Abstract

   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. This study aimed to explore the genetic
   alterations in leukocytes in peripheral blood after
   ischemia–reperfusion, aiming to discover new biomarkers and potential
   therapeutic targets. 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 h
   post-ischemia‒reperfusion. This method integrated genes that exhibited
   regular changes over time. In this study, we thoroughly analyzed the
   dynamic changes in gene expression post-IRI, revealing significant
   enrichment in key signaling pathways that regulate immune responses and
   T-cell activation over time. Our identification of the interleukin-7
   receptor (IL7R) was particularly striking, as it plays a crucial
   molecular role in IRI. Additionally, using 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.
   Using KAS-Seq analysis of leukocytes from peripheral blood, we
   successfully delineated the temporal patterns of gene expression and
   changes 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 provides innovative avenues for the
   development of IRI treatments.

   Keywords: KAS-Seq, Ischemia–reperfusion injury, Leukocytes, IL7R,
   Target

   Subject terms: Genetics, Gene expression, Immunogenetics

Introduction

   Ischemia is a condition in which the body’s blood supply is restricted,
   leading to a lack of oxygen and other nutrients necessary for cell
   metabolism^[46]1,[47]2. Prolonged ischemia can cause irreversible
   damage to many organs, such as the heart, brain, kidneys, liver, lungs,
   and intestines^[48]3. Reperfusion is an immediate intervention to
   restore blood supply, but the damage caused by the sudden restoration
   of blood flow to an organ with long-term ischemia, known as reperfusion
   injury, can be more severe and potentially fatal^[49]4. Numerous
   previous studies have indicated that the pathogenesis of IRI is
   associated with changes related to neuroinflammation, neutrophil
   regulation, and other mechanisms^[50]5–[51]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. These molecules not only exacerbate the local inflammatory
   response but also may further contribute to tissue
   damage.^[52]9–[53]11. In addition, the release of these inflammatory
   cytokines can affect apoptosis, pyroptosis, and
   autophagy^[54]12,[55]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 analysis of differential gene expression in leukocytes.
   This approach can not only enhance our understanding of the immune
   response to IRI but also uncover potential new therapeutic targets,
   thereby 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)^[56]14, which offers a straightforward, effective, and
   sensitive method for studying transcriptional regulation and enhancer
   activity through single-strand DNA sequencing. Instantaneous ssDNA
   during transcription provides a more direct in situ reading of
   transcriptional activity than does the RNA itself^[57]14–[58]20. Thus,
   the application of KAS-Seq in elucidating the molecular mechanism
   underlying IRI has significant implications for advancing disease
   research.

   In this study, KAS-Seq technology was employed to analyze ssDNA in
   peripheral blood leukocytes of porcine IRI model to explore key dynamic
   gene changes and potential mechanisms following IRI, and reveal
   potential therapeutic targets. Specifically, we collected leukocyte
   samples at 0, 24, 48, and 72 h 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, thereby offering new
   insights and inform potential therapeutic strategies for its clinical
   management.

Materials and methods

Animal model

   In this study, we selected three pigs weighing from 20 to 25 kg 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 approximately 400 ml 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 min.
   Throughout the entire balloon intervention process, we continuously
   monitored the animals’ vital signs, including heart rates and blood
   pressures. 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 h 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 evaluate their recovery progress.

Study participants

   In this investigation, we utilized KAS-Seq sequencing technology to map
   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 h and the remaining 9 representing
   the 24-, 48-, and 72-h postischemia reperfusion intervals. The KAS-Seq
   libraries for all samples were sequenced using the Illumina NovaSeq
   6000 platform. Data processing involved comparative analyses at each
   time point—0, 24, 48, and 72 h—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 h post-ischemia reperfusion,
   and leukocytes were isolated and extracted (Becton, Dickinson and
   Company, product No. 367525). All blood samples were sent to the
   laboratory within 24 h. The plasma was separated by centrifugation at
   1350×g for 12 min. The plasma was then transferred to a 2 ml centrifuge
   tube (AXYGEN, MCT-200-C) and centrifuged again at 1350×g for 12 min.
   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, [59]Q33216). The extracted DNA
   samples were then stored at − 80 °C for future use. Prior to
   constructing the KAS-Seq library, we performed nucleic acid
   electrophoresis to determine the size of the DNA fragments, ensuring
   their suitability for library construction.

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% CO[2] for 10 min. 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. Subsequently, 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 h. Next, 5 µL of RNase A
   (Thermo, 12091039) was added to the reaction system, and the mixture
   was incubated at 37 °C for 5 min. 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 s pulses/30 s 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 min to
   enrich the biotin-labeled DNA. After incubation, the beads were washed
   and heated in 15 µL of H2O at 95 °C for 10 min 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^[60]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^[61]21 and
   filtered with SAMtools (version 1.3.1)^[62]22 to preserve unique,
   nonrepetitive matches. Pair-end reads were extended and converted into
   BedGraph format via Bedtool2 (version 2.19.1)^[63]23 and then converted
   to BigWig format for visualization via bedGraphToBigWig from the
   Integrated Genomics Viewer. MACS2 (version 2.1.1) was used to identify
   potential KAS-Seq-enriched regions in each sample^[64]22. Regions of
   less than 1000 base pairs (bp) that occur in two or more samples are
   used as a uniform reference for each sample. To improve the reliability
   of the data, we specifically excluded genomic regions that could
   generate false signals, as identified by data from the ENCODE project
   (Encyclopedia of DNA Elements). 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 ChIPseeker package to annotate the KAS-Seq-enriched
   regions, using the gene closest to each region for annotation.

Differential analysis and functional analysis

   RStudio 3.5.0 (version) of the DESeq2 package (version 3.24.3)^[65]24
   was used to identify KAS-Seq difference loci (root filter thresholds: p
   values < 0.05 and |log twofold change|> 0.5). Genes were selected for
   GO (Gene Ontology) analysis^[66]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^[67]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^[68]27, we
   constructed a protein–protein interaction (PPI) network for
   visualization of the collated data. Each protein is depicted as a node,
   and its 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.^[69]28.

GEO database

   Three ischemia-reperfusion injury-related microarray datasets,
   [70]GSE9634, [71]GSE72646, and [72]GSE23160, were downloaded from the
   GEO Expression Synthesis database^[73]29.

Cell culture and cell IR model construction

   In this study, we utilized two human leukemia T lymphoid cell lines
   (MOLT-4 and MOLT-6) to establish an IRI model. These cell lines were
   obtained from the ATCC cell bank and cultured in DMEM/F12 medium
   supplemented with 10% fetal bovine serum and 1% penicillin–streptomycin
   at 37 °C in a humidified atmosphere containing 95% CO2. To simulate the
   IRI process, cells were initially incubated in a three-gas incubator at
   37 °C with a gas composition of 1% O2, 94% N2, and 5% CO2 for a
   duration of 12 h to mimic ischemic conditions. Subsequently, the cells
   were transferred to a standard incubator for an additional six hours to
   replicate the reperfusion phase. Through this approach, we successfully
   simulated the IRI process in vitro, thereby providing a reliable
   experimental framework for investigating the responses of leukemia
   cells under conditions of ischemia and reperfusion. This work
   contributes to a deeper understanding of the mechanisms underlying IRI
   and offers an experimental basis for developing novel therapeutic
   strategies.

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 h post-IRI, with the 0-h samples
   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 employed visualization and machine
   learning algorithms to investigate the underlying mechanisms and
   potential biomarkers of IRI, with the aim of providing new insights and
   strategies for clinical diagnosis and treatment (Fig. [74]1A).

Fig. 1.

   [75]Fig. 1
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   Schematic overview of the study. (A) Porcine femoral artery
   ischemia–reperfusion was 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.

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 h 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 at 48 h
   post-IRI, and the KAS-Seq signals began to recover by 72 h post-IRI
   (Fig. [77]2A).Visual analysis revealed that KAS-Seq differences were
   predominantly distributed in gene functional regions, such as
   transcriptional start sites, introns, and distal regions (Fig. [78]2B).
   Notably, significant disparities in the distribution characteristics of
   these genomic regions were observed at different time points
   (Fig. [79]2C). Furthermore, we identified distinct variations among
   gene functional regions, particularly in transcription start sites and
   introns, across the groups at each time point (Fig. [80]2D,E). The
   subsequent principal component analysis demonstrated significant
   aggregation and differentiation among the groups at 0, 24, 48, and 72 h
   (Fig. [81]2F). Therefore, KAS-Seq markers can effectively differentiate
   between these time points post-ischemia–reperfusion, holding
   considerable significance for distinguishing among these four groups.

Fig. 2.

   [82]Fig. 2
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   Global and dynamic ssDNA distribution characteristics of leukocytes
   from KAS-Seq in ischemia–reperfusion. (A) Genome-wide ssDNA
   distribution from samples collected 0, 24, 48, and 72 h after
   ischemia–reperfusion(0 h in green, 24 h in black,48 h in yellow, 72 h
   in pink); (B) Differences in the distribution of gene function at 0 h,
   24 h, 48 h and 72 h(0 h in green, 24 h in black, 48 h in yellow, 72 h
   in pink); (C) 5hmC signal profile of ischemia–reperfusion at 0, 24, 48,
   and 72 h. (D,E) Peak number of promoter and intron regions at 0 h,24
   h,48 h, and 72 h after ischemia–reperfusion D, is promoter, E, is
   intron regions. (F) PCA (principal component analysis) distinguished
   injured white blood cell samples(0 h in green, 24 h in black,48 h in
   yellow, 72 h in orange) after ischemia–reperfusion 0, 24, 48, and 72 h.

GO signaling pathway and functional enrichment analysis

   We conducted a differential gene expression analysis using significance
   criteria of p < 0.05 and |log2FoldChange|≥ 0.5. This analysis
   identified 1651 differentially expressed genes (DEGs), including 690
   upregulated and 941 downregulated genes, in the 24-h
   ischemia–reperfusion samples compared to the healthy controls
   (Fig. [84]3A, Supplementary Table [85]S1). Compared with those in the
   24-h sample, there were 1578 DEGs in the 48-h sample, including 858
   upregulated and 720 downregulated genes (Fig. [86]3B, Supplementary
   Table [87]S2). Similarly, there were 1577 DEGs in the 72-h sample
   compared with the 48-h sample, including 802 upregulated and 775
   downregulated genes (Fig. [88]3C, Supplementary Table [89]S3). 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 h (Fig. [90]S1A). Our analysis revealed 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^[91]30. Subsequently, signaling pathways related to the
   immune response became apparent 48 h post-ischemia–reperfusion, and
   other pathways related to myocardial tissue development and hypoxia
   level regulation began to appear 72 h post-ischemia–reperfusion (Fig.
   [92]S1B–D). 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^[93]31,[94]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 h.
   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 h,
   whereas Cluster 1, Cluster 3, and Cluster 4 tended to be downregulated
   within 48 h (Fig. [95]3D) (Supplementary Table [96]S5). Concurrently,
   we performed pathway enrichment analysis on clusters exhibiting similar
   time trends. It was observed that some typical pathways were
   functionally rich (Fig. [97]3E,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 Clusters 1, 3, and 4, the
   related genes were primarily enriched in pathways related to epithelial
   cell proliferation, leukocyte functions, T-cell regulation, and other
   immune responses. Previous studies have reported that IRI is associated
   with nervous system regulation, immune system regulation, and
   functions^[98]32,[99]33. Therefore, the results suggest that the
   gene-related changes identified via KAS-Seq may be significantly
   related to the molecular mechanisms and clinical symptoms of IRI.

Fig. 3.

   [100]Fig. 3
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   GO enrichment analysis and function exploration of biomarkers. (A)
   Volcano map (0 h vs. 24 h). DEGs significantly changed (p-value < 0.05
   & |log2FoldChange|≥ 0.5) are marked in red (up) or green (down). The
   black dots represent the DEGs with no difference. (B) Volcano map (24 h
   vs. 48 h). DEGs significantly changed (p-value < 0.05 &
   |log2FoldChange|≥ 0.5) are marked in red (up) or green (down). The
   black dots represent the DEGs with no difference. (C) Volcano map (48 h
   vs. 72 h). DEGs significantly changed (p-value < 0.05 &
   |log2FoldChange|≥ 0.5) are marked in red (up) or green (down). The
   black dots represent the DEGs with no difference. (D) The four groups
   identified by Mfuzz analysis showed regulatory trends in
   ischemia–reperfusion time progression. (E) 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.

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

   Since the overall signal level was highest at 48 h post-injury, we
   focused on biomarkers in Cluster 2 that exhibited regulatory trends
   similar to those observed during disease progression (Supplementary
   Table [102]S4). 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. [103]4A and Supplementary Table [104]S6). We used the STRING
   database to generate a PPI network of the 178 intersecting genes. As
   shown in Fig. [105]4B, each node represents a cross-gene-encoded
   protein, each edge represents the correlation confidence between the
   two targets, and the thickness of the edge indicates the strength of
   data support. The P value of PPI enrichment was less than 1.0e-16,
   indicating significant protein interactions in the PPI network.To
   further clarify the intrinsic biological differences among the 178
   immune-related genes, we used the CIBERSORT algorithm to analyze the
   composition of immune cell types, focusing on the 178 genes with a
   major distribution, and found that their proportion was significantly
   increased, particularly in activated memory CD4 T cells (Fig. [106]4C).
   Additionally, we intersected genes from Clusters 1, 3, and 4 with
   immune-related genes to identify 212 differentially expressed genes
   (Fig. [107]S2A and Supplementary Table [108]S7). We used the STRING
   database to generate a PPI network of 212 target genes (Fig. [109]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 (Fig. [110]S2C), which was consistent
   with the results of Cluster 2 (Fig. [111]4C). These results suggest
   that IRI may be associated with T-cell activity through the regulation
   of immune-related genes.

Fig. 4.

   [112]Fig. 4
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   Immune infiltration analysis was performed for immune-related genes.
   (A) Venn diagram shows the intersection between immune-related genes
   and differentially expressed genes (DEGs) in Cluster2; (B) PPI network
   analysis of Cluster2 and immune-related gene intersection; (C)
   Characteristic analysis of 178 intergene-mediated immune cell
   infiltration landscape.

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. [114]4A). According to the maximum cluster centrality (MCC)
   algorithm, the top 10 hub genes were identified in the immune-related
   dataset of Cluster 2 (Fig. [115]5A). A total of 10 genes in the dataset
   were identified as hub genes (IFNG, IL7R, TLR4, IL2RA, CCRL2, CD40,
   CCR7, CCL20, CD5, and CD38). In the PPI network analysis, we identified
   two hub genes: interferon-gamma (IFNG) and the interleukin-7 receptor
   (IL7R). The expression levels of these genes were consistent with the
   overall temporal trend of the signal, showing upregulation from 0 to
   48 h and downregulation from 48 to 72 h post-ischemia–reperfusion
   (Figs. [116]5B,C and [117]2A). Many studies have reported a close
   association between IFNG and the occurrence and progression of IRI,
   confirming our results from KAS-Seq sequencing^[118]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 ([119]GSE9634,
   [120]GSE72646, and [121]GSE23160) (Fig. [122]5D–F). In each dataset,
   the IL7R expression level was increased in the IRI group, which aligns
   with our findings (Fig. [123]5D–F). Additionally, reports have
   indicated a correlation between IFNG and IL7R in the context of other
   diseases^[124]34–[125]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. [126]5G). Through pathway analysis of these target genes, we
   identified a series of typical biological pathways responsible for
   regulating and activating leukocytes and the immune system, as well as
   pathways associated with T-cell activation. These findings are
   consistent with the high expression of T-cell specificity observed in
   our previous analysis, suggesting a critical role for these pathways in
   the immune response to IRI (Fig. [127]4C). IRI is reported to be
   closely related to T-cell activation and immune system
   regulation^[128]37,[129]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.

Fig. 5.

   [130]Fig. 5
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   Hub genes recognized by the cytoHubba plug-in. (A) TOP10 Hub genes
   recognized by cytoHubba plugin. According to the MCC method, the
   gradient of color represents the value of the fraction. (B,C) The
   changing trend of IFNG and IL7R with ischemia–reperfusion injury time.
   (D–F) Expressions of IL7R in [132]GSE9634, [133]GSE72646, and
   [134]GSE23160 (blue represents 0 h, red represents IRI group). (G) Gene
   target networks of IL7R and IFNG in IRI-associated pathways. (Yellow
   represents target genes; Orange represents related genes; Blue
   indicates the functional annotation of the target genes).

Expression of IL7R in cell models

   To validate the aforementioned mechanisms and investigate the
   species-specific differences between pigs and humans, we analyzed the
   expression of IL7R in human T lymphocytes (MOLT-4 and MOLT-6) under
   simulated ischemia–reperfusion injury (IRI) hypoxia-reoxygenation
   conditions. Flow cytometry was employed to detect differences in IL7R
   expression. The results demonstrated that in both MOLT-4 and MOLT-6
   cells cultured under simulated IRI hypoxia-reoxygenation (HR)
   conditions, the expression levels of IL7R were significantly higher
   compared to the isotype control antibody staining group (ISO) and the
   normal culture condition group (Normal) (Fig. [135]6A–G). These
   findings further corroborate our previous sequencing results.

Fig. 6.

   [136]Fig. 6
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   The expression of IL7R in CD4^+ T cells under IRI mimic
   hypoxia-reoxygenation condition analyzed by flow cytometry. (A) Flow
   cytometry analysis of MOLT-4 cells stained with isotype control
   antibody. (B) Flow cytometry analysis of the IL7R expression in MOLT-4
   cells cultured under normal condition. (C) Flow cytometry analysis of
   the IL7R expression in MOLT-4 cells cultured under the IRI mimic
   hypoxia-reoxygenation (HR) condition. (D) Quantitative analysis of the
   IL7R positive MOLT-4 cells under different conditions shown in (A–C).
   (E) Flow cytometry analysis of MOLT-6 cells stained with isotype
   control antibody. (F) Flow cytometry analysis of the IL7R expression in
   MOLT-6 cells cultured under normal condition. (G) Flow cytometry
   analysis of the IL7R expression in MOLT-6 cells cultured under the IRI
   mimic hypoxia-reoxygenation (HR) condition. (H) Quantitative analysis
   of the IL7R positive MOLT-6 cells under different conditions shown in
   (A–C). Data are expressed as mean ± SD. N = 3. ***p < 0.001,
   ****p < 0.0001.

Discussion

   IRI is a complex pathological process involving multiple organs and
   mechanisms. Despite the continuous deepening of our understanding of
   IRI, there remains a significant gap in effective clinical treatment
   options, highlighting the need for further research and development of
   targeted therapies. Future research needs to further explore the
   specific mechanisms of IRI and search for new therapeutic targets and
   strategies^[138]2. Although numerous studies have indicated that the
   pathogenesis of IRI is closely related to immune responses, such as
   T-cell activation, neutrophil burst, and macrophage
   polarization^[139]39–[140]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 demonstrated that the pig model holds
   significant scientific value in IRI studies due to its high degree of
   homology with human physiology^[141]42. Therefore, in our study, we
   established a pig model of IRI and collected blood samples at 0, 24,
   48, and 72 h post-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 h, 24 h, and 48 h and began to decrease
   at 72 h, as shown in Fig. [142]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. [143]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^[144]43–[145]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 these genes. This
   analysis revealed that these 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^[146]51. In
   allo-orthotopic liver transplantation, depleting anti-CD4 antibodies
   can reduce neutrophil and macrophage infiltration and proinflammatory
   gene expression caused by IRI^[147]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^[148]31.
   Consequently, we investigated IL7R as a novel target for IRI and
   validated its differential expression using external datasets. Previous
   studies have demonstrated that IL7R, an essential cell surface
   receptor, promotes T cell survival, proliferation, and differentiation
   primarily through its interaction with IL-7^[149]53–[150]55. In the
   context of IRI, the expression of IL7R may influence immune cell
   function, thereby affecting the recovery and repair processes. Unlike
   previously reported targets, such as oxidative stress, IL7R addresses a
   broader spectrum of immune responses, offering a unique approach to
   mitigate IRI. For instance, NRF2 within the NRF2-ARE pathway functions
   as a transcription factor that activates antioxidant genes and
   diminishes oxidative stress^[151]56–[152]58. While effective, its
   influence is confined to the antioxidant response. Similarly, the
   inhibition of HDAC3 may alleviate inflammation by modulating the
   JAK1/STAT3 signaling pathway, but its effects are restricted to
   specific cell types^[153]59–[154]61. Additionally, ALOX12, a
   lipoxygenase implicated in myocardial IRI, primarily affects lipid
   metabolism by catalyzing the production of peroxidation products from
   polyunsaturated fatty acids, rather than engaging in immune
   regulation^[155]62,[156]63. In contrast, IL7R exerts a more extensive
   impact on immune responses by regulating T cell activation and
   promoting the release of cytokines such as IFN-γ.

   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 associated with IL7R and IFNG are cross-enriched
   in signaling pathways related to immune regulation, including leukocyte
   activation, migration, and T-cell activation pathways. Given that
   previous analyses have revealed that global immune-related genes are
   highly expressed specifically in T cells, we speculate that following
   IRI, the upregulation of IL7R may regulate the upregulation of IFNG,
   thereby influencing T-cell activity. To verify the above mechanisms and
   investigate species-specific differences between pigs and humans, we
   established a human T-lymphocyte model and verified that IL7R
   expression was significantly higher in the model group than in the
   control group. These findings suggest that IL7R is a potential new
   target for IRI treatment.

   Compared to established pathways such as macrophage polarization and
   neutrophil burst, the mechanism of action of IL7R is similar in some
   respects but may present new challenges in others. Macrophage
   polarization plays an important role in IRI, as these cells can
   differentiate into the pro-inflammatory M1 type or the
   anti-inflammatory M2 type, thereby affecting the inflammatory response
   and tissue repair^[157]64,[158]65. IL7R may influence the polarization
   of macrophages by regulating T cell function, thus affecting the
   outcome of IRI. Additionally, neutrophil bursts are a key component of
   the early inflammatory response in IRI, and IL7R may regulate this
   process by influencing interactions between T cells and
   neutrophils^[159]66,[160]67.

   In summary, this study not only provides new insights into clinical
   treatment strategies for IRI but also identifies potential future
   therapeutic targets. Future studies should further explore the
   mechanisms underlying IL7R’s role in IRI and evaluate its clinical
   potential as a therapeutic target.

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. These findings not only deepen our understanding of the
   pathophysiological mechanism of IRI but also open up new ideas and
   potential directions for future clinical treatment. Targeting IL7R is
   expected to provide entirely new strategies for the development of
   therapeutic interventions. For example, anti-IL7R monoclonal antibodies
   can be used to block IL-7 signaling, thereby reducing inflammatory
   responses and tissue damage in IRI. In conclusion, the results of this
   study further expand our understanding of the pathophysiological
   mechanism of IRI and provide new perspectives and potential therapeutic
   clues for future clinical treatment.

Strengths and limitations

   In this study, the KAS-Seq technique was employed for the first time to
   detect single-stranded DNA in peripheral blood leukocytes, aiming to
   evaluate its role in IRI. This approach offers a novel perspective for
   investigating the molecular mechanisms underlying IRI. Furthermore,
   this research delves into the intricate relationship between IRI and
   immune regulation, as well as T cell activation. While prior studies
   have indicated a potential involvement of immune cells in IRI, our
   investigation further elucidates their roles by analyzing changes in
   KAS-Seq signaling at specific temporal points—particularly concerning T
   cell activation and immunomodulatory pathways. These findings
   underscore IL7R as a promising target for therapeutic intervention in
   IRI; specifically, the upregulation of IL7R and IFNG may influence T
   cell activation and significantly impact the progression of IRI.
   Compared to previous investigations, this study not only provides fresh
   insights into clinical treatment strategies for IRI but also identifies
   potential future therapeutic targets.

   Although our study has achieved initial results in exploring the role
   of IL7R in IRI, we must acknowledge some limitations in the study.
   First, due to the small sample size, we may not be able to capture all
   biological variations comprehensively. This sample size limitation can
   lead to statistical inefficiencies, which in turn affect the
   reliability and broad applicability of our findings, and limit our
   ability to dig deeper into the underlying mechanisms. To overcome these
   limitations, we plan to take the following steps in future studies:
   Increase the sample size, which will enhance the statistical power of
   the study and allow us to more accurately assess the role of IL7R in
   IRI. By increasing the sample size, we expect to be able to more fully
   reveal the detailed mechanisms of how IL7R affects immune response and
   tissue damage. Second, while the pig model shares similarities with
   humans in various aspects, there may be differences in the immune
   responses between pigs and humans. These discrepancies could have
   significant implications for understanding the pathological mechanisms
   of IRI and developing effective treatment strategies. Furthermore, our
   current study lacks direct validation using human IRI samples, which
   may impact the clinical translation and applicability of future
   research findings. To address this limitation, future research will be
   directed towards two primary avenues. First, in vitro experiments will
   be conducted utilizing IL7R-expressing cell lines (such as T cells) or
   human primary cells. CRISPR-Cas9 technology will be employed to either
   knock out or overexpress the IL7R gene. Subsequently, these cells will
   be subjected to ischemia–reperfusion conditions to evaluate cell
   viability, apoptosis rates, and the secretion of inflammatory factors.
   Furthermore, cells will be treated with IL-7 agonists or inhibitors to
   further elucidate their effects on cellular function. These in vitro
   studies will provide critical insights into the specific mechanisms by
   which IL7R modulates cellular responses during ischemia–reperfusion
   injury, thereby laying the groundwork for targeted therapeutic
   interventions. Second, in vivo studies using animal models will involve
   the development of IL7R gene knockout or overexpression models. For
   instance, CRISPR-Cas9 technology will be used to knock out the IL7R
   gene in IRI mouse models to validate its in vivo role. By confirming
   the role of IL7R in IRI through these animal models, we will be able to
   bridge the gap between basic research and clinical application,
   ultimately facilitating the development of more effective therapeutic
   strategies for IRI. This approach aims not only to enhance our
   understanding of the mechanisms by which IL7R contributes to IRI but
   also to provide a critical experimental foundation for developing
   targeted therapeutic strategies.

   There are many challenges to translating IL7R into a human therapeutic
   target. First, drug development needs to consider the complex role of
   IL7R in the immune system. IL7R plays a key role in the
   differentiation, development, and maturation of T and B cells, and is
   essential for adaptive immunity and maintenance of immune homeostasis.
   However, abnormal IL7R levels are also associated with immunopathology,
   for example in autoimmune diseases such as type I diabetes and multiple
   sclerosis, as well as chronic inflammatory diseases such as rheumatoid
   arthritis, ankylosing spondylitis, and inflammatory bowel
   disease^[161]68–[162]72. Therefore, drug development targeting IL7R
   requires careful evaluation of its potential off-target effects and
   immune system variabilit. Additionally, current drugs targeting IL7R
   have shown some efficacy in clinical trials, but further optimization
   is still needed.

Conclusions

   In this study, we conducted KAS-Seq sequencing analysis of leukocytes
   in porcine blood following ischemia-reperfusion to investigate the
   consistent changes in gene expression from 0 to 72 h
   post-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.

Supplementary Information

   [163]Supplementary Information.^ (1.4MB, zip)

Acknowledgements