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Mulè MP, Martins AJ, Cheung F, Farmer R, Sellers B, Quiel JA, Jain A, Kotliarov Y, Bansal N, Chen J, Schwartzberg PL, Tsang JS. Multiscale integration of human and single-cell variations reveals unadjuvanted vaccine high responders are naturally adjuvanted. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.20.23287474. [PMID: 37090674 PMCID: PMC10120791 DOI: 10.1101/2023.03.20.23287474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Advances in multimodal single cell analysis can empower high-resolution dissection of human vaccination responses. The resulting data capture multiple layers of biological variations, including molecular and cellular states, vaccine formulations, inter- and intra-subject differences, and responses unfolding over time. Transforming such data into biological insight remains a major challenge. Here we present a systematic framework applied to multimodal single cell data obtained before and after influenza vaccination without adjuvants or pandemic H5N1 vaccination with the AS03 adjuvant. Our approach pinpoints responses shared across or unique to specific cell types and identifies adjuvant specific signatures, including pro-survival transcriptional states in B lymphocytes that emerged one day after vaccination. We also reveal that high antibody responders to the unadjuvanted vaccine have a distinct baseline involving a rewired network of cell type specific transcriptional states. Remarkably, the status of certain innate immune cells in this network in high responders of the unadjuvanted vaccine appear "naturally adjuvanted": they resemble phenotypes induced early in the same cells only by vaccination with AS03. Furthermore, these cell subsets have elevated frequency in the blood at baseline and increased cell-intrinsic phospho-signaling responses after LPS stimulation ex vivo in high compared to low responders. Our findings identify how variation in the status of multiple immune cell types at baseline may drive robust differences in innate and adaptive responses to vaccination and thus open new avenues for vaccine development and immune response engineering in humans.
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Affiliation(s)
- Matthew P. Mulè
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
- NIH-Oxford-Cambridge Scholars Program; Department of Medicine, University of Cambridge, Cambridge, UK
| | - Andrew J. Martins
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Foo Cheung
- NIH Center for Human Immunology, NIAID, NIH, Bethesda, MD, USA
| | - Rohit Farmer
- NIH Center for Human Immunology, NIAID, NIH, Bethesda, MD, USA
| | - Brian Sellers
- NIH Center for Human Immunology, NIAID, NIH, Bethesda, MD, USA
| | - Juan A. Quiel
- NIH Center for Human Immunology, NIAID, NIH, Bethesda, MD, USA
| | - Arjun Jain
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Yuri Kotliarov
- NIH Center for Human Immunology, NIAID, NIH, Bethesda, MD, USA
| | - Neha Bansal
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Jinguo Chen
- NIH Center for Human Immunology, NIAID, NIH, Bethesda, MD, USA
| | - Pamela L. Schwartzberg
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- Cell Signaling and Immunity Section, NIAID, NIH, Bethesda, MD, USA
| | - John S. Tsang
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
- NIH Center for Human Immunology, NIAID, NIH, Bethesda, MD, USA
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Mongiardino Koch N, Thompson JR, Hiley AS, McCowin MF, Armstrong AF, Coppard SE, Aguilera F, Bronstein O, Kroh A, Mooi R, Rouse GW. Phylogenomic analyses of echinoid diversification prompt a re-evaluation of their fossil record. eLife 2022; 11:72460. [PMID: 35315317 PMCID: PMC8940180 DOI: 10.7554/elife.72460] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 03/03/2022] [Indexed: 12/25/2022] Open
Abstract
Echinoids are key components of modern marine ecosystems. Despite a remarkable fossil record, the emergence of their crown group is documented by few specimens of unclear affinities, rendering their early history uncertain. The origin of sand dollars, one of its most distinctive clades, is also unclear due to an unstable phylogenetic context. We employ 18 novel genomes and transcriptomes to build a phylogenomic dataset with a near-complete sampling of major lineages. With it, we revise the phylogeny and divergence times of echinoids, and place their history within the broader context of echinoderm evolution. We also introduce the concept of a chronospace - a multidimensional representation of node ages - and use it to explore methodological decisions involved in time calibrating phylogenies. We find the choice of clock model to have the strongest impact on divergence times, while the use of site-heterogeneous models and alternative node prior distributions show minimal effects. The choice of loci has an intermediate impact, affecting mostly deep Paleozoic nodes, for which clock-like genes recover dates more congruent with fossil evidence. Our results reveal that crown group echinoids originated in the Permian and diversified rapidly in the Triassic, despite the relative lack of fossil evidence for this early diversification. We also clarify the relationships between sand dollars and their close relatives and confidently date their origins to the Cretaceous, implying ghost ranges spanning approximately 50 million years, a remarkable discrepancy with their rich fossil record.
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Affiliation(s)
- Nicolás Mongiardino Koch
- Department of Earth & Planetary Sciences, Yale University, New Haven, United States.,Scripps Institution of Oceanography, University of California San Diego, La Jolla, United States
| | - Jeffrey R Thompson
- Department of Earth Sciences, Natural History Museum, London, United Kingdom.,University College London Center for Life's Origins and Evolution, London, United Kingdom
| | - Avery S Hiley
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, United States
| | - Marina F McCowin
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, United States
| | - A Frances Armstrong
- Department of Invertebrate Zoology and Geology, California Academy of Sciences, San Francisco, United States
| | - Simon E Coppard
- Bader International Study Centre, Queen's University, Herstmonceux Castle, East Sussex, United Kingdom
| | - Felipe Aguilera
- Departamento de Bioquímica y Biología Molecular, Facultad de Ciencias Biológicas, Universidad de Concepción, Concepción, Chile
| | - Omri Bronstein
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.,Steinhardt Museum of Natural History, Tel-Aviv, Israel
| | - Andreas Kroh
- Department of Geology and Palaeontology, Natural History Museum Vienna, Vienna, Austria
| | - Rich Mooi
- Department of Invertebrate Zoology and Geology, California Academy of Sciences, San Francisco, United States
| | - Greg W Rouse
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, United States
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3
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Bauch A, Pellet J, Schleicher T, Yu X, Gelemanović A, Cristella C, Fraaij PL, Polasek O, Auffray C, Maier D, Koopmans M, de Jong MD. Informing epidemic (research) responses in a timely fashion by knowledge management - a Zika virus use case. Biol Open 2020; 9:bio053934. [PMID: 33148605 PMCID: PMC7725600 DOI: 10.1242/bio.053934] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 10/28/2020] [Indexed: 01/24/2023] Open
Abstract
The response of pathophysiological research to emerging epidemics often occurs after the epidemic and, as a consequence, has little to no impact on improving patient outcomes or on developing high-quality evidence to inform clinical management strategies during the epidemic. Rapid and informed guidance of epidemic (research) responses to severe infectious disease outbreaks requires quick compilation and integration of existing pathophysiological knowledge. As a case study we chose the Zika virus (ZIKV) outbreak that started in 2015 to develop a proof-of-concept knowledge repository. To extract data from available sources and build a computationally tractable and comprehensive molecular interaction map we applied generic knowledge management software for literature mining, expert knowledge curation, data integration, reporting and visualization. A multi-disciplinary team of experts, including clinicians, virologists, bioinformaticians and knowledge management specialists, followed a pre-defined workflow for rapid integration and evaluation of available evidence. While conventional approaches usually require months to comb through the existing literature, the initial ZIKV KnowledgeBase (ZIKA KB) was completed within a few weeks. Recently we updated the ZIKA KB with additional curated data from the large amount of literature published since 2016 and made it publicly available through a web interface together with a step-by-step guide to ensure reproducibility of the described use case. In addition, a detailed online user manual is provided to enable the ZIKV research community to generate hypotheses, share knowledge, identify knowledge gaps, and interactively explore and interpret data. A workflow for rapid response during outbreaks was generated, validated and refined and is also made available. The process described here can be used for timely structuring of pathophysiological knowledge for future threats. The resulting structured biological knowledge is a helpful tool for computational data analysis and generation of predictive models and opens new avenues for infectious disease research. ZIKV Knowledgebase is available at www.zikaknowledgebase.eu.
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Affiliation(s)
| | - Johann Pellet
- European Institute of Systems Biology and Medicine, 69390 Lyon, France
| | | | - Xiao Yu
- Department of Medical Microbiology, Amsterdam UMC, University of Amsterdam, Amsterdam 1105 AZ, the Netherlands
| | - Andrea Gelemanović
- Department of Public Health, University of Split School of Medicine, 21000 Split, Croatia
| | - Cosimo Cristella
- Department of Medical Microbiology, Amsterdam UMC, University of Amsterdam, Amsterdam 1105 AZ, the Netherlands
| | - Pieter L Fraaij
- Department of Viroscience and Department of Paediatrics, Erasmus Medical Centre, 3000 CA Rotterdam, the Netherlands
| | - Ozren Polasek
- Department of Public Health, University of Split School of Medicine, 21000 Split, Croatia
| | - Charles Auffray
- European Institute of Systems Biology and Medicine, 69390 Lyon, France
| | | | - Marion Koopmans
- Department of Viroscience and Department of Paediatrics, Erasmus Medical Centre, 3000 CA Rotterdam, the Netherlands
| | - Menno D de Jong
- Department of Medical Microbiology, Amsterdam UMC, University of Amsterdam, Amsterdam 1105 AZ, the Netherlands
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Dugourd A, Saez-Rodriguez J. Footprint-based functional analysis of multiomic data. ACTA ACUST UNITED AC 2019; 15:82-90. [PMID: 32685770 PMCID: PMC7357600 DOI: 10.1016/j.coisb.2019.04.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 03/19/2019] [Accepted: 04/03/2019] [Indexed: 02/07/2023]
Abstract
Omic technologies allow us to generate extensive data, including transcriptomic, proteomic, phosphoproteomic and metabolomic. These data can be used to study signal transduction, gene regulation and metabolism. In this review, we summarise resources and methods to analysis these types of data. We focus on methods developed to recover functional insights using footprints. Footprints are signatures defined by the effect of molecules or processes of interest. They integrate information from multiple measurements whose abundances are under the influence of a common regulator. For example, transcripts controlled by a transcription factor or peptides phosphorylated by a kinase. Footprints can also be generalised across multiple types of omic data. Thus, we also present methods to integrate multiple types of omic data and features (such as the ones derived from footprints) together. We highlight some examples of studies that leverage such approaches to discover new biological mechanisms. Functional information on signalling pathways, metabolism and gene regulation can be found across multiple types of omic data. One way to extract such information is to consider these data as the footprint of the activity of enzymes and pathways. Information on enzyme/pathway activities and omic data can be integrated together to contextualise multi-scale networks. Such an approach can lead to the discovery of regulatory events spanning across multiple biological processes.
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Affiliation(s)
- Aurelien Dugourd
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute of Computational Biomedicine, Bioquant, 69120 Heidelberg, Germany.,RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), 52074, Aachen, Germany
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute of Computational Biomedicine, Bioquant, 69120 Heidelberg, Germany.,RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), 52074, Aachen, Germany
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5
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Tényi Á, Cano I, Marabita F, Kiani N, Kalko SG, Barreiro E, de Atauri P, Cascante M, Gomez-Cabrero D, Roca J. Network modules uncover mechanisms of skeletal muscle dysfunction in COPD patients. J Transl Med 2018; 16:34. [PMID: 29463285 PMCID: PMC5819708 DOI: 10.1186/s12967-018-1405-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 02/12/2018] [Indexed: 02/08/2023] Open
Abstract
Background Chronic obstructive pulmonary disease (COPD) patients often show skeletal muscle dysfunction that has a prominent negative impact on prognosis. The study aims to further explore underlying mechanisms of skeletal muscle dysfunction as a characteristic systemic effect of COPD, potentially modifiable with preventive interventions (i.e. muscle training). The research analyzes network module associated pathways and evaluates the findings using independent measurements. Methods We characterized the transcriptionally active network modules of interacting proteins in the vastus lateralis of COPD patients (n = 15, FEV1 46 ± 12% pred, age 68 ± 7 years) and healthy sedentary controls (n = 12, age 65 ± 9 years), at rest and after an 8-week endurance training program. Network modules were functionally evaluated using experimental data derived from the same study groups. Results At baseline, we identified four COPD specific network modules indicating abnormalities in creatinine metabolism, calcium homeostasis, oxidative stress and inflammatory responses, showing statistically significant associations with exercise capacity (VO2 peak, Watts peak, BODE index and blood lactate levels) (P < 0.05 each), but not with lung function (FEV1). Training-induced network modules displayed marked differences between COPD and controls. Healthy subjects specific training adaptations were significantly associated with cell bioenergetics (P < 0.05) which, in turn, showed strong relationships with training-induced plasma metabolomic changes; whereas, effects of training in COPD were constrained to muscle remodeling. Conclusion In summary, altered muscle bioenergetics appears as the most striking finding, potentially driving other abnormal skeletal muscle responses. Trial registration The study was based on a retrospectively registered trial (May 2017), ClinicalTrials.gov identifier: NCT03169270 Electronic supplementary material The online version of this article (10.1186/s12967-018-1405-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ákos Tényi
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain. .,Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain.
| | - Isaac Cano
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain.,Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain
| | - Francesco Marabita
- Unit of Computational Medicine, Department of Medicine, Karolinska Institute, 171 77, Stockholm, Sweden.,Center for Molecular Medicine, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Narsis Kiani
- Unit of Computational Medicine, Department of Medicine, Karolinska Institute, 171 77, Stockholm, Sweden.,Center for Molecular Medicine, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Susana G Kalko
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain.,Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain.,Bioinformatics Core Facility, IDIBAPS-CEK, Hospital Clínic, University de Barcelona, Barcelona, Spain
| | - Esther Barreiro
- Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain.,Pulmonology Dept, Muscle and Respiratory System Research Unit, IMIM-Hospital del Mar, Universitat Pompeu Fabra, PRBB, Barcelona, Spain
| | - Pedro de Atauri
- Departament de Bioquimica i Biologia Molecular, Facultat de Biologia-IBUB, Universitat de Barcelona, 08028, Barcelona, Spain
| | - Marta Cascante
- Departament de Bioquimica i Biologia Molecular, Facultat de Biologia-IBUB, Universitat de Barcelona, 08028, Barcelona, Spain
| | - David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine, Karolinska Institute, 171 77, Stockholm, Sweden.,Center for Molecular Medicine, Karolinska Institutet, 171 77, Stockholm, Sweden.,Mucosal and Salivary Biology Division, King's College London Dental Institute, London, SE1 9RT, UK
| | - Josep Roca
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain. .,Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain.
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