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Nenclares P, Larkeryd A, Manodoro F, Lee JY, Lalondrelle S, Gilbert DC, Punta M, O’Leary B, Rullan A, Sadanandam A, Chain B, Melcher A, Harrington KJ, Bhide SA. T-cell receptor determinants of response to chemoradiation in locally-advanced HPV16-driven malignancies. Front Oncol 2024; 13:1296948. [PMID: 38234396 PMCID: PMC10791873 DOI: 10.3389/fonc.2023.1296948] [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] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/28/2023] [Indexed: 01/19/2024] Open
Abstract
Background The effect of chemoradiation on the anti-cancer immune response is being increasingly acknowledged; however, its clinical implications in treatment responses are yet to be fully understood. Human papillomavirus (HPV)-driven malignancies express viral oncogenic proteins which may serve as tumor-specific antigens and represent ideal candidates for monitoring the peripheral T-cell receptor (TCR) changes secondary to chemoradiotherapy (CRT). Methods We performed intra-tumoral and pre- and post-treatment peripheral TCR sequencing in a cohort of patients with locally-advanced HPV16-positive cancers treated with CRT. An in silico computational pipeline was used to cluster TCR repertoire based on epitope-specificity and to predict affinity between these clusters and HPV16-derived epitopes. Results Intra-tumoral repertoire diversity, intra-tumoral and post-treatment peripheral CDR3β similarity clustering were predictive of response. In responders, CRT triggered an increase peripheral TCR clonality and clonal relatedness. Post-treatment expansion of baseline peripheral dominant TCRs was associated with response. Responders showed more baseline clustered structures of TCRs maintained post-treatment and displayed significantly more maintained clustered structures. When applying clustering by TCR-specificity methods, responders displayed a higher proportion of intra-tumoral TCRs predicted to recognise HPV16 peptides. Conclusions Baseline TCR characteristics and changes in the peripheral T-cell clones triggered by CRT are associated with treatment outcome. Maintenance and boosting of pre-existing clonotypes are key elements of an effective anti-cancer immune response driven by CRT, supporting a paradigm in which the immune system plays a central role in the success of CRT in current standard-of-care protocols.
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Affiliation(s)
- Pablo Nenclares
- Radiotherapy and Imaging Division, The Institute of Cancer Research, London, United Kingdom
- Head and Neck Unit, The Royal Marsden Hospital, London, United Kingdom
| | - Adrian Larkeryd
- Bioinformatics Unit, The Centre for Translational Immunotherapy, The Institute of Cancer Research, London, United Kingdom
| | - Floriana Manodoro
- Genomics Facility, The Institute of Cancer Research, London, United Kingdom
| | - Jen Y. Lee
- Radiotherapy and Imaging Division, The Institute of Cancer Research, London, United Kingdom
| | - Susan Lalondrelle
- Radiotherapy and Imaging Division, The Institute of Cancer Research, London, United Kingdom
| | - Duncan C. Gilbert
- Sussex Cancer Centre, University Hospitals Sussex NHS Foundation Trust, Brighton, United Kingdom
| | - Marco Punta
- Unit of Immunogenetic, Leukemia Genomics and Immunobiology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Ben O’Leary
- Radiotherapy and Imaging Division, The Institute of Cancer Research, London, United Kingdom
- Head and Neck Unit, The Royal Marsden Hospital, London, United Kingdom
| | - Antonio Rullan
- Radiotherapy and Imaging Division, The Institute of Cancer Research, London, United Kingdom
- Head and Neck Unit, The Royal Marsden Hospital, London, United Kingdom
| | - Anguraj Sadanandam
- Systems and Precision Cancer Medicine Team, The Institute of Cancer Research, London, United Kingdom
| | - Benny Chain
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Alan Melcher
- Radiotherapy and Imaging Division, The Institute of Cancer Research, London, United Kingdom
| | - Kevin J. Harrington
- Radiotherapy and Imaging Division, The Institute of Cancer Research, London, United Kingdom
- Head and Neck Unit, The Royal Marsden Hospital, London, United Kingdom
| | - Shreerang A. Bhide
- Radiotherapy and Imaging Division, The Institute of Cancer Research, London, United Kingdom
- Head and Neck Unit, The Royal Marsden Hospital, London, United Kingdom
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Rovatti PE, Muccini C, Punta M, Galli L, Mainardi I, Ponta G, Vago LAE, Castagna A. Impact of predicted HLA class I immunopeptidome on viral reservoir in a cohort of people living with HIV in Italy. HLA 2024; 103:e15298. [PMID: 37962099 DOI: 10.1111/tan.15298] [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: 10/19/2022] [Revised: 10/30/2023] [Accepted: 11/04/2023] [Indexed: 11/15/2023]
Abstract
The class I HLA genotype has been widely recognized as a factor influencing HIV disease progression in treatment-naïve subjects. However, little is known regarding its role in HIV disease course and how it influences the size of the viral reservoir once anti-retroviral therapy (ART) is started. Here, leveraging on cutting-edge bioinformatic tools, we explored the relationship between HLA class I and the HIV reservoir in a cohort of 90 people living with HIV (PLWH) undergoing ART and who achieved viral suppression. Analysis of HLA allele distribution among patients with high and low HIV reservoir allowed us to document a predominant role of HLA-B and -C genes in regulating the size of HIV reservoir. We then focused on the analysis of HIV antigen (Ag) repertoire, by investigating immunogenetic parameters such as the degree of homozygosity, HLA evolutionary distance and Ag load. In particular, we used two different bioinformatic algorithms, NetMHCpan and MixMHCpred, to predict HLA presentation of immunogenic HIV-derived peptides and identified HLA-B*57:01 and HLA-B*58:01 among the highest ranking HLAs in terms of total load, suggesting that their previously reported protective role against HIV disease progression might be linked to a more effective viral recognition and presentation to Cytotoxic T lymphocytes (CTLs). Further, we speculated that some peptide-HLA complexes, including those produced by the interaction between HLA-B*27 and the HIV Gag protein, might be particularly relevant for the efficient regulation of HIV replication and containment of the HIV reservoir. Last, we provide evidence of a possible synergistic effect between the CCR5 ∆32 mutation and Ag load in controlling HIV reservoir.
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Affiliation(s)
- Pier Edoardo Rovatti
- Unit of Immunogenetics, Leukemia Genomics and Immunobiology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Camilla Muccini
- Vita-Salute San Raffaele University, Milan, Italy
- Infectious Diseases Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco Punta
- Unit of Immunogenetics, Leukemia Genomics and Immunobiology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Laura Galli
- Infectious Diseases Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | | | - Luca Aldo Edoardo Vago
- Unit of Immunogenetics, Leukemia Genomics and Immunobiology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Hematology and Bone Marrow Transplantation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Antonella Castagna
- Vita-Salute San Raffaele University, Milan, Italy
- Infectious Diseases Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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3
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Manfredi F, Stasi L, Buonanno S, Marzuttini F, Noviello M, Mastaglio S, Abbati D, Potenza A, Balestrieri C, Cianciotti BC, Tassi E, Feola S, Toffalori C, Punta M, Magnani Z, Camisa B, Tiziano E, Lupo-Stanghellini MT, Branca RM, Lehtiö J, Sikanen TM, Haapala MJ, Cerullo V, Casucci M, Vago L, Ciceri F, Bonini C, Ruggiero E. Harnessing T cell exhaustion and trogocytosis to isolate patient-derived tumor-specific TCR. Sci Adv 2023; 9:eadg8014. [PMID: 38039364 PMCID: PMC10691777 DOI: 10.1126/sciadv.adg8014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 11/02/2023] [Indexed: 12/03/2023]
Abstract
To study and then harness the tumor-specific T cell dynamics after allogeneic hematopoietic stem cell transplant, we typed the frequency, phenotype, and function of lymphocytes directed against tumor-associated antigens (TAAs) in 39 consecutive transplanted patients, for 1 year after transplant. We showed that TAA-specific T cells circulated in 90% of patients but display a limited effector function associated to an exhaustion phenotype, particularly in the subgroup of patients deemed to relapse, where exhausted stem cell memory T cells accumulated. Accordingly, cancer-specific cytolytic functions were relevant only when the TAA-specific T cell receptors (TCRs) were transferred into healthy, genome-edited T cells. We then exploited trogocytosis and ligandome-on-chip technology to unveil the specificities of tumor-specific TCRs retrieved from the exhausted T cell pool. Overall, we showed that harnessing circulating TAA-specific and exhausted T cells allow to isolate TCRs against TAAs and previously not described acute myeloid leukemia antigens, potentially relevant for T cell-based cancer immunotherapy.
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Affiliation(s)
- Francesco Manfredi
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation, and Infectious Diseases, Experimental Hematology Unit, via Olgettina 60, Milan 20132, Italy
| | - Lorena Stasi
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation, and Infectious Diseases, Experimental Hematology Unit, via Olgettina 60, Milan 20132, Italy
| | - Silvia Buonanno
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation, and Infectious Diseases, Experimental Hematology Unit, via Olgettina 60, Milan 20132, Italy
| | - Francesca Marzuttini
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation, and Infectious Diseases, Experimental Hematology Unit, via Olgettina 60, Milan 20132, Italy
| | - Maddalena Noviello
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation, and Infectious Diseases, Experimental Hematology Unit, via Olgettina 60, Milan 20132, Italy
| | - Sara Mastaglio
- IRCCS San Raffaele Scientific Institute, Hematology and Hematopoietic Stem Cell Transplantation Unit, via Olgettina 60, Milan 20132, Italy
| | - Danilo Abbati
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation, and Infectious Diseases, Experimental Hematology Unit, via Olgettina 60, Milan 20132, Italy
| | - Alessia Potenza
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation, and Infectious Diseases, Experimental Hematology Unit, via Olgettina 60, Milan 20132, Italy
| | - Chiara Balestrieri
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation, and Infectious Diseases, Experimental Hematology Unit, via Olgettina 60, Milan 20132, Italy
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, via Olgettina 60, Milan 20132, Italy
| | - Beatrice Claudia Cianciotti
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation, and Infectious Diseases, Experimental Hematology Unit, via Olgettina 60, Milan 20132, Italy
| | - Elena Tassi
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation, and Infectious Diseases, Experimental Hematology Unit, via Olgettina 60, Milan 20132, Italy
| | - Sara Feola
- University of Helsinki, ImmunoVirotherapy Lab, Yliopistonkatu 4, 00100 Helsinki, Finland
| | - Cristina Toffalori
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation and Infectious Disease, Unit of Immunogenetics, Leukemia Genomics and Immunobiology, via Olgettina 60, Milan 20132, Italy
| | - Marco Punta
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, via Olgettina 60, Milan 20132, Italy
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation and Infectious Disease, Unit of Immunogenetics, Leukemia Genomics and Immunobiology, via Olgettina 60, Milan 20132, Italy
| | - Zulma Magnani
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation, and Infectious Diseases, Experimental Hematology Unit, via Olgettina 60, Milan 20132, Italy
| | - Barbara Camisa
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation, and Infectious Diseases, Experimental Hematology Unit, via Olgettina 60, Milan 20132, Italy
| | - Elena Tiziano
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation, and Infectious Diseases, Experimental Hematology Unit, via Olgettina 60, Milan 20132, Italy
| | - Maria Teresa Lupo-Stanghellini
- IRCCS San Raffaele Scientific Institute, Hematology and Hematopoietic Stem Cell Transplantation Unit, via Olgettina 60, Milan 20132, Italy
| | - Rui Mamede Branca
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, 171 65 Solna, Sweden
| | - Janne Lehtiö
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institute, 171 65 Solna, Sweden
| | - Tiina M. Sikanen
- Drug Research Program, Faculty of Pharmacy, Division of Pharmaceutical Chemistry and Technology, Helsinki University,, Viikinkaari 5E, 00014 Helsinki, Finland
| | - Markus J. Haapala
- Drug Research Program, Faculty of Pharmacy, Division of Pharmaceutical Chemistry and Technology, Helsinki University,, Viikinkaari 5E, 00014 Helsinki, Finland
| | - Vincenzo Cerullo
- University of Helsinki, ImmunoVirotherapy Lab, Yliopistonkatu 4, 00100 Helsinki, Finland
| | - Monica Casucci
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation and Infectious Disease, Innovative Immunotherapies Unit, via Olgettina 60, Milan 20132, Italy
| | - Luca Vago
- IRCCS San Raffaele Scientific Institute, Hematology and Hematopoietic Stem Cell Transplantation Unit, via Olgettina 60, Milan 20132, Italy
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation and Infectious Disease, Unit of Immunogenetics, Leukemia Genomics and Immunobiology, via Olgettina 60, Milan 20132, Italy
- Vita Salute San Raffaele University, Milan, Italy
| | - Fabio Ciceri
- IRCCS San Raffaele Scientific Institute, Hematology and Hematopoietic Stem Cell Transplantation Unit, via Olgettina 60, Milan 20132, Italy
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation and Infectious Disease, Innovative Immunotherapies Unit, via Olgettina 60, Milan 20132, Italy
| | - Chiara Bonini
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation, and Infectious Diseases, Experimental Hematology Unit, via Olgettina 60, Milan 20132, Italy
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation and Infectious Disease, Innovative Immunotherapies Unit, via Olgettina 60, Milan 20132, Italy
| | - Eliana Ruggiero
- IRCCS San Raffaele Scientific Institute, Division of Immunology, Transplantation, and Infectious Diseases, Experimental Hematology Unit, via Olgettina 60, Milan 20132, Italy
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García-Mulero S, Fornelino R, Punta M, Lise S, Varela M, del Carpio LP, Moreno R, Costa-García M, Rieder D, Trajanoski Z, Gros A, Alemany R, Piulats JM, Sanz-Pamplona R. Driver mutations in GNAQ and GNA11 genes as potential targets for precision immunotherapy in uveal melanoma patients. Oncoimmunology 2023; 12:2261278. [PMID: 38126027 PMCID: PMC10732647 DOI: 10.1080/2162402x.2023.2261278] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 09/17/2023] [Indexed: 12/23/2023] Open
Abstract
Uveal melanoma (UM) is the most common ocular malignancy in adults. Nearly 95% of UM patients carry the mutually exclusive mutations in the homologous genes GNAQ (amino acid change Q209L/Q209P) and GNA11 (aminoacid change Q209L). UM is located in an immunosuppressed organ and does not suffer immunoediting. Therefore, we hypothesize that driver mutations in GNAQ/11 genes could be recognized by the immune system. Genomic and transcriptomic data from primary uveal tumors were collected from the TCGA-UM dataset (n = 80) and used to assess the immunogenic potential for GNAQ/GNA11 Q209L/Q209P mutations using a variety of tools and HLA type information. All prediction tools showed stronger GNAQ/11 Q209L binding to HLA than GNAQ/11 Q209P. The immunogenicity analysis revealed that Q209L is likely to be presented by more than 73% of individuals in 1000 G databases whereas Q209P is only predicted to be presented in 24% of individuals. GNAQ/11 Q209L showed a higher likelihood to be presented by HLA-I molecules than almost all driver mutations analyzed. Finally, samples carrying Q209L had a higher immune-reactive phenotype. Regarding cancer risk, seven HLA genotypes with low Q209L affinity show higher frequency in uveal melanoma patients than in the general population. However, no clear association was found between any HLA genotype and survival. Results suggest a high potential immunogenicity of the GNAQ/11 Q209L variant that could allow the generation of novel therapeutic tools to treat UM like neoantigen vaccinations.
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Affiliation(s)
- Sandra García-Mulero
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL) and CIBERESP, Barcelona, Spain
- Anatomy Unit, Department of Pathology and Experimental Therapy, and Department of Clinical Sciences, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Roberto Fornelino
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL) and CIBERESP, Barcelona, Spain
| | - Marco Punta
- Bioinformatics Core, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Stefano Lise
- Bioinformatics Core, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Mar Varela
- Department of Pathology, Bellvitge University Hospital, Barcelona, Spain
| | - Luis P. del Carpio
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Rafael Moreno
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Marcel Costa-García
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Dietmar Rieder
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Zlatko Trajanoski
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Alena Gros
- Tumor Immunology and Immunotherapy, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Ramón Alemany
- Procure Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | | | - Rebeca Sanz-Pamplona
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL) and CIBERESP, Barcelona, Spain
- Institute for Health Research Aragon (IISA), ARAID Foundation, Aragon Government, University Hospital Lozano Blesa, Zaragoza, Spain
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5
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Apostolova P, Kreutmair S, Toffalori C, Punta M, Unger S, Burk AC, Wehr C, Maas-Bauer K, Melchinger W, Haring E, Hoefflin R, Shoumariyeh K, Hupfer V, Lauer EM, Duquesne S, Lowinus T, Gonzalo Núñez N, Alberti C, da Costa Pereira S, Merten CH, Power L, Weiss M, Böke C, Pfeifer D, Marks R, Bertz H, Wäsch R, Ihorst G, Gentner B, Duyster J, Boerries M, Andrieux G, Finke J, Becher B, Vago L, Zeiser R. Phase II trial of hypomethylating agent combined with nivolumab for acute myeloid leukaemia relapse after allogeneic haematopoietic cell transplantation-Immune signature correlates with response. Br J Haematol 2023; 203:264-281. [PMID: 37539479 DOI: 10.1111/bjh.19007] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 07/19/2023] [Indexed: 08/05/2023]
Abstract
Acute myeloid leukaemia (AML) relapse after allogeneic haematopoietic cell transplantation (allo-HCT) is often driven by immune-related mechanisms and associated with poor prognosis. Immune checkpoint inhibitors combined with hypomethylating agents (HMA) may restore or enhance the graft-versus-leukaemia effect. Still, data about using this combination regimen after allo-HCT are limited. We conducted a prospective, phase II, open-label, single-arm study in which we treated patients with haematological AML relapse after allo-HCT with HMA plus the anti-PD-1 antibody nivolumab. The response was correlated with DNA-, RNA- and protein-based single-cell technology assessments to identify biomarkers associated with therapeutic efficacy. Sixteen patients received a median number of 2 (range 1-7) nivolumab applications. The overall response rate (CR/PR) at day 42 was 25%, and another 25% of the patients achieved stable disease. The median overall survival was 15.6 months. High-parametric cytometry documented a higher frequency of activated (ICOS+ , HLA-DR+ ), low senescence (KLRG1- , CD57- ) CD8+ effector T cells in responders. We confirmed these findings in a preclinical model. Single-cell transcriptomics revealed a pro-inflammatory rewiring of the expression profile of T and myeloid cells in responders. In summary, the study indicates that the post-allo-HCT HMA/nivolumab combination induces anti-AML immune responses in selected patients and could be considered as a bridging approach to a second allo-HCT. Trial-registration: EudraCT-No. 2017-002194-18.
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Affiliation(s)
- Petya Apostolova
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stefanie Kreutmair
- German Cancer Consortium (DKTK), Partner Site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
| | - Cristina Toffalori
- Unit of Immunogenetics, Leukemia Genomics and Immunobiology, Division of Immunology, Transplantation and Infectious Disease, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco Punta
- Unit of Immunogenetics, Leukemia Genomics and Immunobiology, Division of Immunology, Transplantation and Infectious Disease, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Center for OMICS Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Susanne Unger
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
| | - Ann-Cathrin Burk
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Claudia Wehr
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Kristina Maas-Bauer
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Wolfgang Melchinger
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Eileen Haring
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rouven Hoefflin
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Khalid Shoumariyeh
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Valerie Hupfer
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Eliza Maria Lauer
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Sandra Duquesne
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Theresa Lowinus
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | | | - Chiara Alberti
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
| | | | - Carla Helena Merten
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
| | - Laura Power
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
| | - Matthias Weiss
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Caroline Böke
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Dietmar Pfeifer
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Reinhard Marks
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Hartmut Bertz
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ralph Wäsch
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Gabriele Ihorst
- Clinical Trials Unit, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Bernhard Gentner
- Translational Stem Cell and Leukemia Unit, San Raffaele Telethon Institute for Gene Therapy, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Ludwig Institute for Cancer Research and Department of Oncology, University of Lausanne (UNIL) and Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Justus Duyster
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Melanie Boerries
- German Cancer Consortium (DKTK), Partner Site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Medical Bioinformatics and Systems Medicine, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Geoffroy Andrieux
- Institute of Medical Bioinformatics and Systems Medicine, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Juergen Finke
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Burkhard Becher
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
| | - Luca Vago
- Unit of Immunogenetics, Leukemia Genomics and Immunobiology, Division of Immunology, Transplantation and Infectious Disease, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Hematology and Bone Marrow Transplantation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Robert Zeiser
- Department of Medicine I, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Signalling Research Centres BIOSS and CIBSS-Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany
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Arbore G, Albarello L, Bucci G, Punta M, Cossu A, Fanti L, Maurizio A, Di Mauro F, Bilello V, Arrigoni G, Bonfiglio S, Biancolini D, Puccetti F, Elmore U, Vago L, Cascinu S, Tonon G, Rosati R, Casorati G, Dellabona P. Preexisting Immunity Drives the Response to Neoadjuvant Chemotherapy in Esophageal Adenocarcinoma. Cancer Res 2023; 83:2873-2888. [PMID: 37350667 PMCID: PMC10472105 DOI: 10.1158/0008-5472.can-23-0356] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 05/18/2023] [Accepted: 06/20/2023] [Indexed: 06/24/2023]
Abstract
Current treatment for patients with locally advanced esophageal adenocarcinoma (EAC) is neoadjuvant chemotherapy (nCT), alone or combined with radiotherapy, before surgery. However, fewer than 30% of treated patients show a pathologic complete response to nCT, which correlates with increased 5-year survival compared with nonresponders. Understanding the mechanisms of response to nCT is pivotal to better stratify patients and inform more efficacious therapies. Here, we investigated the immune mechanisms involved in nCT response by multidimensional profiling of pretreatment tumor biopsies and blood from 68 patients with EAC (34 prospectively and 34 retrospectively collected), comparing complete responders versus nonresponders to nCT. At the tumor level, complete response to nCT was associated with molecular signatures of immune response and proliferation, increased putative antitumor tissue-resident memory CD39+ CD103+ CD8+ T cells, and reduced immunosuppressive T regulatory cells (Treg) and M2-like macrophages. Systemically, complete responders showed higher frequencies of immunostimulatory CD14+ CD11c+ HLA-DRhigh cells, and reduced programmed cell death ligand 1-positive (PD-L1+) monocytic myeloid-derived suppressor cells, along with high plasma GM-CSF (proinflammatory) and low IL4, CXCL10, C3a, and C5a (suppressive). Plasma proinflammatory and suppressive cytokines correlated directly and inversely, respectively, with the frequency of tumor-infiltrating CD39+ CD103+ CD8+ T cells. These results suggest that preexisting immunity in baseline tumor drives the clinical activity of nCT in locally advanced EAC. Furthermore, it may be possible to stratify patients based on predictive immune signatures, enabling tailored neoadjuvant and/or adjuvant regimens. SIGNIFICANCE Multidimensional profiling of pretreatment esophageal adenocarcinoma shows patient response to nCT is correlated with active preexisting immunity and indicates molecular pathways of resistance that may be targeted to improve clinical outcomes.
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Affiliation(s)
- Giuseppina Arbore
- Experimental Immunology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Luca Albarello
- Department of Pathology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Gabriele Bucci
- Center for OMICS Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco Punta
- Center for OMICS Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Hematology and Bone Marrow Transplant Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Andrea Cossu
- Department of Gastrointestinal Surgery, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Lorella Fanti
- Division of Gastroenterology & Gastrointestinal Endoscopy, San Raffaele Scientific Institute, Milan, Italy
| | - Aurora Maurizio
- Center for OMICS Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesco Di Mauro
- Experimental Immunology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Vito Bilello
- Experimental Immunology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Gianluigi Arrigoni
- Department of Pathology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Silvia Bonfiglio
- Center for OMICS Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Donatella Biancolini
- Center for OMICS Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesco Puccetti
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Gastrointestinal Surgery, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Ugo Elmore
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Gastrointestinal Surgery, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Luca Vago
- Vita-Salute San Raffaele University, Milan, Italy
- Hematology and Bone Marrow Transplant Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Stefano Cascinu
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giovanni Tonon
- Vita-Salute San Raffaele University, Milan, Italy
- Center for OMICS Sciences, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Riccardo Rosati
- Vita-Salute San Raffaele University, Milan, Italy
- Department of Gastrointestinal Surgery, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giulia Casorati
- Experimental Immunology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paolo Dellabona
- Experimental Immunology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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7
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Russo ET, Barone F, Bateman A, Cozzini S, Punta M, Laio A. DPCfam: Unsupervised protein family classification by Density Peak Clustering of large sequence datasets. PLoS Comput Biol 2022; 18:e1010610. [PMID: 36260616 PMCID: PMC9621593 DOI: 10.1371/journal.pcbi.1010610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 02/15/2022] [Revised: 10/31/2022] [Accepted: 09/26/2022] [Indexed: 11/07/2022] Open
Abstract
Proteins that are known only at a sequence level outnumber those with an experimental characterization by orders of magnitude. Classifying protein regions (domains) into homologous families can generate testable functional hypotheses for yet unannotated sequences. Existing domain family resources typically use at least some degree of manual curation: they grow slowly over time and leave a large fraction of the protein sequence space unclassified. We here describe automatic clustering by Density Peak Clustering of UniRef50 v. 2017_07, a protein sequence database including approximately 23M sequences. We performed a radical re-implementation of a pipeline we previously developed in order to allow handling millions of sequences and data volumes of the order of 3 TeraBytes. The modified pipeline, which we call DPCfam, finds ∼ 45,000 protein clusters in UniRef50. Our automatic classification is in close correspondence to the ones of the Pfam and ECOD resources: in particular, about 81% of medium-large Pfam families and 72% of ECOD families can be mapped to clusters generated by DPCfam. In addition, our protocol finds more than 14,000 clusters constituted of protein regions with no Pfam annotation, which are therefore candidates for representing novel protein families. These results are made available to the scientific community through a dedicated repository.
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Affiliation(s)
| | - Federico Barone
- SISSA, Trieste, Italy
- AREA SCIENCE PARK, Trieste, Italy
- Department of Mathematics and Geosciences, University of Trieste, Trieste, Italy
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, United Kingdom
| | | | - Marco Punta
- Center for Omics Sciences, IRCCS San Raffaele Institute, Milan, Italy
- Unit of Immunogenetics, Leukemia Genomics and Immunobiology, Division of Immunology, Transplantation and Infectious Disease, IRCCS San Raffaele Scientific Institute, Milan, Italy
- * E-mail: (MP); (AL)
| | - Alessandro Laio
- SISSA, Trieste, Italy
- ICTP, Trieste, Italy
- * E-mail: (MP); (AL)
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8
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Ho JNHG, Schmidt D, Lowinus T, Ryoo J, Dopfer EP, Gonzalo Núñez N, Costa-Pereira S, Toffalori C, Punta M, Fetsch V, Wertheimer T, Rittmann MC, Braun LM, Follo M, Briere C, Vinnakota JM, Langenbach M, Koppers F, Shoumariyeh K, Engel H, Rückert T, Märklin M, Holzmayer S, Illert AL, Magon F, Andrieux G, Duquesne S, Pfeifer D, Staniek J, Rizzi M, Miething C, Köhler N, Duyster J, Menssen HD, Boerries M, Buescher JM, Cabezas-Wallscheid N, Blazar BR, Apostolova P, Vago L, Pearce EL, Becher B, Zeiser R. Targeting MDM2 enhances antileukemia immunity after allogeneic transplantation via MHC-II and TRAIL-R1/2 upregulation. Blood 2022; 140:1167-1181. [PMID: 35853161 PMCID: PMC9461473 DOI: 10.1182/blood.2022016082] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 07/01/2022] [Indexed: 11/20/2022] Open
Abstract
Patients with acute myeloid leukemia (AML) often achieve remission after allogeneic hematopoietic cell transplantation (allo-HCT) but subsequently die of relapse driven by leukemia cells resistant to elimination by allogeneic T cells based on decreased major histocompatibility complex II (MHC-II) expression and apoptosis resistance. Here we demonstrate that mouse-double-minute-2 (MDM2) inhibition can counteract immune evasion of AML. MDM2 inhibition induced MHC class I and II expression in murine and human AML cells. Using xenografts of human AML and syngeneic mouse models of leukemia, we show that MDM2 inhibition enhanced cytotoxicity against leukemia cells and improved survival. MDM2 inhibition also led to increases in tumor necrosis factor-related apoptosis-inducing ligand receptor-1 and -2 (TRAIL-R1/2) on leukemia cells and higher frequencies of CD8+CD27lowPD-1lowTIM-3low T cells, with features of cytotoxicity (perforin+CD107a+TRAIL+) and longevity (bcl-2+IL-7R+). CD8+ T cells isolated from leukemia-bearing MDM2 inhibitor-treated allo-HCT recipients exhibited higher glycolytic activity and enrichment for nucleotides and their precursors compared with vehicle control subjects. T cells isolated from MDM2 inhibitor-treated AML-bearing mice eradicated leukemia in secondary AML-bearing recipients. Mechanistically, the MDM2 inhibitor-mediated effects were p53-dependent because p53 knockdown abolished TRAIL-R1/2 and MHC-II upregulation, whereas p53 binding to TRAILR1/2 promotors increased upon MDM2 inhibition. The observations in the mouse models were complemented by data from human individuals. Patient-derived AML cells exhibited increased TRAIL-R1/2 and MHC-II expression on MDM2 inhibition. In summary, we identified a targetable vulnerability of AML cells to allogeneic T-cell-mediated cytotoxicity through the restoration of p53-dependent TRAIL-R1/2 and MHC-II production via MDM2 inhibition.
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Affiliation(s)
- Jenny N H G Ho
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
| | - Dominik Schmidt
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
- Faculty of Biology, Albert-Ludwigs-University, Freiburg, Germany
| | - Theresa Lowinus
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
| | - Jeongmin Ryoo
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
- Faculty of Biology, Albert-Ludwigs-University, Freiburg, Germany
| | - Elaine-Pashupati Dopfer
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
| | | | - Sara Costa-Pereira
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
| | - Cristina Toffalori
- Unit of Immunogenetics, Leukemia Genomics and Immunobiology, Division of Immunology, Transplantation and Infectious Disease, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milano, Italy
| | - Marco Punta
- Unit of Immunogenetics, Leukemia Genomics and Immunobiology, Division of Immunology, Transplantation and Infectious Disease, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milano, Italy
- Center for Omics Sciences, IRCCS San Raffaele Institute, Milano, Italy
| | - Viktor Fetsch
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
| | - Tobias Wertheimer
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
| | - Marie-Claire Rittmann
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
| | - Lukas M Braun
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
- Faculty of Biology, Albert-Ludwigs-University, Freiburg, Germany
| | - Marie Follo
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
| | - Christelle Briere
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
| | - Janaki Manoja Vinnakota
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
- Faculty of Biology, Albert-Ludwigs-University, Freiburg, Germany
| | - Marlene Langenbach
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
- Faculty of Biology, Albert-Ludwigs-University, Freiburg, Germany
| | - Felicitas Koppers
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
| | - Khalid Shoumariyeh
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research, Center (DKFZ), Heidelberg, Germany
| | - Helena Engel
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
| | - Tamina Rückert
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
| | - Melanie Märklin
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tuebingen, Tuebingen, Germany
- Deutsche Forschungsgemeinschaft Cluster of Excellence 2180 "Image-guided and Functional Instructed Tumor Therapy," University of Tuebingen, Tuebingen, Germany
| | - Samuel Holzmayer
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tuebingen, Tuebingen, Germany
- Deutsche Forschungsgemeinschaft Cluster of Excellence 2180 "Image-guided and Functional Instructed Tumor Therapy," University of Tuebingen, Tuebingen, Germany
| | - Anna L Illert
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research, Center (DKFZ), Heidelberg, Germany
| | - Federica Magon
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
| | - Geoffroy Andrieux
- German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research, Center (DKFZ), Heidelberg, Germany
- Institute of Medical Bioinformatics and Systems Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Sandra Duquesne
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
| | - Dietmar Pfeifer
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
| | - Julian Staniek
- Faculty of Biology, Albert-Ludwigs-University, Freiburg, Germany
- Department of Rheumatology and Clinical Immunology, Faculty of Medicine, Medical Centre
| | - Marta Rizzi
- Department of Rheumatology and Clinical Immunology, Faculty of Medicine, Medical Centre
- Signalling Research Centres BIOSS and CIBSS - Centre for Integrative Biological, Signalling Studies, and
| | - Cornelius Miething
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research, Center (DKFZ), Heidelberg, Germany
| | - Natalie Köhler
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
- CIBSS Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| | - Justus Duyster
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research, Center (DKFZ), Heidelberg, Germany
| | | | - Melanie Boerries
- German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research, Center (DKFZ), Heidelberg, Germany
- Institute of Medical Bioinformatics and Systems Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Joerg M Buescher
- Max-Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
| | | | - Bruce R Blazar
- Division of Blood & Marrow Transplant and Cellular Therapy, Department of Pediatrics, University of Minnesota, Minneapolis, MN; and
| | - Petya Apostolova
- Max-Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- The Bloomberg-Kimmel Institute for Cancer Immunotherapy at Johns Hopkins, Johns Hopkins University, Baltimore, MD
| | - Luca Vago
- Unit of Immunogenetics, Leukemia Genomics and Immunobiology, Division of Immunology, Transplantation and Infectious Disease, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milano, Italy
| | - Erika L Pearce
- Max-Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- The Bloomberg-Kimmel Institute for Cancer Immunotherapy at Johns Hopkins, Johns Hopkins University, Baltimore, MD
| | - Burkhard Becher
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
| | - Robert Zeiser
- Clinic of Internal Medicine I, Hematology, Oncology, and Stem Cell Transplantation, Faculty of Medicine, Medical Centre, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) Partner Site Freiburg, German Cancer Research, Center (DKFZ), Heidelberg, Germany
- Signalling Research Centres BIOSS and CIBSS - Centre for Integrative Biological, Signalling Studies, and
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9
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Quilici G, Berardi A, Fabris C, Ghitti M, Punta M, Gourlay LJ, Bolognesi M, Musco G. Solution Structure of the BPSL1445 Protein of Burkholderia pseudomallei Reveals the SYLF Domain Three-Dimensional Fold. ACS Chem Biol 2022; 17:230-239. [PMID: 34968022 DOI: 10.1021/acschembio.1c00886] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The SYLF domain is an evolutionary conserved protein domain with phosphatidylinositol binding ability, whose three-dimensional structure is unknown. Here, we present the solution structure and the dynamics characterization of the SYLF domain of the bacterial BPSL1445 protein. BPSL1445 is a seroreactive antigen and a diagnostic marker of Burkholderia pseudomallei, the etiological agent of melioidosis, a severe infectious disease in the tropics. The BPSL1445 SYLF domain (BPSL1445-SYLF) consists of a β-barrel core, with two flexible loops protruding out of the barrel and three helices packing on its surface. Our structure allows for a more precise definition of the boundaries of the SYLF domain compared to the previously reported one and suggests common ancestry with bacterial EipA domains. We also demonstrate by phosphatidyl-inositol phosphate arrays and nuclear magnetic resonance titrations that BPSL1445-SYLF weakly interacts with phosphoinositides, thus supporting lipid binding abilities of this domain also in prokaryotes.
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Affiliation(s)
- Giacomo Quilici
- Biomolecular NMR Laboratory, I.R.C.C.S. Ospedale San Raffaele, Via Olgettina 58, 20132 Milan, Italy
| | - Andrea Berardi
- Biomolecular NMR Laboratory, I.R.C.C.S. Ospedale San Raffaele, Via Olgettina 58, 20132 Milan, Italy
| | - Chantal Fabris
- Biomolecular NMR Laboratory, I.R.C.C.S. Ospedale San Raffaele, Via Olgettina 58, 20132 Milan, Italy
| | - Michela Ghitti
- Biomolecular NMR Laboratory, I.R.C.C.S. Ospedale San Raffaele, Via Olgettina 58, 20132 Milan, Italy
| | - Marco Punta
- Unit of Immunogenetics, Leukemia Genomics and Immunobiology and Center for Omics Sciences, I.R.C.C.S. Ospedale San Raffaele, Via Olgettina 58, 20132 Milan, Italy
| | - Louise J. Gourlay
- Department of Biosciences, University of Milano, Via Celoria 26, 20133 Milan, Italy
- Centro di Ricerca Pediatrica Romeo ed Enrica Invernizzi, Università degli Studi di Milano, 20133 Milan, Italy
| | - Martino Bolognesi
- Department of Biosciences, University of Milano, Via Celoria 26, 20133 Milan, Italy
- Centro di Ricerca Pediatrica Romeo ed Enrica Invernizzi, Università degli Studi di Milano, 20133 Milan, Italy
| | - Giovanna Musco
- Biomolecular NMR Laboratory, I.R.C.C.S. Ospedale San Raffaele, Via Olgettina 58, 20132 Milan, Italy
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Pettitt SJ, Frankum J, Punta M, Lise S, Alexander J, Chen Y, Haider S, Tutt ANJ, Lord CJ. Abstract P080: Analysis of clinical BRCA1/2 reversions identifies hotspot mutations and predicted neoantigens associated with therapy resistance. Mol Cancer Ther 2021. [DOI: 10.1158/1535-7163.targ-21-p080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Although PARP inhibitors and platinum salts form part of the standard-of-care for cancers with homologous recombination (HR) defects, drug resistance does emerge and is often caused by reversion mutations in BRCA1 or BRCA2. Reversion mutations are secondary mutations that restore functional protein or compensate for frameshift mutations. To better understand the nature and aetiology of these mutations, we collated, codified and analysed over 200 reversion mutations from 24 published studies. Most reversions have been reported as case reports or small clinical series – we have collected all published reports into a single freely-accessible database to which further cases can be submitted. The majority of reported reversion mutations are from BRCA1/2 mutant serous ovarian cancer, reflecting the longer use of platinum and PARP inhibitors in this disease. Our analysis identified reversion “hotspots” and “deserts” in the N- and C-terminal coding regions (respectively) of BRCA2, suggesting that pathogenic mutations in these domains may be at higher or lower chance of reversion, an effect not seen for BRCA1. Missense and splice-site pathogenic mutations in BRCA1/2 also appeared less likely to revert than frameshift-causing mutations. Unexpectedly, whilst most reversions were <100 bp “second site” deletions, microhomology use was not universal, especially in BRCA1-mutant cancers, suggesting that multiple DNA repair processes cause reversion and these vary in BRCA1 vs. BRCA2 mutant tumours. Reversions in BRCA1 mutant tumours were less likely to be mediated by deletions. Many reversions contain novel protein sequence not found in the wild type protein, for example where a stretch of out-of-frame protein sequence is retained between the pathogenic and reversion mutations. We modelled MHC binding affinities for these sequences and found that many reversions were predicted to encode potentially immunogenic neopeptides, suggesting a route to the treatment of reverted disease. These observations have implications for how drug resistance might be managed in BRCA-mutant cancers.
Citation Format: Stephen J. Pettitt, Jessica Frankum, Marco Punta, Stefano Lise, John Alexander, Yi Chen, Syed Haider, Andre N. J. Tutt, Christopher J. Lord. Analysis of clinical BRCA1/2 reversions identifies hotspot mutations and predicted neoantigens associated with therapy resistance [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P080.
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Affiliation(s)
| | | | - Marco Punta
- Institute of Cancer Research, London, United Kingdom
| | - Stefano Lise
- Institute of Cancer Research, London, United Kingdom
| | | | - Yi Chen
- Institute of Cancer Research, London, United Kingdom
| | - Syed Haider
- Institute of Cancer Research, London, United Kingdom
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11
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Russo ET, Laio A, Punta M. Density Peak clustering of protein sequences associated to a Pfam clan reveals clear similarities and interesting differences with respect to manual family annotation. BMC Bioinformatics 2021; 22:121. [PMID: 33711918 PMCID: PMC7955657 DOI: 10.1186/s12859-021-04013-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 02/09/2021] [Indexed: 11/24/2022] Open
Abstract
Background The identification of protein families is of outstanding practical importance for in silico protein annotation and is at the basis of several bioinformatic resources. Pfam is possibly the most well known protein family database, built in many years of work by domain experts with extensive use of manual curation. This approach is generally very accurate, but it is quite time consuming and it may suffer from a bias generated from the hand-curation itself, which is often guided by the available experimental evidence. Results We introduce a procedure that aims to identify automatically putative protein families. The procedure is based on Density Peak Clustering and uses as input only local pairwise alignments between protein sequences. In the experiment we present here, we ran the algorithm on about 4000 full-length proteins with at least one domain classified by Pfam as belonging to the Pseudouridine synthase and Archaeosine transglycosylase (PUA) clan. We obtained 71 automatically-generated sequence clusters with at least 100 members. While our clusters were largely consistent with the Pfam classification, showing good overlap with either single or multi-domain Pfam family architectures, we also observed some inconsistencies. The latter were inspected using structural and sequence based evidence, which suggested that the automatic classification captured evolutionary signals reflecting non-trivial features of protein family architectures. Based on this analysis we identified a putative novel pre-PUA domain as well as alternative boundaries for a few PUA or PUA-associated families. As a first indication that our approach was unlikely to be clan-specific, we performed the same analysis on the P53 clan, obtaining comparable results. Conclusions The clustering procedure described in this work takes advantage of the information contained in a large set of pairwise alignments and successfully identifies a set of putative families and family architectures in an unsupervised manner. Comparison with the Pfam classification highlights significant overlap and points to interesting differences, suggesting that our new algorithm could have potential in applications related to automatic protein classification. Testing this hypothesis, however, will require further experiments on large and diverse sequence datasets. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04013-x.
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Affiliation(s)
| | | | - Marco Punta
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK.,Center for Omics Sciences, IRCCS San Raffaele Hospital, 20132, Milan, Italy
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12
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Punta M, Jennings VA, Melcher AA, Lise S. The Immunogenic Potential of Recurrent Cancer Drug Resistance Mutations: An In Silico Study. Front Immunol 2020; 11:524968. [PMID: 33133066 PMCID: PMC7578429 DOI: 10.3389/fimmu.2020.524968] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 09/14/2020] [Indexed: 12/13/2022] Open
Abstract
Cancer somatic mutations have been identified as a source of antigens that can be targeted by cancer immunotherapy. In this work, expanding on previous studies, we analyze the HLA-presentation properties of mutations that are known to drive resistance to cancer targeted-therapies. We survey a large dataset of mutations that confer resistance to different drugs and occur in numerous genes and tumor types. We show that a significant number of them are predicted in silico to be potentially immunogenic across a large proportion of the human population. Further, by analyzing a cohort of patients carrying a small subset of these resistance mutations, we provide evidence that what is observed in the general population may be indicative of the mutations' immunogenic potential in resistant patients. Two of the mutations in our dataset had previously been experimentally validated by others and it was confirmed that some of their associated neopeptides elicit T-cell responses in vitro. The identification of potent cancer-specific antigens can be instrumental for developing more effective immunotherapies. In this work, we propose a novel list of drug-resistance mutations, several of which are recurrent, that could be of particular interest in the context of off-the-shelf precision immunotherapies such as therapeutic cancer vaccines.
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Affiliation(s)
- Marco Punta
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Victoria A. Jennings
- Department of Immunity and Infection, Leeds Institute of Medical Research, Leeds, United Kingdom
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - Alan A. Melcher
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - Stefano Lise
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
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13
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Pettitt SJ, Frankum JR, Punta M, Lise S, Alexander J, Chen Y, Yap TA, Haider S, Tutt ANJ, Lord CJ. Clinical BRCA1/2 Reversion Analysis Identifies Hotspot Mutations and Predicted Neoantigens Associated with Therapy Resistance. Cancer Discov 2020; 10:1475-1488. [PMID: 32699032 PMCID: PMC7611203 DOI: 10.1158/2159-8290.cd-19-1485] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [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: 12/17/2019] [Revised: 04/16/2020] [Accepted: 07/01/2020] [Indexed: 02/06/2023]
Abstract
Reversion mutations in BRCA1 or BRCA2 are associated with resistance to PARP inhibitors and platinum. To better understand the nature of these mutations, we collated, codified, and analyzed more than 300 reversions. This identified reversion "hotspots" and "deserts" in regions encoding the N and C terminus, respectively, of BRCA2, suggesting that pathogenic mutations in these regions may be at higher or lower risk of reversion. Missense and splice-site pathogenic mutations in BRCA1/2 also appeared less likely to revert than truncating mutations. Most reversions were <100 bp deletions. Although many deletions exhibited microhomology, this was not universal, suggesting that multiple DNA-repair processes cause reversion. Finally, we found that many reversions were predicted to encode immunogenic neopeptides, suggesting a route to the treatment of reverted disease. As well as providing a freely available database for the collation of future reversion cases, these observations have implications for how drug resistance might be managed in BRCA-mutant cancers. SIGNIFICANCE: Reversion mutations in BRCA genes are a major cause of clinical platinum and PARP inhibitor resistance. This analysis of all reported clinical reversions suggests that the position of BRCA2 mutations affects the risk of reversion. Many reversions are also predicted to encode tumor neoantigens, providing a potential route to targeting resistance.This article is highlighted in the In This Issue feature, p. 1426.
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Affiliation(s)
- Stephen J Pettitt
- The CRUK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom.
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, United Kingdom
| | - Jessica R Frankum
- The CRUK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, United Kingdom
| | - Marco Punta
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Stefano Lise
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - John Alexander
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, United Kingdom
| | - Yi Chen
- Scientific Computing Team, The Institute of Cancer Research, London, United Kingdom
| | - Timothy A Yap
- The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Syed Haider
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, United Kingdom
| | - Andrew N J Tutt
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, United Kingdom
| | - Christopher J Lord
- The CRUK Gene Function Laboratory, The Institute of Cancer Research, London, United Kingdom.
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, United Kingdom
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14
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Pettitt S, Frankum J, Punta M, Lise S, Alexander J, Chen Y, Haider S, Tutt AN, Lord CJ. Abstract 4072: Analysis of clinical BRCA1/2 reversions identifies hotspot mutations and predicted neoantigens associated with therapy resistance. Cancer Res 2020. [DOI: 10.1158/1538-7445.am2020-4072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Although PARP inhibitors and platinum salts form part of the standard-of-care for cancers with homologous recombination (HR) defects, drug resistance does emerge and is often caused by reversion mutations in BRCA1 or BRCA2. Reversion mutations are secondary mutations that restore functional protein or compensate for frameshift mutations. To better understand the nature and aetiology of these mutations, we collated, codified and analysed over 200 reversion mutations from 24 published studies. Most reversions have been reported as case reports or small clinical series - we have collected all published reports into a single freely-accessible database to which further cases can be submitted.
The majority of reported reversion mutations are from BRCA1/2 mutant serous ovarian cancer, reflecting the longer use of platinum and PARP inhibitors in this disease. Our analysis identified reversion “hotspots” and “deserts” in the N- and C-terminal coding regions (respectively) of BRCA2, suggesting that pathogenic mutations in these domains may be at higher or lower chance of reversion, an effect not seen for BRCA1. Missense and splice-site pathogenic mutations in BRCA1/2 also appeared less likely to revert than frameshift-causing mutations. Unexpectedly, whilst most reversions were <100 bp “second site” deletions, microhomology use was not universal, especially in BRCA1-mutant cancers, suggesting that multiple DNA repair processes cause reversion and these vary in BRCA1 vs. BRCA2 mutant tumours. Reversions in BRCA1 mutant tumours were less likely to be mediated by deletions.
Many reversions contain novel protein sequence not found in the wild type protein, for example where a stretch of out-of-frame protein sequence is retained between the pathogenic and reversion mutations. We modelled MHC binding affinities for these sequences and found that many reversions were predicted to encode potentially immunogenic neopeptides, suggesting a route to the treatment of reverted disease. These observations have implications for how drug resistance might be managed in BRCA-mutant cancers.
Citation Format: Stephen Pettitt, Jessica Frankum, Marco Punta, Stefano Lise, John Alexander, Yi Chen, Syed Haider, Andrew N. Tutt, Christopher J. Lord. Analysis of clinical BRCA1/2 reversions identifies hotspot mutations and predicted neoantigens associated with therapy resistance [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4072.
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Affiliation(s)
| | | | - Marco Punta
- Institute of Cancer Research, London, United Kingdom
| | - Stefano Lise
- Institute of Cancer Research, London, United Kingdom
| | | | - Yi Chen
- Institute of Cancer Research, London, United Kingdom
| | - Syed Haider
- Institute of Cancer Research, London, United Kingdom
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15
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von Loga K, Woolston A, Punta M, Barber LJ, Griffiths B, Semiannikova M, Spain G, Challoner B, Fenwick K, Simon R, Marx A, Sauter G, Lise S, Matthews N, Gerlinger M. Author Correction: Extreme intratumour heterogeneity and driver evolution in mismatch repair deficient gastro-oesophageal cancer. Nat Commun 2020; 11:675. [PMID: 31996672 PMCID: PMC6989513 DOI: 10.1038/s41467-020-14602-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Affiliation(s)
- Katharina von Loga
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
- Biomedical Research Centre, The Royal Marsden Hospital, London, SM2 5PT, United Kingdom
| | - Andrew Woolston
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Marco Punta
- Bioinformatics Core, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, United Kingdom
| | - Louise J Barber
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Beatrice Griffiths
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Maria Semiannikova
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Georgia Spain
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Benjamin Challoner
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Kerry Fenwick
- Tumour Profiling Unit, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Ronald Simon
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Andreas Marx
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
- Institute of Pathology, University Hospital Fuerth, 90766, Fuerth, Germany
| | - Guido Sauter
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Stefano Lise
- Bioinformatics Core, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, United Kingdom
| | - Nik Matthews
- Tumour Profiling Unit, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Marco Gerlinger
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom.
- Gastrointestinal Cancer Unit, The Royal Marsden Hospital, London, SW3 6JJ, United Kingdom.
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16
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von Loga K, Woolston A, Punta M, Barber LJ, Griffiths B, Semiannikova M, Spain G, Challoner B, Fenwick K, Simon R, Marx A, Sauter G, Lise S, Matthews N, Gerlinger M. Extreme intratumour heterogeneity and driver evolution in mismatch repair deficient gastro-oesophageal cancer. Nat Commun 2020; 11:139. [PMID: 31949146 PMCID: PMC6965135 DOI: 10.1038/s41467-019-13915-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 12/05/2019] [Indexed: 01/09/2023] Open
Abstract
Mismatch repair deficient (dMMR) gastro-oesophageal adenocarcinomas (GOAs) show better outcomes than their MMR-proficient counterparts and high immunotherapy sensitivity. The hypermutator-phenotype of dMMR tumours theoretically enables high evolvability but their evolution has not been investigated. Here we apply multi-region exome sequencing (MSeq) to four treatment-naive dMMR GOAs. This reveals extreme intratumour heterogeneity (ITH), exceeding ITH in other cancer types >20-fold, but also long phylogenetic trunks which may explain the exquisite immunotherapy sensitivity of dMMR tumours. Subclonal driver mutations are common and parallel evolution occurs in RAS, PIK3CA, SWI/SNF-complex genes and in immune evasion regulators. MSeq data and evolution analysis of single region-data from 64 MSI GOAs show that chromosome 8 gains are early genetic events and that the hypermutator-phenotype remains active during progression. MSeq may be necessary for biomarker development in these heterogeneous cancers. Comparison with other MSeq-analysed tumour types reveals mutation rates and their timing to determine phylogenetic tree morphologies.
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Affiliation(s)
- Katharina von Loga
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
- Biomedical Research Centre, The Royal Marsden Hospital, London, SM2 5PT, United Kingdom
| | - Andrew Woolston
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Marco Punta
- Bioinformatics Core, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, United Kingdom
| | - Louise J Barber
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Beatrice Griffiths
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Maria Semiannikova
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Georgia Spain
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Benjamin Challoner
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Kerry Fenwick
- Tumour Profiling Unit, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Ronald Simon
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Andreas Marx
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
- Institute of Pathology, University Hospital Fuerth, 90766, Fuerth, Germany
| | - Guido Sauter
- Institute of Pathology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Stefano Lise
- Bioinformatics Core, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, United Kingdom
| | - Nik Matthews
- Tumour Profiling Unit, The Institute of Cancer Research, London, SW3 6JB, United Kingdom
| | - Marco Gerlinger
- Translational Oncogenomics Laboratory, Centre for Evolution and Cancer, The Institute of Cancer Research, London, SW3 6JB, United Kingdom.
- Gastrointestinal Cancer Unit, The Royal Marsden Hospital, London, SW3 6JJ, United Kingdom.
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17
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Kleftogiannis D, Punta M, Jayaram A, Sandhu S, Wong SQ, Gasi Tandefelt D, Conteduca V, Wetterskog D, Attard G, Lise S. Identification of single nucleotide variants using position-specific error estimation in deep sequencing data. BMC Med Genomics 2019; 12:115. [PMID: 31375105 PMCID: PMC6679440 DOI: 10.1186/s12920-019-0557-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 07/15/2019] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). METHODS To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection. RESULTS Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments. CONCLUSIONS AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve .
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Affiliation(s)
- Dimitrios Kleftogiannis
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Present address: Genome Institute of Singapore (GIS), Agency of Science Research and Technology (A*STAR), Singapore, 138672, Singapore
| | - Marco Punta
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | | | - Shahneen Sandhu
- Peter MacCallum Cancer Centre and University of Melbourne, Melbourne, Victoria, Australia
| | - Stephen Q Wong
- Peter MacCallum Cancer Centre and University of Melbourne, Melbourne, Victoria, Australia
| | - Delila Gasi Tandefelt
- Department of Urology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Vincenza Conteduca
- Department of Medical Oncology, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, 47014, Meldola, Italy
| | | | - Gerhardt Attard
- UCL Cancer Institute, University College London, London, UK.
| | - Stefano Lise
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
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18
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Woolston A, Khan K, Spain G, Barber LJ, Griffiths B, Gonzalez-Exposito R, Hornsteiner L, Punta M, Patil Y, Newey A, Mansukhani S, Davies MN, Furness A, Sclafani F, Peckitt C, Jiménez M, Kouvelakis K, Ranftl R, Begum R, Rana I, Thomas J, Bryant A, Quezada S, Wotherspoon A, Khan N, Fotiadis N, Marafioti T, Powles T, Lise S, Calvo F, Guettler S, von Loga K, Rao S, Watkins D, Starling N, Chau I, Sadanandam A, Cunningham D, Gerlinger M. Genomic and Transcriptomic Determinants of Therapy Resistance and Immune Landscape Evolution during Anti-EGFR Treatment in Colorectal Cancer. Cancer Cell 2019; 36:35-50.e9. [PMID: 31287991 PMCID: PMC6617392 DOI: 10.1016/j.ccell.2019.05.013] [Citation(s) in RCA: 154] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 04/01/2019] [Accepted: 05/23/2019] [Indexed: 01/05/2023]
Abstract
Despite biomarker stratification, the anti-EGFR antibody cetuximab is only effective against a subgroup of colorectal cancers (CRCs). This genomic and transcriptomic analysis of the cetuximab resistance landscape in 35 RAS wild-type CRCs identified associations of NF1 and non-canonical RAS/RAF aberrations with primary resistance and validated transcriptomic CRC subtypes as non-genetic predictors of benefit. Sixty-four percent of biopsies with acquired resistance harbored no genetic resistance drivers. Most of these had switched from a cetuximab-sensitive transcriptomic subtype at baseline to a fibroblast- and growth factor-rich subtype at progression. Fibroblast-supernatant conferred cetuximab resistance in vitro, confirming a major role for non-genetic resistance through stromal remodeling. Cetuximab treatment increased cytotoxic immune infiltrates and PD-L1 and LAG3 immune checkpoint expression, potentially providing opportunities to treat cetuximab-resistant CRCs with immunotherapy.
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Affiliation(s)
- Andrew Woolston
- Translational Oncogenomics Lab, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Khurum Khan
- GI Cancer Unit, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - Georgia Spain
- Translational Oncogenomics Lab, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Louise J Barber
- Translational Oncogenomics Lab, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Beatrice Griffiths
- Translational Oncogenomics Lab, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Reyes Gonzalez-Exposito
- Translational Oncogenomics Lab, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Lisa Hornsteiner
- Translational Oncogenomics Lab, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Marco Punta
- Centre for Evolution and Cancer Bioinformatics Team, The Institute of Cancer Research, London SW3 6JB, UK
| | - Yatish Patil
- Centre for Evolution and Cancer Bioinformatics Team, The Institute of Cancer Research, London SW3 6JB, UK
| | - Alice Newey
- Translational Oncogenomics Lab, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Sonia Mansukhani
- Translational Oncogenomics Lab, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Matthew N Davies
- Translational Oncogenomics Lab, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Andrew Furness
- Cancer Institute, University College London, London WC1E 6AG, UK
| | | | - Clare Peckitt
- GI Cancer Unit, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - Mirta Jiménez
- Translational Oncogenomics Lab, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | | | - Romana Ranftl
- Tumour Microenvironment Lab, The Institute of Cancer Research, London SW3 6JB, UK
| | - Ruwaida Begum
- GI Cancer Unit, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - Isma Rana
- GI Cancer Unit, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - Janet Thomas
- GI Cancer Unit, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - Annette Bryant
- GI Cancer Unit, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - Sergio Quezada
- Cancer Institute, University College London, London WC1E 6AG, UK
| | | | - Nasir Khan
- Department of Radiology, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - Nikolaos Fotiadis
- Department of Radiology, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - Teresa Marafioti
- Departments of Pathology and Histopathology, University College Hospital, London NW1 2PG, UK
| | - Thomas Powles
- Barts Cancer Institute, Queen Mary University, London EC1M 6BQ, UK
| | - Stefano Lise
- Centre for Evolution and Cancer Bioinformatics Team, The Institute of Cancer Research, London SW3 6JB, UK
| | - Fernando Calvo
- Tumour Microenvironment Lab, The Institute of Cancer Research, London SW3 6JB, UK
| | - Sebastian Guettler
- Division of Structural Biology, The Institute of Cancer Research, London SW3 6JB, UK
| | - Katharina von Loga
- Translational Oncogenomics Lab, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Sheela Rao
- GI Cancer Unit, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - David Watkins
- GI Cancer Unit, The Royal Marsden Hospital, London SW3 6JJ, UK
| | | | - Ian Chau
- GI Cancer Unit, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - Anguraj Sadanandam
- Systems and Precision Cancer Medicine Lab, The Institute of Cancer Research, London SW3 6JB, UK
| | | | - Marco Gerlinger
- Translational Oncogenomics Lab, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK; GI Cancer Unit, The Royal Marsden Hospital, London SW3 6JJ, UK.
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19
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Heindl A, Khan AM, Rodrigues DN, Eason K, Sadanandam A, Orbegoso C, Punta M, Sottoriva A, Lise S, Banerjee S, Yuan Y. Microenvironmental niche divergence shapes BRCA1-dysregulated ovarian cancer morphological plasticity. Nat Commun 2018. [PMID: 30254278 DOI: 10.1038/s41467-018-06130-3] [] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
How tumor microenvironmental forces shape plasticity of cancer cell morphology is poorly understood. Here, we conduct automated histology image and spatial statistical analyses in 514 high grade serous ovarian samples to define cancer morphological diversification within the spatial context of the microenvironment. Tumor spatial zones, where cancer cell nuclei diversify in shape, are mapped in each tumor. Integration of this spatially explicit analysis with omics and clinical data reveals a relationship between morphological diversification and the dysregulation of DNA repair, loss of nuclear integrity, and increased disease mortality. Within the Immunoreactive subtype, spatial analysis further reveals significantly lower lymphocytic infiltration within diversified zones compared with other tumor zones, suggesting that even immune-hot tumors contain cells capable of immune escape. Our findings support a model whereby a subpopulation of morphologically plastic cancer cells with dysregulated DNA repair promotes ovarian cancer progression through positive selection by immune evasion.
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Affiliation(s)
- Andreas Heindl
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK.,Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Adnan Mujahid Khan
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK.,Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Daniel Nava Rodrigues
- Division of Cancer Therapeutics, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Katherine Eason
- Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Anguraj Sadanandam
- Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK.,Centre for Molecular Pathology, Royal Marsden Hospital, London, SM2 5NG, UK
| | - Cecilia Orbegoso
- Gynaecology Unit, The Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK
| | - Marco Punta
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Stefano Lise
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Susana Banerjee
- Gynaecology Unit, The Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK.,Division of Clinical Studies, the Institute of Cancer Research, London, UK, SM2 5NG
| | - Yinyin Yuan
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK. .,Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK.
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20
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Heindl A, Khan AM, Rodrigues DN, Eason K, Sadanandam A, Orbegoso C, Punta M, Sottoriva A, Lise S, Banerjee S, Yuan Y. Microenvironmental niche divergence shapes BRCA1-dysregulated ovarian cancer morphological plasticity. Nat Commun 2018; 9:3917. [PMID: 30254278 PMCID: PMC6156340 DOI: 10.1038/s41467-018-06130-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [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: 08/18/2017] [Accepted: 08/15/2018] [Indexed: 12/22/2022] Open
Abstract
How tumor microenvironmental forces shape plasticity of cancer cell morphology is poorly understood. Here, we conduct automated histology image and spatial statistical analyses in 514 high grade serous ovarian samples to define cancer morphological diversification within the spatial context of the microenvironment. Tumor spatial zones, where cancer cell nuclei diversify in shape, are mapped in each tumor. Integration of this spatially explicit analysis with omics and clinical data reveals a relationship between morphological diversification and the dysregulation of DNA repair, loss of nuclear integrity, and increased disease mortality. Within the Immunoreactive subtype, spatial analysis further reveals significantly lower lymphocytic infiltration within diversified zones compared with other tumor zones, suggesting that even immune-hot tumors contain cells capable of immune escape. Our findings support a model whereby a subpopulation of morphologically plastic cancer cells with dysregulated DNA repair promotes ovarian cancer progression through positive selection by immune evasion.
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Affiliation(s)
- Andreas Heindl
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK
- Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Adnan Mujahid Khan
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK
- Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Daniel Nava Rodrigues
- Division of Cancer Therapeutics, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Katherine Eason
- Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Anguraj Sadanandam
- Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK
- Centre for Molecular Pathology, Royal Marsden Hospital, London, SM2 5NG, UK
| | - Cecilia Orbegoso
- Gynaecology Unit, The Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK
| | - Marco Punta
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Stefano Lise
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK
| | - Susana Banerjee
- Gynaecology Unit, The Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK
- Division of Clinical Studies, the Institute of Cancer Research, London, UK, SM2 5NG
| | - Yinyin Yuan
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, SM2 5NG, UK.
- Division of Molecular Pathology, The Institute of Cancer Research, London, SM2 5NG, UK.
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21
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Assur Sanghai Z, Liu Q, Clarke OB, Belcher-Dufrisne M, Wiriyasermkul P, Giese MH, Leal-Pinto E, Kloss B, Tabuso S, Love J, Punta M, Banerjee S, Rajashankar KR, Rost B, Logothetis D, Quick M, Hendrickson WA, Mancia F. Structure-based analysis of CysZ-mediated cellular uptake of sulfate. eLife 2018; 7:27829. [PMID: 29792261 PMCID: PMC5967866 DOI: 10.7554/elife.27829] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Accepted: 04/11/2018] [Indexed: 01/25/2023] Open
Abstract
Sulfur, most abundantly found in the environment as sulfate (SO42-), is an essential element in metabolites required by all living cells, including amino acids, co-factors and vitamins. However, current understanding of the cellular delivery of SO42- at the molecular level is limited. CysZ has been described as a SO42- permease, but its sequence family is without known structural precedent. Based on crystallographic structure information, SO42- binding and flux experiments, we provide insight into the molecular mechanism of CysZ-mediated translocation of SO42- across membranes. CysZ structures from three different bacterial species display a hitherto unknown fold and have subunits organized with inverted transmembrane topology. CysZ from Pseudomonas denitrificans assembles as a trimer of antiparallel dimers and the CysZ structures from two other species recapitulate dimers from this assembly. Mutational studies highlight the functional relevance of conserved CysZ residues.
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Affiliation(s)
- Zahra Assur Sanghai
- Department of Physiology and Cellular Biophysics, Columbia University, New York, United States.,Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States
| | - Qun Liu
- Biology Department, Brookhaven National Laboratory, Upton, United States
| | - Oliver B Clarke
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States
| | - Meagan Belcher-Dufrisne
- Department of Physiology and Cellular Biophysics, Columbia University, New York, United States
| | - Pattama Wiriyasermkul
- Center for Molecular Recognition, Department of Psychiatry, Columbia University, New York, United States
| | - M Hunter Giese
- Department of Physiology and Cellular Biophysics, Columbia University, New York, United States.,Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States
| | - Edgar Leal-Pinto
- Department of Physiology and Biophysics, Virginia Commonwealth University School of Medicine, Richmond, United States.,Department of Pharmaceutical Sciences, School of Pharmacy, Bouvé College of Health Sciences, Northeastern University, Boston, United States
| | - Brian Kloss
- New York Structural Biology Center, New York, United States
| | | | - James Love
- New York Structural Biology Center, New York, United States
| | - Marco Punta
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Surajit Banerjee
- Department of Chemistry and Chemical Biology, Cornell University, NE-CAT, Argonne, United States
| | | | - Burkhard Rost
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Diomedes Logothetis
- Department of Physiology and Biophysics, Virginia Commonwealth University School of Medicine, Richmond, United States.,Department of Pharmaceutical Sciences, School of Pharmacy, Bouvé College of Health Sciences, Northeastern University, Boston, United States
| | - Matthias Quick
- Center for Molecular Recognition, Department of Psychiatry, Columbia University, New York, United States.,Division of Molecular Therapeutics, New York State Psychiatric Institute, New York, United States
| | - Wayne A Hendrickson
- Department of Physiology and Cellular Biophysics, Columbia University, New York, United States.,Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States.,New York Structural Biology Center, New York, United States
| | - Filippo Mancia
- Department of Physiology and Cellular Biophysics, Columbia University, New York, United States
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22
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Lote H, Spiteri I, Ermini L, Vatsiou A, Roy A, McDonald A, Maka N, Balsitis M, Bose N, Simbolo M, Mafficini A, Lampis A, Hahne JC, Trevisani F, Eltahir Z, Mentrasti G, Findlay C, Kalkman EAJ, Punta M, Werner B, Lise S, Aktipis A, Maley C, Greaves M, Braconi C, White J, Fassan M, Scarpa A, Sottoriva A, Valeri N. Carbon dating cancer: defining the chronology of metastatic progression in colorectal cancer. Ann Oncol 2017; 28:1243-1249. [PMID: 28327965 PMCID: PMC5452067 DOI: 10.1093/annonc/mdx074] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Background Patients often ask oncologists how long a cancer has been present before causing symptoms or spreading to other organs. The evolutionary trajectory of cancers can be defined using phylogenetic approaches but lack of chronological references makes dating the exact onset of tumours very challenging. Patients and methods Here, we describe the case of a colorectal cancer (CRC) patient presenting with synchronous lung metastasis and metachronous thyroid, chest wall and urinary tract metastases over the course of 5 years. The chest wall metastasis was caused by needle tract seeding, implying a known time of onset. Using whole genome sequencing data from primary and metastatic sites we inferred the complete chronology of the cancer by exploiting the time of needle tract seeding as an in vivo 'stopwatch'. This approach allowed us to follow the progression of the disease back in time, dating each ancestral node of the phylogenetic tree in the past history of the tumour. We used a Bayesian phylogenomic approach, which accounts for possible dynamic changes in mutational rate, to reconstruct the phylogenetic tree and effectively 'carbon date' the malignant progression. Results The primary colon cancer emerged between 5 and 8 years before the clinical diagnosis. The primary tumour metastasized to the lung and the thyroid within a year from its onset. The thyroid lesion presented as a tumour-to-tumour deposit within a benign Hurthle adenoma. Despite rapid metastatic progression from the primary tumour, the patient showed an indolent disease course. Primary cancer and metastases were microsatellite stable and displayed low chromosomal instability. Neo-antigen analysis suggested minimal immunogenicity. Conclusion Our data provide the first in vivo experimental evidence documenting the timing of metastatic progression in CRC and suggest that genomic instability might be more important than the metastatic potential of the primary cancer in dictating CRC fate.
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Affiliation(s)
- H. Lote
- Division of Molecular Pathology, The Institute of Cancer Research, Sutton
- Gastrointestinal Cancers and Lymphoma Unit, The Royal Marsden NHS Trust, Sutton
| | - I. Spiteri
- Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - L. Ermini
- Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - A. Vatsiou
- Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - A. Roy
- Department of Oncology, Crosshouse Hospital, Crosshouse, Kilmarnock
| | - A. McDonald
- Beatson West of Scotland Cancer Centre, Glasgow
| | - N. Maka
- Department of Pathology, Southern General Hospital, Glasgow
| | - M. Balsitis
- Department of Pathology, Crosshouse Hospital, Crosshouse, Kilmarnock, UK
| | - N. Bose
- Department of Oncology, Crosshouse Hospital, Crosshouse, Kilmarnock
| | - M. Simbolo
- Department of Pathology and Diagnostics, ARC-NET Research Centre University of Verona, Verona, Italy
| | - A. Mafficini
- Department of Pathology and Diagnostics, ARC-NET Research Centre University of Verona, Verona, Italy
| | - A. Lampis
- Division of Molecular Pathology, The Institute of Cancer Research, Sutton
| | - J. C. Hahne
- Division of Molecular Pathology, The Institute of Cancer Research, Sutton
| | - F. Trevisani
- Division of Molecular Pathology, The Institute of Cancer Research, Sutton
| | - Z. Eltahir
- Gastrointestinal Cancers and Lymphoma Unit, The Royal Marsden NHS Trust, Sutton
| | - G. Mentrasti
- Division of Molecular Pathology, The Institute of Cancer Research, Sutton
| | - C. Findlay
- Beatson West of Scotland Cancer Centre, Glasgow
| | | | - M. Punta
- Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - B. Werner
- Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - S. Lise
- Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - A. Aktipis
- Centre for Evolution and Cancer, The Institute of Cancer Research, London
- Center for Evolution and Cancer, University of California San Francisco, San Francisco
- Department of Psychology
| | - C. Maley
- Centre for Evolution and Cancer, The Institute of Cancer Research, London
- Center for Evolution and Cancer, University of California San Francisco, San Francisco
- Biodesign Institute, Arizona State University, Tempe, USA
| | - M. Greaves
- Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - C. Braconi
- Gastrointestinal Cancers and Lymphoma Unit, The Royal Marsden NHS Trust, Sutton
- Division of Cancer Therapeutics, The Institute of Cancer Research, Sutton, UK
| | - J. White
- Beatson West of Scotland Cancer Centre, Glasgow
| | - M. Fassan
- Department of Pathology and Diagnostics, ARC-NET Research Centre University of Verona, Verona, Italy
- Department of Medicine, Surgical Pathology & Cytopathology Unit, University of Padua, Padua, Italy
| | - A. Scarpa
- Department of Pathology and Diagnostics, ARC-NET Research Centre University of Verona, Verona, Italy
| | - A. Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, London
| | - N. Valeri
- Division of Molecular Pathology, The Institute of Cancer Research, Sutton
- Gastrointestinal Cancers and Lymphoma Unit, The Royal Marsden NHS Trust, Sutton
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23
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Punta M, Mistry J. Homology-Based Annotation of Large Protein Datasets. Methods Mol Biol 2016; 1415:153-176. [PMID: 27115632 DOI: 10.1007/978-1-4939-3572-7_8] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Advances in DNA sequencing technologies have led to an increasing amount of protein sequence data being generated. Only a small fraction of this protein sequence data will have experimental annotation associated with them. Here, we describe a protocol for in silico homology-based annotation of large protein datasets that makes extensive use of manually curated collections of protein families. We focus on annotations provided by the Pfam database and suggest ways to identify family outliers and family variations. This protocol may be useful to people who are new to protein data analysis, or who are unfamiliar with the current computational tools that are available.
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Affiliation(s)
- Marco Punta
- Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 15 rue de l'Ecole deMédecine, Paris, France.
| | - Jaina Mistry
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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24
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Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, Potter SC, Punta M, Qureshi M, Sangrador-Vegas A, Salazar GA, Tate J, Bateman A. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res 2015; 44:D279-85. [PMID: 26673716 PMCID: PMC4702930 DOI: 10.1093/nar/gkv1344] [Citation(s) in RCA: 3627] [Impact Index Per Article: 403.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Accepted: 11/17/2015] [Indexed: 11/24/2022] Open
Abstract
In the last two years the Pfam database (http://pfam.xfam.org) has undergone a substantial reorganisation to reduce the effort involved in making a release, thereby permitting more frequent releases. Arguably the most significant of these changes is that Pfam is now primarily based on the UniProtKB reference proteomes, with the counts of matched sequences and species reported on the website restricted to this smaller set. Building families on reference proteomes sequences brings greater stability, which decreases the amount of manual curation required to maintain them. It also reduces the number of sequences displayed on the website, whilst still providing access to many important model organisms. Matches to the full UniProtKB database are, however, still available and Pfam annotations for individual UniProtKB sequences can still be retrieved. Some Pfam entries (1.6%) which have no matches to reference proteomes remain; we are working with UniProt to see if sequences from them can be incorporated into reference proteomes. Pfam-B, the automatically-generated supplement to Pfam, has been removed. The current release (Pfam 29.0) includes 16 295 entries and 559 clans. The facility to view the relationship between families within a clan has been improved by the introduction of a new tool.
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Affiliation(s)
- Robert D Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Penelope Coggill
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Ruth Y Eberhardt
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Sean R Eddy
- Department of Molecular & Cellular Biology, Harvard University, Biological Laboratories 1008, 16 Divinity Avenue, Cambridge, MA 02138, USA John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA
| | - Jaina Mistry
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Alex L Mitchell
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Simon C Potter
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Marco Punta
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, 15 rue de l'Ecole de Médecine, 75006 Paris, France
| | - Matloob Qureshi
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Amaia Sangrador-Vegas
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Gustavo A Salazar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - John Tate
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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25
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Chiang Z, Vastermark A, Punta M, Coggill PC, Mistry J, Finn RD, Saier MH. The complexity, challenges and benefits of comparing two transporter classification systems in TCDB and Pfam. Brief Bioinform 2015; 16:865-72. [PMID: 25614388 PMCID: PMC4570203 DOI: 10.1093/bib/bbu053] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Indexed: 01/04/2023] Open
Abstract
Transport systems comprise roughly 10% of all proteins in a cell, playing critical roles in many processes. Improving and expanding their classification is an important goal that can affect studies ranging from comparative genomics to potential drug target searches. It is not surprising that different classification systems for transport proteins have arisen, be it within a specialized database, focused on this functional class of proteins, or as part of a broader classification system for all proteins. Two such databases are the Transporter Classification Database (TCDB) and the Protein family (Pfam) database. As part of a long-term endeavor to improve consistency between the two classification systems, we have compared transporter annotations in the two databases to understand the rationale for differences and to improve both systems. Differences sometimes reflect the fact that one database has a particular transporter family while the other does not. Differing family definitions and hierarchical organizations were reconciled, resulting in recognition of 69 Pfam ‘Domains of Unknown Function’, which proved to be transport protein families to be renamed using TCDB annotations. Of over 400 potential new Pfam families identified from TCDB, 10% have already been added to Pfam, and TCDB has created 60 new entries based on Pfam data. This work, for the first time, reveals the benefits of comprehensive database comparisons and explains the differences between Pfam and TCDB.
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26
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Natarajan P, Punta M, Kumar A, Yeh AP, Godzik A, Aravind L. Structure and sequence analyses of Bacteroides proteins BVU_4064 and BF1687 reveal presence of two novel predominantly-beta domains, predicted to be involved in lipid and cell surface interactions. BMC Bioinformatics 2015; 16:7. [PMID: 25592227 PMCID: PMC4387736 DOI: 10.1186/s12859-014-0434-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [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: 08/08/2014] [Accepted: 12/16/2014] [Indexed: 11/11/2022] Open
Abstract
Background N-terminal domains of BVU_4064 and BF1687 proteins from Bacteroides vulgatus and Bacteroides fragilis respectively are members of the Pfam family PF12985 (DUF3869). Proteins containing a domain from this family can be found in most Bacteroides species and, in large numbers, in all human gut microbiome samples. Both BVU_4064 and BF1687 proteins have a consensus lipobox motif implying they are anchored to the membrane, but their functions are otherwise unknown. The C-terminal half of BVU_4064 is assigned to protein family PF12986 (DUF3870); the equivalent part of BF1687 was unclassified. Results Crystal structures of both BVU_4064 and BF1687 proteins, solved at the JCSG center, show strikingly similar three-dimensional structures. The main difference between the two is that the two domains in the BVU_4064 protein are connected by a short linker, as opposed to a longer insertion made of 4 helices placed linearly along with a strand that is added to the C-terminal domain in the BF1687 protein. The N-terminal domain in both proteins, corresponding to the PF12985 (DUF3869) domain is a β–sandwich with pre-albumin-like fold, found in many proteins belonging to the Transthyretin clan of Pfam. The structures of C-terminal domains of both proteins, corresponding to the PF12986 (DUF3870) domain in BVU_4064 protein and an unclassified domain in the BF1687 protein, show significant structural similarity to bacterial pore-forming toxins. A helix in this domain is in an analogous position to a loop connecting the second and third strands in the toxin structures, where this loop is implicated to play a role in the toxin insertion into the host cell membrane. The same helix also points to the groove between the N- and C-terminal domains that are loosely held together by hydrophobic and hydrogen bond interactions. The presence of several conserved residues in this region together with these structural determinants could make it a functionally important region in these proteins. Conclusions Structural analysis of BVU_4064 and BF1687 points to possible roles in mediating multiple interactions on the cell-surface/extracellular matrix. In particular the N-terminal domain could be involved in adhesive interactions, the C-terminal domain and the inter-domain groove in lipid or carbohydrate interactions. Electronic supplementary material The online version of this article (doi:10.1186/s12859-014-0434-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Padmaja Natarajan
- Joint Center for Structural Genomics, San Diego, USA. .,Program on Bioinformatics and Systems Biology, Sanford-Burnham Medical Research Institute, La Jolla, CA, USA.
| | - Marco Punta
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
| | - Abhinav Kumar
- Joint Center for Structural Genomics, San Diego, USA. .,Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.
| | - Andrew P Yeh
- Joint Center for Structural Genomics, San Diego, USA. .,Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.
| | - Adam Godzik
- Joint Center for Structural Genomics, San Diego, USA. .,Program on Bioinformatics and Systems Biology, Sanford-Burnham Medical Research Institute, La Jolla, CA, USA.
| | - L Aravind
- National Center for Biotechnology Information, National Library of Medicine, Building 38A, Bethesda, MD, 20894, USA.
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27
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Abstract
Intrinsically disordered proteins and protein regions (IDPs/IDRs) do not adopt a well-defined folded structure under physiological conditions. Instead, these proteins exist as heterogeneous and dynamical conformational ensembles. IDPs are widespread in eukaryotic proteomes and are involved in fundamental biological processes, mostly related to regulation and signaling. At the same time, disordered regions often pose significant challenges to the structure determination process, which generally requires highly homogeneous proteins samples. In this book chapter, we provide a brief overview of protein disorder, describe various bioinformatics resources that have been developed in recent years for their characterization, and give a general outline of their applications in various types of structural genomics projects. Traditionally, disordered segments were filtered out to optimize the yield of structure determination pipelines. However, it is becoming increasingly clear that the structural characterization of proteins cannot be complete without the incorporation of intrinsically disordered regions.
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Affiliation(s)
- Marco Punta
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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28
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Mitchell A, Chang HY, Daugherty L, Fraser M, Hunter S, Lopez R, McAnulla C, McMenamin C, Nuka G, Pesseat S, Sangrador-Vegas A, Scheremetjew M, Rato C, Yong SY, Bateman A, Punta M, Attwood TK, Sigrist CJA, Redaschi N, Rivoire C, Xenarios I, Kahn D, Guyot D, Bork P, Letunic I, Gough J, Oates M, Haft D, Huang H, Natale DA, Wu CH, Orengo C, Sillitoe I, Mi H, Thomas PD, Finn RD. The InterPro protein families database: the classification resource after 15 years. Nucleic Acids Res 2014; 43:D213-21. [PMID: 25428371 PMCID: PMC4383996 DOI: 10.1093/nar/gku1243] [Citation(s) in RCA: 941] [Impact Index Per Article: 94.1] [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] [Indexed: 11/30/2022] Open
Abstract
The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.
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Affiliation(s)
- Alex Mitchell
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Hsin-Yu Chang
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Louise Daugherty
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Matthew Fraser
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Sarah Hunter
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Rodrigo Lopez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Craig McAnulla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Conor McMenamin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Gift Nuka
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Sebastien Pesseat
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Amaia Sangrador-Vegas
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Maxim Scheremetjew
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Claudia Rato
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Siew-Yit Yong
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Marco Punta
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Teresa K Attwood
- Faculty of Life Science and School of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Christian J A Sigrist
- Swiss Institute of Bioinformatics (SIB), CMU - Rue Michel-Servet, 1211 Geneva 4, Switzerland
| | - Nicole Redaschi
- Swiss Institute of Bioinformatics (SIB), CMU - Rue Michel-Servet, 1211 Geneva 4, Switzerland
| | - Catherine Rivoire
- Swiss Institute of Bioinformatics (SIB), CMU - Rue Michel-Servet, 1211 Geneva 4, Switzerland
| | - Ioannis Xenarios
- Swiss Institute of Bioinformatics (SIB), CMU - Rue Michel-Servet, 1211 Geneva 4, Switzerland Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland Department of Biochemistry, University of Geneva, 1211 Geneva, Switzerland
| | - Daniel Kahn
- Pôle Rhône-Alpin de Bio-Informatique (PRABI), Batiment G. Mendel, Universite Claude Bernard, 43 bd du 11 novembre 1918, 69622 Villeurbanne Cedex, France
| | - Dominique Guyot
- Pôle Rhône-Alpin de Bio-Informatique (PRABI), Batiment G. Mendel, Universite Claude Bernard, 43 bd du 11 novembre 1918, 69622 Villeurbanne Cedex, France
| | - Peer Bork
- European Molecular Laboratory (EMBL), Meyerhofstasse 1, 69117 Heidelberg, Germany
| | - Ivica Letunic
- European Molecular Laboratory (EMBL), Meyerhofstasse 1, 69117 Heidelberg, Germany
| | - Julian Gough
- Department of Computer Science, University of Bristol, Woodland Road, Bristol, BS8 1UB, UK
| | - Matt Oates
- Department of Computer Science, University of Bristol, Woodland Road, Bristol, BS8 1UB, UK
| | - Daniel Haft
- J. Craig Venter Institute (JCVI), 9704 Medical Center Drive, Rockville, MD 20850, USA
| | - Hongzhan Huang
- Protein Information Resource (PIR), Georgetown University Medical Center, Washington, DC 20007, USA
| | - Darren A Natale
- Protein Information Resource (PIR), Georgetown University Medical Center, Washington, DC 20007, USA
| | - Cathy H Wu
- Protein Information Resource (PIR), Georgetown University Medical Center, Washington, DC 20007, USA Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA
| | - Christine Orengo
- Structural and Molecular Biology Department, University College London, University of London, London, WC1E 6BT, UK
| | - Ian Sillitoe
- Structural and Molecular Biology Department, University College London, University of London, London, WC1E 6BT, UK
| | - Huaiyu Mi
- Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Paul D Thomas
- Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Robert D Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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Lewis TE, Sillitoe I, Andreeva A, Blundell TL, Buchan DWA, Chothia C, Cozzetto D, Dana JM, Filippis I, Gough J, Jones DT, Kelley LA, Kleywegt GJ, Minneci F, Mistry J, Murzin AG, Ochoa-Montaño B, Oates ME, Punta M, Rackham OJL, Stahlhacke J, Sternberg MJE, Velankar S, Orengo C. Genome3D: exploiting structure to help users understand their sequences. Nucleic Acids Res 2014; 43:D382-6. [PMID: 25348407 PMCID: PMC4384030 DOI: 10.1093/nar/gku973] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Genome3D (http://www.genome3d.eu) is a collaborative resource that provides predicted domain annotations and structural models for key sequences. Since introducing Genome3D in a previous NAR paper, we have substantially extended and improved the resource. We have annotated representatives from Pfam families to improve coverage of diverse sequences and added a fast sequence search to the website to allow users to find Genome3D-annotated sequences similar to their own. We have improved and extended the Genome3D data, enlarging the source data set from three model organisms to 10, and adding VIVACE, a resource new to Genome3D. We have analysed and updated Genome3D's SCOP/CATH mapping. Finally, we have improved the superposition tools, which now give users a more powerful interface for investigating similarities and differences between structural models.
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Affiliation(s)
- Tony E Lewis
- Institute of Structural and Molecular Biology, UCL, 636 Darwin Building, Gower Street, London, WC1E 6BT, UK
| | - Ian Sillitoe
- Institute of Structural and Molecular Biology, UCL, 636 Darwin Building, Gower Street, London, WC1E 6BT, UK
| | - Antonina Andreeva
- MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Old Addenbrooke's Site, 80 Tennis Court Road, Cambridge, CB2 1GA, UK
| | - Daniel W A Buchan
- Department of Computer Science, UCL, Gower Street, London, WC1E 6BT, UK
| | - Cyrus Chothia
- MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK
| | - Domenico Cozzetto
- Department of Computer Science, UCL, Gower Street, London, WC1E 6BT, UK
| | - José M Dana
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Ioannis Filippis
- Centre for Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Julian Gough
- Department of Computer Science, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol, BS8 1UB, UK
| | - David T Jones
- Institute of Structural and Molecular Biology, UCL, 636 Darwin Building, Gower Street, London, WC1E 6BT, UK Department of Computer Science, UCL, Gower Street, London, WC1E 6BT, UK
| | - Lawrence A Kelley
- Centre for Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Gerard J Kleywegt
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Federico Minneci
- Department of Computer Science, UCL, Gower Street, London, WC1E 6BT, UK
| | - Jaina Mistry
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Alexey G Murzin
- MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK
| | - Bernardo Ochoa-Montaño
- Department of Biochemistry, University of Cambridge, Old Addenbrooke's Site, 80 Tennis Court Road, Cambridge, CB2 1GA, UK
| | - Matt E Oates
- Department of Computer Science, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol, BS8 1UB, UK
| | - Marco Punta
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Owen J L Rackham
- MRC Clinical Sciences Centre, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
| | - Jonathan Stahlhacke
- Department of Computer Science, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol, BS8 1UB, UK
| | - Michael J E Sternberg
- Centre for Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Sameer Velankar
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Christine Orengo
- Institute of Structural and Molecular Biology, UCL, 636 Darwin Building, Gower Street, London, WC1E 6BT, UK
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Kumar A, Punta M, Axelrod HL, Das D, Farr CL, Grant JC, Chiu HJ, Miller MD, Coggill PC, Klock HE, Elsliger MA, Deacon AM, Godzik A, Lesley SA, Wilson IA. Crystal structures of three representatives of a new Pfam family PF14869 (DUF4488) suggest they function in sugar binding/uptake. Protein Sci 2014; 23:1380-91. [PMID: 25044324 DOI: 10.1002/pro.2522] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [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: 05/30/2014] [Revised: 07/10/2014] [Accepted: 07/11/2014] [Indexed: 12/27/2022]
Abstract
Crystal structures of three members (BACOVA_00364 from Bacteroides ovatus, BACUNI_03039 from Bacteroides uniformis and BACEGG_00036 from Bacteroides eggerthii) of the Pfam domain of unknown function (DUF4488) were determined to 1.95, 1.66, and 1.81 Å resolutions, respectively. The protein structures adopt an eight-stranded, calycin-like, β-barrel fold and bind an endogenous unknown ligand at one end of the β-barrel. The amino acids interacting with the ligand are not conserved in any other protein of known structure with this particular fold. The size and chemical environment of the bound ligand suggest binding or transport of a small polar molecule(s) as a potential function for these proteins. These are the first structural representatives of a newly defined PF14869 (DUF4488) Pfam family.
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Affiliation(s)
- Abhinav Kumar
- Joint Center for Structural Genomics, http://www.jcsg.org; Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, California, 94025
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31
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Yachdav G, Kloppmann E, Kajan L, Hecht M, Goldberg T, Hamp T, Hönigschmid P, Schafferhans A, Roos M, Bernhofer M, Richter L, Ashkenazy H, Punta M, Schlessinger A, Bromberg Y, Schneider R, Vriend G, Sander C, Ben-Tal N, Rost B. PredictProtein--an open resource for online prediction of protein structural and functional features. Nucleic Acids Res 2014; 42:W337-43. [PMID: 24799431 PMCID: PMC4086098 DOI: 10.1093/nar/gku366] [Citation(s) in RCA: 435] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
PredictProtein is a meta-service for sequence analysis that has been predicting
structural and functional features of proteins since 1992. Queried with a
protein sequence it returns: multiple sequence alignments, predicted aspects of
structure (secondary structure, solvent accessibility, transmembrane helices
(TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered
regions) and function. The service incorporates analysis methods for the
identification of functional regions (ConSurf), homology-based inference of Gene
Ontology terms (metastudent), comprehensive subcellular localization prediction
(LocTree3), protein–protein binding sites (ISIS2),
protein–polynucleotide binding sites (SomeNA) and predictions of the
effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our
goal has always been to develop a system optimized to meet the demands of
experimentalists not highly experienced in bioinformatics. To this end, the
PredictProtein results are presented as both text and a series of intuitive,
interactive and visually appealing figures. The web server and sources are
available at http://ppopen.rostlab.org.
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Affiliation(s)
- Guy Yachdav
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany Biosof LLC, New York, NY 10001, USA TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Edda Kloppmann
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, New York, NY 10032, USA
| | - Laszlo Kajan
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Maximilian Hecht
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Tatyana Goldberg
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Tobias Hamp
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Peter Hönigschmid
- Department of Genome Oriented Bioinformatics, Technische Universität München, Wissenschaftszentrum Weihenstephan, Freising 85354, Germany
| | - Andrea Schafferhans
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Manfred Roos
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Michael Bernhofer
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Lothar Richter
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
| | - Haim Ashkenazy
- The Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, 69978 Tel Aviv, Israel
| | - Marco Punta
- Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK Institute for Food and Plant Sciences WZW-Weihenstephan, Alte Akademie 8, Freising 85350, Germany
| | - Avner Schlessinger
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Yana Bromberg
- Biosof LLC, New York, NY 10001, USA Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Reinhard Schneider
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08901, USA
| | - Gerrit Vriend
- Luxembourg University & Luxembourg Centre for Systems Biomedicine, 4362 Belval, Luxembourg
| | - Chris Sander
- CMBI, NCMLS, Radboudumc Nijmegen Medical Centre, 6525 GA Nijmegen, The Netherlands
| | - Nir Ben-Tal
- Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, 10065 NY, USA
| | - Burkhard Rost
- Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany Biosof LLC, New York, NY 10001, USA New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, New York, NY 10032, USA The Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, 69978 Tel Aviv, Israel Department of Biochemistry and Molecular Biophysics & New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, New York, NY 10032, USA Institute for Advanced Study (TUM-IAS), Garching/Munich 85748, Germany
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Trame CB, Chang Y, Axelrod HL, Eberhardt RY, Coggill P, Punta M, Rawlings ND. New mini- zincin structures provide a minimal scaffold for members of this metallopeptidase superfamily. BMC Bioinformatics 2014; 15:1. [PMID: 24383880 PMCID: PMC3890501 DOI: 10.1186/1471-2105-15-1] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [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: 07/30/2013] [Accepted: 12/17/2013] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The Acel_2062 protein from Acidothermus cellulolyticus is a protein of unknown function. Initial sequence analysis predicted that it was a metallopeptidase from the presence of a motif conserved amongst the Asp-zincins, which are peptidases that contain a single, catalytic zinc ion ligated by the histidines and aspartic acid within the motif (HEXXHXXGXXD). The Acel_2062 protein was chosen by the Joint Center for Structural Genomics for crystal structure determination to explore novel protein sequence space and structure-based function annotation. RESULTS The crystal structure confirmed that the Acel_2062 protein consisted of a single, zincin-like metallopeptidase-like domain. The Met-turn, a structural feature thought to be important for a Met-zincin because it stabilizes the active site, is absent, and its stabilizing role may have been conferred to the C-terminal Tyr113. In our crystallographic model there are two molecules in the asymmetric unit and from size-exclusion chromatography, the protein dimerizes in solution. A water molecule is present in the putative zinc-binding site in one monomer, which is replaced by one of two observed conformations of His95 in the other. CONCLUSIONS The Acel_2062 protein is structurally related to the zincins. It contains the minimum structural features of a member of this protein superfamily, and can be described as a "mini- zincin". There is a striking parallel with the structure of a mini-Glu-zincin, which represents the minimum structure of a Glu-zincin (a metallopeptidase in which the third zinc ligand is a glutamic acid). Rather than being an ancestral state, phylogenetic analysis suggests that the mini-zincins are derived from larger proteins.
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Affiliation(s)
| | | | | | | | | | | | - Neil D Rawlings
- Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK.
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Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, Heger A, Hetherington K, Holm L, Mistry J, Sonnhammer ELL, Tate J, Punta M. Pfam: the protein families database. Nucleic Acids Res 2013; 42:D222-30. [PMID: 24288371 PMCID: PMC3965110 DOI: 10.1093/nar/gkt1223] [Citation(s) in RCA: 4192] [Impact Index Per Article: 381.1] [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] [Indexed: 01/17/2023] Open
Abstract
Pfam, available via servers in the UK (http://pfam.sanger.ac.uk/) and the USA (http://pfam.janelia.org/), is a widely used database of protein families, containing 14 831 manually curated entries in the current release, version 27.0. Since the last update article 2 years ago, we have generated 1182 new families and maintained sequence coverage of the UniProt Knowledgebase (UniProtKB) at nearly 80%, despite a 50% increase in the size of the underlying sequence database. Since our 2012 article describing Pfam, we have also undertaken a comprehensive review of the features that are provided by Pfam over and above the basic family data. For each feature, we determined the relevance, computational burden, usage statistics and the functionality of the feature in a website context. As a consequence of this review, we have removed some features, enhanced others and developed new ones to meet the changing demands of computational biology. Here, we describe the changes to Pfam content. Notably, we now provide family alignments based on four different representative proteome sequence data sets and a new interactive DNA search interface. We also discuss the mapping between Pfam and known 3D structures.
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Affiliation(s)
- Robert D Finn
- HHMI Janelia Farm Research Campus, 19700 Helix Drive, Ashburn, VA 20147 USA, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK, MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, OX1 3QX, UK, Institute of Biotechnology and Department of Biological and Environmental Sciences, University of Helsinki, PO Box 56 (Viikinkaari 5), 00014 Helsinki, Finland and Stockholm Bioinformatics Center, Swedish eScience Research Center, Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, PO Box 1031, SE-17121 Solna, Sweden
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Hwang WC, Bakolitsa C, Punta M, Coggill PC, Bateman A, Axelrod HL, Rawlings ND, Sedova M, Peterson SN, Eberhardt RY, Aravind L, Pascual J, Godzik A. LUD, a new protein domain associated with lactate utilization. BMC Bioinformatics 2013; 14:341. [PMID: 24274019 PMCID: PMC3924224 DOI: 10.1186/1471-2105-14-341] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [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: 07/03/2013] [Accepted: 11/19/2013] [Indexed: 11/24/2022] Open
Abstract
Background A novel highly conserved protein domain, DUF162 [Pfam: PF02589], can be mapped to two proteins: LutB and LutC. Both proteins are encoded by a highly conserved LutABC operon, which has been implicated in lactate utilization in bacteria. Based on our analysis of its sequence, structure, and recent experimental evidence reported by other groups, we hereby redefine DUF162 as the LUD domain family. Results JCSG solved the first crystal structure [PDB:2G40] from the LUD domain family: LutC protein, encoded by ORF DR_1909, of Deinococcus radiodurans. LutC shares features with domains in the functionally diverse ISOCOT superfamily. We have observed that the LUD domain has an increased abundance in the human gut microbiome. Conclusions We propose a model for the substrate and cofactor binding and regulation in LUD domain. The significance of LUD-containing proteins in the human gut microbiome, and the implication of lactate metabolism in the radiation-resistance of Deinococcus radiodurans are discussed.
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Affiliation(s)
- William C Hwang
- Joint Center for Structural Genomics, La Jolla, CA 92037, USA.
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35
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Mistry J, Kloppmann E, Rost B, Punta M. An estimated 5% of new protein structures solved today represent a new Pfam family. Acta Crystallogr D Biol Crystallogr 2013; 69:2186-93. [PMID: 24189229 PMCID: PMC3817691 DOI: 10.1107/s0907444913027157] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2013] [Accepted: 10/02/2013] [Indexed: 01/09/2023]
Abstract
High-resolution structural knowledge is key to understanding how proteins function at the molecular level. The number of entries in the Protein Data Bank (PDB), the repository of all publicly available protein structures, continues to increase, with more than 8000 structures released in 2012 alone. The authors of this article have studied how structural coverage of the protein-sequence space has changed over time by monitoring the number of Pfam families that acquired their first representative structure each year from 1976 to 2012. Twenty years ago, for every 100 new PDB entries released, an estimated 20 Pfam families acquired their first structure. By 2012, this decreased to only about five families per 100 structures. The reasons behind the slower pace at which previously uncharacterized families are being structurally covered were investigated. It was found that although more than 50% of current Pfam families are still without a structural representative, this set is enriched in families that are small, functionally uncharacterized or rich in problem features such as intrinsically disordered and transmembrane regions. While these are important constraints, the reasons why it may not yet be time to give up the pursuit of a targeted but more comprehensive structural coverage of the protein-sequence space are discussed.
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Affiliation(s)
- Jaina Mistry
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, England
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Mistry J, Coggill P, Eberhardt RY, Deiana A, Giansanti A, Finn RD, Bateman A, Punta M. The challenge of increasing Pfam coverage of the human proteome. Database (Oxford) 2013; 2013:bat023. [PMID: 23603847 PMCID: PMC3630804 DOI: 10.1093/database/bat023] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
It is a worthy goal to completely characterize all human proteins in terms of their domains. Here, using the Pfam database, we asked how far we have progressed in this endeavour. Ninety per cent of proteins in the human proteome matched at least one of 5494 manually curated Pfam-A families. In contrast, human residue coverage by Pfam-A families was <45%, with 9418 automatically generated Pfam-B families adding a further 10%. Even after excluding predicted signal peptide regions and short regions (<50 consecutive residues) unlikely to harbour new families, for ∼38% of the human protein residues, there was no information in Pfam about conservation and evolutionary relationship with other protein regions. This uncovered portion of the human proteome was found to be distributed over almost 25 000 distinct protein regions. Comparison with proteins in the UniProtKB database suggested that the human regions that exhibited similarity to thousands of other sequences were often either divergent elements or N- or C-terminal extensions of existing families. Thirty-four per cent of regions, on the other hand, matched fewer than 100 sequences in UniProtKB. Most of these did not appear to share any relationship with existing Pfam-A families, suggesting that thousands of new families would need to be generated to cover them. Also, these latter regions were particularly rich in amino acid compositional bias such as the one associated with intrinsic disorder. This could represent a significant obstacle toward their inclusion into new Pfam families. Based on these observations, a major focus for increasing Pfam coverage of the human proteome will be to improve the definition of existing families. New families will also be built, prioritizing those that have been experimentally functionally characterized. Database URL: http://pfam.sanger.ac.uk/
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Affiliation(s)
- Jaina Mistry
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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Abstract
Detection of protein homology via sequence similarity has important applications in biology, from protein structure and function prediction to reconstruction of phylogenies. Although current methods for aligning protein sequences are powerful, challenges remain, including problems with homologous overextension of alignments and with regions under convergent evolution. Here, we test the ability of the profile hidden Markov model method HMMER3 to correctly assign homologous sequences to >13,000 manually curated families from the Pfam database. We identify problem families using protein regions that match two or more Pfam families not currently annotated as related in Pfam. We find that HMMER3 E-value estimates seem to be less accurate for families that feature periodic patterns of compositional bias, such as the ones typically observed in coiled-coils. These results support the continued use of manually curated inclusion thresholds in the Pfam database, especially on the subset of families that have been identified as problematic in experiments such as these. They also highlight the need for developing new methods that can correct for this particular type of compositional bias.
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Affiliation(s)
- Jaina Mistry
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
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38
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Eberhardt RY, Bartholdson SJ, Punta M, Bateman A. The SHOCT domain: a widespread domain under-represented in model organisms. PLoS One 2013; 8:e57848. [PMID: 23451277 PMCID: PMC3581485 DOI: 10.1371/journal.pone.0057848] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [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: 10/30/2012] [Accepted: 01/29/2013] [Indexed: 11/18/2022] Open
Abstract
We have identified a new protein domain, which we have named the SHOCT domain (Short C-terminal domain). This domain is widespread in bacteria with over a thousand examples. But we found it is missing from the most commonly studied model organisms, despite being present in closely related species. It's predominantly C-terminal location, co-occurrence with numerous other domains and short size is reminiscent of the Gram-positive anchor motif, however it is present in a much wider range of species. We suggest several hypotheses about the function of SHOCT, including oligomerisation and nucleic acid binding. Our initial experiments do not support its role as an oligomerisation domain.
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Affiliation(s)
- Ruth Y Eberhardt
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom.
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39
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Perez-Riverol Y, Hermjakob H, Kohlbacher O, Martens L, Creasy D, Cox J, Leprevost F, Shan BP, Pérez-Nueno VI, Blazejczyk M, Punta M, Vierlinger K, Valiente PA, Leon K, Chinea G, Guirola O, Bringas R, Cabrera G, Guillen G, Padron G, Gonzalez LJ, Besada V. Computational proteomics pitfalls and challenges: HavanaBioinfo 2012 workshop report. J Proteomics 2013; 87:134-8. [PMID: 23376229 DOI: 10.1016/j.jprot.2013.01.019] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2013] [Accepted: 01/22/2013] [Indexed: 10/27/2022]
Abstract
The workshop "Bioinformatics for Biotechnology Applications (HavanaBioinfo 2012)", held December 8-11, 2012 in Havana, aimed at exploring new bioinformatics tools and approaches for large-scale proteomics, genomics and chemoinformatics. Major conclusions of the workshop include the following: (i) development of new applications and bioinformatics tools for proteomic repository analysis is crucial; current proteomic repositories contain enough data (spectra/identifications) that can be used to increase the annotations in protein databases and to generate new tools for protein identification; (ii) spectral libraries, de novo sequencing and database search tools should be combined to increase the number of protein identifications; (iii) protein probabilities and FDR are not yet sufficiently mature; (iv) computational proteomics software needs to become more intuitive; and at the same time appropriate education and training should be provided to help in the efficient exchange of knowledge between mass spectrometrists and experimental biologists and bioinformaticians in order to increase their bioinformatics background, especially statistics knowledge.
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40
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Wu Y, Punta M, Xiao R, Acton TB, Sathyamoorthy B, Dey F, Fischer M, Skerra A, Rost B, Montelione GT, Szyperski T. NMR structure of lipoprotein YxeF from Bacillus subtilis reveals a calycin fold and distant homology with the lipocalin Blc from Escherichia coli. PLoS One 2012; 7:e37404. [PMID: 22693626 PMCID: PMC3367933 DOI: 10.1371/journal.pone.0037404] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [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: 02/13/2012] [Accepted: 04/19/2012] [Indexed: 11/18/2022] Open
Abstract
The soluble monomeric domain of lipoprotein YxeF from the Gram positive bacterium B. subtilis was selected by the Northeast Structural Genomics Consortium (NESG) as a target of a biomedical theme project focusing on the structure determination of the soluble domains of bacterial lipoproteins. The solution NMR structure of YxeF reveals a calycin fold and distant homology with the lipocalin Blc from the Gram-negative bacterium E.coli. In particular, the characteristic β-barrel, which is open to the solvent at one end, is extremely well conserved in YxeF with respect to Blc. The identification of YxeF as the first lipocalin homologue occurring in a Gram-positive bacterium suggests that lipocalins emerged before the evolutionary divergence of Gram positive and Gram negative bacteria. Since YxeF is devoid of the α-helix that packs in all lipocalins with known structure against the β-barrel to form a second hydrophobic core, we propose to introduce a new lipocalin sub-family named ‘slim lipocalins’, with YxeF and the other members of Pfam family PF11631 to which YxeF belongs constituting the first representatives. The results presented here exemplify the impact of structural genomics to enhance our understanding of biology and to generate new biological hypotheses.
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Affiliation(s)
- Yibing Wu
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York, United States of America
- Northeast Structural Genomics Consortium
| | - Marco Punta
- Department of Computer Science and Institute for Advanced Study, Technical University of Munich, Munich, Germany
- Northeast Structural Genomics Consortium
| | - Rong Xiao
- Center of Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Robert Wood Johnson Medical School, The State University of New Jersey, Piscataway, New Jersey, United States of America
- Northeast Structural Genomics Consortium
| | - Thomas B. Acton
- Center of Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Robert Wood Johnson Medical School, The State University of New Jersey, Piscataway, New Jersey, United States of America
- Northeast Structural Genomics Consortium
| | - Bharathwaj Sathyamoorthy
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York, United States of America
| | - Fabian Dey
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America
- Northeast Structural Genomics Consortium
| | - Markus Fischer
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America
- Northeast Structural Genomics Consortium
| | - Arne Skerra
- Munich Center for Integrated Protein Science, CIPS-M, and Lehrstuhl für Biologische Chemie, Technische Universität München, Freising-Weihenstephan, Germany
| | - Burkhard Rost
- Department of Computer Science and Institute for Advanced Study, Technical University of Munich, Munich, Germany
- Northeast Structural Genomics Consortium
| | - Gaetano T. Montelione
- Center of Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Robert Wood Johnson Medical School, The State University of New Jersey, Piscataway, New Jersey, United States of America
- Northeast Structural Genomics Consortium
| | - Thomas Szyperski
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York, United States of America
- Northeast Structural Genomics Consortium
- * E-mail:
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41
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Kloppmann E, Punta M, Rost B. Structural genomics plucks high-hanging membrane proteins. Curr Opin Struct Biol 2012; 22:326-32. [PMID: 22622032 DOI: 10.1016/j.sbi.2012.05.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2012] [Revised: 03/28/2012] [Accepted: 05/01/2012] [Indexed: 01/21/2023]
Abstract
Recent years have seen the establishment of structural genomics centers that explicitly target integral membrane proteins. Here, we review the advances in targeting these extremely high-hanging fruits of structural biology in high-throughput mode. We observe that the experimental determination of high-resolution structures of integral membrane proteins is increasingly successful both in terms of getting structures and of covering important protein families, for example, from Pfam. Structural genomics has begun to contribute significantly toward this progress. An important component of this contribution is the set up of robotic pipelines that generate a wealth of experimental data for membrane proteins. We argue that prediction methods for the identification of membrane regions and for the comparison of membrane proteins largely suffice to meet the challenges of target selection for structural genomics of membrane proteins. In contrast, we need better methods to prioritize the most promising members in a family of closely related proteins and to annotate protein function from sequence and structure in absence of homology.
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Affiliation(s)
- Edda Kloppmann
- Department of Bioinformatics and Computational Biology, Technical University Munich, Germany.
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42
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Eberhardt RY, Haft DH, Punta M, Martin M, O'Donovan C, Bateman A. AntiFam: a tool to help identify spurious ORFs in protein annotation. Database (Oxford) 2012; 2012:bas003. [PMID: 22434837 PMCID: PMC3308159 DOI: 10.1093/database/bas003] [Citation(s) in RCA: 28] [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] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
As the deluge of genomic DNA sequence grows the fraction of protein sequences that have been manually curated falls. In turn, as the number of laboratories with the ability to sequence genomes in a high-throughput manner grows, the informatics capability of those labs to accurately identify and annotate all genes within a genome may often be lacking. These issues have led to fears about transitive annotation errors making sequence databases less reliable. During the lifetime of the Pfam protein families database a number of protein families have been built, which were later identified as composed solely of spurious open reading frames (ORFs) either on the opposite strand or in a different, overlapping reading frame with respect to the true protein-coding or non-coding RNA gene. These families were deleted and are no longer available in Pfam. However, we realized that these may perform a useful function to identify new spurious ORFs. We have collected these families together in AntiFam along with additional custom-made families of spurious ORFs. This resource currently contains 23 families that identified 1310 spurious proteins in UniProtKB and a further 4119 spurious proteins in a collection of metagenomic sequences. UniProt has adopted AntiFam as a part of the UniProtKB quality control process and will investigate these spurious proteins for exclusion.
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Affiliation(s)
- Ruth Y Eberhardt
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA. UK.
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43
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Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate J, Boursnell C, Pang N, Forslund K, Ceric G, Clements J, Heger A, Holm L, Sonnhammer ELL, Eddy SR, Bateman A, Finn RD. The Pfam protein families database. Nucleic Acids Res 2011; 40:D290-301. [PMID: 22127870 PMCID: PMC3245129 DOI: 10.1093/nar/gkr1065] [Citation(s) in RCA: 2852] [Impact Index Per Article: 219.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Pfam is a widely used database of protein families, currently containing more than 13,000 manually curated protein families as of release 26.0. Pfam is available via servers in the UK (http://pfam.sanger.ac.uk/), the USA (http://pfam.janelia.org/) and Sweden (http://pfam.sbc.su.se/). Here, we report on changes that have occurred since our 2010 NAR paper (release 24.0). Over the last 2 years, we have generated 1840 new families and increased coverage of the UniProt Knowledgebase (UniProtKB) to nearly 80%. Notably, we have taken the step of opening up the annotation of our families to the Wikipedia community, by linking Pfam families to relevant Wikipedia pages and encouraging the Pfam and Wikipedia communities to improve and expand those pages. We continue to improve the Pfam website and add new visualizations, such as the 'sunburst' representation of taxonomic distribution of families. In this work we additionally address two topics that will be of particular interest to the Pfam community. First, we explain the definition and use of family-specific, manually curated gathering thresholds. Second, we discuss some of the features of domains of unknown function (also known as DUFs), which constitute a rapidly growing class of families within Pfam.
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Affiliation(s)
- Marco Punta
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.
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44
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Hunter S, Jones P, Mitchell A, Apweiler R, Attwood TK, Bateman A, Bernard T, Binns D, Bork P, Burge S, de Castro E, Coggill P, Corbett M, Das U, Daugherty L, Duquenne L, Finn RD, Fraser M, Gough J, Haft D, Hulo N, Kahn D, Kelly E, Letunic I, Lonsdale D, Lopez R, Madera M, Maslen J, McAnulla C, McDowall J, McMenamin C, Mi H, Mutowo-Muellenet P, Mulder N, Natale D, Orengo C, Pesseat S, Punta M, Quinn AF, Rivoire C, Sangrador-Vegas A, Selengut JD, Sigrist CJA, Scheremetjew M, Tate J, Thimmajanarthanan M, Thomas PD, Wu CH, Yeats C, Yong SY. InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res 2011; 40:D306-12. [PMID: 22096229 PMCID: PMC3245097 DOI: 10.1093/nar/gkr948] [Citation(s) in RCA: 800] [Impact Index Per Article: 61.5] [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] [Indexed: 02/07/2023] Open
Abstract
InterPro (http://www.ebi.ac.uk/interpro/) is a database that integrates diverse information about protein families, domains and functional sites, and makes it freely available to the public via Web-based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large-scale analysis of whole genomes and meta-genomes, as well as in characterizing individual protein sequences. Herein we give an overview of new developments in the database and its associated software since 2009, including updates to database content, curation processes and Web and programmatic interfaces.
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Affiliation(s)
- Sarah Hunter
- EMBL Outstation European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD Cambridge, UK.
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45
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Cao Y, Jin X, Levin EJ, Huang H, Zong Y, Quick M, Weng J, Pan Y, Love J, Punta M, Rost B, Hendrickson WA, Javitch JA, Rajashankar KR, Zhou M. Crystal structure of a phosphorylation-coupled saccharide transporter. Nature 2011; 473:50-4. [PMID: 21471968 PMCID: PMC3201810 DOI: 10.1038/nature09939] [Citation(s) in RCA: 68] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2010] [Accepted: 02/11/2011] [Indexed: 01/07/2023]
Abstract
Saccharides have a central role in the nutrition of all living organisms. Whereas several saccharide uptake systems are shared between the different phylogenetic kingdoms, the phosphoenolpyruvate-dependent phosphotransferase system exists almost exclusively in bacteria. This multi-component system includes an integral membrane protein EIIC that transports saccharides and assists in their phosphorylation. Here we present the crystal structure of an EIIC from Bacillus cereus that transports diacetylchitobiose. The EIIC is a homodimer, with an expansive interface formed between the amino-terminal halves of the two protomers. The carboxy-terminal half of each protomer has a large binding pocket that contains a diacetylchitobiose, which is occluded from both sides of the membrane with its site of phosphorylation near the conserved His250 and Glu334 residues. The structure shows the architecture of this important class of transporters, identifies the determinants of substrate binding and phosphorylation, and provides a framework for understanding the mechanism of sugar translocation.
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Affiliation(s)
- Yu Cao
- Department of Physiology & Cellular Biophysics, College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY 10032, USA
| | - Xiangshu Jin
- Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biophysics, Howard Hughes Medical Institute, Columbia University, 1130 St. Nicholas Ave, Room 815, New York, NY 10032
| | - Elena J. Levin
- Department of Physiology & Cellular Biophysics, College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY 10032, USA
| | - Hua Huang
- Department of Physiology & Cellular Biophysics, College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY 10032, USA
| | - Yinong Zong
- Sanford-Burnham Institute, La Jolla, CA 92037
| | - Matthias Quick
- Department of Psychiatry and Center for Molecular Recognition, Columbia University, 630 West 168th Street, New York, NY 10032, USA,New York State Psychiatric Institute, Division of Molecular Therapeutics; 1051 Riverside Drive, New York, NY 10032
| | - Jun Weng
- Department of Physiology & Cellular Biophysics, College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY 10032, USA
| | - Yaping Pan
- Department of Physiology & Cellular Biophysics, College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY 10032, USA
| | - James Love
- New York Consortium on Membrane Protein Structure, New York Structural Biology Center, 89 Convent Avenue, New York, NY 10027, USA
| | - Marco Punta
- New York Consortium on Membrane Protein Structure, New York Structural Biology Center, 89 Convent Avenue, New York, NY 10027, USA,Department of Computer Science and Institute for Advanced Study, Technical University of Munich, D-85748 Munich, Germany
| | - Burkhard Rost
- New York Consortium on Membrane Protein Structure, New York Structural Biology Center, 89 Convent Avenue, New York, NY 10027, USA,Department of Computer Science and Institute for Advanced Study, Technical University of Munich, D-85748 Munich, Germany
| | - Wayne A. Hendrickson
- New York Consortium on Membrane Protein Structure, New York Structural Biology Center, 89 Convent Avenue, New York, NY 10027, USA,Department of Biochemistry and Molecular Biophysics, Howard Hughes Medical Institute, Columbia University, 630 West 168th Street, New York, NY 10032
| | - Jonathan A. Javitch
- Department of Psychiatry and Center for Molecular Recognition, Columbia University, 630 West 168th Street, New York, NY 10032, USA,New York State Psychiatric Institute, Division of Molecular Therapeutics; 1051 Riverside Drive, New York, NY 10032,Department of Pharmacology, Columbia University, 630 West 168th Street, New York, NY 10032, USA
| | - Kanagalaghatta R. Rajashankar
- Department of Chemistry and Chemical Biology, Cornell University, NE-CAT, Advanced Photon Source, Argonne, Illinois 60439, USA
| | - Ming Zhou
- Department of Physiology & Cellular Biophysics, College of Physicians and Surgeons, Columbia University, 630 West 168th Street, New York, NY 10032, USA
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46
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Shi W, Punta M, Bohon J, Sauder JM, D'Mello R, Sullivan M, Toomey J, Abel D, Lippi M, Passerini A, Frasconi P, Burley SK, Rost B, Chance MR. Characterization of metalloproteins by high-throughput X-ray absorption spectroscopy. Genome Res 2011; 21:898-907. [PMID: 21482623 DOI: 10.1101/gr.115097.110] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
High-throughput X-ray absorption spectroscopy was used to measure transition metal content based on quantitative detection of X-ray fluorescence signals for 3879 purified proteins from several hundred different protein families generated by the New York SGX Research Center for Structural Genomics. Approximately 9% of the proteins analyzed showed the presence of transition metal atoms (Zn, Cu, Ni, Co, Fe, or Mn) in stoichiometric amounts. The method is highly automated and highly reliable based on comparison of the results to crystal structure data derived from the same protein set. To leverage the experimental metalloprotein annotations, we used a sequence-based de novo prediction method, MetalDetector, to identify Cys and His residues that bind to transition metals for the redundancy reduced subset of 2411 sequences sharing <70% sequence identity and having at least one His or Cys. As the HT-XAS identifies metal type and protein binding, while the bioinformatics analysis identifies metal- binding residues, the results were combined to identify putative metal-binding sites in the proteins and their associated families. We explored the combination of this data with homology models to generate detailed structure models of metal-binding sites for representative proteins. Finally, we used extended X-ray absorption fine structure data from two of the purified Zn metalloproteins to validate predicted metalloprotein binding site structures. This combination of experimental and bioinformatics approaches provides comprehensive active site analysis on the genome scale for metalloproteins as a class, revealing new insights into metalloprotein structure and function.
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Affiliation(s)
- Wuxian Shi
- New York SGX Research Center for Structural Genomics (NYSGXRC), Case Western Reserve University, Center for Proteomics and Bioinformatics, Case Center for Synchrotron Biosciences, Upton, New York 11973, USA.
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47
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Chen YH, Hu L, Punta M, Bruni R, Hillerich B, Kloss B, Rost B, Love J, Siegelbaum SA, Hendrickson WA. Homologue structure of the SLAC1 anion channel for closing stomata in leaves. Nature 2010; 467:1074-80. [PMID: 20981093 PMCID: PMC3548404 DOI: 10.1038/nature09487] [Citation(s) in RCA: 100] [Impact Index Per Article: 7.1] [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: 03/28/2010] [Accepted: 09/10/2010] [Indexed: 01/17/2023]
Abstract
The plant SLAC1 anion channel controls turgor pressure in the aperture-defining guard cells of plant stomata, thereby regulating exchange of water vapor and photosynthetic gases in response to environmental signals such as drought or high levels of carbon dioxide. We determined the crystal structure of a bacterial homolog of SLAC1 at 1.20Å resolution, and we have used structure-inspired mutagenesis to analyze the conductance properties of SLAC1 channels. SLAC1 is a symmetric trimer composed from quasi-symmetric subunits, each having ten transmembrane helices arranged from helical hairpin pairs to form a central five-helix transmembrane pore that is gated by an extremely conserved phenylalanine residue. Conformational features suggest a mechanism for control of gating by kinase activation, and electrostatic features of the pore coupled with electrophysiological characteristics suggest that selectivity among different anions is largely a function of the energetic cost of ion dehydration.
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Affiliation(s)
- Yu-Hang Chen
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032, USA
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48
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Love J, Mancia F, Shapiro L, Punta M, Rost B, Girvin M, Wang DN, Zhou M, Hunt JF, Szyperski T, Gouaux E, MacKinnon R, McDermott A, Honig B, Inouye M, Montelione G, Hendrickson WA. The New York Consortium on Membrane Protein Structure (NYCOMPS): a high-throughput platform for structural genomics of integral membrane proteins. ACTA ACUST UNITED AC 2010; 11:191-9. [PMID: 20690043 DOI: 10.1007/s10969-010-9094-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2010] [Accepted: 07/13/2010] [Indexed: 10/19/2022]
Abstract
The New York Consortium on Membrane Protein Structure (NYCOMPS) was formed to accelerate the acquisition of structural information on membrane proteins by applying a structural genomics approach. NYCOMPS comprises a bioinformatics group, a centralized facility operating a high-throughput cloning and screening pipeline, a set of associated wet labs that perform high-level protein production and structure determination by x-ray crystallography and NMR, and a set of investigators focused on methods development. In the first three years of operation, the NYCOMPS pipeline has so far produced and screened 7,250 expression constructs for 8,045 target proteins. Approximately 600 of these verified targets were scaled up to levels required for structural studies, so far yielding 24 membrane protein crystals. Here we describe the overall structure of NYCOMPS and provide details on the high-throughput pipeline.
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Affiliation(s)
- James Love
- New York Structural Biology Center, New York, 10027, USA
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49
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Singarapu KK, Mills JL, Xiao R, Acton T, Punta M, Fischer M, Honig B, Rost B, Montelione GT, Szyperski T. Solution NMR structures of proteins VPA0419 from
Vibrio parahaemolyticus
and yiiS from
Shigella flexneri
provide structural coverage for protein domain family PFAM 04175. Proteins 2010; 78:779-84. [PMID: 19927321 PMCID: PMC2860719 DOI: 10.1002/prot.22630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
- Kiran Kumar Singarapu
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260
- Northeast Structural Genomics Consortium
| | - Jeffrey L. Mills
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260
- Northeast Structural Genomics Consortium
| | - Rong Xiao
- Northeast Structural Genomics Consortium
- Center of Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey 08854
- Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, New Jersey 08854
| | - Thomas Acton
- Northeast Structural Genomics Consortium
- Center of Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey 08854
- Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, New Jersey 08854
| | - Marco Punta
- Northeast Structural Genomics Consortium
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032
- Computational Biology and Bioinformatics (C2B2), Columbia University, New York, New York 10032
| | - Markus Fischer
- Northeast Structural Genomics Consortium
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032
| | - Barry Honig
- Northeast Structural Genomics Consortium
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032
- Howard Hughes Medical Institute, Center for Computational Biology and Bioinformatics, Columbia University, New York, New York 10032
| | - Burkhard Rost
- Northeast Structural Genomics Consortium
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032
- Computational Biology and Bioinformatics (C2B2), Columbia University, New York, New York 10032
| | - Gaetano T. Montelione
- Northeast Structural Genomics Consortium
- Center of Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey 08854
- Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, New Jersey 08854
| | - Thomas Szyperski
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260
- Northeast Structural Genomics Consortium
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50
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Punta M, Love J, Handelman S, Hunt JF, Shapiro L, Hendrickson WA, Rost B. Structural genomics target selection for the New York consortium on membrane protein structure. ACTA ACUST UNITED AC 2009; 10:255-68. [PMID: 19859826 PMCID: PMC2780672 DOI: 10.1007/s10969-009-9071-1] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.7] [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: 04/22/2009] [Accepted: 09/30/2009] [Indexed: 01/02/2023]
Abstract
The New York Consortium on Membrane Protein Structure (NYCOMPS), a part of the Protein Structure Initiative (PSI) in the USA, has as its mission to establish a high-throughput pipeline for determination of novel integral membrane protein structures. Here we describe our current target selection protocol, which applies structural genomics approaches informed by the collective experience of our team of investigators. We first extract all annotated proteins from our reagent genomes, i.e. the 96 fully sequenced prokaryotic genomes from which we clone DNA. We filter this initial pool of sequences and obtain a list of valid targets. NYCOMPS defines valid targets as those that, among other features, have at least two predicted transmembrane helices, no predicted long disordered regions and, except for community nominated targets, no significant sequence similarity in the predicted transmembrane region to any known protein structure. Proteins that feed our experimental pipeline are selected by defining a protein seed and searching the set of all valid targets for proteins that are likely to have a transmembrane region structurally similar to that of the seed. We require sequence similarity aligning at least half of the predicted transmembrane region of seed and target. Seeds are selected according to their feasibility and/or biological interest, and they include both centrally selected targets and community nominated targets. As of December 2008, over 6,000 targets have been selected and are currently being processed by the experimental pipeline. We discuss how our target list may impact structural coverage of the membrane protein space.
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Affiliation(s)
- Marco Punta
- Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, New York, NY, 10032, USA.
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