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Genetic code expansion facilitates position‐selective labeling of rna for biophysical studies
(2019)
Nature relies on reading and synthesizing the genetic code with high fidelity. Nucleic acid building blocks that are orthogonal to the canonical A‐T and G‐C base‐pairs are therefore uniquely suitable to facilitate position‐specific labeling of nucleic acids. Here, we employ the orthogonal kappa‐xanthosine‐base‐pair for in vitro transcription of labeled RNA. We devised an improved synthetic route to obtain the phosphoramidite of the deoxy‐version of the kappa nucleoside in solid phase synthesis. From this DNA template, we demonstrate the reliable incorporation of xanthosine during in vitro transcription. Using NMR spectroscopy, we show that xanthosine introduces only minor structural changes in an RNA helix. We furthermore synthesized a clickable 7‐deaza‐xanthosine, which allows to site‐specifically modify transcribed RNA molecules with fluorophores or other labels.
Background: Germinal center-derived B cell lymphomas are tumors of the lymphoid tissues representing one of the most heterogeneous malignancies. Here we characterize the variety of transcriptomic phenotypes of this disease based on 873 biopsy specimens collected in the German Cancer Aid MMML (Molecular Mechanisms in Malignant Lymphoma) consortium. They include diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), Burkitt’s lymphoma, mixed FL/DLBCL lymphomas, primary mediastinal large B cell lymphoma, multiple myeloma, IRF4-rearranged large cell lymphoma, MYC-negative Burkitt-like lymphoma with chr. 11q aberration and mantle cell lymphoma.
Methods: We apply self-organizing map (SOM) machine learning to microarray-derived expression data to generate a holistic view on the transcriptome landscape of lymphomas, to describe the multidimensional nature of gene regulation and to pursue a modular view on co-expression. Expression data were complemented by pathological, genetic and clinical characteristics.
Results: We present a transcriptome map of B cell lymphomas that allows visual comparison between the SOM portraits of different lymphoma strata and individual cases. It decomposes into one dozen modules of co-expressed genes related to different functional categories, to genetic defects and to the pathogenesis of lymphomas. On a molecular level, this disease rather forms a continuum of expression states than clearly separated phenotypes. We introduced the concept of combinatorial pattern types (PATs) that stratifies the lymphomas into nine PAT groups and, on a coarser level, into five prominent cancer hallmark types with proliferation, inflammation and stroma signatures. Inflammation signatures in combination with healthy B cell and tonsil characteristics associate with better overall survival rates, while proliferation in combination with inflammation and plasma cell characteristics worsens it. A phenotypic similarity tree is presented that reveals possible progression paths along the transcriptional dimensions. Our analysis provided a novel look on the transition range between FL and DLBCL, on DLBCL with poor prognosis showing expression patterns resembling that of Burkitt’s lymphoma and particularly on "double-hit" MYC and BCL2 transformed lymphomas.
Conclusions: The transcriptome map provides a tool that aggregates, refines and visualizes the data collected in the MMML study and interprets them in the light of previous knowledge to provide orientation and support in current and future studies on lymphomas and on other cancer entities.