Research ArticleNephrology Open Access | 10.1172/JCI180117

Segewkal Hawaze Heruye,1 Jered Myslinski,1 Chao Zeng,2 Amy Zollman,1 Shinichi Makino,1 Azuma Nanamatsu,1 Quoseena Mir,3 Sarath Chandra Janga,3 Emma H. Doud,4 Michael T. Eadon,1 Bernhard Maier,1 Michiaki Hamada,2,5,6 Tuan M. Tran,1,7 Pierre C. Dagher,1 and Takashi Hato1,7,8

1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

2Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana, USA.

4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA.

5AIST–Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.

6Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

7Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana, USA.

8Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Address correspondence to: Takashi Hato, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street R2-202A, Indianapolis, Indiana 46202, USA. Phone: 317.278.4286; Email: thato@iu.edu.

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1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

2Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana, USA.

4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA.

5AIST–Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.

6Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

7Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana, USA.

8Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Address correspondence to: Takashi Hato, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street R2-202A, Indianapolis, Indiana 46202, USA. Phone: 317.278.4286; Email: thato@iu.edu.

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1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

2Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana, USA.

4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA.

5AIST–Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.

6Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

7Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana, USA.

8Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Address correspondence to: Takashi Hato, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street R2-202A, Indianapolis, Indiana 46202, USA. Phone: 317.278.4286; Email: thato@iu.edu.

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1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

2Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana, USA.

4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA.

5AIST–Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.

6Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

7Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana, USA.

8Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Address correspondence to: Takashi Hato, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street R2-202A, Indianapolis, Indiana 46202, USA. Phone: 317.278.4286; Email: thato@iu.edu.

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1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

2Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana, USA.

4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA.

5AIST–Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.

6Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

7Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana, USA.

8Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Address correspondence to: Takashi Hato, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street R2-202A, Indianapolis, Indiana 46202, USA. Phone: 317.278.4286; Email: thato@iu.edu.

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1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

2Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana, USA.

4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA.

5AIST–Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.

6Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

7Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana, USA.

8Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Address correspondence to: Takashi Hato, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street R2-202A, Indianapolis, Indiana 46202, USA. Phone: 317.278.4286; Email: thato@iu.edu.

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1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

2Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana, USA.

4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA.

5AIST–Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.

6Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

7Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana, USA.

8Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Address correspondence to: Takashi Hato, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street R2-202A, Indianapolis, Indiana 46202, USA. Phone: 317.278.4286; Email: thato@iu.edu.

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1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

2Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana, USA.

4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA.

5AIST–Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.

6Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

7Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana, USA.

8Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Address correspondence to: Takashi Hato, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street R2-202A, Indianapolis, Indiana 46202, USA. Phone: 317.278.4286; Email: thato@iu.edu.

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1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

2Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana, USA.

4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA.

5AIST–Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.

6Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

7Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana, USA.

8Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Address correspondence to: Takashi Hato, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street R2-202A, Indianapolis, Indiana 46202, USA. Phone: 317.278.4286; Email: thato@iu.edu.

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1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

2Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana, USA.

4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA.

5AIST–Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.

6Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

7Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana, USA.

8Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Address correspondence to: Takashi Hato, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street R2-202A, Indianapolis, Indiana 46202, USA. Phone: 317.278.4286; Email: thato@iu.edu.

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1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

2Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana, USA.

4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA.

5AIST–Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.

6Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

7Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana, USA.

8Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Address correspondence to: Takashi Hato, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street R2-202A, Indianapolis, Indiana 46202, USA. Phone: 317.278.4286; Email: thato@iu.edu.

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1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

2Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana, USA.

4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA.

5AIST–Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.

6Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

7Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana, USA.

8Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Address correspondence to: Takashi Hato, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street R2-202A, Indianapolis, Indiana 46202, USA. Phone: 317.278.4286; Email: thato@iu.edu.

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1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

2Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana, USA.

4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA.

5AIST–Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.

6Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

7Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana, USA.

8Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Address correspondence to: Takashi Hato, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street R2-202A, Indianapolis, Indiana 46202, USA. Phone: 317.278.4286; Email: thato@iu.edu.

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1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

2Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana, USA.

4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA.

5AIST–Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.

6Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

7Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana, USA.

8Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Address correspondence to: Takashi Hato, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street R2-202A, Indianapolis, Indiana 46202, USA. Phone: 317.278.4286; Email: thato@iu.edu.

Find articles by Dagher, P. in: JCI | PubMed | Google Scholar

1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA.

2Faculty of Science and Engineering, Waseda University, Tokyo, Japan.

3Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana, USA.

4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA.

5AIST–Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.

6Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.

7Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana, USA.

8Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Address correspondence to: Takashi Hato, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street R2-202A, Indianapolis, Indiana 46202, USA. Phone: 317.278.4286; Email: thato@iu.edu.

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Published July 2, 2024 - More info

Published in Volume 134, Issue 17 on September 3, 2024
J Clin Invest. 2024;134(17):e180117. https://doi.org/10.1172/JCI180117.
© 2024 Heruye et al. This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Published July 2, 2024 - Version history
Received: February 7, 2024; Accepted: June 25, 2024 View PDF Abstract

The progression of kidney disease varies among individuals, but a general methodology to quantify disease timelines is lacking. Particularly challenging is the task of determining the potential for recovery from acute kidney injury following various insults. Here, we report that quantitation of post-transcriptional adenosine-to-inosine (A-to-I) RNA editing offers a distinct genome-wide signature, enabling the delineation of disease trajectories in the kidney. A well-defined murine model of endotoxemia permitted the identification of the origin and extent of A-to-I editing, along with temporally discrete signatures of double-stranded RNA stress and adenosine deaminase isoform switching. We found that A-to-I editing of antizyme inhibitor 1 (AZIN1), a positive regulator of polyamine biosynthesis, serves as a particularly useful temporal landmark during endotoxemia. Our data indicate that AZIN1 A-to-I editing, triggered by preceding inflammation, primes the kidney and activates endogenous recovery mechanisms. By comparing genetically modified human cell lines and mice locked in either A-to-I–edited or uneditable states, we uncovered that AZIN1 A-to-I editing not only enhances polyamine biosynthesis but also engages glycolysis and nicotinamide biosynthesis to drive the recovery phenotype. Our findings implicate that quantifying AZIN1 A-to-I editing could potentially identify individuals who have transitioned to an endogenous recovery phase. This phase would reflect their past inflammation and indicate their potential for future recovery.

Graphical Abstract Introduction

The polyamines — namely putrescine, spermidine, and spermine — are involved in a variety of fundamental biological processes, such as transcription, translation, cell growth, differentiation, DNA repair, and aging (1–3). Polyamines are fully protonated at physiological pH, and a substantial fraction of polyamines are associated with ribosomes (~15%) and RNA (~80%) (4). These nucleotide-bound polyamines facilitate global protein synthesis through their direct interaction with the translation machinery (5, 6). The critical role of polyamines in protein synthesis is further supported by the fact that cancer cells frequently exploit the polyamine pathway to enhance their growth (7). Conversely, polyamines are also essential for the activation of immune cells (8, 9), blurring the boundaries between therapeutic advantages and disadvantages in a variety of settings.

The regulation of polyamine bioavailability is determined by a multitude of mechanisms, including gut absorption, de novo synthesis, and the salvage pathways. In addition, polyamines significantly influence their own pathway through various post-transcriptional mechanisms (1). These mechanisms include ribosomal frameshifting (ornithine decarboxylase antizyme 1), ribosomal occupancy of upstream open reading frames (spermine synthase and spermidine/spermine N1-acetyltransferase 1), stop codon readthrough (adenosylmethionine decarboxylase 1), and posttranslational modification of eukaryotic translation initiation factor 5A (hypusination), as well as post-transcriptional mRNA editing of antizyme inhibitor 1 (AZIN1) from adenosine to inosine (A-to-I). This A-to-I editing results in a non-synonymous amino acid mutation, as inosines are translated as guanosines (10). The presence of these intricate regulatory mechanisms within this pathway underscores the crucial importance of controlling polyamine levels in response to various environmental stresses.

The kidney is an organ with exceptionally high metabolic demands (11), making it susceptible to various stressors such as diabetes and sepsis, which can disrupt polyamine homeostasis. Indeed, a recent study has highlighted that altered polyamine metabolism is a unifying feature across more than 10 different kidney injury models in mice, as well as in the post–kidney transplantation context in humans (12, 13). Although the importance of polyamines in kidney biology is indisputable, their exact role under stress conditions remains unclear. The supplementation of polyamines and the modulation of the polyamine pathway have yielded diverse outcomes in multiple models of kidney injury, ranging from providing protection to exacerbating tissue damage (14–21). These varying results underscore the need for a more systematic examination of the roles of polyamines across specific disease timelines and trajectories.

Defining timelines and stages of any kidney disease is highly challenging. Because of variations in disease progression among patients, a uniform physical timescale cannot be universally applied. We reasoned that the precisely controlled, stepwise reactions embedded in the polyamine pathway could serve as the basis for constructing a molecular clock. This path of investigation has led to our present findings, which demonstrate that AZIN1 A-to-I editing is strikingly prevalent and occurs at specific points along disease timelines in both mouse models and humans. As such, AZIN1 A-to-I editing can serve as a molecular clock to stage various forms of kidney disease.

AZIN1 is a key regulatory enzyme that controls the initial entry point into the polyamine pathway by augmenting the activity of ornithine decarboxylase 1 (22). The A-to-I editing of AZIN1 confers a gain-of-function phenotype, thereby further increasing polyamine biosynthesis. Such gain-of-function AZIN1 A-to-I editing has been described in several forms of cancer, contributing to aggressive tumor behavior (23–26). The role of AZIN1 editing is also implicated in hematopoietic stem cell differentiation (27). More recently, transient AZIN1 editing has been reported in cases of COVID-19 infection (28). However, the clinical implications of AZIN1 editing in non-cancerous kidney diseases remain unclear.

By combining a series of sequencing and genetic approaches, we found that AZIN1 edited state confers an advantage over the unedited state by upregulating the polyamine pathway and co-opting glycolysis and nicotinamide biosynthesis, culminating in a metabolically robust phenotype. Using an extensively characterized murine model of endotoxemia, we also provide a genome-wide, time-anchored map of A-to-I editing, serving as a novel framework for the development of molecular staging in kidney disease.

Results

AZIN1 A-to-I editing is widespread in non-cancerous conditions. Using a model of endotoxin preconditioning, we have previously identified that increased polyamine levels are a key feature of the robust protective phenotype against severe sepsis (14). Increases in polyamine levels are also reported by others during the recovery phase of ischemia/reperfusion injury (29). Conversely, inhibiting a branch of the polyamine pathway can also lead to tissue protection against multiple models of kidney diseases (e.g., inhibition of ornithine decarboxylase or eukaryotic translation initiation factor 5A hypusination) (15, 16, 30–32). These contrasting findings suggest that the role of polyamines is context dependent, such as the severity of tissue injury or timing of intervention. To understand the role of polyamines broadly in various stress conditions, here we first interrogated a large clinical data set in which stranded RNA sequencing (RNA-Seq) was performed on whole blood collected from children before and after they contracted malaria (33). Through prospective surveillance, the patients were categorized into (a) early fever (infection with concurrent fever), (b) delayed fever (infection with a delay of 2–14 days until development of fever), and (c) immune (infection without progression to fever). We found that AZIN1 A-to-I editing at chromosome 8:102829408 (hg38), a known A-to-I editing site (34), was highly prevalent in this cohort, albeit at different time points among the 3 groups (Figure 1, A and B, and Supplemental Figure 1, A–C; supplemental material available online with this article; https://doi.org/10.1172/JCI180117DS1). Notably, children in the early fever group had low levels of AZIN1 A-to-I editing at baseline but showed an increase in editing after malaria infection. In contrast, children in the delayed and immune groups exhibited surprisingly high levels of A-to-I editing at baseline that were sustained over time. This raises the possibility that AZIN1 A-to-I editing early in the course of malaria infection could have a beneficial role in controlling disease progression.

Figure 1

AZIN1 A-to-I editing status in non-cancerous diseases in humans. (A) Distribution of AZIN1 A-to-I editing rates (percent of edited reads over total reads) in prospectively collected blood from male children aged 6–11 years, before and after Plasmodium falciparum malaria infection. Individuals were classified as early fever (symptomatic and first-time infection), delayed fever (asymptomatic and first-time infection, subsequently developing malarial symptoms), and immune (infected but never developing symptoms). (B) Representative read coverage near the AZIN1 editing site for one sample. Note that inosine is sequenced as guanosine. The human AZIN1 gene is encoded on the minus strand, hence the T-to-C mutation, not A-to-G, in the coverage track. Light-blue-colored reads (F2R1 paired-end orientation) indicate the proper directionality of reads mapped to the minus strand. (C) Distribution of AZIN1 A-to-I editing rates in kidney biopsies with a pathology diagnosis of diabetic kidney disease (DKD), acute kidney injury (AKI), or reference nephrectomy samples. Each column represents one sample. (D) Stacked bar chart summarizing total numbers of differentially expressed A-to-I editing sites genome-wide under the indicated conditions. For each comparison, editing sites are divided on the x axis based on the direction of fold change. For example, in the DKD versus reference comparison, approximately 20,000 sites are more edited in DKD, whereas approximately 10,000 sites are more edited in reference nephrectomy samples. (E) Heatmap displaying the top 500 differentially expressed A-to-I editing sites between diabetic nephropathy and reference nephrectomy samples. The differentially expressed sites are categorized based on repeat classes. (F) Comparison between AKI biopsies and reference nephrectomy samples.

Next, we interrogated stranded RNA-Seq data of human kidney biopsies obtained from our biobank and the Kidney Precision Medicine Project (35, 36). We found that AZIN1 editing is common in non-cancerous kidney tissues, including those with diabetic kidney disease, acute kidney injury (AKI), and even reference nephrectomy (Figure 1C). However, no difference was found in the extent of AZIN1 editing among the 3 groups. This may be due to the fact that these biopsies were obtained at various stages in the diabetes and AKI timelines (Supplemental Figure 2, A–G; https://connect.posit.iu.edu/bulk_kidney_bx/). Similarly, the reference biopsies are known to sustain variable degrees of ischemic injury, thus exhibiting some AKI phenotype. In addition, some reference nephrectomy samples were derived from tissues adjacent to renal cell carcinoma, which may also influence AZIN1 A-to-I editing status. Nevertheless, genome-wide examination did reveal significant differences among the 3 groups in A-to-I editing at tens of thousands of sites (Figure 1D; see Methods). Overall, diabetic kidneys showed more extensive genome-wide A-to-I editing than nephrectomies and AKI samples. Focusing on the top differentially edited sites, reference nephrectomy samples had A-to-I editing predominantly within simple repeat regions, whereas AKI and diabetic samples had A-to-I editing within short interspersed nuclear elements (SINEs, such as Alu elements; Figure 1, E and F). The differential editing in transposable elements such as SINEs may have profound implications for disease unfolding (37). No significant A-to-I editing was identified in mitochondrial transcripts for all conditions, implicating no breach of mitochondrial RNA into the cytoplasm (Supplemental Figure 2H) (38).

Changes in AZIN1 A-to-I editing and polyamine metabolism across AKI timelines. To understand the role of AZIN1 editing and polyamine metabolism in the kidney, we next interrogated a well-characterized animal model of endotoxemia (39–41). In this specific model, the kidney goes through precise stages, starting with classic NF-κB–mediated acute inflammation, followed by interferon responses and the integrated stress response, and culminating in metabolic and translation shutdown (Figure 2, A–C). Single-cell RNA-Seq revealed that Azin1 is expressed in all cell types in the kidney (Supplemental Figure 3A). Furthermore, Ribo-Seq analysis (ribosome profiling) showed that Azin1 translation remained nearly constant throughout the course of endotoxemia (Figure 2D). However, we found that Azin1 A-to-I editing status varied significantly over the same time period (Figure 2E and Supplemental Figure 3B). While the extent of A-to-I editing was minimal at baseline and during the early phases of endotoxemia, it significantly increased during the later stages of sepsis in this model. In fact, we observed a consistent and robust increase in Azin1 A-to-I editing at around 16 hours and later time points after endotoxin exposure. We have previously shown that this 16-hour time point corresponds to a critical transition phase between translation shutdown and subsequent tissue recovery (39, 40). Thus, editing of Azin1 at this precise time point may serve as a clock to stage endotoxemia. Furthermore, since AZIN1 A-to-I editing confers a gain of function (23–26), it may also signal a change in polyamine metabolism that aids tissue healing.

Figure 2

Azin1 A-to-I editing status in murine models of AKI. (A) Bulk RNA-Seq analysis on a murine model of endotoxemia (LPS). Gene set coregulation analysis showing sequential upregulation of pathways involved in NF-κB–mediated acute inflammation and in antiviral/interferon responses, followed by the integrated stress response, as indicated by enrichment of the Molecular Signatures Database Hallmark Gene Sets. Each dot corresponds to each animal. The colored lines in the background depict scaled expression of individual genes. ***Pairwise t test adjusted P < 0.05 compared with the preceding time point. (B) Principal component analysis showing overall gene expression changes over the course of endotoxemia in the kidney. (C) Serum creatinine levels at indicated time points after administration of LPS (4 mg/kg in C57BL/6J male mice). (D) Combined Ribo-Seq and RNA-Seq read coverage graphs for Azin1 after LPS challenge in the kidney. Reads are mapped to Ensembl transcript Azin1-201. Gray-colored reads represent RNA-Seq, whereas red/green/blue-colored reads represent codon frames for ribosome-protected fragments in Ribo-Seq. The top right panel confirms the translation of A-to-I–edited Azin1 (reanalysis of GEO GSE120877). (E) Percentage of Azin1 A-to-I editing under indicated conditions (based on stranded total RNA-Seq data). (F–H) Measurements of kidney tissue putrescine and spermidine levels by HPLC under indicated conditions. Representative HPLC chromatograms are also shown. For clarity, the traces are slightly shifted from each other on the x axis elution time. (I and J) Quantitation of RNA-Seq read counts (in counts per million) at the indicated time points. (K) Sanger sequencing showing timeline-specific Azin1 A-to-I editing observed in wild-type mouse kidneys after ischemia/reperfusion injury (IRI; arrowheads). (L) Measurements of kidney tissue spermidine levels by HPLC after IRI. *P < 0.05 vs. 0-hour control samples, 1-way ANOVA followed by Dunnett’s test for multiple treatment comparisons. 0** indicates kidney tissues harvested 20 minutes after ischemia without reperfusion.

Indeed, quantitation of polyamines in kidney tissues revealed a notable increase in spermidine levels during the recovery phase of endotoxemia (Figure 2, F–H). This increase was observed despite a significant decrease in the expression of ornithine decarboxylase 1, the rate-limiting step of polyamine biosynthesis, and an increase in spermidine/spermine N1-acetyltransferase 1, the main polyamine catabolic enzyme (Figure 2, I and J, and Supplemental Figure 3C). These findings suggest that the gain-of-function Azin1 A-to-I editing plays a crucial role in limiting polyamine depletion at the peak of injury and expediting the restoration of tissue polyamine levels during recovery. Single-cell RNA-Seq data implicate that the source of polyamines could be cell type specific, with arginine serving as the substrate for myeloid cells, S3 proximal tubule, and the thick ascending loop of Henle, while proline serves as the substrate for other tubular segments (Supplemental Figure 3, D and E).

Finally, using a murine model of renal ischemia/reperfusion injury, we further extended our analysis of Azin1 A-to-I editing and polyamine levels. We observed overlapping editing kinetics and polyamine trajectories over the course of ischemic kidney injury compared with endotoxemia. However, the exact timelines differed between the 2 models, and the peak of Azin1 A-to-I editing and polyamine rebound were delayed after ischemia/reperfusion injury (Figure 2, K and L, and Supplemental Figure 4, A–F).

AZIN1 A-to-I–uneditable cells are compromised upon nutrient deprivation and mitochondrial inhibition. To elucidate the functional significance of AZIN1 editing, we next designed 2 homozygous clonal cell lines using the CRISPR knockin strategy (Figure 3A and Supplemental Figure 5A). The first cell line contains a constitutively edited AZIN1, resulting in an A-to-I–locked state (AGC serine to GGC glycine). The second cell line is an A-to-I–uneditable variant in which the editing site is disrupted while preserving the codon composition (AGC serine to TCC serine). A-to-I–locked or uneditable state did not lead to changes in the abundance or stability of the AZIN1 protein (Figure 3, B and C). We found that A-to-I–locked cells exhibited accelerated cell growth compared with wild-type and A-to-I–uneditable cells, all of which share an otherwise identical genetic background (HEK293T; Figure 3, D and E, and Supplemental Figure 5, B and C). The level of A-to-I editing in the wild-type cells was minimal (~0%). However, the growth curve of the wild-type cells fell between those of the A-to-I–locked and uneditable cells. This suggests that transient and low-grade AZIN1 editing is operative under normal conditions, contributing to healthy cellular growth. In support of the rapid growth rate observed in the A-to-I–edited state, multiple genes involved in cell growth and differentiation were upregulated in the A-to-I–locked cell line (e.g., BMP2/bone morphogenetic protein 2, IGFBPL1/insulin-like growth factor–binding protein like 1, PGF/placental growth factor; Figure 3F and Supplemental Figure 5E; https://connect.posit.iu.edu/azin1/).

Figure 3

Azin1 A-to-I–uneditable state hinders cell growth and limits glycolytic capacity. (A) Sanger sequencing chromatograms for wild-type (HEK293T; top), AZIN1 A-to-I–locked (middle), and AZIN1 A-to-I–uneditable homozygous cell lines (bottom). Homology-directed repair donor oligonucleotides used for CRISPR knockin are shown in Supplemental Figure 5A. (B) Western blotting for AZIN1 under indicated conditions (~70% confluence). (C) Determination of AZIN1 protein turnover under indicated conditions. Nascent protein synthesis was inhibited with 250 μg/mL cycloheximide. Arrow points to AZIN1. Bands below AZIN1 result from inhibition of proteasomal degradation with MG132. n = 2 biological replicates. (D) Real-time monitoring of cell growth for AZIN1 A-to-I–locked, uneditable, and wild-type cells. n = 3 independent experiments with n = 6 technical replicates for each experiment. *P < 0.05 at all time points for indicated conditions, except the stationary phase between AZIN1 A-to-I–locked and wild-type cells. Representative images are shown in Supplemental Figure 5C. (E) Polyribosome profiling of AZIN1 A-to-I–locked and uneditable cell lines. n = 3 independent experiments. Mean polysome/monosome ratios for A-to-I–locked and uneditable genotypes are 4.1 and 3.6, respectively. (F) Heatmap of the top 20 differentially expressed genes between AZIN1 A-to-I–locked and uneditable cell lines as determined by RNA-Seq (https://connect.posit.iu.edu/azin1/). (G) Cell growth under indicated conditions. Representative images are shown in Supplemental Figure 5D. *P < 0.05, **P < 0.05 after day 1 and day 2.5 for indicated conditions, respectively. (H) Extracellular acidification rates under indicated conditions (Seahorse glycolysis stress test). n = 3 independent experiments with n = 3 technical replicates for each experiment. *P < 0.05 vs. AZIN1-uneditable cells at indicated time points. (I) Identification of AZIN1-interacting molecules by mass spectrometry. Top: Coomassie staining for input, flow-through, and immunoprecipitated unfractionated lysates from IgG control and transfection of FLAG-tagged AZIN1 or AZIN1 without FLA