A Locus for Spondylocarpotarsal Synostosis Syndrome At
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Sequence Analysis and Comparative Study of the Protein Subunits of Archaeal Rnase P
biomolecules Review Sequence Analysis and Comparative Study of the Protein Subunits of Archaeal RNase P Manoj P. Samanta 1,*,†, Stella M. Lai 2,3,†, Charles J. Daniels 3,4 and Venkat Gopalan 2,3,* 1 Systemix Institute, Redmond, WA 98053, USA 2 Department of Chemistry & Biochemistry, The Ohio State University, Columbus, OH 43210, USA; [email protected] 3 Center for RNA Biology, The Ohio State University, Columbus, OH 43210, USA; [email protected] 4 Department of Microbiology, The Ohio State University, Columbus, OH 43210, USA * Correspondence: [email protected] (M.P.S.); [email protected] (V.G.); Tel.: +1-425-898-8187 (M.P.S.); +1-614-292-1332 (V.G.) † These authors contributed equally to this work. Academic Editor: Denis Drainas Received: 1 March 2016; Accepted: 8 April 2016; Published: 20 April 2016 Abstract: RNase P, a ribozyme-based ribonucleoprotein (RNP) complex that catalyzes tRNA 51-maturation, is ubiquitous in all domains of life, but the evolution of its protein components (RNase P proteins, RPPs) is not well understood. Archaeal RPPs may provide clues on how the complex evolved from an ancient ribozyme to an RNP with multiple archaeal and eukaryotic (homologous) RPPs, which are unrelated to the single bacterial RPP. Here, we analyzed the sequence and structure of archaeal RPPs from over 600 available genomes. All five RPPs are found in eight archaeal phyla, suggesting that these RPPs arose early in archaeal evolutionary history. The putative ancestral genomic loci of archaeal RPPs include genes encoding several members of ribosome, exosome, and proteasome complexes, which may indicate coevolution/coordinate regulation of RNase P with other core cellular machineries. -
1 Supporting Information for a Microrna Network Regulates
Supporting Information for A microRNA Network Regulates Expression and Biosynthesis of CFTR and CFTR-ΔF508 Shyam Ramachandrana,b, Philip H. Karpc, Peng Jiangc, Lynda S. Ostedgaardc, Amy E. Walza, John T. Fishere, Shaf Keshavjeeh, Kim A. Lennoxi, Ashley M. Jacobii, Scott D. Rosei, Mark A. Behlkei, Michael J. Welshb,c,d,g, Yi Xingb,c,f, Paul B. McCray Jr.a,b,c Author Affiliations: Department of Pediatricsa, Interdisciplinary Program in Geneticsb, Departments of Internal Medicinec, Molecular Physiology and Biophysicsd, Anatomy and Cell Biologye, Biomedical Engineeringf, Howard Hughes Medical Instituteg, Carver College of Medicine, University of Iowa, Iowa City, IA-52242 Division of Thoracic Surgeryh, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Canada-M5G 2C4 Integrated DNA Technologiesi, Coralville, IA-52241 To whom correspondence should be addressed: Email: [email protected] (M.J.W.); yi- [email protected] (Y.X.); Email: [email protected] (P.B.M.) This PDF file includes: Materials and Methods References Fig. S1. miR-138 regulates SIN3A in a dose-dependent and site-specific manner. Fig. S2. miR-138 regulates endogenous SIN3A protein expression. Fig. S3. miR-138 regulates endogenous CFTR protein expression in Calu-3 cells. Fig. S4. miR-138 regulates endogenous CFTR protein expression in primary human airway epithelia. Fig. S5. miR-138 regulates CFTR expression in HeLa cells. Fig. S6. miR-138 regulates CFTR expression in HEK293T cells. Fig. S7. HeLa cells exhibit CFTR channel activity. Fig. S8. miR-138 improves CFTR processing. Fig. S9. miR-138 improves CFTR-ΔF508 processing. Fig. S10. SIN3A inhibition yields partial rescue of Cl- transport in CF epithelia. -
Association of Gene Ontology Categories with Decay Rate for Hepg2 Experiments These Tables Show Details for All Gene Ontology Categories
Supplementary Table 1: Association of Gene Ontology Categories with Decay Rate for HepG2 Experiments These tables show details for all Gene Ontology categories. Inferences for manual classification scheme shown at the bottom. Those categories used in Figure 1A are highlighted in bold. Standard Deviations are shown in parentheses. P-values less than 1E-20 are indicated with a "0". Rate r (hour^-1) Half-life < 2hr. Decay % GO Number Category Name Probe Sets Group Non-Group Distribution p-value In-Group Non-Group Representation p-value GO:0006350 transcription 1523 0.221 (0.009) 0.127 (0.002) FASTER 0 13.1 (0.4) 4.5 (0.1) OVER 0 GO:0006351 transcription, DNA-dependent 1498 0.220 (0.009) 0.127 (0.002) FASTER 0 13.0 (0.4) 4.5 (0.1) OVER 0 GO:0006355 regulation of transcription, DNA-dependent 1163 0.230 (0.011) 0.128 (0.002) FASTER 5.00E-21 14.2 (0.5) 4.6 (0.1) OVER 0 GO:0006366 transcription from Pol II promoter 845 0.225 (0.012) 0.130 (0.002) FASTER 1.88E-14 13.0 (0.5) 4.8 (0.1) OVER 0 GO:0006139 nucleobase, nucleoside, nucleotide and nucleic acid metabolism3004 0.173 (0.006) 0.127 (0.002) FASTER 1.28E-12 8.4 (0.2) 4.5 (0.1) OVER 0 GO:0006357 regulation of transcription from Pol II promoter 487 0.231 (0.016) 0.132 (0.002) FASTER 6.05E-10 13.5 (0.6) 4.9 (0.1) OVER 0 GO:0008283 cell proliferation 625 0.189 (0.014) 0.132 (0.002) FASTER 1.95E-05 10.1 (0.6) 5.0 (0.1) OVER 1.50E-20 GO:0006513 monoubiquitination 36 0.305 (0.049) 0.134 (0.002) FASTER 2.69E-04 25.4 (4.4) 5.1 (0.1) OVER 2.04E-06 GO:0007050 cell cycle arrest 57 0.311 (0.054) 0.133 (0.002) -
Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement. -
Supplementary Materials
Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine -
(P -Value<0.05, Fold Change≥1.4), 4 Vs. 0 Gy Irradiation
Table S1: Significant differentially expressed genes (P -Value<0.05, Fold Change≥1.4), 4 vs. 0 Gy irradiation Genbank Fold Change P -Value Gene Symbol Description Accession Q9F8M7_CARHY (Q9F8M7) DTDP-glucose 4,6-dehydratase (Fragment), partial (9%) 6.70 0.017399678 THC2699065 [THC2719287] 5.53 0.003379195 BC013657 BC013657 Homo sapiens cDNA clone IMAGE:4152983, partial cds. [BC013657] 5.10 0.024641735 THC2750781 Ciliary dynein heavy chain 5 (Axonemal beta dynein heavy chain 5) (HL1). 4.07 0.04353262 DNAH5 [Source:Uniprot/SWISSPROT;Acc:Q8TE73] [ENST00000382416] 3.81 0.002855909 NM_145263 SPATA18 Homo sapiens spermatogenesis associated 18 homolog (rat) (SPATA18), mRNA [NM_145263] AA418814 zw01a02.s1 Soares_NhHMPu_S1 Homo sapiens cDNA clone IMAGE:767978 3', 3.69 0.03203913 AA418814 AA418814 mRNA sequence [AA418814] AL356953 leucine-rich repeat-containing G protein-coupled receptor 6 {Homo sapiens} (exp=0; 3.63 0.0277936 THC2705989 wgp=1; cg=0), partial (4%) [THC2752981] AA484677 ne64a07.s1 NCI_CGAP_Alv1 Homo sapiens cDNA clone IMAGE:909012, mRNA 3.63 0.027098073 AA484677 AA484677 sequence [AA484677] oe06h09.s1 NCI_CGAP_Ov2 Homo sapiens cDNA clone IMAGE:1385153, mRNA sequence 3.48 0.04468495 AA837799 AA837799 [AA837799] Homo sapiens hypothetical protein LOC340109, mRNA (cDNA clone IMAGE:5578073), partial 3.27 0.031178378 BC039509 LOC643401 cds. [BC039509] Homo sapiens Fas (TNF receptor superfamily, member 6) (FAS), transcript variant 1, mRNA 3.24 0.022156298 NM_000043 FAS [NM_000043] 3.20 0.021043295 A_32_P125056 BF803942 CM2-CI0135-021100-477-g08 CI0135 Homo sapiens cDNA, mRNA sequence 3.04 0.043389246 BF803942 BF803942 [BF803942] 3.03 0.002430239 NM_015920 RPS27L Homo sapiens ribosomal protein S27-like (RPS27L), mRNA [NM_015920] Homo sapiens tumor necrosis factor receptor superfamily, member 10c, decoy without an 2.98 0.021202829 NM_003841 TNFRSF10C intracellular domain (TNFRSF10C), mRNA [NM_003841] 2.97 0.03243901 AB002384 C6orf32 Homo sapiens mRNA for KIAA0386 gene, partial cds. -
Nuclear PTEN Safeguards Pre-Mrna Splicing to Link Golgi Apparatus for Its Tumor Suppressive Role
ARTICLE DOI: 10.1038/s41467-018-04760-1 OPEN Nuclear PTEN safeguards pre-mRNA splicing to link Golgi apparatus for its tumor suppressive role Shao-Ming Shen1, Yan Ji2, Cheng Zhang1, Shuang-Shu Dong2, Shuo Yang1, Zhong Xiong1, Meng-Kai Ge1, Yun Yu1, Li Xia1, Meng Guo1, Jin-Ke Cheng3, Jun-Ling Liu1,3, Jian-Xiu Yu1,3 & Guo-Qiang Chen1 Dysregulation of pre-mRNA alternative splicing (AS) is closely associated with cancers. However, the relationships between the AS and classic oncogenes/tumor suppressors are 1234567890():,; largely unknown. Here we show that the deletion of tumor suppressor PTEN alters pre-mRNA splicing in a phosphatase-independent manner, and identify 262 PTEN-regulated AS events in 293T cells by RNA sequencing, which are associated with significant worse outcome of cancer patients. Based on these findings, we report that nuclear PTEN interacts with the splicing machinery, spliceosome, to regulate its assembly and pre-mRNA splicing. We also identify a new exon 2b in GOLGA2 transcript and the exon exclusion contributes to PTEN knockdown-induced tumorigenesis by promoting dramatic Golgi extension and secretion, and PTEN depletion significantly sensitizes cancer cells to secretion inhibitors brefeldin A and golgicide A. Our results suggest that Golgi secretion inhibitors alone or in combination with PI3K/Akt kinase inhibitors may be therapeutically useful for PTEN-deficient cancers. 1 Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China. 2 Institute of Health Sciences, Shanghai Institutes for Biological Sciences of Chinese Academy of Sciences and SJTU-SM, Shanghai 200025, China. -
The Database of Chromosome Imbalance Regions and Genes
Lo et al. BMC Cancer 2012, 12:235 http://www.biomedcentral.com/1471-2407/12/235 RESEARCH ARTICLE Open Access The database of chromosome imbalance regions and genes resided in lung cancer from Asian and Caucasian identified by array-comparative genomic hybridization Fang-Yi Lo1, Jer-Wei Chang1, I-Shou Chang2, Yann-Jang Chen3, Han-Shui Hsu4, Shiu-Feng Kathy Huang5, Fang-Yu Tsai2, Shih Sheng Jiang2, Rajani Kanteti6, Suvobroto Nandi6, Ravi Salgia6 and Yi-Ching Wang1* Abstract Background: Cancer-related genes show racial differences. Therefore, identification and characterization of DNA copy number alteration regions in different racial groups helps to dissect the mechanism of tumorigenesis. Methods: Array-comparative genomic hybridization (array-CGH) was analyzed for DNA copy number profile in 40 Asian and 20 Caucasian lung cancer patients. Three methods including MetaCore analysis for disease and pathway correlations, concordance analysis between array-CGH database and the expression array database, and literature search for copy number variation genes were performed to select novel lung cancer candidate genes. Four candidate oncogenes were validated for DNA copy number and mRNA and protein expression by quantitative polymerase chain reaction (qPCR), chromogenic in situ hybridization (CISH), reverse transcriptase-qPCR (RT-qPCR), and immunohistochemistry (IHC) in more patients. Results: We identified 20 chromosomal imbalance regions harboring 459 genes for Caucasian and 17 regions containing 476 genes for Asian lung cancer patients. Seven common chromosomal imbalance regions harboring 117 genes, included gain on 3p13-14, 6p22.1, 9q21.13, 13q14.1, and 17p13.3; and loss on 3p22.2-22.3 and 13q13.3 were found both in Asian and Caucasian patients. -
Different Requirements for EDS1 and NDR1 by Disease Resistance Genes Define at Least Two R Gene-Mediated Signaling Pathways in Arabidopsis
Proc. Natl. Acad. Sci. USA Vol. 95, pp. 10306–10311, August 1998 Plant Biology Different requirements for EDS1 and NDR1 by disease resistance genes define at least two R gene-mediated signaling pathways in Arabidopsis NICOLE AARTS*, MATTHEW METZ†,ERIC HOLUB‡,BRIAN J. STASKAWICZ†,MICHAEL J. DANIELS*, AND JANE E. PARKER*§ *Sainsbury Laboratory, John Innes Centre, Colney Lane, Norwich NR4 7UH, United Kingdom; †Department of Plant Biology, Koshland Hall, University of California, Berkeley, CA 94720-3102; and ‡Horticultural Research International, Wellesbourne, Warwick CV35 9EF, United Kingdom Edited by Frederick M. Ausubel, Harvard Medical School, Boston, MA, and approved June 11, 1998 (received for review April 3, 1998) ABSTRACT The Arabidopsis genes EDS1 and NDR1 were localized either extracellularly or within the cytoplasm are shown previously by mutational analysis to encode essential structurally different to Pto and contain leucine-rich repeats components of race-specific disease resistance. Here, we ex- (LRRs) (5), implicating protein–protein interactions as part of amined the relative requirements for EDS1 and NDR1 by a the recognition process. In the putatively cytoplasmic R pro- broad spectrum of Resistance (R) genes present in three tein members, the LRRs lie adjacent to sequences that con- Arabidopsis accessions (Columbia, Landsberg-erecta, and stitute a nucleotide binding site (NBS) (5). The NBSyLRR Wassilewskija). We show that there is a strong requirement class of R proteins can be subdivided into members that for EDS1 by a subset of R loci (RPP2, RPP4, RPP5, RPP21, and possess an amino-terminal leucine zipper (LZ) motif or those RPS4), conferring resistance to the biotrophic oomycete Per- that have amino-terminal similarity to the cytoplasmic do- onospora parasitica, and to Pseudomonas bacteria expressing mains of the Drosophila Toll and mammalian interleukin 1 the avirulence gene avrRps4. -
A Point Mutation in HIV-1 Integrase Redirects Proviral Integration Into
bioRxiv preprint doi: https://doi.org/10.1101/2021.01.12.426369; this version posted January 12, 2021. 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. A point mutation in HIV-1 integrase redirects proviral integration into centromeric repeats Shelby Winansa,b,c and Stephen P. Goffa.b,c# aDepartment of Biochemistry and Molecular Biophysics Columbia University Medical Center, New York, NY bDepartment of Microbiology and Immunology Columbia University Medical Center, New York, NY cHoward Hughes Medical Institute, Columbia University, New York, NY #Lead Contact: address correspondence to Stephen P. Goff, [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2021.01.12.426369; this version posted January 12, 2021. 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 Abstract 2 Retroviruses utilize the viral integrase (IN) protein to integrate a DNA copy of their 3 genome into the host chromosomal DNA. HIV-1 integration sites are highly biased towards 4 actively transcribed genes, likely mediated by binding of the IN protein to specific host 5 factors, particularly LEDGF, located at these gene regions. We here report a dramatic 6 redirection of integration site distribution induced by a single point mutation in HIV-1 IN. -
Computational Prediction of Associations Between Long Non
Lu et al. BMC Genomics 2013, 14:651 http://www.biomedcentral.com/1471-2164/14/651 METHODOLOGY ARTICLE Open Access Computational prediction of associations between long non-coding RNAs and proteins Qiongshi Lu1,2,5, Sijin Ren1,3, Ming Lu1, Yong Zhang4, Dahai Zhu4, Xuegong Zhang2 and Tingting Li1,3* Abstract Background: Though most of the transcripts are long non-coding RNAs (lncRNAs), little is known about their functions. lncRNAs usually function through interactions with proteins, which implies the importance of identifying the binding proteins of lncRNAs in understanding the molecular mechanisms underlying the functions of lncRNAs. Only a few approaches are available for predicting interactions between lncRNAs and proteins. In this study, we introduce a new method lncPro. Results: By encoding RNA and protein sequences into numeric vectors, we used matrix multiplication to score each RNA–protein pair. This score can be used to measure the interactions between an RNA–protein pair. This method effectively discriminates interacting and non-interacting RNA–protein pairs and predicts RNA–protein interactions within a given complex. Applying this method on all human proteins, we found that the long non- coding RNAs we collected tend to interact with nuclear proteins and RNA-binding proteins. Conclusions: Compared with the existing approaches, our method shortens the time for training matrix and obtains optimal results based on the model being used. The ability of predicting the associations between lncRNAs and proteins has also been enhanced. Our method provides an idea on how to integrate different information into the prediction process. Keywords: Long non-coding RNA, RNA–protein interaction, Computation Background biological processes, including epigenetic regulation, Recent studies show that only a small part of the human chromatin remodeling, and cell proliferation and differ- transcriptome is involved in the protein-coding process entiation, the molecular mechanisms underlying the [1]. -
393LN V 393P 344SQ V 393P Probe Set Entrez Gene
393LN v 393P 344SQ v 393P Entrez fold fold probe set Gene Gene Symbol Gene cluster Gene Title p-value change p-value change chemokine (C-C motif) ligand 21b /// chemokine (C-C motif) ligand 21a /// chemokine (C-C motif) ligand 21c 1419426_s_at 18829 /// Ccl21b /// Ccl2 1 - up 393 LN only (leucine) 0.0047 9.199837 0.45212 6.847887 nuclear factor of activated T-cells, cytoplasmic, calcineurin- 1447085_s_at 18018 Nfatc1 1 - up 393 LN only dependent 1 0.009048 12.065 0.13718 4.81 RIKEN cDNA 1453647_at 78668 9530059J11Rik1 - up 393 LN only 9530059J11 gene 0.002208 5.482897 0.27642 3.45171 transient receptor potential cation channel, subfamily 1457164_at 277328 Trpa1 1 - up 393 LN only A, member 1 0.000111 9.180344 0.01771 3.048114 regulating synaptic membrane 1422809_at 116838 Rims2 1 - up 393 LN only exocytosis 2 0.001891 8.560424 0.13159 2.980501 glial cell line derived neurotrophic factor family receptor alpha 1433716_x_at 14586 Gfra2 1 - up 393 LN only 2 0.006868 30.88736 0.01066 2.811211 1446936_at --- --- 1 - up 393 LN only --- 0.007695 6.373955 0.11733 2.480287 zinc finger protein 1438742_at 320683 Zfp629 1 - up 393 LN only 629 0.002644 5.231855 0.38124 2.377016 phospholipase A2, 1426019_at 18786 Plaa 1 - up 393 LN only activating protein 0.008657 6.2364 0.12336 2.262117 1445314_at 14009 Etv1 1 - up 393 LN only ets variant gene 1 0.007224 3.643646 0.36434 2.01989 ciliary rootlet coiled- 1427338_at 230872 Crocc 1 - up 393 LN only coil, rootletin 0.002482 7.783242 0.49977 1.794171 expressed sequence 1436585_at 99463 BB182297 1 - up 393