Digital spatial profiling reveals the molecular signatures of BK virus infection in renal transplant recipients

Introduction

BK polyomavirus (BKPyV)-associated nephropathy (BKVAN) is a severe complication in renal transplant recipients, caused by the reactivation of BKPyV due to immunosuppressive therapy, and is characterized by tubulointerstitial nephritis (1). BKVAN occurs in approximately 5% of kidney transplant recipients, and accounts for about 29% of graft losses (2). The diagnosis of BKVAN relies on the histological evaluation of renal biopsy specimens and positive immunohistochemical staining for the SV40 T antigen (3). However, due to focal or medullary-restricted lesions, there is a 33% risk of underdiagnosis (4).

High viral loads of BKPyV (>10⁷ copies/mL in urine or >10³ copies/mL in plasma) are considered markers of reactivation (5), and persistent BK polyomavirus viremia (BKV) is associated with a high risk of developing BKVAN (6). Routine screening for BKV in renal transplant recipients can detect 90% of early BKVAN cases, potentially slowing disease progression (7). Understanding the distinct pathological mechanisms of BKV and BKVAN could improve patient outcomes.

The exact mechanisms by which BKPyV infection and reactivation lead to BKVAN remain unclear. Previous research indicates that renal tubular epithelial cells are the primary sites for BKPyV latency and reactivation (8). Viral replication may disrupt both innate and adaptive immune responses, leading to necrosis and the shedding of epithelial cells (9). The resultant cell damage and persistent inflammation contribute to interstitial fibrosis, tubular atrophy, and progressive nephron loss (10). Interactions among various cell types, particularly epithelial cells, immune cells, and fibroblasts, may play critical roles in BKVAN. An et al. showed that BKPyV can infect nine different human cell types, including primary epithelial, endothelial, and fibroblast cells from various tissues, but these cells exhibit distinct responses (11). Multiple studies have highlighted the significant heterogeneity and complexity of BKPyV infection across different cell types and time points (12,13). Dynamic changes in cell signaling and tissue microenvironments may influence BKVAN progression, affecting the prognosis of renal transplant recipients.

Several key challenges remain in understanding the molecular mechanisms of BKVAN. First, most existing studies rely on bulk-sequencing and single-cell sequencing, which lack spatial information and cannot distinguish molecular features across anatomically distinct regions (13-15). Second, the focal and heterogeneous distribution of BKVAN lesions further complicates molecular characterization using conventional approaches. GeoMx digital spatial profiling (DSP) technology offers a unique technical advantage by enabling the precise localization of gene expression in specific cells or tissue compartments. It provides insights into cell-cell interactions and dynamic microenvironment changes, and has been successfully applied to renal biopsy samples (16,17). In this study, we used DSP to investigate the spatial transcriptomic alterations across different stages of BKPyV infection. By doing so, we aimed to fill the critical gap in understanding how spatially distinct cell populations, including epithelial cells, immune cells, and fibroblasts, contribute to the pathogenesis and progression of BKVAN. We present this article in accordance with the STREGA reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-451/rc).

Methods

Human samples

The study population comprised normal functioning (n=3), BKV (n=2), and BKVAN (n=3) patients who underwent kidney transplantation at the General Hospital of Northern Theater Command between 2017 and 2023. The blood and urine BK viral DNA loads of the patients were monitored monthly for the first 3 to 9 months post-kidney transplantation, and then every 3 months for 10 to 48 months thereafter (18). All the recipients diagnosed with BKVAN had urinary DNA loads exceeding 1.0×10⁷ copies/mL (19). Independent of the urinary BKPyV levels, the patients with plasma BK viral loads ≥1.0×10³ copies/mL were classified as having BKV. Recipients with persistent BKV and unexplained elevations in serum creatinine underwent renal allograft biopsy, with SV40 immunohistochemical staining used to confirm the BKVAN diagnosis (20). The clinical characteristics of the patients are shown in Table 1. Tissues derived from the kidney transplant patients via renal biopsy were preserved as formalin-fixed and paraffin-embedded (FFPE) samples, and securely stored in the pathology database. This study was approved by the Medical Ethics Committee of the General Hospital of Northern Theater Command (No. Y[2024]245), and informed consent was taken from all the patients. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Histological analyses

The FFPE sections were stained with hematoxylin and eosin for pathological analysis. Immunohistochemistry was also performed on these samples using anti-simian virus 40 (SV40) antibodies. The results were evaluated by pathologists in a single-blind manner. The positive area of SV40 was assessed by Image J (21).

DSP

NanoString GeoMx DSP was employed to conduct digital spatial RNA profiling on the FFPE sections (22). These sections were prepared using the GeoMx DSP Protein Slide Prep Kit (Nanostring). Prior to analysis, the FFPE sections underwent pyrorepair in Tris-ethylenediaminetetraacetic acid buffer and were enzymatically digested with proteinase K. Following digestion, the samples were incubated at 140 ℃ to facilitate further nucleic acid denaturation, resulting in fragments ranging from 140 to 160 nucleotides. In situ hybridization was conducted using antibodies from the Whole Transcriptome Atlas (WTA) Panel as specified in the protocol. The slides were then stained with the immunofluorescent markers provided in the GeoMx Solid Tumor TME Morphology Kit Human Protein Compatible (Nanostring), and the GeoMx Panel and Seqcode kit (Nanostring), including Nuclear Stain SYTO13 for nuclear visualization, pan-cytokeratin (PanCK) for epithelial cell identification, CD45 for hematopoietic cell identification, and smooth muscle actin (SMA) for smooth muscle cell and fibroblast marking.

Regions of interest (ROIs) were delineated based on histological characteristics and cellular density, using rectangular selections. These ROIs were subsequently subdivided into multiple areas of interest (AOIs). Library preparation was conducted in accordance with the NanoString GeoMx Library Preparation Manual. The purified products underwent stringent quality control (QC) checks using both QFX and Qsep100 analyzers. Finally, the prepared libraries were analyzed through whole transcriptome sequencing following established protocols.

Quality control and normalization

QC was executed using thresholds recommended by NanoString. For each AOI/ROI, the following criteria were applied: raw reads >1,000, aligned reads percentage >80%, and sequencing saturation >50%. The negative control (no template control) count was >1,000. The negative probe count, which was used to quantify technical noise in the WTA experiments, was considered significant if it reached five or higher. Each AOI/ROI was required to have >200 nuclei counts and a surface area >16,000 µm². The DSP probes targeted 18,676 genes, and a ROI was excluded if it failed to capture 90% of these genes, or if the captured genes exhibited very low expression; the threshold was set at a count of one to determine low expression. The limit of detection (LOD) for the targets was calculated using the following formula: LOD = GeoMean (NegProbe) × GeoSD (NegProbe)^threshold. The threshold value was set to 2.0. Normalization was performed using the upper quartile (Q3) method, wherein gene expression in the 75th percentile served as the benchmark. The probes that failed the QC were eliminated from all the ROIs. Only the ROIs that passed the QC were entered into the downstream analysis (Figure S1). All the subsequent analyses were conducted using R software (version 4.0.3).

Identification of differentially expressed genes (DEGs) and functional enrichment

An unpaired t-test was used to perform the differential expression analysis of disease across various regions and stages of viral infection to investigate the effects of BKPyV on gene expression. Adjustments for multiple comparisons were made using the Benjamini-Hochberg method, with the screening parameters set to a false discovery rate <0.05 and |log₂fold change| >1. To further understand the biological implications of the DEGs, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was used to analyze the protein-protein interactions. Enrichment analyses through WikiPathways and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted to elucidate the biological processes involving the DEGs, maintaining an adjusted P value (pval_adj) threshold <0.05. The key genes were ranked by intersecting the top 10 genes identified by the following four algorithms in Cytoscape: maximum clique centrality, maximum neighborhood component, degree, and edge percolated component. Additionally, cell deconvolution was conducted using the SpatialDecon package (version 0.99.1) (https://github.com/Nanostring-Biostats/SpatialDecon/) to analyze the cellular composition in the context of the spatial gene expression data.

Statistical analysis

All statistical analyses were performed using R software (version 4.0.3). Differential expression analysis was conducted using the unpaired t-test, and P values were adjusted using the Benjamini-Hochberg method. An adjusted P value <0.05 was considered statistically significant.

Results

Histopathological analysis of the study cohort

This study included renal transplant normal functional (n=3), BKV (n=2), and BKVAN (n=3) patients. There were no significant differences between the included patients in terms of age or gender, and the included patients had similar underlying renal conditions (Table 1). The renal histology of the patients in the normal group was unremarkable. Conversely, the patients in the BKV group displayed renal interstitial edema, tubular epithelial cell degeneration, and focal tubular atrophy (Figure 1A). While the patients in the BKVAN group displayed marked mononuclear cell infiltration in the renal interstitium, and nuclear anisotropy in tubular epithelial cells. Immunohistochemical staining for SV40 revealed that the patients in the normal and BKV groups were negative for SV40, while those in the BKVAN group exhibited strong SV40 positivity (Figure 1B). Some SV40-infected tubular cells were necrotic and detached into the lumen or infiltrated the renal interstitium. The three BKVAN patients had SV40-positive areas of 2.675%, 0.156%, and 3.83%, respectively.

Figure 1 Histopathological analyses of the study cohort. Histologic evaluation of kidney transplant recipients categorized into the following three groups: normal group (n=3), BKV group (n=2), and BKVAN group (n=3). (A) Representative hematoxylin and eosin staining images for each group, illustrating the histopathological differences across the cohort. The left panel shows a 100× field, and the black square is magnified at 400× in the right panel. Scale bar =200 µm. (B) Immunohistochemical staining for SV40 large T antigen, highlighting polyomavirus infection in the renal tubular epithelial cells in the BKVAN group. The left panel shows a 100× field, and the black square is magnified at 400× in the right panel. Scale bar =200 µm. (C) Workflow schematic of GeoMx DSP. Created by Figdraw (www.figdraw.com). Formalin-fixed and paraffin-embedded renal biopsy tissues were analyzed using four-color immunofluorescence staining targeting PanCK (an epithelial marker), CD45 (an immune marker), SMA (a fibroblast marker), and nuclear stain SYTO13, and a total of 71 regions of interest were selected. Scale bar =1 mm. (D) Representative image of the four-color immunofluorescence staining used for the region of interest selection. Scale bar =200 µm. BKV, BK polyomavirus viremia; BKVAN, BK polyomavirus-associated nephropathy; FFPE, formalin-fixed and paraffin-embedded; ROI, region of interest; SMA, smooth muscle actin.

In total, 71 ROIs were delineated in the renal transplant normal functional patients (ROI n=18), BKV patients (ROI n=26; 1 ROI was excluded due to QC failure), and BKVAN patients (ROI n=27) (Figure 1C). PanCK was used to label the epithelial cells, CD45 to label the immune cells, and SMA to label the fibroblasts. The DSP four-color fluorescence analysis results indicated that the BKV patients exhibited mild hyperplasia of tubular epithelial cells, focal inflammatory cell aggregation in the interstitium, occasional tubulitis, and some degree of interstitial fibrosis (Figure 1D). Conversely, the BKVAN patients showed pronounced tubular epithelial cell hyperplasia, extensive inflammatory cell infiltration in the interstitium, occasional glomerulitis, and more severe interstitial fibrosis.

Gene expression profiling of kidney transplant patients during BKPyV infection

We analyzed eight kidney tissue FFPE samples using GeoMx DSP technology. The gene expression profiles for each ROI are shown in Figure 2A. The ROIs were categorized into three groups based on cellular markers (PanCK⁺, CD45⁺, and SMA⁺). The principal component analysis (PCA) revealed that in the PanCK⁺ segment, the BKV group differed significantly from the normal and BKVAN groups, while most BKVAN ROIs, except for two from the second sample, also displayed distinct expression patterns (Figure 2B). In the CD45⁺ segment, the normal group differed slightly from the BKV and BKVAN groups. In the SMA⁺ segment, the samples from the normal group were tightly clustered, while those from the BKV and BKVAN groups were more dispersed, indicating higher inter-sample heterogeneity. These findings suggest that BKPyV infection induces transcriptional changes not only in epithelial cells (PanCK⁺) but also in surrounding immune and stromal cells. We then performed differential expression analyses across different disease states and segments (Figure 2C-2E). As expected, fewer differences were observed between the BKV and BKVAN groups in the CD45⁺ and SMA⁺ segments, while more significant differences were noted in the PanCK⁺ segment.

Figure 2 Gene expression profiling across different stages of BK virus infection in kidney transplant recipients. (A) Heatmap depicting gene expression profiles of distinct groups and tissue segments analyzed using GeoMx DSP transcriptomic sequencing. (B) Principal component analysis plots illustrating the separation between groups and segments, highlighting the distinct gene expression signatures associated with each stage of BK virus infection. (C) Volcano plots showing the DEGs in the PanCK⁺, CD45⁺, and SMA⁺ segments in the BKV group compared to the normal group. Red and blue dots represent DEGs (false discovery rate <0.05 and |log2fold change| >1), with red indicating the upregulated genes in the BKV group and blue indicating the downregulated genes. Black dots represent genes with no significant difference. (D) Volcano plots of the DEGs in the PanCK⁺, CD45⁺, and SMA⁺ segments in the BKVAN group compared to the normal group. Red and blue dots represent DEGs (false discovery rate <0.05 and |log2fold change| >1), with red indicating the upregulated genes in the BKV group and blue indicating the downregulated genes. Black dots represent genes with no significant difference. (E) Volcano plot comparing the DEGs in the PanCK⁺, CD45⁺, and SMA⁺ segments between the BKVAN and BKV groups. Red and blue dots represent DEGs (false discovery rate <0.05 and |log2fold change| >1), with red indicating the upregulated genes in the BKV group and blue indicating the downregulated genes. Black dots represent genes with no significant difference. The top five upregulated and downregulated genes are highlighted and labeled. BKV, BK polyomavirus viremia; BKVAN, BK polyomavirus-associated nephropathy; DEGs, differentially expressed genes; DSP, digital spatial profiling; FC, fold change.

In the comparison of the normal and BKV groups (Figure 2C), 461 DEGs (262 upregulated and 199 downregulated) were identified in the PanCK⁺ segment (Table S1). The upregulated genes were associated with the immune response (B2M, HLA-B, HLA-E, HLA-A, and DEFB1) and metabolic regulation (ATP6AP2, ATP5IF1, NDUFA5, MDH1, and OGDHL), while the downregulated genes were linked to protein synthesis and transcriptional regulation (TMA7 and ELOA2). In the CD45⁺ segment, 744 DEGs (479 upregulated and 265 downregulated) were detected (Table S2). The upregulated genes included those involved in energy metabolism (NDUFA1, ATP5IF1, and TXN), the immune response (HLA-DPA1, HLA-DRB1, CD74, and APOE), and innate immunity (CXCL9, CTSB, LYZ, SERPINA1, LGMN, and CTS), while the downregulated genes were primarily associated with protein synthesis and structural integrity (TMA7, ELOA2, ELOA3P, KRT38, MYO5B, and KRTAP9-6). In the SMA⁺ segment, 620 DEGs (414 upregulated and 206 downregulated) were identified (Table S3). The upregulated genes were related to antigen presentation (B2M, HLA-DRB1, HLA-B, HLA-DRA, CD74, HLA-DQB1, and HLA-DPA1), extracellular matrix (ECM) remodeling and fibrosis (COL3A1, LUM, and SPP1), and lipid metabolism (APOE and APOL1).

In the comparison of the normal and BKVAN groups (Figure 2D), 558 DEGs (332 upregulated and 226 downregulated) were identified in the PanCK⁺ segment (Table S4). The downregulated genes were related to protein synthesis and modification (TMA7 and USP17L3) and the tumor immune response (BAGE5), while the upregulated genes were associated with humoral immunity (IGHM and PIGR), antibody-mediated injury (HLA-DRB1), and monocyte/macrophage activation (CD74). In the CD45⁺ segment, 271 DEGs (258 upregulated and 13 downregulated) were identified (Table S5). The downregulated genes included those involved in renal function (UMOD) and lipid metabolism (DHRS1), while the upregulated genes were linked to humoral immunity (HLA-DQA2), innate immunity (LYZ), and other immune responses. In the SMA⁺ segment, 241 DEGs (161 upregulated and 80 downregulated) were identified (Table S6).

In the comparison of the BKV and BKVAN groups (Figure 2E), 157 DEGs (94 upregulated and 63 downregulated) were identified in the PanCK⁺ segment (Table S7). The downregulated genes were associated with cellular structure and function (CALB1, CA2, ATP6V0A4, CLDN8, and CLCNK), while the upregulated genes were related to the immune response (IGHM, HLA-DQA2, CD68, CD4, TYROBP, and LYZ) and complement activation (C1QC and C1QB). Notably, the upregulation of SOCS3, a negative regulator of inflammation, suggests an overactive inflammatory response in BKVAN patients (23). The significant upregulation of HAVCR1 (KIM-1), a marker of kidney injury, indicates severe epithelial cell damage in BKVAN (24). In the CD45⁺ segment, 34 DEGs (14 upregulated and 20 downregulated) were identified (Table S8). The upregulated genes were associated with humoral immunity (CD19, RASGRP2, BANK1, CD22, and FCMR), innate immunity (SPIB), and cellular immunity (HLA-DQA2), while the downregulated genes were linked to metabolism (FBP1, ALDOB, PDK4, and GSTA1). In the SMA⁺ segment, 33 DEGs (8 upregulated and 25 downregulated) were detected (Table S9). The upregulated genes were related to B-cell activation (HLA-DQA2, IGHM, and CD79A), while the downregulated genes were related to ECM remodeling (ITGA3, SERPINE1, SPARCL1, and SORBS2).

Taken together, the DEGs revealed that the progression from viral infection to the BKV stage is marked by the upregulation of genes associated with innate immunity and metabolic processes. Conversely, the transition to the BKVAN stage is characterized by the enhanced expression of adaptive immune genes, as well as the downregulation of genes related to cellular structure and function.

KEGG and WikiPathways enrichment analyses were conducted to explore pathway differences across spatial segments and disease progression (DEGs from BKV vs. normal and BKVAN vs. BKV) (Figure 3A,3B). The KEGG analysis indicated that these genes were significantly enriched across all three segments (PanCK⁺, CD45⁺, and SMA⁺) in pathways related to viral infection (Epstein-Barr virus infection) and the immune response (phagosome, antigen processing, and presentation). In the early stage of BKPyV infection (BKV vs. normal), the DEGs in the PanCK⁺ segment were uniquely enriched in pathways associated with protein synthesis (ribosome), distinguishing this segment from CD45⁺ and SMA⁺. This distinction was further emphasized in the WikiPathways analysis, where the PanCK⁺ segment showed a pronounced enrichment in ribosome-related processes. Additionally, T-cell-mediated immune responses (T1, T2, and Th17) were prominent in BKV vs. normal group, which is consistent with the activation and proliferation of T cells in early response to BKPyV infection (25). In BKVAN vs. BKV, although overall enrichment was weak, the upregulated pathways in the CD45⁺ and SMA⁺ segments were mainly metabolism-related, indicating persistent metabolic reprogramming during disease progression.

Figure 3 Gene expression profiling analysis associated with the progression of BK virus infection. (A) KEGG pathway analysis of the DEGs across BKV vs. normal and BKVAN vs. BKV comparisons in different segments (PanCK⁺, CD45⁺, and SMA⁺). (B) WikiPathways analysis of the same DEGs, providing insights into the biological processes implicated in BK virus progression. (C-E) Identification of the hub genes in different segments using Cytoscape. (C) Hub genes identified in the PanCK⁺ segment, (D) in the CD45⁺ segment, and (E) in the SMA⁺ segment. BKV, BK polyomavirus viremia; BKVAN, BK polyomavirus-associated nephropathy; DEGs, differentially expressed genes; FDR, false discovery rate; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Expression profiling of genes associated with the onset of BKPyV infection

To investigate the molecular changes associated with BKPyV infection, we identified the DEGs by comparing the normal group with the BKV and BKVAN groups. The DEGs that exhibited consistent expression trends (either significantly upregulated or downregulated) in both comparisons were selected. This analysis yielded 171 intersecting genes in the PanCK⁺ segment, 177 in the CD45⁺ segment, and 133 in the SMA⁺ segment. The protein-protein interaction analysis revealed that most of these intersecting genes formed coherent networks within each segment (Figure S2).

The key genes associated with BKPyV infection were identified using Cytoscape. In the PanCK⁺ segment, RPL4, RPL30, RPS8, and CD74 were identified (Figure 3C). RPL4, RPL30, and RPS8 encode ribosomal proteins (26), which suggests that BKPyV may exploit host ribosome biosynthesis to facilitate viral replication and mediate immune evasion (27). In the CD45⁺ segment, HLA-DRA, CD74, B2M, and CD4 were identified as key genes (Figure 3D). These genes are involved in antigen presentation and immune regulation. The SMA⁺ segment revealed similar key genes to those identified in the CD45⁺ segment, including B2M, HLA-DPA1, HLA-DRA, CD74, and CD4 (Figure 3E), suggesting shared immunoregulatory mechanisms across the two segments. B2M plays a role in cellular immunity. Urinary B2M levels can serve as a marker of renal tubular injury and dysfunction (28).

Expression profiling of genes related to disease progression

To identify the genes associated with disease progression, we performed an intersection analysis of the DEGs between the normal vs. BKV groups, and BKV vs. BKVAN groups, selecting the genes with consistent expression trends across both groups (Figure 4A). In the PanCK⁺ segment, HLA-DPA1 and LYZ were notably upregulated. Lysozyme LYZ is an essential component of the innate immune system and is commonly used in the Cre recombinase system to elicit antiviral responses (29). In the CD45⁺ segment, HLA-DQA2 was notably upregulated, while in the SMA⁺ segment, IGHM was significantly upregulated. IGHM is associated with pro-B cell differentiation, which has been implicated in mediating inflammation and fibrosis in conditions such as diabetic nephropathy (30,31). Notably, HLA-DPA1 and HLA-DQA2, both major histocompatibility complex (MHC) class II molecules responsible for antigen presentation, are consistently upregulated during the progression of BKPyV infection, and may indicate a sustained immune response in patients (32).

Figure 4 Gene expression profiling with viral progression and BKVAN specific signatures. (A) Identification of the intersecting DEGs with consistent expression trends across comparisons between the BKV vs. normal groups, and the BKVAN vs. BKV groups in different tissue segments (PanCK⁺, CD45⁺, and SMA⁺). (B) Identification of the intersecting DEGs showing consistent expression trends across comparisons between the BKVAN vs. normal groups, and the BKVAN vs. BKV groups in the same segments (PanCK⁺, CD45⁺, and SMA⁺). BKV, BK polyomavirus viremia; BKVAN, BK polyomavirus-associated nephropathy; DEGs, differentially expressed genes.

Analysis of unique gene expression profiles in BKVAN

As stated above, the patients in the BKVAN group showed positive SV40 staining, while those in the other two groups showed negative SV40 staining. This prompted further investigation into whether BKVAN has a unique gene expression profile. An intersection analysis of the DEGs between the normal vs. BKVAN groups, and BKV vs. BKVAN groups was conducted, focusing on the genes with consistent expression trends across both comparisons (Figure 4B). In the PanCK⁺ segment, 53 intersecting genes were identified. Notably, we found that several genes, including PLTP, TNFAIP2, IRF7, and APOC1, are specifically upregulated in BKVAN and may be unique to this condition. APOC1 and PLTP are associated with lipid metabolism, which could influence virus replication and viral envelope formation (33). Additionally, APOC1 promotes the M2 polarization of macrophages through interactions with CD163 and CD206 (34). IRF7 is associated with the interferon-gamma (IFNγ) response. Previous research has shown that BKPyV infection in renal transplant patients is linked to the overexpression of the IFNγ gene, which may contribute to immune dysregulation (35). In the CD45⁺ segment, only HLA-DQA2 and HLA-DQA1 were detected as intersecting genes. In the SMA⁺ segment, the following six intersecting genes were identified: IGHM, ADAM15, HLA-DQA2, DSTN, HSPB1, and SPARCL1. ADAM15, which is expressed by activated leukocytes and fibroblasts in vitro, has been associated with tissue remodeling (36). HSPB1 (HSP27) has been shown to facilitate enterovirus A71 replication by enhancing viral translation via the internal ribosomal entry site, suggesting its potential role in promoting viral infection (37). The endothelial overexpression of SPARCL1 leads to harmful lung inflammation, the generation of M1-like macrophages, and an increase in pro-inflammatory factors (38). The specific roles of these genes in the pathogenesis of BKPyV infection require further investigation.

Cellular changes in renal transplant patients during BKPyV infection

An analysis of the immune cell landscapes in the renal transplant patients during BKPyV infection revealed distinct patterns of immune cell distribution across different tissue segments and stages of infection (Figure 5A,5B). For instance, the number of B memory cells infiltrating the PanCK⁺ and SMA⁺ segments increased as BKPyV infection progressed, while their infiltration was least abundant in the CD45⁺ segment in the BKV group. Additionally, there was an upward trend in the number of CD4⁺ memory T cells, CD8⁺ memory T cells, macrophages, and mature dendritic cells (mDCs) infiltrating the PanCK⁺ and SMA⁺ segments during the course of BKPyV infection. Conversely, the number of neutrophils and regulatory T cells infiltrating these segments decreased as the infection advanced. These findings suggest an activated immune response in renal transplant patients following BKPyV infection, potentially indicating an active inflammatory process. This observation aligns with findings from Lubetzky et al. (39) support the notion that the immune system is engaged in combating BKPyV in this patient population.

Figure 5 Immune cell profiling during BK virus infection. (A) Cellular abundance analysis based on expression data using deconvolution methods. Bar graphs depict the distribution of 18 immune cell types across different tissue segments (PanCK⁺, CD45⁺, and SMA⁺) and groups (normal, BKV, and BKVAN). (B) Comparative analysis of the abundance of nine selected immune cell types across different tissue segments, highlighting differences in immune cell composition during the progression from normal to BKVAN. BKV, BK polyomavirus viremia; BKVAN, BK polyomavirus-associated nephropathy; DC, dendritic cell; NK, natural killer.

Discussion

We evaluated the biological changes associated with the progression from BKPyV infection to BKVAN in kidney transplant recipients by conducting transcriptomic analyses across multiple regions using GeoMx DSP technology. Additionally, we identified candidate genes linked to BKPyV infection and progression as potential biomarkers, providing a more comprehensive understanding of the pathogenesis of BKVAN.

On BKPyV activation in renal transplant recipients, we observed that epithelial cells, immune cells, and fibroblasts exhibit similar functional roles but are characterized by cellular compartmental heterogeneity. In addition to the pathways associated with viral infection, significant alterations were observed in immune, metabolic, and ECM-related processes. Fibroblasts are recognized as key effector cells in fibrosis and tissue remodeling, and they likely play a crucial role in renal interstitial fibrosis (40). Similarly, the fibroblast segment showed a strong association with ECM-related genetic alterations in this study. Additionally, the KEGG and Wikipathways results suggest that allograft rejection remains a significant concern. The need to reduce immunosuppression in BKVAN patients may increase the risk of allograft rejection (41). Importantly, we also observed pronounced inter- and intra-patient heterogeneity in spatial gene expression. The same cell type (e.g., PanCK⁺ epithelial cells) exhibited marked inter-patient variability in gene expression, as evidenced by distinct clustering patterns in PCA, indicating individualized responses to BKPyV infection. Furthermore, within a single patient, different compartments exhibited distinct gene expression patterns, with the epithelial region displaying the most dynamic changes during disease progression. This was supported by its unique enrichment in ribosome-related pathways in early-stage infection, indicating compartment-specific viral activity. Given that BKPyV has been shown to regulate CXCL10 translation rather than transcription to drive inflammation (42) and that many viruses manipulate ribosomal function to evade host immunity (43), it is implied that BKPyV may preferentially exploit epithelial ribosomes to facilitate replication and immune evasion. This heterogeneity may partly explain the variable clinical manifestations and progression rates of BKVAN in renal transplant recipients (44) and highlights the need for compartment-specific molecular assessments in diagnostic and therapeutic strategies.

The roles of both innate and adaptive immunity in BKVAN pathogenesis have been well documented (5,45). BKPyV may evade cytotoxicity by interacting with natural killer (NK) cell-associated ligands through viral microRNAs, thereby inhibiting NK cell recognition (46). Increased dendritic cell (DC) counts can enhance immune responses against BKPyV by promoting virus-specific CD8⁺ T cell proliferation (47). However, CD1c⁺ mDCs, unlike conventional DCs, may capture BKPyV particles via the GRAF-1 endocytic pathway, allowing these virions to be efficiently delivered to susceptible cells while evading antibody-mediated neutralization (48). Previous research has also highlighted changes in T cell subsets; BKPyV infection has been linked to the increased infiltration of type 1 T helper and type 17 T helper cells (14). Consistent with our observations in the CD45⁺ segment, the numbers of CD4⁺ and CD8⁺ T cells increase in allografts as BKVAN progresses (49). Therefore, cellular immune responses play a critical role in viral clearance (50), although virus-specific CD4⁺ and CD8⁺ T cell immunity is often significantly impaired in patients with high BKV viral loads (51).

Key genes linked to BKPyV infection were identified, many of which are associated with immunoregulation and have not been previously reported in renal transplant recipients with BK infection. CD74 was consistently upregulated across the PanCK⁺, CD45⁺, and SMA⁺ segments, alongside MHC class II molecules (HLA-DRA, HLA-DPA1) in the CD45⁺ and SMA⁺ segments. CD74 functions as a chaperone for MHC class II molecules, facilitating antigen presentation by stabilizing the MHC complex and directing its trafficking (52,53). Macrophages and DCs initiate adaptive immunity by presenting antigens to CD4⁺ and CD8⁺ T cells via CD74 and MHC class II molecules (52,54). This interaction between CD74 and MHC class II molecules may represent a key immunoregulatory axis across epithelial, immune, and fibroblast cells during BKPyV infection. Previous research has reported that HLA-DR is undetectable in renal tubular cells from BKVAN patients, but is present in adjacent mesangial inflammatory cell infiltrates (55). Thus, renal tubular epithelial cells may not be directly involved in antigen presentation but could modulate immune responses through CD74 upregulation. Moreover, CD74 has been reported to be involved in kidney injury and fibrosis, and to act as a signaling molecule mediating intercellular communication between immune cells and renal tubular epithelial cells (56,57). The continuous upregulation of CD74 and MHC class II genes across different segments during BKVAN progression, combined with the critical role of CD74 in immune regulation and renal function, suggests that the CD74-MHC II axis may serve as a promising immunoregulatory target for BKVAN. Future studies are warranted to validate the role of the axis in BKVAN and assess its potential as a therapeutic target to modulate immune responses and improve clinical outcomes.

A major limitation of this study is the small sample size, which may affect the statistical power of the analysis and the generalizability of the results. However, BKVAN remains a relatively rare complication of kidney transplantation, with a reported incidence of only 5–6% within the first year after surgery in China (58). Importantly, definitive diagnosis of BKVAN requires renal biopsy and immunohistochemical confirmation, which limits the availability of high-quality tissue samples. Moreover, as this study is based on bioinformatic analysis alone, further experimental validation is needed to confirm the functional relevance of the identified key genes in the progression of BKVAN. Despite this, our findings provide spatially resolved molecular features, offering a preliminary exploration into the pathogenesis of BKVAN.

Conclusions

In conclusion, our findings provide new insights into the mechanisms underlying BKVAN. First, distinct expression profiles were observed across different cellular segments. Notably, the PanCK⁺ segments were more prominently associated with BKPyV infection and primarily involved in ribosomal protein synthesis. The SMA⁺ segment was linked to ECM-related processes, while the SMA⁺ and CD45⁺ segments may share common immune-mediated mechanisms in BKPyV infection. Second, the host response triggered by BKPyV infection appears to include immune activation, antigen presentation, and changes in cellular function related to viral defense. Finally, the key genes identified in this study as being associated with BKPyV infection may serve as potential targets for future antiviral therapies and infection control strategies.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STREGA reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-451/rc

Data Sharing Statement: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-451/dss

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-451/prf

Funding: This study was supported by the Clinical Study on the Evaluation Model for Organ Donor Assessment in Donation After Citizen’s Death Based on Donor-Derived DNA Using High-Throughput Sequencing (No. 2022JH2/101500039).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-451/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was approved by the Medical Ethics Committee of the General Hospital of Northern Theater Command (No. Y[2024]245) and complied with the Declaration of Helsinki and its subsequent amendments. Written consent was obtained from all participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: L. Huleatt)