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      A Cell-Based Systems Biology Assessment of Human Blood to Monitor Immune Responses after Influenza Vaccination

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          Abstract

          Systems biology is an approach to comprehensively study complex interactions within a biological system. Most published systems vaccinology studies have utilized whole blood or peripheral blood mononuclear cells (PBMC) to monitor the immune response after vaccination. Because human blood is comprised of multiple hematopoietic cell types, the potential for masking responses of under-represented cell populations is increased when analyzing whole blood or PBMC. To investigate the contribution of individual cell types to the immune response after vaccination, we established a rapid and efficient method to purify human T and B cells, natural killer (NK) cells, myeloid dendritic cells (mDC), monocytes, and neutrophils from fresh venous blood. Purified cells were fractionated and processed in a single day. RNA-Seq and quantitative shotgun proteomics were performed to determine expression profiles for each cell type prior to and after inactivated seasonal influenza vaccination. Our results show that transcriptomic and proteomic profiles generated from purified immune cells differ significantly from PBMC. Differential expression analysis for each immune cell type also shows unique transcriptomic and proteomic expression profiles as well as changing biological networks at early time points after vaccination. This cell type-specific information provides a more comprehensive approach to monitor vaccine responses.

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          Most cited references22

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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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

                Contributors
                Role: Academic Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                23 February 2015
                2015
                : 10
                : 2
                : e0118528
                Affiliations
                [1 ]Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN, 37232, United States of America
                [2 ]Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, 37232, United States of America
                [3 ]Vanderbilt Vaccine Research Program; Division of Infectious Diseases, Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, TN, 37232, United States of America
                [4 ]HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, United States of America
                [5 ]Department of Chemistry, Vanderbilt University, Nashville, TN, 27232, United States of America
                [6 ]Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, 37232, United States of America
                [7 ]Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN, 37232, United States of America
                [8 ]Department of Cancer Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, 37232, United States of America
                Massachusetts General Hospital, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: KLH PS SJ KME AJL. Performed the experiments: KLH PS NP TMA KAF. Analyzed the data: KLH PS XN NP AG QL YG. Contributed reagents/materials/analysis tools: YG YS SL. Wrote the paper: KLH PS AG AJL. Wrote the parental study protocol, obtained IRB approval, collected blood samples from subjects and processed blood samples: LMH.

                Article
                PONE-D-14-38979
                10.1371/journal.pone.0118528
                4338067
                25706537
                16c44c39-366b-4cc4-b35a-36d3d223ec53
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 29 August 2014
                : 16 December 2014
                Page count
                Figures: 9, Tables: 1, Pages: 24
                Funding
                This project was funded in part with Federal funds from the National Institutes of Allergy and Infectious Disease, National Institutes of Health, Department of Health and Human Services, under Contract No. 272200800007C, the Vanderbilt Clinical and Translational Science Award grant NIH RR024975, the Childhood Infections Research Program grant T32-AI095202-01, the Immunobiology of Blood and Vascular Systems training grant 5 T32 HL69765-12, and NIH grant GM064779. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium ( http://proteomecentral.proteomexchange.org) via the PRIDE partner repository [ 1] with the dataset identifier PXD001657 and DOI 10.6019/PXD001657. RNA data have been deposited to the GEO database with the dataset identifier GSE64655, ( http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64655).

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