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

Genome-wide KAS-Seq mapping of leukocytes in ischemia-reperfusion model reveals IL7R as a potential therapeutic target for ischemia-reperfusion injury | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genome-wide KAS-Seq mapping of leukocytes in ischemia-reperfusion model reveals IL7R as a potential therapeutic target for ischemia-reperfusion injury Lei Zhang, Maimaitiyasen Duolikun, Hangyu Chen, Zihao Wang, Xuehui Li, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4968181/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract 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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction 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. Materials and methods 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 0.5). 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 ]. Results 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. 1 A). 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. 2 A). Visual analysis revealed that KAS-Seq differences were predominantly distributed in gene functional regions, such as transcriptional start sites, introns, and distal regions (Fig. 2 B), with notable disparities in genome region distribution characteristics observed across the time points (Fig. 2 C). 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. 2 D- 2 E). The subsequent principal component analysis demonstrated significant aggregation and differentiation among the groups at 0, 24, 48, and 72 hours (Fig. 2 F). 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. 3 A, 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. 3 B, 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. 3 C, 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 S1 A). 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 S1 B-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. 3 D) (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. 3 E- 3 F). 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. 4 A and Supplementary Table 6). We used the STRING database to generate a PPI network of 178 intersecting genes. As shown in Fig. 4 B, 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. 4 C). 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. 4 C). 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. 4 A). According to the maximum cluster centrality (MCC) algorithm, the top 10 Hub genes were identified in the immune-related dataset of Cluster 2 (Fig. 5 A). 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. 5 B- 5 C and 2 A). 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. 5 D-F). In each dataset, the IL7R expression level was increased in the IRI group, which aligns with our findings (Fig. 5 D-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. 5 G). 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. 4 C). 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. Discussion 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. Conclusions 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. Abbreviations 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 Declarations 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. References Randhawa, P. K., Bali, A. & Jaggi, A. S. RIPC for multiorgan salvage in clinical settings: evolution of concept, evidences and mechanisms. Eur. J. Pharmacol. 746 , 317–332 (2015). Peralta, C., Jiménez-Castro, M. B. & Gracia-Sancho, J. Hepatic ischemia and reperfusion injury: effects on the liver sinusoidal milieu. J. Hepatol. 59 (5), 1094–1106 (2013). Iliodromitis, E. K., Andreadou, I., Iliodromitis, K. & Dagres, N. Ischemic and postischemic conditioning of the myocardium in clinical practice: challenges, expectations and obstacles. Cardiology . 129 (2), 117–125 (2014). Zerva, K. et al. Effect of thrombolytic therapy in plaque inflammation and structural remodeling in acute myocardial infarction patients. Hellenic J. Cardiol. 58 (6), 446–449 (2017). Kalogeris, T., Baines, C. P., Krenz, M. & Korthuis, R. J. Cell biology of ischemia/reperfusion injury. Int. Rev. Cell. Mol. Biol. 298 , 229–317 (2012). Jiang, L., Li, L., Shen, J., Qi, Z. & Guo, L. Effect of dexmedetomidine on lung ischemia–reperfusion injury. Mol. Med. Rep. 9 (2), 419–426 (2014). Wu, M. Y. et al. Current Mechanistic Concepts in Ischemia and Reperfusion Injury. Cell. Physiol. Biochem. 46 (4), 1650–1667 (2018). Carniglia, L. et al. Neuropeptides and Microglial Activation in Inflammation, Pain, and Neurodegenerative Diseases. Mediators Inflamm. 2017 , 5048616 (2017). Chi, W. et al. Caspase-8 promotes NLRP1/NLRP3 inflammasome activation and IL-1β production in acute glaucoma. Proc. Natl. Acad. Sci. U S A . 111 (30), 11181–11186 (2014). Qi, Y. et al. Retinal ischemia/reperfusion injury is mediated by Toll-like receptor 4 activation of NLRP3 inflammasomes. Invest. Ophthalmol. Vis. Sci. 55 (9), 5466–5475 (2014). Abcouwer, S. F. et al. Inflammatory resolution and vascular barrier restoration after retinal ischemia reperfusion injury. J. Neuroinflammation . 18 (1), 186 (2021). Du, H. Y. et al. Ligustrazine induces viability, suppresses apoptosis and autophagy of retinal ganglion cells with ischemia/reperfusion injury through the PI3K/Akt/mTOR signaling pathway. Bioengineered . 12 (1), 507–515 (2021). Wan, P. et al. LncRNA H19 initiates microglial pyroptosis and neuronal death in retinal ischemia/reperfusion injury. Cell. Death Differ. 27 (1), 176–191 (2020). Wu, T., Lyu, R., You, Q. & He, C. Kethoxal-assisted single-stranded DNA sequencing captures global transcription dynamics and enhancer activity in situ. Nat. Methods . 17 (5), 515–523 (2020). Lyu, R. et al. KAS-seq: genome-wide sequencing of single-stranded DNA by N(3)-kethoxal-assisted labeling. Nat. Protoc. 17 (2), 402–420 (2022). Chen, F. X., Smith, E. R. & Shilatifard, A. Born to run: control of transcription elongation by RNA polymerase II. Nat. Rev. Mol. Cell. Biol. 19 (7), 464–478 (2018). Burgers, P. M. J. & Kunkel, T. A. Eukaryotic DNA Replication Fork. Annu. Rev. Biochem. 86 , 417–438 (2017). Hustedt, N. & Durocher, D. The control of DNA repair by the cell cycle. Nat. Cell. Biol. 19 (1), 1–9 (2016). Li, X. & Heyer, W. D. Homologous recombination in DNA repair and DNA damage tolerance. Cell. Res. 18 (1), 99–113 (2008). Ginno, P. A., Lott, P. L., Christensen, H. C., Korf, I. & Chédin, F. R-loop formation is a distinctive characteristic of unmethylated human CpG island promoters. Mol. Cell. 45 (6), 814–825 (2012). Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods . 9 (4), 357–359 (2012). Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9 (9), R137 (2008). Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics . 26 (6), 841–842 (2010). McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40 (10), 4288–4297 (2012). Mahajan, P., Fiehn, O. & Barupal, D. IDSL.GOA: Gene Ontology Analysis for Interpreting Metabolomic datasets. bioRxiv (2024). Szklarczyk, D. et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39 (Database issue), D561–568 (2011). Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13 (11), 2498–2504 (2003). Chin, C. H. et al. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 8 (Suppl 4(Suppl 4), S11 (2014). Barrett, T. et al. NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res. 41 (Database issue), D991–995 (2013). Gong, Y. et al. Mitochondria-associated membrane-modulated Ca(2+) transfer: A potential treatment target in cardiac ischemia reperfusion injury and heart failure. Life Sci. 278 , 119511 (2021). Li, L. et al. IL-17 produced by neutrophils regulates IFN-gamma-mediated neutrophil migration in mouse kidney ischemia-reperfusion injury. J. Clin. Invest. 120 (1), 331–342 (2010). Lu, L. et al. Innate Immune Regulations and Liver Ischemia-Reperfusion Injury. Transplantation . 100 (12), 2601–2610 (2016). Bavarsad, K., Barreto, G. E., Hadjzadeh, M. A. & Sahebkar, A. Protective Effects of Curcumin Against Ischemia-Reperfusion Injury in the Nervous System. Mol. Neurobiol. 56 (2), 1391–1404 (2019). Shmarov, V. A. et al. Direct effects of interleukin-7 on the function of human T cells in vitro. Eur. Cytokine Netw. 27 (4), 102–107 (2016). Pangault, C. et al. Integrative Analysis of Cell Crosstalk within Follicular Lymphoma Cell Niche: Towards a Definition of the FL Supportive Synapse. Cancers (Basel) 12 (10). (2020). Karwot, R. et al. Protective role of nuclear factor of activated T cells 2 in CD8 + long-lived memory T cells in an allergy model. J. Allergy Clin. Immunol. 121 (4), 992–999e996 (2008). Rao, J., Lu, L. & Zhai, Y. T cells in organ ischemia reperfusion injury. Curr. Opin. Organ. Transpl. 19 (2), 115–120 (2014). Lie, M. L. et al. Lung T lymphocyte trafficking and activation during ischemic acute kidney injury. J. Immunol. 189 (6), 2843–2851 (2012). Jin, Z. et al. Identification of distinct immune infiltration and potential biomarkers in patients with liver ischemia-reperfusion injury. Life Sci. 327 , 121726 (2023). Tang, Q., Dong, C. & Sun, Q. Immune response associated with ischemia and reperfusion injury during organ transplantation. Inflamm. Res. 71 (12), 1463–1476 (2022). Kezić, A., Stajic, N. & Thaiss, F. Innate Immune Response in Kidney Ischemia/Reperfusion Injury: Potential Target for Therapy. J. Immunol. Res. 2017 , 6305439 (2017). Baehr, A., Klymiuk, N. & Kupatt, C. Evaluating Novel Targets of Ischemia Reperfusion Injury in Pig Models. Int. J. Mol. Sci. 20 (19). (2019). Zhai, Y., Busuttil, R. W. & Kupiec-Weglinski, J. W. Liver ischemia and reperfusion injury: new insights into mechanisms of innate-adaptive immune-mediated tissue inflammation. Am. J. Transpl. 11 (8), 1563–1569 (2011). Ni, M. et al. T-Cell Immunoglobulin and Mucin Domain-Containing Protein-4 Is Critical for Kupffer Cell Homeostatic Function in the Activation and Resolution of Liver Ischemia Reperfusion Injury. Hepatology . 74 (4), 2118–2132 (2021). Stabile, E. et al. Impaired arteriogenic response to acute hindlimb ischemia in CD4-knockout mice. Circulation . 108 (2), 205–210 (2003). Brait, V. H., Arumugam, T. V., Drummond, G. R. & Sobey, C. G. Importance of T lymphocytes in brain injury, immunodeficiency, and recovery after cerebral ischemia. J. Cereb. Blood Flow. Metab. 32 (4), 598–611 (2012). Rabb, H. et al. Pathophysiological role of T lymphocytes in renal ischemia-reperfusion injury in mice. Am. J. Physiol. Ren. Physiol. 279 (3), F525–531 (2000). Caldwell, C. C., Tschoep, J. & Lentsch, A. B. Lymphocyte function during hepatic ischemia/reperfusion injury. J. Leukoc. Biol. 82 (3), 457–464 (2007). Yilmaz, G., Arumugam, T. V., Stokes, K. Y. & Granger, D. N. Role of T lymphocytes and interferon-gamma in ischemic stroke. Circulation . 113 (17), 2105–2112 (2006). Burne, M. J. et al. Identification of the CD4(+) T cell as a major pathogenic factor in ischemic acute renal failure. J. Clin. Invest. 108 (9), 1283–1290 (2001). Ren, Y. et al. Regulatory Effects of Toll-like Receptor 4 Knockout on CD4(+) and CD8(+) T Lymphocytes and Interleukin-17 During Myocardial Ischemia. Ann. Clin. Lab. Sci. 50 (6), 761–768 (2020). Kageyama, S. et al. Ischemia-reperfusion Injury in Allogeneic Liver Transplantation: A Role of CD4 T Cells in Early Allograft Injury. Transplantation . 105 (9), 1989–1997 (2021). Additional Declarations No competing interests reported. Supplementary Files Supplementary.zip Cite Share Download PDF Status: Published Journal Publication published 20 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 20 Dec, 2024 Reviews received at journal 20 Dec, 2024 Reviews received at journal 19 Dec, 2024 Reviewers agreed at journal 14 Dec, 2024 Reviewers agreed at journal 30 Nov, 2024 Reviewers agreed at journal 13 Nov, 2024 Reviewers invited by journal 13 Nov, 2024 Editor assigned by journal 13 Nov, 2024 Editor invited by journal 05 Sep, 2024 Submission checks completed at journal 03 Sep, 2024 First submitted to journal 24 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4968181","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":361255882,"identity":"61624b0f-815e-4d3c-b5ab-de40b7bcc1db","order_by":0,"name":"Lei Zhang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""},{"id":361255883,"identity":"b932df94-d8a0-4a30-8e28-051ffcb7650f","order_by":1,"name":"Maimaitiyasen Duolikun","email":"","orcid":"","institution":"Hainan University","correspondingAuthor":false,"prefix":"","firstName":"Maimaitiyasen","middleName":"","lastName":"Duolikun","suffix":""},{"id":361255884,"identity":"043a59da-54b3-4b21-bc51-2d6ba3a23ec1","order_by":2,"name":"Hangyu Chen","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hangyu","middleName":"","lastName":"Chen","suffix":""},{"id":361255885,"identity":"46e340e5-f384-4471-ba8d-44c61ec23230","order_by":3,"name":"Zihao Wang","email":"","orcid":"","institution":"National Engineering Research Center for the Emergency Drug, Beijing Institute of Pharmacology and Toxicology","correspondingAuthor":false,"prefix":"","firstName":"Zihao","middleName":"","lastName":"Wang","suffix":""},{"id":361255886,"identity":"d97b39d6-1cb5-4868-9d18-4c40c6ae601b","order_by":4,"name":"Xuehui Li","email":"","orcid":"","institution":"Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuehui","middleName":"","lastName":"Li","suffix":""},{"id":361255887,"identity":"b774cbe4-124a-48e0-b681-af55fd1f961a","order_by":5,"name":"Hong Xiao","email":"","orcid":"","institution":"Hainan University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Xiao","suffix":""},{"id":361255888,"identity":"e7125503-a725-4fa2-a26e-bd72136bd421","order_by":6,"name":"Yuchao Dong","email":"","orcid":"","institution":"National Engineering Research Center for the Emergency Drug, Beijing Institute of Pharmacology and Toxicology","correspondingAuthor":false,"prefix":"","firstName":"Yuchao","middleName":"","lastName":"Dong","suffix":""},{"id":361255889,"identity":"5264455d-69bc-40ed-b46c-5fe03028f990","order_by":7,"name":"Haoyu Chen","email":"","orcid":"","institution":"Hebei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Haoyu","middleName":"","lastName":"Chen","suffix":""},{"id":361255890,"identity":"a553bd93-eaf1-4991-8d5c-71d509657fa3","order_by":8,"name":"Fengyong Liu","email":"","orcid":"","institution":"The Fifth Medical Center of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fengyong","middleName":"","lastName":"Liu","suffix":""},{"id":361255891,"identity":"f3a6b1d6-915b-4cf4-9a99-a4a4b5f65e76","order_by":9,"name":"Shiyong Fan","email":"","orcid":"","institution":"National Engineering Research Center for the Emergency Drug, Beijing Institute of Pharmacology and Toxicology","correspondingAuthor":false,"prefix":"","firstName":"Shiyong","middleName":"","lastName":"Fan","suffix":""},{"id":361255892,"identity":"9683ada5-3c43-4d9d-a387-b35455ece4e2","order_by":10,"name":"Jian Lin","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Lin","suffix":""},{"id":361255893,"identity":"fec8a1ba-d287-4f4d-bfb2-393a2a433926","order_by":11,"name":"Long Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACPgYGNiBlAcTMBw58+EGEFjaIFgkQM/HgzB7StPAYH+ZgI0YL++FnDz7ukJAz51/z4TADD4M8v9gBAlp40swNZ56RMLac8XbD4QILBsOZsxMIaJHgYZPmbZNI3HDj7IbDM3gYEgxuE6PlL1jLmQeHediI1cII0nK+h4FILTxpZpK9bRLGBjfYDICBLEHYL/zAEJP42WYjZ3D+8OMPH37YyPNLE9CCABJglRLEKgfbd4AU1aNgFIyCUTCSAAC+8T/rogyyTgAAAABJRU5ErkJggg==","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":true,"prefix":"","firstName":"Long","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-08-24 08:47:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4968181/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4968181/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-90457-7","type":"published","date":"2025-02-20T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65912486,"identity":"abba53f0-56bc-4902-8bc1-4390f6635422","added_by":"auto","created_at":"2024-10-04 09:56:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":112545,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic overview of the study.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e porcine femoral artery ischemia-reperfusionwas constructed and blood leukocytes were collected at different times after reperfusion. The leukocytes were subjected to KAS-Seq to dynamically map ssDNA alterations. In-depth data analysis was performed to identify key genes and pathways that may contribute to IRI.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4968181/v1/45d98f511880b97c827a229f.png"},{"id":65912492,"identity":"29213345-35c8-4d04-8d73-c56c22c9c564","added_by":"auto","created_at":"2024-10-04 09:56:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":136383,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal and dynamic ssDNA distribution characteristics of leukocytes from KAS-Seq in ischemia-reperfusion. A\u003c/strong\u003e, Genome-wide ssDNA distribution from samples collected 0, 24, 48, and 72 hours after ischemia-reperfusion(0 hours in green, 24 hours in black,48 hours in yellow, 72 hours in pink);\u003cstrong\u003eB\u003c/strong\u003e, Differences in the distribution of gene function at 0 hours, 24 h, 48 h and 72 h(0 hours in green, 24 hours in black, 48 hours in yellow, 72 hours in pink);\u003cstrong\u003eC\u003c/strong\u003e, 5hmC signal profile of ischemia-reperfusion at 0, 24, 48, and 72 hours. \u003cstrong\u003eD, E\u003c/strong\u003e peak number of promoter and intron regions at 0 hours,24 hours,48 hours, and 72 hours after ischemia-reperfusion D, is promoter, is intron regions. \u003cstrong\u003eF\u003c/strong\u003e, PCA (principal component analysis) distinguished injured white blood cell samples(0 hours in green, 24 hours in black,48 hours in yellow, 72 hours in orange) after ischemia-reperfusion 0, 24, 48, and 72 hours.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4968181/v1/fa71bbe8762df45a6492c607.png"},{"id":65912489,"identity":"e8985317-be34-46b4-b607-ca5d84c9f91c","added_by":"auto","created_at":"2024-10-04 09:56:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116021,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO enrichment analysis and function exploration of biomarkers.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e Volcano map (0 hours vs. 24 hours). DEGs significantly changed (p-value \u0026lt; 0.05 \u0026amp; |log2FoldChange| ≥ 0.5) are marked in red (up) or green (down). The black dots represent the DEGs with no difference. \u003cstrong\u003eB\u003c/strong\u003e, Volcano map (24 hours vs. 48 hours). DEGs significantly changed (p-value \u0026lt; 0.05 \u0026amp; |log2FoldChange| ≥ 0.5) are marked in red (up) or green (down). The black dots represent the DEGs with no difference. \u003cstrong\u003eC\u003c/strong\u003e, Volcano map (48 hours vs. 72 hours). DEGs significantly changed (p-value \u0026lt; 0.05 \u0026amp; |log2FoldChange| ≥ 0.5) are marked in red (up) or green (down). The black dots represent the DEGs with no difference. \u003cstrong\u003eD\u003c/strong\u003e, The four groups identified by Mfuzz analysis showed regulatory trends in ischemia-reperfusion time progression. \u003cstrong\u003eE\u003c/strong\u003e,Differential genes in Cluster 2 were analyzed for GO enrichment pathway. F, Differential genes in Cluster1, Cluster3, and Cluster 4 were analyzed for signaling pathway enrichment.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4968181/v1/510c57427f8177d82ab5dabe.png"},{"id":65912490,"identity":"881da4e8-5188-401b-addc-710fe062a6c6","added_by":"auto","created_at":"2024-10-04 09:56:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":126572,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune infiltration analysis was performed for immune-related genes. A\u003c/strong\u003e, Venn diagram shows the intersection between immune-related genes and differentially expressed genes (DEGs) in Cluster2; \u003cstrong\u003eB\u003c/strong\u003e, PPI network analysis of Cluster2 and immune-related gene intersection; \u003cstrong\u003eC\u003c/strong\u003e, Characteristic analysis of 178 intergene-mediated immune cell infiltration landscape.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4968181/v1/2d5aeb127f5b819c2df892ce.png"},{"id":65912487,"identity":"deb42d5f-7fe2-4592-94a7-1665ed2c2000","added_by":"auto","created_at":"2024-10-04 09:56:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":163855,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHub genes recognized by the cytoHubba plug-in\u003c/strong\u003e. \u003cstrong\u003eA\u003c/strong\u003e, TOP10 Hub genes recognized by cytoHubba plugin. According to the MCC method, the gradient of color represents the value of the fraction. \u003cstrong\u003eB\u003c/strong\u003e, \u003cstrong\u003eC\u003c/strong\u003e The changing trend of \u003cem\u003eIFNG\u003c/em\u003e and \u003cem\u003eIL7R\u003c/em\u003e with ischemia-reperfusion injury time. \u003cstrong\u003eD-F\u003c/strong\u003e, Expressions of \u003cem\u003eIL7R\u003c/em\u003e in GSE9634, GSE72646, and GSE23160 (blue represents 0 hours, red represents IRI group). \u003cstrong\u003eG\u003c/strong\u003e, Gene target networks of \u003cem\u003eIL7R\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e in IRI-associated pathways. (Yellow represents target genes; Orange represents related genes; Blue indicates the functional annotation of the target genes).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4968181/v1/09e6f8b489a19c3b59788e04.png"},{"id":77052516,"identity":"1630ed4f-8730-4fd4-81b5-7fb6c2ea27c9","added_by":"auto","created_at":"2025-02-24 16:13:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1759707,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4968181/v1/5437d55a-278c-4314-bfa7-e8f641a1ce0b.pdf"},{"id":65912488,"identity":"b5803384-4019-4bcc-bd57-88c98d3b5f8f","added_by":"auto","created_at":"2024-10-04 09:56:05","extension":"zip","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1483980,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.zip","url":"https://assets-eu.researchsquare.com/files/rs-4968181/v1/cf9fb68d834f5ced0cbe8e71.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide KAS-Seq mapping of leukocytes in ischemia-reperfusion model reveals IL7R as a potential therapeutic target for ischemia-reperfusion injury","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIschemia 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[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Prolonged ischemia can cause irreversible damage to many organs, such as the heart, brain, kidneys, liver, lungs, and intestines [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Numerous previous studies have indicated that the pathogenesis of IRI is associated with changes related to neuroinflammation, neutrophil regulation, and other various mechanisms[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003ePrevious 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[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, the release of these inflammatory cytokines can affect apoptosis, pyroptosis, and autophagy[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eSecond-generation sequencing technology has played a significant role in exploring the molecular mechanisms underlying IRI.\u003c/p\u003e \u003cp\u003eOne innovative approach is KAS-Seq (kethoxal-assisted single-strand DNA sequencing) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], 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[\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Hence, the application of KAS-Seq in investigating the molecular mechanism underlying IRI has significant implications for advancing disease research.\u003c/p\u003e \u003cp\u003eIn 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 \u003cem\u003eIL7R\u003c/em\u003e is a potential key target in IRI, suggesting a role for \u003cem\u003eIL7R\u003c/em\u003e 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.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimal model\u003c/h2\u003e \u003cp\u003eIn 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eIn 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\u0026mdash;0, 24, 48, and 72 hours\u0026mdash;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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLeukocyte gDNA extraction and quantity assessment\u003c/h2\u003e \u003cp\u003eFive-milliliter porcine peripheral blood samples were collected in BD Vacutainer\u0026reg;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 \u0026times; g for 12 minutes. The plasma was then transferred to a 2 ml centrifuge tube (AXYGEN, MCT-200-C) and centrifuged again at 1350 \u0026times; 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\u0026trade; 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\u0026deg;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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eKAS-Seq library construction and sequencing\u003c/h2\u003e \u003cp\u003eFirst, the ground tissues were cultured in complete medium containing 5 mM N3-ketoaldehydes at 37\u0026deg;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 \u0026micro;L of DNA elution buffer, 5 \u0026micro;L of 20 mM DBCO-PEG4-biotin (DMSO solution, Sigma, 760749) and 25 mM K3BO3 were added, and the mixture was incubated at 37\u0026deg;C with gentle shaking for 1.5 hours. Next, 5 \u0026micro;L of RNase A (Thermo, 12091039) was added to the reaction system, and the mixture was incubated at 37\u0026deg;C for 5 minutes. Biotinized DNA was purified via a DNA Clean \u0026amp; Concentrator-5 kit (Zymo, D4013). The purified gDNA was dissolved in 100 \u0026micro;L of water, and the DNA fragments were broken to a size of 150\u0026ndash;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 \u0026micro;L 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 \u0026micro;L of H2O at 95\u0026deg;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\u0026nbsp;million reads for each library[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMapping and identification of enriched regions via KAS-Seq\u003c/h2\u003e \u003cp\u003eA 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.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and filtered with SAMtools (version 1.3.1) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] to preserve unique, nonrepetitive matches. Pair-end reads were extended and converted into BedGraph format via Bedtool2 (version 2.19.1) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] 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 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDifferential analysis and functional analysis\u003c/h2\u003e \u003cp\u003eRStudio 3.5.0 (version) of the DESeq2 package (version 3.24.3) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] was used to identify KAS-Seq difference loci (root filter thresholds: p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and | log 2-fold change| \u0026gt; 0.5). Genes were selected for GO (Gene Ontology) analysis[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and BP (biological process), CC (cell component) and MF (molecular function) terms were enriched.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eProtein‒Protein Interaction (PPI) Network Analysis\u003c/h2\u003e \u003cp\u003eWe utilized the interactive Gene/Protein Retrieval Search Tool (STRING) database to search for protein‒protein interaction (PPI) data[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. 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[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], we constructed a protein\u0026ndash;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[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eReferences to the \u003cb\u003eGEO database\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThree ischemia‒reperfusion injury-related microarray datasets, GSE9634, GSE72646, and GSE23160, were downloaded from the GEO Expression Synthesis database[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eOverview of the Research Process Flow\u003c/h2\u003e \u003cp\u003eBlood 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eKAS-Seq global signal distribution expression profile\u003c/h2\u003e \u003cp\u003eTo 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Visual analysis revealed that KAS-Seq differences were predominantly distributed in gene functional regions, such as transcriptional start sites, introns, and distal regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), with notable disparities in genome region distribution characteristics observed across the time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). The subsequent principal component analysis demonstrated significant aggregation and differentiation among the groups at 0, 24, 48, and 72 hours (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Therefore, KAS-Seq markers can effectively differentiate between these time points after ischemia‒reperfusion and hold considerable significance for distinguishing among these four groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGO signaling pathway and functional enrichment analysis\u003c/h2\u003e \u003cp\u003eWe conducted a differential gene expression analysis, using criteria for significance of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FoldChange| \u0026ge; 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Supplementary Table\u0026nbsp;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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Supplementary Table\u0026nbsp;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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, Supplementary Table\u0026nbsp;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 \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). 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[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. 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 \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB-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[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) (Supplementary Table\u0026nbsp;5). Concurrently, we performed pathway enrichment analysis on clusters exhibiting similar time trends. It was observed that some typical pathways were functionally rich (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). 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[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Cluster 2 and immune-related functions and temporal cell landscape changes in Cluster 2\u003c/h2\u003e \u003cp\u003eSince 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\u0026nbsp;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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and Supplementary Table\u0026nbsp;6). We used the STRING database to generate a PPI network of 178 intersecting genes. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). 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\u0026nbsp;7). We used the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database to generate a protein\u0026ndash;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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). These results suggest that IRI may be associated with T-cell activity through the regulation of immune-related genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of hub genes and potential targets for IRI\u003c/h2\u003e \u003cp\u003eWe further analyzed the hub genes among the 178 immune-related genes identified in Cluster 2 via the Cytoscape plugin CytoHubba (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). According to the maximum cluster centrality (MCC) algorithm, the top 10 Hub genes were identified in the immune-related dataset of Cluster 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). A total of 10 genes in the dataset were identified as hub genes (\u003cem\u003eIFNG, IL7R, TLR4, IL2RA, CCRL2, CD40, CCR7, CCL20, CD5, and CD38\u003c/em\u003e). In the PPI network analysis, we identified two hub genes: interferon-gamma (\u003cem\u003eIFNG\u003c/em\u003e) and the interleukin-7 receptor (\u003cem\u003eIL7R\u003c/em\u003e). 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.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Many studies have reported a close association between \u003cem\u003eIFNG\u003c/em\u003e and the occurrence and progression of IRI, confirming our results from KAS-Seq sequencing. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, in the subsequent phase of our study, we concentrated on exploring the correlation between \u003cem\u003eIL7R\u003c/em\u003e and IRI. Initially, we confirmed the expression of \u003cem\u003eIL7R\u003c/em\u003e across three GEO datasets (GSE9634, GSE72646, and GSE23160) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-F). In each dataset, the \u003cem\u003eIL7R\u003c/em\u003e expression level was increased in the IRI group, which aligns with our findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-F). Additionally, reports have indicated a correlation between \u003cem\u003eIFNG\u003c/em\u003e and \u003cem\u003eIL7R\u003c/em\u003e in the context of other diseases[\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, to explore the role of \u003cem\u003eIFNG\u003c/em\u003e and \u003cem\u003eIL7R\u003c/em\u003e in IRI, we used the STRING database to study the correlation between \u003cem\u003eIFNG\u003c/em\u003e and \u003cem\u003eIL7R\u003c/em\u003e target genes in IRI. GO signaling pathway enrichment was utilized to analyze potentially relevant pathways in IRI (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). IRI is reported to be closely related to T-cell activation and immune system regulation[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The results of this study suggest that \u003cem\u003eIL7R\u003c/em\u003e may be a key target of IRI and may coregulate T cells with \u003cem\u003eIFNG\u003c/em\u003e 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.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIRI 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[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite many studies indicating that the pathogenesis of IRI is closely related to immunity, such as T-cell activation, neutrophil burst, and macrophage polarization[\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], 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[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eWe 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We subsequently performed enrichment analysis on the DEGs whose expression changed regularly at the four time points of ischemia‒reperfusion. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, 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[\u003cspan additionalcitationids=\"CR44 CR45 CR46 CR47 CR48 CR49\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], thus confirming the obvious correlation between our data and this disease.\u003c/p\u003e \u003cp\u003eTo 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\u0026thinsp;+\u0026thinsp;T cells in organs such as the heart and liver changes. For example, in studies of myocardial IRI, CD4\u0026thinsp;+\u0026thinsp;T cells participate in the process of myocardial ischemic injury through the HMGB1-TLR4 signaling pathway[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In allo-orthotopic liver transplantation, depleting anti-CD4 antibodies can reduce neutrophil/macrophage infiltration and proinflammatory gene expression caused by IRI [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], indicating that CD4\u0026thinsp;+\u0026thinsp;T-cell activation can affect IRI.\u003c/p\u003e \u003cp\u003eWe subsequently screened for hub genes from the 178 genes and found that \u003cem\u003eIL7R\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e are key target genes. In mouse kidney IRI, \u003cem\u003eIFNG\u003c/em\u003e can regulate the migration of neutrophils together with \u003cem\u003eIL17\u003c/em\u003e [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]; thus, we focused on \u003cem\u003eIL7R\u003c/em\u003e as a new target for IRI, and in an external dataset, we also confirmed the differential expression of \u003cem\u003eIL7R\u003c/em\u003e. These findings suggest that \u003cem\u003eIL7R\u003c/em\u003e plays a key role in IRI.\u003c/p\u003e \u003cp\u003eTo further study the correlation between \u003cem\u003eIL7R\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e and IRI, we analyzed all target genes related to \u003cem\u003eIL7R\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e. Interestingly, we found that the genes related to \u003cem\u003eIL7R\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e 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, \u003cem\u003eIL7R\u003c/em\u003e upregulation regulates the upregulation of \u003cem\u003eIFNG\u003c/em\u003e, thereby affecting the activity of T cells. These findings suggest that \u003cem\u003eIL7R\u003c/em\u003e is a potential new target for IRI treatment.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications\u003c/h2\u003e \u003cp\u003eIn 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\u0026thinsp;+\u0026thinsp;T cells. Notably, \u003cem\u003eIL7R\u003c/em\u003e, identified as a key target in this study, plays a significant role in regulating T-cell activation in conjunction with the upregulation of \u003cem\u003eIFNG\u003c/em\u003e, 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eIn 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 \u003cem\u003eIL7R\u003c/em\u003e as a potential key target of IRI, where the upregulation of \u003cem\u003eIL7R\u003c/em\u003e and \u003cem\u003eIFNG\u003c/em\u003e 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.\u003c/p\u003e \u003cp\u003eNevertheless, 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.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn 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 \u003cem\u003eIL7R\u003c/em\u003e as a key molecular target for IRI. The upregulation of \u003cem\u003eIL7R\u003c/em\u003e and its coregulation with \u003cem\u003eIFNG\u003c/em\u003e 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.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAbbreviation\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eFull name\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eKAS-Seq\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eKethoxal-assisted single-stranded DNA sequencing\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eIRI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eIschemia-Reperfusion Injury\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003essDNA\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSingle-Stranded DNA\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDEGs\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eDifferentially expressed genes\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eGO\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eGene ontology\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003ePPI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eProtein-Protein Interaction\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSTRING\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSearch Tool for the Retrieval of Interacting Genes\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eIL7R\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eInterleukin-7 Receptor\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eIFNG\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eInterferon-gamma\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe use of experimental animals in this study has been approved by the Ethics Committee of the General Hospital of the People\u0026apos;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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLZ 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the essential contributions of all staff and students who participated in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement on ARRIVE Guidelines Compliance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRandhawa, P. K., Bali, A. \u0026amp; Jaggi, A. S. RIPC for multiorgan salvage in clinical settings: evolution of concept, evidences and mechanisms. \u003cem\u003eEur. J. Pharmacol.\u003c/em\u003e \u003cb\u003e746\u003c/b\u003e, 317\u0026ndash;332 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeralta, C., Jim\u0026eacute;nez-Castro, M. B. \u0026amp; Gracia-Sancho, J. Hepatic ischemia and reperfusion injury: effects on the liver sinusoidal milieu. \u003cem\u003eJ. Hepatol.\u003c/em\u003e \u003cb\u003e59\u003c/b\u003e (5), 1094\u0026ndash;1106 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIliodromitis, E. K., Andreadou, I., Iliodromitis, K. \u0026amp; Dagres, N. Ischemic and postischemic conditioning of the myocardium in clinical practice: challenges, expectations and obstacles. \u003cem\u003eCardiology\u003c/em\u003e. \u003cb\u003e129\u003c/b\u003e (2), 117\u0026ndash;125 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZerva, K. et al. Effect of thrombolytic therapy in plaque inflammation and structural remodeling in acute myocardial infarction patients. \u003cem\u003eHellenic J. Cardiol.\u003c/em\u003e \u003cb\u003e58\u003c/b\u003e (6), 446\u0026ndash;449 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalogeris, T., Baines, C. P., Krenz, M. \u0026amp; Korthuis, R. J. Cell biology of ischemia/reperfusion injury. \u003cem\u003eInt. Rev. Cell. Mol. Biol.\u003c/em\u003e \u003cb\u003e298\u003c/b\u003e, 229\u0026ndash;317 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, L., Li, L., Shen, J., Qi, Z. \u0026amp; Guo, L. Effect of dexmedetomidine on lung ischemia\u0026ndash;reperfusion injury. \u003cem\u003eMol. Med. Rep.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (2), 419\u0026ndash;426 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, M. Y. et al. Current Mechanistic Concepts in Ischemia and Reperfusion Injury. \u003cem\u003eCell. Physiol. Biochem.\u003c/em\u003e \u003cb\u003e46\u003c/b\u003e (4), 1650\u0026ndash;1667 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarniglia, L. et al. Neuropeptides and Microglial Activation in Inflammation, Pain, and Neurodegenerative Diseases. \u003cem\u003eMediators Inflamm.\u003c/em\u003e \u003cb\u003e2017\u003c/b\u003e, 5048616 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChi, W. et al. Caspase-8 promotes NLRP1/NLRP3 inflammasome activation and IL-1β production in acute glaucoma. \u003cem\u003eProc. Natl. Acad. Sci. U S A\u003c/em\u003e. \u003cb\u003e111\u003c/b\u003e (30), 11181\u0026ndash;11186 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi, Y. et al. Retinal ischemia/reperfusion injury is mediated by Toll-like receptor 4 activation of NLRP3 inflammasomes. \u003cem\u003eInvest. Ophthalmol. Vis. Sci.\u003c/em\u003e \u003cb\u003e55\u003c/b\u003e (9), 5466\u0026ndash;5475 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbcouwer, S. F. et al. Inflammatory resolution and vascular barrier restoration after retinal ischemia reperfusion injury. \u003cem\u003eJ. Neuroinflammation\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e (1), 186 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu, H. Y. et al. Ligustrazine induces viability, suppresses apoptosis and autophagy of retinal ganglion cells with ischemia/reperfusion injury through the PI3K/Akt/mTOR signaling pathway. \u003cem\u003eBioengineered\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e (1), 507\u0026ndash;515 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWan, P. et al. LncRNA H19 initiates microglial pyroptosis and neuronal death in retinal ischemia/reperfusion injury. \u003cem\u003eCell. Death Differ.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e (1), 176\u0026ndash;191 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, T., Lyu, R., You, Q. \u0026amp; He, C. Kethoxal-assisted single-stranded DNA sequencing captures global transcription dynamics and enhancer activity in situ. \u003cem\u003eNat. Methods\u003c/em\u003e. \u003cb\u003e17\u003c/b\u003e (5), 515\u0026ndash;523 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLyu, R. et al. KAS-seq: genome-wide sequencing of single-stranded DNA by N(3)-kethoxal-assisted labeling. \u003cem\u003eNat. Protoc.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (2), 402\u0026ndash;420 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, F. X., Smith, E. R. \u0026amp; Shilatifard, A. Born to run: control of transcription elongation by RNA polymerase II. \u003cem\u003eNat. Rev. Mol. Cell. Biol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (7), 464\u0026ndash;478 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgers, P. M. J. \u0026amp; Kunkel, T. A. Eukaryotic DNA Replication Fork. \u003cem\u003eAnnu. Rev. Biochem.\u003c/em\u003e \u003cb\u003e86\u003c/b\u003e, 417\u0026ndash;438 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHustedt, N. \u0026amp; Durocher, D. The control of DNA repair by the cell cycle. \u003cem\u003eNat. Cell. Biol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (1), 1\u0026ndash;9 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, X. \u0026amp; Heyer, W. D. Homologous recombination in DNA repair and DNA damage tolerance. \u003cem\u003eCell. Res.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (1), 99\u0026ndash;113 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGinno, P. A., Lott, P. L., Christensen, H. C., Korf, I. \u0026amp; Ch\u0026eacute;din, F. R-loop formation is a distinctive characteristic of unmethylated human CpG island promoters. \u003cem\u003eMol. Cell.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e (6), 814\u0026ndash;825 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLangmead, B. \u0026amp; Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. \u003cem\u003eNat. Methods\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e (4), 357\u0026ndash;359 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (9), R137 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuinlan, A. R. \u0026amp; Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. \u003cem\u003eBioinformatics\u003c/em\u003e. \u003cb\u003e26\u003c/b\u003e (6), 841\u0026ndash;842 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCarthy, D. J., Chen, Y. \u0026amp; Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e (10), 4288\u0026ndash;4297 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahajan, P., Fiehn, O. \u0026amp; Barupal, D. IDSL.GOA: Gene Ontology Analysis for Interpreting Metabolomic datasets. \u003cem\u003ebioRxiv\u003c/em\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzklarczyk, D. et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e39\u003c/b\u003e (Database issue), D561\u0026ndash;568 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. \u003cem\u003eGenome Res.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (11), 2498\u0026ndash;2504 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChin, C. H. et al. cytoHubba: identifying hub objects and sub-networks from complex interactome. \u003cem\u003eBMC Syst. Biol.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (Suppl 4(Suppl 4), S11 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarrett, T. et al. NCBI GEO: archive for functional genomics data sets\u0026ndash;update. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e (Database issue), D991\u0026ndash;995 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong, Y. et al. Mitochondria-associated membrane-modulated Ca(2+) transfer: A potential treatment target in cardiac ischemia reperfusion injury and heart failure. \u003cem\u003eLife Sci.\u003c/em\u003e \u003cb\u003e278\u003c/b\u003e, 119511 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, L. et al. IL-17 produced by neutrophils regulates IFN-gamma-mediated neutrophil migration in mouse kidney ischemia-reperfusion injury. \u003cem\u003eJ. Clin. Invest.\u003c/em\u003e \u003cb\u003e120\u003c/b\u003e (1), 331\u0026ndash;342 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, L. et al. Innate Immune Regulations and Liver Ischemia-Reperfusion Injury. \u003cem\u003eTransplantation\u003c/em\u003e. \u003cb\u003e100\u003c/b\u003e (12), 2601\u0026ndash;2610 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBavarsad, K., Barreto, G. E., Hadjzadeh, M. A. \u0026amp; Sahebkar, A. Protective Effects of Curcumin Against Ischemia-Reperfusion Injury in the Nervous System. \u003cem\u003eMol. Neurobiol.\u003c/em\u003e \u003cb\u003e56\u003c/b\u003e (2), 1391\u0026ndash;1404 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShmarov, V. A. et al. Direct effects of interleukin-7 on the function of human T cells in vitro. \u003cem\u003eEur. Cytokine Netw.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e (4), 102\u0026ndash;107 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePangault, C. et al. Integrative Analysis of Cell Crosstalk within Follicular Lymphoma Cell Niche: Towards a Definition of the FL Supportive Synapse. \u003cem\u003eCancers (Basel)\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e(10). (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarwot, R. et al. Protective role of nuclear factor of activated T cells 2 in CD8\u0026thinsp;+\u0026thinsp;long-lived memory T cells in an allergy model. \u003cem\u003eJ. Allergy Clin. Immunol.\u003c/em\u003e \u003cb\u003e121\u003c/b\u003e (4), 992\u0026ndash;999e996 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao, J., Lu, L. \u0026amp; Zhai, Y. T cells in organ ischemia reperfusion injury. \u003cem\u003eCurr. Opin. Organ. Transpl.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (2), 115\u0026ndash;120 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLie, M. L. et al. Lung T lymphocyte trafficking and activation during ischemic acute kidney injury. \u003cem\u003eJ. Immunol.\u003c/em\u003e \u003cb\u003e189\u003c/b\u003e (6), 2843\u0026ndash;2851 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin, Z. et al. Identification of distinct immune infiltration and potential biomarkers in patients with liver ischemia-reperfusion injury. \u003cem\u003eLife Sci.\u003c/em\u003e \u003cb\u003e327\u003c/b\u003e, 121726 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang, Q., Dong, C. \u0026amp; Sun, Q. Immune response associated with ischemia and reperfusion injury during organ transplantation. \u003cem\u003eInflamm. Res.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e (12), 1463\u0026ndash;1476 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKezić, A., Stajic, N. \u0026amp; Thaiss, F. Innate Immune Response in Kidney Ischemia/Reperfusion Injury: Potential Target for Therapy. \u003cem\u003eJ. Immunol. Res.\u003c/em\u003e \u003cb\u003e2017\u003c/b\u003e, 6305439 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaehr, A., Klymiuk, N. \u0026amp; Kupatt, C. Evaluating Novel Targets of Ischemia Reperfusion Injury in Pig Models. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e(19). (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhai, Y., Busuttil, R. W. \u0026amp; Kupiec-Weglinski, J. W. Liver ischemia and reperfusion injury: new insights into mechanisms of innate-adaptive immune-mediated tissue inflammation. \u003cem\u003eAm. J. Transpl.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (8), 1563\u0026ndash;1569 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNi, M. et al. T-Cell Immunoglobulin and Mucin Domain-Containing Protein-4 Is Critical for Kupffer Cell Homeostatic Function in the Activation and Resolution of Liver Ischemia Reperfusion Injury. \u003cem\u003eHepatology\u003c/em\u003e. \u003cb\u003e74\u003c/b\u003e (4), 2118\u0026ndash;2132 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStabile, E. et al. Impaired arteriogenic response to acute hindlimb ischemia in CD4-knockout mice. \u003cem\u003eCirculation\u003c/em\u003e. \u003cb\u003e108\u003c/b\u003e (2), 205\u0026ndash;210 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrait, V. H., Arumugam, T. V., Drummond, G. R. \u0026amp; Sobey, C. G. Importance of T lymphocytes in brain injury, immunodeficiency, and recovery after cerebral ischemia. \u003cem\u003eJ. Cereb. Blood Flow. Metab.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e (4), 598\u0026ndash;611 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabb, H. et al. Pathophysiological role of T lymphocytes in renal ischemia-reperfusion injury in mice. \u003cem\u003eAm. J. Physiol. Ren. Physiol.\u003c/em\u003e \u003cb\u003e279\u003c/b\u003e (3), F525\u0026ndash;531 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaldwell, C. C., Tschoep, J. \u0026amp; Lentsch, A. B. Lymphocyte function during hepatic ischemia/reperfusion injury. \u003cem\u003eJ. Leukoc. Biol.\u003c/em\u003e \u003cb\u003e82\u003c/b\u003e (3), 457\u0026ndash;464 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYilmaz, G., Arumugam, T. V., Stokes, K. Y. \u0026amp; Granger, D. N. Role of T lymphocytes and interferon-gamma in ischemic stroke. \u003cem\u003eCirculation\u003c/em\u003e. \u003cb\u003e113\u003c/b\u003e (17), 2105\u0026ndash;2112 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurne, M. J. et al. Identification of the CD4(+) T cell as a major pathogenic factor in ischemic acute renal failure. \u003cem\u003eJ. Clin. Invest.\u003c/em\u003e \u003cb\u003e108\u003c/b\u003e (9), 1283\u0026ndash;1290 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen, Y. et al. Regulatory Effects of Toll-like Receptor 4 Knockout on CD4(+) and CD8(+) T Lymphocytes and Interleukin-17 During Myocardial Ischemia. \u003cem\u003eAnn. Clin. Lab. Sci.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e (6), 761\u0026ndash;768 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKageyama, S. et al. Ischemia-reperfusion Injury in Allogeneic Liver Transplantation: A Role of CD4 T Cells in Early Allograft Injury. \u003cem\u003eTransplantation\u003c/em\u003e. \u003cb\u003e105\u003c/b\u003e (9), 1989\u0026ndash;1997 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"KAS-Seq, ischemia‒reperfusion injury, leukocytes, IL7R, target","lastPublishedDoi":"10.21203/rs.3.rs-4968181/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4968181/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIschemia‒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.\u003c/p\u003e\u003ch2\u003eObjective(s):\u003c/h2\u003e \u003cp\u003eThe 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.\u003c/p\u003e\u003ch2\u003eStudy Design:\u003c/h2\u003e \u003cp\u003eKAS-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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn 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 (\u003cem\u003eIL7R\u003c/em\u003e), which plays a crucial molecular role in IRI. Additionally, via database mining technology, we confirmed the close relationship between \u003cem\u003eIL7R\u003c/em\u003e and IRI, explored the interaction between interferon-γ (\u003cem\u003eIFNG\u003c/em\u003e) and \u003cem\u003eIL7R\u003c/em\u003e in T-cell activation, and clarified their joint influence on ischemia‒reperfusion injury.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eUtilizing 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 \u003cem\u003eIL7R\u003c/em\u003e 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.\u003c/p\u003e","manuscriptTitle":"Genome-wide KAS-Seq mapping of leukocytes in ischemia-reperfusion model reveals IL7R as a potential therapeutic target for ischemia-reperfusion injury","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-04 09:56:00","doi":"10.21203/rs.3.rs-4968181/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-20T16:05:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-20T15:28:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-19T12:30:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24031423367909277641506855473811836896","date":"2024-12-14T10:30:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124332772512893377396296037668214942188","date":"2024-11-30T14:39:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262004636938639369613128293232671182416","date":"2024-11-13T19:15:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-13T19:00:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-13T18:44:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-05T21:07:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-03T10:33:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-08-24T08:46:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"48df4580-52a1-4e33-b883-cd468d2b67db","owner":[],"postedDate":"October 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":38436240,"name":"Biological sciences/Genetics"},{"id":38436241,"name":"Biological sciences/Genetics/Gene expression"},{"id":38436242,"name":"Biological sciences/Genetics/Immunogenetics"}],"tags":[],"updatedAt":"2025-02-24T15:59:58+00:00","versionOfRecord":{"articleIdentity":"rs-4968181","link":"https://doi.org/10.1038/s41598-025-90457-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-02-20 15:57:11","publishedOnDateReadable":"February 20th, 2025"},"versionCreatedAt":"2024-10-04 09:56:00","video":"","vorDoi":"10.1038/s41598-025-90457-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-90457-7","workflowStages":[]},"version":"v1","identity":"rs-4968181","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4968181","identity":"rs-4968181","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}