehensive Cancer Center [W.B.I., A.M.D] , The Johns Hopkins University, School of Medicine, Baltimore, Maryland 21287; Department of Epidemiology, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD 21205 [E.P.]; Funded by Public Health Services NIH/NCI Specialized Program in Research Excellence (SPORE) in Prostate Cancer #P50CA58236 (Johns Hopkins), The Prostate Cancer Foundation, and by Philanthropic Support from Susan and Donald Sturm Correspondence and Reprint Requests: Angelo M. De Marzo, M.D., Ph.D., Jun Luo, Ph.D. Department of Pathology, Marburg 411 The Johns Hopkins University, Brady Urological Institute CRB 153 The Johns Hopkins University School 1650 Orleans Street, of Medicine Baltimore, MD 21231-1000 600 N. Wolfe St Phone: (410) 614-5686 Baltimore, MD 21287 Fax: (410) 502-9817 E-mail: [email protected] Funded by Public Health Services NIH/NCI #R01CA084997, NIH/#R01CA70196 and NIH/NCI Specialized Program in Research Excellence (SPORE) in Prostate Cancer #P50CA58236. TFF3 Expression in Prostate Cancer Page 2 ABSTRACT Human intestinal trefoil factor 3 (TFF3) is a member of a family of polypeptides encoded by a cluster of genes on chromosome 21. -
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,