Genome-wide KAS-Seq mapping of leukocytes in ischemia-reperfusion model reveals IL7R as a potential therapeutic target for ischemia-reperfusion injury

doi:10.21203/rs.3.rs-4968181/v1

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Genome-wide KAS-Seq mapping of leukocytes in ischemia-reperfusion model reveals IL7R as a potential therapeutic target for ischemia-reperfusion injury

https://doi.org/10.21203/rs.3.rs-4968181/v1

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Background

Ischemia‒reperfusion injury (IRI) is one of the leading causes of mortality and disability worldwide. Owing to its complex pathogenesis, there is still a lack of effective therapeutic targets in clinical practice, and exploring the mechanism and targets of IRI is still a major clinical challenge.

Objective(s):

The goal of this study was to explore the genetic alterations that cause leukocytes in peripheral blood after ischemia‒reperfusion to discover new biomarkers and potential therapeutic targets.

Study Design:

KAS-Seq (Kethoxal-assisted single-strand DNA sequencing) was used to obtain gene expression profiles of circulating leukocytes in a porcine ischemia‒reperfusion model at 24, 48, and 72 hours after ischemia‒reperfusion, which integrated genes that exhibited regular changes over time.

Results

In this study, we thoroughly analyzed the dynamic changes in gene expression post-IRI, revealing changes that were significantly enriched in key signaling pathways regulating immune responses and T-cell activation over time. Particularly striking was our identification of the interleukin-7 receptor (IL7R), which plays a crucial molecular role in IRI. Additionally, via database mining technology, we confirmed the close relationship between IL7R and IRI, explored the interaction between interferon-γ (IFNG) and IL7R in T-cell activation, and clarified their joint influence on ischemia‒reperfusion injury.

Conclusions

Utilizing KAS-Seq analysis of leukocytes from peripheral blood, we successfully delineated the temporal patterns of gene expression and alterations in signal transduction pathways in porcine models of ischemia‒reperfusion. Subsequent in-depth analysis identified IL7R as a potential novel therapeutic target for IRI. The pivotal role of this gene in modulating immune responses offers innovative avenues for the development of IRI treatments.

Biological sciences/Genetics

Biological sciences/Genetics/Gene expression

Biological sciences/Genetics/Immunogenetics

KAS-Seq

ischemia‒reperfusion injury

leukocytes

IL7R

target

Ischemia is a condition in which the body's blood supply is restricted, which subsequently results in a lack of oxygen and other nutrients necessary for cell metabolism[1, 2]. Prolonged ischemia can cause irreversible damage to many organs, such as the heart, brain, kidneys, liver, lungs, and intestines [3]. Reperfusion is an immediate intervention to rescue the blood supply, but the damage caused by the sudden restoration of the blood supply to an organ with long-term ischemia, which is called reperfusion injury, is more serious and fatal [4]. Numerous previous studies have indicated that the pathogenesis of IRI is associated with changes related to neuroinflammation, neutrophil regulation, and other various mechanisms[5–8]. However, owing to the complexity of these underlying mechanisms, there is currently no fully effective treatment for IRI. The limitations of current clinical treatments for IRI have prompted the search for more effective treatments. Therefore, further exploration into new mechanisms and targets of IRI is still necessary.

Previous studies have clearly indicated that inflammation plays a central role in IRI, mainly through the activation of key immune cells such as neutrophils and macrophages. During the activation of these cells, a variety of proteases and proinflammatory cytokines are released, which not only aggravate the local inflammatory response but also may further aggravate tissue damage[9–11]. In addition, the release of these inflammatory cytokines can affect apoptosis, pyroptosis, and autophagy[12, 13]. Although previous studies have focused on specific cell types or mechanisms, such as neutrophil activation and macrophage polarization, they have often overlooked the overall impact of white blood cell populations on IRI and changes in global gene expression. Therefore, there is an urgent need to conduct a comprehensive differential gene expression analysis of leukocytes. This approach can not only increase our understanding of the immune response to IRI but also reveal potential new therapeutic targets, providing more comprehensive and precise strategies for clinical treatment.

Second-generation sequencing technology has played a significant role in exploring the molecular mechanisms underlying IRI.

One innovative approach is KAS-Seq (kethoxal-assisted single-strand DNA sequencing) [14], which offers a straightforward, effective, and sensitive method for studying transcriptional regulation and enhancer activity through single-strand DNA sequencing. Instantaneous ssDNA in transcription provides a more direct in situ reading of transcriptional activity than does the RNA itself[14–20]. Hence, the application of KAS-Seq in investigating the molecular mechanism underlying IRI has significant implications for advancing disease research.

In this study, we utilized KAS-Seq technology to analyze leukocyte single-strand DNA (ssDNA) in peripheral blood from a porcine IRI model to explore key dynamic gene changes, underlying mechanisms, and potential targets post-IRI. Specifically, we collected leukocyte samples at 0, 24, 48, and 72 hours post-IRI and used KAS-Seq to map the dynamic changes in ssDNA. In-depth analysis revealed that IL7R is a potential key target in IRI, suggesting a role for IL7R in T-cell activation pathways. Our goal is to pinpoint the core pathways and targets involved in the immune response to IRI, aiming to offer new insights and inform potential therapeutic strategies for its clinical management.

Animal model

In this study, we selected three pigs weighing between 20 to 25 kilograms and approximately 4 months old as experimental subjects for intravenous induced anesthesia. Under strict aseptic procedures, we intubated the pigs and maintained gas anesthesia. Subsequently, we made an accurate skin incision at the femoral artery and controlled the total blood loss to about 400 milliliters through an arterial catheter to simulate ischemia in the body. Then, we carefully inserted a pre-inflated balloon catheter into the artery, blocking blood flow for 30 minutes.Throughout the entire balloon intervention process, we continuously monitored the animals' vital signs, including heart rate and blood pressure. As the balloon was slowly deflated and blood flow in the femoral artery was restored, it marked the beginning of the reperfusion phase. We collected blood samples at key time points of 0, 24, 48, and 72 hours after blood withdrawal and reperfusion. In light of the animals' blood loss and fluctuations in blood pressure, we promptly supplemented physiological saline or lactated Ringer's solution to maintain stable blood volume and blood pressure.Postoperatively, we conducted a meticulous examination of the surgical site to ensure that there were no complications such as bleeding or hematoma. Ultimately, these animals were safely transferred to the recovery area, where they were closely monitored to assess their recovery progress.

Study participants

In this investigation, we harnessed the power of KAS-Seq sequencing technology to chart the genomic landscape of pigs during various stages of ischemia‒reperfusion. The study included a total of 12 samples, with 3 designated as control samples at 0 hours and the remaining 3 representing the 24-, 48-, and 72-hour postischemia reperfusion intervals. The KAS-Seq libraries for all the samples were sequenced via the Illumina NovaSeq 6000 platform. Data processing involved a comparative analysis at each time point—0, 24, 48, and 72 hours—to construct a comprehensive KAS-Seq atlas. Through temporal analysis, we sought to pinpoint the key mechanisms driving IRI over time. By examining these temporal shifts, we identified potential therapeutic targets. Furthermore, we conducted target analysis utilizing relevant single-cell sequencing omics data, corroborating that the identified key targets are indeed closely linked to transcriptional activity and are both genuine and efficacious, as revealed by KAS-Seq comparisons.

Leukocyte gDNA extraction and quantity assessment

Five-milliliter porcine peripheral blood samples were collected in BD Vacutainer®EDTA tubes at 0, 24, 48, and 72 hours postischemia reperfusion, and leukocytes were isolated and extracted (Becton, Dickinson and Company, product No. 367525). All blood samples were sent to the laboratory within 24 hours. The plasma was separated by centrifugation at 1350 × g for 12 minutes. The plasma was then transferred to a 2 ml centrifuge tube (AXYGEN, MCT-200-C) and centrifuged again at 1350 × g for 12 minutes. The upper plasma was carefully removed and retained, while the bottom precipitate containing leukocytes was transferred to a new 2 ml centrifuge tube and immediately cryopreserved in a gradient. Leukocyte DNA was extracted from plasma via the Quick-DNA™ Miniprep Plus Kit (ZYMO, D4069), and DNA concentrations were measured via a Qubit 3.0 fluorometer (Thermo Fisher Scientific, Q33216). The extracted DNA sample was then stored at -80°C for future use. Before constructing the Kas-Seq library, we performed nucleic acid electrophoresis to detect the size of the DNA fragments to ensure that they were suitable for the construction of the library.

KAS-Seq library construction and sequencing

First, the ground tissues were cultured in complete medium containing 5 mM N3-ketoaldehydes at 37°C and 5% CO2 for 10 minutes. After culture, tissue samples were collected, and genomic DNA (gDNA) was extracted from the cells via the PureLink Genomic DNA Mini-kit (Thermo, K182002). One microgram of extracted gDNA was dissolved in 95 µL of DNA elution buffer, 5 µL of 20 mM DBCO-PEG4-biotin (DMSO solution, Sigma, 760749) and 25 mM K3BO3 were added, and the mixture was incubated at 37°C with gentle shaking for 1.5 hours. Next, 5 µL of RNase A (Thermo, 12091039) was added to the reaction system, and the mixture was incubated at 37°C for 5 minutes. Biotinized DNA was purified via a DNA Clean & Concentrator-5 kit (Zymo, D4013). The purified gDNA was dissolved in 100 µL of water, and the DNA fragments were broken to a size of 150–350 bp via a Bioruptor Pico ultrasound apparatus for 30 cycles in the mode of 30 second pulses/30 second intervals. Five percent of the DNA fragments were retained as control samples, and the remaining 95% of the DNA was incubated with 10 µL of prewashed Dynabeads MyOne Streptavidin C1 (Thermo, 65001) at room temperature for 15 minutes to enrich the biotin-labeled DNA. After incubation, the beads were washed and heated in 15 µL of H2O at 95°C for 10 minutes to elute the DNA. Finally, the DNA library was constructed via an Accel-NGS Methyl-Seq DNA library kit (Swift, 30024), and the library was sequenced on an Illumina NovaSeq 6000 sequencing platform in the double-ended 150-bp mode. The goal was to obtain approximately 30 million reads for each library[15].

Mapping and identification of enriched regions via KAS-Seq

A comprehensive quality control analysis of the obtained raw sequencing data was performed via FastQC software (version 0.11.5). This step is essential to ensure the accuracy and reliability of the data and to build a solid foundation for subsequent analysis. We then used Bowtie2 software (version 2.2.9) to align these qualitatively screened raw sequencing data precisely with the reference pig genome.[21] and filtered with SAMtools (version 1.3.1) [22] to preserve unique, nonrepetitive matches. Pair-end reads were extended and converted into BedGraph format via Bedtool2 (version 2.19.1) [23] and then converted to BigWig format for visualization via bedGraphToBig-Wig from Integrated Genomics Viewer. MACS2 (version 2.1.1) was used to identify potential KAS-Seq-enriched regions in each sample [22]. A region of less than 1000 base pairs (bp) occurs in two or more samples and is used as a uniform reference catalog for each sample. To improve the reliability of the data, we specifically excluded those genomic regions that could generate false signals according to the ENCODE project (Encyclopedia of DNA Elements) data. Next, we successfully identified specific regions rich in KAS-Seq signals by comparing the individual peak detection files for each sample with the combined peak detection files. We used the CHIP seeker package to annotate the KAS-Seq-enriched region and used the gene closest to the region for annotation.

Differential analysis and functional analysis

RStudio 3.5.0 (version) of the DESeq2 package (version 3.24.3) [24] was used to identify KAS-Seq difference loci (root filter thresholds: p values < 0.05 and | log 2-fold change| > 0.5). Genes were selected for GO (Gene Ontology) analysis[25], and BP (biological process), CC (cell component) and MF (molecular function) terms were enriched.

Protein‒Protein Interaction (PPI) Network Analysis

We utilized the interactive Gene/Protein Retrieval Search Tool (STRING) database to search for protein‒protein interaction (PPI) data[26]. Interactions with a confidence score greater than 0.7 were selected to ensure the reliability of the network data. The collected data underwent rigorous preprocessing to eliminate redundant and self-interacting entries. Using Cytoscape software[27], we constructed a protein–protein interaction (PPI) network for visualization of the collated data. Each protein is depicted as a node, while their interactions are represented as edges, forming a complex network topology. The first 10 highly connected proteins, referred to as central genes, are considered potentially key components of this network[28].

References to the GEO database

Three ischemia‒reperfusion injury-related microarray datasets, GSE9634, GSE72646, and GSE23160, were downloaded from the GEO Expression Synthesis database[29].

Overview of the Research Process Flow

Blood is a type of easy-to-obtain biological sample that provides an ideal method for studying the effects of IRI on the immune system. This study aimed to track immune response changes at various time points post-IRI. We collected peripheral blood samples from four groups of model pigs at 0, 24, 48, and 72 hours after IRI, with the 0-hour sample serving as the control. After centrifuging the blood to isolate leukocytes, we constructed KAS-Seq libraries from the leukocyte single-stranded DNA and performed in-depth sequencing analysis via high-throughput techniques. We used visualization and machine learning algorithms to explore the underlying mechanisms and potential biomarkers of IRI, aiming to provide new insights and strategies for clinical diagnosis and treatment. (Fig. 1A).

KAS-Seq global signal distribution expression profile

To explore the dynamic changes in KAS-Seq signals induced by IRI over time, KAS-Seq sequencing was performed on leukocyte ssDNA in pig models at 0, 24, 48, and 72 hours after IRI. Our observations show that the number of ischemic peaks in different genomic regions varies significantly over time. In particular, significant spikes were observed 48 hours after IRI, and the KAS-Seq signals began to recover again 72 hours after IRI. (Fig. 2A). Visual analysis revealed that KAS-Seq differences were predominantly distributed in gene functional regions, such as transcriptional start sites, introns, and distal regions (Fig. 2B), with notable disparities in genome region distribution characteristics observed across the time points (Fig. 2C). Furthermore, distinct variations were identified among gene functional regions, particularly in the transcription start sites and introns among the groups at each time point (Fig. 2D-2E). The subsequent principal component analysis demonstrated significant aggregation and differentiation among the groups at 0, 24, 48, and 72 hours (Fig. 2F). Therefore, KAS-Seq markers can effectively differentiate between these time points after ischemia‒reperfusion and hold considerable significance for distinguishing among these four groups.

GO signaling pathway and functional enrichment analysis

We conducted a differential gene expression analysis, using criteria for significance of p < 0.05 and |log2FoldChange| ≥ 0.5, and identified 1651 differentially expressed genes (DEGs), including 690 upregulated and 941 downregulated genes, in the 24-hour ischemia‒reperfusion samples compared with the healthy controls (Fig. 3A, Supplementary Table 1). Compared with those in the 24-hour sample, there were 1578 DEGs in the 48-hour sample, including 858 upregulated and 720 downregulated genes (Fig. 3B, Supplementary Table 2). Similarly, there were 1577 DEGs in the 72-hour sample compared with the 48-hour sample, including 802 upregulated and 775 downregulated genes (Fig. 3C, Supplementary Table 3). We conducted unsupervised hierarchical clustering analysis on the top 100 KAS-seq differential sites, which allowed for preliminary differentiation of samples at ischemia‒reperfusion time points of 0, 24, 48, and 72 hours (Figure S1A). We observed that the signaling pathways enriched with the DEGs were closely related to damage development. For example, multiple pathways are involved in regulating the homeostasis of calcium ions within 24 h, which is consistent with the findings that calcium homeostasis is closely related to IRI[30]. Subsequently, signaling pathways related to the immune response became apparent 48 hours after ischemia‒reperfusion, and other pathways related to myocardial tissue development and hypoxia level regulation began to appear 72 hours after ischemia‒reperfusion (Figure S1B-1D). Studies have shown that IRI can lead to changes in the immune system and that changes in various inflammatory factors can lead to multiple types of organ damage, including myocardial ischemia[31, 32]. To identify biomarkers of ischemia‒reperfusion progression, we conducted a comparative analysis of different stages of detection, including mild ischemia‒reperfusion at 24, 48, and 72 hours. Mfuzz was utilized to cluster the identified biomarkers into four discrete clusters according to four time points of ischemia‒reperfusion. Cluster 2 tended to be upregulated within 48 hours, whereas Cluster 1, Cluster 3, and Cluster 4 tended to be downregulated within 48 hours (Fig. 3D) (Supplementary Table 5). Concurrently, we performed pathway enrichment analysis on clusters exhibiting similar time trends. It was observed that some typical pathways were functionally rich (Fig. 3E-3F). For example, in Cluster 2, the major enriched gene signaling pathways were involved in nervous system regulation and development, the immune response, and leukocyte regulation-related functions. In contrast, in Cluster 1, Cluster 3, and Cluster 4, the related genes were enriched primarily in epithelial cell proliferation, leukocytes, T-cell regulation, and other immune-related pathways. Previous studies have reported that IRI is associated with nervous system regulation, immune system regulation, and functions[32, 33]. Therefore, the results suggest that the gene-related changes identified via KAS-Seq may be significantly related to the molecular mechanism and clinical symptoms of IRI.

Analysis of Cluster 2 and immune-related functions and temporal cell landscape changes in Cluster 2

Since the overall signal level was observed to be highest at 48 hours, we focused on biomarkers in cluster 2 that showed regulatory trends similar to those of disease progression (Supplementary Table 4). Several immune-related biological functions were significantly enriched in the functional enrichment analysis of ischemia‒reperfusion in Cluster 2. Therefore, we identified common immune-related genes. A total of 5276 immune-related genes were retrieved from the database, Cluster 2 intersected with immune-related genes, and 178 differential genes were identified at the intersection (Fig. 4A and Supplementary Table 6). We used the STRING database to generate a PPI network of 178 intersecting genes. As shown in Fig. 4B, each node represents a cross-gene-encoded protein, each edge represents the correlation confidence between the two targets, and the thickness of the edge indicates the strength of data support. The P value of PPI enrichment was less than 1.0e-16, indicating significant protein interactions in the PPI network. To further clarify the intrinsic biological differences in 178 immune-related genomes, we used the CIBERSORT algorithm to analyze the composition of 178 immune cells with a major distribution of genes and found that their proportion was significantly increased, mainly in activated memory CD4 T cells (Fig. 4C). In addition, we hybridized genes in Cluster 1, Cluster 3, and Cluster 4 and crossed them with immune-related genes to identify 212 differential genes (Figure S2A and Supplementary Table 7). We used the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database to generate a protein–protein interaction (PPI) network of 212 target genes (Figure S2B). Similarly, we clarified the composition of immune cells with 212 immune-related genes and found that their proportion of memory CD4 T cells decreased significantly (Figure S2C), which was consistent with the results of Cluster 2 (Fig. 4C). These results suggest that IRI may be associated with T-cell activity through the regulation of immune-related genes.

Identification of hub genes and potential targets for IRI

We further analyzed the hub genes among the 178 immune-related genes identified in Cluster 2 via the Cytoscape plugin CytoHubba (Fig. 4A). According to the maximum cluster centrality (MCC) algorithm, the top 10 Hub genes were identified in the immune-related dataset of Cluster 2 (Fig. 5A). A total of 10 genes in the dataset were identified as hub genes (IFNG, IL7R, TLR4, IL2RA, CCRL2, CD40, CCR7, CCL20, CD5, and CD38). In the PPI network analysis, we identified two hub genes: interferon-gamma (IFNG) and the interleukin-7 receptor (IL7R). The expression levels of these genes were consistent with the overall signal temporal trend, being upregulated from 0 to 48 hours and downregulated from 48 to 72 hours after ischemia‒reperfusion. (Figs. 5B-5C and 2A). Many studies have reported a close association between IFNG and the occurrence and progression of IRI, confirming our results from KAS-Seq sequencing. [31]. Therefore, in the subsequent phase of our study, we concentrated on exploring the correlation between IL7R and IRI. Initially, we confirmed the expression of IL7R across three GEO datasets (GSE9634, GSE72646, and GSE23160) (Fig. 5D-F). In each dataset, the IL7R expression level was increased in the IRI group, which aligns with our findings (Fig. 5D-F). Additionally, reports have indicated a correlation between IFNG and IL7R in the context of other diseases[34–36]. Therefore, to explore the role of IFNG and IL7R in IRI, we used the STRING database to study the correlation between IFNG and IL7R target genes in IRI. GO signaling pathway enrichment was utilized to analyze potentially relevant pathways in IRI (Fig. 5G). Through pathway analysis of these target genes, we identified a series of typical biological pathways responsible for regulating and activating leukocytes and the immune system, as well as pathways associated with T-cell activation, which is consistent with the high expression of T-cell specificity in our previous analysis (Fig. 4C). IRI is reported to be closely related to T-cell activation and immune system regulation[37, 38]. The results of this study suggest that IL7R may be a key target of IRI and may coregulate T cells with IFNG to affect IRI. This insight offers a novel perspective on the intricate immune regulatory mechanisms involved in IRI and could guide the development of effective clinical treatment strategies.

IRI is a complex pathological process involving multiple organs and mechanisms. Although our understanding of IRI is continuously deepening, there is still a lack of effective treatment options clinically. Future research needs to further explore the specific mechanisms of IRI and search for new therapeutic targets and strategies[2]. Despite many studies indicating that the pathogenesis of IRI is closely related to immunity, such as T-cell activation, neutrophil burst, and macrophage polarization[39–41], the role of immune responses in IRI has not been fully elucidated. To further investigate the correlation between immunity and IRI, we used Kas-Seq sequencing technology to analyze the single-stranded DNA of leukocytes in the blood to explore the immune-related molecular mechanisms of immune cells in IRI. In addition, research has shown that the pig model has important scientific importance in the study of IRI because of its high degree of homology[42]. Therefore, in our study, we established a pig model of IRI and collected blood samples at 0, 24, 48, and 72 hours after ischemia‒reperfusion, centrifuged them to separate leukocytes for Kas-Seq sequencing, and searched for new therapeutic targets related to immunity.

We first analyzed the overall distribution of Kas-Seq signals at different time points after the start of ischemia‒reperfusion and found that the signals increased at 0 hours, 24 hours, and 48 hours and began to decrease at 72 hours, as shown in Fig. 1. We subsequently performed enrichment analysis on the DEGs whose expression changed regularly at the four time points of ischemia‒reperfusion. As shown in Fig. 2, the DEGs included upregulated (Cluster 2) and downregulated (Cluster 1, Cluster 3, Cluster 4) genes, most of which are closely related to leukocyte regulation pathways. The literature reports that T-cell activation and immune system regulation are related to the IRI process[43–50], thus confirming the obvious correlation between our data and this disease.

To further explore the correlation between IRI and immune system regulation, we screened 178 immune-related genes from the upregulated genes and performed immune infiltration analysis on the 178 genes, which revealed that 178 genes were significantly enriched in activated memory CD4 T cells. Studies have shown that within a certain period of ischemia‒reperfusion, the function of CD4 + T cells in organs such as the heart and liver changes. For example, in studies of myocardial IRI, CD4 + T cells participate in the process of myocardial ischemic injury through the HMGB1-TLR4 signaling pathway[51]. In allo-orthotopic liver transplantation, depleting anti-CD4 antibodies can reduce neutrophil/macrophage infiltration and proinflammatory gene expression caused by IRI [52], indicating that CD4 + T-cell activation can affect IRI.

We subsequently screened for hub genes from the 178 genes and found that IL7R and IFNG are key target genes. In mouse kidney IRI, IFNG can regulate the migration of neutrophils together with IL17 [31]; thus, we focused on IL7R as a new target for IRI, and in an external dataset, we also confirmed the differential expression of IL7R. These findings suggest that IL7R plays a key role in IRI.

To further study the correlation between IL7R and IFNG and IRI, we analyzed all target genes related to IL7R and IFNG. Interestingly, we found that the genes related to IL7R and IFNG are cross-enriched in immune regulation-related signaling pathways, including leukocyte activation, migration, and T-cell activation signaling pathways. Given that previous analyses revealed that global immune-related genes are highly expressed specifically in T cells, we speculate that after IRI, IL7R upregulation regulates the upregulation of IFNG, thereby affecting the activity of T cells. These findings suggest that IL7R is a potential new target for IRI treatment.

Clinical Implications

In summary, our findings clearly indicate that the release of single-stranded DNA (ssDNA) markers by leukocytes in peripheral blood can serve as powerful epigenetic biomarkers for revealing the mechanisms of IRI. The data from our study revealed a close association between IRI and the activation of T cells, particularly CD4 + T cells. Notably, IL7R, identified as a key target in this study, plays a significant role in regulating T-cell activation in conjunction with the upregulation of IFNG, which has a marked effect on the progression of IRI. The discoveries made in this study not only deepen our understanding of the pathophysiological mechanisms of IRI but also provide new perspectives and potential therapeutic leads for future clinical treatment.

Strengths and Limitations

In this study, the KAS-Seq technique was used to detect the single-strand DNA of leukocytes in peripheral blood to evaluate its role in IRI, especially its close relationship with immune regulation and the regulation of T-cell activation. In particular, these findings highlight IL7R as a potential key target of IRI, where the upregulation of IL7R and IFNG may play a role in regulating T-cell activation and have an important impact on the progression of IRI. These results not only provide a new perspective on clinical treatment strategies for IRI but also point to possible future therapeutic targets.

Nevertheless, we are aware of the limitations of this study. First, the small sample size limits our ability to explore the underlying mechanisms in depth, so we recommend expanding the sample size in future studies to validate our findings. Second, although we validated the mechanism exploration results to a certain extent through external databases, these results still need to be further validated through experimental methods.

In this study, we conducted KAS-Seq sequencing analysis of leukocytes in porcine blood after ischemia‒reperfusion to investigate the regular changes in gene expression from 0 h to 72 h after ischemia‒reperfusion. Our results suggest that analysis of leukocytes via the KAS-Seq technique can capture not only the dynamics of gene expression at different time points after IRI but also the underlying pathological mechanisms closely related to T-cell activation. Importantly, we identified IL7R as a key molecular target for IRI. The upregulation of IL7R and its coregulation with IFNG during T-cell activation may play crucial roles in IRI. This discovery provides a new perspective and treatment strategy for the future clinical treatment of IRI and is expected to lead to more effective treatment plans for patients.

Abbreviation	Full name
KAS-Seq	Kethoxal-assisted single-stranded DNA sequencing
IRI	Ischemia-Reperfusion Injury
ssDNA	Single-Stranded DNA
DEGs	Differentially expressed genes
GO	Gene ontology
PPI	Protein-Protein Interaction
STRING	Search Tool for the Retrieval of Interacting Genes
IL7R	Interleukin-7 Receptor
IFNG	Interferon-gamma

Ethics statement

The use of experimental animals in this study has been approved by the Ethics Committee of the General Hospital of the People's Liberation Army of China (Approval Number: IACUC-2023-0017). Throughout the research process, we have strictly adhered to all relevant ethical guidelines and operational standards set by the center to ensure the scientific and ethical integrity of the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no conflict of interests.

Authors’ contributions

LZ conceived the study and designed the experiment. LZ conducted the experiment with the help of HX and XH-L. LZ uses MDK and HY-C to analyze the data. FY-L provided the animals, FY-L ZH-W collected the blood, and YC-D collected the sample data. LZ wrote the manuscript with input and comments from HY-C, all authors read and approved the final manuscript, SY-F participated in the study design and data interpretation, and LC and JL participated in the study design, data interpretation and paper writing.

Funding

No funding was received for this research.

Acknowledgments

We would like to acknowledge the essential contributions of all staff and students who participated in this work.

Statement on ARRIVE Guidelines Compliance

The in vivo experiments presented in this study have been executed with meticulous adherence to the ARRIVE guidelines. We affirm that our methodology and reporting are in strict compliance with these standards, ensuring a transparent and rigorous approach to the design, conduct, and communication of our research. This commitment extends to a thorough delineation of animal care protocols, procedural specifics of the experiments, and a robust statistical framework, all of which are articulated to ensure the reproducibility and scientific integrity of our work.

Data availability

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive of the BIG Data Center at the Beijing Institute of Genomics, Chinese Academy of Science, under accession number CRA018510 (accessible at https://ngdc.cncb.ac.cn). Code is available from the corresponding author on reason able request.

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Genome-wide KAS-Seq mapping of leukocytes in ischemia-reperfusion model reveals IL7R as a potential therapeutic target for ischemia-reperfusion injury

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Abstract

Background

Objective(s):

Study Design:

Results

Conclusions

Figures

Introduction

Materials and methods

Animal model

Study participants

Leukocyte gDNA extraction and quantity assessment

KAS-Seq library construction and sequencing

Mapping and identification of enriched regions via KAS-Seq

Differential analysis and functional analysis

Protein‒Protein Interaction (PPI) Network Analysis

Results

Overview of the Research Process Flow

KAS-Seq global signal distribution expression profile

GO signaling pathway and functional enrichment analysis

Analysis of Cluster 2 and immune-related functions and temporal cell landscape changes in Cluster 2

Identification of hub genes and potential targets for IRI

Discussion

Clinical Implications

Strengths and Limitations

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1