Biology subjects

Lutsik, P.

Publications and source records attributed to Lutsik, P..

25 records · Page 2Linked to original sources

bioRxiv2020

AmpliconDesign - An interactive web server for the design of high-throughput targeted DNA methylation assays

Targeted analysis of DNA methylation patterns based on bisulfite-treated genomic DNA (BT-DNA) is considered as a gold-standard for epigenetic biomarker development. Existing software tools facilitate primer design, primer quality control or visualization of primer localization. However, high-throughput design of primers for BT-DNA amplification is hampered by limits in throughput and functionality of existing tools, requiring users to repeatedly perform specific tasks manually. Consequently, the design of PCR primers for BT-DNA remains a tedious and time-consuming process. To bridge this gap, we developed AmpliconDesign, a webserver providing a scalable and user-friendly platform for the design and analysis of targeted DNA methylation studies based on BT-DNA, e.g. deep amplicon bisulfite sequencing (ampBS-seq), EpiTYPER MassArray, or pyrosequencing. Core functionality of the web server includes high-throughput primer design and binding site validation based on in silico bisulfite-converted DNA sequences, prediction of fragmentation patterns for EpiTYPER MassArray, an interactive quality control as well as a streamlined analysis workflow for ampBS-seq. Availability and ImplementationThe AmpliconDesign webserver is freely available online at: https://amplicondesign.dkfz.de/. AmpliconDesign has been implemented using the R Shiny framework (Chang et al., 2018). The source code is publicly available under the GNU General Public License v3.0 (https://github.com/MaxSchoenung/AmpliconDesign). ContactDaniel B. Lipka (d.lipka@dkfz.de) & Maximilian Schonung (m.schoenung@dkfz.de)

bioinformatics↗

bioRxiv2020

Versatile workflow for cell type resolved transcriptional and epigenetic profiles from cryopreserved human lung.

The complexity of the lung microenvironment together with changes in cellular composition during disease progression make it exceptionally hard to understand the molecular mechanisms leading to the development of chronic lung diseases. Although recent advances in cell type resolved and single-cell sequencing approaches hold great promise for studying complex diseases, their implementation greatly relies on local access to fresh tissue, as traditional methods to process and store tissue do not allow viable cell isolation. To overcome these hurdles, we developed a novel, versatile workflow that allows long-term storage of human lung tissue with high cell viability, permits thorough sample quality check before cell isolation, and is compatible with next generation sequencing-based profiling, including single-cell approaches. We demonstrate that cryopreservation is suitable for isolation of multiple cell types from different lung locations and is applicable to both healthy and diseased tissue, including COPD and tumor samples. Basal cells isolated from cryopreserved airways retain the ability to differentiate, indicating that cellular identity is not altered by cryopreservation. Importantly, using RNA sequencing (RNA-seq) and Illumina EPIC Array, we show that genome-wide gene expression and DNA methylation signatures are preserved upon cryopreservation, emphasizing the suitability of our workflow for -omics profiling of human lung cells. In addition, we obtained high-quality single-cell RNA sequencing data of cells isolated from cryopreserved human lung, demonstrating that cryopreservation empowers single-cell approaches. Overall, thanks to its simplicity, our cryopreservation workflow is well-suited for prospective tissue collection by academic collaborators and biobanks, opening worldwide access to human tissue.

cell biology↗

bioRxiv2020

Methylome-based cell-of-origin modeling (Methyl-COOM) identifies aberrant expression of immune regulatory molecules in CLL

BackgroundIn cancer, normal epigenetic patterns are disturbed and contribute to gene expression changes, disease onset and progression. The cancer epigenome is composed of the epigenetic patterns present in the tumor-initiating cell at the time of transformation, and the tumor-specific epigenetic alterations that are acquired during tumor initiation and progression. The precise dissection of these two components of the tumor epigenome will facilitate a better understanding of the biological mechanisms underlying malignant transformation. Chronic lymphocytic leukemia (CLL) originates from differentiating B cells, which undergo extensive epigenetic programming. This poses the challenge to precisely determine the epigenomic ground-state of the cell-of-origin in order to identify CLL-specific epigenetic aberrations. MethodsWe developed a linear regression model, methylome-based cell-of-origin modeling (Methyl-COOM), to map the cell-of-origin for individual CLL patients based on the continuum of epigenomic changes during normal B cell differentiation. ResultsMethyl-COOM accurately maps the cell-of-origin of CLL and identifies CLL-specific aberrant DNA methylation events that are not confounded by physiologic epigenetic B cell programming. Furthermore, Methyl-COOM unmasks abnormal action of transcription factors, altered super-enhancer activities, and aberrant transcript expression in CLL. Among the aberrantly regulated transcripts were many genes that have previously been implicated in T cell biology. Flow cytometry analysis of these markers confirmed their aberrant expression on malignant B cells at the protein level. ConclusionsMethyl-COOM analysis of CLL identified disease-specific aberrant gene regulation. The aberrantly expressed genes identified in this study might play a role in immune-evasion in CLL and might serve as novel targets for immunotherapy approaches. In summary, we propose a novel framework for in silico modeling of reference DNA methylomes and for the identification of cancer-specific epigenetic changes, a concept that can be broadly applied to other human malignancies.

cancer biology↗

bioRxiv2019

Reference-free deconvolution of complex DNA methylation data - a detailed protocol

Epigenomic profiling enables unique insights into human development and diseases. Often the analysis of bulk samples remains the only feasible option for studying complex tissues and organs in large patient cohorts, masking the signatures of important cell populations in convoluted signals. DNA methylomes are highly cell type-specific, and enable recovery of hidden components using advanced computational methods without the need for reference profiles. We propose a three-stage protocol for reference-free deconvolution of DNA methylomes comprising: (i) data preprocessing, confounder adjustment and feature selection, (ii) deconvolution with multiple parameters, and (iii) guided biological inference and validation of deconvolution results. Our protocol simplifies the analysis and integration of DNA methylomes derived from complex samples, including tumors. Applying this protocol to lung cancer methylomes from TCGA revealed components linked to stromal cells, tumor-infiltrating immune cells, and associations with clinical parameters. The protocol takes less than four days to complete and requires basic R skills.

bioinformatics↗

bioRxiv2019

Activation-induced deaminase is critical for the establishment of DNA methylation patterns prior to the germinal center reaction

Mutations in activation induced deaminase (AID) lead to hyper-IgM syndrome type 2 (HIGM2), a rare human primary antibody deficiency. AID-mediated cytosine deamination has been proposed as mediating active demethylation, although evidences both support and cast doubt on such a role. We here made use of HIGM2 B cells to investigate direct AID involvement in active DNA demethylation. HIGM2 naive and memory B cells both display widespread DNA methylation defects, of which approximately 25% of these defects correspond to active events. For genes that undergo active demethylation that is impaired in HIGM2 individuals, we did not observe AID involvement but a participation of TET enzymes. DNA methylation alterations in HIGM2 naive B cells are related to premature overstimulation of the B-cell receptor prior to the germinal center reaction. Our data supports a role for AID in B cell central tolerance in preventing the expansion of autoreactive cell clones, affecting the correct establishment of DNA methylation patterns.

immunology↗

bioRxiv2019

Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software

Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking.\n\nHere we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30-35%, and that selection of cell-type informative probes has similar effect. We show that Cattells rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-treatment steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir.

bioinformatics↗

bioRxiv2018

DNA methylation signatures of a large cohort monozygotic twins clinically discordant for multiple sclerosis

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system with a modest concordance rate in monozygotic twins that strongly argues for involvement of epigenetic factors. We observe in 45 MS discordant monozygotic twins highly similar peripheral blood mononuclear cell-based methylomes. However, a few MS-associated differentially methylated positions (DMP) were identified and validated, including a region in the TMEM232 promoter and ZBTB16 enhancer. In CD4+ T cells we observed an MS-associated differentially methylated region in FIRRE. In addition, many regions showed large methylation differences in individual pairs, but were not clearly associated with MS. Furthermore, epigenetic biomarkers for current interferon-beta treatment were identified, and extensive validation revealed the ZBTB16 DMP as a signature of prior glucocorticoid treatment. Altogether, our study represents an important reference for epigenomic MS studies. It identifies new candidate epigenetic markers, highlights treatment effects and genetic background as major confounders, and argues against some previously reported MS-associated epigenetic candidates.

genomics↗