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42 results on '"Marat D. Kazanov"'

1. Analysis of the abundance and diversity of RNA secondary structure elements in RNA viruses using the RNAsselem Python package

Author

Fedor M. Kazanov, Evgenii V. Matveev, Gennady V. Ponomarev, Dmitry N. Ivankov, and Marat D. Kazanov

Subjects
RNA secondary structure, RNA viruses, Connectivity table, WUSS, Medicine, Science
Abstract

Abstract Recent advancements in experimental and computational methods for RNA secondary structure detection have revealed the crucial role of RNA structural elements in diverse molecular processes within living cells. It has been demonstrated that the secondary structure of the entire viral genome is often responsible for performing crucial functions in the viral life cycle and also influences virus evolution. To investigate the role of viral RNA secondary structure, alongside experimental techniques, the use of bioinformatics tools is important for analyzing various secondary structure patterns, including hairpin loops, internal loops, multifurcations, external loops, bulges, stems, and pseudoknots. Here, we have introduced a Python package for analyzing RNA secondary structure elements in viral genomes, which includes the recognition of common secondary structure patterns, the generation of descriptive statistics for these structural elements, and the provision of their basic properties. We applied the developed package to analyze the secondary structures of complete viral genomes collected from the literature, aiming to gain insights into viral function and evolution. Both the package and the collection of secondary structures of viral genomes are available at http://github.com/KazanovLab/RNAsselem .

Published
2024
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2. Genome-wide bioinformatics analysis of human protease capacity for proteolytic cleavage of the SARS-CoV-2 spike glycoprotein

Author

Evgenii V. Matveev, Gennady V. Ponomarev, and Marat D. Kazanov

Subjects
SARS-CoV-2, spike glycoprotein, proteolytic activation, protease, proteolysis, furin, Microbiology, QR1-502
Abstract

ABSTRACTSevere acute respiratory syndrome coronavirus-2 (SARS-CoV-2) primarily enters the cell by binding the virus’s spike (S) glycoprotein to the angiotensin-converting enzyme 2 receptor on the cell surface, followed by proteolytic cleavage by host proteases. Studies have identified furin and transmembrane protease serine 2 proteases in priming and triggering cleavages of the S glycoprotein, converting it into a fusion-competent form and initiating membrane fusion, respectively. Alternatively, SARS-CoV-2 can enter the cell through the endocytic pathway, where activation is triggered by lysosomal cathepsin L. However, other proteases are also suspected to be involved in both entry routes. In this study, we conducted a genome-wide bioinformatics analysis to explore the capacity of human proteases in hydrolyzing peptide bonds of the S glycoprotein. Predictive models of sequence specificity for 169 human proteases were constructed and applied to the S glycoprotein together with the method for predicting structural susceptibility to proteolysis of protein regions. After validating our approach on extensively studied S2′ and S1/S2 cleavage sites, we applied our method to each peptide bond of the S glycoprotein across all 169 proteases. Our results indicate that various members of the proprotein convertase subtilisin/kexin type, type II transmembrane family serine protease, and kallikrein families, as well as specific coagulation factors, are capable of cleaving S2′ or S1/S2 sites. We have also identified a potential cleavage site of cathepsin L at the K790 position within the S2′ loop. Structural analysis suggests that cleavage of this site induces conformational changes similar to the cleavage at the R815 (S2′) position, leading to the exposure of the fusion peptide and subsequent fusion with the membrane. Other potential cleavage sites and the influence of mutations in common SARS-CoV-2 variants on proteolytic efficiency are discussed.IMPORTANCEThe entry of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) into the cell, activated by host proteases, is considerably more complex in coronaviruses than in most other viruses and is not fully understood. There is evidence that other proteases beyond the known furin and transmembrane protease serine 2 can activate the spike protein. Another example of uncertainty is the cleavage site for the alternative endocytic route of SARS-CoV-2 entrance, which is still unknown. Bioinformatics methods, modeling protease specificity and estimating the structural susceptibility of protein regions to proteolysis, can aid in studying this topic by predicting the involved proteases and their cleavage sites, thereby substantially reducing the amount of experimental work. Elucidating the mechanisms of spike protein activation is crucial for preventing possible future coronavirus pandemics and developing antiviral drugs.

Published
2024
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3. APOBEC-Induced Cancer Mutations Are Uniquely Enriched in Early-Replicating, Gene-Dense, and Active Chromatin Regions

Author

Marat D. Kazanov, Steven A. Roberts, Paz Polak, John Stamatoyannopoulos, Leszek J. Klimczak, Dmitry A. Gordenin, and Shamil R. Sunyaev

Subjects
Biology (General), QH301-705.5
Abstract

An antiviral component of the human innate immune system—the APOBEC cytidine deaminases—was recently identified as a prominent source of mutations in cancers. Here, we investigated the distribution of APOBEC-induced mutations across the genomes of 119 breast and 24 lung cancer samples. While the rate of most mutations is known to be elevated in late-replicating regions that are characterized by reduced chromatin accessibility and low gene density, we observed a marked enrichment of APOBEC mutations in early-replicating regions. This unusual mutagenesis profile may be associated with a higher propensity to form single-strand DNA substrates for APOBEC enzymes in early-replicating regions and should be accounted for in statistical analyses of cancer genome mutation catalogs aimed at understanding the mechanisms of carcinogenesis as well as highlighting genes that are significantly mutated in cancer.

Published
2015
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4. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

Author

Esther Rheinbay, Morten Muhlig Nielsen, Federico Abascal, Jeremiah Wala, Ofer Shapira, Grace Tiao, Henrik Hornshøj, Julian M. Hess, Randi Istrup Juul, Ziao Lin, Lars Feuerbach, Radhakrishnan Sabarinathan, Tobias Madsen, Jaegil Kim, Loris Mularoni, Shimin Shuai, Andrés Lanzós, Carl Herrmann, Yosef E. Maruvka, Ciyue Shen, Samirkumar B. Amin, Pratiti Bandopadhayay, Johanna Bertl, Keith A. Boroevich, John Busanovich, Joana Carlevaro-Fita, Dimple Chakravarty, Calvin Wing Yiu Chan, David Craft, Priyanka Dhingra, Klev Diamanti, Nuno A. Fonseca, Abel Gonzalez-Perez, Qianyun Guo, Mark P. Hamilton, Nicholas J. Haradhvala, Chen Hong, Keren Isaev, Todd A. Johnson, Malene Juul, André Kahles, Abdullah Kahraman, Youngwook Kim, Jan Komorowski, Kiran Kumar, Sushant Kumar, Donghoon Lee 0006, Kjong-Van Lehmann, Yilong Li, Eric Minwei Liu, Lucas Lochovsky, Keunchil Park, Oriol Pich, Nicola D. Roberts, Gordon Saksena, Steven E. Schumacher, Nikos Sidiropoulos, Lina Sieverling, Nasa Sinnott-Armstrong, Chip Stewart, David Tamborero, Jose M. C. Tubio, Husen M. Umer, Liis Uusküla-Reimand, Claes Wadelius, Lina Wadi, Xiaotong Yao, Cheng-Zhong Zhang, Jing Zhang 0062, James E. Haber, Asger Hobolth, Marcin Imielinski, Manolis Kellis, Michael S. Lawrence, Christian von Mering, Hidewaki Nakagawa, Benjamin J. Raphael, Mark A. Rubin, Chris Sander, Lincoln D. Stein, Joshua M. Stuart, Tatsuhiko Tsunoda, David A. Wheeler, Rory Johnson, Jüri Reimand, Mark Gerstein, Ekta Khurana, Peter J. Campbell, Núria López-Bigas, Gary D. Bader, Jonathan Barenboim, Rameen Beroukhim, Søren Brunak, Ken Chen, Jung Kyoon Choi, Jordi Deu-Pons, J. Lynn Fink, Joan Frigola, Carlo Gambacorti Passerini, Dale W. Garsed, Gad Getz, Ivo Glynne Gut, David Haan, Arif Ozgun Harmanci, Mohamed Helmy, Ermin Hodzic, José M. G. Izarzugaza, Jong K. Kim, Jan O. Korbel, Erik Larsson, Shantao Li, Xiaotong Li, Shaoke Lou, Kathleen Marchal, Iñigo Martincorena, Alexander Martínez-Fundichely, Patrick D. McGillivray, William Meyerson, Ferran Muiños, Marta Paczkowska, Kiejung Park, Jakob Skou Pedersen, Tirso Pons, Sergio Pulido-Tamayo, Iker Reyes-Salazar, Matthew A. Reyna, Carlota Rubio-Perez, Süleyman Cenk Sahinalp, Leonidas Salichos, Mark Shackleton, Raunak Shrestha, Alfonso Valencia, Miguel Vazquez, Lieven P. C. Verbeke, Jiayin Wang, Jonathan Warrell, Sebastian M. Waszak, Joachim Weischenfeldt, Guanming Wu, Jun Yu, Xuanping Zhang, Yan Zhang 0032, Zhongming Zhao, Lihua Zou, Kadir C. Akdemir, Eva G. Alvarez, Adrian Baez-Ortega, Paul C. Boutros, David D. L. Bowtell, Benedikt Brors, Kathleen H. Burns, Kin Chan, Isidro Cortés-Ciriano, Ana Dueso-Barroso, Andrew J. Dunford, Paul A. Edwards, Xavier Estivill, Dariush Etemadmoghadam, Milana Frenkel-Morgenstern, Dmitry A. Gordenin, Barbara Hutter, David T. W. Jones, Young Seok Ju, Marat D. Kazanov, Leszek J. Klimczak, Youngil Koh, Eunjung Alice Lee, Jake June-Koo Lee, Andy G. Lynch, Geoff MacIntyre, Florian Markowetz, Matthew Meyerson, Satoru Miyano, Fabio C. P. Navarro, Stephan Ossowski, Peter J. Park, John V. Pearson, Montserrat Puiggròs, Karsten Rippe, Steven A. Roberts, Bernardo Rodriguez-Martin, Ralph Scully, David Torrents, Izar Villasante, Nicola Waddell, Jeremiah A. Wala, Lixing Yang, Sung-Soo Yoon, and Jorge Zamora

Published
2020
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8. Evaluation and Comparison of Current Fetal Ultrasound Image Segmentation Methods for Biometric Measurements: A Grand Challenge.

Author

Sylvia Rueda, Sana Fathima, Caroline L. Knight, Mohammad Yaqub, Aris T. Papageorghiou, Bahbibi Rahmatullah, Alessandro Foi, Matteo Maggioni, Antonietta Pepe, Jussi Tohka, Richard V. Stebbing, John McManigle, Anca Ciurte, Xavier Bresson, Meritxell Bach Cuadra, Changming Sun, Gennady V. Ponomarev, Mikhail S. Gelfand, Marat D. Kazanov, Ching-Wei Wang, Hsiang-Chou Chen, Chun-Wei Peng, Chu-Mei Hung, and J. Alison Noble

Published
2014
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11. APOBEC mutagenesis is low in most types of non-B DNA structures

Author

Gennady V. Ponomarev, Bulat Fatykhov, Vladimir A. Nazarov, Ruslan Abasov, Evgeny Shvarov, Nina-Vicky Landik, Alexandra A. Denisova, Almira A. Chervova, Mikhail S. Gelfand, and Marat D. Kazanov

Subjects
Multidisciplinary
Abstract

While somatic mutations are known to be enriched in genome regions with non-canonical DNA secondary structure, the impact of particular mutagens still needs to be elucidated. Here, we demonstrate that in human cancers, the APOBEC mutagenesis is not enriched in direct repeats, mirror repeats, short tandem repeats, and G-quadruplexes, and even decreased below its level in B-DNA for cancer samples with very high APOBEC activity. In contrast, we observe that the APOBEC-induced mutational density is positively associated with APOBEC activity in inverted repeats (cruciform structures), where the impact of cytosine at the 3'-end of the hairpin loop is substantial. Surprisingly, the APOBEC-signature mutation density per TC motif in the single-stranded DNA of a G-quadruplex (G4) is lower than in the four-stranded part of G4 and in B-DNA. The APOBEC mutagenesis, as well as the UV-mutagenesis in melanoma samples, are absent in Z-DNA regions, owing to the depletion of their mutational signature motifs.

Published
2022

12. APOBEC mutagenesis is low in most types of non-B DNA structures, unlike other types of cancer mutagenesis

Author

Nina-Vicky Landik, Ruslan Abasov, Mikhail S. Gelfand, Bulat Fatykhov, Alexandra A. Denisova, Almira Chervova, Gennady V. Ponomarev, Vladimir A. Nazarov, Evgeny Shvarov, and Marat D. Kazanov

Subjects
Genetics, APOBEC, chemistry.chemical_compound, Mutation, Chemistry, Inverted repeat, Mutagenesis, medicine, Direct repeat, medicine.disease_cause, Genome, Cytosine, DNA
Abstract

While somatic mutations are known to be enriched in genome regions with non-canonical DNA secondary structure, the impact of particular mutagens still needs to be elucidated. Here, we demonstrate that in human cancers, the APOBEC mutagenesis is not enriched in direct repeats, mirror repeats, short tandem repeats, and G-quadruplexes, and even decreased below its level in B-DNA for cancer samples with very high APOBEC activity. In contrast, we observe that the APOBEC-induced mutational density is positively associated with APOBEC activity in inverted repeats (cruciform structures), where the impact of cytosine at the 3’-end of the hairpin loop is substantial. Surprisingly, the APOBEC-signature mutation density per TC motif in the single-stranded DNA of a G-quadruplex (G4) is lower than in the four-stranded part of G4 and in B-DNA. The APOBEC mutagenesis, as well as the UV-mutagenesis in melanoma samples are absent in Z-DNA regions, due to depletion of their mutational signature motifs.

Published
2021
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15. Analysis of gene expression and mutation data points on contribution of transcription to the mutagenesis by APOBEC enzymes

Author

Dmitry Vinogradov, Gennady V. Ponomarev, Alexander Koblov, Evgeny Shvarov, Mikhail S. Gelfand, Bulat Fatykhov, Almira Chervova, Marat D. Kazanov, and Julia Preobrazhenskaya

Subjects
APOBEC, AcademicSubjects/SCI01140, AcademicSubjects/SCI01060, AcademicSubjects/SCI00030, Mutagenesis (molecular biology technique), Standard Article, Biology, medicine.disease_cause, AcademicSubjects/SCI01180, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Transcription (biology), Gene expression, APOBEC Deaminases, medicine, Gene, 030304 developmental biology, Genetics, 0303 health sciences, Mutation, Mutagenesis, General Medicine, Sense strand, chemistry, AcademicSubjects/SCI00980, 030217 neurology & neurosurgery, DNA
Abstract

Since the discovery of the role of the APOBEC enzymes in human cancers, the mechanisms of this type of mutagenesis remain little understood. Theoretically, targeting of single-stranded DNA by the APOBEC enzymes could occur during cellular processes leading to the unwinding of DNA double-stranded structure. Some evidence points to the importance of replication in the APOBEC mutagenesis, while the role of transcription is still underexplored. Here, we analyzed gene expression and whole genome sequencing data from five types of human cancers with substantial APOBEC activity to estimate the involvement of transcription in the APOBEC mutagenesis and compare its impact with that of replication. Using the TCN motif as the mutation signature of the APOBEC enzymes, we observed a correlation of active APOBEC mutagenesis with gene expression, confirmed the increase of APOBEC-induced mutations in early-replicating regions, and estimated the relative impact of transcription and replication on the APOBEC mutagenesis, which turned out to be approximately equal in transcribed regions. We also found that the known effect of higher density of APOBEC-induced mutations on the lagging strand was highest in middle-replicating regions, and observed higher APOBEC mutation density on the sense strand, the latter bias positively correlated with the gene expression level.Bullet points–The APOBEC mutagenesis rate is higher in actively expressed genes–The APOBEC mutation density is higher on the sense strand–The lagging/leading strand ratio of the APOBEC mutational density is highest in middle-replicating regions

Published
2020

16. Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Author

Bailey, Matthew H, Meyerson, William U, Dursi, Lewis Jonathan, Wang, Liang-Bo, Dong, Guanlan, Liang, Wen-Wei, Weerasinghe, Amila, Shantao, Li, Kelso, Sean, Saksena, Gordon, Ellrott, Kyle, Wendl, Michael C, Wheeler, David A, Getz, Gad, Simpson, Jared T, Gerstein, Mark B, Ding, Lirehan, Akbani, Pavana, Anur, Matthew, H Bailey, Alex, Buchanan, Kami, Chiotti, Kyle, Covington, Allison, Creason, Ding, Li, Kyle, Ellrott, Fan, Yu, Steven, Foltz, Gad, Getz, Walker, Hale, David, Haussler, Julian, M Hess, Carolyn, M Hutter, Cyriac, Kandoth, Katayoon, Kasaian, Melpomeni, Kasapi, Dave, Larson, Ignaty, Leshchiner, John, Letaw, Singer, Ma, Michael, D McLellan, Yifei, Men, Gordon, B Mills, Beifang, Niu, Myron, Peto, Amie, Radenbaugh, Sheila, M Reynolds, Gordon, Saksena, Heidi, Sofia, Chip, Stewart, Adam, J Struck, Joshua, M Stuart, Wenyi, Wang, John, N Weinstein, David, A Wheeler, Christopher, K Wong, Liu, Xi, Kai, Ye, Matthias, Bieg, Paul, C Boutros, Ivo, Buchhalter, Adam, P Butler, Ken, Chen, Zechen, Chong, Oliver, Drechsel, Lewis Jonathan Dursi, Roland, Eils, Shadrielle M, G Espiritu, Robert, S Fulton, Shengjie, Gao, Josep L, L Gelpi, Mark, B Gerstein, Santiago, Gonzalez, Ivo, G Gut, Faraz, Hach, Michael, C Heinold, Jonathan, Hinton, Taobo, Hu, Vincent, Huang, Huang, Yi, Barbara, Hutter, David, R Jones, Jongsun, Jung, Natalie, Jäger, Hyung-Lae, Kim, Kortine, Kleinheinz, Sushant, Kumar, Yogesh, Kumar, Christopher, M Lalansingh, Ivica, Letunic, Dimitri, Livitz, Eric, Z Ma, Yosef, E Maruvka, R Jay Mashl, Andrew, Menzies, Ana, Milovanovic, Morten Muhlig Nielsen, Stephan, Ossowski, Nagarajan, Paramasivam, Jakob Skou Pedersen, Marc, D Perry, Montserrat, Puiggròs, Keiran, M Raine, Esther, Rheinbay, Romina, Royo, S Cenk Sahinalp, Iman, Sarrafi, Matthias, Schlesner, Jared, T Simpson, Lucy, Stebbings, Miranda, D Stobbe, Jon, W Teague, Grace, Tiao, David, Torrents, Jeremiah, A Wala, Jiayin, Wang, Sebastian, M Waszak, Joachim, Weischenfeldt, Michael, C Wendl, Johannes, Werner, Zhenggang, Wu, Hong, Xue, Sergei, Yakneen, Takafumi, N Yamaguchi, Venkata, D Yellapantula, Christina, K Yung, Junjun, Zhang, Lauri, A Aaltonen, Federico, Abascal, Adam, Abeshouse, Hiroyuki, Aburatani, David, J Adams, Nishant, Agrawal, Keun Soo Ahn, Sung-Min, Ahn, Hiroshi, Aikata, Rehan, Akbani, Kadir, C Akdemir, Hikmat, Al-Ahmadie, Sultan, T Al-Sedairy, Fatima, Al-Shahrour, Malik, Alawi, Monique, Albert, Kenneth, Aldape, Ludmil, B Alexandrov, Adrian, Ally, Kathryn, Alsop, Eva, G Alvarez, Fernanda, Amary, Samirkumar, B Amin, Brice, Aminou, Ole, Ammerpohl, Matthew, J Anderson, Yeng, Ang, Davide, Antonello, Samuel, Aparicio, Elizabeth, L Appelbaum, Yasuhito, Arai, Axel, Aretz, Koji, Arihiro, Shun-Ichi, Ariizumi, Joshua, Armenia, Laurent, Arnould, Sylvia, Asa, Yassen, Assenov, Gurnit, Atwal, Sietse, Aukema, J Todd Auman, Miriam, R Aure, Philip, Awadalla, Marta, Aymerich, Gary, D Bader, Adrian, Baez-Ortega, Peter, J Bailey, Miruna, Balasundaram, Saianand, Balu, Pratiti, Bandopadhayay, Rosamonde, E Banks, Stefano, Barbi, Andrew, P Barbour, Jonathan, Barenboim, Jill, Barnholtz-Sloan, Hugh, Barr, Elisabet, Barrera, John, Bartlett, Javier, Bartolome, Bassi, Claudio, Oliver, F Bathe, Daniel, Baumhoer, Prashant, Bavi, Stephen, B Baylin, Wojciech, Bazant, Duncan, Beardsmore, Timothy, A Beck, Sam, Behjati, Andreas, Behren, Cindy, Bell, Sergi, Beltran, Christopher, Benz, Andrew, Berchuck, Anke, K Bergmann, Erik, N Bergstrom, Benjamin, P Berman, Daniel, M Berney, Stephan, H Bernhart, Rameen, Beroukhim, Mario, Berrios, Samantha, Bersani, Johanna, Bertl, Miguel, Betancourt, Vinayak, Bhandari, Shriram, G Bhosle, Andrew, V Biankin, Darell, Bigner, Hans, Binder, Ewan, Birney, Michael, Birrer, Nidhan, K Biswas, Bodil, Bjerkehagen, Tom, Bodenheimer, Lori, Boice, Giada, Bonizzato, Johann, S De Bono, Arnoud, Boot, Moiz, S Bootwalla, Ake, Borg, Arndt, Borkhardt, Keith, A Boroevich, Ivan, Borozan, Christoph, Borst, Marcus, Bosenberg, Mattia, Bosio, Jacqueline, Boultwood, Guillaume, Bourque, G Steven Bova, David, T Bowen, Reanne, Bowlby, David D, L Bowtell, Sandrine, Boyault, Rich, Boyce, Jeffrey, Boyd, Alvis, Brazma, Paul, Brennan, Daniel, S Brewer, Arie, B Brinkman, Robert, G Bristow, Russell, R Broaddus, Jane, E Brock, Malcolm, Brock, Annegien, Broeks, Angela, N Brooks, Denise, Brooks, Benedikt, Brors, Søren, Brunak, Timothy J, C Bruxner, Alicia, L Bruzos, Christiane, Buchholz, Susan, Bullman, Hazel, Burke, Birgit, Burkhardt, Kathleen, H Burns, John, Busanovich, Carlos, D Bustamante, Atul, J Butte, Niall, J Byrne, Anne-Lise, Børresen-Dale, Samantha, J Caesar-Johnson, Andy, Cafferkey, Declan, Cahill, Claudia, Calabrese, Carlos, Caldas, Fabien, Calvo, Niedzica, Camacho, Peter, J Campbell, Elias, Campo, Cinzia, Cantù, Shaolong, Cao, Thomas, E Carey, Joana, Carlevaro-Fita, Rebecca, Carlsen, Ivana, Cataldo, Mario, Cazzola, Jonathan, Cebon, Robert, Cerfolio, Dianne, E Chadwick, Dimple, Chakravarty, Don, Chalmers, Calvin Wing Yiu Chan, Kin, Chan, Michelle, Chan-Seng-Yue, Vishal, S Chandan, David, K Chang, Stephen, J Chanock, Lorraine, A Chantrill, Aurélien, Chateigner, Nilanjan, Chatterjee, Kazuaki, Chayama, Hsiao-Wei, Chen, Jieming, Chen, Yiwen, Chen, Zhaohong, Chen, Andrew, D Cherniack, Jeremy, Chien, Yoke-Eng, Chiew, Suet-Feung, Chin, Juok, Cho, Sunghoon, Cho, Jung Kyoon Choi, Wan, Choi, Christine, Chomienne, Su Pin Choo, Angela, Chou, Angelika, N Christ, Elizabeth, L Christie, Eric, Chuah, Carrie, Cibulskis, Kristian, Cibulskis, Sara, Cingarlini, Peter, Clapham, Alexander, Claviez, Sean, Cleary, Nicole, Cloonan, Marek, Cmero, Colin, C Collins, Ashton, A Connor, Susanna, L Cooke, Colin, S Cooper, Leslie, Cope, Corbo, Vincenzo, Matthew, G Cordes, Stephen, M Cordner, Isidro, Cortés-Ciriano, Prue, A Cowin, Brian, Craft, David, Craft, Chad, J Creighton, Yupeng, Cun, Erin, Curley, Ioana, Cutcutache, Karolina, Czajka, Bogdan, Czerniak, Rebecca, A Dagg, Ludmila, Danilova, Maria Vittoria Davi, Natalie, R Davidson, Helen, Davies, Ian, J Davis, Brandi, N Davis-Dusenbery, Kevin, J Dawson, Francisco, M De La Vega, Ricardo De Paoli-Iseppi, Timothy, Defreitas, Angelo, P Dei Tos, Olivier, Delaneau, John, A Demchok, Jonas, Demeulemeester, German, M Demidov, Deniz, Demircioğlu, Nening, M Dennis, Robert, E Denroche, Stefan, C Dentro, Nikita, Desai, Vikram, Deshpande, Amit, G Deshwar, Christine, Desmedt, Jordi, Deu-Pons, Noreen, Dhalla, Neesha, C Dhani, Priyanka, Dhingra, Rajiv, Dhir, Anthony, Dibiase, Klev, Diamanti, Shuai, Ding, Huy, Q Dinh, Luc, Dirix, Harshavardhan, Doddapaneni, Nilgun, Donmez, Michelle, T Dow, Ronny, Drapkin, Ruben, M Drews, Serge, Serge, Tim, Dudderidge, Ana, Dueso-Barroso, Andrew, J Dunford, Michael, Dunn, Fraser, R Duthie, Ken, Dutton-Regester, Jenna, Eagles, Douglas, F Easton, Stuart, Edmonds, Paul, A Edwards, Sandra, E Edwards, Rosalind, A Eeles, Anna, Ehinger, Juergen, Eils, Adel, El-Naggar, Matthew, Eldridge, Serap, Erkek, Georgia, Escaramis, Xavier, Estivill, Dariush, Etemadmoghadam, Jorunn, E Eyfjord, Bishoy, M Faltas, Daiming, Fan, William, C Faquin, Claudiu, Farcas, Matteo, Fassan, Aquila, Fatima, Francesco, Favero, Nodirjon, Fayzullaev, Ina, Felau, Sian, Fereday, Martin, L Ferguson, Vincent, Ferretti, Lars, Feuerbach, Matthew, A Field, J Lynn Fink, Gaetano, Finocchiaro, Cyril, Fisher, Matthew, W Fittall, Anna, Fitzgerald, Rebecca, C Fitzgerald, Adrienne, M Flanagan, Neil, E Fleshner, Paul, Flicek, John, A Foekens, Kwun, M Fong, Nuno, A Fonseca, Christopher, S Foster, Natalie, S Fox, Michael, Fraser, Scott, Frazer, Milana, Frenkel-Morgenstern, William, Friedman, Joan, Frigola, Catrina, C Fronick, Akihiro, Fujimoto, Masashi, Fujita, Masashi, Fukayama, Lucinda, A Fulton, Mayuko, Furuta, P Andrew Futreal, Anja, Füllgrabe, Stacey, B Gabriel, Steven, Gallinger, Carlo, Gambacorti-Passerini, Jianjiong, Gao, Levi, Garraway, Øystein, Garred, Erik, Garrison, Dale, W Garsed, Nils, Gehlenborg, Joshy, George, Daniela, S Gerhard, Clarissa, Gerhauser, Jeffrey, E Gershenwald, Moritz, Gerstung, Mohammed, Ghori, Ronald, Ghossein, Nasra, H Giama, Richard, A Gibbs, Anthony, J Gill, Pelvender, Gill, Dilip, D Giri, Dominik, Glodzik, Vincent, J Gnanapragasam, Maria Elisabeth Goebler, Mary, J Goldman, Carmen, Gomez, Abel, Gonzalez-Perez, Dmitry, A Gordenin, James, Gossage, Kunihito, Gotoh, Ramaswamy, Govindan, Dorthe, Grabau, Janet, S Graham, Robert, C Grant, Anthony, R Green, Eric, Green, Liliana, Greger, Nicola, Grehan, Sonia, Grimaldi, Sean, M Grimmond, Robert, L Grossman, Adam, Grundhoff, Gunes, Gundem, Qianyun, Guo, Manaswi, Gupta, Shailja, Gupta, Marta, Gut, Jonathan, Göke, Gavin, Ha, Andrea, Haake, David, Haan, Siegfried, Haas, Kerstin, Haase, James, E Haber, Nina, Habermann, Syed, Haider, Natsuko, Hama, Freddie, C Hamdy, Anne, Hamilton, Mark, P Hamilton, Leng, Han, George, B Hanna, Martin, Hansmann, Nicholas, J Haradhvala, Olivier, Harismendy, Ivon, Harliwong, Arif, O Harmanci, Eoghan, Harrington, Takanori, Hasegawa, Steve, Hawkins, Shinya, Hayami, Shuto, Hayashi, D Neil Hayes, Stephen, J Hayes, Nicholas, K Hayward, Steven, Hazell, Yao, He, Allison, P Heath, Simon, C Heath, David, Hedley, Apurva, M Hegde, David, I Heiman, Zachary, Heins, Lawrence, E Heisler, Eva, Hellstrom-Lindberg, Mohamed, Helmy, Seong Gu Heo, Austin, J Hepperla, José María Heredia-Genestar, Carl, Herrmann, Peter, Hersey, Holmfridur, Hilmarsdottir, Satoshi, Hirano, Nobuyoshi, Hiraoka, Katherine, A Hoadley, Asger, Hobolth, Ermin, Hodzic, Jessica, I Hoell, Steve, Hoffmann, Oliver, Hofmann, Andrea, Holbrook, Aliaksei, Z Holik, Michael, A Hollingsworth, Oliver, Holmes, Robert, A Holt, Chen, Hong, Eun Pyo Hong, Jongwhi, H Hong, Gerrit, K Hooijer, Henrik, Hornshøj, Fumie, Hosoda, Yong, Hou, Volker, Hovestadt, William, Howat, Alan, P Hoyle, Ralph, H Hruban, Jianhong, Hu, Xing, Hua, Kuan-Lin, Huang, Mei, Huang, Mi Ni Huang, Wolfgang, Huber, Thomas, J Hudson, Michael, Hummel, Jillian, A Hung, David, Huntsman, Ted, R Hupp, Jason, Huse, Matthew, R Huska, Daniel, Hübschmann, Christine, A 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Yamamoto, S, Yamaue, H, Yang, F, Yang, H, Yang, J, Yang, L, Yang, S, Yang, T, Yang, Y, Yao, X, Yaspo, M, Yates, L, Yau, C, Ye, C, Yoon, C, Yoon, S, Yousif, F, Yu, J, Yu, K, Yu, W, Yu, Y, Yuan, K, Yuan, Y, Yuen, D, Zaikova, O, Zamora, J, Zapatka, M, Zenklusen, J, Zenz, T, Zeps, N, Zhang, C, Zhang, F, Zhang, H, Zhang, X, Zhang, Y, Zhang, Z, Zhao, Z, Zheng, L, Zheng, X, Zhou, W, Zhou, Y, Bin, Z, Zhu, H, Zhu, J, Zhu, S, Zou, L, Zou, X, Defazio, A, van As, N, van Deurzen, C, van de Vijver, M, van't Veer, L, von Mering, C, Heilbrigðisvísindasvið (HÍ), School of Health Sciences (UI), Háskóli Íslands, University of Iceland, Tampere University, BioMediTech, TAYS Cancer Centre, University of St Andrews. Sir James Mackenzie Institute for Early Diagnosis, University of St Andrews. Cellular Medicine Division, University of St Andrews. Statistics, University of St Andrews. School of Medicine, University of Zurich, Gerstein, Mark B, Ding, Li, Bailey, Matthew H [0000-0003-4526-9727], Wheeler, David A [0000-0002-9056-6299], Gerstein, Mark B [0000-0002-9746-3719], Faculty of Economic and Social Sciences and Solvay Business School, Lauri Antti Aaltonen / Principal Investigator, Genome-Scale Biology (GSB) Research Program, Department of Medical and Clinical Genetics, Organismal and Evolutionary Biology Research Programme, Helsinki Institute for Information Technology, Institute of Biotechnology, Bioinformatics, Department of Computer Science, Faculty of Medicine, and HUS Helsinki and Uusimaa Hospital District

Subjects
VARIANTS, 0302 clinical medicine, 706/648/697/129/2043, Databases, Genetic, Cancer genomics, SOMATIC POINT MUTATIONS, Càncer, lcsh:Science, Exome, Exome sequencing, Cancer, Base Composition, Neoplasms -- genetics, 1184 Genetics, developmental biology, physiology, 3100 General Physics and Astronomy, 3. Good health, 030220 oncology & carcinogenesis, Science & Technology - Other Topics, Transformació genètica, Genetic databases, Erfðarannsóknir, Human, GENES, Science, 1600 General Chemistry, General Biochemistry, Genetics and Molecular Biology, RC0254, 03 medical and health sciences, Genetic, SDG 3 - Good Health and Well-being, 1300 General Biochemistry, Genetics and Molecular Biology, Exome Sequencing, Genetics, Humans, Author Correction, Retrospective Studies, Whole genome sequencing, Comparative genomics, Science & Technology, RC0254 Neoplasms. Tumors. Oncology (including Cancer), INSERTIONS, DNA, PERFORMANCE, Human genetics, Communication and replication, Cancérologie, 692/4028/67/69, Genòmica, 030104 developmental biology, Mutation, Genome mutation, Human genome, lcsh:Q, COMPREHENSIVE CHARACTERIZATION, Genètica, 0301 basic medicine, Medizin, General Physics and Astronomy, Genome, Whole Exome Sequencing, Genetic transformation, International Cancer Genome Consortium, Neoplasms, 631/114/2399, Genamengi, Medicine and Health Sciences, Medicine(all), Women's cancers Radboud Institute for Molecular Life Sciences [Radboudumc 17], Multidisciplinary, 318 Medical biotechnology, Exome -- genetics, article, Exons, Women's cancers Radboud Institute for Health Sciences [Radboudumc 17], Multidisciplinary Sciences, CAPTURE, 1181 Ecology, evolutionary biology, oncology, DNA, Intergenic, 139, Medical Genetics, Biotechnology, ICGC/TCGA Pan-Cancer Analysis, 3122 Cancers, 610 Medicine & health, 45/23, QH426 Genetics, Biology, MC3 Working Group, Databases, Germline mutation, PCAWG novel somatic mutation calling methods working group, Krabbameinsrannsóknir, Cancer Genome Atlas, Genome, Human -- genetics, ddc:610, QH426, Medicinsk genetik, Krabbamein, Intergenic, Whole Genome Sequencing, Genome, Human, Human Genome, PCAWG Consortium, DAS, General Chemistry, DELETIONS, Good Health and Well Being, 10032 Clinic for Oncology and Hematology, 3111 Biomedicine, 631/1647/2217/748
Abstract

MC3 Working Group: Rehan Akbani21, Pavana Anur22, Matthew H. Bailey1,2,3, Alex Buchanan9, Kami Chiotti9, Kyle Covington12,23, Allison Creason9, Li Ding1,2,3,20, Kyle Ellrott9, Yu Fan21, Steven Foltz1,2, Gad Getz8,14,15,16, Walker Hale12, David Haussler24,25, Julian M. Hess8,26, Carolyn M. Hutter27, Cyriac Kandoth28, Katayoon Kasaian29,30, Melpomeni Kasapi27, Dave Larson1 , Ignaty Leshchiner8, John Letaw31, Singer Ma32, Michael D. McLellan1,3,20, Yifei Men32, Gordon B. Mills33,34, Beifang Niu35, Myron Peto22, Amie Radenbaugh24, Sheila M. Reynolds36, Gordon Saksena8, Heidi Sofia27, Chip Stewart8, Adam J. Struck31, Joshua M. Stuart24,37, Wenyi Wang21, John N. Weinstein38, David A. Wheeler12,13, Christopher K. Wong24,39, Liu Xi12 & Kai Ye40,41 21Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 22Molecular and Medical Genetics, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 23Castle Biosciences Inc, Friendswood, TX 77546, USA. 24UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 25Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 26Massachusetts General Hospital Center for Cancer Research, Charlestown, MA 02114, USA. 27National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA. 28Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. 29Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada. 30Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada. 31Computational Biology Program, School of Medicine, Oregon Health and Science University, Portland, OR 97239, USA. 32DNAnexus Inc, Mountain View, CA 94040, USA. 33Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA. 34Precision Oncology, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 35Computer Network Information Center, Chinese Academy of Sciences, Beijing, China. 36Institute for Systems Biology, Seattle, WA 98109, USA. 37Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 38Department of Bioinformatics and Computational Biology and Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 39Biomolecular Engineering Department, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 40School of Elect, PCAWG novel somatic mutation calling methods working group: Matthew H. Bailey1,2,3, Beifang Niu35, Matthias Bieg42,43, Paul C. Boutros6,44,45,46, Ivo Buchhalter43,47,48, Adam P. Butler49, Ken Chen50, Zechen Chong51, Li Ding1,2,3,20, Oliver Drechsel52,53, Lewis Jonathan Dursi6,7, Roland Eils47,48,54,55, Kyle Ellrott9, Shadrielle M. G. Espiritu6, Yu Fan21, Robert S. Fulton1,3,20, Shengjie Gao56, Josep L. l. Gelpi57,58, Mark B. Gerstein5,18,19, Gad Getz8,14,15,16, Santiago Gonzalez59,60, Ivo G. Gut52,61, Faraz Hach62,63, Michael C. Heinold47,48, Julian M. Hess8,26, Jonathan Hinton49, Taobo Hu64, Vincent Huang6, Yi Huang65,66, Barbara Hutter43,67,68, David R. Jones49, Jongsun Jung69, Natalie Jäger47, Hyung-Lae Kim70, Kortine Kleinheinz47,48, Sushant Kumar5,19, Yogesh Kumar64, Christopher M. Lalansingh6, Ignaty Leshchiner8, Ivica Letunic71, Dimitri Livitz8, Eric Z. Ma64, Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Michael D. McLellan1,3,20, Andrew Menzies49, Ana Milovanovic57, Morten Muhlig Nielsen73, Stephan Ossowski52,53,74, Nagarajan Paramasivam43,47, Jakob Skou Pedersen73,75, Marc D. Perry76,77, Montserrat Puiggròs57, Keiran M. Raine49, Esther Rheinbay8,14,72, Romina Royo57, S. Cenk Sahinalp62,78,79, Gordon Saksena8, Iman Sarrafi62,78, Matthias Schlesner47,80, Jared T. Simpson6,17, Lucy Stebbings49, Chip Stewart8, Miranda D. Stobbe52,61, Jon W. Teague49, Grace Tiao8, David Torrents57,81, Jeremiah A. Wala8,14,82, Jiayin Wang1,40,66, Wenyi Wang21, Sebastian M. Waszak60, Joachim Weischenfeldt60,83,84, Michael C. Wendl1,10,11, Johannes Werner47,85, Zhenggang Wu64, Hong Xue64, Sergei Yakneen60, Takafumi N. Yamaguchi6, Kai Ye40,41, Venkata D. Yellapantula20,86, Christina K. Yung76 & Junjun Zhang76, PCAWG Consortium: Lauri A. Aaltonen87, Federico Abascal49, Adam Abeshouse88, Hiroyuki Aburatani89, David J. Adams49, Nishant Agrawal90, Keun Soo Ahn91, Sung-Min Ahn92, Hiroshi Aikata93, Rehan Akbani21, Kadir C. Akdemir50, Hikmat Al-Ahmadie88, Sultan T. Al-Sedairy94, Fatima Al-Shahrour95, Malik Alawi96,97, Monique Albert98, Kenneth Aldape99,100, Ludmil B. Alexandrov49,101,102, Adrian Ally30, Kathryn Alsop103, Eva G. Alvarez104,105,106, Fernanda Amary107, Samirkumar B. Amin108,109,110, Brice Aminou76, Ole Ammerpohl111,112, Matthew J. Anderson113, Yeng Ang114, Davide Antonello115, Pavana Anur22, Samuel Aparicio116, Elizabeth L. Appelbaum1,117, Yasuhito Arai118, Axel Aretz119, Koji Arihiro93, Shun-ichi Ariizumi120, Joshua Armenia121, Laurent Arnould122, Sylvia Asa123,124, Yassen Assenov125, Gurnit Atwal6,126,127, Sietse Aukema112,128, J. Todd Auman129, Miriam R. Aure130, Philip Awadalla6,126, Marta Aymerich131, Gary D. Bader126, Adrian Baez-Ortega132, Matthew H. Bailey1,2,3, Peter J. Bailey133, Miruna Balasundaram30, Saianand Balu134, Pratiti Bandopadhayay8,135,136, Rosamonde E. Banks137, Stefano Barbi138, Andrew P. Barbour139,140, Jonathan Barenboim6, Jill Barnholtz-Sloan141,142, Hugh Barr143, Elisabet Barrera59, John Bartlett98,144, Javier Bartolome57, Claudio Bassi115, Oliver F. Bathe145,146, Daniel Baumhoer147, Prashant Bavi148, Stephen B. Baylin149,150, Wojciech Bazant59, Duncan Beardsmore151, Timothy A. Beck152,153, Sam Behjati49, Andreas Behren154, Beifang Niu35, Cindy Bell155, Sergi Beltran52,61, Christopher Benz156, Andrew Berchuck157, Anke K. Bergmann158, Erik N. Bergstrom101,102, Benjamin P. Berman159,160,161, Daniel M. Berney162, Stephan H. Bernhart163,164,165, Rameen Beroukhim8,14,82, Mario Berrios166, Samantha Bersani167, Johanna Bertl73,168, Miguel Betancourt169, Vinayak Bhandari6,44, Shriram G. Bhosle49, Andrew V. Biankin133,170,171,172, Matthias Bieg42,43, Darell Bigner173, Hans Binder163,164, Ewan Birney59, Michael Birrer72, Nidhan K. Biswas174, Bodil Bjerkehagen147,175, Tom Bodenheimer134, Lori Boice176, Giada Bonizzato177, Johann S. De Bono178, Arnoud Boot179,180, Moiz S. Bootwalla166, Ake Borg181, Arndt Borkhardt182, Keith A. Boroevich183,184, Ivan Borozan6, Christoph Borst185, Marcus Bosenberg186, Mattia Bosio52,53,57, Jacqueline Boultwood187, Guillaume Bourque188,189, Paul C. Boutros6,44,45,46, G. Steven Bova190, David T. Bowen49,191, Reanne Bowlby30, David D. L. Bowtell103, Sandrine Boyault192, Rich Boyce59, Jeffrey Boyd193, Alvis Brazma59, Paul Brennan194, Daniel S. Brewer195,196, Arie B. Brinkman197, Robert G. Bristow44,198,199,200,201, Russell R. Broaddus99, Jane E. Brock202, Malcolm Brock203, Annegien Broeks204, Angela N. Brooks8,24,37,82, Denise Brooks30, Benedikt Brors67,205,206, Søren Brunak207,208, Timothy J. C. Bruxner113,209, Alicia L. Bruzos104,105,106, Alex Buchanan9, Ivo Buchhalter43,47,48, Christiane Buchholz210, Susan Bullman8,82, Hazel Burke211, Birgit Burkhardt212, Kathleen H. Burns213,214, John Busanovich8,215, Carlos D. Bustamante216,217, Adam P. Butler49, Atul J. Butte218, Niall J. Byrne76, Anne-Lise Børresen-Dale130,219, Samantha J. Caesar-Johnson220, Andy Cafferkey59, Declan Cahill221, Claudia Calabrese59,60, Carlos Caldas222,223, Fabien Calvo224, Niedzica Camacho178, Peter J. Campbell49,225, Elias Campo226,227, Cinzia Cantù177, Shaolong Cao21, Thomas E. Carey228, Joana Carlevaro-Fita229,230,231, Rebecca Carlsen30, Ivana Cataldo167,177, Mario Cazzola232, Jonathan Cebon154, Robert Cerfolio233, Dianne E. Chadwick234, Dimple Chakravarty235, Don Chalmers236, Calvin Wing Yiu Chan47,237, Kin Chan238, Michelle Chan-Seng-Yue148, Vishal S. Chandan239, David K. Chang133,170, Stephen J. Chanock240, Lorraine A. Chantrill170,241, Aurélien Chateigner76,242, Nilanjan Chatterjee149,243, Kazuaki Chayama93, Hsiao-Wei Chen114,121, Jieming Chen218, Ken Chen50, Yiwen Chen21, Zhaohong Chen244, Andrew D. Cherniack8,82, Jeremy Chien245, Yoke-Eng Chiew246,247, Suet-Feung Chin222,223, Juok Cho8, Sunghoon Cho248, Jung Kyoon Choi249, Wan Choi250, Christine Chomienne251, Zechen Chong51, Su Pin Choo252, Angela Chou170,246, Angelika N. Christ113, Elizabeth L. Christie103, Eric Chuah30, Carrie Cibulskis8, Kristian Cibulskis8, Sara Cingarlini253, Peter Clapham49, Alexander Claviez254, Sean Cleary148,255, Nicole Cloonan256, Marek Cmero257,258,259, Colin C. Collins62, Ashton A. Connor255,260, Susanna L. Cooke133, Colin S. Cooper178,196,261, Leslie Cope149, Vincenzo Corbo138,177, Matthew G. Cordes1,262, Stephen M. Cordner263, Isidro Cortés-Ciriano264,265,266, Kyle Covington12,23, Prue A. Cowin267, Brian Craft24, David Craft8,268, Chad J. Creighton269, Yupeng Cun270, Erin Curley271, Ioana Cutcutache179,180, Karolina Czajka272, Bogdan Czerniak99,273, Rebecca A. Dagg274, Ludmila Danilova149, Maria Vittoria Davi275, Natalie R. Davidson276,277,278,279,280, Helen Davies49,281,282, Ian J. Davis283, Brandi N. Davis-Dusenbery284, Kevin J. Dawson49, Francisco M. De La Vega216,217,285, Ricardo De Paoli-Iseppi211, Timothy Defreitas8, Angelo P. Dei Tos286, Olivier Delaneau287,288,289, John A. Demchok220, Jonas Demeulemeester290,291, German M. Demidov52,53,74, Deniz Demircioğlu292,293, Nening M. Dennis221, Robert E. Denroche148, Stefan C. Dentro49,290,294, Nikita Desai76, Vikram Deshpande72, Amit G. Deshwar295, Christine Desmedt296,297, Jordi Deu-Pons298,299, Noreen Dhalla30, Neesha C. Dhani300, Priyanka Dhingra301,302, Rajiv Dhir303, Anthony DiBiase304, Klev Diamanti305, Li Ding1,2,3,20, Shuai Ding306, Huy Q. Dinh159, Luc Dirix307, HarshaVardhan Doddapaneni12, Nilgun Donmez62,78, Michelle T. Dow244, Ronny Drapkin308, Oliver Drechsel52,53, Ruben M. Drews223, Serge Serge49, Tim Dudderidge150,221, Ana Dueso-Barroso57, Andrew J. Dunford8, Michael Dunn309, Lewis Jonathan Dursi6,7, Fraser R. Duthie133,310, Ken Dutton-Regester311, Jenna Eagles272, Douglas F. Easton312,313, Stuart Edmonds314, Paul A. Edwards223,315, Sandra E. Edwards178, Rosalind A. Eeles178,221, Anna Ehinger316, Juergen Eils54,55, Roland Eils47,48,54,55, Adel El-Naggar99,273, Matthew Eldridge223, Kyle Ellrott9, Serap Erkek60, Georgia Escaramis53,317,318, Shadrielle M. G. Espiritu6, Xavier Estivill53,319, Dariush Etemadmoghadam103, Jorunn E. Eyfjord320, Bishoy M. Faltas280, Daiming Fan321, Yu Fan21, William C. Faquin72, Claudiu Farcas244, Matteo Fassan322, Aquila Fatima323, Francesco Favero324, Nodirjon Fayzullaev76, Ina Felau220, Sian Fereday103, Martin L. Ferguson325, Vincent Ferretti76,326, Lars Feuerbach205, Matthew A. Field327, J. Lynn Fink57,113, Gaetano Finocchiaro328, Cyril Fisher221, Matthew W. Fittall290, Anna Fitzgerald329, Rebecca C. Fitzgerald282, Adrienne M. Flanagan330, Neil E. Fleshner331, Paul Flicek59, John A. Foekens332, Kwun M. Fong333, Nuno A. Fonseca59,334, Christopher S. Foster335,336, Natalie S. Fox6, Michael Fraser6, Scott Frazer8, Milana Frenkel-Morgenstern337, William Friedman338, Joan Frigola298, Catrina C. Fronick1,262, Akihiro Fujimoto184, Masashi Fujita184, Masashi Fukayama339, Lucinda A. Fulton1 , Robert S. Fulton1,3,20, Mayuko Furuta184, P. Andrew Futreal340, Anja Füllgrabe59, Stacey B. Gabriel8, Steven Gallinger148,255,260, Carlo Gambacorti-Passerini341, Jianjiong Gao121, Shengjie Gao56, Levi Garraway82, Øystein Garred342, Erik Garrison49, Dale W. Garsed103, Nils Gehlenborg8,343, Josep L. l. Gelpi57,58, Joshy George110, Daniela S. Gerhard344, Clarissa Gerhauser345, Jeffrey E. Gershenwald346,347, Mark B. Gerstein5,18,19, Moritz Gerstung59,60, Gad Getz8,14,15,16, Mohammed Ghori49, Ronald Ghossein348, Nasra H. Giama349, Richard A. Gibbs12, Anthony J. Gill170,350, Pelvender Gill351, Dilip D. Giri348, Dominik Glodzik49, Vincent J. Gnanapragasam352,353, Maria Elisabeth Goebler354, Mary J. Goldman24, Carmen Gomez355, Santiago Gonzalez59,60, Abel Gonzalez-Perez298,299,356, Dmitry A. Gordenin357, James Gossage358, Kunihito Gotoh359, Ramaswamy Govindan3, Dorthe Grabau360, Janet S. Graham133,361, Robert C. Grant148,260, Anthony R. Green315, Eric Green27, Liliana Greger59, Nicola Grehan282, Sonia Grimaldi177, Sean M. Grimmond362, Robert L. Grossman363, Adam Grundhoff97,364, Gunes Gundem88, Qianyun Guo75, Manaswi Gupta8, Shailja Gupta365, Ivo G. Gut52,61, Marta Gut52,61, Jonathan Göke292,366, Gavin Ha8, Andrea Haake111, David Haan37, Siegfried Haas185, Kerstin Haase290, James E. Haber367, Nina Habermann60, Faraz Hach62,63, Syed Haider6, Natsuko Hama118, Freddie C. Hamdy351, Anne Hamilton267, Mark P. Hamilton368, Leng Han369, George B. Hanna370, Martin Hansmann371, Nicholas J. Haradhvala8,72, Olivier Harismendy102,372, Ivon Harliwong113, Arif O. Harmanci5,373, Eoghan Harrington374, Takanori Hasegawa375, David Haussler24,25, Steve Hawkins223, Shinya Hayami376, Shuto Hayashi375, D. Neil Hayes134,377,378, Stephen J. Hayes379,380, Nicholas K. Hayward211,311, Steven Hazell221, Yao He381, Allison P. Heath382, Simon C. Heath52,61, David Hedley300, Apurva M. Hegde38, David I. Heiman8, Michael C. Heinold47,48, Zachary Heins88, Lawrence E. Heisler152, Eva Hellstrom-Lindberg383, Mohamed Helmy384, Seong Gu Heo385, Austin J. Hepperla134, José María Heredia-Genestar386, Carl Herrmann47,48,387, Peter Hersey211, Julian M. Hess8,26, Holmfridur Hilmarsdottir320, Jonathan Hinton49, Satoshi Hirano388, Nobuyoshi Hiraoka389, Katherine A. Hoadley134,390, Asger Hobolth75,168, Ermin Hodzic78, Jessica I. Hoell182, Steve Hoffmann163,164,165,391, Oliver Hofmann392, Andrea Holbrook166, Aliaksei Z. Holik53, Michael A. Hollingsworth393, Oliver Holmes209,311, Robert A. Holt30, Chen Hong205,237, Eun Pyo Hong385, Jongwhi H. Hong394, Gerrit K. Hooijer395, Henrik Hornshøj73, Fumie Hosoda118, Yong Hou56,396, Volker Hovestadt397, William Howat352, Alan P. Hoyle134, Ralph H. Hruban149, Jianhong Hu12, Taobo Hu64, Xing Hua240, Kuan-lin Huang1,398, Mei Huang176, Mi Ni Huang179,180, Vincent Huang6, Yi Huang65,66, Wolfgang Huber60, Thomas J. Hudson272,399, Michael Hummel400, Jillian A. Hung246,247, David Huntsman401, Ted R. Hupp402, Jason Huse88, Matthew R. Huska403, Barbara Hutter43,67,68, Carolyn M. Hutter27, Daniel Hübschmann48,54,404,405,406, Christine A. Iacobuzio-Donahue348, Charles David Imbusch205, Marcin Imielinski407,408, Seiya Imoto375, William B. Isaacs409, Keren Isaev6,44, Shumpei Ishikawa410, Murat Iskar397, S. M. Ashiqul Islam244, Michael Ittmann411,412,413, Sinisa Ivkovic284, Jose M. G. Izarzugaza414, Jocelyne Jacquemier415, Valerie Jakrot211, Nigel B. Jamieson133,172,416, Gun Ho Jang148, Se Jin Jang417, Joy C. Jayaseelan12, Reyka Jayasinghe1 , Stuart R. Jefferys134, Karine Jegalian418, Jennifer L. Jennings419, Seung-Hyup Jeon250, Lara Jerman60,420, Yuan Ji421,422, Wei Jiao6, Peter A. Johansson311, Amber L. Johns170, Jeremy Johns272, Rory Johnson230,423, Todd A. Johnson183, Clemency Jolly290, Yann Joly424, Jon G. Jonasson320, Corbin D. Jones425, David R. Jones49, David T. W. Jones426,427, Nic Jones428, Steven J. M. Jones30, Jos Jonkers204, Young Seok Ju49,249, Hartmut Juhl429, Jongsun Jung69, Malene Juul73, Randi Istrup Juul73, Sissel Juul374, Natalie Jäger47, Rolf Kabbe47, Andre Kahles276,277,278,279,430, Abdullah Kahraman431,432,433, Vera B. Kaiser434, Hojabr Kakavand211, Sangeetha Kalimuthu148, Christof von Kalle405, Koo Jeong Kang91, Katalin Karaszi351, Beth Karlan435, Rosa Karlić436, Dennis Karsch437, Katayoon Kasaian29,30, Karin S. Kassahn113,438, Hitoshi Katai439, Mamoru Kato440, Hiroto Katoh410, Yoshiiku Kawakami93, Jonathan D. Kay117, Stephen H. Kazakoff209,311, Marat D. Kazanov441,442,443, Maria Keays59, Electron Kebebew444,445, Richard F. Kefford446, Manolis Kellis8,447, James G. Kench170,350,448, Catherine J. Kennedy246,247, Jules N. A. Kerssemakers47, David Khoo273, Vincent Khoo221, Narong Khuntikeo115,449, Ekta Khurana301,302,450,451, Helena Kilpinen117, Hark Kyun Kim452, Hyung-Lae Kim70, Hyung-Yong Kim415, Hyunghwan Kim250, Jaegil Kim8, Jihoon Kim453, Jong K. Kim454, Youngwook Kim455,456, Tari A. King457,458,459, Wolfram Klapper128, Kortine Kleinheinz47,48, Leszek J. Klimczak460, Stian Knappskog49,461, Michael Kneba437, Bartha M. Knoppers424, Youngil Koh462,463, Jan Komorowski305,464, Daisuke Komura410, Mitsuhiro Komura375, Gu Kong415, Marcel Kool426,465, Jan O. Korbel59,60, Viktoriya Korchina12, Andrey Korshunov465, Michael Koscher465, Roelof Koster466, Zsofia Kote-Jarai178, Antonios Koures244, Milena Kovacevic284, Barbara Kremeyer49, Helene Kretzmer164,165, Markus Kreuz467, Savitri Krishnamurthy99,468, Dieter Kube469, Kiran Kumar8, Pardeep Kumar221, Sushant Kumar5,19, Yogesh Kumar64, Ritika Kundra114,121, Kirsten Kübler8,14,72, Ralf Küppers470, Jesper Lagergren383,471, Phillip H. Lai166, Peter W. Laird472, Sunil R. Lakhani473, Christopher M. Lalansingh6, Emilie Lalonde6, Fabien C. Lamaze6, Adam Lambert351, Eric Lander8, Pablo Landgraf474,475, Luca Landoni115, Anita Langerød130, Andrés Lanzós230,231,423, Denis Larsimont476, Erik Larsson477, Mark Lathrop189, Loretta M. S. Lau478, Chris Lawerenz55, Rita T. Lawlor177, Michael S. Lawrence8,72,183, Alexander J. Lazar99,108, Xuan Le479, Darlene Lee30, Donghoon Lee5, Eunjung Alice Lee480, Hee Jin Lee417, Jake June-Koo Lee264,266, Jeong-Yeon Lee481, Juhee Lee482, Ming Ta Michael Lee340, Henry Lee-Six49, Kjong-Van Lehmann276,277,278,279,430, Hans Lehrach483, Dido Lenze400, Conrad R. Leonard209,311, Daniel A. Leongamornlert49,178, Ignaty Leshchiner8, Louis Letourneau484, Ivica Letunic71, Douglas A. Levine88,485, Lora Lewis12, Tim Ley486, Chang Li56,396, Constance H. Li6,44, Haiyan Irene Li30, Jun Li21, Lin Li56, Shantao Li5, Siliang Li56,396, Xiaobo Li56,396, Xiaotong Li5, Xinyue Li56, Yilong Li49, Han Liang21, Sheng-Ben Liang234, Peter Lichter68,397, Pei Lin8, Ziao Lin8,487, W. M. Linehan488, Ole Christian Lingjærde489, Dongbing Liu56,396, Eric Minwei Liu88,301,302, Fei-Fei Liu201,490, Fenglin Liu381,491, Jia Liu492, Xingmin Liu56,396, Julie Livingstone6, Dimitri Livitz8, Naomi Livni221, Lucas Lochovsky5,19,110, Markus Loeffler467, Georgina V. Long211, Armando Lopez-Guillermo493, Shaoke Lou5,19, David N. Louis72, Laurence B. Lovat117, Yiling Lu38, Yong-Jie Lu162,494, Youyong Lu495,496,497, Claudio Luchini167, Ilinca Lungu144,148, Xuemei Luo152, Hayley J. Luxton117, Andy G. Lynch223,315,498, Lisa Lype36, Cristina López111,112, Carlos López-Otín499, Eric Z. Ma64, Yussanne Ma30, Gaetan MacGrogan500, Shona MacRae501, Geoff Macintyre223, Tobias Madsen73, Kazuhiro Maejima184, Andrea Mafficini177, Dennis T. Maglinte166,502, Arindam Maitra174, Partha P. Majumder174, Luca Malcovati232, Salem Malikic62,78, Giuseppe Malleo115, Graham J. Mann211,246,503, Luisa Mantovani-Löffler504, Kathleen Marchal505,506, Giovanni Marchegiani115, Elaine R. Mardis1,193,507, Adam A. Margolin31, Maximillian G. Marin37, Florian Markowetz223,315, Julia Markowski403, Jeffrey Marks508, Tomas Marques-Bonet61,81,386,509, Marco A. Marra30, Luke Marsden351, John W. M. Martens332, Sancha Martin49,510, Jose I. Martin-Subero81,511, Iñigo Martincorena49, Alexander Martinez-Fundichely301,302,451 Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Charlie E. Massie223, Thomas J. Matthew37, Lucy Matthews178, Erik Mayer221,512, Simon Mayes513, Michael Mayo30, Faridah Mbabaali272, Karen McCune514, Ultan McDermott49, Patrick D. McGillivray19, Michael D. McLellan1,3,20, John D. McPherson148,272,515, John R. McPherson179,180, Treasa A. McPherson260, Samuel R. Meier8, Alice Meng516, Shaowu Meng134, Andrew Menzies49, Neil D. Merrett115,517, Sue Merson178, Matthew Meyerson8,14,82, William U. Meyerson4,5, Piotr A. Mieczkowski518, George L. Mihaiescu76, Sanja Mijalkovic284, Ana Mijalkovic Mijalkovic-Lazic284, Tom Mikkelsen519, Michele Milella253, Linda Mileshkin103, Christopher A. Miller1 , David K. Miller113,170, Jessica K. Miller272, Gordon B. Mills33,34, Ana Milovanovic57, Sarah Minner520, Marco Miotto115, Gisela Mir Arnau267, Lisa Mirabello240, Chris Mitchell103, Thomas J. Mitchell49,315,352, Satoru Miyano375, Naoki Miyoshi375, Shinichi Mizuno521, Fruzsina Molnár-Gábor522, Malcolm J. Moore300, Richard A. Moore30, Sandro Morganella49, Quaid D. Morris127,490, Carl Morrison523,524, Lisle E. Mose134, Catherine D. Moser349, Ferran Muiños298,299, Loris Mularoni298,299, Andrew J. Mungall30, Karen Mungall30, Elizabeth A. Musgrove133, Ville Mustonen525,526,527, David Mutch528, Francesc Muyas52,53,74, Donna M. Muzny12, Alfonso Muñoz59, Jerome Myers529, Ola Myklebost461, Peter Möller530, Genta Nagae89, Adnan M. Nagrial170, Hardeep K. Nahal-Bose76, Hitoshi Nakagama531, Hidewaki Nakagawa184, Hiromi Nakamura118, Toru Nakamura388, Kaoru Nakano184, Tannistha Nandi532, Jyoti Nangalia49, Mia Nastic284, Arcadi Navarro61,81,386, Fabio C. P. Navarro19, David E. Neal223,352, Gerd Nettekoven533, Felicity Newell209,311, Steven J. Newhouse59, Yulia Newton37, Alvin Wei Tian Ng534, Anthony Ng535, Jonathan Nicholson49, David Nicol221, Yongzhan Nie321,536, G. Petur Nielsen72, Morten Muhlig Nielsen73, Serena Nik-Zainal49,281,282,537, Michael S. Noble8, Katia Nones209,311, Paul A. Northcott538, Faiyaz Notta148,539, Brian D. O’Connor76,540, Peter O’Donnell541, Maria O’Donovan282, Sarah O’Meara49, Brian Patrick O’Neill542, J. Robert O’Neill543, David Ocana59, Angelica Ochoa88, Layla Oesper544, Christopher Ogden221, Hideki Ohdan93, Kazuhiro Ohi375, Lucila Ohno-Machado244, Karin A. Oien523,545, Akinyemi I. Ojesina546,547,548, Hidenori Ojima549, Takuji Okusaka550, Larsson Omberg551, Choon Kiat Ong552, Stephan Ossowski52,53,74, German Ott553, B. F. Francis Ouellette76,554, Christine P’ng6, Marta Paczkowska6, Salvatore Paiella115, Chawalit Pairojkul523, Marina Pajic170, Qiang Pan-Hammarström56,555, Elli Papaemmanuil49, Irene Papatheodorou59, Nagarajan Paramasivam43,47, Ji Wan Park385, Joong-Won Park556, Keunchil Park557,558, Kiejung Park559, Peter J. Park264,266, Joel S. Parker518, Simon L. Parsons124, Harvey Pass560, Danielle Pasternack272, Alessandro Pastore276, Ann-Marie Patch209,311, Iris Pauporté251, Antonio Pea115, John V. Pearson209,311, Chandra Sekhar Pedamallu8,14,82, Jakob Skou Pedersen73,75, Paolo Pederzoli115, Martin Peifer270, Nathan A. Pennell561, Charles M. Perou129,518, Marc D. Perry76,77, Gloria M. Petersen562, Myron Peto22, Nicholas Petrelli563, Robert Petryszak59, Stefan M. Pfister426,465,564, Mark Phillips424, Oriol Pich298,299, Hilda A. Pickett478, Todd D. Pihl565, Nischalan Pillay566, Sarah Pinder567, Mark Pinese170, Andreia V. Pinho568, Esa Pitkänen60, Xavier Pivot569, Elena Piñeiro-Yáñez95, Laura Planko533, Christoph Plass345, Paz Polak8,14,15, Tirso Pons570, Irinel Popescu571, Olga Potapova572, Aparna Prasad52, Shaun R. Preston573, Manuel Prinz47, Antonia L. Pritchard311, Stephenie D. Prokopec6, Elena Provenzano574, Xose S. Puente499, Sonia Puig176, Montserrat Puiggròs57, Sergio Pulido-Tamayo505,506, Gulietta M. Pupo246, Colin A. Purdie575, Michael C. Quinn209,311, Raquel Rabionet52,53,576, Janet S. Rader577, Bernhard Radlwimmer397, Petar Radovic284, Benjamin Raeder60, Keiran M. Raine49, Manasa Ramakrishna49, Kamna Ramakrishnan49, Suresh Ramalingam578, Benjamin J. Raphael579, W. Kimryn Rathmell580, Tobias Rausch60, Guido Reifenberger475, Jüri Reimand6,44, Jorge Reis-Filho348, Victor Reuter348, Iker Reyes-Salazar298, Matthew A. Reyna579, Sheila M. Reynolds36, Esther Rheinbay8,14,72, Yasser Riazalhosseini189, Andrea L. Richardson323, Julia Richter111,128, Matthew Ringel581, Markus Ringnér181, Yasushi Rino582, Karsten Rippe405, Jeffrey Roach583, Lewis R. Roberts349, Nicola D. Roberts49, Steven A. Roberts584, A. Gordon Robertson30, Alan J. Robertson113, Javier Bartolomé Rodriguez57, Bernardo Rodriguez-Martin104,105,106, F. Germán Rodríguez-González83,332, Michael H. A. Roehrl44,123,148,234,585,586, Marius Rohde587, Hirofumi Rokutan440, Gilles Romieu588, Ilse Rooman170, Tom Roques262, Daniel Rosebrock8, Mara Rosenberg8,72, Philip C. Rosenstiel589, Andreas Rosenwald590, Edward W. Rowe221,591, Romina Royo57, Steven G. Rozen179,180,592, Yulia Rubanova17,127, Mark A. Rubin423,593,594,595,596, Carlota Rubio-Perez298,299,597, Vasilisa A. Rudneva60, Borislav C. Rusev177, Andrea Ruzzenente598, Gunnar Rätsch276,277,278,279,280,430, Radhakrishnan Sabarinathan298,299,599, Veronica Y. Sabelnykova6, Sara Sadeghi30, S. Cenk Sahinalp62,78,79, Natalie Saini357, Mihoko Saito-Adachi440, Gordon Saksena8, Adriana Salcedo6, Roberto Salgado600, Leonidas Salichos5,19, Richard Sallari8, Charles Saller601, Roberto Salvia115, Michelle Sam272, Jaswinder S. Samra115,602, Francisco Sanchez-Vega114,121, Chris Sander276,603,604, Grant Sanders134, Rajiv Sarin605, Iman Sarrafi62,78, Aya Sasaki-Oku184, Torill Sauer489, Guido Sauter520, Robyn P. M. Saw211, Maria Scardoni167, Christopher J. Scarlett170,606, Aldo Scarpa177, Ghislaine Scelo194, Dirk Schadendorf68,607, Jacqueline E. Schein30, Markus B. Schilhabel589, Matthias Schlesner47,80, Thorsten Schlomm84,608, Heather K. Schmidt1 , Sarah-Jane Schramm246, Stefan Schreiber609, Nikolaus Schultz121, Steven E. Schumacher8,323, Roland F. Schwarz59,403,405,610, Richard A. Scolyer211,448,602, David Scott428, Ralph Scully611, Raja Seethala612, Ayellet V. Segre8,613, Iris Selander260, Colin A. Semple434, Yasin Senbabaoglu276, Subhajit Sengupta614, Elisabetta Sereni115, Stefano Serra585, Dennis C. Sgroi72, Mark Shackleton103, Nimish C. Shah352, Sagedeh Shahabi234, Catherine A. Shang329, Ping Shang211, Ofer Shapira8,323, Troy Shelton271, Ciyue Shen603,604, Hui Shen615, Rebecca Shepherd49, Ruian Shi490, Yan Shi134, Yu-Jia Shiah6, Tatsuhiro Shibata118,616, Juliann Shih8,82, Eigo Shimizu375, Kiyo Shimizu617, Seung Jun Shin618, Yuichi Shiraishi375, Tal Shmaya285, Ilya Shmulevich36, Solomon I. Shorser6, Charles Short59, Raunak Shrestha62, Suyash S. Shringarpure217, Craig Shriver619, Shimin Shuai6,126, Nikos Sidiropoulos83, Reiner Siebert112,620, Anieta M. Sieuwerts332, Lina Sieverling205,237, Sabina Signoretti202,621, Katarzyna O. Sikora177, Michele Simbolo138, Ronald Simon520, Janae V. Simons134, Jared T. Simpson6,17, Peter T. Simpson473, Samuel Singer115,458, Nasa Sinnott-Armstrong8,217, Payal Sipahimalani30, Tara J. Skelly390, Marcel Smid332, Jaclyn Smith622, Karen Smith-McCune514, Nicholas D. Socci276, Heidi J. Sofia27, Matthew G. Soloway134, Lei Song240, Anil K. Sood623,624,625, Sharmila Sothi626, Christos Sotiriou244, Cameron M. Soulette37, Paul N. Span627, Paul T. Spellman22, Nicola Sperandio177, Andrew J. Spillane211, Oliver Spiro8, Jonathan Spring628, Johan Staaf181, Peter F. Stadler163,164,165, Peter Staib629, Stefan G. Stark277,279,618,630, Lucy Stebbings49, Ólafur Andri Stefánsson631, Oliver Stegle59,60,632, Lincoln D. Stein6,126, Alasdair Stenhouse633, Chip Stewart8, Stephan Stilgenbauer634, Miranda D. Stobbe52,61, Michael R. Stratton49, Jonathan R. Stretch211, Adam J. Struck31, Joshua M. Stuart24,37, Henk G. Stunnenberg396,635, Hong Su56,396, Xiaoping Su99, Ren X. Sun6, Stephanie Sungalee60, Hana Susak52,53, Akihiro Suzuki89,636, Fred Sweep637, Monika Szczepanowski128, Holger Sültmann67,638, Takashi Yugawa617, Angela Tam30, David Tamborero298,299, Benita Kiat Tee Tan639, Donghui Tan518, Patrick Tan180,532,592,640, Hiroko Tanaka375, Hirokazu Taniguchi616, Tomas J. Tanskanen641, Maxime Tarabichi49,290, Roy Tarnuzzer220, Patrick Tarpey642, Morgan L. Taschuk152, Kenji Tatsuno89, Simon Tavaré223,643, Darrin F. Taylor113, Amaro Taylor-Weiner8, Jon W. Teague49, Bin Tean Teh180,592,640,644,645, Varsha Tembe246, Javier Temes104,105, Kevin Thai76, Sarah P. Thayer393, Nina Thiessen30, Gilles Thomas646, Sarah Thomas221, Alan Thompson221, Alastair M. Thompson633, John F. Thompson211, R. Houston Thompson647, Heather Thorne103, Leigh B. Thorne176, Adrian Thorogood424, Grace Tiao8, Nebojsa Tijanic284, Lee E. Timms272, Roberto Tirabosco648, Marta Tojo106, Stefania Tommasi649, Christopher W. Toon170, Umut H. Toprak48,650, David Torrents57,81, Giampaolo Tortora651,652, Jörg Tost653, Yasushi Totoki118, David Townend654, Nadia Traficante103, Isabelle Treilleux655,656, Jean-Rémi Trotta61, Lorenz H. P. Trümper469, Ming Tsao124,539, Tatsuhiko Tsunoda183,657,658,659, Jose M. C. Tubio104,105,106, Olga Tucker660, Richard Turkington661, Daniel J. Turner513, Andrew Tutt323, Masaki Ueno376, Naoto T. Ueno662, Christopher Umbricht151,213,663, Husen M. Umer305,664, Timothy J. Underwood665, Lara Urban59,60, Tomoko Urushidate616, Tetsuo Ushiku339, Liis Uusküla-Reimand666,667, Alfonso Valencia57,81, David J. Van Den Berg166, Steven Van Laere307, Peter Van Loo290,291, Erwin G. Van Meir668, Gert G. Van den Eynden307, Theodorus Van der Kwast123, Naveen Vasudev137, Miguel Vazquez57,669, Ravikiran Vedururu267, Umadevi Veluvolu518, Shankar Vembu490,670, Lieven P. C. Verbeke506,671, Peter Vermeulen307, Clare Verrill351,672, Alain Viari177, David Vicente57, Caterina Vicentini177, K. Vijay Raghavan365, Juris Viksna673, Ricardo E. Vilain674, Izar Villasante57, Anne Vincent-Salomon635, Tapio Visakorpi190, Douglas Voet8, Paresh Vyas311,351, Ignacio Vázquez-García49,86,675,676, Nick M. Waddell209, Nicola Waddell209,311, Claes Wadelius677, Lina Wadi6, Rabea Wagener111,112, Jeremiah A. Wala8,14,82, Jian Wang56, Jiayin Wang1,40,66, Linghua Wang12, Qi Wang465, Wenyi Wang21, Yumeng Wang21, Zhining Wang220, Paul M. Waring523, Hans-Jörg Warnatz483, Jonathan Warrell5,19, Anne Y. Warren352,678, Sebastian M. Waszak60, David C. Wedge49,294,679, Dieter Weichenhan345, Paul Weinberger680, John N. Weinstein38, Joachim Weischenfeldt60,83,84, Daniel J. Weisenberger166, Ian Welch681, Michael C. Wendl1,10,11, Johannes Werner47,85, Justin P. Whalley61,682, David A. Wheeler12,13, Hayley C. Whitaker117, Dennis Wigle683, Matthew D. Wilkerson518, Ashley Williams244, James S. Wilmott211, Gavin W. Wilson6,148, Julie M. Wilson148, Richard K. Wilson1,684, Boris Winterhoff685, Jeffrey A. Wintersinger17,127,384, Maciej Wiznerowicz686,687, Stephan Wolf688, Bernice H. Wong689, Tina Wong1,30, Winghing Wong690, Youngchoon Woo250, Scott Wood209,311, Bradly G. Wouters44, Adam J. Wright6, Derek W. Wright133,691, Mark H. Wright217, Chin-Lee Wu72, Dai-Ying Wu285, Guanming Wu692, Jianmin Wu170, Kui Wu56,396, Yang Wu179,180, Zhenggang Wu64, Liu Xi12, Tian Xia693, Qian Xiang76, Xiao Xiao66, Rui Xing497, Heng Xiong56,396, Qinying Xu209,311, Yanxun Xu694, Hong Xue64, Shinichi Yachida118,695, Sergei Yakneen60, Rui Yamaguchi375, Takafumi N. Yamaguchi6, Masakazu Yamamoto120, Shogo Yamamoto89, Hiroki Yamaue376, Fan Yang490, Huanming Yang56, Jean Y. Yang696, Liming Yang220, Lixing Yang697, Shanlin Yang306, Tsun-Po Yang270, Yang Yang369, Xiaotong Yao408,698, Marie-Laure Yaspo483, Lucy Yates49, Christina Yau156, Chen Ye56,396, Kai Ye40,41, Venkata D. Yellapantula20,86, Christopher J. Yoon249, Sung-Soo Yoon463, Fouad Yousif6, Jun Yu699, Kaixian Yu700, Willie Yu701, Yingyan Yu702, Ke Yuan223,510,703, Yuan Yuan21, Denis Yuen6, Takashi Yugawa617, Christina K. Yung76, Olga Zaikova704, Jorge Zamora49,104,105,106, Marc Zapatka397, Jean C. Zenklusen220, Thorsten Zenz67, Nikolajs Zeps705,706, Cheng-Zhong Zhang8,707, Fan Zhang381, Hailei Zhang8, Hongwei Zhang494, Hongxin Zhang121, Jiashan Zhang220, Jing Zhang5, Junjun Zhang76, Xiuqing Zhang56, Xuanping Zhang66,369, Yan Zhang5,708,709, Zemin Zhang381,710, Zhongming Zhao711, Liangtao Zheng381, Xiuqing Zheng381, Wanding Zhou615, Yong Zhou56, Bin Zhu240, Hongtu Zhu700,712, Jingchun Zhu24, Shida Zhu56,396, Lihua Zou713, Xueqing Zou49, Anna deFazio246,247,714, Nicholas van As221, Carolien H. M. van Deurzen715, Marc J. van de Vijver523, L. van’t Veer716 & Christian von Mering433,717, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

Published
2020
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17. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data

Author

Justin Guinney, Tao Wang, Teemu D Laajala, Kimberly Kanigel Winner, J Christopher Bare, Elias Chaibub Neto, Suleiman A Khan, Gopal Peddinti, Antti Airola, Tapio Pahikkala, Tuomas Mirtti, Thomas Yu, Brian M Bot, Liji Shen, Kald Abdallah, Thea Norman, Stephen Friend, Gustavo Stolovitzky, Howard Soule, Christopher J Sweeney, Charles J Ryan, Howard I Scher, Oliver Sartor, Yang Xie, Tero Aittokallio, Fang Liz Zhou, James C Costello, Catalina Anghe, Helia Azima, Robert Baertsch, Pedro J Ballester, Chris Bare, Vinayak Bhandari, Cuong C Dang, Maria Bekker-Nielsen Dunbar, Ann-Sophie Buchardt, Ljubomir Buturovic, Da Cao, Prabhakar Chalise, Junwoo Cho, Tzu-Ming Chu, R Yates Coley, Sailesh Conjeti, Sara Correia, Ziwei Dai, Junqiang Dai, Philip Dargatz, Sam Delavarkhan, Detian Deng, Ankur Dhanik, Yu Du, Aparna Elangovan, Shellie Ellis, Laura L Elo, Shadrielle M Espiritu, Fan Fan, Ashkan B Farshi, Ana Freitas, Brooke Fridley, Christiane Fuchs, Eyal Gofer, Gopalacharyulu Peddinti, Stefan Graw, Russ Greiner, Yuanfang Guan, Jing Guo, Pankaj Gupta, Anna I Guyer, Jiawei Han, Niels R Hansen, Billy HW Chang, Outi Hirvonen, Barbara Huang, Chao Huang, Jinseub Hwang, Joseph G Ibrahim, Vivek Jayaswa, Jouhyun Jeon, Zhicheng Ji, Deekshith Juvvadi, Sirkku Jyrkkiö, Kimberly Kanigel-Winner, Amin Katouzian, Marat D Kazanov, Shahin Khayyer, Dalho Kim, Agnieszka K Golinska, Devin Koestler, Fernanda Kokowicz, Ivan Kondofersky, Norbert Krautenbacher, Damjan Krstajic, Luke Kumar, Christoph Kurz, Matthew Kyan, Michael Laimighofer, Eunjee Lee, Wojciech Lesinski, Miaozhu Li, Ye Li, Qiuyu Lian, Xiaotao Liang, Minseong Lim, Henry Lin, Xihui Lin, Jing Lu, Mehrad Mahmoudian, Roozbeh Manshaei, Richard Meier, Dejan Miljkovic, Krzysztof Mnich, Nassir Navab, Elias C Neto, Yulia Newton, Subhabrata Pal, Byeongju Park, Jaykumar Patel, Swetabh Pathak, Alejandrina Pattin, Donna P Ankerst, Jian Peng, Anne H Petersen, Robin Philip, Stephen R Piccolo, Sebastian Pölsterl, Aneta Polewko-Klim, Karthik Rao, Xiang Ren, Miguel Rocha, Witold R. Rudnicki, Hyunnam Ryu, Hagen Scherb, Raghav Sehgal, Fatemeh Seyednasrollah, Jingbo Shang, Bin Shao, Howard Sher, Motoki Shiga, Artem Sokolov, Julia F Söllner, Lei Song, Josh Stuart, Ren Sun, Nazanin Tahmasebi, Kar-Tong Tan, Lisbeth Tomaziu, Joseph Usset, Yeeleng S Vang, Roberto Vega, Vitor Vieira, David Wang, Difei Wang, Junmei Wang, Lichao Wang, Sheng Wang, Yue Wang, Russ Wolfinger, Chris Wong, Zhenke Wu, Jinfeng Xiao, Xiaohui Xie, Doris Xin, Hojin Yang, Nancy Yu, Xiang Yu, Sulmaz Zahedi, Massimiliano Zanin, Chihao Zhang, Jingwen Zhang, Shihua Zhang, Yanchun Zhang, Hongtu Zhu, Shanfeng Zhu, Yuxin Zhu, Universidade do Minho, Institute for Molecular Medicine Finland, University of Helsinki, Department of Pathology, Medicum, Clinicum, HUSLAB, Tero Aittokallio / Principal Investigator, and Bioinformatics

Subjects
Male, 0301 basic medicine, Oncology, BIOMEDICAL-RESEARCH, law.invention, DOUBLE-BLIND, Prostate cancer, 0302 clinical medicine, Randomized controlled trial, law, Antineoplastic Combined Chemotherapy Protocols, health care economics and organizations, DOCETAXEL, PLACEBO, Hazard ratio, MEN, Middle Aged, CHEMOTHERAPY, Prognosis, 3. Good health, Survival Rate, Prostatic Neoplasms, Castration-Resistant, Docetaxel, 030220 oncology & carcinogenesis, Crowdsourcing, Taxoids, medicine.drug, Adult, PREDNISONE, medicine.medical_specialty, ENZALUTAMIDE, Adolescent, 3122 Cancers, Young Adult, 03 medical and health sciences, SDG 3 - Good Health and Well-being, Internal medicine, medicine, Humans, Survival rate, Aged, Neoplasm Staging, Models, Statistical, Science & Technology, business.industry, Proportional hazards model, Clinical study design, Bayes Theorem, medicine.disease, PHASE-III, SIPULEUCEL-T IMMUNOTHERAPY, Surgery, Clinical trial, Nomograms, 030104 developmental biology, business, Follow-Up Studies
Abstract

Background: Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interestnamely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trialENTHUSE M1in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·394·62, p, European Union within the ERC grant LatentCauses supported the work of C.F and I.K. German Research Foundation (DFG) within the Collaborative Research Centre 1243, subproject A17 awarded to C.F. German Federal Ministry of Education and Research (BMBF) through the Research Consortium e:AtheroMED (Systems medicine of myocardial infarction and stroke) under the auspices of the e:Med Programme (grant # 01ZX1313C) supported the work of D.P.A., P.D., C.F., C.K., I.K., N.K., M.L., H.S. and J.F.S. at the Institute of Computational Biology. NIH Grants RR025747-01, MH086633 and 1UL1TR001111, and NSF Grants SES-1357666, DMS-14-07655 and BCS0826844 supported the work of C.H., J.I., E.L., Y.W., H.Y., H.Z. and J.Z. NSFC Grant Nos. 61332013, 61572139 supported the work of X.L, Y.L, Y.Z., and S.Z. National Natural Science Foundation of China grants [Nos. 61422309, 61379092] was awarded to S.Z. The Patrick C. Walsh Prostate Research Fund and the Johns Hopkins Individualized Health Initiative supported the work of R.Y.C., D.D., Y.D., Z.J., K.R., Z.W. and Y.Z. FCT Ph.D. Grant SFRH/BD/80925/2011 was awarded to S.C. Clinical Persona Inc., East Palo Alto, CA supported the work of L.B. and D.K. The Finnish Cultural Foundation and the Drug Research Doctoral Programme (DRDP) at the University of Turku supported T.D.L. The National Research Foundation Singapore and the Singapore Ministry of Education, under its Research Centres of Excellence initiative, supported the work of J.G. and K.T. A grant from the Russian Science Foundation 14-24-00155 was awarded to M.D.K. A*MIDEX grant (no. ANR-11-IDEX-0001-02) was awarded to P.J.B. NSERC supported the work of R.G. The Israeli Centers of Research Excellence (I-CORE) program (Center No. 4/11) supported the work of E.G. Academy of Finland (grants 292611, 269862, 272437, 279163, 295504), National Cancer Institute (16X064), and Cancer Society of Finland supported the work of T.A. Academy of Finland (grant 268531) supported the work of T.M

Published
2017
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18. Mycobacterial Nicotinate Mononucleotide Adenylyltransferase

Author

Chen Ting Ma, Marat D. Kazanov, Christopher Locher, Leonardo Sorci, Alexander E. Aleshin, Eduard Sergienko, Irina A. Rodionova, Andrei L. Osterman, Eric J. Rubin, and Harmon J. Zuccola

Subjects
Nicotinamide-nucleotide adenylyltransferase, biology, Stereochemistry, Active site, Cell Biology, Protein engineering, Biochemistry, Enzyme structure, Cofactor, Protein structure, Non-competitive inhibition, biology.protein, NAD+ kinase, Molecular Biology
Abstract

Nicotinate mononucleotide adenylyltransferase NadD is an essential enzyme in the biosynthesis of the NAD cofactor, which has been implicated as a target for developing new antimycobacterial therapies. Here we report the crystal structure of Mycobacterium tuberculosis NadD (MtNadD) at a resolution of 2.4 A. A remarkable new feature of the MtNadD structure, compared with other members of this enzyme family, is a 310 helix that locks the active site in an over-closed conformation. As a result, MtNadD is rendered inactive as it is topologically incompatible with substrate binding and catalysis. Directed mutagenesis was also used to further dissect the structural elements that contribute to the interactions of the two MtNadD substrates, i.e. ATP and nicotinic acid mononucleotide (NaMN). For inhibitory profiling of partially active mutants and wild type MtNadD, we used a small molecule inhibitor of MtNadD with moderate affinity (Ki ∼ 25 μm) and antimycobacterial activity (MIC80) ∼ 40–80 μm). This analysis revealed interferences with some of the residues in the NaMN binding subsite consistent with the competitive inhibition observed for the NaMN substrate (but not ATP). A detailed steady-state kinetic analysis of MtNadD suggests that ATP must first bind to allow efficient NaMN binding and catalysis. This sequential mechanism is consistent with the requirement of transition to catalytically competent (open) conformation hypothesized from structural modeling. A possible physiological significance of this mechanism is to enable the down-regulation of NAD synthesis under ATP-limiting dormancy conditions. These findings point to a possible new strategy for designing inhibitors that lock the enzyme in the inactive over-closed conformation.

Published
2015
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19. Sequence and Conformational Specificity in Substrate Recognition

Author

Ruiying Wang, Alexandra Hockla, Alexei S. Soares, Evette S. Radisky, Marat D. Kazanov, Benjamin J. Madden, Rachel D. Henin, and Devon F. Pendlebury

Subjects
Serine protease, Protease, medicine.medical_treatment, Cell Biology, Plasma protein binding, Biology, Protein degradation, Biochemistry, Protein–protein interaction, Protein structure, biology.protein, Amyloid precursor protein, medicine, Binding site, Molecular Biology
Abstract

Mesotrypsin is an isoform of trypsin that is uniquely resistant to polypeptide trypsin inhibitors and can cleave some inhibitors rapidly. Previous studies have shown that the amyloid precursor protein Kunitz protease inhibitor domain (APPI) is a specific substrate of mesotrypsin and that stabilization of the APPI cleavage site in a canonical conformation contributes to recognition by mesotrypsin. We hypothesized that other proteins possessing potential cleavage sites stabilized in a similar conformation might also be mesotrypsin substrates. Here we evaluated a series of candidate substrates, including human Kunitz protease inhibitor domains from amyloid precursor-like protein 2 (APLP2), bikunin, hepatocyte growth factor activator inhibitor type 2 (HAI2), tissue factor pathway inhibitor-1 (TFPI1), and tissue factor pathway inhibitor-2 (TFPI2), as well as E-selectin, an unrelated protein possessing a potential cleavage site displaying canonical conformation. We find that Kunitz domains within APLP2, bikunin, and HAI2 are cleaved by mesotrypsin with kinetic profiles of specific substrates. TFPI1 and TFPI2 Kunitz domains are cleaved less efficiently by mesotrypsin, and E-selectin is not cleaved at the anticipated site. Cocrystal structures of mesotrypsin with HAI2 and bikunin Kunitz domains reveal the mode of mesotrypsin interaction with its canonical substrates. Our data suggest that major determinants of mesotrypsin substrate specificity include sequence preferences at the P1 and P′2 positions along with conformational stabilization of the cleavage site in the canonical conformation. Mesotrypsin up-regulation has been implicated previously in cancer progression, and proteolytic clearance of Kunitz protease inhibitors offers potential mechanisms by which mesotrypsin may mediate pathological effects in cancer.

Published
2014
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20. Evaluation and Comparison of Current Fetal Ultrasound Image Segmentation Methods for Biometric Measurements: A Grand Challenge

Author

Aris T. Papageorghiou, Sylvia Rueda, Chu-Mei Hung, John E. McManigle, Mikhail S. Gelfand, Hsiang-Chou Chen, Bahbibi Rahmatullah, Anca Ciurte, Mohammad Yaqub, Matteo Maggioni, J. Alison Noble, Ching-Wei Wang, Xavier Bresson, Marat D. Kazanov, Chun-Wei Peng, Gennady V. Ponomarev, Changming Sun, Meritxell Bach Cuadra, Alessandro Foi, Jussi Tohka, Sana Fathima, Richard V. Stebbing, C. L. Knight, Antonietta Pepe, Tampere University, Research group: Signal and Image Restoration-RST, Department of Signal Processing, and Signal Processing Research Community (SPRC)

Subjects
medicine.medical_specialty, Biometry, Biometrics, Image quality, fetal biometry, Gestational Age, Image processing, CIBM-SPC, Ultrasonography, Prenatal, Pregnancy, Image Processing, Computer-Assisted, Medical imaging, medicine, image quality, Humans, Computer vision, Segmentation, Femur, Challenge, Electrical and Electronic Engineering, Ground truth, evaluation, Radiological and Ultrasound Technology, business.industry, segmentation, LTS5, Image segmentation, 113 Computer and information sciences, Computer Science Applications, ultrasound (US), Female, Artificial intelligence, Radiology, business, Software
Abstract

This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal ultrasound image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal ultrasound images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.

Published
2014
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21. Predictive models of protease specificity based on quantitative protease-activity profiling data

Author

Gennady V. Ponomarev, Mikhail S. Gelfand, Piotr Cieplak, Gennady G. Fedonin, Evgenii V. Matveev, Marat D. Kazanov, Boris I. Ratnikov, and Alexey Eroshkin

Subjects
Proteases, Proteolysis, medicine.medical_treatment, Biophysics, Computational biology, Biochemistry, Article, Analytical Chemistry, 03 medical and health sciences, 0302 clinical medicine, Activity profiling, medicine, Humans, Molecular Biology, 030304 developmental biology, 0303 health sciences, Protease, Training set, medicine.diagnostic_test, Chemistry, Computational Biology, Experimental data, Position weight matrix, Algorithms, 030217 neurology & neurosurgery, Predictive modelling, Peptide Hydrolases
Abstract

Bioinformatics-based prediction of protease substrates can help to elucidate regulatory proteolytic pathways that control a broad range of biological processes such as apoptosis and blood coagulation. The majority of published predictive models are position weight matrices (PWM) reflecting specificity of proteases towards target sequence. These models are typically derived from experimental data on positions of hydrolyzed peptide bonds and show a reasonable predictive power. New emerging techniques that not only register the cleavage position but also measure catalytic efficiency of proteolysis are expected to improve the quality of predictions or at least substantially reduce the number of tested substrates required for confident predictions. The main goal of this study was to develop new prediction models based on such data and to estimate the performance of the constructed models. We used data on catalytic efficiency of proteolysis measured for eight major human matrix metalloproteinases to construct predictive models of protease specificity using a variety of regression analysis techniques. The obtained results suggest that efficiency-based (quantitative) models show a comparable performance with conventional PWM-based algorithms, while less training data are required. The derived list of candidate cleavage sites in human secreted proteins may serve as a starting point for experimental analysis.

Published
2019
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22. Sequence-derived structural features driving proteolytic processing

Author

Alexander Belushkin, Andrei L. Osterman, Dmitry Vinogradov, Mikhail S. Gelfand, Piotr Cieplak, and Marat D. Kazanov

Subjects
Proteases, Models, Statistical, Protease, medicine.diagnostic_test, Protein Conformation, medicine.medical_treatment, Proteolysis, Wnt signaling pathway, Computational Biology, Proteins, Computational biology, Biology, Biochemistry, Article, Protein structure, ROC Curve, medicine, Amino Acid Sequence, Molecular Biology, Hedgehog, Protein secondary structure, Peptide sequence
Abstract

Proteolytic signaling, or regulated proteolysis, is an essential part of many important pathways such as Notch, Wnt, and Hedgehog. How the structure of the cleaved substrate regions influences the efficacy of proteolytic processing remains underexplored. Here, we analyzed the relative importance in proteolysis of various structural features derived from substrate sequences using a dataset of more than five thousand experimentally verified proteolytic events captured in CutDB. Accessibility to the solvent was recognized as an essential property of a proteolytically processed polypeptide chain. Proteolytic events were found nearly uniformly distributed among three types of secondary structure, although with some enrichment in loops. Cleavages in α-helices were found to be relatively abundant in regions apparently prone to unfolding, while cleavages in β-structures tended to be located at the periphery of β-sheets. Application of the same statistical procedures to proteolytic events divided into separate sets according to the catalytic classes of proteases proved consistency of the results and confirmed that the structural mechanisms of proteolysis are universal. The estimated prediction power of sequence-derived structural features, which turned out to be sufficiently high, presents a rationale for their use in bioinformatic prediction of proteolytic events.

Published
2013
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23. Evolution of Pan-Genomes of Escherichia coli, Shigella spp., and Salmonella enterica

Author

Evgeny N. Gordienko, Marat D. Kazanov, and Mikhail S. Gelfand

Subjects
Genetics, Phylogenetic tree, Strain (biology), Computational Biology, Salmonella enterica, Articles, Biology, medicine.disease_cause, biology.organism_classification, Microbiology, Genome, Evolution, Molecular, Horizontal gene transfer, Escherichia coli, medicine, Shigella, Molecular Biology, Gene, Genome, Bacterial, Phylogeny
Abstract

Multiple sequencing of genomes belonging to a bacterial species allows one to analyze and compare statistics and dynamics of the gene complements of species, their pan-genomes. Here, we analyzed multiple genomes of Escherichia coli , Shigella spp., and Salmonella enterica . We demonstrate that the distribution of the number of genomes harboring a gene is well approximated by a sum of two power functions, describing frequent genes (present in many strains) and rare genes (present in few strains). The virtual absence of Shigella -specific genes not present in E. coli genomes confirms previous observations that Shigella is not an independent genus. While the pan-genome size is increasing with each new strain, the number of genes present in a fixed fraction of strains stabilizes quickly. For instance, slightly fewer than 4,000 genes are present in at least half of any group of E. coli genomes. Comparison of S. enterica and E. coli pan-genomes revealed the existence of a common periphery, that is, genes present in some but not all strains of both species. Analysis of phylogenetic trees demonstrates that rare genes from the periphery likely evolve under horizontal transfer, whereas frequent periphery genes may have been inherited from the periphery genome of the common ancestor.

Published
2013
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24. Structural Determinants of Limited Proteolysis

Author

Piotr Cieplak, Alexey Eroshkin, Andrei L. Osterman, Jeffrey W. Smith, Adam Godzik, Zhanwen Li, Boris I. Ratnikov, Marat D. Kazanov, Yoshinobu Igarashi, and Ying Zhang

Subjects
Models, Molecular, Proteases, Protease, medicine.diagnostic_test, medicine.medical_treatment, Proteolysis, Molecular Sequence Data, Proteins, General Chemistry, Biology, Biochemistry, Article, Protein Structure, Secondary, Scissile bond, Protein structure, medicine, Peptide bond, Amino Acid Sequence, Protein secondary structure, Peptide sequence
Abstract

Limited or regulatory proteolysis plays a critical role in many important biological pathways like blood coagulation, cell proliferation, and apoptosis. A better understanding of mechanisms that control this process is required for discovering new proteolytic events and for developing inhibitors with potential therapeutic value. Two features that determine the susceptibility of peptide bonds to proteolysis are the sequence in the vicinity of the scissile bond and the structural context in which the bond is displayed. In this study, we assessed statistical significance and predictive power of individual structural descriptors and combination thereof for the identification of cleavage sites. The analysis was performed on a data set of >200 proteolytic events documented in CutDB for a variety of mammalian regulatory proteases and their physiological substrates with known 3D structures. The results confirmed the significance and provided a ranking within three main categories of structural features: exposure > flexibility > local interactions. Among secondary structure elements, the largest frequency of proteolytic cleavage was confirmed for loops and lower but significant frequency for helices. Limited proteolysis has lower albeit appreciable frequency of occurrence in certain types of β-strands, which is in contrast with some previous reports. Descriptors deduced directly from the amino acid sequence displayed only marginal predictive capabilities. Homology-based structural models showed a predictive performance comparable to protein substrates with experimentally established structures. Overall, this study provided a foundation for accurate automated prediction of segments of protein structure susceptible to proteolytic processing and, potentially, other post-translational modifications.

Published
2011
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25. Evolution of Prokaryotic Genes by Shift of Stop Codons

Author

Andrey A. Mironov, Marat D. Kazanov, Anna A. Vakhrusheva, and Georgii A. Bazykin

Subjects
Genetics, Silent mutation, Bacteria, Models, Genetic, Nonsense mutation, Reading frame, Biology, Stop codon, Evolution, Molecular, Open Reading Frames, Open reading frame, INDEL Mutation, Start codon, Genes, Bacterial, Codon usage bias, Databases, Genetic, Codon, Terminator, Cluster Analysis, Point Mutation, Coding region, Molecular Biology, Algorithms, Ecology, Evolution, Behavior and Systematics
Abstract

De novo origin of coding sequence remains an obscure issue in molecular evolution. One of the possible paths for addition (subtraction) of DNA segments to (from) a gene is stop codon shift. Single nucleotide substitutions can destroy the existing stop codon, leading to uninterrupted translation up to the next stop codon in the gene's reading frame, or create a premature stop codon via a nonsense mutation. Furthermore, short indels-caused frameshifts near gene's end may lead to premature stop codons or to translation past the existing stop codon. Here, we describe the evolution of the length of coding sequence of prokaryotic genes by change of positions of stop codons. We observed cases of addition of regions of 3'UTR to genes due to mutations at the existing stop codon, and cases of subtraction of C-terminal coding segments due to nonsense mutations upstream of the stop codon. Many of the observed stop codon shifts cannot be attributed to sequencing errors or rare deleterious variants segregating within bacterial populations. The additions of regions of 3'UTR tend to occur in those genes in which they are facilitated by nearby downstream in-frame triplets which may serve as new stop codons. Conversely, subtractions of coding sequence often give rise to in-frame stop codons located nearby. The amino acid composition of the added region is significantly biased, compared to the overall amino acid composition of the genes. Our results show that in prokaryotes, shift of stop codon is an underappreciated contributor to functional evolution of gene length.

Published
2010
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26. Comparative immunogenicity and structural analysis of epitopes of different bacterial L-asparaginases

Author

S. S. Aleksandrova, Marat D. Kazanov, Ilya N. Dyakov, Vadim S. Pokrovsky, and M. V. Pokrovskaya

Subjects
0301 basic medicine, Cancer Research, Yersinia, medicine.disease_cause, Rhodospirillum rubrum, Epitope, Microbiology, Drug Hypersensitivity, 03 medical and health sciences, Epitopes, Mice, 0302 clinical medicine, Immune system, Cell Line, Tumor, Genetics, medicine, Escherichia coli, Yersinia pseudotuberculosis, Animals, Asparaginase, Humans, Amino Acid Sequence, Peptide sequence, chemistry.chemical_classification, biology, Immunogenicity, Precursor Cell Lymphoblastic Leukemia-Lymphoma, biology.organism_classification, L-asparaginase, 030104 developmental biology, Enzyme, Pectobacterium carotovorum, Oncology, chemistry, 030220 oncology & carcinogenesis, Genetic Engineering, Research Article
Abstract

Background E.coli type II L-asparaginase is widely used for treatment of acute lymphoblastic leukemia. However, serious side effects such as allergic or hypersensitivity reactions are common for L-asparaginase treatment. Methods for minimizing immune response on L-asparaginase treatment in human include bioengeneering of less immunogenic version of the enzyme or utilizing the homologous enzymes of different origin. To rationalize these approaches we compared immunogenicity of L-asparaginases from five bacterial organisms and performed sequence-structure analysis of the presumable epitope regions. Methods IgG and IgM immune response in C57B16 mice after immunization with Wollinella succinogenes type II (WsA), Yersinia pseudotuberculosis type II (YpA), Erwinia carotovora type II (EwA), and Rhodospirillum rubrum type I (RrA) and Escherichia coli type II (EcA) L-asparaginases was evaluated using standard ELISA method. The comparative bioinformatics analysis of structure and sequence of the bacterial L-asparaginases presumable epitope regions was performed. Results We showed different immunogenic properties of five studied L-asparaginases and confirmed the possibility of replacement of EcA with L-asparaginase from different origin as a second-line treatment. Studied L-asparaginases might be placed in the following order based on the immunogenicity level: YpA > RrA, WsA ≥ EwA > EcA. Most significant cross-immunogenicity was shown between EcA and YpA. We propose that a long N-terminus of YpA enzyme enriched with charged aminoacids and tryptophan could be a reason of higher immunogenicity of YpA in comparison with other considered enzymes. Although the recognized structural and sequence differences in putative epitope regions among five considered L-asparaginases does not fully explain experimental observation of the immunogenicity of the enzymes, the performed analysis set the foundation for further research in this direction. Conclusions The performed studies showed different immunogenic properties of L-asparaginases and confirmed the possibility of replacement of EcA with L-asparaginase from different origin. The preferable enzymes for the second line treatment are WsA, RrA, or EwA. Electronic supplementary material The online version of this article (doi:10.1186/s12885-016-2125-4) contains supplementary material, which is available to authorized users.

Published
2016

27. Mycobacterial nicotinate mononucleotide adenylyltransferase: structure, mechanism, and implications for drug discovery

Author

Irina A, Rodionova, Harmon J, Zuccola, Leonardo, Sorci, Alexander E, Aleshin, Marat D, Kazanov, Chen-Ting, Ma, Eduard, Sergienko, Eric J, Rubin, Christopher P, Locher, and Andrei L, Osterman

Subjects
Models, Molecular, Sequence Homology, Amino Acid, Protein Conformation, Molecular Sequence Data, Antitubercular Agents, Mycobacterium tuberculosis, Crystallography, X-Ray, Kinetics, Structure-Activity Relationship, Drug Discovery, Protein Structure and Folding, Mutagenesis, Site-Directed, Amino Acid Sequence, Nicotinamide-Nucleotide Adenylyltransferase, Enzyme Inhibitors
Abstract

Nicotinate mononucleotide adenylyltransferase NadD is an essential enzyme in the biosynthesis of the NAD cofactor, which has been implicated as a target for developing new antimycobacterial therapies. Here we report the crystal structure of Mycobacterium tuberculosis NadD (MtNadD) at a resolution of 2.4 Å. A remarkable new feature of the MtNadD structure, compared with other members of this enzyme family, is a 310 helix that locks the active site in an over-closed conformation. As a result, MtNadD is rendered inactive as it is topologically incompatible with substrate binding and catalysis. Directed mutagenesis was also used to further dissect the structural elements that contribute to the interactions of the two MtNadD substrates, i.e. ATP and nicotinic acid mononucleotide (NaMN). For inhibitory profiling of partially active mutants and wild type MtNadD, we used a small molecule inhibitor of MtNadD with moderate affinity (Ki ∼ 25 μM) and antimycobacterial activity (MIC80) ∼ 40-80 μM). This analysis revealed interferences with some of the residues in the NaMN binding subsite consistent with the competitive inhibition observed for the NaMN substrate (but not ATP). A detailed steady-state kinetic analysis of MtNadD suggests that ATP must first bind to allow efficient NaMN binding and catalysis. This sequential mechanism is consistent with the requirement of transition to catalytically competent (open) conformation hypothesized from structural modeling. A possible physiological significance of this mechanism is to enable the down-regulation of NAD synthesis under ATP-limiting dormancy conditions. These findings point to a possible new strategy for designing inhibitors that lock the enzyme in the inactive over-closed conformation.

Published
2015

28. CleavPredict: A Platform for Reasoning about Matrix Metalloproteinases Proteolytic Events

Author

Boris I. Ratnikov, Jeffrey W. Smith, Sonu Kumar, Piotr Cieplak, and Marat D. Kazanov

Subjects
Multidisciplinary, business.industry, Statement (logic), lcsh:R, Library science, Correction, Reproducibility of Results, lcsh:Medicine, Web Browser, Bioinformatics, Matrix Metalloproteinases, ROC Curve, Basic research, Databases, Genetic, Proteolysis, Medicine, lcsh:Q, business, lcsh:Science, Algorithms, Software
Abstract

CleavPredict (http://cleavpredict.sanfordburnham.org) is a Web server for substrate cleavage prediction for matrix metalloproteinases (MMPs). It is intended as a computational platform aiding the scientific community in reasoning about proteolytic events. CleavPredict offers in silico prediction of cleavage sites specific for 11 human MMPs. The prediction method employs the MMP specific position weight matrices (PWMs) derived from statistical analysis of high-throughput phage display experimental results. To augment the substrate cleavage prediction process, CleavPredict provides information about the structural features of potential cleavage sites that influence proteolysis. These include: secondary structure, disordered regions, transmembrane domains, and solvent accessibility. The server also provides information about subcellular location, co-localization, and co-expression of proteinase and potential substrates, along with experimentally determined positions of single nucleotide polymorphism (SNP), and posttranslational modification (PTM) sites in substrates. All this information will provide the user with perspectives in reasoning about proteolytic events. CleavPredict is freely accessible, and there is no login required.

Published
2015

29. Basis for substrate recognition and distinction by matrix metalloproteinases

Author

Marat D. Kazanov, Boris I. Ratnikov, Alex Y. Strongin, Kosi Gramatikoff, Alexey Eroshkin, Yoshinobu Igarashi, Qing Sun, Andrei L. Osterman, Boguslaw Stec, Jeffrey W. Smith, Piotr Cieplak, James C. Pierce, and Adam Godzik

Subjects
Models, Molecular, medicine.medical_treatment, In silico, Molecular Sequence Data, Sequence Homology, Peptide, Computational biology, Biology, Matrix metalloproteinase, Cleavage (embryo), Substrate Specificity, Structural genomics, Models, Catalytic Domain, medicine, Humans, Amino Acid Sequence, Phylogeny, chemistry.chemical_classification, Binding Sites, Multidisciplinary, Protease, Sequence Homology, Amino Acid, Phylogenetic tree, Molecular, protease, specificity-determining positions, Matrix Metalloproteinases, Amino Acid, Kinetics, PNAS Plus, Biochemistry, chemistry, Nat, Mutation, Proteolysis, Biocatalysis, MMPs, Generic health relevance, Peptides, Algorithms
Abstract

Genomic sequencing and structural genomics produced a vast amount of sequence and structural data, creating an opportunity for structure–function analysis in silico [Radivojac P, et al. (2013) Nat Methods 10(3):221–227]. Unfortunately, only a few large experimental datasets exist to serve as benchmarks for function-related predictions. Furthermore, currently there are no reliable means to predict the extent of functional similarity among proteins. Here, we quantify structure–function relationships among three phylogenetic branches of the matrix metalloproteinase (MMP) family by comparing their cleavage efficiencies toward an extended set of phage peptide substrates that were selected from ∼64 million peptide sequences (i.e., a large unbiased representation of substrate space). The observed second-order rate constants [ k (obs) ] across the substrate space provide a distance measure of functional similarity among the MMPs. These functional distances directly correlate with MMP phylogenetic distance. There is also a remarkable and near-perfect correlation between the MMP substrate preference and sequence identity of 50–57 discontinuous residues surrounding the catalytic groove. We conclude that these residues represent the specificity-determining positions (SDPs) that allowed for the expansion of MMP proteolytic function during evolution. A transmutation of only a few selected SDPs proximal to the bound substrate peptide, and contributing the most to selectivity among the MMPs, is sufficient to enact a global change in the substrate preference of one MMP to that of another, indicating the potential for the rational and focused redesign of cleavage specificity in MMPs.

Published
2014
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30. Horizontal gene transfer and genome evolution in Methanosarcina

Author

Sofya K. Garushyants, Marat D. Kazanov, and Mikhail S. Gelfand

Subjects
Genetics, Genome evolution, biology, Bacteria, Gene Transfer, Horizontal, Operon, Methanosarcina, Horizontal gene transfer, biology.organism_classification, Genome, Archaea, Evolution, Molecular, Evolutionary biology, Phylogenetics, Genome, Archaeal, Databases, Genetic, Gene, Ecology, Evolution, Behavior and Systematics, Phylogeny, Methanosarcinaceae, Research Article
Abstract

Background Genomes of Methanosarcina spp. are among the largest archaeal genomes. One suggested reason for that is massive horizontal gene transfer (HGT) from bacteria. Genes of bacterial origin may be involved in the central metabolism and solute transport, in particular sugar synthesis, sulfur metabolism, phosphate metabolism, DNA repair, transport of small molecules etc. Horizontally transferred (HT) genes are considered to play the key role in the ability of Methanosarcina spp. to inhabit diverse environments. At the moment, genomes of three Methanosarcina spp. have been sequenced, and while these genomes vary in length and number of protein-coding genes, they all have been shown to accumulate HT genes. However, previous estimates had been made when fewer archaeal genomes were known. Moreover, several Methanosarcinaceae genomes from other genera have been sequenced recently. Here, we revise the census of genes of bacterial origin in Methanosarcinaceae. Results About 5 % of Methanosarcina genes have been shown to be horizontally transferred from various bacterial groups to the last common ancestor either of Methanosarcinaceae, or Methanosarcina, or later in the evolution. Simulation of the composition of the NCBI protein non-redundant database for different years demonstrates that the estimates of the HGT rate have decreased drastically since 2002, the year of publication of the first Methanosarcina genome. The phylogenetic distribution of HT gene donors is non-uniform. Most HT genes were transferred from Firmicutes and Proteobacteria, while no HGT events from Actinobacteria to the common ancestor of Methanosarcinaceae were found. About 50 % of HT genes are involved in metabolism. Horizontal transfer of transcription factors is not common, while 46 % of horizontally transferred genes have demonstrated differential expression in a variety of conditions. HGT of complete operons is relatively infrequent and half of HT genes do not belong to operons. Conclusions While genes of bacterial origin are still more frequent in Methanosarcinaceae than in other Archaea, most HGT events described earlier as Methanosarcina-specific seem to have occurred before the divergence of Methanosarcinaceae. Genes horizontally transferred from bacteria to archaea neither tend to be transferred with their regulators, nor in long operons. Electronic supplementary material The online version of this article (doi:10.1186/s12862-015-0393-2) contains supplementary material, which is available to authorized users.

Published
2014

31. Characterization of bacterial NMN deamidase as a Ser/Lys hydrolase expands diversity of serine amidohydrolases

Author

Lucia Brunetti, Leonardo Sorci, Lucia Cialabrini, Silverio Ruggieri, Marat D. Kazanov, Francesca Mazzola, Nadia Raffaelli, and Sabato D'Auria

Subjects
Models, Molecular, Pyridine nucleotide, Stereochemistry, Molecular Sequence Data, Biophysics, Biochemistry, Protein Structure, Secondary, Amidohydrolases, Serine, chemistry.chemical_compound, Apoenzymes, Structural Biology, Catalytic Domain, Hydrolase, Enzyme Stability, Genetics, Amino Acid Sequence, Site-directed mutagenesis, Molecular Biology, Catalytic dyad, Conserved Sequence, Nicotinamide Mononucleotide, Nicotinamide mononucleotide, chemistry.chemical_classification, Amidohydrolase, biology, Sequence Homology, Amino Acid, Escherichia coli Proteins, Active site, Cell Biology, NMN deamidase, Kinetics, Enzyme, chemistry, Amino Acid Substitution, biology.protein, Mutagenesis, Site-Directed, PMSF
Abstract

NMN deamidase (PncC) is a bacterial enzyme involved in NAD biosynthesis. We have previously demonstrated that PncC is structurally distinct from other known amidohydrolases. Here, we extended PncC characterization by mutating all potential catalytic residues and assessing their individual roles in catalysis through kinetic analyses. Inspection of these residues’ spatial arrangement in the active site, allowed us to conclude that PncC is a serine-amidohydrolase, employing a Ser/Lys dyad for catalysis. Analysis of the PncC structure in complex with a modeled NMN substrate supported our conclusion, and enabled us to propose the catalytic mechanism.

Published
2013

32. Comparative genomics of metabolic capacities of regulons controlled by cis-regulatory RNA motifs in bacteria

Author

Semen A. Leyn, Dmitry A. Rodionov, Eric I. Sun, Milton H. Saier, Pavel S. Novichkov, and Marat D. Kazanov

Subjects
Riboswitch, In silico, Rfam, Biology, Regulon, 03 medical and health sciences, RNA regulatory motif, Gene expression, Genetics, Nucleotide Motifs, Gene, 030304 developmental biology, 2. Zero hunger, Comparative genomics, 0303 health sciences, Comparative Genomic Hybridization, Bacteria, 030302 biochemistry & molecular biology, RNA, Gene Expression Regulation, Bacterial, RNA, Bacterial, DNA microarray, Gene function, Genome, Bacterial, Biotechnology, Research Article
Abstract

Background In silico comparative genomics approaches have been efficiently used for functional prediction and reconstruction of metabolic and regulatory networks. Riboswitches are metabolite-sensing structures often found in bacterial mRNA leaders controlling gene expression on transcriptional or translational levels. An increasing number of riboswitches and other cis-regulatory RNAs have been recently classified into numerous RNA families in the Rfam database. High conservation of these RNA motifs provides a unique advantage for their genomic identification and comparative analysis. Results A comparative genomics approach implemented in the RegPredict tool was used for reconstruction and functional annotation of regulons controlled by RNAs from 43 Rfam families in diverse taxonomic groups of Bacteria. The inferred regulons include ~5200 cis-regulatory RNAs and more than 12000 target genes in 255 microbial genomes. All predicted RNA-regulated genes were classified into specific and overall functional categories. Analysis of taxonomic distribution of these categories allowed us to establish major functional preferences for each analyzed cis-regulatory RNA motif family. Overall, most RNA motif regulons showed predictable functional content in accordance with their experimentally established effector ligands. Our results suggest that some RNA motifs (including thiamin pyrophosphate and cobalamin riboswitches that control the cofactor metabolism) are widespread and likely originated from the last common ancestor of all bacteria. However, many more analyzed RNA motifs are restricted to a narrow taxonomic group of bacteria and likely represent more recent evolutionary innovations. Conclusions The reconstructed regulatory networks for major known RNA motifs substantially expand the existing knowledge of transcriptional regulation in bacteria. The inferred regulons can be used for genetic experiments, functional annotations of genes, metabolic reconstruction and evolutionary analysis. The obtained genome-wide collection of reference RNA motif regulons is available in the RegPrecise database (http://regprecise.lbl.gov/).

Published
2013
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33. Genomic reconstruction of the transcriptional regulatory network in Bacillus subtilis

Author

Ekaterina O. Ermakova, Semen A. Leyn, Pavel S. Novichkov, Dmitry A. Rodionov, Natalia V. Sernova, and Marat D. Kazanov

Subjects
Carbohydrates, Context (language use), Rfam, Bacillus subtilis, Microbiology, Genome, Regulon, Bacterial Proteins, Stress, Physiological, Gene Regulatory Networks, Amino Acids, Nucleotide Motifs, Molecular Biology, Gene, Transcription factor, Phylogeny, Genetics, Bacillales, biology, RNA, Gene Expression Regulation, Bacterial, Articles, biology.organism_classification, RNA, Bacterial, Carbohydrate Metabolism, Genome, Bacterial, Protein Binding, Transcription Factors
Abstract

The adaptation of microorganisms to their environment is controlled by complex transcriptional regulatory networks (TRNs), which are still only partially understood even for model species. Genome scale annotation of regulatory features of genes and TRN reconstruction are challenging tasks of microbial genomics. We used the knowledge-driven comparative-genomics approach implemented in the RegPredict Web server to infer TRN in the model Gram-positive bacterium Bacillus subtilis and 10 related Bacillales species. For transcription factor (TF) regulons, we combined the available information from the DBTBS database and the literature with bioinformatics tools, allowing inference of TF binding sites (TFBSs), comparative analysis of the genomic context of predicted TFBSs, functional assignment of target genes, and effector prediction. For RNA regulons, we used known RNA regulatory motifs collected in the Rfam database to scan genomes and analyze the genomic context of new RNA sites. The inferred TRN in B. subtilis comprises regulons for 129 TFs and 24 regulatory RNA families. First, we analyzed 66 TF regulons with previously known TFBSs in B. subtilis and projected them to other Bacillales genomes, resulting in refinement of TFBS motifs and identification of novel regulon members. Second, we inferred motifs and described regulons for 28 experimentally studied TFs with previously unknown TFBSs. Third, we discovered novel motifs and reconstructed regulons for 36 previously uncharacterized TFs. The inferred collection of regulons is available in the RegPrecise database ( http://regprecise.lbl.gov/ ) and can be used in genetic experiments, metabolic modeling, and evolutionary analysis.

Published
2013

34. Genomic reconstruction of transcriptional regulatory networks in lactic acid bacteria

Author

Pavel S. Novichkov, Dmitry A. Rodionov, Dmitry A. Ravcheev, Natalia V. Sernova, Marat D. Kazanov, and Aaron A. Best

Subjects
Streptococcaceae, Gene regulatory network, Carbohydrate metabolism, Multidisciplinary, general & others [F99] [Life sciences], Biology, Proteomics, Genome, Regulon, 03 medical and health sciences, Multidisciplinaire, généralités & autres [F99] [Sciences du vivant], Bacterial Proteins, Lactobacillales, Stress, Physiological, Genetics, Lactic acid bacteria, Gene Regulatory Networks, Amino Acids, Gene, 030304 developmental biology, 2. Zero hunger, Comparative genomics, Comparative Genomic Hybridization, 0303 health sciences, 030306 microbiology, biology.organism_classification, Metals, Lactobacillaceae, DNA microarray, Transcription factor, Genome, Bacterial, Transcriptional regulatory network, Transcription Factors, Research Article, Biotechnology
Abstract

Background Genome scale annotation of regulatory interactions and reconstruction of regulatory networks are the crucial problems in bacterial genomics. The Lactobacillales order of bacteria collates various microorganisms having a large economic impact, including both human and animal pathogens and strains used in the food industry. Nonetheless, no systematic genome-wide analysis of transcriptional regulation has been previously made for this taxonomic group. Results A comparative genomics approach was used for reconstruction of transcriptional regulatory networks in 30 selected genomes of lactic acid bacteria. The inferred networks comprise regulons for 102 orthologous transcription factors (TFs), including 47 novel regulons for previously uncharacterized TFs. Numerous differences between regulatory networks of the Streptococcaceae and Lactobacillaceae groups were described on several levels. The two groups are characterized by substantially different sets of TFs encoded in their genomes. Content of the inferred regulons and structure of their cognate TF binding motifs differ for many orthologous TFs between the two groups. Multiple cases of non-orthologous displacements of TFs that control specific metabolic pathways were reported. Conclusions The reconstructed regulatory networks substantially expand the existing knowledge of transcriptional regulation in lactic acid bacteria. In each of 30 studied genomes the obtained regulatory network contains on average 36 TFs and 250 target genes that are mostly involved in carbohydrate metabolism, stress response, metal homeostasis and amino acids biosynthesis. The inferred networks can be used for genetic experiments, functional annotations of genes, metabolic reconstruction and evolutionary analysis. All reconstructed regulons are captured within the Streptococcaceae and Lactobacillaceae collections in the RegPrecise database (http://regprecise.lbl.gov).

Published
2013

35. Functional diversification of ROK-family transcriptional regulators of sugar catabolism in the Thermotogae phylum

Author

Andrei L. Osterman, Mikhail S. Gelfand, Xiaoqing Li, Dmitry A. Rodionov, and Marat D. Kazanov

Subjects
Genetics, DNA, Bacterial, Binding Sites, biology, Bacteria, Repressor, Gene Regulation, Chromatin and Epigenetics, biology.organism_classification, Regulon, Open reading frame, Bacterial Proteins, Phylogenetics, Thermotoga maritima, Transcriptional regulation, Carbohydrate Metabolism, Nucleotide Motifs, Gene, Transcription factor, Genome, Bacterial, Phylogeny, Transcription Factors
Abstract

Large and functionally heterogeneous families of transcription factors have complex evolutionary histories. What shapes specificities toward effectors and DNA sites in paralogous regulators is a fundamental question in biology. Bacteria from the deep-branching lineage Thermotogae possess multiple paralogs of the repressor, open reading frame, kinase (ROK) family regulators that are characterized by carbohydrate-sensing domains shared with sugar kinases. We applied an integrated genomic approach to study functions and specificities of regulators from this family. A comparative analysis of 11 Thermotogae genomes revealed novel mechanisms of transcriptional regulation of the sugar utilization networks, DNA-binding motifs and specific functions. Reconstructed regulons for seven groups of ROK regulators were validated by DNA-binding assays using purified recombinant proteins from the model bacterium Thermotoga maritima. All tested regulators demonstrated specific binding to their predicted cognate DNA sites, and this binding was inhibited by specific effectors, mono- or disaccharides from their respective sugar catabolic pathways. By comparing ligand-binding domains of regulators with structurally characterized kinases from the ROK family, we elucidated signature amino acid residues determining sugar-ligand regulator specificity. Observed correlations between signature residues and the sugar-ligand specificities provide the framework for structure functional classification of the entire ROK family.

Published
2012

36. Glutamine versus ammonia utilization in the NAD synthetase family

Author

Jessica De Ingeniis, Konstantin Shatalin, Leonardo Sorci, Andrei L. Osterman, Mikhail S. Gelfand, and Marat D. Kazanov

Subjects
Methanocaldococcus, Evolutionary Processes, Glutamine, lcsh:Medicine, Biochemistry, Cofactor, 03 medical and health sciences, Amide Synthases, Ammonia, Genome Analysis Tools, Glutamine synthetase, Macromolecular Structure Analysis, lcsh:Science, Biology, Phylogeny, 030304 developmental biology, Enzyme Kinetics, 0303 health sciences, Evolutionary Biology, Multidisciplinary, biology, Glutaminase, Reverse Transcriptase Polymerase Chain Reaction, Enzyme Classes, Thermus thermophilus, 030302 biochemistry & molecular biology, lcsh:R, Computational Biology, Genomics, Comparative Genomics, biology.organism_classification, Enzymes, biology.protein, lcsh:Q, NAD+ kinase, Sequence Analysis, Archaea, Research Article
Abstract

NAD is a ubiquitous and essential metabolic redox cofactor which also functions as a substrate in certain regulatory pathways. The last step of NAD synthesis is the ATP-dependent amidation of deamido-NAD by NAD synthetase (NADS). Members of the NADS family are present in nearly all species across the three kingdoms of Life. In eukaryotic NADS, the core synthetase domain is fused with a nitrilase-like glutaminase domain supplying ammonia for the reaction. This two-domain NADS arrangement enabling the utilization of glutamine as nitrogen donor is also present in various bacterial lineages. However, many other bacterial members of NADS family do not contain a glutaminase domain, and they can utilize only ammonia (but not glutamine) in vitro. A single-domain NADS is also characteristic for nearly all Archaea, and its dependence on ammonia was demonstrated here for the representative enzyme from Methanocaldococcus jannaschi. However, a question about the actual in vivo nitrogen donor for single-domain members of the NADS family remained open: Is it glutamine hydrolyzed by a committed (but yet unknown) glutaminase subunit, as in most ATP-dependent amidotransferases, or free ammonia as in glutamine synthetase? Here we addressed this dilemma by combining evolutionary analysis of the NADS family with experimental characterization of two representative bacterial systems: a two-subunit NADS from Thermus thermophilus and a single-domain NADS from Salmonella typhimurium providing evidence that ammonia (and not glutamine) is the physiological substrate of a typical single-domain NADS. The latter represents the most likely ancestral form of NADS. The ability to utilize glutamine appears to have evolved via recruitment of a glutaminase subunit followed by domain fusion in an early branch of Bacteria. Further evolution of the NADS family included lineage-specific loss of one of the two alternative forms and horizontal gene transfer events. Lastly, we identified NADS structural elements associated with glutamine-utilizing capabilities.

Published
2012

37. Comparative genomic reconstruction of transcriptional networks controlling central metabolism in the Shewanella genus

Author

James K. Fredrickson, Andrei L. Osterman, Elena D. Stavrovskaya, Dmitry A. Rodionov, Galina Yu Kovaleva, Pavel S. Novichkov, Adam P. Arkin, Dmitry A. Ravcheev, Xiaoqing Li, Alexey E. Kazakov, Mikhail S. Gelfand, Marat D. Kazanov, Elizabeth A. Permina, Irina A. Rodionova, Ross Overbeek, Anna Gerasimova, Margaret F. Romine, Inna Dubchak, and Olga N. Laikova

Subjects
Shewanella, Gene regulatory network, Genome, Medical and Health Sciences, Multidisciplinaire, généralités & autres [F99] [Sciences du vivant], 2.2 Factors relating to the physical environment, Gene Regulatory Networks, Amino Acids, Aetiology, Genetics, Regulation of gene expression, 0303 health sciences, biology, Escherichia coli Proteins, Fatty Acids, Bacterial, Genomics, Biological Sciences, DNA-Binding Proteins, Infectious Diseases, Multigene Family, Carbohydrate Metabolism, Infection, Biotechnology, lcsh:QH426-470, Bioinformatics, lcsh:Biotechnology, Multidisciplinary, general & others [F99] [Life sciences], Regulon, Acetylglucosamine, 03 medical and health sciences, Bacterial Proteins, Information and Computing Sciences, lcsh:TP248.13-248.65, Escherichia coli, 030304 developmental biology, Comparative genomics, Binding Sites, 030306 microbiology, Research, Human Genome, Gene Expression Regulation, Bacterial, biology.organism_classification, DNA binding site, Repressor Proteins, lcsh:Genetics, Emerging Infectious Diseases, Gene Expression Regulation, Riboswitch, bacteria, Genome, Bacterial, Transcription Factors
Abstract

Background Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in bacteria is one of the critical tasks of modern genomics. The Shewanella genus is comprised of metabolically versatile gamma-proteobacteria, whose lifestyles and natural environments are substantially different from Escherichia coli and other model bacterial species. The comparative genomics approaches and computational identification of regulatory sites are useful for the in silico reconstruction of transcriptional regulatory networks in bacteria. Results To explore conservation and variations in the Shewanella transcriptional networks we analyzed the repertoire of transcription factors and performed genomics-based reconstruction and comparative analysis of regulons in 16 Shewanella genomes. The inferred regulatory network includes 82 transcription factors and their DNA binding sites, 8 riboswitches and 6 translational attenuators. Forty five regulons were newly inferred from the genome context analysis, whereas others were propagated from previously characterized regulons in the Enterobacteria and Pseudomonas spp.. Multiple variations in regulatory strategies between the Shewanella spp. and E. coli include regulon contraction and expansion (as in the case of PdhR, HexR, FadR), numerous cases of recruiting non-orthologous regulators to control equivalent pathways (e.g. PsrA for fatty acid degradation) and, conversely, orthologous regulators to control distinct pathways (e.g. TyrR, ArgR, Crp). Conclusions We tentatively defined the first reference collection of ~100 transcriptional regulons in 16 Shewanella genomes. The resulting regulatory network contains ~600 regulated genes per genome that are mostly involved in metabolism of carbohydrates, amino acids, fatty acids, vitamins, metals, and stress responses. Several reconstructed regulons including NagR for N-acetylglucosamine catabolism were experimentally validated in S. oneidensis MR-1. Analysis of correlations in gene expression patterns helps to interpret the reconstructed regulatory network. The inferred regulatory interactions will provide an additional regulatory constrains for an integrated model of metabolism and regulation in S. oneidensis MR-1.

Published
2011

38. CleavPredict: A Platform for Reasoning about Matrix Metalloproteinases Proteolytic Events

Author

Piotr Cieplak, Boris I. Ratnikov, Sonu Kumar, Marat D. Kazanov, and Jeffrey W. Smith

Subjects
0303 health sciences, Multidisciplinary, Phage display, medicine.diagnostic_test, Proteolysis, In silico, lcsh:R, 030302 biochemistry & molecular biology, lcsh:Medicine, Computational biology, Protein structure prediction, Biology, Cleavage (embryo), Molecular biology, 03 medical and health sciences, Transmembrane domain, Protein sequencing, medicine, lcsh:Q, lcsh:Science, Protein secondary structure, Research Article, 030304 developmental biology
Abstract

CleavPredict (http://cleavpredict.sanfordburnham.org) is a Web server for substrate cleavage prediction for matrix metalloproteinases (MMPs). It is intended as a computational platform aiding the scientific community in reasoning about proteolytic events. CleavPredict offers in silico prediction of cleavage sites specific for 11 human MMPs. The prediction method employs the MMP specific position weight matrices (PWMs) derived from statistical analysis of high-throughput phage display experimental results. To augment the substrate cleavage prediction process, CleavPredict provides information about the structural features of potential cleavage sites that influence proteolysis. These include: secondary structure, disordered regions, transmembrane domains, and solvent accessibility. The server also provides information about subcellular location, co-localization, and co-expression of proteinase and potential substrates, along with experimentally determined positions of single nucleotide polymorphism (SNP), and posttranslational modification (PTM) sites in substrates. All this information will provide the user with perspectives in reasoning about proteolytic events. CleavPredict is freely accessible, and there is no login required.

Published
2015
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39. Evolution of transcriptional regulation in closely related bacteria

Author

Marat D. Kazanov, Mikhail S. Gelfand, Olga Tsoy, and Mikhail A. Pyatnitskiy

Subjects
Enterobacteriales, Evolution, Gene regulatory network, Bacterial genome size, Genome, Conserved sequence, Evolution, Molecular, Transcriptional regulation, QH359-425, Escherichia coli, Gene Regulatory Networks, Selection, Genetic, Gene, Ecology, Evolution, Behavior and Systematics, Conserved Sequence, Genetics, Binding Sites, biology, Salmonella enterica, Promoter, Gene Expression Regulation, Bacterial, biology.organism_classification, DNA, Intergenic, Genome, Bacterial, Research Article, Transcription Factors
Abstract

Background The exponential growth of the number of fully sequenced genomes at varying taxonomic closeness allows one to characterize transcriptional regulation using comparative-genomics analysis instead of time-consuming experimental methods. A transcriptional regulatory unit consists of a transcription factor, its binding site and a regulated gene. These units constitute a graph which contains so-called “network motifs”, subgraphs of a given structure. Here we consider genomes of closely related Enterobacteriales and estimate the fraction of conserved network motifs and sites as well as positions under selection in various types of non-coding regions. Results Using a newly developed technique, we found that the highest fraction of positions under selection, approximately 50%, was observed in synvergon spacers (between consecutive genes from the same strand), followed by ~45% in divergon spacers (common 5’-regions), and ~10% in convergon spacers (common 3’-regions). The fraction of selected positions in functional regions was higher, 60% in transcription factor-binding sites and ~45% in terminators and promoters. Small, but significant differences were observed between Escherichia coli and Salmonella enterica. This fraction is similar to the one observed in eukaryotes. The conservation of binding sites demonstrated some differences between types of regulatory units. In E. coli, strains the interactions of the type “local transcriptional factor ➝ gene” turned out to be more conserved in feed-forward loops (FFLs) compared to non-motif interactions. The coherent FFLs tend to be less conserved than the incoherent FFLs. A natural explanation is that the former imply functional redundancy. Conclusions A naïve hypothesis that FFL would be highly conserved turned out to be not entirely true: its conservation depends on its status in the transcriptional network and also from its usage. The fraction of positions under selection in intergenic regions of bacterial genomes is roughly similar to that of eukaryotes. Known regulatory sites explain 20±5% of selected positions.

Published
2012

41. RegPrecise 3.0 – A resource for genome-scale exploration of transcriptional regulation in bacteria

Author

Dmitry A. Ravcheev, Semen A. Leyn, Alexey E. Kazakov, Galina Yu Kovaleva, Pavel S. Novichkov, Adam P. Arkin, Inna Dubchak, William J. Riehl, Dmitry A. Rodionov, Marat D. Kazanov, and Roman A. Sutormin

Subjects
Riboswitch, Systems biology, Genomics, Bacterial genome size, Computational biology, Multidisciplinary, general & others [F99] [Life sciences], Biology, Proteomics, Genome, Regulon, Database, Multidisciplinaire, généralités & autres [F99] [Sciences du vivant], User-Computer Interface, Databases, Genetic, Genetics, Gene Regulatory Networks, Comparative genomics, Internet, Bacteria, Regulatory network, Transcription factor, Genome, Bacterial, Metabolic Networks and Pathways, Transcription Factors, Biotechnology
Abstract

Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in prokaryotes is one of the critical tasks of modern genomics. Bacteria from different taxonomic groups, whose lifestyles and natural environments are substantially different, possess highly diverged transcriptional regulatory networks. The comparative genomics approaches are useful for in silico reconstruction of bacterial regulons and networks operated by both transcription factors (TFs) and RNA regulatory elements (riboswitches). RegPrecise ( http://regprecise.lbl.gov ) is a web resource for collection, visualization and analysis of transcriptional regulons reconstructed by comparative genomics. We significantly expanded a reference collection of manually curated regulons we introduced earlier. RegPrecise 3.0 provides access to inferred regulatory interactions organized by phylogenetic, structural and functional properties. Taxonomy-specific collections include 781 TF regulogs inferred in more than 160 genomes representing 14 taxonomic groups of Bacteria. TF-specific collections include regulogs for a selected subset of 40 TFs reconstructed across more than 30 taxonomic lineages. Novel collections of regulons operated by RNA regulatory elements (riboswitches) include near 400 regulogs inferred in 24 bacterial lineages. RegPrecise 3.0 provides four classifications of the reference regulons implemented as controlled vocabularies: 55 TF protein families; 43 RNA motif families; ~150 biological processes or metabolic pathways; and ~200 effectors or environmental signals. Genome-wide visualization of regulatory networks and metabolic pathways covered by the reference regulons are available for all studied genomes. A separate section of RegPrecise 3.0 contains draft regulatory networks in 640 genomes obtained by an conservative propagation of the reference regulons to closely related genomes. RegPrecise 3.0 gives access to the transcriptional regulons reconstructed in bacterial genomes. Analytical capabilities include exploration of: regulon content, structure and function; TF binding site motifs; conservation and variations in genome-wide regulatory networks across all taxonomic groups of Bacteria. RegPrecise 3.0 was selected as a core resource on transcriptional regulation of the Department of Energy Systems Biology Knowledgebase, an emerging software and data environment designed to enable researchers to collaboratively generate, test and share new hypotheses about gene and protein functions, perform large-scale analyses, and model interactions in microbes, plants, and their communities.

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42. Genomics-guided analysis of NAD recycling yields functional elucidation of COG1058 as a new family of pyrophosphatases.

Author

Lucia Cialabrini, Silverio Ruggieri, Marat D Kazanov, Leonardo Sorci, Francesca Mazzola, Giuseppe Orsomando, Andrei L Osterman, and Nadia Raffaelli

Subjects
Medicine, Science
Abstract

We have recently identified the enzyme NMN deamidase (PncC), which plays a key role in the regeneration of NAD in bacteria by recycling back to the coenzyme the pyridine by-products of its non redox consumption. In several bacterial species, PncC is fused to a COG1058 domain of unknown function, highly conserved and widely distributed in all living organisms. Here, we demonstrate that the PncC-fused domain is endowed with a novel Co(+2)- and K(+)-dependent ADP-ribose pyrophosphatase activity, and discuss the functional connection of such an activity with NAD recycling. An in-depth phylogenetic analysis of the COG1058 domain evidenced that in most bacterial species it is fused to PncC, while in α- and some δ-proteobacteria, as well as in archaea and fungi, it occurs as a stand-alone protein. Notably, in mammals and plants it is fused to FAD synthase. We extended the enzymatic characterization to a representative bacterial single-domain protein, which resulted to be a more versatile ADP-ribose pyrophosphatase, active also towards diadenosine 5'-diphosphate and FAD. Multiple sequence alignment analysis, and superposition of the available three-dimensional structure of an archaeal COG1058 member with the structure of the enzyme MoeA of the molybdenum cofactor biosynthesis, allowed identification of residues likely involved in catalysis. Their role has been confirmed by site-directed mutagenesis.

Published
2013
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