- Research article
- Open Access
In silico analysis of autoimmune diseases and genetic relationships to vaccination against infectious diseases
© McGarvey et al.; licensee BioMed Ltd. 2014
- Received: 7 November 2014
- Accepted: 1 December 2014
- Published: 9 December 2014
Near universal administration of vaccines mandates intense pharmacovigilance for vaccine safety and a stringently low tolerance for adverse events. Reports of autoimmune diseases (AID) following vaccination have been challenging to evaluate given the high rates of vaccination, background incidence of autoimmunity, and low incidence and variable times for onset of AID after vaccinations. In order to identify biologically plausible pathways to adverse autoimmune events of vaccine-related AID, we used a systems biology approach to create a matrix of innate and adaptive immune mechanisms active in specific diseases, responses to vaccine antigens, adjuvants, preservatives and stabilizers, for the most common vaccine-associated AID found in the Vaccine Adverse Event Reporting System.
This report focuses on Guillain-Barre Syndrome (GBS), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Idiopathic (or immune) Thrombocytopenic Purpura (ITP). Multiple curated databases and automated text mining of PubMed literature identified 667 genes associated with RA, 448 with SLE, 49 with ITP and 73 with GBS. While all data sources provided valuable and unique gene associations, text mining using natural language processing (NLP) algorithms provided the most information but required curation to remove incorrect associations. Six genes were associated with all four AIDs. Thirty-three pathways were shared by the four AIDs. Classification of genes into twelve immune system related categories identified more “Th17 T-cell subtype” genes in RA than the other AIDs, and more “Chemokine plus Receptors” genes associated with RA than SLE. Gene networks were visualized and clustered into interconnected modules with specific gene clusters for each AID, including one in RA with ten C-X-C motif chemokines. The intersection of genes associated with GBS, GBS peptide auto-antigens, influenza A infection, and influenza vaccination created a subnetwork of genes that inferred a possible role for the MAPK signaling pathway in influenza vaccine related GBS.
Results showing unique and common gene sets, pathways, immune system categories and functional clusters of genes in four autoimmune diseases suggest it is possible to develop molecular classifications of autoimmune and inflammatory events. Combining this information with cellular and other disease responses should greatly aid in the assessment of potential immune-mediated adverse events following vaccination.
- Rheumatoid Arthritis
- Systemic Lupus Erythematosus
- Gene Ontology
- Influenza Vaccination
Vaccines are profoundly important to global health in preventing infectious diseases. However, like any medication, there are potential adverse events reported after vaccination that warrant evaluation. Adverse events reported after vaccination can be transient and common responses like fever or in rare cases, autoimmune diseases (AID) . Although AIDs have been reported, to date there is no evidence to demonstrate a causal association . Nonetheless, autoimmune diseases occurring after vaccination (either new onset or flairs) must be thoroughly evaluated. Biologic plausibility is one key component to reported adverse events following immunization (AEFI). Evaluation of AID as AEFI is challenging because of the complex innate and adaptive immune responses to vaccine antigens, adjuvants, excipient preservatives and stabilizers that may contribute to reactogenic responses. In addition, the genetic factors that may predispose to susceptibility to autoimmune disease are not well understood.
To contribute to the understanding of vaccinomics in the context of evaluating autoimmune AEFIs, a bioinformatics, systems biology approach was used to model the overlapping immune components involved in vaccine response and autoimmune disease. We propose that the genes and proteins that are induced or participate in vaccine immune responses, natural infections, and specific autoimmune diseases reported after vaccinations would form cross-referenced, interactive networks that could lead to hypothesis-generation for potential mechanisms and risk factors for induced autoimmune adverse events. A database was created so that the rapidly expanding universe of literature on immune mechanisms and increasingly sophisticated biological and genomic data could be searched in an expeditious manner by investigators with an interest in evaluating the safety of vaccines and other biological therapeutics. We argue that analysis of this information can significantly improve our understanding of the relevant gene networks and molecular pathways pertinent to vaccinology. Here we describe our methodology and initial results in developing curated lists of genes and vaccine components associated with autoimmune diseases and integration of this information using knowledge of biological pathways and functional processes. Our ultimate goal is to develop an online resource with genomic, immunological and molecular data to explore plausible mechanisms of autoimmune adverse event development and identification of potential risk factors for these rare events. This may contribute to hypotheses for genomic/vaccine safety studies, potential biomarkers, and improve future vaccine development.
Selection of autoimmune diseases
Autoimmune disease reports in VAERS
Reports in VAERS
Top three vaccines reported (Number of reports)
Guillain-Barre Syndrome (GBS)
Flu H1N1 (144)
Rheumatoid Arthritis (RA)
Systemic Lupus Erythematosus (SLE)
Human Papillomavirus (36)
Idiopathic Thrombocytopenic Purpura (ITP)
Measles, Mumps & Rubella (64)
Others (N = 39)
Data collection and curation
Pairwise comparison of gene to autoimmune disease data sources
Unique to source
PS + Curation
Our collection contains 667 genes associated with RA, 448 with SLE, 49 with ITP and 73 with GBS. Many genes were associated with multiple AIDs. The list of genes associated with each AID including additional functional information on each gene is provided in Additional file 1. The literature sources for gene to disease association are contained in Additional file 2 and consist of a database reference and/or a PubMed identifier for a publication that claims an association. This gene set represents the largest, most complete, high quality source of genes and protein products associated with these four AIDs. DisGeNet  provides a similar quality resource with computationally mapped and classified genes associated with many diseases from multiple data resources including text mining. A comparison between our gene lists and those provided by DisGeNet for the four AIDs showed substantial overlap but with more genes identified in our more focused resource. This is likely due to some differences in the original data sources used and differences in methods used for mining the data sources we have in common. In addition our manual screening of associations derived from text mining, eliminated some erroneous associations.
Gene interactions with vaccine ingredients were obtained from the curated Comparative Toxicogenomics Database (CTD)  and PubChem . The CTD list was filtered to include direct ingredient to gene interactions with indirect interactions, those with intermediate genes or compounds, removed. The filtered list contained 64 interactions between 6 ingredients and 46 genes (Additional file 3). However, these sources are incomplete from an adjuvant perspective. Alum is the most commonly used adjuvant, but this term is generic and encompasses several aluminum salts such as aluminum hydroxide, phosphoaluminum sulfate, and aluminum sulfate. The toxicological databases do not reflect this diversity since entries were found only for aluminum sulfate and the general term aluminum. Few studies of the effects of these adjuvants on human leukocyte or other transcriptomes were available.
Functional analysis and classification
Functional annotations for each gene/protein were collected from UniProt , Reactome BioMart  Protein Information Resource  and Gene Ontology (GO) . Annotations included alternate gene names, protein names, function (if known), involvement in other diseases and pathways. Immune system gene classifications were obtained from the ImmPort website (http://immport.niad.nih.gov). Annotations are included in Additional file 1.
Only six genes (TGFB1, IFNG, CD4, FCGR3A, FCGR2A, HLA-DRB1) and thirty-three pathways were common to all AIDs. Not surprisingly all six common genes are well known to participate in many immune response and disease pathways. CD4 and INFG implicate TH1 lymphocytes. TGFB1 suggests roles for T-regulatory cells. The two IgG FC receptors suggest antibody mediated immunopathology or immunomodulation. HLA-DRB1 suggests antigen processing for presentation to T-cells. Twenty-two genes were common to RA, SLE and GBS, and RA, SLE and ITP shared fifteen genes. Two hundred and twenty genes were associated with two or more AIDs. This list is likely to expand as more studies are reported.
The thirty-three pathways shared by the four AIDs were likely due to inclusion of well-characterized signaling proteins that have been implicated in many pathways and functional processes. Fourteen of the thirty-three were KEGG disease pathways. Pathways related to natural influenza and other infections were included to cross-reference mechanisms potentially related to disease-induced AIDs. The pathway list may be somewhat redundant since the four individual pathway databases overlap and have differences in classifying the boundaries of a pathway. It is likely some interactions appear in multiple pathways.
Disease associated genes in different categories and pathways
Systemic lupus erythematosus
Immune thrombocytopenic purpura
Number of Genes Associated >>
v Categories v
Antigen Processing & Presentation
BCR Signaling Pathway
Cytokines + Receptors
> Chemokines + Receptors
> Interferons + Receptors
> Interleukins + Receptors
> TGF-b Family Members + Receptors
> TNF Family Members + Receptors
Natural Killer Cell Cytotoxicity
TCR Signaling Pathway
Immune Disease Pathways
Infectious Disease Pathways
Genes associated with T-cell types
All four AIDs
Cytotoxic T cell (n = 13)
Exhausted T cell (n = 7)
Anergic T cell (n = 11)
TR1 cell (n = 9)
Natural TReg cell (n = 15)
Inducible TReg cell (n = 17)
NKT cell (n = 10)
CD8αα T cell (n = 10)
CD4+ αβ T cell (n = 10)
CD8+ αβ T cell (n = 9)
TH1 cell (n = 14)
TH 2 cell (n = 19)
TH 9 cell (n = 8)
TH 17 cell (n = 20)
TH 22 cell (n = 8)
TFH cell (n = 16)
Central memory T cell (n = 18)
Effector memory T cell (n = 8)
γδ T cell (n = 10)
Although the smaller numbers of genes and pathways for ITP and GBS represent the best available sample from the current literature, they were unlikely to present the complete mechanistic schema. GBS had a much larger proportion of genes in the antimicrobial and infectious disease categories (Table 3), which were consistent with the association of GBS and Campylobacter, influenza, and other bacterial and viral infections .
Gene networks, data integration and hypothesis building
These gene associations were derived from experimental data about mechanisms of autoimmune diseases, infectious diseases and vaccination biology. Analysis of them provides insights into molecular mechanisms of autoimmune disease and can assist in the development of new hypotheses that promote vaccine safety. Gene interaction networks were built for each AID and clustered into interconnected modules. The network data and Cytoscape files for each are included in the supplemental material.
As another example, Guillain-Barré Syndrome (GBS) is an acute polyneuropathy with demyelination of the peripheral nervous system. Its causes are not fully known, but about one third of cases are preceded by Campylobacter jejuni, cytomegalovirus, influenza, and other bacterial and viral infections . The influenza virus prepared for the 1976–77 swine flu pandemic , led to an excess of GBS cases within 42 days of immunizations. This created a legacy of warnings for influenza vaccine – related to GBS. Retrospective studies have not demonstrated an increased risk for GBS . Prospective tracking and meta-analysis of the 2009 Influenza A (H1N1) monovalent inactivated vaccine in the U.S. concluded there were 1.6 excess cases of GBS per 1 million persons vaccinated . This is similar to rates after natural influenza infection. However, a global consortium analyzed Influenza A (H1N1) 2009 monovalent vaccination and reported measurable risk of GBS . Investigations of persons who develop GBS after influenza infections or immunization may identify genetic risks factors related to GBS pathophysiology.
Five genes were uniquely associated with GBS and not with RA, SLE or ITP including MPZ (Myelin protein P0), TUBB6 (Tubulin beta-6 chain), PMP22 (Peripheral myelin protein 22), which are auto-antigens, plus CASP1 (Caspase 1), CREBBP (CREB-binding protein), and IL12A (Interleukin-12 subunit alpha). Links to UniProtKB, KEGG pathways, and annotated literature collected in this study suggested mechanistic implications. CREBBP and EP300 (Histone acetyltransferase p300) acetylate histone and non-histone proteins for transcriptional regulation of antiviral interferons -. JUN is proposed to help promote IL1 and IL12 expression in influenza A. Microarray studies of cultured lymphocytes show that the vaccine ingredient thimerosal increased expression of JUN .
PIK3R1 and CASP1 interact with the influenza A NS1 protein. PIK3R1 (phosphatidylinositol 3-kinase regulatory subunit alpha) interacts with multiple proteins and signaling pathways. PIK3R1 is activated by double stranded RNA docked on NS1 (dsRNA-loaded NS1) and may promote viral replication by inhibiting premature apoptosis and promoting viral protein expression and nucleocapsid export -. PIK3R1 also has an upstream role in promoting the production of interferons via CREBBP and EP300. CASP1 is a component of the inflammasome formed in response to viral infection and cleaves the precursor forms of IL1B and IL18 into mature forms that are released from macrophages . NS1 may inhibit CASP1 and the production of IL18 . IL1B was also in the subnetwork, and is involved in stimulation of ”thymocyte proliferation”, “B-cell maturation and proliferation”, and “fibroblast growth factor activity”. The IL1B precursor mRNA was significantly up-regulated by aluminum sulfate in neural cell cultures . STAT1 (signal transducer and activator of transcription 1-alpha/beta) functions as a signal transducer and transcription activator that mediates responses to interferons, cytokines and growth factors via the Jak/STAT pathway. Its role in influenza A may be to promote expression of antiviral proteins and initiate expression of HLA class II genes like HLA-DRB1 that were also present in the subnetwork.
One of the linker genes added to the network was MAPK1 (mitogen-activated protein kinase 1), a multifunctional serine/threonine kinase that is an essential component of the MAP kinase signal transduction pathway along with STAT1 and JUN. Though not directly associated with GBS in our data set, MAPK1 was an extensively connected vertex that contacted almost all of the genes in this network. MAPK1 was associated with RA, SLE, influenza A infection, and was up-regulated following trivalent inactivated Influenza vaccine (TIV) and live-attenuated influenza vaccine (LAIV) administration . Gene expression studies in peripheral blood leukocytes from GBS patients found the MAPK signaling pathway to be one of the most significantly up-regulated pathways . In addition, the vaccine ingredients polymyxin B, neomycin, and deoxycholic acid increased MAPK1 phosphorylation and activity -, while thimerosal may decrease its activity . In influenza A, MAPK appears to promote viral protein expression and nucleocapsid export. PIK3R1 may have similar functions but the exact mechanisms remain unclear.
On the periphery of the subnetwork in Figure 4 with fewer connections to other genes in the network are four genes that IEDB identified with peptide epitopes in GBS: MBP (myelin basic protein), MPZ (myelin protein P0), PMP22 peripheral myelin protein 22), and TUBB6 (tubulin B6). MBP, MPZ and PMP22 were predicted to be connected to each other and eventually to MAPK1 (indicated by dotted lines) based on the shared GO biological process annotation “synaptic transmission” (in UniProtKB), and membership in an “axon guidance” canonical pathway in Reactome. MBP, MPZ and PMP22 received this GO annotation based on maintenance of myelin sheath integrity and mutations that cause demyelination -. MAPK signaling has been shown to be involved in the demyelination process and in Schwann cell differentiation , although full details of all the molecular mechanisms remain to be determined. TUBB6 is connected via an interaction with DYNLL2 (Dynein light chain 2) in transporting antigen loaded MHCII molecules to the cell surface.
Improved understanding of the systems biology related to adverse events occurring after vaccines and medications in general is critical to enhancing the efforts to evaluate safety. One first step is to identify all the genes and molecular processes and pathways involved. The data presented analyzed genes involved in four autoimmune diseases commonly reported as following viral infections and also reported following vaccination against the virus. Our analysis has identified common and unique genes and pathways for each AID. Classification of genes into immune system categories identified more “Chemokine plus Receptors” genes associated with RA than SLE. RA also had more genes associated with the “Th17 T-cell subtype” than the other AIDs. These results suggest it is possible, with additional data and effort to develop molecular classifications of autoimmune and other inflammatory events. Combining this information with cellular and other disease responses  should greatly aid in the assessment of potential immune-mediated adverse events following vaccination.
A benefit of having a reliable curated list of gene associations is that it facilitates integration and analysis with other data resources and experimental data from the literature, to develop hypotheses, enhance understanding of the systems biology of vaccines and vaccine preventable diseases. Some limitations to this approach are that the body of knowledge in the literature is incomplete, imperfect and biased toward specific diseases that affect more individuals and receive corresponding increases in research and funding.
Network analysis of AIDs demonstrated integration and analysis from outside resources by using the gene lists to build functional gene interaction networks using data from multiple databases collected in the ReactomeFI tool. The analysis for RA allowed identification of functional gene clusters unique for RA and clusters common for multiple AIDs. Analysis of the GBS network included data from KEGG’s Influenza A infection pathway and experimental data from a systems biology study of influenza vaccination. This helped to define a subnetwork of genes and pathways involved in all three processes Influenza infection, Guillain-Barré Syndrome and Influenza vaccination and inferred a possible role for the MAPK signaling pathway in influenza vaccine – related GBS.
Systems, methods and tools to collect organize and integrate the increasing volumes of data are essential for medical researchers and regulatory agencies to evaluate molecular data and develop testable hypotheses related to vaccine safety and efficacy. Literature mining together with rigorous network modeling and statistical approaches can help improve vaccine safety monitoring and evaluation. We hope this data will inform experimental studies on the relationships between these diseases and vaccination, assist in the analysis of new experimental data, new in silico models of vaccine related adverse events, and in the development of novel therapeutic strategies. Such models will help enable rapid classification of immune-mediated diseases. Our results and observations are based on what is available in published literature and genetic databases to date. We expect the list of gene associations to grow, especially for ITP and GBS. As more studies are conducted, a more complete picture of the genetic map associated with these diseases will emerge. All comparisons between AIDs should be viewed with these caveats in mind. Importantly, any of the suggestions we may infer in our analysis here still need to be further evaluated in independent studies (e.g., using prospective study designs) to confirm any of the results.
Data collection and curation
To define an initial set of autoimmune diseases we downloaded and queried the national VAERS  reports submitted between Jan 1990 and April, 2012. SAS software was used to query the dataset. Lists of vaccines, their manufacturers with brand names, associated adverse events and numbers of cases reported are collected. Using the MedDRA  thesaurus from BioPortal  all the adverse events that fall under “Autoimmune disorders” term were filtered. Four autoimmune diseases Rheumatoid arthritis (RA), Systemic Lupus Erythematosus (SLE), Guillain-Barre Syndrome (GBS) and Immune Thrombocytopenic Purpura (ITP) were chosen for further study to collect genes associated with each disease. The goal was to generate a high quality list of human genes associated with the four AIDs. The criteria for inclusion was broad in that the gene association could be of any type such as significant changes in gene or protein expression, GWAS association, sequence variations associated with the disease and others. The criteria were strict in that our sources must be either from a well-respected human curated database and/or traceable to published literature. As much as possible evidence was restricted to be from human studies not mouse model systems, though often mouse and human studies are presented in the same publication. Only protein-encoding genes were included and most associations with a genomic locus without a known gene were excluded. The one exception to this was SLE where several well-studied loci associated with inherited SLE from OMIM were included. Additional associations to non-protein encoding loci may be included in the future. Genes with associations from high-throughput analysis only, for example genes up-regulated in a relevant microarray experiments, were not included in the associated set unless the published evidence contained additional corroborating data for an association. Initial sources included UniProt , OMIM , the Genetic Association Database (GAD) , and KEGG Pathways . In addition proteins with epitopes in the Immune Epitope Database (IEDB)  were included. Searches were conducted using multiple MedDRA terms for the autoimmune disease. For UniProt searches we searched the Disease Association comment field first followed by all fields. For GAD only positive associations validated by GAD curators that corresponded to MedDRA terms for the AID were used. Associations corresponding to multiple disease terms were not used, as it was often difficult to assign the association found to only one disease. For IEDB only genes coding for peptide B- or T –cell epitopes were used. To expand the list of gene associations we used Pathway Studio 9 (PS9) software  and searched its internal ResNet 9 Mammalian database, which contains functional relationships for humans, mouse and rat extracted via natural language processing (NLP) algorithms from the PubMed database. The PS9 gene associations were manually reviewed before adding them to gene lists from the curated databases. Review involved rapid assessments of the manuscript text for the context of a gene – disease relationship. Often this text gave a clear statement supporting, or occasionally the statement negated the association. In other cases the gene symbol/name was the same as an abbreviation for a drug or other substance in a study or the statement was otherwise ambiguous. To resolve such cases required reviewing the paper and its supplementary materials. One issue with the ResNet database was that it was not possible to determine if outcomes were derived from human, mouse or rat studies, particularly if data from human and model organisms were compared in the same manuscript. About 20% of the associations originally identified by text mining were removed after review leaving remaining 621 associations. In the process of curation a few additional genes and references were noted by curators checking the literature and added to the results.
All protein encoding genes were mapped to UniProt/SwissProt protein accessions. Functional annotations for each gene/protein were collected using UniProt APIs , BioMart  service APIs provided by Reactome and the Protein Information Resource website  and ID mapping services . Annotations included alternate gene names, protein names, function (if known), known involvement in other diseases, pathways, and Gene Ontology (GO)  terms. Immune system gene classifications were downloaded from the ImmPort website (http://immport.niad.nih.gov). These annotations are included in Additional file 1. Gene networks and pathways for Influenza A and Measles infection were derived from KEGG pathways for those diseases. Only current HUGO gene symbols were used, alternate or retired gene symbols were updated via UniProt.
Gene classifications, network, pathway and GO analysis
Genes were classified into groups using the ImmPort (http://immport.niaid.nih.gov) list of immune-related genes and were additionally classified into Immune diseases and Infectious diseases based on KEGG pathway mappings. These categories are not exclusive so a gene can be grouped in more than one. Further classification of genes related to T-cell types was done manually using the genes surface phenotype, transcription factors, secreted effector molecules and other characteristic functions as defined in (www.nature.com/nri/posters/tcellsubsets/index.html) to group the genes.
Genes associated with each autoimmune disease were used to create functional interaction networks using Cytoscape 2.8  and the ReactomeFI plug-in ,. The 2012 version of the FI database was used and a minimal set of linker genes were included to help connect the network. The resulting network produces a good summary of the known and predicted functional relationships derived from multiple pathway databases, protein interaction databases and the Gene Ontology . Pathway and GO enrichment data were generated using the Reactome FI plug-in, excluding any linker genes from the analysis, with a P value of 0.05 or lower and false discovery rate of 0.01 unless otherwise noted. Pathway analysis for the genes in Figure 4 was done including the linker genes. Clustering the networked genes was done using a spectral partition based network clustering algorithm  as implemented in the ReactomeFI plug-in. Complete details of all genes and interactions plus additional images for RA, GBS, SLE and ITP are available in Additional files 5, 6, 7 and 8 respectively.
Overlaps in genes associated with the four autoimmune diseases (Figure 1) were tested for significance in two ways. First, two-sided Fisher’s exact tests (hypergeometric tests) were performed on the overlaps for each of the 6 possible pairs of diseases. For each pair, a contingency table was constructed with the gene intersection count; the counts of genes associated with one AID and not the other, and the count of genes not associated with either disease. 9,310 disease-mapped genes (see description below of how they were obtained) were considered as the universe of discoverable genes. Second, the counts of gene overlaps were also compared to a background distribution of overlap counts. 255,970 unique disease pairs were generated, and the count of the intersection of associated genes for each disease was recorded. The gene overlaps among the four autoimmune diseases were compared to this set of values by calculating the proportion of times the AID overlap count exceeded the overlap counts in the background distribution. Overlapping pathways in the four autoimmune diseases were compared in a similar manner. For each of the 255,970 unique disease pairs, an associated set of genes was obtained for each disease in the pair. Pathway enrichment was performed on each of these sets of genes using a one-sided Fisher’s exact test, and those pathways with FDR-adjusted p-values less than 0.01  were considered associated with the disease. The count of pathways in the intersection of associated pathways for the two diseases was recorded for each pair. The pathway overlap counts among the four autoimmune diseases were compared to this set of values in the same manner as the gene overlaps.
Enrichment for immune genes in each of the AIDs (Table 3) was determined using two-sided Fisher’s exact tests. The AID-associated genes were compared with the remainder of genes in the universe for each AID and immune category. P-values were adjusted using a Bonferroni correction, and only those below 0.05 deemed significant.
For these analyses the universe of discoverable genes was derived from a dataset of 34,942 unique gene–disease associations containing 9,310 unique HGNC gene names and 716 unique UMLS disease terms. The dataset was assembled from the AIDs analyzed here and additional immune related diseases we collected by similar methods and supplemented with curated disease associated genes from DisGeNet . For DisGeNet associations only disease terms from curated sources were used and associations from Text-Mining sources were excluded. All diseases were mapped to common MedDRA and UMLS terminology. Detailed results from each analysis above plus additional supporting analysis and methods are provided in Additional file 9: Table S9.
We thank Simina Boca for advice on the statistical analysis.
This work was partly funded by the FDA though the CERSI (Centers in Excellence in Regulatory Sciences) program 1U01FD004319-01.
- Koenig HC, Sutherland A, Izurieta HS, McGonagle D: Application of the immunological disease continuum to study autoimmune and other inflammatory events after vaccination. Vaccine. 2011, 29 (5): 913-919. 10.1016/j.vaccine.2010.10.044.PubMedView ArticleGoogle Scholar
- Stratton K, Ford A, Rusch E, Clayton EW: Adverse Effects of Vaccines: Evidence and Causality. 2012, The National Academies Press, Washington D.CGoogle Scholar
- Chen RT, Rastogi SC, Mullen JR, Hayes SW, Cochi SL, Donlon JA, Wassilak SG: The Vaccine Adverse Event Reporting System (VAERS). Vaccine. 1994, 12 (6): 542-550. 10.1016/0264-410X(94)90315-8.PubMedView ArticleGoogle Scholar
- Brown EG, Wood L, Wood S: The medical dictionary for regulatory activities (MedDRA). Drug Saf. 1999, 20 (2): 109-117. 10.2165/00002018-199920020-00002.PubMedView ArticleGoogle Scholar
- UniProt C: Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucleic Acids Res. 2013, 41 (Database issue): D43-D47.Google Scholar
- McKusick VA: Mendelian Inheritance in Man and its online version, OMIM. Am J Hum Genet. 2007, 80 (4): 588-604. 10.1086/514346.PubMedPubMed CentralView ArticleGoogle Scholar
- Becker KG, Barnes KC, Bright TJ, Wang SA: The genetic association database. Nat Genet. 2004, 36 (5): 431-432. 10.1038/ng0504-431.PubMedView ArticleGoogle Scholar
- Aoki-Kinoshita KF, Kanehisa M: Gene annotation and pathway mapping in KEGG. Methods Mol Biol. 2007, 396: 71-91. 10.1007/978-1-59745-515-2_6.PubMedView ArticleGoogle Scholar
- Vita R, Zarebski L, Greenbaum JA, Emami H, Hoof I, Salimi N, Damle R, Sette A, Peters B: The immune epitope database 2.0. Nucleic Acids Res. 2010, 38 (Database issue): D854-D862. 10.1093/nar/gkp1004.PubMedPubMed CentralView ArticleGoogle Scholar
- Bauer-Mehren A, Bundschus M, Rautschka M, Mayer MA, Sanz F, Furlong LI: Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases. PLoS One. 2011, 6 (6): e20284-10.1371/journal.pone.0020284.PubMedPubMed CentralView ArticleGoogle Scholar
- Davis AP, Murphy CG, Johnson R, Lay JM, Lennon-Hopkins K, Saraceni-Richards C, Sciaky D, King BL, Rosenstein MC, Wiegers TC, Mattingly CJ: The Comparative Toxicogenomics Database: update 2013. Nucleic Acids Res. 2013, 41 (Database issue): D1104-D1114. 10.1093/nar/gks994.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH: PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009, 37 (2): W623-W633. 10.1093/nar/gkp456.PubMedPubMed CentralView ArticleGoogle Scholar
- Jain E, Bairoch A, Duvaud S, Phan I, Redaschi N, Suzek BE, Martin MJ, McGarvey P, Gasteiger E: Infrastructure for the life sciences: design and implementation of the UniProt website. BMC Bioinformatics. 2009, 10: 136-10.1186/1471-2105-10-136.PubMedPubMed CentralView ArticleGoogle Scholar
- Guberman JM, Ai J, Arnaiz O, Baran J, Blake A, Baldock R, Chelala C, Croft D, Cros A, Cutts RJ, Di Genova A, Forbes S, Fujisawa T, Gadaleta E, Goodstein DM, Gundem G, Haggarty B, Haider S, Hall M, Harris T, Haw R, Hu S, Hubbard S, Hsu J, Iyer V, Jones P, Katayama T, Kinsella R, Kong L, Lawson D: BioMart Central Portal: an open database network for the biological community. Database. 2011, 2011: bar041-10.1093/database/bar041.PubMedPubMed CentralView ArticleGoogle Scholar
- Wu CH, Yeh LS, Huang H, Arminski L, Castro-Alvear J, Chen Y, Hu Z, Kourtesis P, Ledley RS, Suzek BE, Vinayaka CR, Zhang J, Barker WC: The Protein Information Resource. Nucleic Acids Res. 2003, 31 (1): 345-347. 10.1093/nar/gkg040.PubMedPubMed CentralView ArticleGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25 (1): 25-29. 10.1038/75556.PubMedPubMed CentralView ArticleGoogle Scholar
- Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T: Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011, 27 (3): 431-432. 10.1093/bioinformatics/btq675.PubMedPubMed CentralView ArticleGoogle Scholar
- Reactome FI Cytoscape Plugin , [http://wiki.reactome.org/index.php/Reactome_FI_Cytoscape_Plugin]
- Wu G, Feng X, Stein L: A human functional protein interaction network and its application to cancer data analysis. Genome Biol. 2010, 11 (5): R53-10.1186/gb-2010-11-5-r53.PubMedPubMed CentralView ArticleGoogle Scholar
- Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH: PID: the Pathway Interaction Database. Nucleic Acids Res. 2009, 37 (Database issue): D674-D679. 10.1093/nar/gkn653.PubMedPubMed CentralView ArticleGoogle Scholar
- Croft D, O’Kelly G, Wu G, Haw R, Gillespie M, Matthews L, Caudy M, Garapati P, Gopinath G, Jassal B, Jupe S, Kalatskaya I, Mahajan S, May B, Ndegwa N, Schmidt E, Shamovsky V, Yung C, Birney E, Hermjakob H, D’Eustachio P, Stein L: Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011, 39 (Database issue): D691-D697. 10.1093/nar/gkq1018.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhernakova A, van Diemen CC, Wijmenga C: Detecting shared pathogenesis from the shared genetics of immune-related diseases. Nat Rev Genet. 2009, 10 (1): 43-55. 10.1038/nrg2489.PubMedView ArticleGoogle Scholar
- Jacobs BC, Rothbarth PH, van der Meche FG, Herbrink P, Schmitz PI, de Klerk MA, van Doorn PA: The spectrum of antecedent infections in Guillain-Barre syndrome: a case–control study. Neurology. 1998, 51 (4): 1110-1115. 10.1212/WNL.51.4.1110.PubMedView ArticleGoogle Scholar
- Shahrara S, Huang Q, Mandelin AM, Pope RM: TH-17 cells in rheumatoid arthritis. Arthritis Res Ther. 2008, 10 (4): R93-10.1186/ar2477.PubMedPubMed CentralView ArticleGoogle Scholar
- Chabaud M, Durand JM, Buchs N, Fossiez F, Page G, Frappart L, Miossec P: Human interleukin-17: A T cell-derived proinflammatory cytokine produced by the rheumatoid synovium. Arthritis Rheum. 1999, 42 (5): 963-970. 10.1002/1529-0131(199905)42:5<963::AID-ANR15>3.0.CO;2-E.PubMedView ArticleGoogle Scholar
- Ma J, Zhu C, Ma B, Tian J, Baidoo SE, Mao C, Wu W, Chen J, Tong J, Yang M, Jiao Z, Xu H, Lu L, Wang S: Increased frequency of circulating follicular helper T cells in patients with rheumatoid arthritis. Clin Dev Immunol. 2012, 2012: 827480-PubMedPubMed CentralGoogle Scholar
- Feng X, Wang D, Chen J, Lu L, Hua B, Li X, Tsao BP, Sun L: Inhibition of aberrant circulating Tfh cell proportions by corticosteroids in patients with systemic lupus erythematosus. PLoS One. 2012, 7 (12): e51982-10.1371/journal.pone.0051982.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhang X, Ing S, Fraser A, Chen M, Khan O, Zakem J, Davis W, Quinet R: Follicular helper T cells: new insights into mechanisms of autoimmune diseases. Ochsner J. 2013, 13 (1): 131-139.PubMedPubMed CentralGoogle Scholar
- Han L, Wang Y, Bryant SH: A survey of across-target bioactivity results of small molecules in PubChem. Bioinformatics. 2009, 25 (17): 2251-2255. 10.1093/bioinformatics/btp380.PubMedPubMed CentralView ArticleGoogle Scholar
- Rho J, Takami M, Choi Y: Osteoimmunology: interactions of the immune and skeletal systems. Mol Cells. 2004, 17 (1): 1-9.PubMedGoogle Scholar
- Takayanagi H: Osteoimmunology: shared mechanisms and crosstalk between the immune and bone systems. Nat Rev Immunol. 2007, 7 (4): 292-304. 10.1038/nri2062.PubMedView ArticleGoogle Scholar
- Heinemann T, Bulwin GC, Randall J, Schnieders B, Sandhoff K, Volk HD, Milford E, Gullans SR, Utku N: Genomic organization of the gene coding for TIRC7, a novel membrane protein essential for T cell activation. Genomics. 1999, 57 (3): 398-406. 10.1006/geno.1999.5751.PubMedView ArticleGoogle Scholar
- Li YP, Chen W, Stashenko P: Molecular cloning and characterization of a putative novel human osteoclast-specific 116-kDa vacuolar proton pump subunit. Biochem Biophys Res Commun. 1996, 218 (3): 813-821. 10.1006/bbrc.1996.0145.PubMedView ArticleGoogle Scholar
- Sivadon-Tardy V, Orlikowski D, Porcher R, Sharshar T, Durand MC, Enouf V, Rozenberg F, Caudie C, Annane D, van der Werf S, Lebon P, Raphael JC, Gaillard JL, Gault E: Guillain-Barre syndrome and influenza virus infection. Clin Infect Dis. 2009, 48 (1): 48-56. 10.1086/594124.PubMedView ArticleGoogle Scholar
- Langmuir AD, Bregman DJ, Kurland LT, Nathanson N, Victor M: An epidemiologic and clinical evaluation of Guillain-Barre syndrome reported in association with the administration of swine influenza vaccines. Am J Epidemiol. 1984, 119 (6): 841-879.PubMedGoogle Scholar
- Lehmann HC, Hartung HP, Kieseier BC, Hughes RA: Guillain-Barre syndrome after exposure to influenza virus. Lancet Infect Dis. 2010, 10 (9): 643-651. 10.1016/S1473-3099(10)70140-7.PubMedView ArticleGoogle Scholar
- Salmon DA, Proschan M, Forshee R, Gargiullo P, Bleser W, Burwen DR, Cunningham F, Garman P, Greene SK, Lee GM, Vellozzi , Yih WK, Gellin B, Lurie N: Association between Guillain-BarrÈ syndrome and influenza A (H1N1) 2009 monovalent inactivated vaccines in the USA: a meta-analysis. Lancet. 2013, 381 (9876): 1461-1468. 10.1016/S0140-6736(12)62189-8.PubMedView ArticleGoogle Scholar
- Dodd CN, Romio SA, Black S, Vellozzi C, Andrews N, Sturkenboom M, Zuber P, Hua W, Bonhoeffer J, Buttery J, Crawford N, Deceuninck G, de Vries C, De Wals P, Gutierrez-Gimeno MV, Heijbel H, Hughes H, Hur K, Hviid A, Kelman J, Kilpi T, Chuang SK, Macartney K, Rett M, Lopez-Callada VR, Salmon D, Gimenez-Sanchez F, Sanz N, Silverman B, Storsaeter J: International collaboration to assess the risk of Guillain Barre Syndrome following Influenza A (H1N1) 2009 monovalent vaccines. Vaccine. 2013, 31 (40): 4448-4458. 10.1016/j.vaccine.2013.06.032.PubMedView ArticleGoogle Scholar
- Nakaya HI, Wrammert J, Lee EK, Racioppi L, Marie-Kunze S, Haining WN, Means AR, Kasturi SP, Khan N, Li GM, McCausland M, Kanchan V, Kokko KE, Li S, Elbein R, Mehta AK, Aderem A, Subbarao K, Ahmed R, Pulendran B: Systems biology of vaccination for seasonal influenza in humans. Nat Immunol. 2011, 12 (8): 786-795. 10.1038/ni.2067.PubMedPubMed CentralView ArticleGoogle Scholar
- Goldman PS, Tran VK, Goodman RH: The multifunctional role of the co-activator CBP in transcriptional regulation. Recent Prog Horm Res. 1997, 52: 103-119. discussion 119–120PubMedGoogle Scholar
- Ogryzko VV, Schiltz RL, Russanova V, Howard BH, Nakatani Y: The transcriptional coactivators p300 and CBP are histone acetyltransferases. Cell. 1996, 87 (5): 953-959. 10.1016/S0092-8674(00)82001-2.PubMedView ArticleGoogle Scholar
- Yang XJ, Ogryzko VV, Nishikawa J, Howard BH, Nakatani Y: A p300/CBP-associated factor that competes with the adenoviral oncoprotein E1A. Nature. 1996, 382 (6589): 319-324. 10.1038/382319a0.PubMedView ArticleGoogle Scholar
- Arany Z, Huang LE, Eckner R, Bhattacharya S, Jiang C, Goldberg MA, Bunn HF, Livingston DM: An essential role for p300/CBP in the cellular response to hypoxia. Proc Natl Acad Sci U S A. 1996, 93 (23): 12969-12973. 10.1073/pnas.93.23.12969.PubMedPubMed CentralView ArticleGoogle Scholar
- Dornan D, Eckert M, Wallace M, Shimizu H, Ramsay E, Hupp TR, Ball KL: Interferon regulatory factor 1 binding to p300 stimulates DNA-dependent acetylation of p53. Mol Cell Biol. 2004, 24 (22): 10083-10098. 10.1128/MCB.24.22.10083-10098.2004.PubMedPubMed CentralView ArticleGoogle Scholar
- Masumi A, Yamakawa Y, Fukazawa H, Ozato K, Komuro K: Interferon regulatory factor-2 regulates cell growth through its acetylation. J Biol Chem. 2003, 278 (28): 25401-25407. 10.1074/jbc.M213037200.PubMedView ArticleGoogle Scholar
- Walker SJ, Segal J, Aschner M: Cultured lymphocytes from autistic children and non-autistic siblings up-regulate heat shock protein RNA in response to thimerosal challenge. Neurotoxicology. 2006, 27 (5): 685-692. 10.1016/j.neuro.2006.06.003.PubMedView ArticleGoogle Scholar
- Ehrhardt C, Wolff T, Pleschka S, Planz O, Beermann W, Bode JG, Schmolke M, Ludwig S: Influenza A virus NS1 protein activates the PI3K/Akt pathway to mediate antiapoptotic signaling responses. J Virol. 2007, 81 (7): 3058-3067. 10.1128/JVI.02082-06.PubMedPubMed CentralView ArticleGoogle Scholar
- Ehrhardt C, Marjuki H, Wolff T, Nurnberg B, Planz O, Pleschka S, Ludwig S: Bivalent role of the phosphatidylinositol-3-kinase (PI3K) during influenza virus infection and host cell defence. Cell Microbiol. 2006, 8 (8): 1336-1348. 10.1111/j.1462-5822.2006.00713.x.PubMedView ArticleGoogle Scholar
- Sarkar SN, Peters KL, Elco CP, Sakamoto S, Pal S, Sen GC: Novel roles of TLR3 tyrosine phosphorylation and PI3 kinase in double-stranded RNA signaling. Nat Struct Mol Biol. 2004, 11 (11): 1060-1067. 10.1038/nsmb847.PubMedView ArticleGoogle Scholar
- Pang IK, Iwasaki A: Inflammasomes as mediators of immunity against influenza virus. Trends Immunol. 2011, 32 (1): 34-41. 10.1016/j.it.2010.11.004.PubMedPubMed CentralView ArticleGoogle Scholar
- Stasakova J, Ferko B, Kittel C, Sereinig S, Romanova J, Katinger H, Egorov A: Influenza A mutant viruses with altered NS1 protein function provoke caspase-1 activation in primary human macrophages, resulting in fast apoptosis and release of high levels of interleukins 1beta and 18. J Gen Virol. 2005, 86 (Pt 1): 185-195. 10.1099/vir.0.80422-0.PubMedView ArticleGoogle Scholar
- Lukiw WJ, Percy ME, Kruck TP: Nanomolar aluminum induces pro-inflammatory and pro-apoptotic gene expression in human brain cells in primary culture. J Inorg Biochem. 2005, 99 (9): 1895-1898. 10.1016/j.jinorgbio.2005.04.021.PubMedView ArticleGoogle Scholar
- Chang KH, Chuang TJ, Lyu RK, Ro LS, Wu YR, Chang HS, Huang CC, Kuo HC, Hsu WC, Chu CC, Chen CM: Identification of gene networks and pathways associated with Guillain-Barre syndrome. PLoS One. 2012, 7 (1): e29506-10.1371/journal.pone.0029506.PubMedPubMed CentralView ArticleGoogle Scholar
- Im E, Martinez JD: Ursodeoxycholic acid (UDCA) can inhibit deoxycholic acid (DCA)-induced apoptosis via modulation of EGFR/Raf-1/ERK signaling in human colon cancer cells. J Nutr. 2004, 134 (2): 483-486.PubMedGoogle Scholar
- Valentinis B, Bianchi A, Zhou D, Cipponi A, Catalanotti F, Russo V, Traversari C: Direct effects of polymyxin B on human dendritic cells maturation. The role of IkappaB-alpha/NF-kappaB and ERK1/2 pathways and adhesion. J Biol Chem. 2005, 280 (14): 14264-14271. 10.1074/jbc.M410791200.PubMedView ArticleGoogle Scholar
- Molostvov G, James S, Fletcher S, Bennett J, Lehnert H, Bland R, Zehnder D: Extracellular calcium-sensing receptor is functionally expressed in human artery. Am J Physiol Renal Physiol. 2007, 293 (3): F946-F955. 10.1152/ajprenal.00474.2006.PubMedView ArticleGoogle Scholar
- Trompezinski S, Migdal C, Tailhardat M, Le Varlet B, Courtellemont P, Haftek M, Serres M: Characterization of early events involved in human dendritic cell maturation induced by sensitizers: cross talk between MAPK signalling pathways. Toxicol Appl Pharmacol. 2008, 230 (3): 397-406. 10.1016/j.taap.2008.03.012.PubMedView ArticleGoogle Scholar
- Roa BB, Dyck PJ, Marks HG, Chance PF, Lupski JR: Dejerine-Sottas syndrome associated with point mutation in the peripheral myelin protein 22 (PMP22) gene. Nat Genet. 1993, 5 (3): 269-273. 10.1038/ng1193-269.PubMedView ArticleGoogle Scholar
- Hayasaka K, Himoro M, Sato W, Takada G, Uyemura K, Shimizu N, Bird TD, Conneally PM, Chance PF: Charcot-Marie-Tooth neuropathy type 1B is associated with mutations of the myelin P0 gene. Nat Genet. 1993, 5 (1): 31-34. 10.1038/ng0993-31.PubMedView ArticleGoogle Scholar
- Popko B, Puckett C, Lai E, Shine HD, Readhead C, Takahashi N, Hunt SW, Sidman RL, Hood L: Myelin deficient mice: expression of myelin basic protein and generation of mice with varying levels of myelin. Cell. 1987, 48 (4): 713-721. 10.1016/0092-8674(87)90249-2.PubMedView ArticleGoogle Scholar
- Ogata T, Iijima S, Hoshikawa S, Miura T, Yamamoto S, Oda H, Nakamura K, Tanaka S: Opposing extracellular signal-regulated kinase and Akt pathways control Schwann cell myelination. J Neurosci. 2004, 24 (30): 6724-6732. 10.1523/JNEUROSCI.5520-03.2004.PubMedView ArticleGoogle Scholar
- Harrisingh MC, Perez-Nadales E, Parkinson DB, Malcolm DS, Mudge AW, Lloyd AC: The Ras/Raf/ERK signalling pathway drives Schwann cell dedifferentiation. EMBO J. 2004, 23 (15): 3061-3071. 10.1038/sj.emboj.7600309.PubMedPubMed CentralView ArticleGoogle Scholar
- Noy NF, Shah NH, Whetzel PL, Dai B, Dorf M, Griffith N, Jonquet C, Rubin DL, Storey MA, Chute CG, Musen MA: BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res. 2009, 37 (Web Server issue): W170-W173. 10.1093/nar/gkp440.PubMedPubMed CentralView ArticleGoogle Scholar
- Huang H, McGarvey PB, Suzek BE, Mazumder R, Zhang J, Chen Y, Wu CH: A comprehensive protein-centric ID mapping service for molecular data integration. Bioinformatics. 2011, 27 (8): 1190-1191. 10.1093/bioinformatics/btr101.PubMedPubMed CentralView ArticleGoogle Scholar
- Camon E, Magrane M, Barrell D, Lee V, Dimmer E, Maslen J, Binns D, Harte N, Lopez R, Apweiler R: The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology. Nucleic Acids Res. 2004, 32 (Database issue): D262-D266. 10.1093/nar/gkh021.PubMedPubMed CentralView ArticleGoogle Scholar
- Newman ME: Modularity and community structure in networks. Proc Natl Acad Sci U S A. 2006, 103 (23): 8577-8582. 10.1073/pnas.0601602103.PubMedPubMed CentralView ArticleGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 1995, 57 (1): 289-300.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.