Prenatal maternal distress and immune cell epigenetic profiles at 3-months of age.

BACKGROUND: Prenatal maternal distress predicts altered offspring immune outcomes, potentially via altered epigenetics. The role of different kinds of prenatal maternal distress on DNA methylation profiles is not understood. METHODS: A sample of 117 women (APrON cohort) were followed from pregnancy to the postpartum period. Maternal distress (depressive symptoms, pregnancy-specific anxiety, stressful life events) were assessed mid-pregnancy, late-pregnancy, and 3-months postpartum. DNA methylation profiles were obtained from 3-month-old blood samples. Principal component analysis identified two epigenetic components, characterized as Immune Signaling and DNA Transcription through gene network analysis. Covariates were maternal demographics, pre-pregnancy body mass index, child sex, birth gestational age, and postpartum maternal distress. Penalized regression (LASSO) models were used. RESULTS: Late-pregnancy stressful life events, b = 0.006, early-pregnancy depressive symptoms, b = 0.027, late-pregnancy depressive symptoms, b = 0.014, and pregnancy-specific anxiety during late pregnancy, b = -0.631, were predictive of the Immune Signaling component, suggesting that these aspects of maternal distress could affect methylation in offspring immune signaling pathways. Only early-pregnancy depressive symptoms was predictive of the DNA Transcription component, b = -0.0004, suggesting that this aspect of maternal distress is implicated in methylation of offspring DNA transcription pathways. CONCLUSIONS: Exposure timing and kind of prenatal maternal distress could matter in the prediction of infant immune epigenetic profiles.

Identification of differentially expressed peptides in high-throughput proteomics data.

With the advent of high-throughput proteomics, the type and amount of data pose a significant challenge to statistical approaches used to validate current quantitative analysis. Whereas many studies focus on the analysis at the protein level, the analysis of peptide-level data provides insight into changes at the sub-protein level, including splice variants, isoforms and a range of post-translational modifications. Statistical evaluation of liquid chromatography-mass spectrometry/mass spectrometry peptide-based label-free differential data is most commonly performed using a t-test or analysis of variance, often after the application of data imputation to reduce the number of missing values. In high-throughput proteomics, statistical analysis methods and imputation techniques are difficult to evaluate, given the lack of gold standard data sets. Here, we use experimental and resampled data to evaluate the performance of four statistical analysis methods and the added value of imputation, for different numbers of biological replicates. We find that three or four replicates are the minimum requirement for high-throughput data analysis and confident assignment of significant changes. Data imputation does increase sensitivity in some cases, but leads to a much higher actual false discovery rate. Additionally, we find that empirical Bayes method (limma) achieves the highest sensitivity, and we thus recommend its use for performing differential expression analysis at the peptide level.

Proinflammatory Cytokine Responses in Extra-Respiratory Tissues During Severe Influenza.

Severe influenza is often associated with disease manifestations outside the respiratory tract. While proinflammatory cytokines can be detected in the lungs and blood of infected patients, the role of extra-respiratory organs in the production of proinflammatory cytokines is unknown. Here, we show that both 2009 pandemic H1N1 influenza A (H1N1) virus and highly pathogenic avian influenza A (H5N1) virus induce expression of tumor necrosis factor α, interleukin-6, and interleukin-8 in the respiratory tract and central nervous system. In addition, H5N1 virus induced cytokines in the heart, pancreas, spleen, liver, and jejunum. Together, these data suggest that extra-respiratory tissues contribute to systemic cytokine responses, which may increase the severity of influenza.

Meta-analysis approach as a gene selection method in class prediction: does it improve model performance? A case study in acute myeloid leukemia.

BACKGROUND: Aggregating gene expression data across experiments via meta-analysis is expected to increase the precision of the effect estimates and to increase the statistical power to detect a certain fold change. This study evaluates the potential benefit of using a meta-analysis approach as a gene selection method prior to predictive modeling in gene expression data. RESULTS: Six raw datasets from different gene expression experiments in acute myeloid leukemia (AML) and 11 different classification methods were used to build classification models to classify samples as either AML or healthy control. First, the classification models were trained on gene expression data from single experiments using conventional supervised variable selection and externally validated with the other five gene expression datasets (referred to as the individual-classification approach). Next, gene selection was performed through meta-analysis on four datasets, and predictive models were trained with the selected genes on the fifth dataset and validated on the sixth dataset. For some datasets, gene selection through meta-analysis helped classification models to achieve higher performance as compared to predictive modeling based on a single dataset; but for others, there was no major improvement. Synthetic datasets were generated from nine simulation scenarios. The effect of sample size, fold change and pairwise correlation between differentially expressed (DE) genes on the difference between MA- and individual-classification model was evaluated. The fold change and pairwise correlation significantly contributed to the difference in performance between the two methods. The gene selection via meta-analysis approach was more effective when it was conducted using a set of data with low fold change and high pairwise correlation on the DE genes. CONCLUSION: Gene selection through meta-analysis on previously published studies potentially improves the performance of a predictive model on a given gene expression data.

Transcriptome assists prognosis of disease severity in respiratory syncytial virus infected infants.

Respiratory syncytial virus (RSV) causes infections that range from common cold to severe lower respiratory tract infection requiring high-level medical care. Prediction of the course of disease in individual patients remains challenging at the first visit to the pediatric wards and RSV infections may rapidly progress to severe disease. In this study we investigate whether there exists a genomic signature that can accurately predict the course of RSV. We used early blood microarray transcriptome profiles from 39 hospitalized infants that were followed until recovery and of which the level of disease severity was determined retrospectively. Applying support vector machine learning on age by sex standardized transcriptomic data, an 84 gene signature was identified that discriminated hospitalized infants with eventually less severe RSV infection from infants that suffered from most severe RSV disease. This signature yielded an area under the receiver operating characteristic curve (AUC) of 0.966 using leave-one-out cross-validation on the experimental data and an AUC of 0.858 on an independent validation cohort consisting of 53 infants. A combination of the gene signature with age and sex yielded an AUC of 0.971. Thus, the presented signature may serve as the basis to develop a prognostic test to support clinical management of RSV patients.

Selecting a classification function for class prediction with gene expression data.

MOTIVATION: Class predicting with gene expression is widely used to generate diagnostic and/or prognostic models. The literature reveals that classification functions perform differently across gene expression datasets. The question, which classification function should be used for a given dataset remains to be answered. In this study, a predictive model for choosing an optimal function for class prediction on a given dataset was devised. RESULTS: To achieve this, gene expression data were simulated for different values of gene-pairs correlations, sample size, genes' variances, deferentially expressed genes and fold changes. For each simulated dataset, ten classifiers were built and evaluated using ten classification functions. The resulting accuracies from 1152 different simulation scenarios by ten classification functions were then modeled using a linear mixed effects regression on the studied data characteristics, yielding a model that predicts the accuracy of the functions on a given data. An application of our model on eight real-life datasets showed positive correlations (0.33-0.82) between the predicted and expected accuracies. CONCLUSION: The here presented predictive model might serve as a guide to choose an optimal classification function among the 10 studied functions, for any given gene expression data. AVAILABILITY AND IMPLEMENTATION: The R source code for the analysis and an R-package 'SPreFuGED' are available at Bioinformatics online. CONTACT: v.l.jong@umcutecht.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An Application of Sequential Meta-Analysis to Gene Expression Studies.

Most of the discoveries from gene expression data are driven by a study claiming an optimal subset of genes that play a key role in a specific disease. Meta-analysis of the available datasets can help in getting concordant results so that a real-life application may be more successful. Sequential meta-analysis (SMA) is an approach for combining studies in chronological order while preserving the type I error and pre-specifying the statistical power to detect a given effect size. We focus on the application of SMA to find gene expression signatures across experiments in acute myeloid leukemia. SMA of seven raw datasets is used to evaluate whether the accumulated samples show enough evidence or more experiments should be initiated. We found 313 differentially expressed genes, based on the cumulative information of the experiments. SMA offers an alternative to existing methods in generating a gene list by evaluating the adequacy of the cumulative information.

Factors affecting the accuracy of a class prediction model in gene expression data.

BACKGROUND: Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends on the type of classification function. While a substantial amount of information is known about the characteristics of classification functions, little has been done to determine which characteristics of gene expression data have impact on the performance of a classifier. This study aims to empirically identify data characteristics that affect the predictive accuracy of classification models, outside of the field of cancer. RESULTS: Datasets from twenty five studies meeting predefined inclusion and exclusion criteria were downloaded. Nine classification functions were chosen, falling within the categories: discriminant analyses or Bayes classifiers, tree based, regularization and shrinkage and nearest neighbors methods. Consequently, nine class prediction models were built for each dataset using the same procedure and their performances were evaluated by calculating their accuracies. The characteristics of each experiment were recorded, (i.e., observed disease, medical question, tissue/cell types and sample size) together with characteristics of the gene expression data, namely the number of differentially expressed genes, the fold changes and the within-class correlations. Their effects on the accuracy of a class prediction model were statistically assessed by random effects logistic regression. The number of differentially expressed genes and the average fold change had significant impact on the accuracy of a classification model and gave individual explained-variation in prediction accuracy of up to 72% and 57%, respectively. Multivariable random effects logistic regression with forward selection yielded the two aforementioned study factors and the within class correlation as factors affecting the accuracy of classification functions, explaining 91.5% of the between study variation. CONCLUSIONS: We evaluated study- and data-related factors that might explain the varying performances of classification functions in non-cancerous datasets. Our results showed that the number of differentially expressed genes, the fold change, and the correlation in gene expression data significantly affect the accuracy of class prediction models.

Host proteome correlates of vaccine-mediated enhanced disease in a mouse model of respiratory syncytial virus infection.

UNLABELLED: Respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infections in infants. Despite over 50 years of research, to date no safe and efficacious RSV vaccine has been licensed. Many experimental vaccination strategies failed to induce balanced T-helper (Th) responses and were associated with adverse effects such as hypersensitivity and immunopathology upon challenge. In this study, we explored the well-established recombinant vaccinia virus (rVV) RSV-F/RSV-G vaccination-challenge mouse model to study phenotypically distinct vaccine-mediated host immune responses at the proteome level. In this model, rVV-G priming and not rVV-F priming results in the induction of Th2 skewed host responses upon RSV challenge. Mass spectrometry-based spectral count comparisons enabled us to identify seven host proteins for which expression in lung tissue is associated with an aberrant Th2 skewed response characterized by the influx of eosinophils and neutrophils. These proteins are involved in processes related to the direct influx of eosinophils (eosinophil peroxidase [Epx]) and to chemotaxis and extravasation processes (Chil3 [chitinase-like-protein 3]) as well as to eosinophil and neutrophil homing signals to the lung (Itgam). In addition, the increased levels of Arg1 and Chil3 proteins point to a functional and regulatory role for alternatively activated macrophages and type 2 innate lymphoid cells in Th2 cytokine-driven RSV vaccine-mediated enhanced disease. IMPORTANCE: RSV alone is responsible for 80% of acute bronchiolitis cases in infants worldwide and causes substantial mortality in developing countries. Clinical trials performed with formalin-inactivated RSV vaccine preparations in the 1960s failed to induce protection upon natural RSV infection and even predisposed patients for enhanced disease. Despite the clinical need, to date no safe and efficacious RSV vaccine has been licensed. Since RSV vaccines have a tendency to prime for unbalanced responses associated with an exuberant influx of inflammatory cells and enhanced disease, detailed characterization of primed host responses has become a crucial element in RSV vaccine research. We investigated the lung proteome of mice challenged with RSV upon priming with vaccine preparations known to induce phenotypically distinct host responses. Seven host proteins whose expression levels are associated with vaccine-mediated enhanced disease have been identified. The identified protein biomarkers support the development as well as detailed evaluation of next-generation RSV vaccines.

Exploring homogeneity of correlation structures of gene expression datasets within and between etiological disease categories.

The literature shows that classifiers perform differently across datasets and that correlations within datasets affect the performance of classifiers. The question that arises is whether the correlation structure within datasets differ significantly across diseases. In this study, we evaluated the homogeneity of correlation structures within and between datasets of six etiological disease categories; inflammatory, immune, infectious, degenerative, hereditary and acute myeloid leukemia (AML). We also assessed the effect of filtering; detection call and variance filtering on correlation structures. We downloaded microarray datasets from ArrayExpress for experiments meeting predefined criteria and ended up with 12 datasets for non-cancerous diseases and six for AML. The datasets were preprocessed by a common procedure incorporating platform-specific recommendations and the two filtering methods mentioned above. Homogeneity of correlation matrices between and within datasets of etiological diseases was assessed using the Box's M statistic on permuted samples. We found that correlation structures significantly differ between datasets of the same and/or different etiological disease categories and that variance filtering eliminates more uncorrelated probesets than detection call filtering and thus renders the data highly correlated.