A longitudinal study of gene expression in healthy individuals

Diagnosis of acute myeloid leukaemia on microarray gene expression data using categorical gradient boosted trees

We define an iterative method for dimensionality reduction using categorical gradient boosted trees and Shapley values and created four machine learning models which potentially could be used as diagnostic tests for acute myeloid leukaemia (AML). For the final Catboost model we use a dataset of 2177 individuals using as features 16 probe sets and the age in order to classify if someone has AML or is healthy. The dataset is multicentric and consists of data from 27 organizations, 25 cities, 15 countries and 4 continents. The performance of our last model is specificity: 0.9909, sensitivity: 0.9985, F1-score: 0.9976 and its ROC-AUC: 0.9962 using ten fold cross validation. On an inference dataset the perormance is: specificity: 0.9909, sensitivity: 0.9969, F1-score: 0.9969 and its ROC-AUC: 0.9939. To the best of our knowledge the performance of our model is the best one in the literature, as regards the diagnosis of AML using similar or not data. Moreover, there has not been any bibliographic reference which associates AML or any other type of cancer with the 16 probe sets we used as features in our final model.

Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach

Quantitative proteomics has matured into an established tool and longitudinal proteomics experiments have begun to emerge. However, no effective, simple-to-use differential expression method for longitudinal proteomics data has been released. Typically, such data is noisy, contains missing values, and has only few time points and biological replicates. To address this need, we provide a comprehensive evaluation of several existing differential expression methods for high-throughput longitudinal omics data and introduce a Robust longitudinal Differential Expression (RolDE) approach. The methods are evaluated using over 3000 semi-simulated spike-in proteomics datasets and three large experimental datasets. In the comparisons, RolDE performs overall best; it is most tolerant to missing values, displays good reproducibility and is the top method in ranking the results in a biologically meaningful way. Furthermore, RolDE is suitable for different types of data with typically unknown patterns in longitudinal expression and can be applied by non-experienced users.

Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence

Recent developments allowed generating multiple high-quality 'omics' data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using the best linear unbiased prediction, while nonparametric models were implemented using the gradient boosting machine algorithm. We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While gradient boosting machine models captured more phenotypic variation, their predictive performance did not exceed the best linear unbiased prediction models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the gradient boosting machine model was able to outperform best linear unbiased prediction for most traits except body weight, but the same pattern was not observed when using both single nucleotide polymorphism genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed the highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values.

Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE)

Batch-effects present challenges in the analysis of high-throughput molecular data and are particularly problematic in longitudinal studies when interest lies in identifying genes/features whose expression changes over time, but time is confounded with batch. While many methods to correct for batch-effects exist, most assume independence across samples; an assumption that is unlikely to hold in longitudinal microarray studies. We propose Batch effect Reduction of mIcroarray data with Dependent samples usinGEmpirical Bayes (BRIDGE), a three-step parametric empirical Bayes approach that leverages technical replicate samples profiled at multiple timepoints/batches, so-called "bridge samples", to inform batch-effect reduction/attenuation in longitudinal microarray studies. Extensive simulation studies and an analysis of a real biological data set were conducted to benchmark the performance of BRIDGE against both ComBat and longitudinalComBat. Our results demonstrate that while all methods perform well in facilitating accurate estimates of time effects, BRIDGE outperforms both ComBat and longitudinal ComBat in the removal of batch-effects in data sets with bridging samples, and perhaps as a result, was observed to have improved statistical power for detecting genes with a time effect. BRIDGE demonstrated competitive performance in batch effect reduction of confounded longitudinal microarray studies, both in simulated and a real data sets, and may serve as a useful preprocessing method for researchers conducting longitudinal microarray studies that include bridging samples.

Variability of multi-omics profiles in a population-based child cohort

BACKGROUND

Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood.

METHODS

We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability.

RESULTS

All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability.

CONCLUSIONS

Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both.

Integration of microarray data and literature mining identifies a sex bias in DPP4+CD4+ T cells in HIV-1 infection

HIV-1 infection exhibits a significant sex bias. This study aimed at identifying and examining lymphocyte associated sex differences in HIV-1 pathogenesis using a data-driven approach. To select targets for investigating sex differences in lymphocytes, data of microarray experiments and literature mining were integrated. Data from three large-scale microarray experiments were obtained from NCBI/GEO and screened for sex differences in gene expression. Literature mining was employed to identify sex biased genes in the microarray data, which were relevant to HIV-1 pathogenesis and lymphocyte biology. Sex differences in gene expression of selected genes were investigated by RT-qPCR and flowcytometry in healthy individuals and persons living with HIV-1. A significant and consistent sex bias was identified in 31 genes, the majority of which were related to immunity and expressed at higher levels in women. Using literature mining, three genes (DPP4, FCGR1A and SOCS3) were selected for analysis by qPCR because of their relevance to HIV, as well as, B and T cell biology. DPP4 exhibited the most significant sex bias in mRNA expression (p = 0.00029). Therefore, its expression was further analyzed on B and T cells using flowcytometry. In HIV-1 infected controllers and healthy individuals, frequencies of CD4+DPP4+ T cells were higher in women compared to men (p = 0.037 and p = 0.027). In women, CD4 T cell counts correlated with a predominant decreased in DPP4+CD4+ T cells (p = 0.0032). Sex differences in DPP4 expression abrogated in progressive HIV-1 infection. In conclusion, we found sex differences in the pathobiology of T cells in HIV-1 infection using a data-driven approach. Our results indicate that DPP4 expression on CD4+ T cells might contribute to the immunological sex differences observed in chronic HIV‑1 infection.

Untangling the effects of cellular composition on coexpression analysis

Coexpression analysis is widely used for inferring regulatory networks, predicting gene function, and interpretation of transcriptome profiling studies, based on methods such as clustering. The majority of such studies use data collected from bulk tissue, where the effects of cellular composition present a potential confound. However, the impact of composition on coexpression analysis has not been studied in detail. Here, we examine this issue for the case of human RNA analysis. Focusing on brain tissue, we found that, for most genes, differences in expression levels across cell types account for a large fraction of the variance of their measured RNA levels (median R 2 = 0.68). We then show that genes that have similar expression patterns across cell types will have correlated RNA levels in bulk tissue, due to the effect of variation in cellular composition. We demonstrate that much of the coexpression and the formation of coexpression clusters can be attributed to this effect for both brain and blood transcriptomes. For brain, we further show how this composition-induced coexpression masks underlying intra-cell-type coexpression observed in single-cell data. An attempt to correct for composition yielded mixed results. Our conclusion is that the dominant coexpression signal in brain, blood, and, likely, other complex tissues can be attributed to cellular compositional effects, rather than intra-cell-type regulatory relationships. These results have implications for the relevance and interpretation of coexpression analysis.

Multi-study reanalysis of 2,213 acute myeloid leukemia patients reveals age- and sex-dependent gene expression signatures

In 2019 it is estimated that more than 21,000 new acute myeloid leukemia (AML) patients will be diagnosed in the United States, and nearly 11,000 are expected to die from the disease. AML is primarily diagnosed among the elderly (median 68 years old at diagnosis). Prognoses have significantly improved for younger patients, but as much as 70% of patients over 60 years old will die within a year of diagnosis. In this study, we conducted a reanalysis of 2,213 acute myeloid leukemia patients compared to 548 healthy individuals, using curated publicly available microarray gene expression data. We carried out an analysis of normalized batch corrected data, using a linear model that included considerations for disease, age, sex, and tissue. We identified 974 differentially expressed probe sets and 4 significant pathways associated with AML. Additionally, we identified 375 age- and 70 sex-related probe set expression signatures relevant to AML. Finally, we trained a k nearest neighbors model to classify AML and healthy subjects with 90.9% accuracy. Our findings provide a new reanalysis of public datasets, that enabled the identification of new gene sets relevant to AML that can potentially be used in future experiments and possible stratified disease diagnostics.

Predicting Autism Spectrum Disorder Using Blood-based Gene Expression Signatures and Machine Learning

Objective

The aim of this study was to identify a transcriptomic signature that could be used to classify subjects with autism spectrum disorder (ASD) compared to controls on the basis of blood gene expression profiles. The gene expression profiles could ultimately be used as diagnostic biomarkers for ASD.

Methods

We used the published microarray data (GSE26415) from the Gene Expression Omnibus database, which included 21 young adults with ASD and 21 age- and sex-matched unaffected controls. Nineteen differentially expressed probes were identified from a training dataset (n=26, 13 ASD cases and 13 controls) using the limma package in R language (adjusted p value <0.05) and were further analyzed in a test dataset (n=16, 8 ASD cases and 8 controls) using machine learning algorithms.

Results

Hierarchical cluster analysis showed that subjects with ASD were relatively well-discriminated from controls. Based on the support vector machine and K-nearest neighbors analysis, validation of 19-DE probes with a test dataset resulted in an overall class prediction accuracy of 93.8% as well as a sensitivity and specificity of 100% and 87.5%, respectively.

Conclusion

The results of our exploratory study suggest that the gene expression profiles identified from the peripheral blood samples of young adults with ASD can be used to identify a biological signature for ASD. Further study using a larger cohort and more homogeneous datasets is required to improve the diagnostic accuracy.

The pulmonary inflammatory response to multiwalled carbon nanotubes is influenced by gender and glutathione synthesis

Inhalation of multiwalled carbon nanotubes (MWCNTs) during their manufacture or incorporation into various commercial products may cause lung inflammation, fibrosis, and oxidative stress in exposed workers. Some workers may be more susceptible to these effects because of differences in their ability to synthesize the major antioxidant and immune system modulator glutathione (GSH). Accordingly, in this study we examined the influence of GSH synthesis and gender on MWCNT-induced lung inflammation in C57BL/6 mice. GSH synthesis was impaired through genetic manipulation of Gclm, the modifier subunit of glutamate cysteine ligase, the rate-limiting enzyme in GSH synthesis. Twenty-four hours after aspirating 25µg of MWCNTs, all male mice developed neutrophilia in their lungs, regardless of Gclm genotype. However, female mice with moderate (Gclm heterozygous) and severe (Gclm null) GSH deficiencies developed significantly less neutrophilia. We found no indications of MWCNT-induced oxidative stress as reflected in the GSH content of lung tissue and epithelial lining fluid, 3-nitrotyrosine formation, or altered mRNA or protein expression of several redox-responsive enzymes. Our results indicate that GSH-deficient female mice are rendered uniquely susceptible to an attenuated neutrophil response. If the same effects occur in humans, GSH-deficient women manufacturing MWCNTs may be at greater risk for impaired neutrophil-dependent clearance of MWCNTs from the lung. In contrast, men may have effective neutrophil-dependent clearance, but may be at risk for lung neutrophilia regardless of their GSH levels.

RNA-Seq analysis of seasonal and individual variation in blood transcriptomes of healthy managed bottlenose dolphins

Background

The blood transcriptome can reflect both systemic exposures and pathological changes in other organs of the body because immune cells recirculate through the blood, lymphoid tissues, and affected sites. In human and veterinary medicine, blood transcriptome analysis has been used successfully to identify markers of disease or pathological conditions, but can be confounded by large seasonal changes in expression. In comparison, the use of transcriptomic based analyses in wildlife has been limited. Here we report a longitudinal study of four managed bottlenose dolphins located in Waikoloa, Hawaii, serially sampled (approximately monthly) over the course of 1 year to establish baseline information on the content and variation of the dolphin blood transcriptome.

Results

Illumina based RNA-seq analyses were carried out using both the Ensembl dolphin genome and a de novo blood transcriptome as guides. Overall, the blood transcriptome encompassed a wide array of cellular functions and processes and was relatively stable within and between animals over the course of 1 year. Principal components analysis revealed moderate clustering by sex associated with the variation among global gene expression profiles (PC1, 22 % of variance). Limited seasonal change was observed, with < 2.5 % of genes differentially expressed between winter and summer months (FDR < 0.05). Among the differentially expressed genes, cosinor analysis identified seasonal rhythmicity for the observed changes in blood gene expression, consistent with studies in humans. While the proportion of seasonally variant genes in these dolphins is much smaller than that reported in humans, the majority of those identified in dolphins were also shown to vary with season in humans. Gene co-expression network analysis identified several gene modules with significant correlation to age, sex, or hematological parameters.

Conclusions

This longitudinal analysis of healthy managed dolphins establishes a preliminary baseline for blood transcriptome analysis in this species. Correlations with hematological parameters, distinct from muted seasonal effects, suggest that the otherwise relatively stable blood transcriptome may be a useful indicator of health and exposure. A robust database of gene expression in free-ranging and managed dolphins across seasons with known adverse health conditions or contaminant exposures will be needed to establish predictive gene expression profiles suitable for biomonitoring.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3020-8) contains supplementary material, which is available to authorized users.

Improving power to detect changes in blood miRNA expression by accounting for sources of variability in experimental designs

BACKGROUND

Blood miRNAs are a new promising area of disease research, but variability in miRNA measurements may limit detection of true-positive findings. Here, we measured sources of miRNA variability and determine whether repeated measures can improve power to detect fold-change differences between comparison groups.

METHODS

Blood from healthy volunteers (N = 12) was collected at three time points. The miRNAs were extracted by a method predetermined to give the highest miRNA yield. Nine different miRNAs were quantified using different qPCR assays and analyzed using mixed models to identify sources of variability. A larger number of miRNAs from a publicly available blood miRNA microarray dataset with repeated measures were used for a bootstrapping procedure to investigate effects of repeated measures on power to detect fold changes in miRNA expression for a theoretical case-control study.

RESULTS

Technical variability in qPCR replicates was identified as a significant source of variability (P < 0.05) for all nine miRNAs tested. Variability was larger in the TaqMan qPCR assays (SD = 0.15-0.61) versus the qScript qPCR assays (SD = 0.08-0.14). Inter- and intraindividual and extraction variability also contributed significantly for two miRNAs. The bootstrapping procedure demonstrated that repeated measures (20%-50% of N) increased detection of a 2-fold change for approximately 10% to 45% more miRNAs.

CONCLUSION

Statistical power to detect small fold changes in blood miRNAs can be improved by accounting for sources of variability using repeated measures and choosing appropriate methods to minimize variability in miRNA quantification.

IMPACT

This study demonstrates the importance of including repeated measures in experimental designs for blood miRNA research. See all the articles in this CEBP Focus section, "Biomarkers, Biospecimens, and New Technologies in Molecular Epidemiology."

IL6 secreted by Ewing sarcoma tumor microenvironment confers anti-apoptotic and cell-disseminating paracrine responses in Ewing sarcoma cells

Background

The prognosis of patients with Ewing sarcoma (ES) has improved over the course of the last decades. However, those patients suffering from metastatic and recurrent ES still have only poor chances of survival and require new therapeutic approaches. Interleukin-6 (IL6) is a pleiotropic cytokine expressed by immune cells and a great variety of cancer cells. It induces inflammatory responses, enhances proliferation and inhibits apoptosis in cancer cells, thereby promoting chemoresistance.

Methods

We investigated expression of IL6, its receptors and the IL6 signal transduction pathway in ES tumor samples and cell lines applying reverse transcriptase PCR, immunoblot and immunohistochemistry. The impact of IL6 on cell viability and apoptosis in ES cell lines was analyzed by MTT and propidium iodide staining, migration assessed by chorioallantoic membrane (CAM) assay.

Results

Immunohistochemistry proved IL6 expression in the stroma of ES tumor samples. IL6 receptor subunits IL6R and IL6ST were expressed on the surface of ES cells. Treatment of ES cells with rhIL6 resulted in phosphorylation of STAT3. rhIL6 protected ES cells from serum starvation-induced apoptosis and promoted migration. IL6 blood serum levels were elevated in a subgroup of ES patients with poor prognosis.

Conclusions

These data suggest that IL6 contributes to ES tumor progression by increasing resistance to apoptosis in conditions of cellular stress, such as serum starvation, and by promotion of metastasis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1564-7) contains supplementary material, which is available to authorized users.

Future clinical implications emerging from recent genome-wide expression studies in asthma

Host susceptibility to environmental triggers is the most likely explanation for the development of asthma. Quantifying gene expression levels in disease-relevant tissues and cell types using fast evolving genomic technologies have generated new hypotheses about the pathogenesis of asthma and identified new therapeutic targets to treat asthma and asthma-exacerbations. New biomarkers and distinct transcriptomic phenotypes in blood, sputum and other tissues were also identified and proved effective to refine asthma classification and guide targeted therapies. The wealth of information provided by transcriptomic studies in asthma is yet to be fully exploited, but discoveries in this field may soon be implemented in clinical settings to improve diagnosis and treatment of patients afflicted with this common disease.

Characterization of the peripheral blood transcriptome in a repeated measures design using a panel of healthy individuals

A repeated measures microarray design with 22 healthy, non-smoking volunteers (aging 32±5years) was set up to study transcriptome profiles in whole blood samples. The results indicate that repeatable data can be obtained with high within-subject correlation. Probes that could discriminate between individuals are associated with immune and inflammatory functions. When investigating possible time trends in the microarray data, we have found no differential expression within a sampling period (within-season effect). Differential expression was observed between sampling seasons and the data suggest a weak response of genes related to immune system functioning. Finally, a high number of probes showed significant season-specific expression variability within subjects. Expression variability increased in springtime and there was an association of the probe list with immune system functioning. Our study suggests that the blood transcriptome of healthy individuals is reproducible over a time period of several months.

Iron deficiency modifies gene expression variation induced by augmented hypoxia sensing

In congenital Chuvash polycythemia (CP), VHL(R200W) homozygosity leads to elevated hypoxia inducible factor (HIF) levels at normoxia. CP is often treated by phlebotomy resulting in iron deficiency, permitting us to examine the separate and synergistic effects of iron deficiency and HIF signaling on gene expression. We compared peripheral blood mononuclear cell gene expression profiles of eight VHL(R200W) homozygotes with 17 wildtype individuals with normal iron status and found 812 up-regulated and 2120 down-regulated genes at false discovery rate of 0.05. Among differential genes we identified three major gene regulation modules involving induction of innate immune responses, alteration of carbohydrate and lipid metabolism, and down-regulation of cell proliferation, stress-induced apoptosis and T-cell activation. These observations suggest molecular mechanisms for previous observations in CP of lower blood sugar without increased insulin and low oncogenic potential. Studies including 16 additional VHL(R200W) homozygotes with low ferritin indicated that iron deficiency enhanced the induction effect of VHL(R200W) for 50 genes including hemoglobin synthesis loci but suppressed the effect for 107 genes enriched for HIF-2 targets. This pattern is consistent with potentiation of HIF-1α protein stability by iron deficiency but a trend for down-regulation of HIF-2α translation by iron deficiency overriding an increase in HIF-2α protein stability.

Performance in omics analyses of blood samples in long-term storage: opportunities for the exploitation of existing biobanks in environmental health research

BACKGROUND

The suitability for omic analysis of biosamples collected in previous decades and currently stored in biobanks is unknown.

OBJECTIVES

We evaluated the influence of handling and storage conditions of blood-derived biosamples on transcriptomic, epigenomic (CpG methylation), plasma metabolomic [UPLC-ToFMS (ultra performance liquid chromatography-time-of-flight mass spectrometry)], and wide-target proteomic profiles.

METHODS

We collected fresh blood samples without RNA preservative in heparin, EDTA, or citrate and held them at room temperature for ≤ 24 hr before fractionating them into buffy coat, erythrocytes, and plasma and freezing the fractions at -80oC or in liquid nitrogen. We developed methodology for isolating RNA from the buffy coats and conducted omic analyses. Finally, we analyzed analogous samples from the EPIC-Italy and Northern Sweden Health and Disease Study biobanks.

RESULTS

Microarray-quality RNA could be isolated from buffy coats (including most biobank samples) that had been frozen within 8 hr of blood collection by thawing the samples in RNA preservative. Different anticoagulants influenced the metabolomic, proteomic, and to a lesser extent transcriptomic profiles. Transcriptomic profiles were most affected by the delay (as little as 2 hr) before blood fractionation, whereas storage temperature had minimal impact. Effects on metabolomic and proteomic profiles were noted in samples processed ≥ 8 hr after collection, but no effects were due to storage temperature. None of the variables examined significantly influenced the epigenomic profiles. No systematic influence of time-in-storage was observed in samples stored over a period of 13-17 years.

CONCLUSIONS

Most samples currently stored in biobanks are amenable to meaningful omics analysis, provided that they satisfy collection and storage criteria defined in this study.

Validation of reference genes for the determination of platelet transcript level in healthy individuals and in patients with the history of myocardial infarction

RT-qPCR is the standard method for studying changes in relative transcript level in different experimental and clinical conditions and in different tissues. No validated reference genes have been reported for the normalization of transcript level in platelets. The very low level of platelet RNA and the elimination of leukocyte contamination represented special methodological difficulties. Our aims were to apply a simple technique to separate platelets for transcript level studies, and select the most stable reference genes for platelets from healthy individuals and from patients with the history of myocardial infarction. We developed a simple, straightforward method of platelet separation for RNA isolation. Platelet activation was inhibited by using acid-citrate-dextrose for anticoagulation and by prostaglandin E1. Leukocyte contamination was eliminated by three consecutive centrifugations. Samples prepared by this method were free of leukocytes, showed no inhibition in PCR reaction and no RNA degradation. The assay demands low blood volume, which complies with the requirements of everyday laboratory routine. Seventeen potential reference genes were investigated, but eight of them were excluded during optimization. The stability of the remaining genes, EEF2, EAR, ACTB, GAPDH, ANAPC5, OAZ1, HDGF, GNAS, and CFL1, were determined by four different descriptive statistics. GAPDH, GNAS, and ACTB were shown to be the most stable genes in platelets of healthy individuals, while HDGF, GNAS, and ACTB were the most stable in platelets of patients with the history of myocardial infarction. The results confirm that data normalization needs assessment of appropriate reference genes for a particular sample set.

A novel approach for biomarker selection and the integration of repeated measures experiments from two assays

BACKGROUND

High throughput 'omics' experiments are usually designed to compare changes observed between different conditions (or interventions) and to identify biomarkers capable of characterizing each condition. We consider the complex structure of repeated measurements from different assays where different conditions are applied on the same subjects.

RESULTS

We propose a two-step analysis combining a multilevel approach and a multivariate approach to reveal separately the effects of conditions within subjects from the biological variation between subjects. The approach is extended to two-factor designs and to the integration of two matched data sets. It allows internal variable selection to highlight genes able to discriminate the net condition effect within subjects. A simulation study was performed to demonstrate the good performance of the multilevel multivariate approach compared to a classical multivariate method. The multilevel multivariate approach outperformed the classical multivariate approach with respect to the classification error rate and the selection of relevant genes. The approach was applied to an HIV-vaccine trial evaluating the response with gene expression and cytokine secretion. The discriminant multilevel analysis selected a relevant subset of genes while the integrative multilevel analysis highlighted clusters of genes and cytokines that were highly correlated across the samples.

CONCLUSIONS

Our combined multilevel multivariate approach may help in finding signatures of vaccine effect and allows for a better understanding of immunological mechanisms activated by the intervention. The integrative analysis revealed clusters of genes, that were associated with cytokine secretion. These clusters can be seen as gene signatures to predict future cytokine response. The approach is implemented in the R package mixOmics (http://cran.r-project.org/) with associated tutorials to perform the analysis(a).

Using blood informative transcripts in geographical genomics: impact of lifestyle on gene expression in fijians

In previous geographical genomics studies of the impact of lifestyle on gene expression inferred from microarray analysis of peripheral blood samples, we described the complex influences of culture, ethnicity, and gender in Morocco, and of pregnancy in Brisbane. Here we describe the use of nanofluidic Fluidigm quantitative RT-PCR arrays targeted at a set of 96 transcripts that are broadly informative of the major axes of immune gene expression, to explore the population structure of transcription in Fiji. As in Morocco, major differences are seen between the peripheral blood transcriptomes of rural villagers and residents of the capital city, Suva. The effect is much greater in Indian villages than in Melanesian highlanders and appears to be similar with respect to the nature of at least two axes of variation. Gender differences are much smaller than ethnicity or lifestyle effects. Body mass index is shown to associate with one of the axes as it does in Atlanta and Brisbane, establishing a link between the epidemiological transition of human metabolic disease, and gene expression profiles.

Blood-based gene expression signatures of infants and toddlers with autism

OBJECTIVE

Autism spectrum disorders (ASDs) are highly heritable neurodevelopmental disorders that onset clinically during the first years of life. ASD risk biomarkers expressed early in life could significantly impact diagnosis and treatment, but no transcriptome-wide biomarker classifiers derived from fresh blood samples from children with autism have yet emerged.

METHOD

Using a community-based, prospective, longitudinal method, we identified 60 infants and toddlers at risk for ASDs (autistic disorder and pervasive developmental disorder), 34 at-risk for language delay, 17 at-risk for global developmental delay, and 68 typically developing comparison children. Diagnoses were confirmed via longitudinal follow-up. Each child's mRNA expression profile in peripheral blood mononuclear cells was determined by microarray.

RESULTS

Potential ASD biomarkers were discovered in one-half of the sample and used to build a classifier, with high diagnostic accuracy in the remaining half of the sample.

CONCLUSIONS

The mRNA expression abnormalities reliably observed in peripheral blood mononuclear cells, which are safely and easily assayed in infants, offer the first potential peripheral blood-based, early biomarker panel of risk for autism in infants and toddlers. Future work should verify these biomarkers and evaluate whether they may also serve as indirect indices of deviant molecular neural mechanisms in autism.

Elevated type I interferon-like activity in a subset of multiple sclerosis patients: molecular basis and clinical relevance

Background

A subset of patients with multiple sclerosis (MS) shows an increased endogenous IFN-like activity before initiation of IFN-beta treatment. The molecular basis of this phenomenon and its relevance to predict individual therapy outcomes are not yet fully understood. We studied the expression patterns of these patients, the prognostic value of an elevated IFN-like activity, and the gene regulatory effects of exogenously administered IFN-beta.

Methods

Microarray gene expression profiling was performed for 61 MS patients using peripheral blood mononuclear cells obtained before and after 1 month of IFN-beta therapy. Expression levels of genes involved in pathways either inducing or being activated by IFN-beta were compared between patients with high (MX1_high cohort) and low (MX1_low cohort) endogenous IFN-like activity. Patients were followed for 5 years and relapses as well as progression on the expanded disability status scale (EDSS) were documented.

Results

Before the start of therapy, 11 patients presented elevated mRNA levels of IFN-stimulated genes indicative of a relatively high endogenous IFN-like activity (MX1_high). In these patients, pathogen receptors (for example, TLR7, RIG-I and IFIH1) and transcription factors were also expressed more strongly, which could be attributed to an overactivity of IFN-stimulated gene factor 3 (ISGF3, a complex formed by STAT1, STAT2 and IFN regulatory factor 9). After 1 month of IFN-beta therapy, the expression of many pathway genes was significantly induced in MX1_low patients, but remained unaltered in MX1_high patients. During follow-up, relapse rate and changes in EDSS were comparable between both patient groups, with differences seen between different types of IFN-beta drug application.

Conclusions

Therapeutic IFN-beta induces the transcription of several genes involved in IFN-related pathways. In a subgroup of MS patients, the expression of these genes is already increased before therapy initiation, possibly driven by an overexpression of ISGF3. Patients with high and low endogenous IFN-like activity showed similar clinical long-term courses of disease. Different results were obtained for different IFN-beta drug preparations, and this merits further investigation.

Investigation of variation in gene expression profiling of human blood by extended principle component analysis

Background

Human peripheral blood is a promising material for biomedical research. However, various kinds of biological and technological factors result in a large degree of variation in blood gene expression profiles.

Methodology/Principal Findings

Human peripheral blood samples were drawn from healthy volunteers and analysed using the Human Genome U133Plus2 Microarray. We applied a novel approach using the Principle Component Analysis and Eigen-R² methods to dissect the overall variation of blood gene expression profiles with respect to the interested biological and technological factors. The results indicated that the predominating sources of the variation could be traced to the individual heterogeneity of the relative proportions of different blood cell types (leukocyte subsets and erythrocytes). The physiological factors like age, gender and BMI were demonstrated to be associated with 5.3% to 9.2% of the total variation in the blood gene expression profiles. We investigated the gene expression profiles of samples from the same donors but with different levels of RNA quality. Although the proportion of variation associated to the RNA Integrity Number was mild (2.1%), the significant impact of RNA quality on the expression of individual genes was observed.

Conclusions

By characterizing the major sources of variation in blood gene expression profiles, such variability can be minimized by modifications to study designs. Increasing sample size, balancing confounding factors between study groups, using rigorous selection criteria for sample quality, and well controlled experimental processes will significantly improve the accuracy and reproducibility of blood transcriptome study.

Balanced caloric macronutrient composition downregulates immunological gene expression in human blood cells-adipose tissue diverges

Cardiovascular disease, obesity, and type 2 diabetes are conditions characterized by low-grade systemic inflammation, strongly influenced by lifestyle, but the mechanisms that link these characteristics are poorly understood. Our first objective was to investigate if a normocaloric diet with a calorically balanced macronutrient composition influenced immunological gene expression. Findings regarding the suitability of blood as biological material in nutrigenomics and gene expression profiling have been inconclusive. Our second objective was to compare blood and adipose tissue sample quality in terms of adequacy for DNA-microarray analyses, and to determine tissue-specific gene expression patterns. Blood and adipose tissue samples were collected for gene expression profiling from three obese men before, during, and after a 28-day normocaloric diet intervention where each meal contained an approximately equal caloric load of macronutrients. Time series analyses of blood gene expression revealed a cluster of downregulated genes involved in immunological processes. Blood RNA quality and yield were satisfactory, and DNA-microarray analysis reproducibility was similar in blood and adipose tissue. Gene expression correlation between blood and adipose tissue varied according to gene function, and was especially low for genes involved in immunological and metabolic processes. This suggests that diet composition is of importance in inflammatory processes in blood cells. The findings also suggest that a systems biology approach, in which tissues are studied in parallel, should be employed to fully understand the impact of dietary challenges on the human body.

Deducing corticotropin-releasing hormone receptor type 1 signaling networks from gene expression data by usage of genetic algorithms and graphical Gaussian models

BACKGROUND

Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis is a hallmark of complex and multifactorial psychiatric diseases such as anxiety and mood disorders. About 50-60% of patients with major depression show HPA axis dysfunction, i.e. hyperactivity and impaired negative feedback regulation. The neuropeptide corticotropin-releasing hormone (CRH) and its receptor type 1 (CRHR1) are key regulators of this neuroendocrine stress axis. Therefore, we analyzed CRH/CRHR1-dependent gene expression data obtained from the pituitary corticotrope cell line AtT-20, a well-established in vitro model for CRHR1-mediated signal transduction. To extract significantly regulated genes from a genome-wide microarray data set and to deduce underlying CRHR1-dependent signaling networks, we combined supervised and unsupervised algorithms.

RESULTS

We present an efficient variable selection strategy by consecutively applying univariate as well as multivariate methods followed by graphical models. First, feature preselection was used to exclude genes not differentially regulated over time from the dataset. For multivariate variable selection a maximum likelihood (MLHD) discriminant function within GALGO, an R package based on a genetic algorithm (GA), was chosen. The topmost genes representing major nodes in the expression network were ranked to find highly separating candidate genes. By using groups of five genes (chromosome size) in the discriminant function and repeating the genetic algorithm separately four times we found eleven genes occurring at least in three of the top ranked result lists of the four repetitions. In addition, we compared the results of GA/MLHD with the alternative optimization algorithms greedy selection and simulated annealing as well as with the state-of-the-art method random forest. In every case we obtained a clear overlap of the selected genes independently confirming the results of MLHD in combination with a genetic algorithm. With two unsupervised algorithms, principal component analysis and graphical Gaussian models, putative interactions of the candidate genes were determined and reconstructed by literature mining. Differential regulation of six candidate genes was validated by qRT-PCR.

CONCLUSIONS

The combination of supervised and unsupervised algorithms in this study allowed extracting a small subset of meaningful candidate genes from the genome-wide expression data set. Thereby, variable selection using different optimization algorithms based on linear classifiers as well as the nonlinear random forest method resulted in congruent candidate genes. The calculated interacting network connecting these new target genes was bioinformatically mapped to known CRHR1-dependent signaling pathways. Additionally, the differential expression of the identified target genes was confirmed experimentally.

Imaging and 'omic' methods for the molecular diagnosis of cancer

Molecular imaging methods can noninvasively detect specific biological processes that are aberrant in cancer, including upregulated glycolytic metabolism, increased cellular proliferation and altered receptor expression. PET using the glucose analogue 18F-fluoro-2-deoxyglucose, which detects the increased glucose uptake that is a characteristic of tumor cells, has been widely used in the clinic to detect tumors and their responses to treatment; however, there are many new PET tracers being developed for a wide range of biological targets. Magnetic resonance spectroscopy (MRS), which can be used to detect cellular metabolites, can also provide prognostic information, particularly in brain, breast and prostate cancers. An emerging technique, which by hyperpolarizing 13C-labeled cell substrates dramatically enhances their sensitivity to detection, could further extend the use of MRS in molecular imaging in the clinic. Molecular diagnostics applied to serum samples or tumor samples obtained by biopsy, can measure changes at the individual cell level and the underlying changes in gene or protein expression. DNA microarrays enable high-throughput gene-expression profiling, while mass spectrometry can detect thousands of proteins that may be used in the future as biomarkers of cancer. Probing molecular changes will aid not only cancer diagnosis, but also provide tumor grading, based on gene-expression analysis and imaging measurements of cell proliferation and changes in metabolism; staging, based on imaging of metastatic spread and elevation of protein biomarkers; and the detection of therapeutic response, using serial molecular imaging measurements or monitoring of serum markers. The present article provides a summary of the molecular diagnostic methods that are currently being trialed in the clinic.

Analysis of Correlated Gene Expression Data on Ordered Categories

A bootstrap based methodology is introduced for analyzing repeated measures/longitudinal microarray gene expression data over ordered categories. The proposed non-parametric procedure uses order-restricted inference to compare gene expressions among ordered experimental conditions. The null distribution for determining significance is derived by suitably bootstrapping the residuals. The procedure addresses two potential sources of correlation in the data, namely, (a) correlations among genes within a chip ("intra-chip" correlation), and (b) correlation within subject due to repeated/longitudinal measurements ("temporal" correlation). To make the procedure computationally efficient, the adaptive bootstrap methodology of Guo and Peddada (2008) is implemented such that the resulting procedure controls the false discovery rate (FDR) at the desired nominal level.