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Immunologic classification of acute lymphoblastic leukemia.

Abstract

The study of cell surface antigens in acute lymphoblastic leukemia (ALL) has provided the basic information for classification of ALL into B cell or T cell lineage. Each immunologic type may present at an immature or more mature stage of maturation. This classification has prognostic significance since mature B cell and T cell ALL phenotypes have a worse prognosis, compared to common acute lymphoblastic leukemia-associated antigen (CALLA) positive leukemias that belong to an immature B cell lineage and have the best prognosis. By performing these immunologic studies together with those of molecular biology, it is now possible to establish the precise level of cell differentiation, the point at which the malignant transformation occurred. Further studies may allow correlation of these different maturation stages, thus providing insight into the biologic behavior of lymphoblastic leukemia.

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BibTeXRIS

M E Cabrera. 1990. Immunologic classification of acute lymphoblastic leukemia.. https://doi.org/10.1097/00043426-199023000-00006

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A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase↗

5',8-cyclo-dAdo and 8-oxo-dAdo DNA Lesions Are Both Substrates of Adenosine Deaminase: A Preliminary Study.

Genetic information, whether inside or outside the nucleus, is exposed to a variety of harmful physico-chemical factors. Although DNA damage repair systems have been extensively studied, little information about post-repair and non-genomic DNA damage metabolism is available in the literature. Adenosine deaminase (ADA) is an abundant enzyme found on both sides of the cell membrane that regulates the concentration of adenine derivatives. In this article, it has been shown that 7,8-dihydro-8-oxo-2'-deoxyadenosine (OXOdAdo) and (5'R/S) 5',8-cyclo-2'-deoxyadenosine ((5'R/S)cdAdo) are suitable substrates for ADA. For this purpose, theoretical Density Functional Tight Binding and RP-HPLC analyses were applied. The products of ADA activity, i.e., OXOdIno (7,8-dihydro-8-oxo-2'-deoxyinosine) and (5'R/S) cdIno ((5'R/S) 8-cyclo-2'-deoxyinosine), were identified and confirmed by high-resolution mass spectroscopy. Although the (5'R) and (5'S)cdAdo enzymatic deamination processes are much slower (34% and 32% after 168 h, respectively) than the process observed for dAdo, 5',8-cyclo-2'-deoxyinosine should be considered when monitoring cyclopurine levels in physiological fluids. The same should be considered in the case of OXOdAdo, which is completely converted to OXOdIno within one minute and may therefore be less visible than OXOdGuo during mass spectroscopy analysis. Both these observations are important, given the abundance of 2'-deoxyadenosine on both sides of the cell membrane and its potential conversion into OXOdAdo and (5'R/S)cdAdo. They may also explain why the observed level of OXOdAdo is much lower than that of OXOdGuo in cells and physiological fluids, even though their difference in ionisation potential is only 0.25 eV. Future studies are needed to further investigate the metabolism of DNA damage and to identify the enzymes involved in nucleic acid biochemistry.

Adenosine Deaminase↗

A high resolution A-to-I editing map in the mouse identifies editing events controlled by pre-mRNA splicing.

Pre-mRNA-splicing and adenosine to inosine (A-to-I) RNA-editing occur mostly cotranscriptionally. During A-to-I editing, a genomically encoded adenosine is deaminated to inosine by adenosine deaminases acting on RNA (ADARs). Editing-competent stems are frequently formed between exons and introns. Consistently, studies using reporter assays have shown that splicing efficiency can affect editing levels. Here, we use Nascent-seq and identify ∼90,000 novel A-to-I editing events in the mouse brain transcriptome. Most novel sites are located in intronic regions. Unlike previously assumed, we show that both ADAR (ADAR1) and ADARB1 (ADAR2) can edit repeat elements and regular transcripts to the same extent. We find that inhibition of splicing primarily increases editing levels at hundreds of sites, suggesting that reduced splicing efficiency extends the exposure of intronic and exonic sequences to ADAR enzymes. Lack of splicing factors NOVA1 or NOVA2 changes global editing levels, demonstrating that alternative splicing factors can modulate RNA editing. Finally, we show that intron retention rates correlate with editing levels across different brain tissues. We therefore demonstrate that splicing efficiency is a major factor controlling tissue-specific differences in editing levels.

Adenosine Deaminase↗