Dataset Information

Facile detection of RNA phospho-methylation in cells and tissues.

ABSTRACT: RNAs from various cells and tissues are modified in nearly 200 chemically distinct ways. These modifications can be deposited either on the 5' or 3' ends, or internally on the nucleobases or sugar backbone. 5'-end modifications are crucial for protecting RNAs from untimely degradation/processing, regulating their cellular functions, or discriminating endogenous RNAs from pathogenic RNAs. 5'-end phospho-methylation is a remarkable RNA modification that is enzymatically deposited either on the γ-phosphate of nascent triphosphorylated RNAs by human BCDIN3/MePCE, or on the α-phosphate of processed monophosphorylated RNAs by human BCDIN3D. These 5'-phospho-methyltransferases are part of the BIN3 family of O-methyltransferases conserved from S. pombe to humans and play important cellular and biological roles, many of which await further elucidation. Here, we quickly recapitulate historical methods for the detection of 5'-end phospho-methyl modifications, and focus more specifically on a method that can be used to detect and quantify α-monophosphate methylation from as low as 10-100ng of total RNA from cells or tissues. This method is important for deciphering the roles of BCDIN3D and its homologs across species, as well as serves as starting point for the development of new methods for detection of 5'-end modifications.

SUBMITTER: Devanathan SK

PROVIDER: S-EPMC8890445 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Facile detection of RNA phospho-methylation in cells and tissues.

Devanathan Sravan K SK Debnath Turja K TK Xhemalçe Blerta B

Methods in enzymology 20210715

RNAs from various cells and tissues are modified in nearly 200 chemically distinct ways. These modifications can be deposited either on the 5' or 3' ends, or internally on the nucleobases or sugar backbone. 5'-end modifications are crucial for protecting RNAs from untimely degradation/processing, regulating their cellular functions, or discriminating endogenous RNAs from pathogenic RNAs. 5'-end phospho-methylation is a remarkable RNA modification that is enzymatically deposited either on the γ-p ...[more]

PMID: 34517959

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Project description:Single-cell technologies are currently widely applied to obtain a deeper understanding of the phenotype of single-cells in heterogenous mixtures. However, integrated multilayer approaches including simultaneous detection of mRNA, protein expression, and intracellular phospho-proteins are still challenging. Here, we combined an adapted method to in vitro-differentiate peripheral B-cells into antibody-secreting cells (ASCs) (i.e., plasmablasts and plasma cells) with integrated multi-omic single-cell sequencing technologies to detect and quantify immunoglobulin subclass-specific surface markers, transcriptional profiles, and signaling transduction pathway components. Using a common set of surface proteins, we integrated two multimodal datasets to combine mRNA, protein expression, and phospho-protein detection in one integrated dataset. Next, we tested whether ASCs that only seem to differ in its ability to secrete different IgM, IgA, or IgG antibodies exhibit other differences that characterize these different ASCs. Our approach detected differential expression of plasmablast and plasma cell markers, homing receptors, and TNF receptors. In addition, differential sensitivity was observed for the different cytokine stimulations that were applied during in vitro differentiation. For example, IgM ASCs were more sensitive to IL-15, while IgG ASC responded more to IL-6 and IFN addition. Furthermore, tonic BCR activity was detected in IgA and IgM ASCs, while IgG ASC exhibited active BCR-independent SYK activity and NF-κB and mTOR signaling. We confirmed these findings using flow cytometry and small molecules inhibitors, demonstrating the importance of SYK, NF-κB, and mTOR activity for plasmablast/plasma cell differentiation/survival and/or IgG secretion. Taken together, our integrated multi-omics approach allowed high-resolution phenotypic characterization of single cells in a heterogenous sample of in vitro-differentiated human ASCs. Our strategy is expected to further our understanding of human ASCs in healthy and diseased samples and provide a valuable tool to identify novel biomarkers and potential drug targets.

Project description:IntroductionThe current methodology involving diagnosis of prostate cancer (PCa) relies on the pathology examination of prostate needle biopsies, a method with high false negative rates partly due to temporospatial, molecular, and morphological heterogeneity of prostate adenocarcinoma. It is postulated that molecular markers have a potential to assign diagnosis to a considerable portion of undetected prostate tumors. This study examines the genome-wide DNA methylation changes in PCa in search of genomic markers for the development of a diagnostic algorithm for PCa screening.MethodsArchival PCa and normal tissues were assessed using genomic DNA methylation arrays. Differentially methylated sites and regions (DMRs) were used for functional assessment, gene-set enrichment and protein interaction analyses, and examination of transcription factor-binding patterns. Raw signal intensity data were used for identification of recurrent copy number variations (CNVs). Non-redundant fully differentiating cytosine-phosphate-guanine sites (CpGs), which did not overlap CNV segments, were used in an L1 regularized logistic regression model (LASSO) to train a classification algorithm. Validation of this algorithm was performed using a large external cohort of benign and tumor prostate arrays.ResultsApproximately 6,000 probes and 600 genomic regions showed significant DNA methylation changes, primarily involving hypermethylation. Gene-set enrichment and protein interaction analyses found an overrepresentation of genes related to cell communications, neurogenesis, and proliferation. Motif enrichment analysis demonstrated enrichment of tumor suppressor-binding sites nearby DMRs. Several of these regions were also found to contain copy number amplifications. Using four non-redundant fully differentiating CpGs, we trained a classification model with 100% accuracy in discriminating tumors from benign samples. Validation of this algorithm using an external cohort of 234 tumors and 92 benign samples yielded 96% sensitivity and 98% specificity. The model was found to be highly sensitive to detect metastatic lesions in bone, lymph node, and soft tissue, while being specific enough to differentiate the benign hyperplasia of prostate from tumor.ConclusionA considerable component of PCa DNA methylation profile represent driver events potentially established/maintained by disruption of tumor suppressor activity. As few as four CpGs from this profile can be used for screening of PCa.

Dataset Information

Facile detection of RNA phospho-methylation in cells and tissues.

Publications

Facile detection of RNA phospho-methylation in cells and tissues.

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