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Gerstung M, Jolly C, Leshchiner I, Dentro SC, Gonzalez S, Rosebrock D, Mitchell TJ, Rubanova Y, Anur P, Yu K, Tarabichi M, Deshwar A, Wintersinger J, Kleinheinz K, Vázquez-García I, Haase K, Jerman L, Sengupta S, Macintyre G, Malikic S, Donmez N, Livitz DG, Cmero M, Demeulemeester J, Schumacher S, Fan Y, Yao X, Lee J, Schlesner M, Boutros PC, Bowtell DD, Zhu H, Getz G, Imielinski M, Beroukhim R, Sahinalp SC, Ji Y, Peifer M, Markowetz F, Mustonen V, Yuan K, Wang W, Morris QD, Spellman PT, Wedge DC, Van Loo P, Tarabichi M, Wintersinger J, Deshwar AG, Yu K, Gonzalez S, Rubanova Y, Macintyre G, Adams DJ, Anur P, Beroukhim R, Boutros PC, Bowtell DD, Campbell PJ, Cao S, Christie EL, Cmero M, Cun Y, Dawson KJ, Demeulemeester J, Donmez N, Drews RM, Eils R, Fan Y, Fittall M, Garsed DW, Getz G, Ha G, Imielinski M, Jerman L, Ji Y, Kleinheinz K, Lee J, Lee-Six H, Livitz DG, Malikic S, Markowetz F, Martincorena I, Mitchell TJ, Mustonen V, Oesper L, Peifer M, Peto M, Raphael BJ, Rosebrock D, Sahinalp SC, Salcedo A, Schlesner M, Schumacher S, Sengupta S, Shi R, Shin SJ, Spiro O, Pitkänen E, Pivot X, Piñeiro-Yáñez E, Planko L, Plass C, Polak P, Pons T, Popescu I, Potapova O, Prasad A, Stein LD, Preston SR, Prinz M, Pritchard AL, Prokopec SD, Provenzano E, Puente XS, Puig S, Puiggròs M, Pulido-Tamayo S, Pupo GM, Vázquez-García I, Purdie CA, Quinn MC, Rabionet R, Rader JS, Radlwimmer B, Radovic P, Raeder B, Raine KM, Ramakrishna M, Ramakrishnan K, Vembu S, Ramalingam S, Raphael BJ, Rathmell WK, Rausch T, Reifenberger G, Reimand J, Reis-Filho J, Reuter V, Reyes-Salazar I, Reyna MA, Wheeler DA, Reynolds SM, Rheinbay E, Riazalhosseini Y, Richardson AL, Richter J, Ringel M, Ringnér M, Rino Y, Rippe K, Roach J, Yang TP, Roberts LR, Roberts ND, Roberts SA, Robertson AG, Robertson AJ, Rodriguez JB, Rodriguez-Martin B, Rodríguez-González FG, Roehrl MHA, Rohde M, Yao X, Rokutan H, Romieu G, Rooman I, Roques T, Rosebrock D, Rosenberg M, Rosenstiel PC, Rosenwald A, Rowe EW, Royo R, Yuan K, Rozen SG, Rubanova Y, Rubin MA, Rubio-Perez C, Rudneva VA, Rusev BC, Ruzzenente A, Rätsch G, Sabarinathan R, Sabelnykova VY, Zhu H, Sadeghi S, Sahinalp SC, Saini N, Saito-Adachi M, Saksena G, Salcedo A, Salgado R, Salichos L, Sallari R, Saller C, Wang W, Salvia R, Sam M, Samra JS, Sanchez-Vega F, Sander C, Sanders G, Sarin R, Sarrafi I, Sasaki-Oku A, Sauer T, Morris QD, Sauter G, Saw RPM, Scardoni M, Scarlett CJ, Scarpa A, Scelo G, Schadendorf D, Schein JE, Schilhabel MB, Schlesner M, Spellman PT, Schlomm T, Schmidt HK, Schramm SJ, Schreiber S, Schultz N, Schumacher SE, Schwarz RF, Scolyer RA, Scott D, Scully R, Wedge DC, Seethala R, Segre AV, Selander I, Semple CA, Senbabaoglu Y, Sengupta S, Sereni E, Serra S, Sgroi DC, Shackleton M, Van Loo P, Shah NC, Shahabi S, Shang CA, Shang P, Shapira O, Shelton T, Shen C, Shen H, Shepherd R, Shi R, Spellman PT, Shi Y, Shiah YJ, Shibata T, Shih J, Shimizu E, Shimizu K, Shin SJ, Shiraishi Y, Shmaya T, Shmulevich I, Wedge DC, Shorser SI, Short C, Shrestha R, Shringarpure SS, Shriver C, Shuai S, Sidiropoulos N, Siebert R, Sieuwerts AM, Sieverling L, Van Loo P, Signoretti S, Sikora KO, Simbolo M, Simon R, Simons JV, Simpson JT, Simpson PT, Singer S, Sinnott-Armstrong N, Sipahimalani P, Aaltonen LA, Skelly TJ, Smid M, Smith J, Smith-McCune K, Socci ND, Sofia HJ, Soloway MG, Song L, Sood AK, Sothi S, Abascal F, Sotiriou C, Soulette CM, Span PN, Spellman PT, Sperandio N, Spillane AJ, Spiro O, Spring J, Staaf J, Stadler PF, Abeshouse A, Staib P, Stark SG, Stebbings L, Stefánsson ÓA, Stegle O, Stein LD, Stenhouse A, Stewart C, Stilgenbauer S, Stobbe MD, Aburatani H, Stratton MR, Stretch JR, Struck AJ, Stuart JM, Stunnenberg HG, Su H, Su X, Sun RX, Sungalee S, Susak H, Adams DJ, Suzuki A, Sweep F, Szczepanowski M, Sültmann H, Yugawa T, Tam A, Tamborero D, Tan BKT, Tan D, Tan P, Agrawal N, Tanaka H, Taniguchi H, Tanskanen TJ, Tarabichi M, Tarnuzzer R, Tarpey P, Taschuk ML, Tatsuno K, Tavaré S, Taylor DF, Ahn KS, Taylor-Weiner A, Teague JW, Teh BT, Tembe V, Temes J, Thai K, Thayer SP, Thiessen N, Thomas G, Thomas S, Ahn SM, Thompson A, Thompson AM, Thompson JFF, Thompson RH, Thorne H, Thorne LB, Thorogood A, Tiao G, Tijanic N, Timms LE, Aikata H, Tirabosco R, Tojo M, Tommasi S, Toon CW, Toprak UH, Torrents D, Tortora G, Tost J, Totoki Y, Townend D, Akbani R, Traficante N, Treilleux I, Trotta JR, Trümper LHP, Tsao M, Tsunoda T, Tubio JMC, Tucker O, Turkington R, Turner DJ, Akdemir KC, Tutt A, Ueno M, Ueno NT, Umbricht C, Umer HM, Underwood TJ, Urban L, Urushidate T, Ushiku T, Uusküla-Reimand L, Al-Ahmadie H, Valencia A, Van Den Berg DJ, Van Laere S, Van Loo P, Van Meir EG, Van den Eynden GG, Van der Kwast T, Vasudev N, Vazquez M, Vedururu R, Al-Sedairy ST, Veluvolu U, Vembu S, Verbeke LPC, Vermeulen P, Verrill C, Viari A, Vicente D, Vicentini C, VijayRaghavan K, Viksna J, Al-Shahrour F, Vilain RE, Villasante I, Vincent-Salomon A, Visakorpi T, Voet D, Vyas P, Vázquez-García I, Waddell NM, Waddell N, Wadelius C, Alawi M, Wadi L, Wagener R, Wala JA, Wang J, Wang J, Wang L, Wang Q, Wang W, Wang Y, Wang Z, Albert M, Waring PM, Warnatz HJ, Warrell J, Warren AY, Waszak SM, Wedge DC, Weichenhan D, Weinberger P, Weinstein JN, Weischenfeldt J, Aldape K, Weisenberger DJ, Welch I, Wendl MC, Werner J, Whalley JP, Wheeler DA, Whitaker HC, Wigle D, Wilkerson MD, Williams A, Alexandrov LB, Wilmott JS, Wilson GW, Wilson JM, Wilson RK, Winterhoff B, Wintersinger JA, Wiznerowicz M, Wolf S, Wong BH, Wong T, Ally A, Wong W, Woo Y, Wood S, Wouters BG, Wright AJ, Wright DW, Wright MH, Wu CL, Wu DY, Wu G, Alsop K, Wu J, Wu K, Wu Y, Wu Z, Xi L, Xia T, Xiang Q, Xiao X, Xing R, Xiong H, Alvarez EG, Xu Q, Xu Y, Xue H, Yachida S, Yakneen S, Yamaguchi R, Yamaguchi TN, Yamamoto M, Yamamoto S, Yamaue H, Amary F, Yang F, Yang H, Yang JY, Yang L, Yang L, Yang S, Yang TP, Yang Y, Yao X, Yaspo ML, Amin SB, Yates L, Yau C, Ye C, Ye K, Yellapantula VD, Yoon CJ, Yoon SS, Yousif F, Yu J, Yu K, Aminou B, Yu W, Yu Y, Yuan K, Yuan Y, Yuen D, Yung CK, Zaikova O, Zamora J, Zapatka M, Zenklusen JC, Ammerpohl O, Zenz T, Zeps N, Zhang CZ, Zhang F, Zhang H, Zhang H, Zhang H, Zhang J, Zhang J, Zhang J, Anderson MJ, Zhang X, Zhang X, Zhang Y, Zhang Z, Zhao Z, Zheng L, Zheng X, Zhou W, Zhou Y, Zhu B, Ang Y, Zhu H, Zhu J, Zhu S, Zou L, Zou X, deFazio A, van As N, van Deurzen CHM, van de Vijver MJ, van’t Veer L, Antonello D, von Mering C, Anur P, Aparicio S, Appelbaum EL, Arai Y, Aretz A, Arihiro K, Ariizumi SI, Armenia J, Arnould L, Asa S, Assenov Y, Atwal G, Aukema S, Auman JT, Aure MRR, Awadalla P, Aymerich M, Bader GD, Baez-Ortega A, Bailey MH, Bailey PJ, Balasundaram M, Balu S, Bandopadhayay P, Banks RE, Barbi S, Barbour AP, Barenboim J, Barnholtz-Sloan J, Barr H, Barrera E, Bartlett J, Bartolome J, Bassi C, Bathe OF, Baumhoer D, Bavi P, Baylin SB, Bazant W, Beardsmore D, Beck TA, Behjati S, Behren A, Niu B, Bell C, Beltran S, Benz C, Berchuck A, Bergmann AK, Bergstrom EN, Berman BP, Berney DM, Bernhart SH, Beroukhim R, 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Chan-Seng-Yue M, Chandan VS, Chang DK, Chanock SJ, Chantrill LA, Chateigner A, Chatterjee N, Chayama K, Chen HW, Chen J, Chen K, Chen Y, Chen Z, Cherniack AD, Chien J, Chiew YE, Chin SF, Cho J, Cho S, Choi JK, Choi W, Chomienne C, Chong Z, Choo SP, Chou A, Christ AN, Christie EL, Chuah E, Cibulskis C, Cibulskis K, Cingarlini S, Clapham P, Claviez A, Cleary S, Cloonan N, Cmero M, Collins CC, Connor AA, Cooke SL, Cooper CS, Cope L, Corbo V, Cordes MG, Cordner SM, Cortés-Ciriano I, Covington K, Cowin PA, Craft B, Craft D, Creighton CJ, Cun Y, Curley E, Cutcutache I, Czajka K, Czerniak B, Dagg RA, Danilova L, Davi MV, Davidson NR, Davies H, Davis IJ, Davis-Dusenbery BN, Dawson KJ, De La Vega FM, De Paoli-Iseppi R, Defreitas T, Tos APD, Delaneau O, Demchok JA, Demeulemeester J, Demidov GM, Demircioğlu D, Dennis NM, Denroche RE, Dentro SC, Desai N, Deshpande V, Deshwar AG, Desmedt C, Deu-Pons J, Dhalla N, Dhani NC, Dhingra P, Dhir R, DiBiase A, Diamanti K, Ding L, Ding S, Dinh HQ, Dirix L, 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George J, Gerhard DS, Gerhauser C, Gershenwald JE, Gerstein M, Gerstung M, Getz G, Ghori M, Ghossein R, Giama NH, Gibbs RA, Gibson B, Gill AJ, Gill P, Giri DD, Glodzik D, Gnanapragasam VJ, Goebler ME, Goldman MJ, Gomez C, Gonzalez S, Gonzalez-Perez A, Gordenin DA, Gossage J, Gotoh K, Govindan R, Grabau D, Graham JS, Grant RC, Green AR, Green E, Greger L, Grehan N, Grimaldi S, Grimmond SM, Grossman RL, Grundhoff A, Gundem G, Guo Q, Gupta M, Gupta S, Gut IG, Gut M, Göke J, Ha G, Haake A, Haan D, Haas S, Haase K, Haber JE, Habermann N, Hach F, Haider S, Hama N, Hamdy FC, Hamilton A, Hamilton MP, Han L, Hanna GB, Hansmann M, Haradhvala NJ, Harismendy O, Harliwong I, Harmanci AO, Harrington E, Hasegawa T, Haussler D, Hawkins S, Hayami S, Hayashi S, Hayes DN, Hayes SJ, Hayward NK, Hazell S, He Y, Heath AP, Heath SC, Hedley D, Hegde AM, Heiman DI, Heinold MC, Heins Z, Heisler LE, Hellstrom-Lindberg E, Helmy M, Heo SG, Hepperla AJ, Heredia-Genestar JM, Herrmann C, Hersey P, Hess JM, Hilmarsdottir H, Hinton J, Hirano S, Hiraoka N, Hoadley KA, Hobolth A, Hodzic E, Hoell JI, Hoffmann S, Hofmann O, Holbrook A, Holik AZ, Hollingsworth MA, Holmes O, Holt RA, Hong C, Hong EP, Hong JH, Hooijer GK, Hornshøj H, Hosoda F, Hou Y, Hovestadt V, Howat W, Hoyle AP, Hruban RH, Hu J, Hu T, Hua X, Huang KL, Huang M, Huang MN, Huang V, Huang Y, Huber W, Hudson TJ, Hummel M, Hung JA, Huntsman D, Hupp TR, Huse J, Huska MR, Hutter B, Hutter CM, Hübschmann D, Iacobuzio-Donahue CA, Imbusch CD, Imielinski M, Imoto S, Isaacs WB, Isaev K, Ishikawa S, Iskar M, Islam SMA, Ittmann M, Ivkovic S, Izarzugaza JMG, Jacquemier J, Jakrot V, Jamieson NB, Jang GH, Jang SJ, Jayaseelan JC, Jayasinghe R, Jefferys SR, Jegalian K, Jennings JL, Jeon SH, Jerman L, Ji Y, Jiao W, Johansson PA, Johns AL, Johns J, Johnson R, Johnson TA, Jolly C, Joly Y, Jonasson JG, Jones CD, Jones DR, Jones DTW, Jones N, Jones SJM, Jonkers J, Ju YS, Juhl H, Jung J, Juul M, Juul RI, Juul S, Jäger N, Kabbe R, Kahles A, Kahraman A, Kaiser VB, Kakavand H, Kalimuthu S, von Kalle C, Kang KJ, Karaszi K, Karlan B, Karlić R, Karsch D, Kasaian K, Kassahn KS, Katai H, Kato M, Katoh H, Kawakami Y, Kay JD, Kazakoff SH, Kazanov MD, Keays M, Kebebew E, Kefford RF, Kellis M, Kench JG, Kennedy CJ, Kerssemakers JNA, Khoo D, Khoo V, Khuntikeo N, Khurana E, Kilpinen H, Kim HK, Kim HL, Kim HY, Kim H, Kim J, Kim J, Kim JK, Kim Y, King TA, Klapper W, Kleinheinz K, Klimczak LJ, Knappskog S, Kneba M, Knoppers BM, Koh Y, Komorowski J, Komura D, Komura M, Kong G, Kool M, Korbel JO, Korchina V, Korshunov A, Koscher M, Koster R, Kote-Jarai Z, Koures A, Kovacevic M, Kremeyer B, Kretzmer H, Kreuz M, Krishnamurthy S, Kube D, Kumar K, Kumar P, Kumar S, Kumar Y, Kundra R, Kübler K, Küppers R, Lagergren J, Lai PH, Laird PW, Lakhani SR, Lalansingh CM, Lalonde E, Lamaze FC, Lambert A, Lander E, Landgraf P, Landoni L, Langerød A, Lanzós A, Larsimont D, Larsson E, Lathrop M, Lau LMS, Lawerenz C, Lawlor RT, Lawrence MS, Lazar AJ, Lazic AM, Le X, Lee D, Lee D, Lee EA, Lee HJ, Lee JJK, Lee JY, Lee J, Lee MTM, Lee-Six H, Lehmann KV, Lehrach H, Lenze D, Leonard CR, Leongamornlert DA, Leshchiner I, Letourneau L, Letunic I, Levine DA, Lewis L, Ley T, Li C, Li CH, Li HI, Li J, Li L, Li S, Li S, Li X, Li X, Li X, Li Y, Liang H, Liang SB, Lichter P, Lin P, Lin Z, Linehan WM, Lingjærde OC, Liu D, Liu EM, Liu FFF, Liu F, Liu J, Liu X, Livingstone J, Livitz D, Livni N, Lochovsky L, Loeffler M, Long GV, Lopez-Guillermo A, Lou S, Louis DN, Lovat LB, Lu Y, Lu YJ, Lu Y, Luchini C, Lungu I, Luo X, Luxton HJ, Lynch AG, Lype L, López C, López-Otín C, Ma EZ, Ma Y, MacGrogan G, MacRae S, Macintyre G, Madsen T, Maejima K, Mafficini A, Maglinte DT, Maitra A, Majumder PP, Malcovati L, Malikic S, Malleo G, Mann GJ, Mantovani-Löffler L, Marchal K, Marchegiani G, Mardis ER, Margolin AA, Marin MG, Markowetz F, Markowski J, Marks J, Marques-Bonet T, Marra MA, Marsden L, Martens JWM, Martin S, Martin-Subero JI, Martincorena I, Martinez-Fundichely A, Maruvka YE, Mashl RJ, Massie CE, Matthew TJ, Matthews L, Mayer E, Mayes S, Mayo M, Mbabaali F, McCune K, McDermott U, McGillivray PD, McLellan MD, McPherson JD, McPherson JR, McPherson TA, Meier SR, Meng A, Meng S, Menzies A, Merrett ND, Merson S, Meyerson M, Meyerson W, Mieczkowski PA, Mihaiescu GL, Mijalkovic S, Mikkelsen T, Milella M, Mileshkin L, Miller CA, Miller DK, Miller JK, Mills GB, Milovanovic A, Minner S, Miotto M, Arnau GM, Mirabello L, Mitchell C, Mitchell TJ, Miyano S, Miyoshi N, Mizuno S, Molnár-Gábor F, Moore MJ, Moore RA, Morganella S, Morris QD, Morrison C, Mose LE, Moser CD, Muiños F, Mularoni L, Mungall AJ, Mungall K, Musgrove EA, Mustonen V, Mutch D, Muyas F, Muzny DM, Muñoz A, Myers J, Myklebost O, Möller P, Nagae G, Nagrial AM, Nahal-Bose HK, Nakagama H, Nakagawa H, Nakamura H, Nakamura T, Nakano K, Nandi T, Nangalia J, Nastic M, Navarro A, Navarro FCP, Neal DE, Nettekoven G, Newell F, Newhouse SJ, Newton Y, Ng AWT, Ng A, Nicholson J, Nicol D, Nie Y, Nielsen GP, Nielsen MM, Nik-Zainal S, Noble MS, Nones K, Northcott PA, Notta F, O’Connor BD, O’Donnell P, O’Donovan M, O’Meara S, O’Neill BP, O’Neill JR, Ocana D, Ochoa A, Oesper L, Ogden C, Ohdan H, Ohi K, Ohno-Machado L, Oien KA, Ojesina AI, Ojima H, Okusaka T, Omberg L, Ong CK, Ossowski S, Ott G, Ouellette BFF, P’ng C, Paczkowska M, Paiella S, Pairojkul C, Pajic M, Pan-Hammarström Q, Papaemmanuil E, Papatheodorou I, Paramasivam N, Park JW, Park JW, Park K, Park K, Park PJ, Parker JS, Parsons SL, Pass H, Pasternack D, Pastore A, Patch AM, Pauporté I, Pea A, Pearson JV, Pedamallu CS, Pedersen JS, Pederzoli P, Peifer M, Pennell NA, Perou CM, Perry MD, Petersen GM, Peto M, Petrelli N, Petryszak R, Pfister SM, Phillips M, Pich O, Pickett HA, Pihl TD, Pillay N, Pinder S, Pinese M, Pinho AV. Author Correction: The evolutionary history of 2,658 cancers. Nature 2023; 614:E42. [PMID: 36697833 PMCID: PMC9931577 DOI: 10.1038/s41586-022-05601-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Moritz Gerstung
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK. .,European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany. .,Wellcome Sanger Institute, Cambridge, UK.
| | - Clemency Jolly
- grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK
| | - Ignaty Leshchiner
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Stefan C. Dentro
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK ,grid.4991.50000 0004 1936 8948Big Data Institute, University of Oxford, Oxford, UK
| | - Santiago Gonzalez
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Daniel Rosebrock
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Thomas J. Mitchell
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.5335.00000000121885934University of Cambridge, Cambridge, UK
| | - Yulia Rubanova
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | - Pavana Anur
- grid.5288.70000 0000 9758 5690Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR USA
| | - Kaixian Yu
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Maxime Tarabichi
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK
| | - Amit Deshwar
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | - Jeff Wintersinger
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | - Kortine Kleinheinz
- grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany ,grid.7700.00000 0001 2190 4373Heidelberg University, Heidelberg, Germany
| | - Ignacio Vázquez-García
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.5335.00000000121885934University of Cambridge, Cambridge, UK
| | - Kerstin Haase
- grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK
| | - Lara Jerman
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK ,grid.8954.00000 0001 0721 6013University of Ljubljana, Ljubljana, Slovenia
| | - Subhajit Sengupta
- grid.240372.00000 0004 0400 4439NorthShore University HealthSystem, Evanston, IL USA
| | - Geoff Macintyre
- grid.5335.00000000121885934Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Salem Malikic
- grid.61971.380000 0004 1936 7494Simon Fraser University, Burnaby, British Columbia Canada ,grid.412541.70000 0001 0684 7796Vancouver Prostate Centre, Vancouver, British Columbia Canada
| | - Nilgun Donmez
- grid.61971.380000 0004 1936 7494Simon Fraser University, Burnaby, British Columbia Canada ,grid.412541.70000 0001 0684 7796Vancouver Prostate Centre, Vancouver, British Columbia Canada
| | - Dimitri G. Livitz
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Marek Cmero
- grid.1008.90000 0001 2179 088XUniversity of Melbourne, Melbourne, Victoria Australia ,grid.1042.70000 0004 0432 4889Walter and Eliza Hall Institute, Melbourne, Victoria Australia
| | - Jonas Demeulemeester
- grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK ,grid.5596.f0000 0001 0668 7884University of Leuven, Leuven, Belgium
| | - Steven Schumacher
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Yu Fan
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Xiaotong Yao
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA ,grid.429884.b0000 0004 1791 0895New York Genome Center, New York, NY USA
| | - Juhee Lee
- grid.205975.c0000 0001 0740 6917University of California Santa Cruz, Santa Cruz, CA USA
| | - Matthias Schlesner
- grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Paul C. Boutros
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.419890.d0000 0004 0626 690XOntario Institute for Cancer Research, Toronto, Ontario Canada ,grid.19006.3e0000 0000 9632 6718University of California, Los Angeles, CA USA
| | - David D. Bowtell
- grid.1055.10000000403978434Peter MacCallum Cancer Centre, Melbourne, Victoria Australia
| | - Hongtu Zhu
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Gad Getz
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA ,grid.32224.350000 0004 0386 9924Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA USA ,grid.32224.350000 0004 0386 9924Department of Pathology, Massachusetts General Hospital, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | - Marcin Imielinski
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA ,grid.429884.b0000 0004 1791 0895New York Genome Center, New York, NY USA
| | - Rameen Beroukhim
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA
| | - S. Cenk Sahinalp
- grid.412541.70000 0001 0684 7796Vancouver Prostate Centre, Vancouver, British Columbia Canada ,grid.411377.70000 0001 0790 959XIndiana University, Bloomington, IN USA
| | - Yuan Ji
- grid.240372.00000 0004 0400 4439NorthShore University HealthSystem, Evanston, IL USA ,grid.170205.10000 0004 1936 7822The University of Chicago, Chicago, IL USA
| | - Martin Peifer
- grid.6190.e0000 0000 8580 3777University of Cologne, Cologne, Germany
| | - Florian Markowetz
- grid.5335.00000000121885934Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Ville Mustonen
- grid.7737.40000 0004 0410 2071University of Helsinki, Helsinki, Finland
| | - Ke Yuan
- grid.5335.00000000121885934Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK ,grid.8756.c0000 0001 2193 314XUniversity of Glasgow, Glasgow, UK
| | - Wenyi Wang
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Quaid D. Morris
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | | | - Paul T. Spellman
- grid.5288.70000 0000 9758 5690Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR USA
| | - David C. Wedge
- grid.4991.50000 0004 1936 8948Big Data Institute, University of Oxford, Oxford, UK ,grid.454382.c0000 0004 7871 7212Oxford NIHR Biomedical Research Centre, Oxford, UK
| | - Peter Van Loo
- The Francis Crick Institute, London, UK. .,University of Leuven, Leuven, Belgium.
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Calabrese C, Davidson NR, Demircioğlu D, Fonseca NA, He Y, Kahles A, Lehmann KV, Liu F, Shiraishi Y, Soulette CM, Urban L, Greger L, Li S, Liu D, Perry MD, Xiang Q, Zhang F, Zhang J, Bailey P, Erkek S, Hoadley KA, Hou Y, Huska MR, Kilpinen H, Korbel JO, Marin MG, Markowski J, Nandi T, Pan-Hammarström Q, Pedamallu CS, Siebert R, Stark SG, Su H, Tan P, Waszak SM, Yung C, Zhu S, Awadalla P, Creighton CJ, Meyerson M, Ouellette BFF, Wu K, Yang H, Brazma A, Brooks AN, Göke J, Rätsch G, Schwarz RF, Stegle O, Zhang Z, Wu K, Yang H, Fonseca NA, Kahles A, Lehmann KV, Urban L, Soulette CM, Shiraishi Y, Liu F, He Y, Demircioğlu D, Davidson NR, Calabrese C, Zhang J, Perry MD, Xiang Q, Greger L, Li S, Liu D, Stark SG, Zhang F, Amin SB, Bailey P, Chateigner A, Cortés-Ciriano I, Craft B, Erkek S, Frenkel-Morgenstern M, Goldman M, Hoadley KA, Hou Y, Huska MR, Khurana E, Kilpinen H, Korbel JO, Lamaze FC, Li C, Li X, Li X, Liu X, Marin MG, Markowski J, Nandi T, Nielsen MM, Ojesina AI, Pan-Hammarström Q, 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KH, Busanovich J, Bustamante CD, Butler AP, Butte AJ, Byrne NJ, Børresen-Dale AL, Caesar-Johnson SJ, Cafferkey A, Cahill D, Calabrese C, Caldas C, Calvo F, Camacho N, Campbell PJ, Campo E, Cantù C, Cao S, Carey TE, Carlevaro-Fita J, Carlsen R, Cataldo I, Cazzola M, Cebon J, Cerfolio R, Chadwick DE, Chakravarty D, Chalmers D, Chan CWY, Chan K, Chan-Seng-Yue M, Chandan VS, Chang DK, Chanock SJ, Chantrill LA, Chateigner A, Chatterjee N, Chayama K, Chen HW, Chen J, Chen K, Chen Y, Chen Z, Cherniack AD, Chien J, Chiew YE, Chin SF, Cho J, Cho S, Choi JK, Choi W, Chomienne C, Chong Z, Choo SP, Chou A, Christ AN, Christie EL, Chuah E, Cibulskis C, Cibulskis K, Cingarlini S, Clapham P, Claviez A, Cleary S, Cloonan N, Cmero M, Collins CC, Connor AA, Cooke SL, Cooper CS, Cope L, Corbo V, Cordes MG, Cordner SM, Cortés-Ciriano I, Covington K, Cowin PA, Craft B, Craft D, Creighton CJ, Cun Y, Curley E, Cutcutache I, Czajka K, Czerniak B, Dagg RA, Danilova L, Davi MV, Davidson NR, Davies H, Davis IJ, 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Author Correction: Genomic basis for RNA alterations in cancer. Nature 2023; 614:E37. [PMID: 36697831 PMCID: PMC9931574 DOI: 10.1038/s41586-022-05596-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
| | - Claudia Calabrese
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Natalie R. Davidson
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medical College, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Deniz Demircioğlu
- grid.4280.e0000 0001 2180 6431National University of Singapore, Singapore, Singapore ,grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore
| | - Nuno A. Fonseca
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Yao He
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
| | - André Kahles
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Kjong-Van Lehmann
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Fenglin Liu
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
| | - Yuichi Shiraishi
- grid.26999.3d0000 0001 2151 536XThe University of Tokyo, Minato-ku, Japan
| | - Cameron M. Soulette
- grid.205975.c0000 0001 0740 6917University of California, Santa Cruz, Santa Cruz, CA USA
| | - Lara Urban
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Liliana Greger
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Siliang Li
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Dongbing Liu
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Marc D. Perry
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada ,grid.266102.10000 0001 2297 6811University of California, San Francisco, San Francisco, CA USA
| | - Qian Xiang
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Fan Zhang
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
| | - Junjun Zhang
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Peter Bailey
- grid.8756.c0000 0001 2193 314XUniversity of Glasgow, Glasgow, UK
| | - Serap Erkek
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Katherine A. Hoadley
- grid.10698.360000000122483208The University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Yong Hou
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Matthew R. Huska
- grid.419491.00000 0001 1014 0849Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany
| | - Helena Kilpinen
- grid.83440.3b0000000121901201University College London, London, UK
| | - Jan O. Korbel
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Maximillian G. Marin
- grid.205975.c0000 0001 0740 6917University of California, Santa Cruz, Santa Cruz, CA USA
| | - Julia Markowski
- grid.419491.00000 0001 1014 0849Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany
| | - Tannistha Nandi
- grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore
| | - Qiang Pan-Hammarström
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.4714.60000 0004 1937 0626Karolinska Institutet, Stockholm, Sweden
| | - Chandra Sekhar Pedamallu
- grid.66859.340000 0004 0546 1623Broad Institute, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | - Reiner Siebert
- grid.410712.10000 0004 0473 882XUlm University and Ulm University Medical Center, Ulm, Germany
| | - Stefan G. Stark
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Hong Su
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Patrick Tan
- grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore ,grid.428397.30000 0004 0385 0924Duke-NUS Medical School, Singapore, Singapore
| | - Sebastian M. Waszak
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Christina Yung
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Shida Zhu
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Philip Awadalla
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada ,grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada
| | - Chad J. Creighton
- grid.39382.330000 0001 2160 926XBaylor College of Medicine, Houston, TX USA
| | - Matthew Meyerson
- grid.66859.340000 0004 0546 1623Broad Institute, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | | | - Kui Wu
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Huanming Yang
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China
| | | | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
| | - Angela N. Brooks
- grid.205975.c0000 0001 0740 6917University of California, Santa Cruz, Santa Cruz, CA USA ,grid.66859.340000 0004 0546 1623Broad Institute, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA
| | - Jonathan Göke
- grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore ,grid.410724.40000 0004 0620 9745National Cancer Centre Singapore, Singapore, Singapore
| | - Gunnar Rätsch
- ETH Zurich, Zurich, Switzerland. .,Memorial Sloan Kettering Cancer Center, New York, NY, USA. .,Weill Cornell Medical College, New York, NY, USA. .,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland. .,University Hospital Zurich, Zurich, Switzerland.
| | - Roland F. Schwarz
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK ,grid.419491.00000 0001 1014 0849Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), partner site Berlin, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oliver Stegle
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK ,grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Zemin Zhang
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
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Aaltonen LA, Abascal F, Abeshouse A, Aburatani H, Adams DJ, Agrawal N, Ahn KS, Ahn SM, Aikata H, Akbani R, Akdemir KC, Al-Ahmadie H, Al-Sedairy ST, Al-Shahrour F, Alawi M, Albert M, Aldape K, Alexandrov LB, Ally A, Alsop K, Alvarez EG, Amary F, Amin SB, Aminou B, Ammerpohl O, Anderson MJ, Ang Y, Antonello D, Anur P, Aparicio S, Appelbaum EL, Arai Y, Aretz A, Arihiro K, Ariizumi SI, Armenia J, Arnould L, Asa S, Assenov Y, Atwal G, Aukema S, Auman JT, Aure MRR, Awadalla P, Aymerich M, Bader GD, Baez-Ortega A, Bailey MH, Bailey PJ, Balasundaram M, Balu S, Bandopadhayay P, Banks RE, Barbi S, Barbour AP, Barenboim J, Barnholtz-Sloan J, Barr H, Barrera E, Bartlett J, Bartolome J, Bassi C, Bathe OF, Baumhoer D, Bavi P, Baylin SB, Bazant W, Beardsmore D, Beck TA, Behjati S, Behren A, Niu B, Bell C, Beltran S, Benz C, Berchuck A, Bergmann AK, Bergstrom EN, Berman BP, Berney DM, Bernhart SH, Beroukhim R, Berrios M, Bersani S, Bertl J, Betancourt M, Bhandari V, Bhosle SG, Biankin AV, Bieg M, 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Pan-cancer analysis of whole genomes. Nature 2020; 578:82-93. [PMID: 32025007 PMCID: PMC7025898 DOI: 10.1038/s41586-020-1969-6] [Citation(s) in RCA: 1435] [Impact Index Per Article: 358.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Accepted: 12/11/2019] [Indexed: 02/07/2023]
Abstract
Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.
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4
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López C, Kleinheinz K, Aukema SM, Rohde M, Bernhart SH, Hübschmann D, Wagener R, Toprak UH, Raimondi F, Kreuz M, Waszak SM, Huang Z, Sieverling L, Paramasivam N, Seufert J, Sungalee S, Russell RB, Bausinger J, Kretzmer H, Ammerpohl O, Bergmann AK, Binder H, Borkhardt A, Brors B, Claviez A, Doose G, Feuerbach L, Haake A, Hansmann ML, Hoell J, Hummel M, Korbel JO, Lawerenz C, Lenze D, Radlwimmer B, Richter J, Rosenstiel P, Rosenwald A, Schilhabel MB, Stein H, Stilgenbauer S, Stadler PF, Szczepanowski M, Weniger MA, Zapatka M, Eils R, Lichter P, Loeffler M, Möller P, Trümper L, Klapper W, Hoffmann S, Küppers R, Burkhardt B, Schlesner M, Siebert R. Genomic and transcriptomic changes complement each other in the pathogenesis of sporadic Burkitt lymphoma. Nat Commun 2019; 10:1459. [PMID: 30926794 PMCID: PMC6440956 DOI: 10.1038/s41467-019-08578-3] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Accepted: 01/18/2019] [Indexed: 12/17/2022] Open
Abstract
Burkitt lymphoma (BL) is the most common B-cell lymphoma in children. Within the International Cancer Genome Consortium (ICGC), we performed whole genome and transcriptome sequencing of 39 sporadic BL. Here, we unravel interaction of structural, mutational, and transcriptional changes, which contribute to MYC oncogene dysregulation together with the pathognomonic IG-MYC translocation. Moreover, by mapping IGH translocation breakpoints, we provide evidence that the precursor of at least a subset of BL is a B-cell poised to express IGHA. We describe the landscape of mutations, structural variants, and mutational processes, and identified a series of driver genes in the pathogenesis of BL, which can be targeted by various mechanisms, including IG-non MYC translocations, germline and somatic mutations, fusion transcripts, and alternative splicing. Burkitt lymphoma (BL) is the most common pediatric B-cell lymphoma. Here, within the International Cancer Genome Consortium, the authors performed whole genome and transcriptome sequencing of 39 sporadic BL, describing the landscape of mutations, structural variants, and mutational processes that underpin this disease how alterations on different cellular levels cooperate in deregulating key pathways and complexes.
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Affiliation(s)
- Cristina López
- Institute of Human Genetics, Ulm University and Ulm University Medical Center, 89081, Ulm, Germany.,Institute of Human Genetics, Christian-Albrechts-University, 24105, Kiel, Germany
| | - Kortine Kleinheinz
- Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.,Department for Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology and Bioquant, University of Heidelberg, 69120, Heidelberg, Germany
| | - Sietse M Aukema
- Institute of Human Genetics, Christian-Albrechts-University, 24105, Kiel, Germany.,Hematopathology Section, Christian-Albrechts-University, 24105, Kiel, Germany
| | - Marius Rohde
- Pediatric Hematology and Oncology, University Hospital Giessen, 35392, Giessen, Germany
| | - Stephan H Bernhart
- Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107, Leipzig, Germany.,Bioinformatics Group, Department of Computer, University of Leipzig, 04107, Leipzig, Germany.,Transcriptome Bioinformatics, LIFE Research Center for Civilization Diseases, University of Leipzig, 04107, Leipzig, Germany
| | - Daniel Hübschmann
- Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.,Department of Pediatric Immunology, Hematology and Oncology, University Hospital, 69120, Heidelberg, Germany.,German Cancer Research Center (DKFZ), Division of Stem Cells and Cancer, Heidelberg, Germany and Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGmbH), 69120, Heidelberg, Germany
| | - Rabea Wagener
- Institute of Human Genetics, Ulm University and Ulm University Medical Center, 89081, Ulm, Germany.,Institute of Human Genetics, Christian-Albrechts-University, 24105, Kiel, Germany
| | - Umut H Toprak
- Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.,Bioinformatics and Omics Data Analytics (B240), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.,Faculty of Biosciences, Heidelberg University, 69120, Heidelberg, Germany
| | - Francesco Raimondi
- Cell Networks, Bioquant and Biochemistry CenterBiochemie Zentrum Heidelberg (BZH), University of Heidelberg, 69120, Heidelberg, Germany
| | - Markus Kreuz
- Institute for Medical Informatics Statistics and Epidemiology, 04107, Leipzig, Germany
| | | | - Zhiqin Huang
- Division of Molecular Genetics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Lina Sieverling
- Faculty of Biosciences, Heidelberg University, 69120, Heidelberg, Germany.,Division of Applied Bioinformatics (G200), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Nagarajan Paramasivam
- Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.,Medical Faculty Heidelberg, Heidelberg University, 69120, Heidelber, Germany
| | - Julian Seufert
- Bioinformatics and Omics Data Analytics (B240), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | | | - Robert B Russell
- Cell Networks, Bioquant and Biochemistry CenterBiochemie Zentrum Heidelberg (BZH), University of Heidelberg, 69120, Heidelberg, Germany
| | - Julia Bausinger
- Institute of Human Genetics, Ulm University and Ulm University Medical Center, 89081, Ulm, Germany
| | - Helene Kretzmer
- Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107, Leipzig, Germany.,Bioinformatics Group, Department of Computer, University of Leipzig, 04107, Leipzig, Germany.,Transcriptome Bioinformatics, LIFE Research Center for Civilization Diseases, University of Leipzig, 04107, Leipzig, Germany.,Department of Genome Regulation, Max Planck Institute for Molecular Genetics, 14195, Berlin, Germany
| | - Ole Ammerpohl
- Institute of Human Genetics, Ulm University and Ulm University Medical Center, 89081, Ulm, Germany
| | - Anke K Bergmann
- Institute of Human Genetics, Christian-Albrechts-University, 24105, Kiel, Germany.,Department of Pediatrics, University Hospital Schleswig-Holstein, Campus Kiel, 24105, Kiel, Germany
| | - Hans Binder
- Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107, Leipzig, Germany.,Bioinformatics Group, Department of Computer, University of Leipzig, 04107, Leipzig, Germany
| | - Arndt Borkhardt
- Medical Faculty, Department of Pediatric Oncology, Hematology and Clinical Immunology, Heinrich-Heine-University, 40225, Düsseldorf, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics (G200), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Alexander Claviez
- Department of Pediatrics, University Hospital Schleswig-Holstein, Campus Kiel, 24105, Kiel, Germany
| | - Gero Doose
- Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107, Leipzig, Germany.,Bioinformatics Group, Department of Computer, University of Leipzig, 04107, Leipzig, Germany.,Transcriptome Bioinformatics, LIFE Research Center for Civilization Diseases, University of Leipzig, 04107, Leipzig, Germany
| | - Lars Feuerbach
- Division of Applied Bioinformatics (G200), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Andrea Haake
- Institute of Human Genetics, Christian-Albrechts-University, 24105, Kiel, Germany
| | - Martin-Leo Hansmann
- Senckenberg Institute of Pathology, University of Frankfurt Medical School, 60590, Frankfurt am Main, Germany
| | - Jessica Hoell
- Medical Faculty, Department of Pediatric Oncology, Hematology and Clinical Immunology, Heinrich-Heine-University, 40225, Düsseldorf, Germany
| | - Michael Hummel
- Institute of Pathology, Charité - University Medicine Berlin, 10117, Berlin, Germany
| | - Jan O Korbel
- Genome Biology Unit, EMBL Heidelberg, 69117, Heidelberg, Germany
| | - Chris Lawerenz
- Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Dido Lenze
- Institute of Pathology, Charité - University Medicine Berlin, 10117, Berlin, Germany
| | - Bernhard Radlwimmer
- Division of Molecular Genetics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Julia Richter
- Institute of Human Genetics, Christian-Albrechts-University, 24105, Kiel, Germany.,Hematopathology Section, Christian-Albrechts-University, 24105, Kiel, Germany
| | - Philip Rosenstiel
- Institute of Clinical Molecular Biology, Christian-Albrechts-University, 24105, Kiel, Germany
| | - Andreas Rosenwald
- Institute of Pathology, Comprehensive Cancer Center Mainfranken, University of Würzburg, 97080, Würzburg, Germany
| | - Markus B Schilhabel
- Institute of Clinical Molecular Biology, Christian-Albrechts-University, 24105, Kiel, Germany
| | | | | | - Peter F Stadler
- Bioinformatics Group, Department of Computer, University of Leipzig, 04107, Leipzig, Germany
| | | | - Marc A Weniger
- Institute of Cell Biology (Cancer Research), Medical School, University of Duisburg-Essen, 45147, Essen, Germany
| | - Marc Zapatka
- Division of Molecular Genetics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Roland Eils
- Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.,Department for Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology and Bioquant, University of Heidelberg, 69120, Heidelberg, Germany
| | - Peter Lichter
- Division of Molecular Genetics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Markus Loeffler
- Institute for Medical Informatics Statistics and Epidemiology, 04107, Leipzig, Germany
| | - Peter Möller
- Institute of Pathology, University of Ulm and University Hospital of Ulm, 89081, Ulm, Germany
| | - Lorenz Trümper
- Department of Hematology and Oncology, Georg-August-University of Göttingen, 37075, Göttingen, Germany
| | - Wolfram Klapper
- Hematopathology Section, Christian-Albrechts-University, 24105, Kiel, Germany
| | | | - Steve Hoffmann
- Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107, Leipzig, Germany.,Bioinformatics Group, Department of Computer, University of Leipzig, 04107, Leipzig, Germany.,Transcriptome Bioinformatics, LIFE Research Center for Civilization Diseases, University of Leipzig, 04107, Leipzig, Germany.,Computational Biology, Leibniz Institute on Ageing-Fritz Lipmann Institut (FLI), 07745, Jena, Germany
| | - Ralf Küppers
- Institute of Cell Biology (Cancer Research), Medical School, University of Duisburg-Essen, 45147, Essen, Germany
| | - Birgit Burkhardt
- University Hospital Münster - Pediatric Hematology and Oncology, 48149, Münster, Germany
| | - Matthias Schlesner
- Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany. .,Bioinformatics and Omics Data Analytics (B240), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.
| | - Reiner Siebert
- Institute of Human Genetics, Ulm University and Ulm University Medical Center, 89081, Ulm, Germany. .,Institute of Human Genetics, Christian-Albrechts-University, 24105, Kiel, Germany.
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5
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Harney E, Dubief B, Boudry P, Basuyaux O, Schilhabel MB, Huchette S, Paillard C, Nunes FLD. De novo assembly and annotation of the European abalone Haliotis tuberculata transcriptome. Mar Genomics 2016; 28:11-16. [PMID: 26971316 DOI: 10.1016/j.margen.2016.03.002] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 03/03/2016] [Indexed: 02/08/2023]
Abstract
The European abalone Haliotis tuberculata is a delicacy and consequently a commercially valuable gastropod species. Aquaculture production and wild populations are subjected to multiple climate-associated stressors and anthropogenic pressures, including rising sea-surface temperatures, ocean acidification and an emerging pathogenic Vibrio infection. Transcript expression data provides a valuable resource for understanding abalone responses to variation in the biotic and abiotic environment. To generate an extensive transcriptome, we performed next-generation sequencing of RNA on larvae exposed to temperature and pH variation and on haemolymph of adults from two wild populations after experimental infection with Vibrio harveyi. We obtained more than 1.5 billion raw paired-end reads, which were assembled into 328,519 contigs. Filtration and clustering produced a transcriptome of 41,099 transcripts, of which 10,626 (25.85%) were annotated with Blast hits, and 7380 of these were annotated with Gene Ontology (GO) terms in Blast2Go. A differential expression analysis comparing all samples from the two life stages identified 5690 and 10,759 transcripts with significantly higher expression in larvae and adult haemolymph respectively. This is the greatest sequencing effort yet in the Haliotis genus, and provides the first high-throughput transcriptomic resource for H. tuberculata.
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Affiliation(s)
- Ewan Harney
- Laboratoire des Sciences de l'Environnement Marin (LEMAR), UMR6539 CNRS/UBO/IRD/Ifremer, Institut Universitaire Européen de la Mer, University of Brest (UBO), Université Européenne de Bretagne (UEB), Place Nicolas Copernic, 29280, Plouzané, France.
| | - Bruno Dubief
- Laboratoire des Sciences de l'Environnement Marin (LEMAR), UMR6539 CNRS/UBO/IRD/Ifremer, Institut Universitaire Européen de la Mer, University of Brest (UBO), Université Européenne de Bretagne (UEB), Place Nicolas Copernic, 29280, Plouzané, France
| | - Pierre Boudry
- Ifremer, Laboratoire des Sciences de l'Environnement Marin (LEMAR), UMR 6539 CNRS/UBO/IRD/Ifremer, Centre Bretagne Z.I. Pointe du Diable, 29280 Plouzané, France
| | - Olivier Basuyaux
- SMEL (Synergie Mer Et Littoral), Centre Expérimental, 50560 Blainville-sur-Mer, France
| | - Markus B Schilhabel
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, 24105 Kiel, Germany
| | | | - Christine Paillard
- Laboratoire des Sciences de l'Environnement Marin (LEMAR), UMR6539 CNRS/UBO/IRD/Ifremer, Institut Universitaire Européen de la Mer, University of Brest (UBO), Université Européenne de Bretagne (UEB), Place Nicolas Copernic, 29280, Plouzané, France
| | - Flavia L D Nunes
- Laboratoire des Sciences de l'Environnement Marin (LEMAR), UMR6539 CNRS/UBO/IRD/Ifremer, Institut Universitaire Européen de la Mer, University of Brest (UBO), Université Européenne de Bretagne (UEB), Place Nicolas Copernic, 29280, Plouzané, France
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6
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Breusing C, Biastoch A, Drews A, Metaxas A, Jollivet D, Vrijenhoek RC, Bayer T, Melzner F, Sayavedra L, Petersen JM, Dubilier N, Schilhabel MB, Rosenstiel P, Reusch TBH. Biophysical and Population Genetic Models Predict the Presence of "Phantom" Stepping Stones Connecting Mid-Atlantic Ridge Vent Ecosystems. Curr Biol 2016; 26:2257-67. [PMID: 27476600 DOI: 10.1016/j.cub.2016.06.062] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Revised: 06/26/2016] [Accepted: 06/28/2016] [Indexed: 12/13/2022]
Abstract
Deep-sea hydrothermal vents are patchily distributed ecosystems inhabited by specialized animal populations that are textbook meta-populations. Many vent-associated species have free-swimming, dispersive larvae that can establish connections between remote populations. However, connectivity patterns among hydrothermal vents are still poorly understood because the deep sea is undersampled, the molecular tools used to date are of limited resolution, and larval dispersal is difficult to measure directly. A better knowledge of connectivity is urgently needed to develop sound environmental management plans for deep-sea mining. Here, we investigated larval dispersal and contemporary connectivity of ecologically important vent mussels (Bathymodiolus spp.) from the Mid-Atlantic Ridge by using high-resolution ocean modeling and population genetic methods. Even when assuming a long pelagic larval duration, our physical model of larval drift suggested that arrival at localities more than 150 km from the source site is unlikely and that dispersal between populations requires intermediate habitats ("phantom" stepping stones). Dispersal patterns showed strong spatiotemporal variability, making predictions of population connectivity challenging. The assumption that mussel populations are only connected via additional stepping stones was supported by contemporary migration rates based on neutral genetic markers. Analyses of population structure confirmed the presence of two southern and two hybridizing northern mussel lineages that exhibited a substantial, though incomplete, genetic differentiation. Our study provides insights into how vent animals can disperse between widely separated vent habitats and shows that recolonization of perturbed vent sites will be subject to chance events, unless connectivity is explicitly considered in the selection of conservation areas.
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Affiliation(s)
- Corinna Breusing
- GEOMAR Helmholtz Centre for Ocean Research, 24105 Kiel, Germany.
| | - Arne Biastoch
- GEOMAR Helmholtz Centre for Ocean Research, 24105 Kiel, Germany
| | - Annika Drews
- GEOMAR Helmholtz Centre for Ocean Research, 24105 Kiel, Germany
| | - Anna Metaxas
- Department of Oceanography, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Didier Jollivet
- CNRS, Sorbonne Universités, UMR 7144 CNRS-UPMC, Adaptation et Diversité en Milieu Marin, Équipe ABICE, Station Biologique de Roscoff, 29688 Roscoff Cedex, France
| | | | - Till Bayer
- GEOMAR Helmholtz Centre for Ocean Research, 24105 Kiel, Germany
| | - Frank Melzner
- GEOMAR Helmholtz Centre for Ocean Research, 24105 Kiel, Germany
| | - Lizbeth Sayavedra
- Symbiosis Department, Max Planck Institute for Marine Microbiology, 28359 Bremen, Germany
| | - Jillian M Petersen
- Symbiosis Department, Max Planck Institute for Marine Microbiology, 28359 Bremen, Germany; Department of Microbiology and Ecosystem Science, University of Vienna, 1090 Vienna, Austria
| | - Nicole Dubilier
- Symbiosis Department, Max Planck Institute for Marine Microbiology, 28359 Bremen, Germany
| | - Markus B Schilhabel
- Institute of Clinical Molecular Biology (IKMB), Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
| | - Philip Rosenstiel
- Institute of Clinical Molecular Biology (IKMB), Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
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7
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Sayavedra L, Kleiner M, Ponnudurai R, Wetzel S, Pelletier E, Barbe V, Satoh N, Shoguchi E, Fink D, Breusing C, Reusch TBH, Rosenstiel P, Schilhabel MB, Becher D, Schweder T, Markert S, Dubilier N, Petersen JM. Abundant toxin-related genes in the genomes of beneficial symbionts from deep-sea hydrothermal vent mussels. eLife 2015; 4:e07966. [PMID: 26371554 PMCID: PMC4612132 DOI: 10.7554/elife.07966] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Accepted: 09/14/2015] [Indexed: 01/06/2023] Open
Abstract
Bathymodiolus mussels live in symbiosis with intracellular sulfur-oxidizing (SOX) bacteria that provide them with nutrition. We sequenced the SOX symbiont genomes from two Bathymodiolus species. Comparison of these symbiont genomes with those of their closest relatives revealed that the symbionts have undergone genome rearrangements, and up to 35% of their genes may have been acquired by horizontal gene transfer. Many of the genes specific to the symbionts were homologs of virulence genes. We discovered an abundant and diverse array of genes similar to insecticidal toxins of nematode and aphid symbionts, and toxins of pathogens such as Yersinia and Vibrio. Transcriptomics and proteomics revealed that the SOX symbionts express the toxin-related genes (TRGs) in their hosts. We hypothesize that the symbionts use these TRGs in beneficial interactions with their host, including protection against parasites. This would explain why a mutualistic symbiont would contain such a remarkable 'arsenal' of TRGs.
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Affiliation(s)
| | - Manuel Kleiner
- Max Planck Institute for Marine Microbiology, Bremen, Germany
| | - Ruby Ponnudurai
- Institute of Pharmacy, Ernst-Moritz-Arndt-University, Greifswald, Germany
| | - Silke Wetzel
- Max Planck Institute for Marine Microbiology, Bremen, Germany
| | - Eric Pelletier
- Genoscope - Centre National de Séquençage, Commissariat à l'énergie atomique et aux énergies alternatives, Evry, France
- Metabolic Genomics Group, Commissariat à l'énergie atomique et aux énergies alternatives, Evry, France
- University of Évry-Val d'Essonne, Evry, France
| | - Valerie Barbe
- Genoscope - Centre National de Séquençage, Commissariat à l'énergie atomique et aux énergies alternatives, Evry, France
| | - Nori Satoh
- Marine Genomics Unit, Okinawa Institute of Science and Technology, Onna, Japan
| | - Eiichi Shoguchi
- Marine Genomics Unit, Okinawa Institute of Science and Technology, Onna, Japan
| | - Dennis Fink
- Max Planck Institute for Marine Microbiology, Bremen, Germany
| | - Corinna Breusing
- Evolutionary Ecology, GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
| | - Thorsten BH Reusch
- Evolutionary Ecology, GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
| | | | | | - Dörte Becher
- Institute of Marine Biotechnology, Greifswald, Germany
- Institute of Microbiology, Ernst-Moritz-Arndt-University, Greifswald, Germany
| | - Thomas Schweder
- Institute of Pharmacy, Ernst-Moritz-Arndt-University, Greifswald, Germany
- Institute of Marine Biotechnology, Greifswald, Germany
| | - Stephanie Markert
- Institute of Pharmacy, Ernst-Moritz-Arndt-University, Greifswald, Germany
- Institute of Marine Biotechnology, Greifswald, Germany
| | - Nicole Dubilier
- Max Planck Institute for Marine Microbiology, Bremen, Germany
- University of Bremen, Bremen, Germany
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Lorenc A, Linnenbrink M, Montero I, Schilhabel MB, Tautz D. Genetic Differentiation of Hypothalamus Parentally Biased Transcripts in Populations of the House Mouse Implicate the Prader–Willi Syndrome Imprinted Region as a Possible Source of Behavioral Divergence: Table 1. Mol Biol Evol 2015; 32:1914-5. [PMID: 25989982 PMCID: PMC4476165 DOI: 10.1093/molbev/msv108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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9
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Jackson DJ, Mann K, Häussermann V, Schilhabel MB, Lüter C, Griesshaber E, Schmahl W, Wörheide G. The Magellania venosa Biomineralizing Proteome: A Window into Brachiopod Shell Evolution. Genome Biol Evol 2015; 7:1349-62. [PMID: 25912046 PMCID: PMC4453069 DOI: 10.1093/gbe/evv074] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2015] [Indexed: 01/25/2023] Open
Abstract
Brachiopods are a lineage of invertebrates well known for the breadth and depth of their fossil record. Although the quality of this fossil record attracts the attention of paleontologists, geochemists, and paleoclimatologists, modern day brachiopods are also of interest to evolutionary biologists due to their potential to address a variety of questions ranging from developmental biology to biomineralization. The brachiopod shell is a composite material primarily composed of either calcite or calcium phosphate in close association with proteins and polysaccharides which give these composite structures their material properties. The information content of these biomolecules, sequestered within the shell during its construction, has the potential to inform hypotheses focused on describing how brachiopod shell formation evolved. Here, using high throughput proteomic approaches and next generation sequencing, we have surveyed and characterized the first shell-proteome and shell-forming transcriptome of any brachiopod, the South American Magellania venosa (Rhynchonelliformea: Terebratulida). We find that the seven most abundant proteins present in the shell are unique to M. venosa, but that these proteins display biochemical features found in other metazoan biomineralization proteins. We can also detect some M. venosa proteins that display significant sequence similarity to other metazoan biomineralization proteins, suggesting that some elements of the brachiopod shell-forming proteome are deeply evolutionarily conserved. We also employed a variety of preparation methods to isolate shell proteins and find that in comparison to the shells of other spiralian invertebrates (such as mollusks) the shell ultrastructure of M. venosa may explain the effects these preparation strategies have on our results.
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Affiliation(s)
- Daniel J Jackson
- Department of Geobiology, Georg-August University of Göttingen, Germany
| | - Karlheinz Mann
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Munich, Germany
| | - Vreni Häussermann
- Escuela de Ciencias del Mar, Valparaíso, Facultad de Recursos Naturales, Universidad Católica de Valparaíso, Chile
| | - Markus B Schilhabel
- Institute of Clinical Molecular Biology, Christian-Albrechts-Universität Kiel, Germany
| | - Carsten Lüter
- Museum für Naturkunde, Leibniz-Institute for Evolution and Biodiversity Science, Berlin, Germany
| | - Erika Griesshaber
- Department of Earth- and Environmental Sciences & GeoBioCenter, Ludwig-Maximilians-Universität München, Germany
| | - Wolfgang Schmahl
- Department of Earth- and Environmental Sciences & GeoBioCenter, Ludwig-Maximilians-Universität München, Germany
| | - Gert Wörheide
- Department of Earth- and Environmental Sciences & GeoBioCenter, Ludwig-Maximilians-Universität München, Germany Bavarian State Collections of Palaeontology & Geology, München, Germany
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10
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Bäumlisberger M, Youssar L, Schilhabel MB, Jonas D. Influence of a non-hospital medical care facility on antimicrobial resistance in wastewater. PLoS One 2015; 10:e0122635. [PMID: 25821977 PMCID: PMC4379178 DOI: 10.1371/journal.pone.0122635] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Accepted: 02/23/2015] [Indexed: 01/21/2023] Open
Abstract
The global widespread use of antimicrobials and accompanying increase in resistant bacterial strains is of major public health concern. Wastewater systems and wastewater treatment plants are considered a niche for antibiotic resistance genes (ARGs), with diverse microbial communities facilitating ARG transfer via mobile genetic element (MGE). In contrast to hospital sewage, wastewater from other health care facilities is still poorly investigated. At the instance of a nursing home located in south-west Germany, in the present study, shotgun metagenomics was used to investigate the impact on wastewater of samples collected up- and down-stream in different seasons. Microbial composition, ARGs and MGEs were analyzed using different annotation approaches with various databases, including Antibiotic Resistance Ontologies (ARO), integrons and plasmids. Our analysis identified seasonal differences in microbial communities and abundance of ARG and MGE between samples from different seasons. However, no obvious differences were detected between up- and downstream samples. The results suggest that, in contrast to hospitals, sewage from the nursing home does not have a major impact on ARG or MGE in wastewater, presumably due to much less intense antimicrobial usage. Possible limitations of metagenomic studies using high-throughput sequencing for detection of genes that seemingly confer antibiotic resistance are discussed.
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Affiliation(s)
- Mathias Bäumlisberger
- Institute for Environmental Health Sciences and Hospital Infection Control, Medical Center—University of Freiburg, Freiburg, Germany
| | - Loubna Youssar
- Institute for Environmental Health Sciences and Hospital Infection Control, Medical Center—University of Freiburg, Freiburg, Germany
| | - Markus B. Schilhabel
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, University Hospital Schleswig Holstein, Campus Kiel, Kiel, Germany
| | - Daniel Jonas
- Institute for Environmental Health Sciences and Hospital Infection Control, Medical Center—University of Freiburg, Freiburg, Germany
- * E-mail:
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11
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Lorenc A, Linnenbrink M, Montero I, Schilhabel MB, Tautz D. Genetic differentiation of hypothalamus parentally biased transcripts in populations of the house mouse implicate the Prader-Willi syndrome imprinted region as a possible source of behavioral divergence. Mol Biol Evol 2014; 31:3240-9. [PMID: 25172960 PMCID: PMC4245819 DOI: 10.1093/molbev/msu257] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Parentally biased expression of transcripts (genomic imprinting) in adult tissues, including the brain, can influence and possibly drive the evolution of behavioral traits. We have previously found that paternally determined cues are involved in population-specific mate choice decisions between two populations of the Western house mouse (Mus musculus domesticus). Here, we ask whether this could be mediated by genomically imprinted transcripts that are subject to fast differentiation between these populations. We focus on three organs that are of special relevance for mate choice and behavior: The vomeronasal organ (VNO), the hypothalamus, and the liver. To first identify candidate transcripts at a genome-wide scale, we used reciprocal crosses between M. m. domesticus and M. m. musculus inbred strains and RNA sequencing of the respective tissues. Using a false discovery cutoff derived from mock reciprocal cross comparisons, we find a total of 66 imprinted transcripts, 13 of which have previously not been described as imprinted. The largest number of imprinted transcripts were found in the hypothalamus; fewer were found in the VNO, and the least were found in the liver. To assess molecular differentiation and imprinting in the wild-derived M. m. domesticus populations, we sequenced the RNA of the hypothalamus from individuals of these populations. This confirmed the presence of the above identified transcripts also in wild populations and allowed us to search for those that show a high genetic differentiation between these populations. Our results identify the Ube3a–Snrpn imprinted region on chromosome 7 as a region that encompasses the largest number of previously not described transcripts with paternal expression bias, several of which are at the same time highly differentiated. For four of these, we confirmed their imprinting status via single nucleotide polymorphism-specific pyrosequencing assays with RNA from reciprocal crosses. In addition, we find the paternally expressed Peg13 transcript within the Trappc9 gene region on chromosome 15 to be highly differentiated. Interestingly, both regions have been implicated in Prader–Willi nervous system disorder phenotypes in humans. We suggest that these genomically imprinted regions are candidates for influencing the population-specific mate-choice in mice.
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Affiliation(s)
- Anna Lorenc
- Max-Planck Institute for Evolutionary Biology, Department Evolutionary Genetics, Plön, Germany
| | - Miriam Linnenbrink
- Max-Planck Institute for Evolutionary Biology, Department Evolutionary Genetics, Plön, Germany
| | - Inka Montero
- Max-Planck Institute for Evolutionary Biology, Department Evolutionary Genetics, Plön, Germany
| | - Markus B Schilhabel
- Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany
| | - Diethard Tautz
- Max-Planck Institute for Evolutionary Biology, Department Evolutionary Genetics, Plön, Germany
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12
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Pala-Ozkok I, Rehman A, Kor-Bicakci G, Ural A, Schilhabel MB, Ubay-Cokgor E, Jonas D, Orhon D. Effect of sludge age on population dynamics and acetate utilization kinetics under aerobic conditions. Bioresour Technol 2013; 143:68-75. [PMID: 23777847 DOI: 10.1016/j.biortech.2013.05.095] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2013] [Revised: 05/22/2013] [Accepted: 05/23/2013] [Indexed: 06/02/2023]
Abstract
The study addressed acetate utilization by an acclimated mixed microbial culture under different growth conditions. It explored changes in the composition of the microbial community and variable process kinetics induced by different culture history. Sequencing batch reactors were operated at steady-state at different sludge ages of two and ten days. Microbial population structure was determined using high-throughput sequencing of 16S rRNA genes. Parallel batch experiments were conducted with acclimated biomass for respirometric analyses. A lower sludge age sustained a different community, which also reflected as variable kinetics for microbial growth and biopolymer storage. The maximum growth rate was observed to change from 3.9/d to 8.5/d and the substrate storage rate from 3.5/d to 5.9/d when the sludge age was decreased from 10 d to 2.0 d. Results challenge the basic definition of heterotrophic biomass in activated sludge models, at least by means of variable kinetics under different growth conditions.
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Affiliation(s)
- Ilke Pala-Ozkok
- Istanbul Technical University, Faculty of Civil Engineering, Environmental Engineering Department, Maslak, Istanbul TR-34469, Turkey.
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13
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Schunck H, Lavik G, Desai DK, Großkopf T, Kalvelage T, Löscher CR, Paulmier A, Contreras S, Siegel H, Holtappels M, Rosenstiel P, Schilhabel MB, Graco M, Schmitz RA, Kuypers MMM, LaRoche J. Giant hydrogen sulfide plume in the oxygen minimum zone off Peru supports chemolithoautotrophy. PLoS One 2013; 8:e68661. [PMID: 23990875 PMCID: PMC3749208 DOI: 10.1371/journal.pone.0068661] [Citation(s) in RCA: 130] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2012] [Accepted: 06/01/2013] [Indexed: 11/30/2022] Open
Abstract
In Eastern Boundary Upwelling Systems nutrient-rich waters are transported to the ocean surface, fuelling high photoautotrophic primary production. Subsequent heterotrophic decomposition of the produced biomass increases the oxygen-depletion at intermediate water depths, which can result in the formation of oxygen minimum zones (OMZ). OMZs can sporadically accumulate hydrogen sulfide (H2S), which is toxic to most multicellular organisms and has been implicated in massive fish kills. During a cruise to the OMZ off Peru in January 2009 we found a sulfidic plume in continental shelf waters, covering an area >5500 km2, which contained ∼2.2×104 tons of H2S. This was the first time that H2S was measured in the Peruvian OMZ and with ∼440 km3 the largest plume ever reported for oceanic waters. We assessed the phylogenetic and functional diversity of the inhabiting microbial community by high-throughput sequencing of DNA and RNA, while its metabolic activity was determined with rate measurements of carbon fixation and nitrogen transformation processes. The waters were dominated by several distinct γ-, δ- and ε-proteobacterial taxa associated with either sulfur oxidation or sulfate reduction. Our results suggest that these chemolithoautotrophic bacteria utilized several oxidants (oxygen, nitrate, nitrite, nitric oxide and nitrous oxide) to detoxify the sulfidic waters well below the oxic surface. The chemolithoautotrophic activity at our sampling site led to high rates of dark carbon fixation. Assuming that these chemolithoautotrophic rates were maintained throughout the sulfidic waters, they could be representing as much as ∼30% of the photoautotrophic carbon fixation. Postulated changes such as eutrophication and global warming, which lead to an expansion and intensification of OMZs, might also increase the frequency of sulfidic waters. We suggest that the chemolithoautotrophically fixed carbon may be involved in a negative feedback loop that could fuel further sulfate reduction and potentially stabilize the sulfidic OMZ waters.
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Affiliation(s)
- Harald Schunck
- Research Division Marine Biogeochemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
- Institute for General Microbiology, Christian-Albrechts-University, Kiel, Germany
| | - Gaute Lavik
- Department of Biogeochemistry, Max-Planck-Institute for Marine Microbiology, Bremen, Germany
| | - Dhwani K. Desai
- Research Division Marine Biogeochemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
- Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Tobias Großkopf
- Research Division Marine Biogeochemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | - Tim Kalvelage
- Department of Biogeochemistry, Max-Planck-Institute for Marine Microbiology, Bremen, Germany
| | - Carolin R. Löscher
- Institute for General Microbiology, Christian-Albrechts-University, Kiel, Germany
| | - Aurélien Paulmier
- Department of Biogeochemistry, Max-Planck-Institute for Marine Microbiology, Bremen, Germany
- Laboratory for Studies in Geophysics and Spatial Oceanography, Institute of Research for Development, Toulouse, France
- Dirección de Investigaciones Oceanográficas, Instituto del Mar del Perú, Callao, Peru
| | - Sergio Contreras
- Department of Biogeochemistry, Max-Planck-Institute for Marine Microbiology, Bremen, Germany
- Large Lakes Observatory, University of Minnesota Duluth, Duluth, Minnesota, United States of America
| | - Herbert Siegel
- Physical Oceanography and Instrumentation, Leibniz Institute for Baltic Sea Research Warnemünde, Rostock, Germany
| | - Moritz Holtappels
- Department of Biogeochemistry, Max-Planck-Institute for Marine Microbiology, Bremen, Germany
| | - Philip Rosenstiel
- Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany
| | - Markus B. Schilhabel
- Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany
| | - Michelle Graco
- Dirección de Investigaciones Oceanográficas, Instituto del Mar del Perú, Callao, Peru
| | - Ruth A. Schmitz
- Institute for General Microbiology, Christian-Albrechts-University, Kiel, Germany
| | - Marcel M. M. Kuypers
- Department of Biogeochemistry, Max-Planck-Institute for Marine Microbiology, Bremen, Germany
| | - Julie LaRoche
- Research Division Marine Biogeochemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
- Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada
- * E-mail:
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14
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Krause E, Wichels A, Giménez L, Lunau M, Schilhabel MB, Gerdts G. Small changes in pH have direct effects on marine bacterial community composition: a microcosm approach. PLoS One 2012; 7:e47035. [PMID: 23071704 PMCID: PMC3469576 DOI: 10.1371/journal.pone.0047035] [Citation(s) in RCA: 110] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2012] [Accepted: 09/07/2012] [Indexed: 11/18/2022] Open
Abstract
As the atmospheric CO(2) concentration rises, more CO(2) will dissolve in the oceans, leading to a reduction in pH. Effects of ocean acidification on bacterial communities have mainly been studied in biologically complex systems, in which indirect effects, mediated through food web interactions, come into play. These approaches come close to nature but suffer from low replication and neglect seasonality. To comprehensively investigate direct pH effects, we conducted highly-replicated laboratory acidification experiments with the natural bacterial community from Helgoland Roads (North Sea). Seasonal variability was accounted for by repeating the experiment four times (spring, summer, autumn, winter). Three dilution approaches were used to select for different ecological strategies, i.e. fast-growing or low-nutrient adapted bacteria. The pH levels investigated were in situ seawater pH (8.15-8.22), pH 7.82 and pH 7.67, representing the present-day situation and two acidification scenarios projected for the North Sea for the year 2100. In all seasons, both automated ribosomal intergenic spacer analysis and 16S ribosomal amplicon pyrosequencing revealed pH-dependent community shifts for two of the dilution approaches. Bacteria susceptible to changes in pH were different members of Gammaproteobacteria, Flavobacteriaceae, Rhodobacteraceae, Campylobacteraceae and further less abundant groups. Their specific response to reduced pH was often context-dependent. Bacterial abundance was not influenced by pH. Our findings suggest that already moderate changes in pH have the potential to cause compositional shifts, depending on the community assembly and environmental factors. By identifying pH-susceptible groups, this study provides insights for more directed, in-depth community analyses in large-scale and long-term experiments.
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Affiliation(s)
- Evamaria Krause
- Alfred Wegener Institute for Polar and Marine Research, Biologische Anstalt Helgoland, Helgoland, Germany.
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15
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ElSharawy A, Warner J, Olson J, Forster M, Schilhabel MB, Link DR, Rose-John S, Schreiber S, Rosenstiel P, Brayer J, Franke A. Accurate variant detection across non-amplified and whole genome amplified DNA using targeted next generation sequencing. BMC Genomics 2012; 13:500. [PMID: 22994565 PMCID: PMC3534403 DOI: 10.1186/1471-2164-13-500] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2012] [Accepted: 09/13/2012] [Indexed: 01/31/2023] Open
Abstract
Background Many hypothesis-driven genetic studies require the ability to comprehensively and efficiently target specific regions of the genome to detect sequence variations. Often, sample availability is limited requiring the use of whole genome amplification (WGA). We evaluated a high-throughput microdroplet-based PCR approach in combination with next generation sequencing (NGS) to target 384 discrete exons from 373 genes involved in cancer. In our evaluation, we compared the performance of six non-amplified gDNA samples from two HapMap family trios. Three of these samples were also preamplified by WGA and evaluated. We tested sample pooling or multiplexing strategies at different stages of the tested targeted NGS (T-NGS) workflow. Results The results demonstrated comparable sequence performance between non-amplified and preamplified samples and between different indexing strategies [sequence specificity of 66.0% ± 3.4%, uniformity (coverage at 0.2× of the mean) of 85.6% ± 0.6%]. The average genotype concordance maintained across all the samples was 99.5% ± 0.4%, regardless of sample type or pooling strategy. We did not detect any errors in the Mendelian patterns of inheritance of genotypes between the parents and offspring within each trio. We also demonstrated the ability to detect minor allele frequencies within the pooled samples that conform to predicted models. Conclusion Our described PCR-based sample multiplex approach and the ability to use WGA material for NGS may enable researchers to perform deep resequencing studies and explore variants at very low frequencies and cost.
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Affiliation(s)
- Abdou ElSharawy
- Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany
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16
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Forster M, Forster P, Elsharawy A, Hemmrich G, Kreck B, Wittig M, Thomsen I, Stade B, Barann M, Ellinghaus D, Petersen BS, May S, Melum E, Schilhabel MB, Keller A, Schreiber S, Rosenstiel P, Franke A. From next-generation sequencing alignments to accurate comparison and validation of single-nucleotide variants: the pibase software. Nucleic Acids Res 2012; 41:e16. [PMID: 22965131 PMCID: PMC3592472 DOI: 10.1093/nar/gks836] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Scientists working with single-nucleotide variants (SNVs), inferred by next-generation sequencing software, often need further information regarding true variants, artifacts and sequence coverage gaps. In clinical diagnostics, e.g. SNVs must usually be validated by visual inspection or several independent SNV-callers. We here demonstrate that 0.5-60% of relevant SNVs might not be detected due to coverage gaps, or might be misidentified. Even low error rates can overwhelm the true biological signal, especially in clinical diagnostics, in research comparing healthy with affected cells, in archaeogenetic dating or in forensics. For these reasons, we have developed a package called pibase, which is applicable to diploid and haploid genome, exome or targeted enrichment data. pibase extracts details on nucleotides from alignment files at user-specified coordinates and identifies reproducible genotypes, if present. In test cases pibase identifies genotypes at 99.98% specificity, 10-fold better than other tools. pibase also provides pair-wise comparisons between healthy and affected cells using nucleotide signals (10-fold more accurately than a genotype-based approach, as we show in our case study of monozygotic twins). This comparison tool also solves the problem of detecting allelic imbalance within heterozygous SNVs in copy number variation loci, or in heterogeneous tumor sequences.
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Affiliation(s)
- Michael Forster
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, D-24105 Kiel, Germany.
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17
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Lommer M, Specht M, Roy AS, Kraemer L, Andreson R, Gutowska MA, Wolf J, Bergner SV, Schilhabel MB, Klostermeier UC, Beiko RG, Rosenstiel P, Hippler M, LaRoche J. Genome and low-iron response of an oceanic diatom adapted to chronic iron limitation. Genome Biol 2012. [PMID: 22835381 DOI: 10.1186/gb‐2012‐13‐7‐r66] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Biogeochemical elemental cycling is driven by primary production of biomass via phototrophic phytoplankton growth, with 40% of marine productivity being assigned to diatoms. Phytoplankton growth is widely limited by the availability of iron, an essential component of the photosynthetic apparatus. The oceanic diatom Thalassiosira oceanica shows a remarkable tolerance to low-iron conditions and was chosen as a model for deciphering the cellular response upon shortage of this essential micronutrient. RESULTS The combined efforts in genomics, transcriptomics and proteomics reveal an unexpected metabolic flexibility in response to iron availability for T. oceanica CCMP1005. The complex response comprises cellular retrenchment as well as remodeling of bioenergetic pathways, where the abundance of iron-rich photosynthetic proteins is lowered, whereas iron-rich mitochondrial proteins are preserved. As a consequence of iron deprivation, the photosynthetic machinery undergoes a remodeling to adjust the light energy utilization with the overall decrease in photosynthetic electron transfer complexes. CONCLUSIONS Beneficial adaptations to low-iron environments include strategies to lower the cellular iron requirements and to enhance iron uptake. A novel contribution enhancing iron economy of phototrophic growth is observed with the iron-regulated substitution of three metal-containing fructose-bisphosphate aldolases involved in metabolic conversion of carbohydrates for enzymes that do not contain metals. Further, our data identify candidate components of a high-affinity iron-uptake system, with several of the involved genes and domains originating from duplication events. A high genomic plasticity, as seen from the fraction of genes acquired through horizontal gene transfer, provides the platform for these complex adaptations to a low-iron world.
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18
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Lommer M, Specht M, Roy AS, Kraemer L, Andreson R, Gutowska MA, Wolf J, Bergner SV, Schilhabel MB, Klostermeier UC, Beiko RG, Rosenstiel P, Hippler M, LaRoche J. Genome and low-iron response of an oceanic diatom adapted to chronic iron limitation. Genome Biol 2012; 13:R66. [PMID: 22835381 PMCID: PMC3491386 DOI: 10.1186/gb-2012-13-7-r66] [Citation(s) in RCA: 143] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2011] [Revised: 05/21/2012] [Accepted: 07/26/2012] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Biogeochemical elemental cycling is driven by primary production of biomass via phototrophic phytoplankton growth, with 40% of marine productivity being assigned to diatoms. Phytoplankton growth is widely limited by the availability of iron, an essential component of the photosynthetic apparatus. The oceanic diatom Thalassiosira oceanica shows a remarkable tolerance to low-iron conditions and was chosen as a model for deciphering the cellular response upon shortage of this essential micronutrient. RESULTS The combined efforts in genomics, transcriptomics and proteomics reveal an unexpected metabolic flexibility in response to iron availability for T. oceanica CCMP1005. The complex response comprises cellular retrenchment as well as remodeling of bioenergetic pathways, where the abundance of iron-rich photosynthetic proteins is lowered, whereas iron-rich mitochondrial proteins are preserved. As a consequence of iron deprivation, the photosynthetic machinery undergoes a remodeling to adjust the light energy utilization with the overall decrease in photosynthetic electron transfer complexes. CONCLUSIONS Beneficial adaptations to low-iron environments include strategies to lower the cellular iron requirements and to enhance iron uptake. A novel contribution enhancing iron economy of phototrophic growth is observed with the iron-regulated substitution of three metal-containing fructose-bisphosphate aldolases involved in metabolic conversion of carbohydrates for enzymes that do not contain metals. Further, our data identify candidate components of a high-affinity iron-uptake system, with several of the involved genes and domains originating from duplication events. A high genomic plasticity, as seen from the fraction of genes acquired through horizontal gene transfer, provides the platform for these complex adaptations to a low-iron world.
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Affiliation(s)
- Markus Lommer
- RD2 Marine Biogeochemistry, Helmholtz Centre for Ocean Research Kiel (GEOMAR), Düsternbrooker Weg 20, Kiel, D-24105, Germany
| | - Michael Specht
- Institute of Plant Biology and Biotechnology, University of Münster, Hindenburgplatz 55, Münster, D-48143, Germany
| | - Alexandra-Sophie Roy
- RD2 Marine Biogeochemistry, Helmholtz Centre for Ocean Research Kiel (GEOMAR), Düsternbrooker Weg 20, Kiel, D-24105, Germany
| | - Lars Kraemer
- Institute of Clinical Molecular Biology ICMB, Christian-Albrechts-University Kiel, Schittenhelmstrasse 12, Kiel, D-24105, Germany
| | - Reidar Andreson
- Department of Biology, University of Bergen, Thormøhlensgt. 53 A/B, Bergen, NO-5020, Norway
- Estonian Biocentre, University of Tartu, Riia 23b, Tartu, EE-51010, Estonia
| | - Magdalena A Gutowska
- Institute of Physiology, Christian-Albrechts-University Kiel, Hermann-Rodewald-Strasse 5, Kiel, D-24118, Germany
| | - Juliane Wolf
- Institute of Plant Biology and Biotechnology, University of Münster, Hindenburgplatz 55, Münster, D-48143, Germany
| | - Sonja V Bergner
- Institute of Plant Biology and Biotechnology, University of Münster, Hindenburgplatz 55, Münster, D-48143, Germany
| | - Markus B Schilhabel
- Institute of Clinical Molecular Biology ICMB, Christian-Albrechts-University Kiel, Schittenhelmstrasse 12, Kiel, D-24105, Germany
| | - Ulrich C Klostermeier
- Institute of Clinical Molecular Biology ICMB, Christian-Albrechts-University Kiel, Schittenhelmstrasse 12, Kiel, D-24105, Germany
| | - Robert G Beiko
- Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS B3H 1W5, Canada
| | - Philip Rosenstiel
- Institute of Clinical Molecular Biology ICMB, Christian-Albrechts-University Kiel, Schittenhelmstrasse 12, Kiel, D-24105, Germany
| | - Michael Hippler
- Institute of Plant Biology and Biotechnology, University of Münster, Hindenburgplatz 55, Münster, D-48143, Germany
| | - Julie LaRoche
- RD2 Marine Biogeochemistry, Helmholtz Centre for Ocean Research Kiel (GEOMAR), Düsternbrooker Weg 20, Kiel, D-24105, Germany
- Department of Biology, Dalhousie University, 1355 Oxford Street, Halifax, NS B3H 4J1, Canada
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19
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Klostermeier UC, Barann M, Wittig M, Häsler R, Franke A, Gavrilova O, Kreck B, Sina C, Schilhabel MB, Schreiber S, Rosenstiel P. A tissue-specific landscape of sense/antisense transcription in the mouse intestine. BMC Genomics 2011; 12:305. [PMID: 21663663 PMCID: PMC3125268 DOI: 10.1186/1471-2164-12-305] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2010] [Accepted: 06/10/2011] [Indexed: 11/10/2022] Open
Abstract
Background The intestinal mucosa is characterized by complex metabolic and immunological processes driven highly dynamic gene expression programs. With the advent of next generation sequencing and its utilization for the analysis of the RNA sequence space, the level of detail on the global architecture of the transcriptome reached a new order of magnitude compared to microarrays. Results We report the ultra-deep characterization of the polyadenylated transcriptome in two closely related, yet distinct regions of the mouse intestinal tract (small intestine and colon). We assessed tissue-specific transcriptomal architecture and the presence of novel transcriptionally active regions (nTARs). In the first step, signatures of 20,541 NCBI RefSeq transcripts could be identified in the intestine (74.1% of annotated genes), thereof 16,742 are common in both tissues. Although the majority of reads could be linked to annotated genes, 27,543 nTARs not consistent with current gene annotations in RefSeq or ENSEMBL were identified. By use of a second independent strand-specific RNA-Seq protocol, 20,966 of these nTARs were confirmed, most of them in vicinity of known genes. We further categorized our findings by their relative adjacency to described exonic elements and investigated regional differences of novel transcribed elements in small intestine and colon. Conclusions The current study demonstrates the complexity of an archetypal mammalian intestinal mRNA transcriptome in high resolution and identifies novel transcriptionally active regions at strand-specific, single base resolution. Our analysis for the first time shows a strand-specific comparative picture of nTARs in two tissues and represents a resource for further investigating the transcriptional processes that contribute to tissue identity.
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Affiliation(s)
- Ulrich C Klostermeier
- Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany
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20
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Melum E, May S, Schilhabel MB, Thomsen I, Karlsen TH, Rosenstiel P, Schreiber S, Franke A. SNP discovery performance of two second-generation sequencing platforms in the NOD2 gene region. Hum Mutat 2010; 31:875-85. [PMID: 20506538 DOI: 10.1002/humu.21276] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
A potentially important application of second generation sequencing technologies is to identify disease-associated variation. For comparison of the performance in SNP detection, the Crohn's disease (CD)-associated NOD2 gene was subjected to targeted resequencing using two different second-generation sequencing technologies. Eleven CD patients were selected based on their haplotype background at the NOD2 locus. The 40-kb large NOD2 gene region was amplified using long-range PCR (LR-PCR), and sequenced with the Roche 454/FLX system, an Applied Biosystems SOLiD mate-pair library (2 x 25 bp), and a SOLiD fragment (50 bp) library. The entire NOD2 region was also sequenced using conventional Sanger technology. Four-hundred forty-two single nucleotide polymorphisms (SNPs) were discovered with the SOLiD mate-pair library, 454 with the fragment library, and 441 with the 454/FLX. For the homozygous SNPs, 98% were confirmed by Sanger for the mate-pair library, 100% for the fragment library and 99% for the 454/FLX. Ninety-six percent of the heterozygous SNPs detected with the SOLiD mate-pair library, 91% with the fragment library and 96% with the 454/FLX were confirmed by Sanger. In a simulation, the SNP detection performance fell rapidly when the achieved coverage was below 40 x. Due to uneven representation of the target region when using LR-PCR, oversequencing of other regions is necessary.
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Affiliation(s)
- Espen Melum
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, Kiel, Germany
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Reichwald K, Lauber C, Nanda I, Kirschner J, Hartmann N, Schories S, Gausmann U, Taudien S, Schilhabel MB, Szafranski K, Glöckner G, Schmid M, Cellerino A, Schartl M, Englert C, Platzer M. High tandem repeat content in the genome of the short-lived annual fish Nothobranchius furzeri: a new vertebrate model for aging research. Genome Biol 2009; 10:R16. [PMID: 19210790 PMCID: PMC2688266 DOI: 10.1186/gb-2009-10-2-r16] [Citation(s) in RCA: 75] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2008] [Revised: 01/26/2009] [Accepted: 02/11/2009] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The annual fish Nothobranchius furzeri is the vertebrate with the shortest known life span in captivity. Fish of the GRZ strain live only three to four months under optimal laboratory conditions, show explosive growth, early sexual maturation and age-dependent physiological and behavioral decline, and express aging related biomarkers. Treatment with resveratrol and low temperature significantly extends the maximum life span. These features make N. furzeri a promising new vertebrate model for age research. RESULTS To contribute to establishing N. furzeri as a new model organism, we provide a first insight into its genome and a comparison to medaka, stickleback, tetraodon and zebrafish. The N. furzeri genome contains 19 chromosomes (2n = 38). Its genome of between 1.6 and 1.9 Gb is the largest among the analyzed fish species and has, at 45%, the highest repeat content. Remarkably, tandem repeats comprise 21%, which is 4-12 times more than in the other four fish species. In addition, G+C-rich tandem repeats preferentially localize to centromeric regions. Phylogenetic analysis based on coding sequences identifies medaka as the closest relative. Genotyping of an initial set of 27 markers and multi-locus fingerprinting of one microsatellite provides the first molecular evidence that the GRZ strain is highly inbred. CONCLUSIONS Our work presents a first basis for systematic genomic and genetic analyses aimed at understanding the mechanisms of life span determination in N. furzeri.
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Affiliation(s)
- Kathrin Reichwald
- Leibniz Institute for Age Research - Fritz Lipmann Institute, Beutenbergstr,, 07745 Jena, Germany.
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Kurischko C, Schilhabel MB, Kunze I, Franzl E. The MATA locus of the dimorphic yeast Yarrowia lipolytica consists of two divergently oriented genes. Mol Gen Genet 1999; 262:180-8. [PMID: 10503550 DOI: 10.1007/s004380051073] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The MATA locus of Yarrowia lipolytica, which was on the basis of its ability to induce sporulation in a diploid B/B strain, represses the mating capacity of this strain. The gene functions required for induction of sporulation and repression of conjugation could be separated by subcloning. Sequence analysis revealed two ORFs in the MATA locus. One of them (MATA1) codes for a protein of 119 amino acids which is required to induce sporulation. The other (MATA2) codes for a protein of 291 amino acids that is able to repress conjugation. Both genes are oriented divergently from a central promoter region, which possesses putative TATA and CAAT boxes for both genes. The product of MATA1 shows no homology to any known protein and seems to represent a new class of mating-type genes. MATA2 contains a HMG box with homology to other mating-type genes. Both MATA1 and MATA2 are mating-type specific. In cells of both mating types, the regions flanking the MATA locus contain sequences with homology to either S. cerevisiae SLA2 and ORF YBB9, respectively. From hybridization and subcloning data we estimate that the MATA region is approximately 2 kb long and is present only once in the genome.
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Affiliation(s)
- C Kurischko
- Hans-Knöll-Institut für Naturstoff-Forschung e.V. Jena, Germany.
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