Immune Imbalance in Primary Membranous Nephropathy at Single-cell Resolution

Abstract

Background:

Primary membranous nephropathy (pMN) often progresses to end-stage renal disease (ESRD) in the absence of immunosuppressive therapy. The immunological mechanisms driving pMN progression remain insufficiently understood.

Methods:

We developed a single-cell transcriptomic profile of peripheral blood mononuclear cells (PBMCs) from 11 newly-diagnosed pMN patients and 5 healthy donors. Through correlation analysis, we identified potential biomarkers for disease stratification and poor prognosis.

Results:

Expression levels of several proinflammatory factors were significantly increased in patients compared to healthy donors, such as interleukins (IL1B, IL8, and IL15) and interferon G (IFNG). Multiple pattern recognition receptors involved in proinflammatory signaling were also upregulated in patients, including NOD-like receptors (NLRs) (NLRP1, NLRP3, and NLRC5), RNA helicases (DDX58, IFIH1, DHX9, and DHX36), cGAS (cyclic GMP-AMP synthase) and IFI16 (interferon gamma inducible protein 16). Additionally, human leukocyte antigen molecules HLA-DQA1 and HLA-DRB1 enriched in memory B cells were upregulated in patients. More importantly, we found that the genes for antiviral defense response were significantly elevated in high-risk patients relative to the low-risk group. More than twenty genes were negatively correlated with estimated glomerular filtration rate (eGFR), such as BST2 (bone marrow stromal cell antigen 2) and SLC35F1 (solute carrier family 35 member F1). Their predicted values were confirmed in a larger population with nephrotic syndrome or other chronic kidney diseases from a public database. Furthermore, we developed a series of scoring systems for distinguishing high-risk patients from low- and moderate-risk individuals.

Conclusions:

Our study provides insight into the immunological mechanism of pMN and identifies numerous biomarkers and signaling pathways as potential therapeutic targets for managing the progression of high-risk pMN.

Graphical abstract

Keywords

primary membranous nephropathy (pMN) / single-cell RNA sequencing / peripheral blood mononuclear cells (PBMCs) / immune imbalance / biomarkers

1. Introduction

Membranous nephropathy (MN) is a pathologically diagnosed disease of the kidney glomerulus, which is the leading cause of nephrotic syndrome in adults [1]. The morphological feature of MN is the presence of immune deposits in the subepithelial space of the glomerular filtration barrier. The immune deposits consist of the relevant antigens, autoantibodies and complement components, which might trigger an inflammatory response, leading to further damage and increased permeability of the glomerular basement membrane (GBM) [2]. Proteinuria and generalized edema are major clinical manifestations of MN. Patients with MN may have also other concurrent diseases, such as other autoimmune disorders, malignancies or infections. These patients are often diagnosed as secondary MN (sMN). Those without identifiable underlying cause are considered to be primary MN (pMN) [3]. In our study, we focus on newly-diagnosed pMN with circulating autoantibodies against the phospholipase A2 receptor 1 (PLA2R1) [4].

Prognosis of pMN is highly variable. Among patients with persistent nephrotic syndrome, 40 to 50% of them will progress into end-stage renal disease (ESRD) within 10–15 years, in part due to an incomplete understanding of its pathogenic mechanism [1]. Recent studies attempted to elucidate the underlying mechanisms of pMN using single-cell RNA sequencing of kidney biopsy tissues [5, 6, 7]. However, due to few immune cells in these tissues, it remains unclear about immune abnormalities in patients with newly diagnosed pMN, especially in those at high risk. Despite two study analyzed B cells from patients with pMN, only three blood samples with PBMCs were included [8, 9]. No comprehensive single-cell immune profile of adult with pMN has been reported to date.

Peripheral blood contains a large number of immune cells, making it ideal for mapping the single-cell immune landscape of patients with pMN. In our study, we identified a series of high-expression genes in high-risk patients compared to low-risk population, which plays a critical role in antiviral defense response. Meanwhile, we found a set of novel biomarkers for distinguishing patients from healthy donors.

2. Materials and Methods

2.1 Patients and Samples

In this study, we included 11 patients with newly diagnosed pMN, all of whom had circulating autoantibodies against phospholipase A2 receptor 1 (PLA2R1). None of the patients had a history of other diseases or treatment of steroids and immunosuppressive drugs in the past year. One patient refused to undergo kidney biopsy. Based on the KDIGO 2021 guideline for the management of glomerular diseases [10], the patients were categorized into three groups: low-risk group (3 patients), moderate-risk group (4 patients) and high-risk group (4 patients).

Peripheral venous blood samples were collected from patients before treatment. PBMCs were isolated using density gradient centrifugation with Ficoll-Paque from 5 milliliter fresh anticoagulated blood. The isolated cells were resuspended in cryopreservation medium [900 µL of fetal bovine serum supplemented with 100 µL of dimethyl sulfoxide (DMSO)] for long-term storage liquid nitrogen.

2.2 Single-Cell RNA Library Preparation and Sequencing

Cryopreserved PBMCs from all patients were thawed, and cell viability was assessed, consistently exceeding 90%. Single-cell suspensions containing 10,000 to 20,000 cells were loaded for library construction using the Chromium Single Cell 3^′ Library (10\times Genomics, Inc., Pleasanton, CA, USA) following the manufacturer’s protocol. The quality of the purified libraries was evaluated, and sequencing was performed on an Illumina NovaSeq X Plus platform (Illumina, Inc., San Diego, CA, USA) using 150-bp paired-end reads.

2.3 Single-Cell RNA-seq Data Processing

We performed single-cell RNA sequencing (scRNA-seq) on PBMCs from 11 patients with pMN and integrated a published scRNA-seq dataset from 5 healthy donors as a control group [11]. Samples were stratified into four groups correspondingly: normal (healthy donors), low (low-risk patients), moderate (moderate-risk patients), and high (high-risk patients). Raw sequencing data were processed using the Cell Ranger Software Suite 7.1.0 (10\times Genomics, Inc., Pleasanton, CA, USA), aligning reads to the human reference genome (refdata-gex-GRCh38-2020-A). Filtered count matrices were imported into Seurat 4.3 (https://satijalab.org/seurat/) for downstream analysis. Quality control criteria included: 500–5000 detected genes per cell, total unique molecular identifier (UMI) count \le20,000, and mitochondrial gene expression \le10%. Doublets were identified and removed using DoubletFinder 2.0.3 (https://github.com/chris-mcginnis-ucsf/DoubletFinder). The cleaned data were normalized and scaled using Seurat’s built-in functions.

2.4 Batch Effect Correction and Cell Type Annotation

To integrate cells from different individuals and risk levels, we employed Harmony 1.2 (https://github.com/immunogenomics/harmony) to correct batch effects and create a shared embedding [12, 13, 14, 15]. Dimensionality reduction and clustering were performed using 2000 highly variable genes and 30 principal components. Marker genes for each cluster were identified using Seurat’s FindAllMarkers function with default parameters. Initial cell type annotation was performed using CellTypist 1.6.3 (https://www.celltypist.org/) with the ‘Immune_All_Low.pkl’ model [16], followed by manual refinement based on known marker genes from the literature.

2.5 Differential Expression and Enrichment Analysis

To explore the differences between disease and control groups, we identified differentially expressed genes (DEGs) for each cell type using Seurat’s FindMarkers function, comparing patients (different risks) with healthy donors. Genes with |log2FC| \ge0.5 and adjusted p-value \le 0.05 were considered significant DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler 4.6.2 (https://github.com/YuLab-SMU/clusterProfiler) package with default parameters to elucidate the biological processes and pathways associated with the identified DEGs.

2.6 Gene Set Signature Scoring and Comparison

To assess the expression of specific gene sets, we utilized Seurat’s AddModuleScore function to calculate signature scores for each cell [13]. Comparisons of signature scores among different disease and control groups were performed using analysis of variance (ANOVA) and the Kruskal-Wallis test to identify statistically significant differences.

2.7 Cell-Cell Interaction Analysis

To predict cell-cell communication, we employed CellChat 1.6.1 (https://github.com/sqjin/CellChat) to analyze ligand-receptor pairs [17]. Interaction strength was calculated for individual ligand-receptor pairs between each pair of cell types and aggregated to determine the overall interaction strength of signaling pathways. Comparative analyses were performed between disease groups (low, medium, and high risks) and the control group to identify alterations in cell-cell communication associated with disease progression. Significant signaling pathways were calculated and ranked based on differences in the overall information flow within the inferred networks between disease and control groups.

2.8 Statistical Analysis

All statistical analyses were performed using R 4.2.3 (https://www.r-project.org/). Differential expression testing was conducted using FindMarkers with the Wilcoxon Rank Sum test, and p-values were adjusted by the Bonferroni correction method. Enrichment analyses were performed using the clusterProfiler package with the permutation test, and p-values were adjusted by the Benjamini-Hochberg method. To assess the statistical significance of signature score changes observed in a given cell subtype among different groups, we employed Bonferroni’s test for multiple comparison. To perform Pearson correlation analysis between candidate genes in high-risk patients and estimated glomerular filtration rate (eGFR), a linear regression model was applied to estimate the slope of the regression line and its 95% confidence interval. The data used for correlation analysis were from our own study and a public database Nephroseq v5 (https://nephroseq.org). An adjusted p-value 0.05 was considered statistically significant and annotated above the box plot as follows: p 0.05 *, p 0.01 **, p 0.001 ***, and p 0.0001 ****.

3. Results

3.1 Landscape of Peripheral Blood Mononuclear Cells Using Single-cell RNA Sequencing

Apart from the kidney biopsy tissue, peripheral blood mononuclear cells are also important for dissecting the immunological mechanism of pMN. Using the 10\timesGenomics scRNA-seq method, we obtained our own dataset from 11 patients (Fig. 1A), comprising 3 low-risk, 4 moderate-risk and 4 high-risk individuals. Anti-PLA2R1 antibodies were positive in all patients (Table 1). We compiled our data and a published one with 5 healthy individuals, which served as a negative control group (Supplementary Table 1). After stringent quality control and filtration, a total of 178,185 single-cell transcript data of PBMCs were analyzed. After correcting batch effects between the two datasets, we integrated the data to perform unsupervised graph-based clustering. Five major lineages (T cells, B cells, NK cells, Monocytes and cDC2) were identified depending on a referenced automatically annotation (Fig. 1B,D) [16]. In addition, other kinds of immune cells were not used to perform analysis due to their low abundance, including innate lymphoid cell 3 (ILC3), natural killer T cells (NKT), immature lymphoid cells and platelets and so on (Fig. 1B). The published data contained the same types of cells as ours. Cell annotations were verified independently by two investigators to ensure accuracy. No significant differences were observed in the overall distribution of cell populations across the four groups (Fig. 1C).

We conducted GO enrichment analysis on the five kinds of immune cells, comparing patients with pMN to healthy donors. Top up-regulated and down-regulated signaling pathways were showed in individual type cells. Immune-response related pathways were notably more active in patients across all cell types (Fig. 1E). The average capacity of antigen presentation was reduced in B cells from patients (Fig. 1E).

In order to discovery more positive results, we decided to divided specific type of immune cells into several subtypes. Based on reference annotations, individual cell lineages were categorized as shown in Fig. 2. CD4⁺ T cells were divided into three subgroups, including naive T (Tn) and central memory T (Tcm), effector T (Te) and effector memory T (Tem) and regulatory T cells (Treg). CD8⁺ T cells were classified into three subgroups, including Tn and Tcm, Tem and Temra (CD45RA⁺), Tem and resident memory T cells (Trm) (Fig. 2A–C). B cells included naive (Bn), activated B (Ba), non-switched memory (Bnsm) and switched memory B cells (Bsm) (Fig. 2D–F). NK cells included CD56^+⁣/- NK cells (or CD56^dimCD16^high) and CD56⁺ NK cells (or CD56^brightCD16^low) (Fig. 2G–I). Monocytes consisted of two subgroups, namely classical (CD14⁺CD16^–) and non-classical monocytes (CD14^–CD16⁺) (Fig. 2J–L). Of note, classical dendritic cell 2 (cDC2) and MAIT did not subdivide further in our dataset (Fig. 1B).

3.2 Abnormal Expressions of Immunoglobulins and Components of Complement System

As we know, circulating immunoglobulin G4 (IgG4) are thought to bind to deposited autoantigens and form the subepithelial complex. To investigate the potential correlation between immunoglobulins and disease severity, we analyzed the data of B cells. From naive to memory B cells, expression levels of IGHD and IGHM gradually decreased, confirming that our data was qualified (Fig. 3A). Consistent with IgG4 deposition in glomerular biopsies, IGHG4 expression in memory B cells was higher in patients than in healthy donors (Fig. 3D). Similar trend was observed for IGKC (Fig. 3F). Intriguingly, transcript levels of IGHG3 in memory B cells were gradually upregulated from low-risk to high-risk patients (Fig. 3A,B). Whereas expression levels of IGHA1 and IGHG2 in activated and memory B cells were lower in patients (Fig. 3C,E). Together, these results suggest that IgG3 might participate into the progression of pMN with antibodies against PLA2R1 [18].

Complement activation plays a central role in the pathogenesis of pMN. Subepithelial deposition of circulating immune complexes promotes the formation of the terminal complement component C5b-9, which subsequently damaged the glomerular basement membrane (GBM) [18]. To determine whether the complement system was active in peripheral immune cells, we analyzed complement-related markers in B cells. We found that complement receptor 1 (CR1) expression in B cells was higher in patients compared to controls (Fig. 3G). Similar trend was showed in patients with regard to C1S, C2 and C5. To provide a more accurate assessment, we developed a score using a panel of biomarkers up-regulated in patients, including C1S, C2, C5, CR1, CD46, CD55 and ITGAX. Scores for memory B cells and monocytes were significantly higher in patients than in healthy donors (Fig. 3H). These findings indicate that abnormal activation of complement system was present in patients with pMN, potentially contributing to disease progression.

3.3 Activation of Proinflammatory Response in pMN

Chronic inflammation is a prominent feature of many autoimmune diseases [19], including pMN. Numerous proinflammatory pathways, involving interleukins, interferons, tumor necrosis factors, chemokines, growth factors and their corresponding receptors, may contribute to the development of pMN. Nevertheless, that the relationship between proinflammatory responses and pMN progression remains poorly understood. To address this issue, we investigated the expression levels of these inflammatory factors across different types of immune cells.

With regard to interleukin signalings, we found that the levels of interleukin 1B (IL1B) and IL15 in monocytes and cDC2 were significantly elevated in patients compared to the control group (Fig. 4A–C). Whereas IL16 expression in lymphoid cells was significantly decreased in patients [20], similar results were showed about IL32 and IL18 in T and cDC2 cells, respectively (Fig. 4D, Supplementary Fig. 1A,B). Regarding interleukin receptors, we found that IL2RG and IL6ST in lymphoid cells were significantly higher in patients with pMN (Fig. 4E). The similar trends were observed in monocytes and cDC2 for IL6R and IL10RB. Multiple scoring metrics further confirmed the findings (Fig. 4F).

In terms of interferon signaling, expression levels of interferon gamma (IFNG), IFNG antisense RNA1 (IFNG-AS1) and interferon alpha and beta receptor subunit 2 (IFNAR2) in specific type cells were significantly higher in patients (Fig. 4G–I, Supplementary Fig. 1C–F).

Regarding the tumor necrosis factor superfamily, we noted that TNFRSF13C, TNFAIP3, TNFAIP8 and TNFSF8 in T cells were significantly upregulated in patients, as confirmed by specific score systems (Fig. 4J–N, Supplementary Fig. 1G,I). In contrast, expression levels of TNFSF13B and TNFRSF14 in lymphoid cells were downregulated in patients (Fig. 4J,L,M, Supplementary Fig. 1H).

Additionally, several chemokines in monocytes were higher in patients compared to healthy donors, including CCL3L1, CXCL8 (IL8) and CXCL10 (Fig. 4O–R, Supplementary Fig. 1J), with a similar trend for CXCR4 in lymphoid cells (Supplementary Fig. 1K). In contrast, expression levels of CX3CR1 and CCR7 in specific kinds of cells were relatively lower in patients.

Growth factors also play an essential role in inflammatory responses. Scores of growth-factor family members (TGFBR1, TGFBR3, IGF2R and PDGFD) were significantly higher in patients with pMN, with a similar trend in high-risk patients compared to low-risk group (Fig. 4S,T). Conversely, expression levels of some factors (MIF, FGF23 and PDGFA) significantly decreased in patients than in the control group.

Taken together, these data indicate that a range of proinflammatory pathways is activated, contributing to the development of pMN with anti-PLA2R1 antibodies.

3.4 Pattern Recognition Receptor Signaling Participating in The Development of pMN

It is well established that proinflammatory responses are often activated by pattern recognition receptors (PRRs) signalings [21], which includes toll-like receptors (TLRs) [22], NOD-like receptors (NLRs) [23], RNA sensors (RIG-I like receptors, RLRs) [24], DNA sensors (cyclic GMP-AMP synthase (cGAS) receptors, cGLRs) and so on [25]. To investigate whether PRR pathways participate into the development of pMN, we examined their expression in patients compared to healthy donors. Notably, many of the PRRs in specific type of cells were elevated in patients, including NLRs (NLRP1, NLRP3 and NLRC5) (Fig. 5D–F), RNA sensors (DDX58, IFIH1, DDX9 and DDX36) (Fig. 5G,H), DNA sensors (cGAS and IFI16) (Fig. 5I–L) and TLR1 (Fig. 5A,B). In contrast, some were relatively lower in patients, such as TLR10 (Fig. 5A,C) and TMEM173 (Supplementary Fig. 1L).

Given the downstream effects of PRR signalings, we next examined the activation of transcript factors (TFs)-related pathways that are often implicated in immune response pathways. Interestingly, we found that several TF-related pathways were upregulated in patients than in healthy donors, including MAPKs, AP1 (FOS, JUN, JUNB and JUND), janus kinase (JAK) and signal transducer and activator of transcription (STAT) members (JAK1, JAK2 and STAT3), nuclear factor kappa B family (NFKB1, RELB and NFKBIA), forkhead domain family (FOXO1 and FOXO3), TRAF3, HIF1A and IRF1 (Supplementary Fig. 2) [26, 27, 28].

Together, these results suggest that a broad range of innate immune signaling pathways in PBMCs may play a role in the pathogenesis of pMN with anti-PLA2R1 antibodies.

3.5 Biological Processes Mediated Dysfunction of Immune Cells in pMN

According to the data of different expression genes (DEGs) between patients and healthy donors, we found several interesting biological processes enriched in patients, which might affect the function of immune cells. Cellular senescence plays a key role in chronic diseases, including autoimmune diseases, metabolic diseases and tumors [29]. We found that a senescence-related gene panel was significantly enriched in patients than in the control group (Supplementary Fig. 3A). Statistically, integrated score of cellular senescent genes was higher in immune cells of patients (Supplementary Fig. 3B–D). In terms of circadian rhythm and chromatin remodeling, similar trends were showed in patients (Supplementary Fig. 3E–K) [30, 31, 32]. Altogether, these functional abnormalities of immune cells in pMN need to be explored comprehensively, which would offer more potential therapy targets for patients with pMN.

3.6 Antiviral Defense Response Associated with The Development of High-risk pMN

Although there are some biomarkers used for distinguishing high-risk patients from others, such as proteinuria, serum albumin and eGFR [10], it is limited that our understanding of the immune pathogenesis in the development of high-risk pMN. To address this issue, we analyzed the DEGs between high-risk and low-risk patients.

Intriguingly, genes of defense response to viruses in T, NK cells and monocytes were significantly upregulated in the high-risk group compared to the low-risk group (Fig. 6A,B,G,H,J,K) [33, 34]. Similarly, interferon-mediated signaling pathway in B cells was more active in high-risk group (Fig. 6D,E). In cDC2 cells, genes related to antigen presentation were significantly increased in high-risk patients (Fig. 6M,N), as confirmed by scores of upregulated gene sets (Fig. 6C,F,I,L,O). Of note, all of the scores were also effective for distinguishing high-risk patients from moderate-risk population.

In order to develop a score system for identifying high-risk patients in the overall population, we selected upregulated genes in high-risk patients compared to low-risk individuals in most types of immune cells, including SLC35F1, AC105402.3, STAT1, EPSTI1, MX1 and EIF2AK2 (Supplementary Fig. 4A,B). The gene TRIM22 was excluded due to its high expression in healthy donors (Supplementary Fig. 4A). Importantly, this score system was also effective in distinguishing high-risk patients from moderate-risk individuals.

To further distinguishing moderate-risk patients from high-risk and low-risk populations, we selected genes gradually upregulated from low-risk to high-risk patients in most types of immune cells to develop different score systems (Supplementary Fig. 4C,E–G). Notably, SLC35F1 in B cells was a valuable biomarker for distinguishing the three risk groups (Supplementary Fig. 4D) [35, 36].

3.7 The Predictable Effect of Biomarkers for Identifying High-Risk Patients with pMN

To confirm the predictable value of upregulated biomarkers in high-risk patients compared to low-risk population, we performed correlation analysis between these biomarkers and eGFR in newly-diagnosed patients with pMN. As shown in Table 2 and Supplementary Fig. 5, most effective biomarkers were negatively correlated with eGFR. Of note, upregulation of SLC35F1 and BTS2 in most types of PBMCs could effectively predict poor kidney function. Both GBP5 and KLF6 also had the predictable role in more than one type of immune cells. Conversely, only two genes (LRMDA and MYO1F) were positively associated with eGFR in cDC2 cells.

Given the small sample size of our study, we attempted to figure out whether the screened biomarkers enriched in high-risk patients had a robust capability of predicting declined eGFR in the development of nephrotic syndrome and other chronic kidney diseases (Nephroseq v5 database). Indeed, the predictable values of most biomarkers were confirmed in a larger population (Supplementary Table 2). Despite the validated results were calculated based on kidney tissues, this fact suggests that biomarkers in blood cells have a similar effect on risk stratification.

In addition, we assessed the predictable values of prior selected biomarkers upregulated in total patients with pMN compared to healthy donors. Similarly, most of them were negatively correlated with declined eGFR in patients with nephrotic syndrome or other chronic kidney diseases (Supplementary Table 3).

Altogether, we identified a series of effective biomarkers used for distinguishing high-risk patients from low-risk individuals with pMN and for predicting declined eGFR of patients.

3.8 Abnormalities of Cell-Cell Communications in pMN

Prediction of intercellular communication networks is also necessary for figuring out the pathogenic mechanism of pMN. We analyzed potential cell-cell interactions by examining co-expression patterns of curated ligand-receptor pairs (Fig. 7A and Supplementary Fig. 7).

With regard to the relative information flow, we found that some interaction pairs increased significantly in disease groups, such as cell chemokines (CCL) and cytokines (TGFB and IFN-II) (Fig. 7A, Supplementary Fig. 7A,B,D). And many up-regulated pairs correlated with functional changes in cell adhesion, such as CD6, CD226, ALCAM, NECTIN and NCAM (Fig. 7A) [37]. TIGIT-related pairs may facilitate the inhibition of monocytes by regulatory T cells, suggesting a protective mechanism that could prevent excessive monocyte-driven inflammation (Fig. 7A, Supplementary Fig. 7C) [38].

Conversely, information flow of some interaction pairs decreased remarkably in patients, such as human leukocyte antigen (HLA) molecules (major histocompatibility complex, class I and II) (Fig. 7A–C,E,F). All of the HLA-I molecules from PBMCs were decreased in patients compared to healthy donors (Supplementary Fig. 6A–C, Fig. 7H). Similar trends were showed in B cells for most of the HLA-II molecules (Fig. 7I,J). However, with regard to memory B cells, expression levels of HLA-DQA1 and HLA-DRB1 were significantly higher in patients (Fig. 7J–L). Overall, these findings suggest that memory B cells may play a role in the pathogenic mechanisms of pMN though HLA-DQA1 or HLA-DRB1 [39, 40].

In addition, we found the putative CLEC2 (C-type lectin domain family 2) and KLRB1 (killer cell lectin like receptor B1) pairs were relatively activated in patients (Fig. 7A,D,G). Indeed, the expression levels of CLEC2B, CLEC2C (CD69) and CLEC2D in most cells were significantly upregulated in patients compared to healthy donors (Supplementary Fig. 6H) [41], as confirmed in CLEC score system (Supplementary Fig. 6I). KLRB1 was regarded as an inhibited receptors in CD56^+⁣/- NK cells [42]. CLEC2B was expressed mainly in monocytes, T and NK cells, CLEC2C and CLEC2D were expressed prominently in T, B and NK cells (Supplementary Fig. 6H). We speculated that B and T cells might inhibit cytotoxic NK cells to prevent further kidney injury. This hypothesis was supported by decreased expression of granzyme M (GZMM) in NK (CD56+/-) cells (Supplementary Fig. 6C–G). With regard to perforin 1 (PRF1) and GZMB, similar expression patterns were showed in NK (CD56+/-), Tem and Temra (CD8+) cells (Supplementary Fig. 6C). Furthermore, we found that index of cytotoxicity was significantly lower in patients than in healthy donors using our developed score (including GZMA, GZMB, GZMM, PRF1) (Supplementary Fig. 6G). Abnormal expression of Fc receptors in classical monocytes and cDC2 cells, such as FCAR and FCGR2B (Supplementary Fig. 6J-L), which may further disturb the antibody-dependent cell-mediated cytotoxicity [43, 44]. Altogether, these finding suggests that the presence of a compensatory mechanism could mitigate glomerular injury.

4. Discussion

Membranous nephropathy is the most frequent cause of nephrotic syndrome in adults. Various kinds of autoantibodies have been identified in about 80–90% cases with MN. Patients with anti-PLA2R1 antibodies account for approximately 55% cases of total MN [45]. Recent studies mapped the single-cell profile of kidney tissue from adult patients with pMN, which revealed functional abnormalities of impaired glomerulus and local microenvironment cells [5, 6, 7, 8]. However, the immune pathogenic mechanism of pMN has been elusive until now, partly because of few immune cells in kidney biopsy tissues in these studies. One recent study reported the abnormalities of PBMCs from patients with untreated pMN. However, only three samples were involved in the study [9]. To our knowledge, our study is the first one to depict comprehensively the single-cell transcript profile of PBMCs from adult with newly-diagnosed pMN.

The immune complex deposits consist of immunoglobulin G and its related autoantigen PLA2R1 and complement components on the outer aspect of the basement membrane. It is not well known about the expression distribution of immunoglobulins in circulating B cells. Intriguingly, we found that expression levels of IGHG3 in memory B cells correlated positively with the severity of pMN, suggesting that IGHG3 is a good potential biomarker for distinguishing high-risk from moderate and low-risk patients with pMN [18]. More studies are required to explore the precise roles of IGHG3 in the development of pMN.

Persistent inflammation responses play a key role in many autoimmune diseases. We found that numerous inflammatory signalings were implicated into the development of pMN, including part members of interleulin, interferon, tumor necrosis factor, chemokine and growth factor families. As an upstream signaling, some pattern recognition receptor genes were up-regulated in patients. Similar trends were present with regard to some classical transcript factor signalings, such as MAPK, NF-\kappaB and JAK-STAT and AP-1 and so on [26, 27, 28, 46]. Of course, it remains to be determined how the innate immune pathways mediate the initiation of pMN.

Unexpectedly, we discovered several novel biological processes in patients including cellular senescence, circadian rhythm and chromatin remodeling [29, 30, 31, 32]. These conceptions have been proposed for decades. And they widely participated into the development of autoimmune disease and tumors. However, no relevant articles about pMN have been published until now, which implies that functional changes about peripheral-blood derived immune cells need to be explored comprehensively in near future.

In clinical practice, it is extremely important for doctors to distinguish high-risk patients from others effectively, which would contribute to the decision of treatment strategy. However, only few markers are used in practice, such as quantitative proteinuria and eGFR. In this study, we found that genes involved into the antiviral defense response in monocytes and lymphoid cells were elevated significantly in high-risk patients compared to low-risk population. The result suggests that unknown virus infections may play a critical role in the development of high-risk pMN. Blocking the signaling pathway may do good to controlling the progression of high-risk pMN. Intriguingly, a large number of screened genes upregulated in high-risk patients correlated negatively with declined eGFP, such as SLC35F1 (solute carrier family 35 member F1) and BST2 (bone marrow stromal cell antigen 2). These biomarkers have a potential predictable value for assessing the severity of patients with newly-diagnosed pMN [36]. And we validated their predictable values in a larger population from a public database. Intriguingly, we found that peripheral blood cells have an equivalent role with kidney tissues for predicting the prognosis of kidney diseases. Additionally, we established a score system for distinguishing moderate-risk patients from low-risk and high-risk population.

Regarding cell-cell communication, multiple signaling pathways may contribute to the development of pMN, including antigen presentation, immune inhibition, cell adhesion and migration. First of all, all of the major histocompatibility complex (class I) molecules were significantly decreased in monocytes and lymphoid cells, implying that weakened interactions between HLA-I and CD8 might impair the antigen presentation of monocytes and NK cells to T (CD8+) cells. In terms of HLA-II and CD4 pairs, we found a significant upregulation of HLA-DQA1 and HLA-DRB1 in memory B cells in patients, indicating that the interactions between memory B and T (CD4+) cells might play a pivotal role in the pathogenesis of pMN [39, 40]. Consistent with published articles, that the single nucleotide polymorphism (SNP) of HLA-DQA1 and HLA-DRB1 are associated with MN development. However, the relationship between SNP of HLA molecules and their expression levels requires further investigation. In addition, we discovered an increased interaction intensity of CLEC2-KLRB1 pairs in patients, suggesting function of NK cells may be suppressed by monocytes, T and B cells [41, 42]. However, the direct interaction between CLEC2 and KLRB1 has yet to be confirmed through functional experiments. Further investigation is required to determine whether the inhibition of NK cells could potentially mitigate the progression of pMN.

A primary limitation of our study is that the findings need to be validated in a larger population. Additionally, we did not include healthy donors as a negative control group, which could have helped minimize batch effects between different datasets. To address this limitation, we utilized high-quality scRNA-seq data from a previously published study [11], where cryopreserved blood cells were thawed under conditions similar to ours. While this mitigates some variability, the lack of a dedicated negative control group remains a constraint. Another limitation is the inability to analyze single-cell profiles of kidney tissues from the same patients included in our study. This prevented a direct investigation of cell-cell communications between PBMCs and glomerulus-derived cells, which would have provided deeper insights into immune mechanisms and disease pathology. Furthermore, while our study employed various computational tools to analyze PBMC data, potential biases from cell clustering and annotation, as well as noise inherent to scRNA-seq data, may have slightly influenced the results. For example, the use of DoubletFinder may inadvertently remove some normal single cells that exhibit gene expression profiles similar to doublets. Such false negative results could reduce the number of effective cells used for further analysis. Another potential issue lies in the use of the Harmony algorithm for batch effect correction. This method is highly effective in aligning datasets and reducing technical variability, it may overcorrect batch effects in some cases, potentially masking genuine biological variations.

5. Conclusions

we conducted a comprehensive investigation into the immune profile of PBMCs from patients with pMN. Our findings indicate that antiviral defense response may play a critical role in the development of high-risk pMN. Furthermore, we identified numerous biomarkers capable of predicting declined eGFR in patients with pMN. These will give insight into the precise risk stratification of newly-diagnosed pMN and the optimization of current treatment strategies [4].

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Funding

Shanxi Province Health Commission Science Fund(2022002)

“Yiluqihang & Shenmingyuanyang'' Medical Development and Scientific Research Fund project on kidney diseases(SMYY20220301001)