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Matveev EV, Ponomarev GV, Kazanov MD. Genome-wide bioinformatics analysis of human protease capacity for proteolytic cleavage of the SARS-CoV-2 spike glycoprotein. Microbiol Spectr 2024; 12:e0353023. [PMID: 38189333 PMCID: PMC10846095 DOI: 10.1128/spectrum.03530-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 12/07/2023] [Indexed: 01/09/2024] Open
Abstract
Severe 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.
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Affiliation(s)
- Evgenii V. Matveev
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Moscow, Russia
- Research and Training Center on Bioinformatics, A.A.Kharkevich Institute for Information Transmission Problems, Moscow, Russia
- Laboratory of Cytogenetics and Molecular Genetics, Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
| | - Gennady V. Ponomarev
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Moscow, Russia
- Research and Training Center on Bioinformatics, A.A.Kharkevich Institute for Information Transmission Problems, Moscow, Russia
| | - Marat D. Kazanov
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Moscow, Russia
- Research and Training Center on Bioinformatics, A.A.Kharkevich Institute for Information Transmission Problems, Moscow, Russia
- Laboratory of Cytogenetics and Molecular Genetics, Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
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2
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Hibberd MC, Webber DM, Rodionov DA, Henrissat S, Chen RY, Zhou C, Lynn HM, Wang Y, Chang HW, Lee EM, Lelwala-Guruge J, Kazanov MD, Arzamasov AA, Leyn SA, Lombard V, Terrapon N, Henrissat B, Castillo JJ, Couture G, Bacalzo NP, Chen Y, Lebrilla CB, Mostafa I, Das S, Mahfuz M, Barratt MJ, Osterman AL, Ahmed T, Gordon JI. Bioactive glycans in a microbiome-directed food for children with malnutrition. Nature 2024; 625:157-165. [PMID: 38093016 PMCID: PMC10764277 DOI: 10.1038/s41586-023-06838-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 11/06/2023] [Indexed: 12/26/2023]
Abstract
Evidence is accumulating that perturbed postnatal development of the gut microbiome contributes to childhood malnutrition1-4. Here we analyse biospecimens from a randomized, controlled trial of a microbiome-directed complementary food (MDCF-2) that produced superior rates of weight gain compared with a calorically more dense conventional ready-to-use supplementary food in 12-18-month-old Bangladeshi children with moderate acute malnutrition4. We reconstructed 1,000 bacterial genomes (metagenome-assembled genomes (MAGs)) from the faecal microbiomes of trial participants, identified 75 MAGs of which the abundances were positively associated with ponderal growth (change in weight-for-length Z score (WLZ)), characterized changes in MAG gene expression as a function of treatment type and WLZ response, and quantified carbohydrate structures in MDCF-2 and faeces. The results reveal that two Prevotella copri MAGs that are positively associated with WLZ are the principal contributors to MDCF-2-induced expression of metabolic pathways involved in utilizing the component glycans of MDCF-2. The predicted specificities of carbohydrate-active enzymes expressed by their polysaccharide-utilization loci are correlated with (1) the in vitro growth of Bangladeshi P. copri strains, possessing varying degrees of polysaccharide-utilization loci and genomic conservation with these MAGs, in defined medium containing different purified glycans representative of those in MDCF-2, and (2) the levels of faecal carbohydrate structures in the trial participants. These associations suggest that identifying bioactive glycan structures in MDCFs metabolized by growth-associated bacterial taxa will help to guide recommendations about their use in children with acute malnutrition and enable the development of additional formulations.
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Affiliation(s)
- Matthew C Hibberd
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St Louis, MO, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA
| | - Daniel M Webber
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St Louis, MO, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA
| | - Dmitry A Rodionov
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Suzanne Henrissat
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St Louis, MO, USA
- Architecture et Fonction des Macromolécules Biologiques, CNRS, Aix-Marseille University, Marseille, France
| | - Robert Y Chen
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St Louis, MO, USA
| | - Cyrus Zhou
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St Louis, MO, USA
| | - Hannah M Lynn
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St Louis, MO, USA
| | - Yi Wang
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St Louis, MO, USA
| | - Hao-Wei Chang
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St Louis, MO, USA
| | - Evan M Lee
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St Louis, MO, USA
| | - Janaki Lelwala-Guruge
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St Louis, MO, USA
| | - Marat D Kazanov
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Aleksandr A Arzamasov
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Semen A Leyn
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Vincent Lombard
- Architecture et Fonction des Macromolécules Biologiques, CNRS, Aix-Marseille University, Marseille, France
| | - Nicolas Terrapon
- Architecture et Fonction des Macromolécules Biologiques, CNRS, Aix-Marseille University, Marseille, France
| | - Bernard Henrissat
- Department of Biotechnology and Biomedicine (DTU Bioengineering), Technical University of Denmark, Lyngby, Denmark
- Department of Biological Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Juan J Castillo
- Department of Chemistry, University of California, Davis, Davis, CA, USA
| | - Garret Couture
- Department of Chemistry, University of California, Davis, Davis, CA, USA
| | - Nikita P Bacalzo
- Department of Chemistry, University of California, Davis, Davis, CA, USA
| | - Ye Chen
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St Louis, MO, USA
- Department of Chemistry, University of California, Davis, Davis, CA, USA
| | - Carlito B Lebrilla
- Department of Chemistry, University of California, Davis, Davis, CA, USA
| | - Ishita Mostafa
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Subhasish Das
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Mustafa Mahfuz
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Michael J Barratt
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St Louis, MO, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA
| | - Andrei L Osterman
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Tahmeed Ahmed
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Jeffrey I Gordon
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, MO, USA.
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St Louis, MO, USA.
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA.
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3
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Hibberd MC, Webber DM, Rodionov DA, Henrissat S, Chen RY, Zhou C, Lynn HM, Wang Y, Chang HW, Lee EM, Lelwala-Guruge J, Kazanov MD, Arzamasov AA, Leyn SA, Lombard V, Terrapon N, Henrissat B, Castillo JJ, Couture G, Bacalzo NP, Chen Y, Lebrilla CB, Mostafa I, Das S, Mahfuz M, Barratt MJ, Osterman AL, Ahmed T, Gordon JI. Bioactive glycans in a microbiome-directed food for malnourished children. medRxiv 2023:2023.08.14.23293998. [PMID: 37645824 PMCID: PMC10462212 DOI: 10.1101/2023.08.14.23293998] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Evidence is accumulating that perturbed postnatal development of the gut microbiome contributes to childhood malnutrition1-4. Designing effective microbiome-directed therapeutic foods to repair these perturbations requires knowledge about how food components interact with the microbiome to alter its expressed functions. Here we use biospecimens from a randomized, controlled trial of a microbiome-directed complementary food prototype (MDCF-2) that produced superior rates of weight gain compared to a conventional ready-to-use supplementary food (RUSF) in 12-18-month-old Bangladeshi children with moderate acute malnutrition (MAM)4. We reconstructed 1000 bacterial genomes (metagenome-assembled genomes, MAGs) present in their fecal microbiomes, identified 75 whose abundances were positively associated with weight gain (change in weight-for-length Z score, WLZ), characterized gene expression changes in these MAGs as a function of treatment type and WLZ response, and used mass spectrometry to quantify carbohydrate structures in MDCF-2 and feces. The results reveal treatment-induced changes in expression of carbohydrate metabolic pathways in WLZ-associated MAGs. Comparing participants consuming MDCF-2 versus RUSF, and MDCF-2-treated children in the upper versus lower quartiles of WLZ responses revealed that two Prevotella copri MAGs positively associated with WLZ were principal contributors to MDCF-2-induced expression of metabolic pathways involved in utilization of its component glycans. Moreover, the predicted specificities of carbohydrate active enzymes expressed by polysaccharide utilization loci (PULs) in these two MAGs correlate with the (i) in vitro growth of Bangladeshi P. copri strains, possessing differing degrees of PUL and overall genomic content similarity to these MAGs, cultured in defined medium containing different purified glycans representative of those in MDCF-2, and (ii) levels of carbohydrate structures identified in feces from clinical trial participants. In the accompanying paper5, we use a gnotobiotic mouse model colonized with age- and WLZ-associated bacterial taxa cultured from this study population, and fed diets resembling those consumed by study participants, to directly test the relationship between P. copri, MDCF-2 glycan metabolism, host ponderal growth responses, and intestinal gene expression and metabolism. The ability to identify bioactive glycan structures in MDCFs that are metabolized by growth-associated bacterial taxa will help guide recommendations about use of this MDCF for children with acute malnutrition representing different geographic locales and ages, as well as enable development of bioequivalent, or more efficacious, formulations composed of culturally acceptable and affordable ingredients.
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Affiliation(s)
- Matthew C. Hibberd
- Edison Family Center for Genome Sciences and Systems Biology,
Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington
University School of Medicine, St. Louis, MO 63110 USA
- Department of Pathology and Immunology, Washington University
School of Medicine, St. Louis, MO 63110 USA
| | - Daniel M. Webber
- Edison Family Center for Genome Sciences and Systems Biology,
Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington
University School of Medicine, St. Louis, MO 63110 USA
- Department of Pathology and Immunology, Washington University
School of Medicine, St. Louis, MO 63110 USA
| | - Dmitry A. Rodionov
- Infectious and Inflammatory Disease Center, Sanford Burnham
Prebys Medical Discovery Institute, La Jolla, CA 92037 USA
| | - Suzanne Henrissat
- Edison Family Center for Genome Sciences and Systems Biology,
Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington
University School of Medicine, St. Louis, MO 63110 USA
- Architecture et Fonction des Macromolécules Biologiques,
CNRS, Aix-Marseille University, F-13288, Marseille, France
| | - Robert Y. Chen
- Edison Family Center for Genome Sciences and Systems Biology,
Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington
University School of Medicine, St. Louis, MO 63110 USA
| | - Cyrus Zhou
- Edison Family Center for Genome Sciences and Systems Biology,
Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington
University School of Medicine, St. Louis, MO 63110 USA
| | - Hannah M. Lynn
- Edison Family Center for Genome Sciences and Systems Biology,
Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington
University School of Medicine, St. Louis, MO 63110 USA
| | - Yi Wang
- Edison Family Center for Genome Sciences and Systems Biology,
Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington
University School of Medicine, St. Louis, MO 63110 USA
| | - Hao-Wei Chang
- Edison Family Center for Genome Sciences and Systems Biology,
Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington
University School of Medicine, St. Louis, MO 63110 USA
| | - Evan M. Lee
- Edison Family Center for Genome Sciences and Systems Biology,
Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington
University School of Medicine, St. Louis, MO 63110 USA
| | - Janaki Lelwala-Guruge
- Edison Family Center for Genome Sciences and Systems Biology,
Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington
University School of Medicine, St. Louis, MO 63110 USA
| | - Marat D. Kazanov
- Faculty of Engineering and Natural Sciences, Sabanci University,
Istanbul, Turkey, 34956
| | - Aleksandr A. Arzamasov
- Infectious and Inflammatory Disease Center, Sanford Burnham
Prebys Medical Discovery Institute, La Jolla, CA 92037 USA
| | - Semen A. Leyn
- Infectious and Inflammatory Disease Center, Sanford Burnham
Prebys Medical Discovery Institute, La Jolla, CA 92037 USA
| | - Vincent Lombard
- Architecture et Fonction des Macromolécules Biologiques,
CNRS, Aix-Marseille University, F-13288, Marseille, France
| | - Nicolas Terrapon
- Architecture et Fonction des Macromolécules Biologiques,
CNRS, Aix-Marseille University, F-13288, Marseille, France
| | - Bernard Henrissat
- Department of Biotechnology and Biomedicine (DTU Bioengineering),
Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
- Department of Biological Sciences, King Abdulaziz University,
Jeddah, Saudi Arabia
| | - Juan J. Castillo
- Department of Chemistry, University of California, Davis, CA
95616, USA
| | - Garret Couture
- Department of Chemistry, University of California, Davis, CA
95616, USA
| | - Nikita P. Bacalzo
- Department of Chemistry, University of California, Davis, CA
95616, USA
| | - Ye Chen
- Edison Family Center for Genome Sciences and Systems Biology,
Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington
University School of Medicine, St. Louis, MO 63110 USA
- Department of Chemistry, University of California, Davis, CA
95616, USA
| | | | - Ishita Mostafa
- International Centre for Diarrhoeal Disease Research,
Bangladesh (icddr,b), Dhaka 1212, Bangladesh
| | - Subhasish Das
- International Centre for Diarrhoeal Disease Research,
Bangladesh (icddr,b), Dhaka 1212, Bangladesh
| | - Mustafa Mahfuz
- International Centre for Diarrhoeal Disease Research,
Bangladesh (icddr,b), Dhaka 1212, Bangladesh
| | - Michael J. Barratt
- Edison Family Center for Genome Sciences and Systems Biology,
Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington
University School of Medicine, St. Louis, MO 63110 USA
- Department of Pathology and Immunology, Washington University
School of Medicine, St. Louis, MO 63110 USA
| | - Andrei L. Osterman
- Infectious and Inflammatory Disease Center, Sanford Burnham
Prebys Medical Discovery Institute, La Jolla, CA 92037 USA
| | - Tahmeed Ahmed
- International Centre for Diarrhoeal Disease Research,
Bangladesh (icddr,b), Dhaka 1212, Bangladesh
| | - Jeffrey I. Gordon
- Edison Family Center for Genome Sciences and Systems Biology,
Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington
University School of Medicine, St. Louis, MO 63110 USA
- Department of Pathology and Immunology, Washington University
School of Medicine, St. Louis, MO 63110 USA
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4
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Matveev EV, Safronov VV, Ponomarev GV, Kazanov MD. Predicting Structural Susceptibility of Proteins to Proteolytic Processing. Int J Mol Sci 2023; 24:10761. [PMID: 37445939 DOI: 10.3390/ijms241310761] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/16/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
The importance of 3D protein structure in proteolytic processing is well known. However, despite the plethora of existing methods for predicting proteolytic sites, only a few of them utilize the structural features of potential substrates as predictors. Moreover, to our knowledge, there is currently no method available for predicting the structural susceptibility of protein regions to proteolysis. We developed such a method using data from CutDB, a database that contains experimentally verified proteolytic events. For prediction, we utilized structural features that have been shown to influence proteolysis in earlier studies, such as solvent accessibility, secondary structure, and temperature factor. Additionally, we introduced new structural features, including length of protruded loops and flexibility of protein termini. To maximize the prediction quality of the method, we carefully curated the training set, selected an appropriate machine learning method, and sampled negative examples to determine the optimal positive-to-negative class size ratio. We demonstrated that combining our method with models of protease primary specificity can outperform existing bioinformatics methods for the prediction of proteolytic sites. We also discussed the possibility of utilizing this method for bioinformatics prediction of other post-translational modifications.
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Affiliation(s)
- Evgenii V Matveev
- Skolkovo Institute of Science and Technology, Moscow 121205, Russia
- A.A. Kharkevich Institute for Information Transmission Problems, Moscow 127051, Russia
- Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Moscow 117998, Russia
| | - Vyacheslav V Safronov
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow 119991, Russia
| | - Gennady V Ponomarev
- Skolkovo Institute of Science and Technology, Moscow 121205, Russia
- A.A. Kharkevich Institute for Information Transmission Problems, Moscow 127051, Russia
| | - Marat D Kazanov
- Skolkovo Institute of Science and Technology, Moscow 121205, Russia
- A.A. Kharkevich Institute for Information Transmission Problems, Moscow 127051, Russia
- Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Moscow 117998, Russia
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
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5
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Gerstung M, Jolly C, Leshchiner I, Dentro SC, Gonzalez S, Rosebrock D, Mitchell TJ, Rubanova Y, Anur P, Yu K, Tarabichi M, Deshwar A, Wintersinger J, Kleinheinz K, Vázquez-García I, Haase K, Jerman L, Sengupta S, Macintyre G, Malikic S, Donmez N, Livitz DG, Cmero M, Demeulemeester J, Schumacher S, Fan Y, Yao X, Lee J, Schlesner M, Boutros PC, Bowtell DD, Zhu H, Getz G, Imielinski M, Beroukhim R, Sahinalp SC, Ji Y, Peifer M, Markowetz F, Mustonen V, Yuan K, Wang W, Morris QD, Spellman PT, Wedge DC, Van Loo P, Tarabichi M, Wintersinger J, Deshwar AG, Yu K, Gonzalez S, Rubanova Y, Macintyre G, Adams DJ, Anur P, Beroukhim R, Boutros PC, Bowtell DD, Campbell PJ, Cao S, Christie EL, Cmero M, Cun Y, Dawson KJ, Demeulemeester J, Donmez N, Drews RM, Eils R, Fan Y, Fittall M, Garsed DW, Getz G, Ha G, Imielinski M, Jerman L, Ji Y, Kleinheinz K, Lee J, Lee-Six H, Livitz DG, Malikic S, Markowetz F, Martincorena I, Mitchell TJ, Mustonen V, Oesper L, Peifer M, Peto M, Raphael BJ, Rosebrock D, Sahinalp SC, Salcedo A, Schlesner M, Schumacher S, Sengupta S, Shi R, Shin SJ, Spiro O, Pitkänen E, Pivot X, Piñeiro-Yáñez E, Planko L, Plass C, Polak P, Pons T, Popescu I, Potapova O, Prasad A, Stein LD, Preston SR, Prinz M, Pritchard AL, Prokopec SD, Provenzano E, Puente XS, Puig S, Puiggròs M, Pulido-Tamayo S, Pupo GM, Vázquez-García I, Purdie CA, Quinn MC, Rabionet R, Rader JS, Radlwimmer B, Radovic P, Raeder B, Raine KM, Ramakrishna M, Ramakrishnan K, Vembu S, Ramalingam S, Raphael BJ, Rathmell WK, Rausch T, Reifenberger G, Reimand J, Reis-Filho J, Reuter V, Reyes-Salazar I, Reyna MA, Wheeler DA, Reynolds SM, Rheinbay E, Riazalhosseini Y, Richardson AL, Richter J, Ringel M, Ringnér M, Rino Y, Rippe K, Roach J, Yang TP, Roberts LR, Roberts ND, Roberts SA, Robertson AG, Robertson AJ, Rodriguez JB, Rodriguez-Martin B, Rodríguez-González FG, Roehrl MHA, Rohde M, Yao X, Rokutan H, Romieu G, Rooman I, Roques T, Rosebrock D, Rosenberg M, Rosenstiel PC, Rosenwald A, Rowe EW, Royo R, Yuan K, Rozen SG, Rubanova Y, Rubin MA, Rubio-Perez C, Rudneva VA, Rusev BC, Ruzzenente A, Rätsch G, Sabarinathan R, Sabelnykova VY, Zhu H, Sadeghi S, Sahinalp SC, Saini N, Saito-Adachi M, Saksena G, Salcedo A, Salgado R, Salichos L, Sallari R, Saller C, Wang W, Salvia R, Sam M, Samra JS, Sanchez-Vega F, Sander C, Sanders G, Sarin R, Sarrafi I, Sasaki-Oku A, Sauer T, Morris QD, Sauter G, Saw RPM, Scardoni M, Scarlett CJ, Scarpa A, Scelo G, Schadendorf D, Schein JE, Schilhabel MB, Schlesner M, Spellman PT, Schlomm T, Schmidt HK, Schramm SJ, Schreiber S, Schultz N, Schumacher SE, Schwarz RF, Scolyer RA, Scott D, Scully R, Wedge DC, Seethala R, Segre AV, Selander I, Semple CA, Senbabaoglu Y, Sengupta S, Sereni E, Serra S, Sgroi DC, Shackleton M, Van Loo P, Shah NC, Shahabi S, Shang CA, Shang P, Shapira O, Shelton T, Shen C, Shen H, Shepherd R, Shi R, Spellman PT, Shi Y, Shiah YJ, Shibata T, Shih J, Shimizu E, Shimizu K, Shin SJ, Shiraishi Y, Shmaya T, Shmulevich I, Wedge DC, Shorser SI, Short C, Shrestha R, Shringarpure SS, Shriver C, Shuai S, Sidiropoulos N, Siebert R, Sieuwerts AM, Sieverling L, Van Loo P, Signoretti S, Sikora KO, Simbolo M, Simon R, Simons JV, Simpson JT, Simpson PT, Singer S, Sinnott-Armstrong N, Sipahimalani P, Aaltonen LA, Skelly TJ, Smid M, Smith J, Smith-McCune K, Socci ND, Sofia HJ, Soloway MG, Song L, Sood AK, Sothi S, Abascal F, Sotiriou C, Soulette CM, Span PN, Spellman PT, Sperandio N, Spillane AJ, Spiro O, Spring J, Staaf J, Stadler PF, Abeshouse A, Staib P, Stark SG, Stebbings L, Stefánsson ÓA, Stegle O, Stein LD, Stenhouse A, Stewart C, Stilgenbauer S, Stobbe MD, Aburatani H, Stratton MR, Stretch JR, Struck AJ, Stuart JM, Stunnenberg HG, Su H, Su X, Sun RX, Sungalee S, Susak H, Adams DJ, Suzuki A, Sweep F, Szczepanowski M, Sültmann H, Yugawa T, Tam A, Tamborero D, Tan BKT, Tan D, Tan P, Agrawal N, Tanaka H, Taniguchi H, Tanskanen TJ, Tarabichi M, Tarnuzzer R, Tarpey P, Taschuk ML, Tatsuno K, Tavaré S, Taylor DF, Ahn KS, Taylor-Weiner A, Teague JW, Teh BT, Tembe V, Temes J, Thai K, Thayer SP, Thiessen N, Thomas G, Thomas S, Ahn SM, Thompson A, Thompson AM, Thompson JFF, Thompson RH, Thorne H, Thorne LB, Thorogood A, Tiao G, Tijanic N, Timms LE, Aikata H, Tirabosco R, Tojo M, Tommasi S, Toon CW, Toprak UH, Torrents D, Tortora G, Tost J, Totoki Y, Townend D, Akbani R, Traficante N, Treilleux I, Trotta JR, Trümper LHP, Tsao M, Tsunoda T, Tubio JMC, Tucker O, Turkington R, Turner DJ, Akdemir KC, Tutt A, Ueno M, Ueno NT, Umbricht C, Umer HM, Underwood TJ, Urban L, Urushidate T, Ushiku T, Uusküla-Reimand L, Al-Ahmadie H, Valencia A, Van Den Berg DJ, Van Laere S, Van Loo P, Van Meir EG, Van den Eynden GG, Van der Kwast T, Vasudev N, Vazquez M, Vedururu R, Al-Sedairy ST, Veluvolu U, Vembu S, Verbeke LPC, Vermeulen P, Verrill C, Viari A, Vicente D, Vicentini C, VijayRaghavan K, Viksna J, Al-Shahrour F, Vilain RE, Villasante I, Vincent-Salomon A, Visakorpi T, Voet D, Vyas P, Vázquez-García I, Waddell NM, Waddell N, Wadelius C, Alawi M, Wadi L, Wagener R, Wala JA, Wang J, Wang J, Wang L, Wang Q, Wang W, Wang Y, Wang Z, Albert M, Waring PM, Warnatz HJ, Warrell J, Warren AY, Waszak SM, Wedge DC, Weichenhan D, Weinberger P, Weinstein JN, Weischenfeldt J, Aldape K, Weisenberger DJ, Welch I, Wendl MC, Werner J, Whalley JP, Wheeler DA, Whitaker HC, Wigle D, Wilkerson MD, Williams A, Alexandrov LB, Wilmott JS, Wilson GW, Wilson JM, Wilson RK, Winterhoff B, Wintersinger JA, Wiznerowicz M, Wolf S, Wong BH, Wong T, Ally A, Wong W, Woo Y, Wood S, Wouters BG, Wright AJ, Wright DW, Wright MH, Wu CL, Wu DY, Wu G, Alsop K, Wu J, Wu K, Wu Y, Wu Z, Xi L, Xia T, Xiang Q, Xiao X, Xing R, Xiong H, Alvarez EG, Xu Q, Xu Y, Xue H, Yachida S, Yakneen S, Yamaguchi R, Yamaguchi TN, Yamamoto M, Yamamoto S, Yamaue H, Amary F, Yang F, Yang H, Yang JY, Yang L, Yang L, Yang S, Yang TP, Yang Y, Yao X, Yaspo ML, Amin SB, Yates L, Yau C, Ye C, Ye K, Yellapantula VD, Yoon CJ, Yoon SS, Yousif F, Yu J, Yu K, Aminou B, Yu W, Yu Y, Yuan K, Yuan Y, Yuen D, Yung CK, Zaikova O, Zamora J, Zapatka M, Zenklusen JC, Ammerpohl O, Zenz T, Zeps N, Zhang CZ, Zhang F, Zhang H, Zhang H, Zhang H, Zhang J, Zhang J, Zhang J, Anderson MJ, Zhang X, Zhang X, Zhang Y, Zhang Z, Zhao Z, Zheng L, Zheng X, Zhou W, Zhou Y, Zhu B, Ang Y, Zhu H, Zhu J, Zhu S, Zou L, Zou X, deFazio A, van As N, van Deurzen CHM, van de Vijver MJ, van’t Veer L, Antonello D, von Mering C, Anur P, Aparicio S, Appelbaum EL, Arai Y, Aretz A, Arihiro K, Ariizumi SI, Armenia J, Arnould L, Asa S, Assenov Y, Atwal G, Aukema S, Auman JT, Aure MRR, Awadalla P, Aymerich M, Bader GD, Baez-Ortega A, Bailey MH, Bailey PJ, Balasundaram M, Balu S, Bandopadhayay P, Banks RE, Barbi S, Barbour AP, Barenboim J, Barnholtz-Sloan J, Barr H, Barrera E, Bartlett J, Bartolome J, Bassi C, Bathe OF, Baumhoer D, Bavi P, Baylin SB, Bazant W, Beardsmore D, Beck TA, Behjati S, Behren A, Niu B, Bell C, Beltran S, Benz C, Berchuck A, Bergmann AK, Bergstrom EN, Berman BP, Berney DM, Bernhart SH, Beroukhim R, 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Chan-Seng-Yue M, Chandan VS, Chang DK, Chanock SJ, Chantrill LA, Chateigner A, Chatterjee N, Chayama K, Chen HW, Chen J, Chen K, Chen Y, Chen Z, Cherniack AD, Chien J, Chiew YE, Chin SF, Cho J, Cho S, Choi JK, Choi W, Chomienne C, Chong Z, Choo SP, Chou A, Christ AN, Christie EL, Chuah E, Cibulskis C, Cibulskis K, Cingarlini S, Clapham P, Claviez A, Cleary S, Cloonan N, Cmero M, Collins CC, Connor AA, Cooke SL, Cooper CS, Cope L, Corbo V, Cordes MG, Cordner SM, Cortés-Ciriano I, Covington K, Cowin PA, Craft B, Craft D, Creighton CJ, Cun Y, Curley E, Cutcutache I, Czajka K, Czerniak B, Dagg RA, Danilova L, Davi MV, Davidson NR, Davies H, Davis IJ, Davis-Dusenbery BN, Dawson KJ, De La Vega FM, De Paoli-Iseppi R, Defreitas T, Tos APD, Delaneau O, Demchok JA, Demeulemeester J, Demidov GM, Demircioğlu D, Dennis NM, Denroche RE, Dentro SC, Desai N, Deshpande V, Deshwar AG, Desmedt C, Deu-Pons J, Dhalla N, Dhani NC, Dhingra P, Dhir R, DiBiase A, Diamanti K, Ding L, Ding S, Dinh HQ, Dirix L, 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George J, Gerhard DS, Gerhauser C, Gershenwald JE, Gerstein M, Gerstung M, Getz G, Ghori M, Ghossein R, Giama NH, Gibbs RA, Gibson B, Gill AJ, Gill P, Giri DD, Glodzik D, Gnanapragasam VJ, Goebler ME, Goldman MJ, Gomez C, Gonzalez S, Gonzalez-Perez A, Gordenin DA, Gossage J, Gotoh K, Govindan R, Grabau D, Graham JS, Grant RC, Green AR, Green E, Greger L, Grehan N, Grimaldi S, Grimmond SM, Grossman RL, Grundhoff A, Gundem G, Guo Q, Gupta M, Gupta S, Gut IG, Gut M, Göke J, Ha G, Haake A, Haan D, Haas S, Haase K, Haber JE, Habermann N, Hach F, Haider S, Hama N, Hamdy FC, Hamilton A, Hamilton MP, Han L, Hanna GB, Hansmann M, Haradhvala NJ, Harismendy O, Harliwong I, Harmanci AO, Harrington E, Hasegawa T, Haussler D, Hawkins S, Hayami S, Hayashi S, Hayes DN, Hayes SJ, Hayward NK, Hazell S, He Y, Heath AP, Heath SC, Hedley D, Hegde AM, Heiman DI, Heinold MC, Heins Z, Heisler LE, Hellstrom-Lindberg E, Helmy M, Heo SG, Hepperla AJ, Heredia-Genestar JM, Herrmann C, Hersey P, Hess JM, Hilmarsdottir H, Hinton J, Hirano S, Hiraoka N, Hoadley KA, Hobolth A, Hodzic E, Hoell JI, Hoffmann S, Hofmann O, Holbrook A, Holik AZ, Hollingsworth MA, Holmes O, Holt RA, Hong C, Hong EP, Hong JH, Hooijer GK, Hornshøj H, Hosoda F, Hou Y, Hovestadt V, Howat W, Hoyle AP, Hruban RH, Hu J, Hu T, Hua X, Huang KL, Huang M, Huang MN, Huang V, Huang Y, Huber W, Hudson TJ, Hummel M, Hung JA, Huntsman D, Hupp TR, Huse J, Huska MR, Hutter B, Hutter CM, Hübschmann D, Iacobuzio-Donahue CA, Imbusch CD, Imielinski M, Imoto S, Isaacs WB, Isaev K, Ishikawa S, Iskar M, Islam SMA, Ittmann M, Ivkovic S, Izarzugaza JMG, Jacquemier J, Jakrot V, Jamieson NB, Jang GH, Jang SJ, Jayaseelan JC, Jayasinghe R, Jefferys SR, Jegalian K, Jennings JL, Jeon SH, Jerman L, Ji Y, Jiao W, Johansson PA, Johns AL, Johns J, Johnson R, Johnson TA, Jolly C, Joly Y, Jonasson JG, Jones CD, Jones DR, Jones DTW, Jones N, Jones SJM, Jonkers J, Ju YS, Juhl H, Jung J, Juul M, Juul RI, Juul S, Jäger N, Kabbe R, Kahles A, Kahraman A, Kaiser VB, Kakavand H, Kalimuthu S, von Kalle C, Kang KJ, Karaszi K, Karlan B, Karlić R, Karsch D, Kasaian K, Kassahn KS, Katai H, Kato M, Katoh H, Kawakami Y, Kay JD, Kazakoff SH, Kazanov MD, Keays M, Kebebew E, Kefford RF, Kellis M, Kench JG, Kennedy CJ, Kerssemakers JNA, Khoo D, Khoo V, Khuntikeo N, Khurana E, Kilpinen H, Kim HK, Kim HL, Kim HY, Kim H, Kim J, Kim J, Kim JK, Kim Y, King TA, Klapper W, Kleinheinz K, Klimczak LJ, Knappskog S, Kneba M, Knoppers BM, Koh Y, Komorowski J, Komura D, Komura M, Kong G, Kool M, Korbel JO, Korchina V, Korshunov A, Koscher M, Koster R, Kote-Jarai Z, Koures A, Kovacevic M, Kremeyer B, Kretzmer H, Kreuz M, Krishnamurthy S, Kube D, Kumar K, Kumar P, Kumar S, Kumar Y, Kundra R, Kübler K, Küppers R, Lagergren J, Lai PH, Laird PW, Lakhani SR, Lalansingh CM, Lalonde E, Lamaze FC, Lambert A, Lander E, Landgraf P, Landoni L, Langerød A, Lanzós A, Larsimont D, Larsson E, Lathrop M, Lau LMS, Lawerenz C, Lawlor RT, Lawrence MS, Lazar AJ, Lazic AM, Le X, Lee D, Lee D, Lee EA, Lee HJ, Lee JJK, Lee JY, Lee J, Lee MTM, Lee-Six H, Lehmann KV, Lehrach H, Lenze D, Leonard CR, Leongamornlert DA, Leshchiner I, Letourneau L, Letunic I, Levine DA, Lewis L, Ley T, Li C, Li CH, Li HI, Li J, Li L, Li S, Li S, Li X, Li X, Li X, Li Y, Liang H, Liang SB, Lichter P, Lin P, Lin Z, Linehan WM, Lingjærde OC, Liu D, Liu EM, Liu FFF, Liu F, Liu J, Liu X, Livingstone J, Livitz D, Livni N, Lochovsky L, Loeffler M, Long GV, Lopez-Guillermo A, Lou S, Louis DN, Lovat LB, Lu Y, Lu YJ, Lu Y, Luchini C, Lungu I, Luo X, Luxton HJ, Lynch AG, Lype L, López C, López-Otín C, Ma EZ, Ma Y, MacGrogan G, MacRae S, Macintyre G, Madsen T, Maejima K, Mafficini A, Maglinte DT, Maitra A, Majumder PP, Malcovati L, Malikic S, Malleo G, Mann GJ, Mantovani-Löffler L, Marchal K, Marchegiani G, Mardis ER, Margolin AA, Marin MG, Markowetz F, Markowski J, Marks J, Marques-Bonet T, Marra MA, Marsden L, Martens JWM, Martin S, Martin-Subero JI, Martincorena I, Martinez-Fundichely A, Maruvka YE, Mashl RJ, Massie CE, Matthew TJ, Matthews L, Mayer E, Mayes S, Mayo M, Mbabaali F, McCune K, McDermott U, McGillivray PD, McLellan MD, McPherson JD, McPherson JR, McPherson TA, Meier SR, Meng A, Meng S, Menzies A, Merrett ND, Merson S, Meyerson M, Meyerson W, Mieczkowski PA, Mihaiescu GL, Mijalkovic S, Mikkelsen T, Milella M, Mileshkin L, Miller CA, Miller DK, Miller JK, Mills GB, Milovanovic A, Minner S, Miotto M, Arnau GM, Mirabello L, Mitchell C, Mitchell TJ, Miyano S, Miyoshi N, Mizuno S, Molnár-Gábor F, Moore MJ, Moore RA, Morganella S, Morris QD, Morrison C, Mose LE, Moser CD, Muiños F, Mularoni L, Mungall AJ, Mungall K, Musgrove EA, Mustonen V, Mutch D, Muyas F, Muzny DM, Muñoz A, Myers J, Myklebost O, Möller P, Nagae G, Nagrial AM, Nahal-Bose HK, Nakagama H, Nakagawa H, Nakamura H, Nakamura T, Nakano K, Nandi T, Nangalia J, Nastic M, Navarro A, Navarro FCP, Neal DE, Nettekoven G, Newell F, Newhouse SJ, Newton Y, Ng AWT, Ng A, Nicholson J, Nicol D, Nie Y, Nielsen GP, Nielsen MM, Nik-Zainal S, Noble MS, Nones K, Northcott PA, Notta F, O’Connor BD, O’Donnell P, O’Donovan M, O’Meara S, O’Neill BP, O’Neill JR, Ocana D, Ochoa A, Oesper L, Ogden C, Ohdan H, Ohi K, Ohno-Machado L, Oien KA, Ojesina AI, Ojima H, Okusaka T, Omberg L, Ong CK, Ossowski S, Ott G, Ouellette BFF, P’ng C, Paczkowska M, Paiella S, Pairojkul C, Pajic M, Pan-Hammarström Q, Papaemmanuil E, Papatheodorou I, Paramasivam N, Park JW, Park JW, Park K, Park K, Park PJ, Parker JS, Parsons SL, Pass H, Pasternack D, Pastore A, Patch AM, Pauporté I, Pea A, Pearson JV, Pedamallu CS, Pedersen JS, Pederzoli P, Peifer M, Pennell NA, Perou CM, Perry MD, Petersen GM, Peto M, Petrelli N, Petryszak R, Pfister SM, Phillips M, Pich O, Pickett HA, Pihl TD, Pillay N, Pinder S, Pinese M, Pinho AV. Author Correction: The evolutionary history of 2,658 cancers. Nature 2023; 614:E42. [PMID: 36697833 PMCID: PMC9931577 DOI: 10.1038/s41586-022-05601-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Moritz Gerstung
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK. .,European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany. .,Wellcome Sanger Institute, Cambridge, UK.
| | - Clemency Jolly
- grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK
| | - Ignaty Leshchiner
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Stefan C. Dentro
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK ,grid.4991.50000 0004 1936 8948Big Data Institute, University of Oxford, Oxford, UK
| | - Santiago Gonzalez
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Daniel Rosebrock
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Thomas J. Mitchell
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.5335.00000000121885934University of Cambridge, Cambridge, UK
| | - Yulia Rubanova
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | - Pavana Anur
- grid.5288.70000 0000 9758 5690Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR USA
| | - Kaixian Yu
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Maxime Tarabichi
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK
| | - Amit Deshwar
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | - Jeff Wintersinger
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | - Kortine Kleinheinz
- grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany ,grid.7700.00000 0001 2190 4373Heidelberg University, Heidelberg, Germany
| | - Ignacio Vázquez-García
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.5335.00000000121885934University of Cambridge, Cambridge, UK
| | - Kerstin Haase
- grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK
| | - Lara Jerman
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK ,grid.8954.00000 0001 0721 6013University of Ljubljana, Ljubljana, Slovenia
| | - Subhajit Sengupta
- grid.240372.00000 0004 0400 4439NorthShore University HealthSystem, Evanston, IL USA
| | - Geoff Macintyre
- grid.5335.00000000121885934Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Salem Malikic
- grid.61971.380000 0004 1936 7494Simon Fraser University, Burnaby, British Columbia Canada ,grid.412541.70000 0001 0684 7796Vancouver Prostate Centre, Vancouver, British Columbia Canada
| | - Nilgun Donmez
- grid.61971.380000 0004 1936 7494Simon Fraser University, Burnaby, British Columbia Canada ,grid.412541.70000 0001 0684 7796Vancouver Prostate Centre, Vancouver, British Columbia Canada
| | - Dimitri G. Livitz
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Marek Cmero
- grid.1008.90000 0001 2179 088XUniversity of Melbourne, Melbourne, Victoria Australia ,grid.1042.70000 0004 0432 4889Walter and Eliza Hall Institute, Melbourne, Victoria Australia
| | - Jonas Demeulemeester
- grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK ,grid.5596.f0000 0001 0668 7884University of Leuven, Leuven, Belgium
| | - Steven Schumacher
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Yu Fan
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Xiaotong Yao
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA ,grid.429884.b0000 0004 1791 0895New York Genome Center, New York, NY USA
| | - Juhee Lee
- grid.205975.c0000 0001 0740 6917University of California Santa Cruz, Santa Cruz, CA USA
| | - Matthias Schlesner
- grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Paul C. Boutros
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.419890.d0000 0004 0626 690XOntario Institute for Cancer Research, Toronto, Ontario Canada ,grid.19006.3e0000 0000 9632 6718University of California, Los Angeles, CA USA
| | - David D. Bowtell
- grid.1055.10000000403978434Peter MacCallum Cancer Centre, Melbourne, Victoria Australia
| | - Hongtu Zhu
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Gad Getz
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA ,grid.32224.350000 0004 0386 9924Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA USA ,grid.32224.350000 0004 0386 9924Department of Pathology, Massachusetts General Hospital, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | - Marcin Imielinski
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA ,grid.429884.b0000 0004 1791 0895New York Genome Center, New York, NY USA
| | - Rameen Beroukhim
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA
| | - S. Cenk Sahinalp
- grid.412541.70000 0001 0684 7796Vancouver Prostate Centre, Vancouver, British Columbia Canada ,grid.411377.70000 0001 0790 959XIndiana University, Bloomington, IN USA
| | - Yuan Ji
- grid.240372.00000 0004 0400 4439NorthShore University HealthSystem, Evanston, IL USA ,grid.170205.10000 0004 1936 7822The University of Chicago, Chicago, IL USA
| | - Martin Peifer
- grid.6190.e0000 0000 8580 3777University of Cologne, Cologne, Germany
| | - Florian Markowetz
- grid.5335.00000000121885934Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Ville Mustonen
- grid.7737.40000 0004 0410 2071University of Helsinki, Helsinki, Finland
| | - Ke Yuan
- grid.5335.00000000121885934Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK ,grid.8756.c0000 0001 2193 314XUniversity of Glasgow, Glasgow, UK
| | - Wenyi Wang
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Quaid D. Morris
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | | | - Paul T. Spellman
- grid.5288.70000 0000 9758 5690Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR USA
| | - David C. Wedge
- grid.4991.50000 0004 1936 8948Big Data Institute, University of Oxford, Oxford, UK ,grid.454382.c0000 0004 7871 7212Oxford NIHR Biomedical Research Centre, Oxford, UK
| | - Peter Van Loo
- The Francis Crick Institute, London, UK. .,University of Leuven, Leuven, Belgium.
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Calabrese C, Davidson NR, Demircioğlu D, Fonseca NA, He Y, Kahles A, Lehmann KV, Liu F, Shiraishi Y, Soulette CM, Urban L, Greger L, Li S, Liu D, Perry MD, Xiang Q, Zhang F, Zhang J, Bailey P, Erkek S, Hoadley KA, Hou Y, Huska MR, Kilpinen H, Korbel JO, Marin MG, Markowski J, Nandi T, Pan-Hammarström Q, Pedamallu CS, Siebert R, Stark SG, Su H, Tan P, Waszak SM, Yung C, Zhu S, Awadalla P, Creighton CJ, Meyerson M, Ouellette BFF, Wu K, Yang H, Brazma A, Brooks AN, Göke J, Rätsch G, Schwarz RF, Stegle O, Zhang Z, Wu K, Yang H, Fonseca NA, Kahles A, Lehmann KV, Urban L, Soulette CM, Shiraishi Y, Liu F, He Y, Demircioğlu D, Davidson NR, Calabrese C, Zhang J, Perry MD, Xiang Q, Greger L, Li S, Liu D, Stark SG, Zhang F, Amin SB, Bailey P, Chateigner A, Cortés-Ciriano I, Craft B, Erkek S, Frenkel-Morgenstern M, Goldman M, Hoadley KA, Hou Y, Huska MR, Khurana E, Kilpinen H, Korbel JO, Lamaze FC, Li C, Li X, Li X, Liu X, Marin MG, Markowski J, Nandi T, Nielsen MM, Ojesina AI, Pan-Hammarström Q, Park PJ, Pedamallu CS, Pedersen JS, Pederzoli P, Peifer M, Pennell NA, Perou CM, Perry MD, Petersen GM, Peto M, Petrelli N, Pedamallu CS, Petryszak R, Pfister SM, Phillips M, Pich O, Pickett HA, Pihl TD, Pillay N, Pinder S, Pinese M, Pinho AV, Pedersen JS, Pitkänen E, Pivot X, Piñeiro-Yáñez E, Planko L, Plass C, Polak P, Pons T, Popescu I, Potapova O, Prasad A, Siebert R, Preston SR, Prinz M, Pritchard AL, Prokopec SD, Provenzano E, Puente XS, Puig S, Puiggròs M, Pulido-Tamayo S, Pupo GM, Su H, Purdie CA, Quinn MC, Rabionet R, Rader JS, Radlwimmer B, Radovic P, Raeder B, Raine KM, Ramakrishna M, Ramakrishnan K, Tan P, Ramalingam S, Raphael BJ, Rathmell WK, Rausch T, Reifenberger G, Reimand J, Reis-Filho J, Reuter V, Reyes-Salazar I, Reyna MA, Teh BT, Reynolds SM, Rheinbay E, Riazalhosseini Y, Richardson AL, Richter J, Ringel M, Ringnér M, Rino Y, Rippe K, Roach J, Wang J, Roberts LR, Roberts ND, Roberts SA, Robertson AG, Robertson AJ, Rodriguez JB, Rodriguez-Martin B, Rodríguez-González FG, Roehrl MHA, Rohde M, Waszak SM, Rokutan H, Romieu G, Rooman I, Roques T, Rosebrock D, Rosenberg M, Rosenstiel PC, Rosenwald A, Rowe EW, Royo R, Xiong H, Rozen SG, Rubanova Y, Rubin MA, Rubio-Perez C, Rudneva VA, Rusev BC, Ruzzenente A, Rätsch G, Sabarinathan R, Sabelnykova VY, Yakneen S, Sadeghi S, Sahinalp SC, Saini N, Saito-Adachi M, Saksena G, Salcedo A, Salgado R, Salichos L, Sallari R, Saller C, Ye C, Salvia R, Sam M, Samra JS, Sanchez-Vega F, Sander C, Sanders G, Sarin R, Sarrafi I, Sasaki-Oku A, Sauer T, Yung C, Sauter G, Saw RPM, Scardoni M, Scarlett CJ, Scarpa A, Scelo G, Schadendorf D, Schein JE, Schilhabel MB, Schlesner M, Zhang X, Schlomm T, Schmidt HK, Schramm SJ, Schreiber S, Schultz N, Schumacher SE, Schwarz RF, Scolyer RA, Scott D, Scully R, Zheng L, Seethala R, Segre AV, Selander I, Semple CA, Senbabaoglu Y, Sengupta S, Sereni E, Serra S, Sgroi DC, Shackleton M, Zhu J, Shah NC, Shahabi S, Shang CA, Shang P, Shapira O, Shelton T, Shen C, Shen H, Shepherd R, Shi R, Zhu S, Shi Y, Shiah YJ, Shibata T, Shih J, Shimizu E, Shimizu K, Shin SJ, Shiraishi Y, Shmaya T, Shmulevich I, Awadalla P, Shorser SI, Short C, Shrestha R, Shringarpure SS, Shriver C, Shuai S, Sidiropoulos N, Siebert R, Sieuwerts AM, Sieverling L, Creighton CJ, Signoretti S, Sikora KO, Simbolo M, Simon R, Simons JV, Simpson JT, Simpson PT, Singer S, Sinnott-Armstrong N, Sipahimalani P, Meyerson M, Skelly TJ, Smid M, Smith J, Smith-McCune K, Socci ND, Sofia HJ, Soloway MG, Song L, Sood AK, Sothi S, Ouellette BFF, Sotiriou C, Soulette CM, Span PN, Spellman PT, Sperandio N, Spillane AJ, Spiro O, Spring J, Staaf J, Stadler PF, Wu K, Staib P, Stark SG, Stebbings L, Stefánsson ÓA, Stegle O, Stein LD, Stenhouse A, Stewart C, Stilgenbauer S, Stobbe MD, Yang H, Stratton MR, Stretch JR, Struck AJ, Stuart JM, Stunnenberg HG, Su H, Su X, Sun RX, Sungalee S, Susak H, Göke J, Suzuki A, Sweep F, Szczepanowski M, Sültmann H, Yugawa T, Tam A, Tamborero D, Tan BKT, Tan D, Tan P, Schwarz RF, Tanaka H, Taniguchi H, Tanskanen TJ, Tarabichi M, Tarnuzzer R, Tarpey P, Taschuk ML, Tatsuno K, Tavaré S, Taylor DF, Stegle O, Taylor-Weiner A, Teague JW, Teh BT, Tembe V, Temes J, Thai K, Thayer SP, Thiessen N, Thomas G, Thomas S, Zhang Z, Thompson A, Thompson AM, Thompson JFF, Thompson RH, Thorne H, Thorne LB, Thorogood A, Tiao G, Tijanic N, Timms LE, Brazma A, Tirabosco R, Tojo M, Tommasi S, Toon CW, Toprak UH, Torrents D, Tortora G, Tost J, Totoki Y, Townend D, Rätsch G, Traficante N, Treilleux I, Trotta JR, Trümper LHP, Tsao M, Tsunoda T, Tubio JMC, Tucker O, Turkington R, Turner DJ, Brooks AN, Tutt A, Ueno M, Ueno NT, Umbricht C, Umer HM, Underwood TJ, Urban L, Urushidate T, Ushiku T, Uusküla-Reimand L, Brazma A, Valencia A, Van Den Berg DJ, Van Laere S, Van Loo P, Van Meir EG, Van den Eynden GG, Van der Kwast T, Vasudev N, Vazquez M, Vedururu R, Brooks AN, Veluvolu U, Vembu S, Verbeke LPC, Vermeulen P, Verrill C, Viari A, Vicente D, Vicentini C, VijayRaghavan K, Viksna J, Göke J, Vilain RE, Villasante I, Vincent-Salomon A, Visakorpi T, Voet D, Vyas P, Vázquez-García I, Waddell NM, Waddell N, Wadelius C, Rätsch G, Wadi L, Wagener R, Wala JA, Wang J, Wang J, Wang L, Wang Q, Wang W, Wang Y, Wang Z, Schwarz RF, Waring PM, Warnatz HJ, Warrell J, Warren AY, Waszak SM, Wedge DC, Weichenhan D, Weinberger P, Weinstein JN, Weischenfeldt J, Stegle O, Weisenberger DJ, Welch I, Wendl MC, Werner J, Whalley JP, Wheeler DA, Whitaker HC, Wigle D, Wilkerson MD, Williams A, Zhang Z, Wilmott JS, Wilson GW, Wilson JM, Wilson RK, Winterhoff B, Wintersinger JA, Wiznerowicz M, Wolf S, Wong BH, Wong T, Aaltonen LA, Wong W, Woo Y, Wood S, Wouters BG, Wright AJ, Wright DW, Wright MH, Wu CL, Wu DY, Wu G, Abascal F, Wu J, Wu K, Wu Y, Wu Z, Xi L, Xia T, Xiang Q, Xiao X, Xing R, Xiong H, Abeshouse A, Xu Q, Xu Y, Xue H, Yachida S, Yakneen S, Yamaguchi R, Yamaguchi TN, Yamamoto M, Yamamoto S, Yamaue H, Aburatani H, Yang F, Yang H, Yang JY, Yang L, Yang L, Yang S, Yang TP, Yang Y, Yao X, Yaspo ML, Adams DJ, Yates L, Yau C, Ye C, Ye K, Yellapantula VD, Yoon CJ, Yoon SS, Yousif F, Yu J, Yu K, Agrawal N, Yu W, Yu Y, Yuan K, Yuan Y, Yuen D, Yung CK, Zaikova O, Zamora J, Zapatka M, Zenklusen JC, Ahn KS, Zenz T, Zeps N, Zhang CZ, Zhang F, Zhang H, Zhang H, Zhang H, Zhang J, Zhang J, Zhang J, Ahn SM, Zhang X, Zhang X, Zhang Y, Zhang Z, Zhao Z, Zheng L, Zheng X, Zhou W, Zhou Y, Zhu B, Aikata H, Zhu H, Zhu J, Zhu S, Zou L, Zou X, deFazio A, van As N, van Deurzen CHM, van de Vijver MJ, van’t Veer L, Akbani R, von Mering C, Akdemir KC, Al-Ahmadie H, Al-Sedairy ST, Al-Shahrour F, Alawi M, Albert M, Aldape K, Alexandrov LB, Ally A, Alsop K, Alvarez EG, Amary F, Amin SB, Aminou B, Ammerpohl O, Anderson MJ, Ang Y, Antonello D, Anur P, Aparicio S, Appelbaum EL, Arai Y, Aretz A, Arihiro K, Ariizumi SI, Armenia J, Arnould L, Asa S, Assenov Y, Atwal G, Aukema S, Auman JT, Aure MRR, Awadalla P, Aymerich M, Bader GD, Baez-Ortega A, Bailey MH, Bailey PJ, Balasundaram M, Balu S, Bandopadhayay P, Banks RE, Barbi S, Barbour AP, Barenboim J, Barnholtz-Sloan J, Barr H, Barrera E, Bartlett J, Bartolome J, Bassi C, Bathe OF, Baumhoer D, Bavi P, Baylin SB, Bazant W, Beardsmore D, Beck TA, Behjati S, Behren A, Niu B, Bell C, Beltran S, Benz C, Berchuck A, Bergmann AK, Bergstrom EN, Berman BP, Berney DM, Bernhart SH, Beroukhim R, Berrios M, Bersani S, Bertl J, Betancourt M, Bhandari V, Bhosle SG, Biankin AV, Bieg M, Bigner D, Binder H, Birney E, Birrer M, Biswas NK, Bjerkehagen B, Bodenheimer T, Boice L, Bonizzato G, De Bono JS, Boot A, Bootwalla MS, Borg A, Borkhardt A, Boroevich KA, Borozan I, Borst C, Bosenberg M, Bosio M, Boultwood J, Bourque G, Boutros PC, Bova GS, Bowen DT, Bowlby R, Bowtell DDL, Boyault S, Boyce R, Boyd J, Brazma A, Brennan P, Brewer DS, Brinkman AB, Bristow RG, Broaddus RR, Brock JE, Brock M, Broeks A, Brooks AN, Brooks D, Brors B, Brunak S, Bruxner TJC, Bruzos AL, Buchanan A, Buchhalter I, Buchholz C, Bullman S, Burke H, Burkhardt B, Burns KH, Busanovich J, Bustamante CD, Butler AP, Butte AJ, Byrne NJ, Børresen-Dale AL, Caesar-Johnson SJ, Cafferkey A, Cahill D, Calabrese C, Caldas C, Calvo F, Camacho N, Campbell PJ, Campo E, Cantù C, Cao S, Carey TE, Carlevaro-Fita J, Carlsen R, Cataldo I, Cazzola M, Cebon J, Cerfolio R, Chadwick DE, Chakravarty D, Chalmers D, Chan CWY, Chan K, Chan-Seng-Yue M, Chandan VS, Chang DK, Chanock SJ, Chantrill LA, Chateigner A, Chatterjee N, Chayama K, Chen HW, Chen J, Chen K, Chen Y, Chen Z, Cherniack AD, Chien J, Chiew YE, Chin SF, Cho J, Cho S, Choi JK, Choi W, Chomienne C, Chong Z, Choo SP, Chou A, Christ AN, Christie EL, Chuah E, Cibulskis C, Cibulskis K, Cingarlini S, Clapham P, Claviez A, Cleary S, Cloonan N, Cmero M, Collins CC, Connor AA, Cooke SL, Cooper CS, Cope L, Corbo V, Cordes MG, Cordner SM, Cortés-Ciriano I, Covington K, Cowin PA, Craft B, Craft D, Creighton CJ, Cun Y, Curley E, Cutcutache I, Czajka K, Czerniak B, Dagg RA, Danilova L, Davi MV, Davidson NR, Davies H, Davis IJ, Davis-Dusenbery BN, Dawson KJ, De La Vega FM, De Paoli-Iseppi R, Defreitas T, Tos APD, Delaneau O, Demchok JA, Demeulemeester J, Demidov GM, Demircioğlu D, Dennis NM, Denroche RE, Dentro SC, Desai N, Deshpande V, Deshwar AG, Desmedt C, Deu-Pons J, Dhalla N, Dhani NC, Dhingra P, Dhir R, DiBiase A, Diamanti K, Ding L, Ding S, Dinh HQ, Dirix L, Doddapaneni H, Donmez N, Dow MT, Drapkin R, Drechsel O, Drews RM, Serge S, Dudderidge T, Dueso-Barroso A, Dunford AJ, Dunn M, Dursi LJ, Duthie FR, Dutton-Regester K, Eagles J, Easton DF, Edmonds S, Edwards PA, Edwards SE, Eeles RA, Ehinger A, Eils J, Eils R, El-Naggar A, Eldridge M, Ellrott K, Erkek S, Escaramis G, Espiritu SMG, Estivill X, Etemadmoghadam D, Eyfjord JE, Faltas BM, Fan D, Fan Y, Faquin WC, Farcas C, Fassan M, Fatima A, Favero F, Fayzullaev N, Felau I, Fereday S, Ferguson ML, Ferretti V, Feuerbach L, Field MA, Fink JL, Finocchiaro G, Fisher C, Fittall MW, Fitzgerald A, Fitzgerald RC, Flanagan AM, Fleshner NE, Flicek P, Foekens JA, Fong KM, Fonseca NA, Foster CS, Fox NS, Fraser M, Frazer S, Frenkel-Morgenstern M, Friedman W, Frigola J, Fronick CC, Fujimoto A, Fujita M, Fukayama M, Fulton LA, Fulton RS, Furuta M, Futreal PA, Füllgrabe A, Gabriel SB, Gallinger S, Gambacorti-Passerini C, Gao J, Gao S, Garraway L, Garred Ø, Garrison E, Garsed DW, Gehlenborg N, Gelpi JLL, George J, Gerhard DS, Gerhauser C, Gershenwald JE, Gerstein M, Gerstung M, Getz G, Ghori M, Ghossein R, Giama NH, Gibbs RA, Gibson B, Gill AJ, Gill P, Giri DD, Glodzik D, Gnanapragasam VJ, Goebler ME, Goldman MJ, Gomez C, Gonzalez S, Gonzalez-Perez A, Gordenin DA, Gossage J, Gotoh K, Govindan R, Grabau D, Graham JS, Grant RC, Green AR, Green E, Greger L, Grehan N, Grimaldi S, Grimmond SM, Grossman RL, Grundhoff A, Gundem G, Guo Q, Gupta M, Gupta S, Gut IG, Gut M, Göke J, Ha G, Haake A, Haan D, Haas S, Haase K, Haber JE, Habermann N, Hach F, Haider S, Hama N, Hamdy FC, Hamilton A, Hamilton MP, Han L, Hanna GB, Hansmann M, Haradhvala NJ, Harismendy O, Harliwong I, Harmanci AO, Harrington E, Hasegawa T, Haussler D, Hawkins S, Hayami S, Hayashi S, Hayes DN, Hayes SJ, Hayward NK, Hazell S, He Y, Heath AP, Heath SC, Hedley D, Hegde AM, Heiman DI, Heinold MC, Heins Z, Heisler LE, Hellstrom-Lindberg E, Helmy M, Heo SG, Hepperla AJ, Heredia-Genestar JM, Herrmann C, Hersey P, Hess JM, Hilmarsdottir H, Hinton J, Hirano S, Hiraoka N, Hoadley KA, Hobolth A, Hodzic E, Hoell JI, Hoffmann S, Hofmann O, Holbrook A, Holik AZ, Hollingsworth MA, Holmes O, Holt RA, Hong C, Hong EP, Hong JH, Hooijer GK, Hornshøj H, Hosoda F, Hou Y, Hovestadt V, Howat W, Hoyle AP, Hruban RH, Hu J, Hu T, Hua X, Huang KL, Huang M, Huang MN, Huang V, Huang Y, Huber W, Hudson TJ, Hummel M, Hung JA, Huntsman D, Hupp TR, Huse J, Huska MR, Hutter B, Hutter CM, Hübschmann D, Iacobuzio-Donahue CA, Imbusch CD, Imielinski M, Imoto S, Isaacs WB, Isaev K, Ishikawa S, Iskar M, Islam SMA, Ittmann M, Ivkovic S, Izarzugaza JMG, Jacquemier J, Jakrot V, Jamieson NB, Jang GH, Jang SJ, Jayaseelan JC, Jayasinghe R, Jefferys SR, Jegalian K, Jennings JL, Jeon SH, Jerman L, Ji Y, Jiao W, Johansson PA, Johns AL, Johns J, Johnson R, Johnson TA, Jolly C, Joly Y, Jonasson JG, Jones CD, Jones DR, Jones DTW, Jones N, Jones SJM, Jonkers J, Ju YS, Juhl H, Jung J, Juul M, Juul RI, Juul S, Jäger N, Kabbe R, Kahles A, Kahraman A, Kaiser VB, Kakavand H, Kalimuthu S, von Kalle C, Kang KJ, Karaszi K, Karlan B, Karlić R, Karsch D, Kasaian K, Kassahn KS, Katai H, Kato M, Katoh H, Kawakami Y, Kay JD, Kazakoff SH, Kazanov MD, Keays M, Kebebew E, Kefford RF, Kellis M, Kench JG, Kennedy CJ, Kerssemakers JNA, Khoo D, Khoo V, Khuntikeo N, Khurana E, Kilpinen H, Kim HK, Kim HL, Kim HY, Kim H, Kim J, Kim J, Kim JK, Kim Y, King TA, Klapper W, Kleinheinz K, Klimczak LJ, Knappskog S, Kneba M, Knoppers BM, Koh Y, Komorowski J, Komura D, Komura M, Kong G, Kool M, Korbel JO, Korchina V, Korshunov A, Koscher M, Koster R, Kote-Jarai Z, Koures A, Kovacevic M, Kremeyer B, Kretzmer H, Kreuz M, Krishnamurthy S, Kube D, Kumar K, Kumar P, Kumar S, Kumar Y, Kundra R, Kübler K, Küppers R, Lagergren J, Lai PH, Laird PW, Lakhani SR, Lalansingh CM, Lalonde E, Lamaze FC, Lambert A, Lander E, Landgraf P, Landoni L, Langerød A, Lanzós A, Larsimont D, Larsson E, Lathrop M, Lau LMS, Lawerenz C, Lawlor RT, Lawrence MS, Lazar AJ, Lazic AM, Le X, Lee D, Lee D, Lee EA, Lee HJ, Lee JJK, Lee JY, Lee J, Lee MTM, Lee-Six H, Lehmann KV, Lehrach H, Lenze D, Leonard CR, Leongamornlert DA, Leshchiner I, Letourneau L, Letunic I, Levine DA, Lewis L, Ley T, Li C, Li CH, Li HI, Li J, Li L, Li S, Li S, Li X, Li X, Li X, Li Y, Liang H, Liang SB, Lichter P, Lin P, Lin Z, Linehan WM, Lingjærde OC, Liu D, Liu EM, Liu FFF, Liu F, Liu J, Liu X, Livingstone J, Livitz D, Livni N, Lochovsky L, Loeffler M, Long GV, Lopez-Guillermo A, Lou S, Louis DN, Lovat LB, Lu Y, Lu YJ, Lu Y, Luchini C, Lungu I, Luo X, Luxton HJ, Lynch AG, Lype L, López C, López-Otín C, Ma EZ, Ma Y, MacGrogan G, MacRae S, Macintyre G, Madsen T, Maejima K, Mafficini A, Maglinte DT, Maitra A, Majumder PP, Malcovati L, Malikic S, Malleo G, Mann GJ, Mantovani-Löffler L, Marchal K, Marchegiani G, Mardis ER, Margolin AA, Marin MG, Markowetz F, Markowski J, Marks J, Marques-Bonet T, Marra MA, Marsden L, Martens JWM, Martin S, Martin-Subero JI, Martincorena I, Martinez-Fundichely A, Maruvka YE, Mashl RJ, Massie CE, Matthew TJ, Matthews L, Mayer E, Mayes S, Mayo M, Mbabaali F, McCune K, McDermott U, McGillivray PD, McLellan MD, McPherson JD, McPherson JR, McPherson TA, Meier SR, Meng A, Meng S, Menzies A, Merrett ND, Merson S, Meyerson M, Meyerson W, Mieczkowski PA, Mihaiescu GL, Mijalkovic S, Mikkelsen T, Milella M, Mileshkin L, Miller CA, Miller DK, Miller JK, Mills GB, Milovanovic A, Minner S, Miotto M, Arnau GM, Mirabello L, Mitchell C, Mitchell TJ, Miyano S, Miyoshi N, Mizuno S, Molnár-Gábor F, Moore MJ, Moore RA, Morganella S, Morris QD, Morrison C, Mose LE, Moser CD, Muiños F, Mularoni L, Mungall AJ, Mungall K, Musgrove EA, Mustonen V, Mutch D, Muyas F, Muzny DM, Muñoz A, Myers J, Myklebost O, Möller P, Nagae G, Nagrial AM, Nahal-Bose HK, Nakagama H, Nakagawa H, Nakamura H, Nakamura T, Nakano K, Nandi T, Nangalia J, Nastic M, Navarro A, Navarro FCP, Neal DE, Nettekoven G, Newell F, Newhouse SJ, Newton Y, Ng AWT, Ng A, Nicholson J, Nicol D, Nie Y, Nielsen GP, Nielsen MM, Nik-Zainal S, Noble MS, Nones K, Northcott PA, Notta F, O’Connor BD, O’Donnell P, O’Donovan M, O’Meara S, O’Neill BP, O’Neill JR, Ocana D, Ochoa A, Oesper L, Ogden C, Ohdan H, Ohi K, Ohno-Machado L, Oien KA, Ojesina AI, Ojima H, Okusaka T, Omberg L, Ong CK, Ossowski S, Ott G, Ouellette BFF, P’ng C, Paczkowska M, Paiella S, Pairojkul C, Pajic M, Pan-Hammarström Q, Papaemmanuil E, Papatheodorou I, Paramasivam N, Park JW, Park JW, Park K, Park K, Park PJ, Parker JS, Parsons SL, Pass H, Pasternack D, Pastore A, Patch AM, Pauporté I, Pea A, Pearson JV. Author Correction: Genomic basis for RNA alterations in cancer. Nature 2023; 614:E37. [PMID: 36697831 PMCID: PMC9931574 DOI: 10.1038/s41586-022-05596-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
| | - Claudia Calabrese
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Natalie R. Davidson
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medical College, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Deniz Demircioğlu
- grid.4280.e0000 0001 2180 6431National University of Singapore, Singapore, Singapore ,grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore
| | - Nuno A. Fonseca
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Yao He
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
| | - André Kahles
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Kjong-Van Lehmann
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Fenglin Liu
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
| | - Yuichi Shiraishi
- grid.26999.3d0000 0001 2151 536XThe University of Tokyo, Minato-ku, Japan
| | - Cameron M. Soulette
- grid.205975.c0000 0001 0740 6917University of California, Santa Cruz, Santa Cruz, CA USA
| | - Lara Urban
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Liliana Greger
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Siliang Li
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Dongbing Liu
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Marc D. Perry
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada ,grid.266102.10000 0001 2297 6811University of California, San Francisco, San Francisco, CA USA
| | - Qian Xiang
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Fan Zhang
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
| | - Junjun Zhang
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Peter Bailey
- grid.8756.c0000 0001 2193 314XUniversity of Glasgow, Glasgow, UK
| | - Serap Erkek
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Katherine A. Hoadley
- grid.10698.360000000122483208The University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Yong Hou
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Matthew R. Huska
- grid.419491.00000 0001 1014 0849Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany
| | - Helena Kilpinen
- grid.83440.3b0000000121901201University College London, London, UK
| | - Jan O. Korbel
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Maximillian G. Marin
- grid.205975.c0000 0001 0740 6917University of California, Santa Cruz, Santa Cruz, CA USA
| | - Julia Markowski
- grid.419491.00000 0001 1014 0849Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany
| | - Tannistha Nandi
- grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore
| | - Qiang Pan-Hammarström
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.4714.60000 0004 1937 0626Karolinska Institutet, Stockholm, Sweden
| | - Chandra Sekhar Pedamallu
- grid.66859.340000 0004 0546 1623Broad Institute, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | - Reiner Siebert
- grid.410712.10000 0004 0473 882XUlm University and Ulm University Medical Center, Ulm, Germany
| | - Stefan G. Stark
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Hong Su
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Patrick Tan
- grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore ,grid.428397.30000 0004 0385 0924Duke-NUS Medical School, Singapore, Singapore
| | - Sebastian M. Waszak
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Christina Yung
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Shida Zhu
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Philip Awadalla
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada ,grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada
| | - Chad J. Creighton
- grid.39382.330000 0001 2160 926XBaylor College of Medicine, Houston, TX USA
| | - Matthew Meyerson
- grid.66859.340000 0004 0546 1623Broad Institute, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | | | - Kui Wu
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Huanming Yang
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China
| | | | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
| | - Angela N. Brooks
- grid.205975.c0000 0001 0740 6917University of California, Santa Cruz, Santa Cruz, CA USA ,grid.66859.340000 0004 0546 1623Broad Institute, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA
| | - Jonathan Göke
- grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore ,grid.410724.40000 0004 0620 9745National Cancer Centre Singapore, Singapore, Singapore
| | - Gunnar Rätsch
- ETH Zurich, Zurich, Switzerland. .,Memorial Sloan Kettering Cancer Center, New York, NY, USA. .,Weill Cornell Medical College, New York, NY, USA. .,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland. .,University Hospital Zurich, Zurich, Switzerland.
| | - Roland F. Schwarz
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK ,grid.419491.00000 0001 1014 0849Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), partner site Berlin, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oliver Stegle
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK ,grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Zemin Zhang
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
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Chervova A, Fatykhov B, Koblov A, Shvarov E, Preobrazhenskaya J, Vinogradov D, Ponomarev GV, Gelfand MS, Kazanov MD. Analysis of gene expression and mutation data points on contribution of transcription to the mutagenesis by APOBEC enzymes. NAR Cancer 2021; 3:zcab025. [PMID: 34316712 PMCID: PMC8253550 DOI: 10.1093/narcan/zcab025] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 06/04/2021] [Accepted: 06/14/2021] [Indexed: 11/30/2022] Open
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. 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.
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Affiliation(s)
- Almira Chervova
- Institute of Oncology, Radiology and Nuclear Medicine, Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, 117997, Russia
| | - Bulat Fatykhov
- Department of Control and Applied Mathematics, Moscow Institute of Physics and Technology, Dolgoprudny, 141700, Russia
| | | | | | - Julia Preobrazhenskaya
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119234, Russia
| | - Dmitry Vinogradov
- Research and Training Center of Bioinformatics, Institute for Information Transmission Problems (the Kharkevich Institute, RAS), Moscow, 127051, Russia
| | - Gennady V Ponomarev
- Research and Training Center of Bioinformatics, Institute for Information Transmission Problems (the Kharkevich Institute, RAS), Moscow, 127051, Russia
| | - Mikhail S Gelfand
- Research and Training Center of Bioinformatics, Institute for Information Transmission Problems (the Kharkevich Institute, RAS), Moscow, 127051, Russia
| | - Marat D Kazanov
- Research and Training Center of Bioinformatics, Institute for Information Transmission Problems (the Kharkevich Institute, RAS), Moscow, 127051, Russia
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Aaltonen LA, Abascal F, Abeshouse A, Aburatani H, Adams DJ, Agrawal N, Ahn KS, Ahn SM, Aikata H, Akbani R, Akdemir KC, Al-Ahmadie H, Al-Sedairy ST, Al-Shahrour F, Alawi M, Albert M, Aldape K, Alexandrov LB, Ally A, Alsop K, Alvarez EG, Amary F, Amin SB, Aminou B, Ammerpohl O, Anderson MJ, Ang Y, Antonello D, Anur P, Aparicio S, Appelbaum EL, Arai Y, Aretz A, Arihiro K, Ariizumi SI, Armenia J, Arnould L, Asa S, Assenov Y, Atwal G, Aukema S, Auman JT, Aure MRR, Awadalla P, Aymerich M, Bader GD, Baez-Ortega A, Bailey MH, Bailey PJ, Balasundaram M, Balu S, Bandopadhayay P, Banks RE, Barbi S, Barbour AP, Barenboim J, Barnholtz-Sloan J, Barr H, Barrera E, Bartlett J, Bartolome J, Bassi C, Bathe OF, Baumhoer D, Bavi P, Baylin SB, Bazant W, Beardsmore D, Beck TA, Behjati S, Behren A, Niu B, Bell C, Beltran S, Benz C, Berchuck A, Bergmann AK, Bergstrom EN, Berman BP, Berney DM, Bernhart SH, Beroukhim R, Berrios M, Bersani S, Bertl J, Betancourt M, Bhandari V, Bhosle SG, Biankin AV, Bieg M, Bigner D, Binder H, Birney E, Birrer M, Biswas NK, Bjerkehagen B, Bodenheimer T, Boice L, Bonizzato G, De Bono JS, Boot A, Bootwalla MS, Borg A, Borkhardt A, Boroevich KA, Borozan I, Borst C, Bosenberg M, Bosio M, Boultwood J, Bourque G, Boutros PC, Bova GS, Bowen DT, Bowlby R, Bowtell DDL, Boyault S, Boyce R, Boyd J, Brazma A, Brennan P, Brewer DS, Brinkman AB, Bristow RG, Broaddus RR, Brock JE, Brock M, Broeks A, Brooks AN, Brooks D, Brors B, Brunak S, Bruxner TJC, Bruzos AL, Buchanan A, Buchhalter I, Buchholz C, Bullman S, Burke H, Burkhardt B, Burns KH, Busanovich J, Bustamante CD, Butler AP, Butte AJ, Byrne NJ, Børresen-Dale AL, Caesar-Johnson SJ, Cafferkey A, Cahill D, Calabrese C, Caldas C, Calvo F, Camacho N, Campbell PJ, Campo E, Cantù C, Cao S, Carey TE, Carlevaro-Fita J, Carlsen R, Cataldo I, Cazzola M, Cebon J, Cerfolio R, Chadwick DE, Chakravarty D, Chalmers D, Chan CWY, Chan K, Chan-Seng-Yue M, Chandan VS, Chang DK, Chanock SJ, Chantrill LA, Chateigner A, Chatterjee N, Chayama K, Chen HW, Chen J, Chen K, Chen Y, Chen Z, Cherniack AD, Chien J, Chiew YE, Chin SF, Cho J, Cho S, Choi JK, Choi W, Chomienne C, Chong Z, Choo SP, Chou A, Christ AN, Christie EL, Chuah E, Cibulskis C, Cibulskis K, Cingarlini S, Clapham P, Claviez A, Cleary S, Cloonan N, Cmero M, Collins CC, Connor AA, Cooke SL, Cooper CS, Cope L, Corbo V, Cordes MG, Cordner SM, Cortés-Ciriano I, Covington K, Cowin PA, Craft B, Craft D, Creighton CJ, Cun Y, Curley E, Cutcutache I, Czajka K, Czerniak B, Dagg RA, Danilova L, Davi MV, Davidson NR, Davies H, Davis IJ, Davis-Dusenbery BN, Dawson KJ, De La Vega FM, De Paoli-Iseppi R, Defreitas T, Tos APD, Delaneau O, Demchok JA, Demeulemeester J, Demidov GM, Demircioğlu D, Dennis NM, Denroche RE, Dentro SC, Desai N, Deshpande V, Deshwar AG, Desmedt C, Deu-Pons J, Dhalla N, Dhani NC, Dhingra P, Dhir R, DiBiase A, Diamanti K, Ding L, Ding S, Dinh HQ, Dirix L, Doddapaneni H, Donmez N, Dow MT, Drapkin R, Drechsel O, Drews RM, Serge S, Dudderidge T, Dueso-Barroso A, Dunford AJ, Dunn M, Dursi LJ, Duthie FR, Dutton-Regester K, Eagles J, Easton DF, Edmonds S, Edwards PA, Edwards SE, Eeles RA, Ehinger A, Eils J, Eils R, El-Naggar A, Eldridge M, Ellrott K, Erkek S, Escaramis G, Espiritu SMG, Estivill X, Etemadmoghadam D, Eyfjord JE, Faltas BM, Fan D, Fan Y, Faquin WC, Farcas C, Fassan M, Fatima A, Favero F, Fayzullaev N, Felau I, Fereday S, Ferguson ML, Ferretti V, Feuerbach L, Field MA, Fink JL, Finocchiaro G, Fisher C, Fittall MW, Fitzgerald A, Fitzgerald RC, Flanagan AM, Fleshner NE, Flicek P, Foekens JA, Fong KM, Fonseca NA, Foster CS, Fox NS, Fraser M, Frazer S, Frenkel-Morgenstern M, Friedman W, Frigola J, Fronick CC, Fujimoto A, Fujita M, Fukayama M, Fulton LA, Fulton RS, Furuta M, Futreal PA, Füllgrabe A, Gabriel SB, Gallinger S, Gambacorti-Passerini C, Gao J, Gao S, Garraway L, Garred Ø, Garrison E, Garsed DW, Gehlenborg N, Gelpi JLL, George J, Gerhard DS, Gerhauser C, Gershenwald JE, Gerstein M, Gerstung M, Getz G, Ghori M, Ghossein R, Giama NH, Gibbs RA, Gibson B, Gill AJ, Gill P, Giri DD, Glodzik D, Gnanapragasam VJ, Goebler ME, Goldman MJ, Gomez C, Gonzalez S, Gonzalez-Perez A, Gordenin DA, Gossage J, Gotoh K, Govindan R, Grabau D, Graham JS, Grant RC, Green AR, Green E, Greger L, Grehan N, Grimaldi S, Grimmond SM, Grossman RL, Grundhoff A, Gundem G, Guo Q, Gupta M, Gupta S, Gut IG, Gut M, Göke J, Ha G, Haake A, Haan D, Haas S, Haase K, Haber JE, Habermann N, Hach F, Haider S, Hama N, Hamdy FC, Hamilton A, Hamilton MP, Han L, Hanna GB, Hansmann M, Haradhvala NJ, Harismendy O, Harliwong I, Harmanci AO, Harrington E, Hasegawa T, Haussler D, Hawkins S, Hayami S, Hayashi S, Hayes DN, Hayes SJ, Hayward NK, Hazell S, He Y, Heath AP, Heath SC, Hedley D, Hegde AM, Heiman DI, Heinold MC, Heins Z, Heisler LE, Hellstrom-Lindberg E, Helmy M, Heo SG, Hepperla AJ, Heredia-Genestar JM, Herrmann C, Hersey P, Hess JM, Hilmarsdottir H, Hinton J, Hirano S, Hiraoka N, Hoadley KA, Hobolth A, Hodzic E, Hoell JI, Hoffmann S, Hofmann O, Holbrook A, Holik AZ, Hollingsworth MA, Holmes O, Holt RA, Hong C, Hong EP, Hong JH, Hooijer GK, Hornshøj H, Hosoda F, Hou Y, Hovestadt V, Howat W, Hoyle AP, Hruban RH, Hu J, Hu T, Hua X, Huang KL, Huang M, Huang MN, Huang V, Huang Y, Huber W, Hudson TJ, Hummel M, Hung JA, Huntsman D, Hupp TR, Huse J, Huska MR, Hutter B, Hutter CM, Hübschmann D, Iacobuzio-Donahue CA, Imbusch CD, Imielinski M, Imoto S, Isaacs WB, Isaev K, Ishikawa S, Iskar M, Islam SMA, Ittmann M, Ivkovic S, Izarzugaza JMG, Jacquemier J, Jakrot V, Jamieson NB, Jang GH, Jang SJ, Jayaseelan JC, Jayasinghe R, Jefferys SR, Jegalian K, Jennings JL, Jeon SH, Jerman L, Ji Y, Jiao W, Johansson PA, Johns AL, Johns J, Johnson R, Johnson TA, Jolly C, Joly Y, Jonasson JG, Jones CD, Jones DR, Jones DTW, Jones N, Jones SJM, Jonkers J, Ju YS, Juhl H, Jung J, Juul M, Juul RI, Juul S, Jäger N, Kabbe R, Kahles A, Kahraman A, Kaiser VB, Kakavand H, Kalimuthu S, von Kalle C, Kang KJ, Karaszi K, Karlan B, Karlić R, Karsch D, Kasaian K, Kassahn KS, Katai H, Kato M, Katoh H, Kawakami Y, Kay JD, Kazakoff SH, Kazanov MD, Keays M, Kebebew E, Kefford RF, Kellis M, Kench JG, Kennedy CJ, Kerssemakers JNA, Khoo D, Khoo V, Khuntikeo N, Khurana E, Kilpinen H, Kim HK, Kim HL, Kim HY, Kim H, Kim J, Kim J, Kim JK, Kim Y, King TA, Klapper W, Kleinheinz K, Klimczak LJ, Knappskog S, Kneba M, Knoppers BM, Koh Y, Komorowski J, Komura D, Komura M, Kong G, Kool M, Korbel JO, Korchina V, Korshunov A, Koscher M, Koster R, Kote-Jarai Z, Koures A, Kovacevic M, Kremeyer B, Kretzmer H, Kreuz M, Krishnamurthy S, Kube D, Kumar K, Kumar P, Kumar S, Kumar Y, Kundra R, Kübler K, Küppers R, Lagergren J, Lai PH, Laird PW, Lakhani SR, Lalansingh CM, Lalonde E, Lamaze FC, Lambert A, Lander E, Landgraf P, Landoni L, Langerød A, Lanzós A, Larsimont D, Larsson E, Lathrop M, Lau LMS, Lawerenz C, Lawlor RT, Lawrence MS, Lazar AJ, Lazic AM, Le X, Lee D, Lee D, Lee EA, Lee HJ, Lee JJK, Lee JY, Lee J, Lee MTM, Lee-Six H, Lehmann KV, Lehrach H, Lenze D, Leonard CR, Leongamornlert DA, Leshchiner I, Letourneau L, Letunic I, Levine DA, Lewis L, Ley T, Li C, Li CH, Li HI, Li J, Li L, Li S, Li S, Li X, Li X, Li X, Li Y, Liang H, Liang SB, Lichter P, Lin P, Lin Z, Linehan WM, Lingjærde OC, Liu D, Liu EM, Liu FFF, Liu F, Liu J, Liu X, Livingstone J, Livitz D, Livni N, Lochovsky L, Loeffler M, Long GV, Lopez-Guillermo A, Lou S, Louis DN, Lovat LB, Lu Y, Lu YJ, Lu Y, Luchini C, Lungu I, Luo X, Luxton HJ, Lynch AG, Lype L, López C, López-Otín C, Ma EZ, Ma Y, MacGrogan G, MacRae S, Macintyre G, Madsen T, Maejima K, Mafficini A, Maglinte DT, Maitra A, Majumder PP, Malcovati L, Malikic S, Malleo G, Mann GJ, Mantovani-Löffler L, Marchal K, Marchegiani G, Mardis ER, Margolin AA, Marin MG, Markowetz F, Markowski J, Marks J, Marques-Bonet T, Marra MA, Marsden L, Martens JWM, Martin S, Martin-Subero JI, Martincorena I, Martinez-Fundichely A, Maruvka YE, Mashl RJ, Massie CE, Matthew TJ, Matthews L, Mayer E, Mayes S, Mayo M, Mbabaali F, McCune K, McDermott U, McGillivray PD, McLellan MD, McPherson JD, McPherson JR, McPherson TA, Meier SR, Meng A, Meng S, Menzies A, Merrett ND, Merson S, Meyerson M, Meyerson W, Mieczkowski PA, Mihaiescu GL, Mijalkovic S, Mikkelsen T, Milella M, Mileshkin L, Miller CA, Miller DK, Miller JK, Mills GB, Milovanovic A, Minner S, Miotto M, Arnau GM, Mirabello L, Mitchell C, Mitchell TJ, Miyano S, Miyoshi N, Mizuno S, Molnár-Gábor F, Moore MJ, Moore RA, Morganella S, Morris QD, Morrison C, Mose LE, Moser CD, Muiños F, Mularoni L, Mungall AJ, Mungall K, Musgrove EA, Mustonen V, Mutch D, Muyas F, Muzny DM, Muñoz A, Myers J, Myklebost O, Möller P, Nagae G, Nagrial AM, Nahal-Bose HK, Nakagama H, Nakagawa H, Nakamura H, Nakamura T, Nakano K, Nandi T, Nangalia J, Nastic M, Navarro A, Navarro FCP, Neal DE, Nettekoven G, Newell F, Newhouse SJ, Newton Y, Ng AWT, Ng A, Nicholson J, Nicol D, Nie Y, Nielsen GP, Nielsen MM, Nik-Zainal S, Noble MS, Nones K, Northcott PA, Notta F, O’Connor BD, O’Donnell P, O’Donovan M, O’Meara S, O’Neill BP, O’Neill JR, Ocana D, Ochoa A, Oesper L, Ogden C, Ohdan H, Ohi K, Ohno-Machado L, Oien KA, Ojesina AI, Ojima H, Okusaka T, Omberg L, Ong CK, Ossowski S, Ott G, Ouellette BFF, P’ng C, Paczkowska M, Paiella S, Pairojkul C, Pajic M, Pan-Hammarström Q, Papaemmanuil E, Papatheodorou I, Paramasivam N, Park JW, Park JW, Park K, Park K, Park PJ, Parker JS, Parsons SL, Pass H, Pasternack D, Pastore A, Patch AM, Pauporté I, Pea A, Pearson JV, Pedamallu CS, Pedersen JS, Pederzoli P, Peifer M, Pennell NA, Perou CM, Perry MD, Petersen GM, Peto M, Petrelli N, Petryszak R, Pfister SM, Phillips M, Pich O, Pickett HA, Pihl TD, Pillay N, Pinder S, Pinese M, Pinho AV, Pitkänen E, Pivot X, Piñeiro-Yáñez E, Planko L, Plass C, Polak P, Pons T, Popescu I, Potapova O, Prasad A, Preston SR, Prinz M, Pritchard AL, Prokopec SD, Provenzano E, Puente XS, Puig S, Puiggròs M, Pulido-Tamayo S, Pupo GM, Purdie CA, Quinn MC, Rabionet R, Rader JS, Radlwimmer B, Radovic P, Raeder B, Raine KM, Ramakrishna M, Ramakrishnan K, Ramalingam S, Raphael BJ, Rathmell WK, Rausch T, Reifenberger G, Reimand J, Reis-Filho J, Reuter V, Reyes-Salazar I, Reyna MA, Reynolds SM, Rheinbay E, Riazalhosseini Y, Richardson AL, Richter J, Ringel M, Ringnér M, Rino Y, Rippe K, Roach J, Roberts LR, Roberts ND, Roberts SA, Robertson AG, Robertson AJ, Rodriguez JB, Rodriguez-Martin B, Rodríguez-González FG, Roehrl MHA, Rohde M, Rokutan H, Romieu G, Rooman I, Roques T, Rosebrock D, Rosenberg M, Rosenstiel PC, Rosenwald A, Rowe EW, Royo R, Rozen SG, Rubanova Y, Rubin MA, Rubio-Perez C, Rudneva VA, Rusev BC, Ruzzenente A, Rätsch G, Sabarinathan R, Sabelnykova VY, Sadeghi S, Sahinalp SC, Saini N, Saito-Adachi M, Saksena G, Salcedo A, Salgado R, Salichos L, Sallari R, Saller C, Salvia R, Sam M, Samra JS, Sanchez-Vega F, Sander C, Sanders G, Sarin R, Sarrafi I, Sasaki-Oku A, Sauer T, Sauter G, Saw RPM, Scardoni M, Scarlett CJ, Scarpa A, Scelo G, Schadendorf D, Schein JE, Schilhabel MB, Schlesner M, Schlomm T, Schmidt HK, Schramm SJ, Schreiber S, Schultz N, Schumacher SE, Schwarz RF, Scolyer RA, Scott D, Scully R, Seethala R, Segre AV, Selander I, Semple CA, Senbabaoglu Y, Sengupta S, Sereni E, Serra S, Sgroi DC, Shackleton M, Shah NC, Shahabi S, Shang CA, Shang P, Shapira O, Shelton T, Shen C, Shen H, Shepherd R, Shi R, Shi Y, Shiah YJ, Shibata T, Shih J, Shimizu E, Shimizu K, Shin SJ, Shiraishi Y, Shmaya T, Shmulevich I, Shorser SI, Short C, Shrestha R, Shringarpure SS, Shriver C, Shuai S, Sidiropoulos N, Siebert R, Sieuwerts AM, Sieverling L, Signoretti S, Sikora KO, Simbolo M, Simon R, Simons JV, Simpson JT, Simpson PT, Singer S, Sinnott-Armstrong N, Sipahimalani P, Skelly TJ, Smid M, Smith J, Smith-McCune K, Socci ND, Sofia HJ, Soloway MG, Song L, Sood AK, Sothi S, Sotiriou C, Soulette CM, Span PN, Spellman PT, Sperandio N, Spillane AJ, Spiro O, Spring J, Staaf J, Stadler PF, Staib P, Stark SG, Stebbings L, Stefánsson ÓA, Stegle O, Stein LD, Stenhouse A, Stewart C, Stilgenbauer S, Stobbe MD, Stratton MR, Stretch JR, Struck AJ, Stuart JM, Stunnenberg HG, Su H, Su X, Sun RX, Sungalee S, Susak H, Suzuki A, Sweep F, Szczepanowski M, Sültmann H, Yugawa T, Tam A, Tamborero D, Tan BKT, Tan D, Tan P, Tanaka H, Taniguchi H, Tanskanen TJ, Tarabichi M, Tarnuzzer R, Tarpey P, Taschuk ML, Tatsuno K, Tavaré S, Taylor DF, Taylor-Weiner A, Teague JW, Teh BT, Tembe V, Temes J, Thai K, Thayer SP, Thiessen N, Thomas G, Thomas S, Thompson A, Thompson AM, Thompson JFF, Thompson RH, Thorne H, Thorne LB, Thorogood A, Tiao G, Tijanic N, Timms LE, Tirabosco R, Tojo M, Tommasi S, Toon CW, Toprak UH, Torrents D, Tortora G, Tost J, Totoki Y, Townend D, Traficante N, Treilleux I, Trotta JR, Trümper LHP, Tsao M, Tsunoda T, Tubio JMC, Tucker O, Turkington R, Turner DJ, Tutt A, Ueno M, Ueno NT, Umbricht C, Umer HM, Underwood TJ, Urban L, Urushidate T, Ushiku T, Uusküla-Reimand L, Valencia A, Van Den Berg DJ, Van Laere S, Van Loo P, Van Meir EG, Van den Eynden GG, Van der Kwast T, Vasudev N, Vazquez M, Vedururu R, Veluvolu U, Vembu S, Verbeke LPC, Vermeulen P, Verrill C, Viari A, Vicente D, Vicentini C, VijayRaghavan K, Viksna J, Vilain RE, Villasante I, Vincent-Salomon A, Visakorpi T, Voet D, Vyas P, Vázquez-García I, Waddell NM, Waddell N, Wadelius C, Wadi L, Wagener R, Wala JA, Wang J, Wang J, Wang L, Wang Q, Wang W, Wang Y, Wang Z, Waring PM, Warnatz HJ, Warrell J, Warren AY, Waszak SM, Wedge DC, Weichenhan D, Weinberger P, Weinstein JN, Weischenfeldt J, Weisenberger DJ, Welch I, Wendl MC, Werner J, Whalley JP, Wheeler DA, Whitaker HC, Wigle D, Wilkerson MD, Williams A, Wilmott JS, Wilson GW, Wilson JM, Wilson RK, Winterhoff B, Wintersinger JA, Wiznerowicz M, Wolf S, Wong BH, Wong T, Wong W, Woo Y, Wood S, Wouters BG, Wright AJ, Wright DW, Wright MH, Wu CL, Wu DY, Wu G, Wu J, Wu K, Wu Y, Wu Z, Xi L, Xia T, Xiang Q, Xiao X, Xing R, Xiong H, Xu Q, Xu Y, Xue H, Yachida S, Yakneen S, Yamaguchi R, Yamaguchi TN, Yamamoto M, Yamamoto S, Yamaue H, Yang F, Yang H, Yang JY, Yang L, Yang L, Yang S, Yang TP, Yang Y, Yao X, Yaspo ML, Yates L, Yau C, Ye C, Ye K, Yellapantula VD, Yoon CJ, Yoon SS, Yousif F, Yu J, Yu K, Yu W, Yu Y, Yuan K, Yuan Y, Yuen D, Yung CK, Zaikova O, Zamora J, Zapatka M, Zenklusen JC, Zenz T, Zeps N, Zhang CZ, Zhang F, Zhang H, Zhang H, Zhang H, Zhang J, Zhang J, Zhang J, Zhang X, Zhang X, Zhang Y, Zhang Z, Zhao Z, Zheng L, Zheng X, Zhou W, Zhou Y, Zhu B, Zhu H, Zhu J, Zhu S, Zou L, Zou X, deFazio A, van As N, van Deurzen CHM, van de Vijver MJ, van’t Veer L, von Mering C. Pan-cancer analysis of whole genomes. Nature 2020; 578:82-93. [PMID: 32025007 PMCID: PMC7025898 DOI: 10.1038/s41586-020-1969-6] [Citation(s) in RCA: 1435] [Impact Index Per Article: 358.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Accepted: 12/11/2019] [Indexed: 02/07/2023]
Abstract
Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.
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Fedonin GG, Eroshkin A, Cieplak P, Matveev EV, Ponomarev GV, Gelfand MS, Ratnikov BI, Kazanov MD. Predictive models of protease specificity based on quantitative protease-activity profiling data. Biochim Biophys Acta Proteins Proteom 2019; 1867:140253. [PMID: 31330204 DOI: 10.1016/j.bbapap.2019.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 07/09/2019] [Accepted: 07/17/2019] [Indexed: 10/26/2022]
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 toward 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.
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Affiliation(s)
- Gennady G Fedonin
- Central Research Institute of Epidemiology, Moscow 111123, Russia; A.A.Kharkevich Institute of Information Transmission Problems, Moscow 127051, Russia; Moscow Institute of Physics and Technology, Dolgoprudny 141700, Russia
| | - Alexey Eroshkin
- Sanford-Burnham-Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Piotr Cieplak
- Sanford-Burnham-Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | | | - Gennady V Ponomarev
- A.A.Kharkevich Institute of Information Transmission Problems, Moscow 127051, Russia
| | - Mikhail S Gelfand
- A.A.Kharkevich Institute of Information Transmission Problems, Moscow 127051, Russia; Skolkovo Institute of Science and Technology, Moscow 121205, Russia; National Research University Higher School of Economics, Moscow 101000, Russia
| | - Boris I Ratnikov
- Sanford-Burnham-Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Marat D Kazanov
- A.A.Kharkevich Institute of Information Transmission Problems, Moscow 127051, Russia; Skolkovo Institute of Science and Technology, Moscow 121205, Russia; Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Moscow 117997, Russia.
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Pokrovsky VS, Kazanov MD, Dyakov IN, Pokrovskaya MV, Aleksandrova SS. Comparative immunogenicity and structural analysis of epitopes of different bacterial L-asparaginases. BMC Cancer 2016; 16:89. [PMID: 26867931 PMCID: PMC4750198 DOI: 10.1186/s12885-016-2125-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Accepted: 02/04/2016] [Indexed: 12/02/2022] Open
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.
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Affiliation(s)
- Vadim S Pokrovsky
- V.N. Orekhovich Institute of Biomedical Chemistry, Moscow, Russia. .,N.N. Blokhin Cancer Research Center, Moscow, Russia.
| | - Marat D Kazanov
- Research and Training Center on Bioinformatics, A.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Science, Moscow, Russia.
| | - Ilya N Dyakov
- I.I. Mechnikov Research Institute of Vaccine and Sera, Moscow, Russia.
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Kazanov MD, Roberts SA, Polak P, Stamatoyannopoulos J, Klimczak LJ, Gordenin DA, Sunyaev SR. APOBEC-Induced Cancer Mutations Are Uniquely Enriched in Early-Replicating, Gene-Dense, and Active Chromatin Regions. Cell Rep 2015; 13:1103-1109. [PMID: 26527001 DOI: 10.1016/j.celrep.2015.09.077] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Revised: 08/11/2015] [Accepted: 09/25/2015] [Indexed: 10/22/2022] Open
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.
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Affiliation(s)
- Marat D Kazanov
- Research and Training Center on Bioinformatics, A.A. Kharkevich Institute for Information Transmission Problems, RAS, Moscow 127051, Russia
| | - Steven A Roberts
- National Institute of Environmental Health Sciences, Durham, NC 27709, USA; School of Molecular Biosciences, Washington State University, Pullman, WA 99164, USA
| | - Paz Polak
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - John Stamatoyannopoulos
- Departments of Genome Sciences and Medicine, University of Washington, Seattle, WA 98195, USA
| | - Leszek J Klimczak
- National Institute of Environmental Health Sciences, Durham, NC 27709, USA
| | - Dmitry A Gordenin
- National Institute of Environmental Health Sciences, Durham, NC 27709, USA.
| | - Shamil R Sunyaev
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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Kumar S, Ratnikov BI, Kazanov MD, Smith JW, Cieplak P. Correction: CleavPredict: A Platform for Reasoning about Matrix Metalloproteinases Proteolytic Events. PLoS One 2015; 10:e0131952. [PMID: 26110776 PMCID: PMC4482498 DOI: 10.1371/journal.pone.0131952] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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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.
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Affiliation(s)
- Sofya K Garushyants
- A.A. Kharkevich Institute for Information Transmission Problems, RAS, Bolshoi Karetny per. 19, build.1, Moscow, 127051, Russia.
| | - Marat D Kazanov
- A.A. Kharkevich Institute for Information Transmission Problems, RAS, Bolshoi Karetny per. 19, build.1, Moscow, 127051, Russia.
| | - Mikhail S Gelfand
- A.A. Kharkevich Institute for Information Transmission Problems, RAS, Bolshoi Karetny per. 19, build.1, Moscow, 127051, Russia. .,Faculty of Bioengineering and Bioinformatics, M.V. Lomonosov Moscow State University, Vorobievy Gory 1-73, Moscow, 119991, Russia.
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15
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Kumar S, Ratnikov BI, Kazanov MD, Smith JW, Cieplak P. CleavPredict: A Platform for Reasoning about Matrix Metalloproteinases Proteolytic Events. PLoS One 2015; 10:e0127877. [PMID: 25996941 PMCID: PMC4440711 DOI: 10.1371/journal.pone.0127877] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2015] [Accepted: 04/21/2015] [Indexed: 11/19/2022] Open
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.
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Affiliation(s)
- Sonu Kumar
- Sanford Burnham Medical Research Institute, La Jolla, California, United States of America
| | - Boris I. Ratnikov
- Sanford Burnham Medical Research Institute, La Jolla, California, United States of America
| | - Marat D. Kazanov
- Institute for Information Transmission Problems, Russian Academy of Science, Moscow, Russia
| | - Jeffrey W. Smith
- Sanford Burnham Medical Research Institute, La Jolla, California, United States of America
| | - Piotr Cieplak
- Sanford Burnham Medical Research Institute, La Jolla, California, United States of America
- * E-mail:
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Rodionova IA, Zuccola HJ, Sorci L, Aleshin AE, Kazanov MD, Ma CT, Sergienko E, Rubin EJ, Locher CP, Osterman AL. Mycobacterial nicotinate mononucleotide adenylyltransferase: structure, mechanism, and implications for drug discovery. J Biol Chem 2015; 290:7693-706. [PMID: 25631047 DOI: 10.1074/jbc.m114.628016] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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.
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Affiliation(s)
- Irina A Rodionova
- From the Sanford-Burnham Medical Research Institute, La Jolla, California 92037
| | - Harmon J Zuccola
- Vertex Pharmaceuticals Incorporated, Boston, Massachusetts 02210
| | - Leonardo Sorci
- Department of Clinical Sciences, Section of Biochemistry, Polytechnic University of Marche, Ancona 60131, Italy
| | - Alexander E Aleshin
- From the Sanford-Burnham Medical Research Institute, La Jolla, California 92037
| | - Marat D Kazanov
- A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, 127051 Moscow, Russia, and
| | - Chen-Ting Ma
- From the Sanford-Burnham Medical Research Institute, La Jolla, California 92037
| | - Eduard Sergienko
- From the Sanford-Burnham Medical Research Institute, La Jolla, California 92037
| | - Eric J Rubin
- Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts 02115
| | | | - Andrei L Osterman
- From the Sanford-Burnham Medical Research Institute, La Jolla, California 92037,
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17
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Pendlebury D, Wang R, Henin RD, Hockla A, Soares AS, Madden BJ, Kazanov MD, Radisky ES. Sequence and conformational specificity in substrate recognition: several human Kunitz protease inhibitor domains are specific substrates of mesotrypsin. J Biol Chem 2014; 289:32783-97. [PMID: 25301953 DOI: 10.1074/jbc.m114.609560] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
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.
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Affiliation(s)
- Devon Pendlebury
- From the Department of Cancer Biology, Mayo Clinic Comprehensive Cancer Center, Jacksonville, Florida 32224
| | - Ruiying Wang
- From the Department of Cancer Biology, Mayo Clinic Comprehensive Cancer Center, Jacksonville, Florida 32224
| | - Rachel D Henin
- From the Department of Cancer Biology, Mayo Clinic Comprehensive Cancer Center, Jacksonville, Florida 32224
| | - Alexandra Hockla
- From the Department of Cancer Biology, Mayo Clinic Comprehensive Cancer Center, Jacksonville, Florida 32224
| | - Alexei S Soares
- the Biology Department, Brookhaven National Laboratory, Upton, New York 11973
| | - Benjamin J Madden
- the Medical Genome Facility Proteomics Core, Mayo Clinic, Rochester, Minnesota 55905, and
| | - Marat D Kazanov
- the A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow 127994, Russia
| | - Evette S Radisky
- From the Department of Cancer Biology, Mayo Clinic Comprehensive Cancer Center, Jacksonville, Florida 32224,
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Rueda S, Fathima S, Knight CL, Yaqub M, Papageorghiou AT, Rahmatullah B, Foi A, Maggioni M, Pepe A, Tohka J, Stebbing RV, McManigle JE, Ciurte A, Bresson X, Cuadra MB, Sun C, Ponomarev GV, Gelfand MS, Kazanov MD, Wang CW, Chen HC, Peng CW, Hung CM, Noble JA. Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge. IEEE Trans Med Imaging 2014; 33:797-813. [PMID: 23934664 DOI: 10.1109/tmi.2013.2276943] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
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.
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19
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Sorci L, Brunetti L, Cialabrini L, Mazzola F, Kazanov MD, D'Auria S, Ruggieri S, Raffaelli N. Characterization of bacterial NMN deamidase as a Ser/Lys hydrolase expands diversity of serine amidohydrolases. FEBS Lett 2014; 588:1016-23. [PMID: 24530526 DOI: 10.1016/j.febslet.2014.01.063] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2013] [Revised: 01/23/2014] [Accepted: 01/26/2014] [Indexed: 10/25/2022]
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.
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Affiliation(s)
- Leonardo Sorci
- Department of Clinical Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Lucia Brunetti
- Department of Agricultural, Food and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Lucia Cialabrini
- Department of Agricultural, Food and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Francesca Mazzola
- Department of Clinical Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Marat D Kazanov
- A.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Sabato D'Auria
- Laboratory for Molecular Sensing, IBP-CNR, Napoli, Italy
| | - Silverio Ruggieri
- Department of Agricultural, Food and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Nadia Raffaelli
- Department of Agricultural, Food and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy.
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Belushkin AA, Vinogradov DV, Gelfand MS, Osterman AL, Cieplak P, Kazanov MD. Sequence-derived structural features driving proteolytic processing. Proteomics 2013; 14:42-50. [PMID: 24227478 DOI: 10.1002/pmic.201300416] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2013] [Revised: 10/22/2013] [Accepted: 10/28/2013] [Indexed: 12/11/2022]
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 5000 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.
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Affiliation(s)
- Alexander A Belushkin
- Faculty of Bioengineering and Bioinformatics, M.V. Lomonosov Moscow State University, Moscow, Russia
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21
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Novichkov PS, Kazakov AE, Ravcheev DA, Leyn SA, Kovaleva GY, Sutormin RA, Kazanov MD, Riehl W, Arkin AP, Dubchak I, Rodionov DA. RegPrecise 3.0--a resource for genome-scale exploration of transcriptional regulation in bacteria. BMC Genomics 2013; 14:745. [PMID: 24175918 PMCID: PMC3840689 DOI: 10.1186/1471-2164-14-745] [Citation(s) in RCA: 265] [Impact Index Per Article: 24.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2013] [Accepted: 10/28/2013] [Indexed: 11/27/2022] Open
Abstract
Background 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). Description 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. Conclusions 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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22
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Sun EI, Leyn SA, Kazanov MD, Saier MH, Novichkov PS, Rodionov DA. Comparative genomics of metabolic capacities of regulons controlled by cis-regulatory RNA motifs in bacteria. BMC Genomics 2013; 14:597. [PMID: 24060102 PMCID: PMC3766115 DOI: 10.1186/1471-2164-14-597] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2013] [Accepted: 08/30/2013] [Indexed: 12/21/2022] Open
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/).
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Affiliation(s)
- Eric I Sun
- Sanford-Burnham Medical Research Institute, 92037 La Jolla, CA, USA.
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Cialabrini L, Ruggieri S, Kazanov MD, Sorci L, Mazzola F, Orsomando G, Osterman AL, Raffaelli N. Genomics-guided analysis of NAD recycling yields functional elucidation of COG1058 as a new family of pyrophosphatases. PLoS One 2013; 8:e65595. [PMID: 23776507 PMCID: PMC3680494 DOI: 10.1371/journal.pone.0065595] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2013] [Accepted: 04/29/2013] [Indexed: 12/02/2022] Open
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.
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Affiliation(s)
- Lucia Cialabrini
- Department of Agricultural, Food and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Silverio Ruggieri
- Department of Agricultural, Food and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Marat D. Kazanov
- A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Leonardo Sorci
- Department of Clinical Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Francesca Mazzola
- Department of Clinical Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Giuseppe Orsomando
- Department of Clinical Sciences, Polytechnic University of Marche, Ancona, Italy
| | - Andrei L. Osterman
- Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
| | - Nadia Raffaelli
- Department of Agricultural, Food and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy
- * E-mail:
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Leyn SA, Kazanov MD, Sernova NV, Ermakova EO, Novichkov PS, Rodionov DA. Genomic reconstruction of the transcriptional regulatory network in Bacillus subtilis. J Bacteriol 2013; 195:2463-73. [PMID: 23504016 PMCID: PMC3676070 DOI: 10.1128/jb.00140-13] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Accepted: 03/11/2013] [Indexed: 12/26/2022] Open
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.
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Affiliation(s)
- Semen A. Leyn
- Sanford-Burnham Medical Research Institute, La Jolla, California, USA
- A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Marat D. Kazanov
- A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Natalia V. Sernova
- A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Ekaterina O. Ermakova
- A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | | | - Dmitry A. Rodionov
- Sanford-Burnham Medical Research Institute, La Jolla, California, USA
- A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
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Ravcheev DA, Best AA, Sernova NV, Kazanov MD, Novichkov PS, Rodionov DA. Genomic reconstruction of transcriptional regulatory networks in lactic acid bacteria. BMC Genomics 2013; 14:94. [PMID: 23398941 PMCID: PMC3616900 DOI: 10.1186/1471-2164-14-94] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2012] [Accepted: 02/08/2013] [Indexed: 12/21/2022] Open
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).
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Kazanov MD, Li X, Gelfand MS, Osterman AL, Rodionov DA. Functional diversification of ROK-family transcriptional regulators of sugar catabolism in the Thermotogae phylum. Nucleic Acids Res 2012. [PMID: 23209028 PMCID: PMC3553997 DOI: 10.1093/nar/gks1184] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
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.
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Affiliation(s)
- Marat D Kazanov
- Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA
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Tsoy OV, Pyatnitskiy MA, Kazanov MD, Gelfand MS. Evolution of transcriptional regulation in closely related bacteria. BMC Evol Biol 2012; 12:200. [PMID: 23039862 PMCID: PMC3735044 DOI: 10.1186/1471-2148-12-200] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2012] [Accepted: 09/26/2012] [Indexed: 01/15/2023] Open
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.
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Affiliation(s)
- Olga V Tsoy
- Institute for Information Transmission Problems, RAS, Bolshoi Karetny per. 19, Moscow 127994, Russia
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De Ingeniis J, Kazanov MD, Shatalin K, Gelfand MS, Osterman AL, Sorci L. Glutamine versus ammonia utilization in the NAD synthetase family. PLoS One 2012; 7:e39115. [PMID: 22720044 PMCID: PMC3376133 DOI: 10.1371/journal.pone.0039115] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2012] [Accepted: 05/16/2012] [Indexed: 11/18/2022] Open
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.
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Affiliation(s)
- Jessica De Ingeniis
- Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
| | - Marat D. Kazanov
- Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
- A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Konstantin Shatalin
- Department of Biochemistry, New York University School of Medicine, New York, United States of America
| | - Mikhail S. Gelfand
- A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
- Faculty of Bioengineering and Bioinformatics, M.V. Lomonosov Moscow State University, Moscow, Russia
| | - Andrei L. Osterman
- Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
- * E-mail: (LS); (ALO)
| | - Leonardo Sorci
- Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
- Department of Clinical Sciences, Section of Biochemistry, Polytechnic University of Marche, Ancona, Italy
- * E-mail: (LS); (ALO)
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Kazanov MD, Igarashi Y, Eroshkin AM, Cieplak P, Ratnikov B, Zhang Y, Li Z, Godzik A, Osterman AL, Smith JW. Structural determinants of limited proteolysis. J Proteome Res 2011; 10:3642-51. [PMID: 21682278 DOI: 10.1021/pr200271w] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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.
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Affiliation(s)
- Marat D Kazanov
- Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, California 92037, USA
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Rodionov DA, Novichkov PS, Stavrovskaya ED, Rodionova IA, Li X, Kazanov MD, Ravcheev DA, Gerasimova AV, Kazakov AE, Kovaleva GY, Permina EA, Laikova ON, Overbeek R, Romine MF, Fredrickson JK, Arkin AP, Dubchak I, Osterman AL, Gelfand MS. Comparative genomic reconstruction of transcriptional networks controlling central metabolism in the Shewanella genus. BMC Genomics 2011; 12 Suppl 1:S3. [PMID: 21810205 PMCID: PMC3223726 DOI: 10.1186/1471-2164-12-s1-s3] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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.
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Affiliation(s)
- Dmitry A Rodionov
- Sanford-Burnham Medical Research Institute, La Jolla, California, USA.
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Vakhrusheva AA, Kazanov MD, Mironov AA, Bazykin GA. Evolution of prokaryotic genes by shift of stop codons. J Mol Evol 2010; 72:138-46. [PMID: 21082168 DOI: 10.1007/s00239-010-9408-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2010] [Accepted: 10/29/2010] [Indexed: 11/30/2022]
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.
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Affiliation(s)
- Anna A Vakhrusheva
- Department of Bioengineering and Bioinformatics, M.V. Lomonosov Moscow State University, Vorbyevy Gory 1-73, Moscow 119992, Russia
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Kazanov MD, Vitreschak AG, Gelfand MS. Abundance and functional diversity of riboswitches in microbial communities. BMC Genomics 2007; 8:347. [PMID: 17908319 PMCID: PMC2211319 DOI: 10.1186/1471-2164-8-347] [Citation(s) in RCA: 49] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2007] [Accepted: 10/01/2007] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Several recently completed large-scale enviromental sequencing projects produced a large amount of genetic information about microbial communities ('metagenomes') which is not biased towards cultured organisms. It is a good source for estimation of the abundance of genes and regulatory structures in both known and unknown members of microbial communities. In this study we consider the distribution of RNA regulatory structures, riboswitches, in the Sargasso Sea, Minnesota Soil and Whale Falls metagenomes. RESULTS Over three hundred riboswitches were found in about 2 Gbp metagenome DNA sequences. The abundabce of riboswitches in metagenomes was highest for the TPP, B12 and GCVT riboswitches; the S-box, RFN, YKKC/YXKD, YYBP/YKOY regulatory elements showed lower but significant abundance, while the LYS, G-box, GLMS and YKOK riboswitches were rare. Regions downstream of identified riboswitches were scanned for open reading frames. Comparative analysis of identified ORFs revealed new riboswitch-regulated functions for several classes of riboswitches. In particular, we have observed phosphoserine aminotransferase serC (COG1932) and malate synthase glcB (COG2225) to be regulated by the glycine (GCVT) riboswitch; fatty acid desaturase ole1 (COG1398), by the cobalamin (B12) riboswitch; 5-methylthioribose-1-phosphate isomerase ykrS (COG0182), by the SAM-riboswitch. We also identified conserved riboswitches upstream of genes of unknown function: thiamine (TPP), cobalamine (B12), and glycine (GCVT, upstream of genes from COG4198). CONCLUSION This study demonstrates applicability of bioinformatics to the analysis of RNA regulatory structures in metagenomes.
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Affiliation(s)
- Marat D Kazanov
- Institute for Information Transmission Problems (the Kharkevich Institute) RAS, Bolshoi Karetnyi per. 19, Moscow, 127994, Russia
| | - Alexey G Vitreschak
- Institute for Information Transmission Problems (the Kharkevich Institute) RAS, Bolshoi Karetnyi per. 19, Moscow, 127994, Russia
| | - Mikhail S Gelfand
- Institute for Information Transmission Problems (the Kharkevich Institute) RAS, Bolshoi Karetnyi per. 19, Moscow, 127994, Russia
- Faculty of Bioengineering and Bioinformatics, Moscow State University, Moscow 119992, Russia
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