Comparative methylome analysis of ICF patients identifies heterochromatin loci that require ZBTB24, CDCA7 and HELLS for their methylated state.

Alterations of DNA methylation landscapes and machinery are a hallmark of many human diseases. A prominent case is the ICF syndrome, a rare autosomal recessive immunological/neurological disorder diagnosed by the loss of DNA methylation at (peri)centromeric repeats and its associated chromosomal instability. It is caused by mutations in the de novo DNA methyltransferase DNMT3B in about half of the patients (ICF1). In the remainder, the striking identification of mutations in factors devoid of DNA methyltransferase activity, ZBTB24 (ICF2), CDCA7 (ICF3) or HELLS (ICF4), raised key questions about common or distinguishing DNA methylation alterations downstream of these mutations and hence, about the functional link between the four factors. Here, we established the first comparative methylation profiling in ICF patients with all four genotypes and we provide evidence that, despite unifying hypomethylation of pericentromeric repeats and a few common loci, methylation profiling clearly distinguished ICF1 from ICF2, 3 and 4 patients. Using available genomic and epigenomic annotations to characterize regions prone to loss of DNA methylation downstream of ICF mutations, we found that ZBTB24, CDCA7 and HELLS mutations affect CpG-poor regions with heterochromatin features. Among these, we identified clusters of coding and non-coding genes mostly expressed in a monoallelic manner and implicated in neuronal development, consistent with the clinical spectrum of these patients' subgroups. Hence, beyond providing blood-based biomarkers of dysfunction of ICF factors, our comparative study unveiled new players to consider at certain heterochromatin regions of the human genome.

DNA methylation signature (SAM40) identifies subgroups of the Luminal A breast cancer samples with distinct survival.

Breast cancer patients with Luminal A disease generally have a good prognosis, but among this patient group are patients with good prognosis that are currently overtreated with adjuvant chemotherapy, and also patients that have a bad prognosis and should be given more aggressive treatment. There is no available method for subclassification of this patient group. Here we present a DNA methylation signature (SAM40) that segregates Luminal A patients based on prognosis, and identify one good prognosis group and one bad prognosis group. The prognostic impact of SAM40 was validated in four independent patient cohorts. Being able to subdivide the Luminal A patients may give the two-sided benefit of identifying one subgroup that may benefit from a more aggressive treatment than what is given today, and importantly, identifying a subgroup that may benefit from less treatment.

Systemic epigenetic response to recombinant lentiviral vectors independent of proviral integration.

BACKGROUND: Lentiviral vectors (LV) are widely used for various gene transfer or gene therapy applications. The effects of LV on target cells are expected to be limited to gene delivery. Yet, human hematopoietic CD34+ cells respond to functional LVs as well as several types of non-integrating LVs by genome-wide DNA methylation changes. RESULTS: A new algorithm for the analysis of 450K Illumina data showed that these changes were marked by de novo methylation. The same 4126 cytosines located in islands corresponding to 1059 genes were systematically methylated. This effect required cellular entry of the viral particle in the cells but not the genomic integration of the vector cassette. Some LV preparations induced only mild sporadic changes while others had strong effects suggesting that LV batch heterogeneity may be related to the extent of the epigenetic response. CONCLUSION: These findings identify a previously uncharacterized but consistent cellular response to viral components and provide a novel example of environmentally modified epigenome.

DNA methylation status of key cell-cycle regulators such as CDKNA2/p16 and CCNA1 correlates with treatment response to doxorubicin and 5-fluorouracil in locally advanced breast tumors.

PURPOSE: To explore alterations in gene promoter methylation as a potential cause of acquired drug resistance to doxorubicin or combined treatment with 5-fluorouracil and mitomycin C in human breast cancers. EXPERIMENTAL DESIGN: Paired tumor samples from locally advanced breast cancer patients treated with doxorubicin and 5-fluorouracil-mitomycin C were used in the genome-wide DNA methylation analysis as discovery cohort. An enlarged cohort from the same two prospective studies as those in the discovery cohort was used as a validation set in pyrosequencing analysis. RESULTS: A total of 469 genes were differentially methylated after treatment with doxorubicin and revealed a significant association with canonical pathways enriched for immune cell response and cell-cycle regulating genes including CDKN2A, CCND2, CCNA1, which were also associated to treatment response. Treatment with FUMI resulted in 343 differentially methylated genes representing canonical pathways such as retinoate biosynthesis, gαi signaling, and LXR/RXR activation. Despite the clearly different genes and pathways involved in the metabolism and therapeutic effect of both drugs, 46 genes were differentially methylated before and after treatment with both doxorubicin and FUMI. DNA methylation profiles in genes such as BRCA1, FOXC1, and IGFBP3, and most notably repetitive elements like ALU and LINE1, were associated with TP53 mutations status. CONCLUSION: We identified and validated key cell-cycle regulators differentially methylated before and after neoadjuvant chemotherapy such as CDKN2A and CCNA1 and reported that methylation patterns of these genes may be potential predictive markers to anthracycline/mitomycine sensitivity.

Genome-wide DNA methylation profiles in progression to in situ and invasive carcinoma of the breast with impact on gene transcription and prognosis.

BACKGROUND: Ductal carcinoma in situ (DCIS) of the breast is a precursor of invasive breast carcinoma. DNA methylation alterations are thought to be an early event in progression of cancer, and may prove valuable as a tool in clinical decision making and for understanding neoplastic development. RESULTS: We generate genome-wide DNA methylation profiles of 285 breast tissue samples representing progression of cancer, and validate methylation changes between normal and DCIS in an independent dataset of 15 normal and 40 DCIS samples. We also validate a prognostic signature on 583 breast cancer samples from The Cancer Genome Atlas. Our analysis reveals that DNA methylation profiles of DCIS are radically altered compared to normal breast tissue, involving more than 5,000 genes. Changes between DCIS and invasive breast carcinoma involve around 1,000 genes. In tumors, DNA methylation is associated with gene expression of almost 3,000 genes, including both negative and positive correlations. A prognostic signature based on methylation level of 18 CpGs is associated with survival of breast cancer patients with invasive tumors, as well as with survival of patients with DCIS and mixed lesions of DCIS and invasive breast carcinoma. CONCLUSIONS: This work demonstrates that changes in the epigenome occur early in the neoplastic progression, provides evidence for the possible utilization of DNA methylation-based markers of progression in the clinic, and highlights the importance of epigenetic changes in carcinogenesis.

Differential DNA methylation analysis of breast cancer reveals the impact of immune signaling in radiation therapy.

Radiotherapy (RT) is a central treatment modality for breast cancer patients. The purpose of our study was to investigate the DNA methylation changes in tumors following RT, and to identify epigenetic markers predicting treatment outcome. Paired biopsies from patients with inoperable breast cancer were collected both before irradiation (n = 20) and after receiving 10-24 Gray (Gy) (n = 19). DNA methylation analysis was performed by using Illumina Infinium 27K arrays. Fourteen genes were selected for technical validation by pyrosequencing. Eighty-two differentially methylated genes were identified in irradiated (n = 11) versus nonirradiated (n = 19) samples (false discovery rate, FDR = 1.1%). Methylation levels in pathways belonging to the immune system were most altered after RT. Based on methylation levels before irradiation, a panel of five genes (H2AFY, CTSA, LTC4S, IL5RA and RB1) were significantly associated with clinical response (p = 0.041). Furthermore, the degree of methylation changes for 2,516 probes correlated with the given radiation dose. Within the 2,516 probes, an enrichment for pathways involved in cellular immune response, proliferation and apoptosis was identified (FDR < 5%). Here, we observed clear differences in methylation levels induced by radiation, some associated with response to treatment. Our study adds knowledge on the molecular mechanisms behind radiation response.

Individual and combined effects of DNA methylation and copy number alterations on miRNA expression in breast tumors.

BACKGROUND: The global effect of copy number and epigenetic alterations on miRNA expression in cancer is poorly understood. In the present study, we integrate genome-wide DNA methylation, copy number and miRNA expression and identify genetic mechanisms underlying miRNA dysregulation in breast cancer. RESULTS: We identify 70 miRNAs whose expression was associated with alterations in copy number or methylation, or both. Among these, five miRNA families are represented. Interestingly, the members of these families are encoded on different chromosomes and are complementarily altered by gain or hypomethylation across the patients. In an independent breast cancer cohort of 123 patients, 41 of the 70 miRNAs were confirmed with respect to aberration pattern and association to expression. In vitro functional experiments were performed in breast cancer cell lines with miRNA mimics to evaluate the phenotype of the replicated miRNAs. let-7e-3p, which in tumors is found associated with hypermethylation, is shown to induce apoptosis and reduce cell viability, and low let-7e-3p expression is associated with poorer prognosis. The overexpression of three other miRNAs associated with copy number gain, miR-21-3p, miR-148b-3p and miR-151a-5p, increases proliferation of breast cancer cell lines. In addition, miR-151a-5p enhances the levels of phosphorylated AKT protein. CONCLUSIONS: Our data provide novel evidence of the mechanisms behind miRNA dysregulation in breast cancer. The study contributes to the understanding of how methylation and copy number alterations influence miRNA expression, emphasizing miRNA functionality through redundant encoding, and suggests novel miRNAs important in breast cancer.

Lentiviral transduction of CD34(+) cells induces genome-wide epigenetic modifications.

Epigenetic modifications may occur during in vitro manipulations of stem cells but these effects have remained unexplored in the context of cell and gene therapy protocols. In an experimental model of ex vivo gene modification for hematopoietic gene therapy, human CD34(+) cells were cultured shortly in the presence of cytokines then with a gene transfer lentiviral vector (LV) expected to transduce cells but to have otherwise limited biological effects on the cells. At the end of the culture, the population of cells remained largely similar at the phenotypic level but some epigenetic changes were evident. Exposure of CD34(+) cells to cytokines increased nuclear expression of epigenetic regulators SIRT1 or DNMT1 and caused genome-wide DNA methylation changes. Surprisingly, the LV caused additional and distinct effects. Large-scale genomic DNA methylation analysis showed that balanced methylation changes occurred in about 200 genes following culture of CD34(+) cells in the presence of cytokines but 900 genes were modified following addition of the LV, predominantly increasing CpG methylation. Epigenetic effects resulting from ex vivo culture and from the use of LV may constitute previously unsuspected sources of biological effects in stem cells and may provide new biomarkers to rationally optimize gene and cell therapy protocols.

Complete pipeline for Infinium(®) Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation.

BACKGROUND: Huge progress has been made in the development of array- or sequencing-based technologies for DNA methylation analysis. The Illumina Infinium(®) Human Methylation 450K BeadChip (Illumina Inc., CA, USA) allows the simultaneous quantitative monitoring of more than 480,000 CpG positions, enabling large-scale epigenotyping studies. However, the assay combines two different assay chemistries, which may cause a bias in the analysis if all signals are merged as a unique source of methylation measurement. MATERIALS & METHODS: We confirm in three 450K data sets that Infinium I signals are more stable and cover a wider dynamic range of methylation values than Infinium II signals. We evaluated the methylation profile of Infinium I and II probes obtained with different normalization protocols and compared these results with the methylation values of a subset of CpGs analyzed by pyrosequencing. RESULTS: We developed a subset quantile normalization approach for the processing of 450K BeadChips. The Infinium I signals were used as 'anchors' to normalize Infinium II signals at the level of probe coverage categories. Our normalization approach outperformed alternative normalization or correction approaches in terms of bias correction and methylation signal estimation. We further implemented a complete preprocessing protocol that solves most of the issues currently raised by 450K array users. CONCLUSION: We developed a complete preprocessing pipeline for 450K BeadChip data using an original subset quantile normalization approach that performs both sample normalization and efficient Infinium I/II shift correction. The scripts, being freely available from the authors, will allow researchers to concentrate on the biological analysis of data, such as the identification of DNA methylation signatures.

Inferring biological networks with output kernel trees.

BACKGROUND: Elucidating biological networks between proteins appears nowadays as one of the most important challenges in systems biology. Computational approaches to this problem are important to complement high-throughput technologies and to help biologists in designing new experiments. In this work, we focus on the completion of a biological network from various sources of experimental data. RESULTS: We propose a new machine learning approach for the supervised inference of biological networks, which is based on a kernelization of the output space of regression trees. It inherits several features of tree-based algorithms such as interpretability, robustness to irrelevant variables, and input scalability. We applied this method to the inference of two kinds of networks in the yeast S. cerevisiae: a protein-protein interaction network and an enzyme network. In both cases, we obtained results competitive with existing approaches. We also show that our method provides relevant insights on input data regarding their potential relationship with the existence of interactions. Furthermore, we confirm the biological validity of our predictions in the context of an analysis of gene expression data. CONCLUSION: Output kernel tree based methods provide an efficient tool for the inference of biological networks from experimental data. Their simplicity and interpretability should make them of great value for biologists.

Comparison of the canine and human olfactory receptor gene repertoires.

BACKGROUND: Olfactory receptors (ORs), the first dedicated molecules with which odorants physically interact to arouse an olfactory sensation, constitute the largest gene family in vertebrates, including around 900 genes in human and 1,500 in the mouse. Whereas dogs, like many other mammals, have a much keener olfactory potential than humans, only 21 canine OR genes have been described to date. RESULTS: In this study, 817 novel canine OR sequences were identified, and 640 have been characterized. Of the 661 characterized OR sequences, representing half of the canine repertoire, 18% are predicted to be pseudogenes, compared with 63% in human and 20% in mouse. Phylogenetic analysis of 403 canine OR sequences identified 51 families, and radiation-hybrid mapping of 562 showed that they are distributed on 24 dog chromosomes, in 37 distinct regions. Most of these regions constitute clusters of 2 to 124 closely linked genes. The two largest clusters (124 and 109 OR genes) are located on canine chromosomes 18 and 21. They are orthologous to human clusters located on human chromosomes 11q11-q13 and HSA11p15, containing 174 and 115 ORs respectively. CONCLUSIONS: This study shows a strongly conserved genomic distribution of OR genes between dog and human, suggesting that OR genes evolved from a common mammalian ancestral repertoire by successive duplications. In addition, the dog repertoire appears to have expanded relative to that of humans, leading to the emergence of specific canine OR genes.