DOCSLIB.ORG

Use of Whole-Exome Sequencing to Determine the Genetic Basis of Multiple Mitochondrial Respiratory Chain Complex Deficiency

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Use of Whole-Exome Sequencing to Determine the Genetic Basis of Multiple Mitochondrial Respiratory Chain Complex Deficiency Supplementary Online Content Taylor RW, Pyle A, Griffin H, et al. Use of whole-exome sequencing to determine the genetic basis of multiple mitochondrial respiratory chain complex deficiency. JAMA. doi:10.1001/jama.2014.7184 eTable 1. Genetic Summary eTable 2. Clinical Presentation, Laboratory Investigations, and Exome Sequencing Result for 53 Patients With Multiple Respiratory Chain Complex Defects eTable 3. Exome Coverage and Depth Statistics eReferences This supplementary material is provided by the authors to give readers additional information about their work. © 2014 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/24/2021 eTable 1. Genetic Summary GO- In- ID Gene Chr Position Ref. Var. Mutation Transcript Lieber ESP6500 1000G dbSNP term House P1, P2, P3, Hom. c.1349G>C RMND1 6 151726371 C G NM_017909.3 Y Y 0.002 - - - P4, P5 p.*450Serext*32 c.713A>G P6 RMND1 6 151748614 T C NM_017909.3 Y Y - - - - p.Asn238Ser c.829_830+2delGAGT P6 RMND1 6 151751289 TACTC T NM_017909.3 Y Y - 0.0004 - rs144972972 p.Glu277Glyfs*20 c.2882C>T P7 AARS2 6 44268360 G A NM_020745.3 Y Y - - - - p.Ala961Val P7, P8, P9, c.1774C>T AARS2 6 44272249 G A NM_020745.3 Y Y - 0.0002 - rs138119149 P11 p.Arg592Trp c.1774C>T P10 AARS2 6 44272249 G A NM_020745.3 Y Y - 0.0002 - rs138119149 p.Arg592Trp c.647_648insG P10 AARS2 6 44278832 G GC NM_020745.3 Y Y - - - - p.Cys218Leufs*6 c.631_631delG P12 MTO1 6 74183182 AG A NM_012123.3 Y Y - - - - p.Gly211Aspfs*3 c.1402G>A P12 MTO1 6 74191784 G A NM_012123.3 Y Y - 0.0003 - rs143747297 p.Ala468Thr Hom. c.1232C>T P13, P14 MTO1 6 74190500 C T NM_012123.3 Y Y 0.002 - - - p.Thr411Ile c.122T>G P15 MTO1 6 74171699 T G NM_012123.3 Y Y - - - - p.Val41Gly c.767A>G P15 MTO1 6 74183319 A G NM_012123.3 Y Y - - - - p.His256Arg c.1402G>A P15 MTO1 6 74191784 G A NM_012123.3 Y Y - 0.0003 - rs143747297 p.Ala468Thr Hom. c.193A>G P16 EARS2 16 23563572 T C NM_001083614.1 Y Y - - - - p.Lys65Glu c.322C>T P17 EARS2 16 23546353 C T NM_001083614.1 Y Y - - - - p.Arg108Trp c.814G>A P17 EARS2 16 23555998 G A NM_001083614.1 Y Y - 0.0003 - - p.Ala272Thr © 2014 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/24/2021 GO- In- ID Gene Chr Position Ref. Var. Mutation Transcript Lieber ESP6500 1000G dbSNP term House c.452C>T P18 MTFMT 15 65295576 G A NM_139242.3 Y Y - 0.0001 - rs200286768 p.Pro151Leu c.994C>T P18 MTFMT 15 65316100 G A NM_139242.3 Y Y - - - - p.Arg332* c.1100_1101delTT P19 MTFMT 15 65295468 GAA G NM_139242.3 Y Y - 0.0001 - - p.Phe367Serfs*22 c.626C>T P19 MTFMT 15 65313871 G A NM_139242.3 Y Y - 0.0008 - rs201431517 p.Arg181Serfs*5 c.292C>T P20 MGME1 20 17956347 C T NM_052865.2 Y Y 0.002 0.0032 0.0027 rs143417446 p.Arg98Trp c.554C>T P20 MGME1 20 17968871 C T NM_052865.2 Y Y 0.006 0.0105 0.0100 rs76599088 p.Thr185Ile Hom. c.96_99dupATCC P21 C12ORF65 12 123738316 T TATCC NM_152269 Y Y - - - - p.Pro34Ilefs*25 Hom. c.137G>A P22 YARS2 12 32908672 C T NM_001040436.2 Y Y - - - - p.Gly46Asp Hom. c. 342C>A P23 PUS1 12 132416842 C A NM_025215.5 Y Y - - - - p.Cys114* Hom. c.287A>G P24 TRMU 22 46739197 A G NM_018006.4 Y Y - - - - p.Asn96Ser Hom. c.1A>G P25 TK2 16 66583964 T C NM_004614.3 Y Y - - - - p.Met1Val Hom. c.418G>A P26 SCO2 22 50962423 C T NM_001169111.1 Y Y - 0.0002 - rs74315511 p.Glu140Lys c.1478C>T P27 ELAC2 17 12899902 C T NM_018127.6 Y Y 0.045 0.0288 0.0200 rs5030739 p.Pro493Leu c.1621G>A P27 ELAC2 17 12901771 G A NM_018127.6 Y Y - - - - p.Ala541Thr Hom. c.3G>T P28 ETHE1 19 44031327 C A NM_014297.3 Y Y - - - rs119103249 p.Met1Ile c.1045G>A P29 VARS2 6 30886663 G A NM_001167734.1 Y Y - - - - p.Ala349Thr c.1787C>A P29 VARS2 6 30889753 C A NM_001167734.1 Y Y - - - - p.Ala596Asp © 2014 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/24/2021 GO- In- ID Gene Chr Position Ref. Var. Mutation Transcript Lieber ESP6500 1000G dbSNP term House Hom. c.397_400delTTCT P30 FLAD1 1 154960604 CTTCT C NM_025207.4 N N - - - - p.Phe134Cysfs8 Hom. c.2065C>T P31 GARS 7 30672024 C T NM_002047.2 Y Y - - - - p.Arg689Cys c.550G>A P32 PTCD1 7 99030945 C T NM_015545.3 Y Y - - - - p.Gly184Arg c.337C>T P32 PTCD1 7 99032478 G A NM_015545.3 Y Y - - 0.0005 rs201306967 p.Arg113Trp c.388C>T P32 PTCD1 7 99032529 G A NM_015545.3 Y Y 0.002 0.0038 0.0005 rs35556439 p.Arg130* Hom. c.1333G>A P33 SLC25A12 2 172644389 C T NM_003705.4 Y Y - 0.0004 - rs142912356 p.Ala445Thr Hom. P33 METAP1D 2 172930482 T A NM_199227.1 Y Y - - - - c.497+2T>A c.1157A>C P34 ACSM5 16 20442346 A C NM_017888.2 Y Y 0.006 0.0055 0.0046 rs148243446 p.Lys386Thr c.1273C>A P34 ACSM5 16 20442608 C A NM_017888.2 Y Y 0.009 - - rs79364355 p.Pro425Thr Hom. c.206T>C P35 PERP 6 138428272 A G NM_022121.4 N Y - - - - p.Met69Thr c.1052A>C P35 MEF2A 15 100252738 A C NM_005587.2 N Y 0.007 - - rs201861701 p.Gln351Pro c.1055A>C P35 MEF2A 15 100252741 A C NM_005587.2 N Y 0.004 - - rs199811207 p.Gln352Pro c.68A>G P35 ACSM5 16 20422874 A G NM_017888.2 Y Y 0.007 0.0028 0.0018 rs144606521 p.His23Arg c.73A>C P35 ACSM5 16 20422879 A C NM_017888.2 Y Y 0.007 0.0025 - rs148462851 p.Lys25Gln Hom. c.1276C>T P36 HKDC1 10 71008190 C T NM_025130.3 N Y 0.002 0.0052 0.0018 rs148832840 p.Arg426Cys Hom. c.20C>T P36 ETFA 15 76603710 G A NM_000126.3 Y Y 0.002 - - - p.Pro7Leu Hom. c.2393C>T P36 IREB2 15 78786319 C T NM_004136.2 Y Y - 0.0005 - rs147288797 p.Thr798Ile © 2014 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/24/2021 GO- In- ID Gene Chr Position Ref. Var. Mutation Transcript Lieber ESP6500 1000G dbSNP term House Hom. c.241C>T P36 SMCR7 17 18166493 C T NM_139162.3 N Y - - - - p.Gln81* c.1876C>T P37 PC 11 66619351 C T NM_000920.3 Y Y - - - rs113994145 p.Arg626Trp c.1892G>A P37 PC 11 66619367 G A NM_000920.3 Y Y - - - - p.Arg631Gln Hom. c.230C>T P38 TPO 2 1440117 C T NM_000547.5 Y Y - 0.0002 - - p.Ala77Val c.9979G>A P39 HERC2 15 28419619 C T NM_004667.5 N Y 0.058 - 0.0600 rs141441362 p.Val3327Met c.6448C>G P39 HERC2 15 28459329 G C NM_004667.5 N Y - - - - p.Leu2150Val Hom. c.1648A>C P40 MAGI1 3 65376943 T G NM_015520.1 N N 0.002 0.0019 0.0014 rs61740330 p.Thr550Pro Hom. c.322G>C P40 TAF9 5 68647992 C G NM_032013.3 N N - 0.0003 0.0005 rs190151255 p.Glu108Gln Hom. c.505C>T P40 TPX2 20 30359382 C T NM_012112.4 N N - - - - p.Pro169Ser Hom. c.184G>A P40 NDRG3 20 35309281 C T NM_001015891 N N - - - - p.Gly62Ser c.368T>C P41 FAAH2 X 57337118 T C NM_174912.3 N N - - - - p.Phe123Ser (X-linked) c.493C>T P41 SLC25A43 X 118540640 C T NM_145305.2 Y Y - 0.0002 - rs138285581 p.Arg165* (X-linked) c.1738G>T P42 ARHGEF5 7 144061500 G T NM_005435.3 N N 0.015 - 0.0400 rs201664716 p.Gly580Cys c.4066A>G P42 ARHGEF5 7 144070303 A G NM_005435.3 N N - 0.0001 - - p.Asn1356Asp c.55G>C P42 DLAT 11 111896251 G C NM_001931.4 Y Y 0.004 0.0081 0.0046 rs61757217 p.Glu19Gln c.626A>G P42 DLAT 11 111899635 A G NM_001931.4 Y Y 0.007 0.0490 0.0300 rs11553595 p.Gln209Arg c.34G>A P42 SDHD 11 111957665 G A NM_003002.3 Y Y 0.007 0.0079 0.0100 rs28937576 p.Gly12Ser © 2014 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/24/2021 GO- In- ID Gene Chr Position Ref. Var. Mutation Transcript Lieber ESP6500 1000G dbSNP term House c.269T>C P42 SDHD 11 111965600 T C NM_003002.3 Y Y - - - rs201726097 p.Leu90Ser c.232G>A P42 POLRMT 19 630130 C T NM_005035.3 Y Y 0.006 0.0023 0.0018 rs140649984 p.Val78Met c.112C>T P42 POLRMT 19 632915 G A NM_005035.3 Y Y 0.093 0.0840 0.1400 rs12610885 p.Pro38Ser c.4132A>G P43 LRPPRC 2 44115792 T C NM_133259.3 Y Y - 0.0005 - rs149693840 p.Ser1378Gly c.1210C>T P43 HTRA2 2 74759840 C T NM_013247.4 Y Y - - - - p.Arg404Trp c.1810G>C P43 ALDH1L1 3 125831693 C G NM_001270364.1 Y Y - - - - p.Glu604Gln c.239G>A P43 SLC25A4 4 186066045 G A NM_001151.3 Y Y - - - - p.Arg80His c.23C>T P43 BCKDHB 6 80816433 C T NM_183050.2 Y Y - - - - p.Ala8Val c.1003G>A P43 ACSM2A 16 20487000 G A NM_001010845.2 Y Y 0.002 0.0001 - rs4643305 p.Val335Ile c.242G>A P43 TYMP 22 50967740 C T NM_001113755.2 Y Y 0.002 0.0013 0.0009 rs143789597 p.Arg81Gln c.1003C>A P44 PPL 16 4945687 G T NM_002705.4 N Y 0.074 0.0555 0.0500 rs35340520 p.Leu335Met c.263A>G P44 PPL 16 4953941 T C NM_002705.4 N Y - - - - p.Asp88Gly c.425A>G P44 SLC5A10 17 18874359 A G NM_152351.4 N Y - - - - p.Glu142Gly c.1670T>C P44 SLC5A10 17 18923704 T C NM_152351.4 N Y - - - - p.Leu557Pro c.4240A>G P45 FNDC1 6 159660797 A G NM_032532.2 N Y 0.006 - - - p.Thr1414Ala c.4358C>A P45 FNDC1 6 159660915 C A NM_032532.2 N Y - 0.0013 - rs200218522 p.Thr1453Asn c.2657T>C P45 FASN 17 80046120 A G NM_004104.4 Y Y 0.002 - - - p.Phe886Ser © 2014 American Medical Association.
Load more

Recommended publications
  • Genetics of Amyotrophic Lateral Sclerosis in the Han Chinese
    Genetics of amyotrophic lateral sclerosis in the Han Chinese Ji He A thesis submitted for the degree of Master of Philosophy at The University of Queensland in 2015 The University of Queensland Diamantina Institute 1 Abstract Amyotrophic lateral sclerosis is the most frequently occurring neuromuscular degenerative disorders, and has an obscure aetiology. Whilst major progress has been made, the majority of the genetic variation involved in ALS is, as yet, undefined. In this thesis, multiple genetic studies have been conducted to advance our understanding of the genetic architecture of the disease. In the light of the paucity of comprehensive genetic studies performed in Chinese, the presented study focused on advancing our current understanding in genetics of ALS in the Han Chinese population. To identify genetic variants altering risk of ALS, a genome-wide association study (GWAS) was performed. The study included 1,324 Chinese ALS cases and 3,115 controls. After quality control, a number of analyses were performed in a cleaned dataset of 1,243 cases and 2,854 controls that included: a genome-wide association analysis to identify SNPs associated with ALS; a genomic restricted maximum likelihood (GREML) analysis to estimate the proportion of the phenotypic variance in ALS liability due to common SNPs; and a gene- based analysis to identify genes associated with ALS. There were no genome-wide significant SNPs or genes associated with ALS. However, it was estimated that 17% (SE: 0.05; P=6×10-5) of the phenotypic variance in ALS liability was due to common SNPs. The top associated SNP was within GNAS (rs4812037; p =7×10-7).
    [Show full text]
  • 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
    [Show full text]
  • 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
    [Show full text]
  • Mitochondrial Genetics
    Mitochondrial genetics Patrick Francis Chinnery and Gavin Hudson* Institute of Genetic Medicine, International Centre for Life, Newcastle University, Central Parkway, Newcastle upon Tyne NE1 3BZ, UK Introduction: In the last 10 years the field of mitochondrial genetics has widened, shifting the focus from rare sporadic, metabolic disease to the effects of mitochondrial DNA (mtDNA) variation in a growing spectrum of human disease. The aim of this review is to guide the reader through some key concepts regarding mitochondria before introducing both classic and emerging mitochondrial disorders. Sources of data: In this article, a review of the current mitochondrial genetics literature was conducted using PubMed (http://www.ncbi.nlm.nih.gov/pubmed/). In addition, this review makes use of a growing number of publically available databases including MITOMAP, a human mitochondrial genome database (www.mitomap.org), the Human DNA polymerase Gamma Mutation Database (http://tools.niehs.nih.gov/polg/) and PhyloTree.org (www.phylotree.org), a repository of global mtDNA variation. Areas of agreement: The disruption in cellular energy, resulting from defects in mtDNA or defects in the nuclear-encoded genes responsible for mitochondrial maintenance, manifests in a growing number of human diseases. Areas of controversy: The exact mechanisms which govern the inheritance of mtDNA are hotly debated. Growing points: Although still in the early stages, the development of in vitro genetic manipulation could see an end to the inheritance of the most severe mtDNA disease. Keywords: mitochondria/genetics/mitochondrial DNA/mitochondrial disease/ mtDNA Accepted: April 16, 2013 Mitochondria *Correspondence address. The mitochondrion is a highly specialized organelle, present in almost all Institute of Genetic Medicine, International eukaryotic cells and principally charged with the production of cellular Centre for Life, Newcastle energy through oxidative phosphorylation (OXPHOS).
    [Show full text]
  • Genome-Wide Linkage and Association Study Implicates the 10Q26 Region As a Major Genetic Contributor to Primary Nonsyndromic
    www.nature.com/scientificreports OPEN Genome-wide linkage and association study implicates the 10q26 region as a major Received: 6 July 2017 Accepted: 6 October 2017 genetic contributor to primary Published: xx xx xxxx nonsyndromic vesicoureteric refux John M. Darlow1,2, Rebecca Darlay3, Mark G. Dobson1,2, Aisling Stewart3, Pimphen Charoen4,5, Jennifer Southgate 6, Simon C. Baker 6, Yaobo Xu3, Manuela Hunziker2,7, Heather J. Lambert8, Andrew J. Green1,9, Mauro Santibanez-Koref3, John A. Sayer 3, Timothy H. J. Goodship3, Prem Puri2,10, Adrian S. Woolf 11,12, Rajko B. Kenda13, David E. Barton1,9 & Heather J. Cordell3 Vesicoureteric refux (VUR) is the commonest urological anomaly in children. Despite treatment improvements, associated renal lesions – congenital dysplasia, acquired scarring or both – are a common cause of childhood hypertension and renal failure. Primary VUR is familial, with transmission rate and sibling risk both approaching 50%, and appears highly genetically heterogeneous. It is often associated with other developmental anomalies of the urinary tract, emphasising its etiology as a disorder of urogenital tract development. We conducted a genome-wide linkage and association study in three European populations to search for loci predisposing to VUR. Family-based association analysis of 1098 parent-afected-child trios and case/control association analysis of 1147 cases and 3789 controls did not reveal any compelling associations, but parametric linkage analysis of 460 families (1062 afected individuals) under a dominant model identifed a single region, on 10q26, that showed strong linkage (HLOD = 4.90; ZLRLOD = 4.39) to VUR. The ~9Mb region contains 69 genes, including some good biological candidates.
    [Show full text]
  • Mitochondrial Medicine 2019: Washington DC Scientific and Clinical Meetings June 26-29, 2019 Hilton Alexandria Mark Center Alexandria, VA
    Mitochondrial Medicine 2019: Washington DC Scientific and Clinical Meetings June 26-29, 2019 Hilton Alexandria Mark Center Alexandria, VA 2019 Course Chairs: Amel Kara, MD and Carla Koehler, PhD 2019 CME Chair: Bruce H. Cohen, MD Mitochondrial Medicine 2019: Washington DC Scientific/Clinical Program June 26-29, 2019 Course Description The United Mitochondrial Disease Foundation and PeerPoint Medical Education Institute have joined efforts to sponsor and organize a CME-accredited symposium. Mitochondrial diseases are more common than previously recognized and mitochondrial pathophysiology is now a recognized part of many disease processes, including heart disease, cancer, AIDS and diabetes. There have been significant advances in the molecular genetics, proteomics, epidemiology and clinical aspects of mitochondrial pathophysiology. This conference is directed toward the scientist and clinician interested in all aspects of mitochondrial science. The content of this educational program was determined by rigorous assessment of educational needs and includes surveys, program feedback, expert faculty assessment, literature review, medical practice, chart review and new medical knowledge. The format will include didactic lectures from invited experts intermixed with peer-reviewed platform presentations. There will be ample time for professional discussion both in and out of the meeting room, and peer-reviewed poster presentations will be given throughout the meeting. This will be a four-day scientific meeting aimed at those with scientific and
    [Show full text]
  • 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.
    [Show full text]
  • Leigh Syndrome: One Disorder, More Than 75 Monogenic Causes
    REVIEW Leigh Syndrome: One Disorder, More Than 75 Monogenic Causes Nicole J. Lake, MSc,1,2 Alison G. Compton, PhD,1,2 Shamima Rahman, MD, PhD,3 and David R. Thorburn, PhD1,2,4 Leigh syndrome is the most common pediatric presentation of mitochondrial disease. This neurodegenerative disor- der is genetically heterogeneous, and to date pathogenic mutations in >75 genes have been identified, encoded by 2 genomes (mitochondrial and nuclear). More than one-third of these disease genes have been characterized in the past 5 years alone, reflecting the significant advances made in understanding its etiological basis. We review the diverse biochemical and genetic etiology of Leigh syndrome and associated clinical, neuroradiological, and metabolic features that can provide clues for diagnosis. We discuss the emergence of genotype–phenotype correlations, insights gleaned into the molecular basis of disease, and available therapeutic options. ANN NEUROL 2016;79:190–203 isorders of mitochondrial energy generation are the phenotype relationships for this rare disorder. Advances in Dmost frequent group of inherited metabolic disor- characterizing the genetic basis of LS have consolidated our ders, with an estimated incidence of at least 1 in 5,000 understanding of the molecular basis of disease, revealing a live births.1 The most common clinical presentation of diverse biochemical etiology consistent with the annotation mitochondrial disease in children is a progressive neuro- of LS as a disorder of energy generation.2 degenerative disorder known as Leigh syndrome (LS; Mendelian Inheritance in Man [MIM] 25600). LS has Clinical and Neuroradiological Features 2 an estimated prevalence of 1 per 40,000 live births ; LS is clinically heterogeneous, with significant variation however, much higher incidences have been observed in between patients with respect to age of onset, age of 3,4 specific populations owing to founder mutations.
    [Show full text]
  • 27Th Trna Conference-Abstractsbook-3
    P1.1 The protein-only RNase P PRORP1 interacts with the nuclease MNU2 in Arabidopsis mitochondria G. Bonnard, M. Arrivé, A. Bouchoucha, A. Gobert, C. Schelcher, F. Waltz, P. Giegé Institut de biologie moléculaire des plantes, CNRS, Université de Strasbourg, Strasbourg, France The essential endonuclease activity that removes 5’ leader sequences from transfer RNA precursors is called RNase P. While ribonucleoprotein RNase P enzymes containing a ribozyme are found in all domains of life, another type of RNase P called “PRORP”, for “PROtein-only RNase P”, only composed of protein occurs in a wide variety of eukaryotes, in organelles and the nucleus. Although PRORP proteins function as single subunit enzymes in vitro, we find that PRORP1 occurs in protein complexes and is present in polysome fractions in Arabidopsis mitochondria. The analysis of immuno- precipitated protein complexes identifies proteins involved in mitochondrial gene expression processes. In particular, direct interaction is established between PRORP1 and MNU2 another mitochondrial nuclease involved in RNA 5’ processing. A specific domain of MNU2 and a conserved signature of PRORP1 are found to be directly accountable for this protein interaction. Altogether, results reveal the existence of an RNA 5’ maturation complex in Arabidopsis mitochondria and suggest that PRORP proteins might cooperate with other gene expression regulators for RNA maturation in vivo. 111 P1.2 CytoRP, a cytosolic RNase P to target TLS-RNA phytoviruses 1 1 2 1 A. Gobert , Y. Quan , I. Jupin , P. Giegé 1Institut de biologie moléculaire des plantes, CNRS, Université de Strasbourg, Strasbourg, France 1Institut Jacques Monod, CNRS, Université Paris Diderot, Paris, France In plants, PRORP enzymes are responsible for RNase P activity that involves the removal of the 5’ extremity of tRNA precursors.
    [Show full text]
  • Characterization of a Novel Monogenic Form of Liver Failure
    FAKULTÄT FÜR MEDIZIN INSTITUT FÜR HUMANGENETIK Lehrstuhl: Prof. Dr. Thomas Meitinger Characterization of a Novel Monogenic Form of Liver Failure Marlies Gwendolyn Köpke Vollständiger Abdruck der von der Fakultät für Medizin der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Medizin (Dr. med.) genehmigten Dissertation. Vorsitzender: Prof. Dr. Ernst J. Rummeny Prüfer der DissertAtion: 1. Prof. Dr. Thomas Meitinger 2. Prof. Dr. Percy A. Knolle Die Dissertation wurde am 07.08.2017 bei der Technischen Universität München eingereicht und durch die Fakultät für Medizin am 04.07.2018 angenommen. Marlies G. Köpke ChArActerizAtion of A Novel Monogenic Form of Liver Failure Table of Contents Table of contents List of abbreviations ................................................................................................. V List of prefixes ........................................................................................................ VIII Introduction ............................................................................................................... 1 Acute liver failure in children ............................................................................................ 1 Definition .......................................................................................................................... 1 Epidemiology ................................................................................................................... 2 Pathophysiology ..............................................................................................................
    [Show full text]
  • Genetic Modifiers and Rare Mendelian Disease
    G C A T T A C G G C A T genes Review Genetic Modifiers and Rare Mendelian Disease K. M. Tahsin Hassan Rahit 1,2 and Maja Tarailo-Graovac 1,2,* 1 Departments of Biochemistry, Molecular Biology and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; [email protected] 2 Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada * Correspondence: [email protected] Received: 24 January 2020; Accepted: 21 February 2020; Published: 25 February 2020 Abstract: Despite advances in high-throughput sequencing that have revolutionized the discovery of gene defects in rare Mendelian diseases, there are still gaps in translating individual genome variation to observed phenotypic outcomes. While we continue to improve genomics approaches to identify primary disease-causing variants, it is evident that no genetic variant acts alone. In other words, some other variants in the genome (genetic modifiers) may alleviate (suppress) or exacerbate (enhance) the severity of the disease, resulting in the variability of phenotypic outcomes. Thus, to truly understand the disease, we need to consider how the disease-causing variants interact with the rest of the genome in an individual. Here, we review the current state-of-the-field in the identification of genetic modifiers in rare Mendelian diseases and discuss the potential for future approaches that could bridge the existing gap. Keywords: genetic modifier; mendelian disease; rare disease; GWAS; genome sequencing; genetic interaction; penetrance; expressivity; phenotypic variability; bioinformatics 1. Introduction Diseases that follow Mendelian patterns of inheritance are known as Mendelian disorders.
    [Show full text]
  • MMEJ-Based Precision Gene Editing for Applications in Gene Therapy and Functional Genomics
    bioRxiv preprint doi: https://doi.org/10.1101/2020.04.25.060541; this version posted April 28, 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-ND 4.0 International license. MMEJ-based Precision Gene Editing for applications in Gene Therapy and Functional Genomics Gabriel Martínez-Gálvez1, Armando Manduca1, and Stephen C. Ekker2,* 1 Department of Biomedical Engineering & Physiology, Mayo Clinic, Rochester, MN, 55905, USA 2 Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, 55905, USA * To whom correspondence should be addressed. Tel: +1 507-284-5530; Fax: +1 507-293-1058; Email: [email protected] Biorxiv v1.01 ABSTRACT Experiments in gene editing commonly elicit error-prone non-homologous end joining for DNA double-strand break (DSB) repair. Microhomology-mediated end joining (MMEJ) can generate more predictable outcomes for functional genomic and somatic therapeutic applications. MENTHU is a computational tool that predicts nuclease-targetable sites likely to result in MMEJ-repaired, homogeneous genotypes (PreMAs) in zebrafish. We deployed MENTHU on 5,885 distinct Cas9-mediated DSBs in mouse embryonic stem cells, and compared the predictions to those by inDelphi, another DSB repair predictive algorithm. MENTHU correctly identified 46% of all PreMAs available, doubling the sensitivity of inDelphi. We also introduce MENTHU@4, an MENTHU update trained on this large dataset. We trained two MENTHU-based algorithms on this larger dataset and validated them against each other, MENTHU, and inDelphi.
    [Show full text]