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      Molecular signatures of antibody responses derived from a systems biological study of 5 human vaccines

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          Abstract

          Many vaccines induce protective immunity via antibodies. Recent studies have used systems biological approaches to determine signatures that predict vaccine immunity in humans, but whether there is a ‘universal signature’ that can predict antibody responses to any vaccine, is unknown. Here we performed systems analyses of immune responses to the meningococcal polysaccharide and conjugate vaccines in healthy adults, in the broader context of our previous studies with the yellow fever and two influenza vaccines. To achieve this, we performed a large-scale network integration of public human blood transcriptomes, and systems-scale databases in specific biological contexts, and deduced a set of blood transcription modules. These modules revealed distinct transcriptional signatures of antibody responses to different classes of vaccines providing key insights into primary viral, protein recall and anti-polysaccharide responses. These results illuminate the early transcriptional programs orchestrating vaccine immunity in humans, and demonstrate the power of integrative network modeling.

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          Most cited references49

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          Is Open Access

          Network-based classification of breast cancer metastasis

          Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.
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            Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans.

            A major challenge in vaccinology is to prospectively determine vaccine efficacy. Here we have used a systems biology approach to identify early gene 'signatures' that predicted immune responses in humans vaccinated with yellow fever vaccine YF-17D. Vaccination induced genes that regulate virus innate sensing and type I interferon production. Computational analyses identified a gene signature, including complement protein C1qB and eukaryotic translation initiation factor 2 alpha kinase 4-an orchestrator of the integrated stress response-that correlated with and predicted YF-17D CD8(+) T cell responses with up to 90% accuracy in an independent, blinded trial. A distinct signature, including B cell growth factor TNFRS17, predicted the neutralizing antibody response with up to 100% accuracy. These data highlight the utility of systems biology approaches in predicting vaccine efficacy.
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              Systems Biology of Seasonal Influenza Vaccination in Humans

              We used a systems biological approach to study innate and adaptive responses to influenza vaccination in humans, during 3 consecutive influenza seasons. Healthy adults were vaccinated with inactivated (TIV) or live attenuated (LAIV) influenza vaccines. TIV induced greater antibody titers and enhanced numbers of plasmablasts than LAIV. In TIV vaccinees, early molecular signatures correlated with, and accurately predicted, later antibody titers in two independent trials. Interestingly, the expression of Calcium/calmodulin-dependent kinase IV (CamkIV) at day 3 was inversely correlated with later antibody titers. Vaccination of CamkIV −/− mice with TIV induced enhanced antigen-specific antibody titers, demonstrating an unappreciated role for CaMKIV in the regulation of antibody responses. Thus systems approaches can predict immunogenicity, and reveal new mechanistic insights about vaccines.
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                Author and article information

                Journal
                100941354
                21750
                Nat Immunol
                Nat. Immunol.
                Nature immunology
                1529-2908
                1529-2916
                31 December 2013
                15 December 2013
                February 2014
                01 August 2014
                : 15
                : 2
                : 195-204
                Affiliations
                [1 ]Emory Vaccine Center, Emory University, Atlanta, Georgia, USA
                [2 ]Yerkes National Primate Research Center, Emory University, Atlanta, Georgia, USA
                [3 ]The Hope Clinic of the Emory Vaccine Center, Division of Infectious Diseases, Emory University, Decatur, Georgia, USA
                [4 ]Meningitis and Vaccine Preventable Diseases Branch, Division of Bacterial Diseases, National Center of Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
                [5 ]Benaroya Research Institute, Seattle, Washington, USA
                [6 ]Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, Texas, USA
                [7 ]Department of Microbiology and Immunology, Emory University, Atlanta, Georgia, USA
                [8 ]Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, Georgia, USA
                [9 ]Department of Pathology, Emory University School of Medicine, Atlanta, Georgia, USA
                Author notes
                []Correspondence should be addressed to B.P. ( bpulend@ 123456emory.edu )
                [*]

                These authors contributed equally to this work.

                Article
                NIHMS540680
                10.1038/ni.2789
                3946932
                24336226
                70d3b65c-3e62-4db8-8577-9469f1b7dbfb

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                Immunology
                Immunology

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