Dataset Information

Integration of Immune Cell Populations, mRNA-Seq, and CpG Methylation to Better Predict Humoral Immunity to Influenza Vaccination: Dependence of mRNA-Seq/CpG Methylation on Immune Cell Populations.

ABSTRACT: The development of a humoral immune response to influenza vaccines occurs on a multisystems level. Due to the orchestration required for robust immune responses when multiple genes and their regulatory components across multiple cell types are involved, we examined an influenza vaccination cohort using multiple high-throughput technologies. In this study, we sought a more thorough understanding of how immune cell composition and gene expression relate to each other and contribute to interindividual variation in response to influenza vaccination. We first hypothesized that many of the differentially expressed (DE) genes observed after influenza vaccination result from changes in the composition of participants' peripheral blood mononuclear cells (PBMCs), which were assessed using flow cytometry. We demonstrated that DE genes in our study are correlated with changes in PBMC composition. We gathered DE genes from 128 other publically available PBMC-based vaccine studies and identified that an average of 57% correlated with specific cell subset levels in our study (permutation used to control false discovery), suggesting that the associations we have identified are likely general features of PBMC-based transcriptomics. Second, we hypothesized that more robust models of vaccine response could be generated by accounting for the interplay between PBMC composition, gene expression, and gene regulation. We employed machine learning to generate predictive models of B-cell ELISPOT response outcomes and hemagglutination inhibition (HAI) antibody titers. The top HAI and B-cell ELISPOT model achieved an area under the receiver operating curve (AUC) of 0.64 and 0.79, respectively, with linear model coefficients of determination of 0.08 and 0.28. For the B-cell ELISPOT outcomes, CpG methylation had the greatest predictive ability, highlighting potentially novel regulatory features important for immune response. B-cell ELISOT models using only PBMC composition had lower performance (AUC?=?0.67), but highlighted well-known mechanisms. Our analysis demonstrated that each of the three data sets (cell composition, mRNA-Seq, and DNA methylation) may provide distinct information for the prediction of humoral immune response outcomes. We believe that these findings are important for the interpretation of current omics-based studies and set the stage for a more thorough understanding of interindividual immune responses to influenza vaccination.

SUBMITTER: Zimmermann MT

PROVIDER: S-EPMC5399034 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Integration of Immune Cell Populations, mRNA-Seq, and CpG Methylation to Better Predict Humoral Immunity to Influenza Vaccination: Dependence of mRNA-Seq/CpG Methylation on Immune Cell Populations.

Zimmermann Michael T MT Kennedy Richard B RB Grill Diane E DE Oberg Ann L AL Goergen Krista M KM Ovsyannikova Inna G IG Haralambieva Iana H IH Poland Gregory A GA

Frontiers in immunology 20170421

The development of a humoral immune response to influenza vaccines occurs on a multisystems level. Due to the orchestration required for robust immune responses when multiple genes and their regulatory components across multiple cell types are involved, we examined an influenza vaccination cohort using multiple high-throughput technologies. In this study, we sought a more thorough understanding of how immune cell composition and gene expression relate to each other and contribute to interindivid ...[more]

PMID: 28484452

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Project description:DNA CpG methylation is a widespread epigenetic mark in high eukaryotes including mammals. DNA methylation plays key roles in diverse biological processes such as X chromosome inactivation, transposable element repression, genomic imprinting, and control of gene expression. Recent advancements in sequencing-based DNA methylation profiling methods provide an unprecedented opportunity to measure DNA methylation in a genome-wide fashion, making it possible to comprehensively investigate the role of DNA methylation. Several methods have been developed, such as Whole Genome Bisulfite Sequencing (WGBS), Reduced Representation Bisulfite Sequencing (RRBS), and enrichment-based methods including Methylation Dependent ImmunoPrecipitation followed by sequencing (MeDIP-seq), methyl-CpG binding domain (MBD) protein-enriched genome sequencing (MBD-seq), methyltransferase-directed Transfer of Activated Groups followed by sequencing (mTAG), and Methylation-sensitive Restriction Enzyme digestion followed by sequencing (MRE-seq). These methods differ by their genomic CpG coverage, resolution, quantitative accuracy, cost, and software for analyzing the data. Among these, WGBS is considered the gold standard. However, it is still a cost-prohibitive technology for a typical laboratory due to the required sequencing depth. We found that by integrating two enrichment-based methods that are complementary in nature (i.e., MeDIP-seq and MRE-seq), we can significantly increase the efficiency of whole DNA methylome profiling. By using two recently developed computational algorithms (i.e., M&M and methylCRF), the combination of MeDIP-seq and MRE-seq produces genome-wide CpG methylation measurement at high coverage and high resolution, and robust predictions of differentially methylated regions. Thus, the combination of the two enrichment-based methods provides a cost-effective alternative to WGBS. In this article we describe both the experimental protocols for performing MeDIP-seq and MRE-seq, and the computational protocols for running M&M and methylCRF.

Project description:BackgroundUrinary bladder cancer is a common malignancy worldwide. Environmental factors and chronic inflammation are correlated with the disease risk. Diagnosis is performed by transurethral resection of the bladder, and patients with muscle invasive disease preferably proceed to radical cystectomy, with or without neoadjuvant chemotherapy. The anti-tumour immune responses, known to be initiated in the tumour and draining lymph nodes, may play a major role in future treatment strategies. Thus, increasing the knowledge of tumour-associated immunological processes is important. Activated CD4+ T cells differentiate into four main separate lineages: Th1, Th2, Th17 and Treg, and they are recognized by their effector molecules IFN-?, IL-13, IL-17A, and the transcription factor Foxp3, respectively. We have previously demonstrated signature CpG sites predictive for lineage commitment of these four major CD4+ T cell lineages. Here, we investigate the lineage commitment specifically in tumour, lymph nodes and blood and relate them to the disease stage and response to neoadjuvant chemotherapy.ResultsBlood, tumour and regional lymph nodes were obtained from patients at time of transurethral resection of the bladder and at radical cystectomy. Tumour-infiltrating CD4+ lymphocytes were significantly hypomethylated in all four investigated lineage loci compared to CD4+ lymphocytes in lymph nodes and blood (lymph nodes vs tumour-infiltrating lymphocytes: IFNG -4229 bp p?<?0.0001, IL13 -11 bp p?<?0.05, IL17A -122 bp p?<?0.01 and FOXP3 -77 bp p?>?0.05). Examination of individual lymph nodes displayed different methylation signatures, suggesting possible correlation with future survival. More advanced post-cystectomy tumour stages correlated significantly with increased methylation at the IFNG -4229 bp locus. Patients with complete response to neoadjuvant chemotherapy displayed significant hypomethylation in CD4+ T cells for all four investigated loci, most prominently in IFNG p?<?0.0001. Neoadjuvant chemotherapy seemed to result in a relocation of Th1-committed CD4+ T cells from blood, presumably to the tumour, indicated by shifts in the methylation patterns, whereas no such shifts were seen for lineages corresponding to IL13, IL17A and FOXP3.ConclusionIncreased lineage commitment in CD4+ T cells, as determined by demethylation in predictive CpG sites, is associated with lower post-cystectomy tumour stage, complete response to neoadjuvant chemotherapy and overall better outcome, suggesting epigenetic profiling of CD4+ T cell lineages as a useful readout for clinical staging.

Dataset Information

Integration of Immune Cell Populations, mRNA-Seq, and CpG Methylation to Better Predict Humoral Immunity to Influenza Vaccination: Dependence of mRNA-Seq/CpG Methylation on Immune Cell Populations.

Publications

Integration of Immune Cell Populations, mRNA-Seq, and CpG Methylation to Better Predict Humoral Immunity to Influenza Vaccination: Dependence of mRNA-Seq/CpG Methylation on Immune Cell Populations.

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