The RNA-editing enzyme ADAR1: a regulatory hub that tunes multiple dsRNA-sensing pathways

RNA editing enzymes: structure, biological functions and applications

With the advancement of sequencing technologies and bioinformatics, over than 170 different RNA modifications have been identified. However, only a few of these modifications can lead to base pair changes, which are called RNA editing. RNA editing is a ubiquitous modification in mammalian transcriptomes and is an important co/posttranscriptional modification that plays a crucial role in various cellular processes. There are two main types of RNA editing events: adenosine to inosine (A-to-I) editing, catalyzed by ADARs on double-stranded RNA or ADATs on tRNA, and cytosine to uridine (C-to-U) editing catalyzed by APOBECs. This article provides an overview of the structure, function, and applications of RNA editing enzymes. We discuss the structural characteristics of three RNA editing enzyme families and their catalytic mechanisms in RNA editing. We also explain the biological role of RNA editing, particularly in innate immunity, cancer biogenesis, and antiviral activity. Additionally, this article describes RNA editing tools for manipulating RNA to correct disease-causing mutations, as well as the potential applications of RNA editing enzymes in the field of biotechnology and therapy.

Regulation of inflammatory diseases via the control of mRNA decay

Inflammation orchestrates a finely balanced process crucial for microorganism elimination and tissue injury protection. A multitude of immune and non-immune cells, alongside various proinflammatory cytokines and chemokines, collectively regulate this response. Central to this regulation is post-transcriptional control, governing gene expression at the mRNA level. RNA-binding proteins such as tristetraprolin, Roquin, and the Regnase family, along with RNA modifications, intricately dictate the mRNA decay of pivotal mediators and regulators in the inflammatory response. Dysregulated activity of these factors has been implicated in numerous human inflammatory diseases, underscoring the significance of post-transcriptional regulation. The increasing focus on targeting these mechanisms presents a promising therapeutic strategy for inflammatory and autoimmune diseases. This review offers an extensive overview of post-transcriptional regulation mechanisms during inflammatory responses, delving into recent advancements, their implications in human diseases, and the strides made in therapeutic exploitation.

Impact of Disease-Associated Mutations on the Deaminase Activity of ADAR1

The innate immune system relies on molecular sensors to detect distinctive molecular patterns, including viral double-stranded RNA (dsRNA), which triggers responses resulting in apoptosis and immune infiltration. Adenosine Deaminases Acting on RNA (ADARs) catalyze the deamination of adenosine (A) to inosine (I), serving as a mechanism to distinguish self from non-self RNA and prevent aberrant immune activation. Loss-of-function mutations in the ADAR1 gene are one cause of Aicardi Goutières Syndrome (AGS), a severe autoimmune disorder in children. Although seven out of the eight AGS-associated mutations in ADAR1 occur within the catalytic domain of the ADAR1 protein, their specific effects on the catalysis of adenosine deamination remain poorly understood. In this study, we carried out a biochemical investigation of four AGS-causing mutations (G1007R, R892H, K999N, and Y1112F) in ADAR1 p110 and truncated variants. These studies included adenosine deamination rate measurements with two different RNA substrates derived from human transcripts known to be edited by ADAR1 p110 (glioma-associated oncogene homologue 1 (hGli1), 5-hydroxytryptamine receptor 2C (5-HT2cR)). Our results indicate that AGS-associated mutations at two amino acid positions directly involved in stabilizing the base-flipped conformation of the ADAR-RNA complex (G1007R and R892H) had the most detrimental impact on catalysis. The K999N mutation, positioned near the RNA binding interface, altered catalysis contextually. Finally, the Y1112F mutation had small effects in each of the assays described here. These findings shed light on the differential effects of disease-associated mutations on adenosine deamination by ADAR1, thereby advancing our structural and functional understanding of ADAR1-mediated RNA editing.

dsRID: in silico identification of dsRNA regions using long-read RNA-seq data

MOTIVATION

Double-stranded RNAs (dsRNAs) are potent triggers of innate immune responses upon recognition by cytosolic dsRNA sensor proteins. Identification of endogenous dsRNAs helps to better understand the dsRNAome and its relevance to innate immunity related to human diseases.

RESULTS

Here, we report dsRID (double-stranded RNA identifier), a machine-learning-based method to predict dsRNA regions in silico, leveraging the power of long-read RNA-sequencing (RNA-seq) and molecular traits of dsRNAs. Using models trained with PacBio long-read RNA-seq data derived from Alzheimer's disease (AD) brain, we show that our approach is highly accurate in predicting dsRNA regions in multiple datasets. Applied to an AD cohort sequenced by the ENCODE consortium, we characterize the global dsRNA profile with potentially distinct expression patterns between AD and controls. Together, we show that dsRID provides an effective approach to capture global dsRNA profiles using long-read RNA-seq data.

AVAILABILITY AND IMPLEMENTATION

Software implementation of dsRID, and genomic coordinates of regions predicted by dsRID in all samples are available at the GitHub repository: https://github.com/gxiaolab/dsRID.

RNA-Based Therapeutic Technology

RNA-based therapy has been an expanding area of clinical research since the COVID-19 outbreak. Often, its comparison has been made to DNA-based gene therapy, such as adeno-associated virus- and lentivirus-mediated therapy. These DNA-based therapies show persistent expression, with maximized therapeutic efficacy. However, accumulating data indicate that proper control of gene expression is occasionally required. For example, in cancer immunotherapy, cytokine response syndrome is detrimental for host animals, while excess activation of the immune system induces supraphysiological cytokines. RNA-based therapy seems to be a rather mild therapy, and it has room to fit unmet medical needs, whereas current DNA-based therapy has unclear issues. This review focused on RNA-based therapy for cancer immunotherapy, hematopoietic disorders, and inherited disorders, which have received attention for possible clinical applications.

Aicardi-Goutières syndrome: A monogenic type I interferonopathy

Aicardi-Goutières syndrome (AGS) is a rare monogenic autoimmune disease that primarily affects the brains of children patients. Its main clinical features include encephalatrophy, basal ganglia calcification, leukoencephalopathy, lymphocytosis and increased interferon-α (IFN-α) levels in the patient's cerebrospinal fluid (CSF) and serum. AGS may be caused by mutations in any one of nine genes (TREX1, RNASEH2A, RNASEH2B, RNASEH2C, SAMHD1, ADAR1, IFIH1, LSM11 and RNU7-1) that result in accumulation of self-nucleic acids in the cytoplasm or aberrant sensing of self-nucleic acids. This triggers overproduction of type I interferons (IFNs) and subsequently causes AGS, the prototype of type I interferonopathies. This review describes the discovery history of AGS with various genotypes and provides the latest knowledge of clinical manifestations and causative genes of AGS. The relationship between AGS and type I interferonopathy and potential therapeutic methods for AGS are also discussed in this review.

dsRID: Editing-free in silico identification of dsRNA region using long-read RNA-seq data

Double-stranded RNAs (dsRNAs) are potent triggers of innate immune responses upon recognition by cytosolic dsRNA sensor proteins. Identification of endogenous dsRNAs helps to better understand the dsRNAome and its relevance to innate immunity related to human diseases. Here, we report dsRID (double-stranded RNA identifier), a machine learning-based method to predict dsRNA regions in silico, leveraging the power of long-read RNA-sequencing (RNA-seq) and molecular traits of dsRNAs. Using models trained with PacBio long-read RNA-seq data derived from Alzheimer's disease (AD) brain, we show that our approach is highly accurate in predicting dsRNA regions in multiple datasets. Applied to an AD cohort sequenced by the ENCODE consortium, we characterize the global dsRNA profile with potentially distinct expression patterns between AD and controls. Together, we show that dsRID provides an effective approach to capture global dsRNA profiles using long-read RNA-seq data.