Methylglyoxal: a novel upstream regulator of DNA methylation.

peer reviewed[en] BACKGROUND: Aerobic glycolysis, also known as the Warburg effect, is predominantly upregulated in a variety of solid tumors, including breast cancer. We have previously reported that methylglyoxal (MG), a very reactive by-product of glycolysis, unexpectedly enhanced the metastatic potential in triple negative breast cancer (TNBC) cells. MG and MG-derived glycation products have been associated with various diseases, such as diabetes, neurodegenerative disorders, and cancer. Glyoxalase 1 (GLO1) exerts an anti-glycation defense by detoxifying MG to D-lactate. METHODS: Here, we used our validated model consisting of stable GLO1 depletion to induce MG stress in TNBC cells. Using genome-scale DNA methylation analysis, we report that this condition resulted in DNA hypermethylation in TNBC cells and xenografts. RESULTS: GLO1-depleted breast cancer cells showed elevated expression of DNMT3B methyltransferase and significant loss of metastasis-related tumor suppressor genes, as assessed using integrated analysis of methylome and transcriptome data. Interestingly, MG scavengers revealed to be as potent as typical DNA demethylating agents at triggering the re-expression of representative silenced genes. Importantly, we delineated an epigenomic MG signature that effectively stratified TNBC patients based on survival. CONCLUSION: This study emphasizes the importance of MG oncometabolite, occurring downstream of the Warburg effect, as a novel epigenetic regulator and proposes MG scavengers to reverse altered patterns of gene expression in TNBC

One fungus, which genes?: development and assessment of universal primers for potential secondary fungal DNA barcodes

The aim of this study was to assess potential candidate gene regions and corresponding universal primer pairs as secondary DNA barcodes for the fungal kingdom, additional to ITS rDNA as primary barcode. Amplification efficiencies of 14 (partially) universal primer pairs targeting eight genetic markers were tested across > 1 500 species (1 931 strains or specimens) and the outcomes of almost twenty thousand (19 577) polymerase chain reactions were evaluated. We tested several well-known primer pairs that amplify: i) sections of the nuclear ribosomal RNA gene large subunit (D1-D2 domains of 26/28S); ii) the complete internal transcribed spacer region (ITS1/2); iii) partial beta-tubulin II (TUB2); iv) gamma-actin (ACT); v) translation elongation factor 1-alpha (TEF1 alpha); and vi) the second largest subunit of RNA-polymerase II (partial RPB2, section 5-6). Their PCR efficiencies were compared with novel candidate primers corresponding to: i) the fungal-specific translation elongation factor 3 (TEF3); ii) a small ribosomal protein necessary for t-RNA docking; iii) the 60S L10 (L1) RP; iv) DNA topoisomerase I (TOPI); v) phosphoglycerate kinase (PGK); vi) hypothetical protein LNS2; and vii) alternative sections of TEF1 alpha. Results showed that several gene sections are accessible to universal primers (or primers universal for phyla) yielding a single PCR-product. Barcode gap and multi-dimensional scaling analyses revealed that some of the tested candidate markers have universal properties providing adequate infra- and inter-specific variation that make them attractive barcodes for species identification. Among these gene sections, a novel high fidelity primer pair for TEF1 alpha, already widely used as a phylogenetic marker in mycology, has potential as a supplementary DNA barcode with superior resolution to ITS. Both TOPI and PGK show promise for the Ascomycota, while TOPI and LNS2 are attractive for the Pucciniomycotina, for which universal primers for ribosomal subunits often fail

Circulating unmethylated CHTOP and INS DNA fragments provide evidence of possible islet cell death in youth with obesity and diabetes

Background Identification of islet β cell death prior to the onset of type 1 diabetes (T1D) or type 2 diabetes (T2D) might allow for interventions to protect β cells and reduce diabetes risk. Circulating unmethylated DNA fragments arising from the human INS gene have been proposed as biomarkers of β cell death, but this gene alone may not be sufficiently specific to report β cell death. Results To identify new candidate genes whose CpG sites may show greater specificity for β cells, we performed unbiased DNA methylation analysis using the Infinium HumanMethylation 450 array on 64 human islet preparations and 27 non-islet human tissues. For verification of array results, bisulfite DNA sequencing of human β cells and 11 non-β cell tissues was performed on 5 of the top 10 CpG sites that were found to be differentially methylated. We identified the CHTOP gene as a candidate whose CpGs show a greater frequency of unmethylation in human islets. A digital PCR strategy was used to determine the methylation pattern of CHTOP and INS CpG sites in primary human tissues. Although both INS and CHTOP contained unmethylated CpG sites in non-islet tissues, they occurred in a non-overlapping pattern. Based on Naïve Bayes classifier analysis, the two genes together report 100% specificity for islet damage. Digital PCR was then performed on cell-free DNA from serum from human subjects. Compared to healthy controls (N = 10), differentially methylated CHTOP and INS levels were higher in youth with new onset T1D (N = 43) and, unexpectedly, in healthy autoantibody-negative youth who have first-degree relatives with T1D (N = 23). When tested in lean (N = 32) and obese (N = 118) youth, increased levels of unmethylated INS and CHTOP were observed in obese individuals. Conclusion Our data suggest that concurrent measurement of circulating unmethylated INS and CHTOP has the potential to detect islet death in youth at risk for both T1D and T2D. Our data also support the use of multiple parameters to increase the confidence of detecting islet damage in individuals at risk for developing diabetes

Bioinformatic inference of a prognostic epigenetic signature of immunity in breast cancers

L’altération des marques épigénétiques est de plus en plus reconnue comme une caractéristique fondamentale des cancers. Dans cette thèse, nous avons utilisé des profils de méthylation de l’ADN en vue d’améliorer la classification des patients atteints du cancer du sein grâce à une approche basée sur l’apprentissage automatique. L’objectif à long terme est le développement d’outils cliniques de médecine personnalisée. Les données de méthylation de l’ADN furent acquises à l’aide d’une puce à ADN dédiée à la méthylation, appelée Infinium. Cette technologie est récente comparée, par exemple, aux puces d’expression génique et son prétraitement n’est pas encore standardisé. La première partie de cette thèse fut donc consacrée à l’évaluation des méthodes de normalisation par comparaison des données normalisées avec d’autres technologies (pyroséquençage et RRBS) pour les deux technologies Infinium les plus récentes (450k et 850k). Nous avons également évalué la couverture de régions biologiquement relevantes (promoteurs et amplificateurs) par les deux technologies. Ensuite, nous avons utilisé les données Infinium (correctement prétraitées) pour développer un score, appelé MeTIL score, qui présente une valeur pronostique et prédictive dans les cancers du sein. Nous avons profité de la capacité de la méthylation de l’ADN à refléter la composition cellulaire pour extraire une signature de méthylation (c’est-à-dire un ensemble de positions de l’ADN où la méthylation varie) qui reflète la présence de lymphocytes dans l’échantillon tumoral. Après une sélection de sites présentant une méthylation spécifique aux lymphocytes, nous avons développé une approche basée sur l’apprentissage automatique pour obtenir une signature d’une tailleoptimale réduite à cinq sites permettant potentiellement une utilisation en clinique. Après conversion de cette signature en un score, nous avons montré sa spécificité pour les lymphocytes à l’aide de données externes et de simulations informatiques. Puis, nous avons montré la capacité du MeTIL score à prédire la réponse à la chimiothérapie ainsi que son pouvoir pronostique dans des cohortes indépendantes de cancer du sein et, même, dans d’autres cancers.Epigenetic alterations are increasingly recognised as an hallmark of cancers. In this thesis, we used a machine-learning-based approach to improve breast cancer patients’ classification using DNA methylation profiling with the long term aim to make treatment more personalised. The DNA methylation data were acquired using a high density DNA methylation array called Infinium. This technology is recent compared to expression arrays and its preprocessing is not yet standardised. So, the first part of this thesis was to evaluate the normalisation methods by comparing normalised data against other technologies (pyrosequencing and RRBS) for the two most recent Infinium arrays (450k and 850k).We also went deeper into the evaluation of these arrays by assessing their coverage of biologically relevant regions like promoters and enhancers. Then, we used accurately preprocessed Infinium data to develop a score, called MeTIL score, which shows prognostic and predictive value in breast cancers. We took advantage that DNA methylation can mirror the cell composition to extract a DNA methylation signature (i.e. a set of DNA methylation sites) that reflects presence of lymphocytes within the tumour. After an initial selection of lymphocyte-specific sites we developed a machine-learning-based framework which reduced the predictive set to an optimal size of five methylation sites making it potentially suitable to use in clinics. After conversion of this signature to a score, we showed its specificity to lymphocytes using external datasets and simulations. Then, we showed its ability predict response to chemotherapy and, finally, its prognostic value in independent breast cancer cohorts and even in other cancers.Doctorat en Sciencesinfo:eu-repo/semantics/nonPublishe

BREAST CANCER EPIGENETIC MARKERS USEFUL IN ANTHRACYCLINE TREATMENT PROGNOSIS

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A comprehensive overview of Infinium Human Methylation450 data processing

Infinium HumanMethylation450 beadarray is a popular technology to explore DNA methylomes in health and disease, and there is a current explosion in the use of this technique. Despite experience acquired from gene expression microarrays, analyzing Infinium Methylation arrays appeared more complex than initially thought and several difficulties have been encountered, as those arrays display specific features that need to be taken into consideration during data processing. Here, we review several issues that have been highlighted by the scientific community, and we present an overview of the general data processing scheme and an evaluation of the different normalization methods available to date to guide the 450K users in their analysis and data interpretation.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Improving Infinium MethylationEPIC data processing: re-annotation of enhancers and long noncoding RNA genes and benchmarking of normalization methods

Illumina Infinium DNA Methylation (5mC) arrays are a popular technology for low-cost, high-throughput, genome-scale measurement of 5mC distribution, especially in cancer and other complex diseases. After the success of its HumanMethylation450 array (450k), Illumina released the MethylationEPIC array (850k) featuring increased coverage of enhancers. Despite the widespread use of 850k, analysis of the corresponding data remains suboptimal: it still relies mostly on Illumina’s default annotation, which underestimates enhancerss and long noncoding RNAs. Results: We have thus developed an approach, based on the ENCODE and LNCipedia databases, which greatly improves upon Illumina’s default annotation of enhancers and long noncoding transcripts. We compared the re-annotated 850k with both 450k and reduced-representation bisulphite sequencing (RRBS), another high-throughput 5mC profiling technology. We found 850k to cover at least three times as many enhancers and long noncoding RNAs as either 450k or RRBS. We further investigated the reproducibility of the three technologies, applying various normalization methods to the 850k data. Most of these methods reduced variability to a level below that of RRBS data. We then used 850k with our new annotation and normalization to profile 5mC changes in breast cancer biopsies. 850k highlighted aberrant enhancer methylation as the predominant feature, in agreement with previous reports. Our study provides an updated processing approach for 850k data, based on refined probe annotation and normalization, allowing for improved analysis of methylation at enhancers and long noncoding RNA genes. Our findings will help to further advance understanding of the DNA methylome in health and disease

MIRRI - 3rd Iteration Business Case

This working paper describes the Business Case developed during the Preparatory Phase of the project to establish the Microbial Resource Research Infrastructure (MIRRI-ERIC)

The chromatin remodelling protein LSH/HELLS regulates the amount and distribution of DNA hydroxymethylation in the genome

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