Structure of Voltage-Modulated Sodium-Selective NALCN-FAM155A Channel Complex ✉ Yunlu Kang 1, Jing-Xiang Wu1,2,3 & Lei Chen 1,2,3
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Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database. -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
Structural Insight Into Marburg Virus Nucleoprotein-RNA Complex Formation
bioRxiv preprint doi: https://doi.org/10.1101/2021.07.13.452116; this version posted July 13, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Structural insight into Marburg virus nucleoprotein-RNA complex formation 2 3 Yoko Fujita-Fujiharu1,2,3, Yukihiko Sugita1,2,4, Yuki Takamatsu1,#, Kazuya Houri1,2,3, Manabu 4 Igarashi5, Yukiko Muramoto1,2,3, Masahiro Nakano1,2,3, Yugo Tsunoda1, Stephan Becker6,7, 5 Takeshi Noda1,2,3* 6 7 1 Laboratory of Ultrastructural Virology, Institute for Frontier Life and Medical Sciences, 8 Kyoto University, 53 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan 9 2 Laboratory of Ultrastructural Virology, Graduate School of Biostudies, Kyoto University, 53 10 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan 11 3 CREST, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332- 12 0012, Japan 13 4 Hakubi Center for Advanced Research, Kyoto University, Kyoto, Japan. 14 5 Division of Epidemiology, International Institute for Zoonosis Control, Hokkaido University, 15 Sapporo 001-0020, Japan 16 6 Institute of Virology, University of Marburg, 35043 Marburg, Germany. 17 7 German Center for Infection Research (DZIF), Marburg-Gießen-Langen Site, University of 18 Marburg, 35043 Marburg, Germany. 19 20 # present address: Department of Virology I, National Institute of Infectious Diseases, Gakuen 21 4-7-1, Musashimurayama-city, Tokyo 208-0011, Japan 22 *correspondence to: [email protected] 23 24 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.13.452116; this version posted July 13, 2021. -
A Database of Gene Regulatory Network Models Across Human Conditions
bioRxiv preprint doi: https://doi.org/10.1101/2021.06.18.448997; this version posted June 18, 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 4.0 International license. GRAND: A database of gene regulatory network models across human conditions Marouen Ben Guebila1†, Camila M Lopes-Ramos1†, Deborah Weighill1,7, Abhijeet Rajendra Sonawane2, Rebekka Burkholz1, Behrouz Shamsaei3, John Platig4, Kimberly Glass1,4, Marieke L Kuijjer5,6, John Quackenbush1,4,*. 1Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA. 2Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. 3Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. 4Channing Division of Network Medicine, Department of Medicine, Harvard Medical School and Brigham and Women’s Hospital 5Center for Molecular Medicine Norway, Faculty of Medicine, University of Oslo, Norway 6Leiden University Medical Center, Leiden, the Netherlands 7Current address: UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA † Joint Authors. * To whom correspondence should be addressed. Email: [email protected]. 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.18.448997; this version posted June 18, 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. -
Α/Β Coiled Coils 2 3 Marcus D
1 α/β Coiled Coils 2 3 Marcus D. Hartmann, Claudia T. Mendler†, Jens Bassler, Ioanna Karamichali, Oswin 4 Ridderbusch‡, Andrei N. Lupas* and Birte Hernandez Alvarez* 5 6 Department of Protein Evolution, Max Planck Institute for Developmental Biology, 72076 7 Tübingen, Germany 8 † present address: Nuklearmedizinische Klinik und Poliklinik, Klinikum rechts der Isar, 9 Technische Universität München, Munich, Germany 10 ‡ present address: Vossius & Partner, Siebertstraße 3, 81675 Munich, Germany 11 12 13 14 * correspondence to A. N. Lupas or B. Hernandez Alvarez: 15 Department of Protein Evolution 16 Max-Planck-Institute for Developmental Biology 17 Spemannstr. 35 18 D-72076 Tübingen 19 Germany 20 Tel. –49 7071 601 356 21 Fax –49 7071 601 349 22 [email protected], [email protected] 23 1 24 Abstract 25 Coiled coils are the best-understood protein fold, as their backbone structure can uniquely be 26 described by parametric equations. This level of understanding has allowed their manipulation 27 in unprecedented detail. They do not seem a likely source of surprises, yet we describe here 28 the unexpected formation of a new type of fiber by the simple insertion of two or six residues 29 into the underlying heptad repeat of a parallel, trimeric coiled coil. These insertions strain the 30 supercoil to the breaking point, causing the local formation of short β-strands, which move the 31 path of the chain by 120° around the trimer axis. The result is an α/β coiled coil, which retains 32 only one backbone hydrogen bond per repeat unit from the parent coiled coil. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
A Worldwide Map of Swine Short Tandem Repeats and Their
Wu et al. Genet Sel Evol (2021) 53:39 https://doi.org/10.1186/s12711-021-00631-4 Genetics Selection Evolution RESEARCH ARTICLE Open Access A worldwide map of swine short tandem repeats and their associations with evolutionary and environmental adaptations Zhongzi Wu1, Huanfa Gong1, Mingpeng Zhang1, Xinkai Tong1, Huashui Ai1, Shijun Xiao1, Miguel Perez‑Enciso2,3, Bin Yang1* and Lusheng Huang1* Abstract Background: Short tandem repeats (STRs) are genetic markers with a greater mutation rate than single nucleotide polymorphisms (SNPs) and are widely used in genetic studies and forensics. However, most studies in pigs have focused only on SNPs or on a limited number of STRs. Results: This study screened 394 deep‑sequenced genomes from 22 domesticated pig breeds/populations world‑ wide, wild boars from both Europe and Asia, and numerous outgroup Suidaes, and identifed a set of 878,967 poly‑ morphic STRs (pSTRs), which represents the largest repository of pSTRs in pigs to date. We found multiple lines of evidence that pSTRs in coding regions were afected by purifying selection. The enrichment of trinucleotide pSTRs in coding sequences (CDS), 5′UTR and H3K4me3 regions suggests that trinucleotide STRs serve as important com‑ ponents in the exons and promoters of the corresponding genes. We demonstrated that, compared to SNPs, pSTRs provide comparable or even greater accuracy in determining the breed identity of individuals. We identifed pSTRs that showed signifcant population diferentiation between domestic pigs and wild boars in Asia and Europe. We also observed that some pSTRs were signifcantly associated with environmental variables, such as average annual tem‑ perature or altitude of the originating sites of Chinese indigenous breeds, among which we identifed loss‑of‑function and/or expanded STRs overlapping with genes such as AHR, LAS1L and PDK1. -
Stapled Peptides—A Useful Improvement for Peptide-Based Drugs
molecules Review Stapled Peptides—A Useful Improvement for Peptide-Based Drugs Mattia Moiola, Misal G. Memeo and Paolo Quadrelli * Department of Chemistry, University of Pavia, Viale Taramelli 12, 27100 Pavia, Italy; [email protected] (M.M.); [email protected] (M.G.M.) * Correspondence: [email protected]; Tel.: +39-0382-987315 Received: 30 July 2019; Accepted: 1 October 2019; Published: 10 October 2019 Abstract: Peptide-based drugs, despite being relegated as niche pharmaceuticals for years, are now capturing more and more attention from the scientific community. The main problem for these kinds of pharmacological compounds was the low degree of cellular uptake, which relegates the application of peptide-drugs to extracellular targets. In recent years, many new techniques have been developed in order to bypass the intrinsic problem of this kind of pharmaceuticals. One of these features is the use of stapled peptides. Stapled peptides consist of peptide chains that bring an external brace that force the peptide structure into an a-helical one. The cross-link is obtained by the linkage of the side chains of opportune-modified amino acids posed at the right distance inside the peptide chain. In this account, we report the main stapling methodologies currently employed or under development and the synthetic pathways involved in the amino acid modifications. Moreover, we report the results of two comparative studies upon different kinds of stapled-peptides, evaluating the properties given from each typology of staple to the target peptide and discussing the best choices for the use of this feature in peptide-drug synthesis. Keywords: stapled peptide; structurally constrained peptide; cellular uptake; helicity; peptide drugs 1. -
The Generic Geometry of Helices and Their Close-Packed Structures
Theor Chem Acc (2010) 125:207–215 DOI 10.1007/s00214-009-0639-4 REGULAR ARTICLE The generic geometry of helices and their close-packed structures Kasper Olsen Æ Jakob Bohr Received: 23 May 2009 / Accepted: 9 September 2009 / Published online: 25 September 2009 Ó The Author(s) 2009. This article is published with open access at Springerlink.com Abstract The formation of helices is an ubiquitous 1 Introduction phenomenon for molecular structures whether they are biological, organic, or inorganic, in nature. Helical struc- Helical structures are common in chain molecules such as tures have geometrical constraints analogous to close- proteins, RNA, and DNA, e.g., a-helices and the A, B and Z packing of three-dimensional crystal structures. For helical forms of DNA. In this paper, we consider the packing of packing the geometrical constraints involve parameters idealized helices formed by a continuous tube with the such as the radius of the helical cylinder, the helical pitch purpose to calculate the constraints on such helices which angle, and the helical tube radius. In this communication, arise from close-packing and space filling considerations; the geometrical constraints for single helix, double helix, we consider single helical tubes, as well as sets of two and for double helices with minor and major grooves are identical helical tubes. It is found that the efficiency of the calculated. The results are compared with values from the use of space depends on the helical pitch angle, and the literature for helical polypeptide backbone structures, the optimum helical pitch angle is determined for single and a-, p-, 310-, and c-helices. -
Target Gene Gene Description Validation Diana Miranda
Supplemental Table S1. Mmu-miR-183-5p in silico predicted targets. TARGET GENE GENE DESCRIPTION VALIDATION DIANA MIRANDA MIRBRIDGE PICTAR PITA RNA22 TARGETSCAN TOTAL_HIT AP3M1 adaptor-related protein complex 3, mu 1 subunit V V V V V V V 7 BTG1 B-cell translocation gene 1, anti-proliferative V V V V V V V 7 CLCN3 chloride channel, voltage-sensitive 3 V V V V V V V 7 CTDSPL CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) small phosphatase-like V V V V V V V 7 DUSP10 dual specificity phosphatase 10 V V V V V V V 7 MAP3K4 mitogen-activated protein kinase kinase kinase 4 V V V V V V V 7 PDCD4 programmed cell death 4 (neoplastic transformation inhibitor) V V V V V V V 7 PPP2R5C protein phosphatase 2, regulatory subunit B', gamma V V V V V V V 7 PTPN4 protein tyrosine phosphatase, non-receptor type 4 (megakaryocyte) V V V V V V V 7 EZR ezrin V V V V V V 6 FOXO1 forkhead box O1 V V V V V V 6 ANKRD13C ankyrin repeat domain 13C V V V V V V 6 ARHGAP6 Rho GTPase activating protein 6 V V V V V V 6 BACH2 BTB and CNC homology 1, basic leucine zipper transcription factor 2 V V V V V V 6 BNIP3L BCL2/adenovirus E1B 19kDa interacting protein 3-like V V V V V V 6 BRMS1L breast cancer metastasis-suppressor 1-like V V V V V V 6 CDK5R1 cyclin-dependent kinase 5, regulatory subunit 1 (p35) V V V V V V 6 CTDSP1 CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) small phosphatase 1 V V V V V V 6 DCX doublecortin V V V V V V 6 ENAH enabled homolog (Drosophila) V V V V V V 6 EPHA4 EPH receptor A4 V V V V V V 6 FOXP1 forkhead box P1 V -
Breaking Non-Native Hydrophobic Clusters Is the Rate-Limiting Step In
Shibasish Chowdhury Wei Zhang Breaking Non-Native Chun Wu Guoming Xiong Hydrophobic Clusters is the Yong Duan Rate-Limiting Step in the Department of Chemistry and Biochemistry, Folding of an Alanine-Based Center of Biomedical Research Excellence, Peptide University of Delaware, Newark, DE 19716 Received 13 March 2002; accepted 29 April 2002 Abstract: The formation mechanism of an alanine-based peptide has been studied by all-atom molecular dynamics simulations with a recently developed all-atom point-charge force field and the Generalize Born continuum solvent model at an effective salt concentration of 0.2M. Thirty-two simulations were conducted. Each simulation was performed for 100 ns. A surprisingly complex folding process was observed. The development of the helical content can be divided into three phases with time constants of 0.06–0.08, 1.4–2.3, and 12–13 ns, respectively. Helices initiate extreme rapidly in the first phase similar to that estimated from explicit solvent simulations. Hydrophobic collapse also takes place in this phase. A folding intermediate state develops in the second phase and is unfolded to allow the peptide to reach the transition state in the third phase. The folding intermediate states are characterized by the two-turn short helices and the transition states are helix–turn–helix motifs—both of which are stabilized by hydrophobic clusters. The equilibrium helical content, calculated by both the main-chain ⌽–⌿ torsion angles and the main-chain hydrogen bonds, is 64–66%, which is in remarkable agreement with experiments. After corrected for the solvent viscosity effect, an extrapolated folding time of 16–20 ns is obtained that is in qualitative agreement with experiments. -
Engineered Type 1 Regulatory T Cells Designed for Clinical Use Kill Primary
ARTICLE Acute Myeloid Leukemia Engineered type 1 regulatory T cells designed Ferrata Storti Foundation for clinical use kill primary pediatric acute myeloid leukemia cells Brandon Cieniewicz,1* Molly Javier Uyeda,1,2* Ping (Pauline) Chen,1 Ece Canan Sayitoglu,1 Jeffrey Mao-Hwa Liu,1 Grazia Andolfi,3 Katharine Greenthal,1 Alice Bertaina,1,4 Silvia Gregori,3 Rosa Bacchetta,1,4 Norman James Lacayo,1 Alma-Martina Cepika1,4# and Maria Grazia Roncarolo1,2,4# Haematologica 2021 Volume 106(10):2588-2597 1Department of Pediatrics, Division of Stem Cell Transplantation and Regenerative Medicine, Stanford School of Medicine, Stanford, CA, USA; 2Stanford Institute for Stem Cell Biology and Regenerative Medicine, Stanford School of Medicine, Stanford, CA, USA; 3San Raffaele Telethon Institute for Gene Therapy, Milan, Italy and 4Center for Definitive and Curative Medicine, Stanford School of Medicine, Stanford, CA, USA *BC and MJU contributed equally as co-first authors #AMC and MGR contributed equally as co-senior authors ABSTRACT ype 1 regulatory (Tr1) T cells induced by enforced expression of interleukin-10 (LV-10) are being developed as a novel treatment for Tchemotherapy-resistant myeloid leukemias. In vivo, LV-10 cells do not cause graft-versus-host disease while mediating graft-versus-leukemia effect against adult acute myeloid leukemia (AML). Since pediatric AML (pAML) and adult AML are different on a genetic and epigenetic level, we investigate herein whether LV-10 cells also efficiently kill pAML cells. We show that the majority of primary pAML are killed by LV-10 cells, with different levels of sensitivity to killing. Transcriptionally, pAML sensitive to LV-10 killing expressed a myeloid maturation signature.