Dataset Information

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

ABSTRACT:

Background

In 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.

Methods

We 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.

Results

Methyl-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.

Conclusions

Methyl-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.

SUBMITTER: Wierzbinska JA

PROVIDER: S-EPMC7081711 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

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

Wierzbinska Justyna A JA Toth Reka R Ishaque Naveed N Rippe Karsten K Mallm Jan-Philipp JP Klett Lara C LC Mertens Daniel D Zenz Thorsten T Hielscher Thomas T Seifert Marc M Küppers Ralf R Assenov Yassen Y Lutsik Pavlo P Stilgenbauer Stephan S Roessner Philipp M PM Seiffert Martina M Byrd John J Oakes Christopher C CC Plass Christoph C Lipka Daniel B DB

Genome medicine 20200318 1

<h4>Background</h4>In 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 biolo ...[more]

PMID: 32188505

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Project description:Expression profiles of 22 reference Arabidopsis immunity mutants were collected using the Arabidopsis Pathoarray 464_001 (GPL3638) in order to build a network model predicting the Arabidopsis immune signaling network. Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. Here, we demonstrate that use of mRNA profiling to collect and analyze detailed descriptions of changes in the network state resulting from specific network perturbations is a powerful and economical strategy to elucidate regulatory relationships among the components of a complex signaling network. Specifically, we studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2). This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with the Pto DC3000 AvrRpt2 and used as detailed descriptions of the network states resulting from specific genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting network model accurately predicted 22 of 23 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i) the components of the network are highly interconnected; (ii) negative regulatory relationships are common between signaling sectors. One case of a novel negative regulatory relationship, between the early microbe-associated molecular pattern (MAMP)-activated sector and the salicylic acid (SA)-mediated sector, was further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector-switching" network, which effectively balances two apparently conflicting demands, robustness against pathogenic perturbations and moderation of negative impacts of immune responses on plant fitness. Keywords: Responses of reference Arabidopsis immunity mutants to Pseudomonas syringae pv. tomato DC3000 carrying avrRpt2 This experiment consists of two (group00) or three (group01-04) biological replicates of each genotype (total 25 genotypes [3 are multiple mutants, which were removed for the network modeling, but were used for normalization]). For each genotype, two leaves per plant were pooled from three pots to prepare total RNA.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

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