Evolving DNA methylation and gene expression markers of B-cell chronic lymphocytic leukemia are present in pre-diagnostic blood samples more than 10 years prior to diagnosis

Background B-cell chronic lymphocytic leukemia (CLL) is a common type of adult leukemia. It often follows an indolent course and is preceded by monoclonal B-cell lymphocytosis, an asymptomatic condition, however it is not known what causes subjects with this condition to progress to CLL. Hence the discovery of prediagnostic markers has the potential to improve the identification of subjects likely to develop CLL and may also provide insights into the pathogenesis of the disease of potential clinical relevance. Results We employed peripheral blood buffy coats of 347 apparently healthy subjects, of whom 28 were diagnosed with CLL 2.0–15.7 years after enrollment, to derive for the first time genome-wide DNA methylation, as well as gene and miRNA expression, profiles associated with the risk of future disease. After adjustment for white blood cell composition, we identified 722 differentially methylated CpG sites and 15 differentially expressed genes (Bonferroni-corrected p < 0.05) as well as 2 miRNAs (FDR < 0.05) which were associated with the risk of future CLL. The majority of these signals have also been observed in clinical CLL, suggesting the presence in prediagnostic blood of CLL-like cells. Future CLL cases who, at enrollment, had a relatively low B-cell fraction (<10%), and were therefore less likely to have been suffering from undiagnosed CLL or a precursor condition, showed profiles involving smaller numbers of the same differential signals with intensities, after adjusting for B-cell content, generally smaller than those observed in the full set of cases. A similar picture was obtained when the differential profiles of cases with time-to-diagnosis above the overall median period of 7.4 years were compared with those with shorted time-to-disease. Differentially methylated genes of major functional significance include numerous genes that encode for transcription factors, especially members of the homeobox family, while differentially expressed genes include, among others, multiple genes related to WNT signaling as well as the miRNAs miR-150-5p and miR-155-5p. Conclusions Our findings demonstrate the presence in prediagnostic blood of future CLL patients, more than 10 years before diagnosis, of CLL-like cells which evolve as preclinical disease progresses, and point to early molecular alterations with a pathogenetic potential

Comparison of statistical methods and the use of quality control samples for batch effect correction in human transcriptome data

Batch effects are technical sources of variation introduced by the necessity of conducting gene expression analyses on different dates due to the large number of biological samples in population-based studies. The aim of this study is to evaluate the performances of linear mixed models (LMM) and Combat in batch effect removal. We also assessed the utility of adding quality control samples in the study design as technical replicates. In order to do so, we simulated gene expression data by adding “treatment” and batch effects to a real gene expression dataset. The performances of LMM and Combat, with and without quality control samples, are assessed in terms of sensitivity and specificity while correcting for the batch effect using a wide range of effect sizes, statistical noise, sample sizes and level of balanced/unbalanced designs. The simulations showed small differences among LMM and Combat. LMM identifies stronger relationships between big effect sizes and gene expression than Combat, while Combat identifies in general more true and false positives than LMM. However, these small differences can still be relevant depending on the research goal. When any of these methods are applied, quality control samples did not reduce the batch effect, showing no added value for including them in the study design

Persistence of epigenomic effects after recovery from repeated treatment with two nephrocarcinogens

The discovery of the epigenetic regulation of transcription has provided a new source of mechanistic understanding to long lasting effects of chemicals. However, this information is still seldom exploited in a toxicological context and studies of chemical effect after washout remain rare. Here we studied the effects of two nephrocarcinogens on the human proximal tubule cell line RPTEC/TERT1 using high-content mRNA microarrays coupled with miRNA, histone acetylation (HA) and DNA methylation (DM) arrays and metabolomics during a 5-day repeat-dose exposure and 3 days after washout. The mycotoxin ochratoxin A (OTA) was chosen as a model compound for its known impact on HA and DM. The foremost effect observed was the modulation of thousands of mRNAs and histones by OTA during and after exposure. In comparison, the oxidant potassium bromate (KBrO3) had a milder impact on gene expression and epigenetics. However, there was no strong correlation between epigenetic modifications and mRNA changes with OTA while with KBrO3 the gene expression data correlated better with HA for both up- and down-regulated genes. Even when focusing on the genes with persistent epigenetic modifications after washout, only half were coupled to matching changes in gene expression induced by OTA, suggesting that while OTA causes a major effect on the two epigenetic mechanisms studied, these alone cannot explain its impact on gene expression. Mechanistic analysis confirmed the known activation of Nrf2 and p53 by KBrO3, while OTA inhibited most of the same genes, and genes involved in the unfolded protein response. A few miRNAs could be linked to these effects of OTA, albeit without clear contribution of epigenetics to the modulation of the pathways at large. Metabolomics revealed disturbances in amino acid balance, energy catabolism, nucleotide metabolism and polyamine metabolism with both chemicals. In conclusion, the large impact of OTA on transcription was confirmed at the mRNA level but also with two high-content epigenomic methodologies. Transcriptomic data confirmed the previously reported activation (by KBrO3) and inhibition (by OTA) of protective pathways. However, the integration of omic datasets suggested that HA and DM were not driving forces in the gene expression changes induced by either chemical

Short-term transcriptome and microRNAs responses to exposure to different air pollutants in two population studies

Diesel vehicle emissions are the major source of genotoxic compounds in ambient air from urban areas. These pollutants are linked to risks of cardiovascular diseases, lung cancer, respiratory infections and adverse neurological effects. Biological events associated with exposure to some air pollutants are widely unknown but applying omics techniques may help to identify the molecular processes that link exposure to disease risk. Most data on health risks are related to long-term exposure, so the aim of this study is to investigate the impact of short-term exposure (two hours) to air pollutants on the blood transcriptome and microRNA expression levels. We analyzed transcriptomics and microRNA expression using microarray technology on blood samples from volunteers participating in studies in London, the Oxford Street cohort, and, in Barcelona, the TAPAS cohort. Personal exposure levels measurements of particulate matter (PM10, PM2.5), ultrafine particles (UFPC), nitrogen oxides (NO2, NO and NOx), black carbon (BC) and carbon oxides (CO and CO2) were registered for each volunteer. Associations between air pollutant levels and gene/microRNA expression were evaluated using multivariate normal models (MVN). MVN-models identified compound-specific expression of blood cell genes and microRNAs associated with air pollution despite the low exposure levels, the short exposure periods and the relatively small-sized cohorts. Hsa-miR-197-3p, hsa-miR-29a-3p, hsa-miR-15a-5p, hsa-miR-16-5p and hsa-miR-92a-3p are found significantly expressed in association with exposures. These microRNAs target also relevant transcripts, indicating their potential relevance in the research of omics-biomarkers responding to air pollution. Furthermore, these microRNAs are also known to be associated with diseases previously linked to air pollution exposure including several cancers such lung cancer and Alzheimer's disease. In conclusion, we identified in this study promising compound-specific mRNA and microRNA biomarkers after two hours of exposure to low levels of air pollutants during two hours that suggest increased cancer risks

Associations between genome-wide gene expression and ambient nitrogen Oxides

Background: We hypothesize that biological perturbations due to exposure to ambient air pollution are reflected in gene expression levels in peripheral blood mononuclear cells. Methods: We assessed the association between exposure to ambient air pollution and genome-wide gene expression levels in peripheral blood mononuclear cells collected from 550 healthy subjects participating in cohorts from Italy and Sweden. Annual air pollution estimates of nitrogen oxides (NOx) at time of blood collection (1990–2006) were available from the ESCAPE study. In addition to univariate analysis and two variable selection methods to investigate the association between expression and exposure to NOx, we applied gene set enrichment analysis to assess overlap between our most perturbed genes and gene sets hypothesized to be related to air pollution and cigarette smoking. Finally, we assessed associations between NOx and CpG island methylation at the identified genes. Results: Annual average NOx exposure in the Italian and Swedish cohorts was 94.2 and 6.7 µg/m3, respectively. Long-term exposure to NOx was associated with seven probes in the Italian cohort and one probe in the Swedish (and combined) cohorts. For genes AHCYL2 and MTMR2, changes were also seen in the methylome. Genes hypothesized to be downregulated due to cigarette smoking were enriched among the most strongly downregulated genes from our study. Conclusion: This study provides evidence of subtle changes in gene expression related to exposure to long-term NOx. On a global level, the observed changes in the transcriptome may indicate similarities between air pollution and tobacco induced changes in the transcriptome