Immune Cell Infiltration is Discriminative for ABMR and TCMR

doi:10.21203/rs.3.rs-3562569/v1

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Immune Cell Infiltration is Discriminative for ABMR and TCMR

https://doi.org/10.21203/rs.3.rs-3562569/v1

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Antibody-mediated rejection (ABMR) and T cell-mediated rejection (TCMR) are the important causes of kidney transplant failure. Here, we aimed to evaluate the different levels and composition of infiltrating immune cells in kidney allografts of patients with ABMR or TCMR. Gene expression profiles GSE36059 and GSE48581 were obtained from the GEO database. Then, an established algorithm method called CIBERSORT was used to estimate the abundance of 22 immune cells based on microarray profiles in the GSE36059 dataset. The GSE48581 dataset was used as a validation cohort to confirm the different infiltrating immune cells between the ABMR and the TCMR groups. Receiver operating characteristic (ROC) curve analysis was used to assess the value of the infiltrating immune cells in discriminating ABMR from TCMR. Immune infiltration analysis by CIBERSORT method showed that TCMR samples had a higher level of T cell subsets compared with the ABMR samples. In the TCMR group, a significant increase in CD4 + activated memory T cells, follicular helper T cells, gamma delta (GD) T cells was observed. In the ABMR group, we found that plasma cells, resting NK cells, activated NK cells, and resting dendritic cells were increased. Similar results were also obtained in the validation cohort. The combined analysis of these infiltrating immune cells in discriminating ABMR from TCMR had an AUC of 0.839 (95% CI: 0.753–0.924) in the GSE36059 dataset and an AUC of 0.899 (95% CI: 0.823–0.975) in the validation cohort. Our data suggest that there was an obvious difference in immune infiltration between ABMR and TCMR. Plasma cells, resting NK cells, activated NK cells, resting dendritic cells, CD4 + activated memory T cells, follicular helper T cells combined with gamma delta T cells could better discriminate ABMR from TCMR.

CIBERSORT

Antibody-mediated rejection

T cell-mediated rejection

immune infiltration

Receiver operating characteristic

Kidney transplantation is considered to be the preferred treatment for patients with end stage renal disease (ESRD). However, the existing immunosuppressive armamentarium is not enough in preventing kidney transplant recipients from the occurrence of graft rejection. Allograft rejection is a strong predictor of graft failure. Accurate diagnosis of rejection in kidney transplant biopsies is vital for therapeutic choice and clinical decisions[1, 2]. Based on the immune mechanism, there are mainly two types of rejection—antibody-mediated rejection (ABMR) and T cell-mediated rejection (TCMR).

Antibody-mediated rejection (ABMR) is the leading cause of kidney transplant failure[3, 4]. Currently, the diagnosis of ABMR is mainly based on histological assessment of invasive kidney biopsies according to Banff international consensus[5]. However, histologic diagnosis of ABMR and TCMR by the Banff classification has many limitations. The diagnostic rules were based on expert opinion with considerable interobserver disagreement rather than data[6, 7]. Besides, TCMR and ABMR diagnoses are poorly reproducible by the Banff classification. ABMR diagnosis based on relatively subtle microcirculation changes is also not specific[8]. In many cases, it was very difficult to distinguish ABMR from TCMR. A more precise assessment of rejection would help reduce transplant failure. Kidney allograft rejection is closed related to molecular changes in renal allograft tissue, which can reflect changes in cell populations. Immune cell infiltration plays an important role in the occurrence and development of many diseases[9–12]. Some previous studies have shown that resident cell types play an important role in allograft rejection. Immunohistochemical markers are usually used to analyze the kinds of immune cell types. However, it is limited because of the available fluorescent channels.

CIBERSORT is a useful method that uses gene expression data to evaluate the expression of immune cell subsets and obtain their proportions from samples[13]. It has been widely used in many diseases[14, 15], particularly in many cancer types[16–18]. In the present study, we aimed to explore the alteration and proportion of immune cells in ABMR and TCMR. In this study, the microarray profiles of ABMR and TCMR tissues were downloaded from a gene expression omnibus (GEO) data set and CIBERSORT was applied to investigate the quantity of 22 immune cell types within these tissues.

2.1. Immune Cell Infiltration

22 subpopulations of immune cells in ABMR and TCMR are shown in Table 1 and Fig. 1. We found that the ABMR group had a significantly higher relative percentage of plasma cells, resting NK cells, activated NK cells, and resting Dendritic cells in comparison to the TCMR group (Fig. 2). In addition, the correlation between the subpopulations of infiltrating immune cells was explored and we found that gamma delta T cells had a positive correlation with follicular helper T cells, and plasma cells had a negative correlation with M1 macrophages (Fig. 3). Finally, hierarchical clustering of 22 immune cell subpopulations was performed, and we found that some types of immune cells had a similar expression in some modules, suggesting a synergetic role in ABMR (Fig. 4).

Table 1

Immune Cell Fractions between ABMR and TCMR tissues
Immune cell types	ABMR(%)	TCMR(%)	p
B cells naive	2.38 ± 2.53	2.88 ± 2.74	0.330
B cells memory	4.52 ± 5.53	3.41 ± 4.38	0.658
Plasma cells	19.35 ± 11.63	11.61 ± 11.97	0.001
T cells CD8	13.52 ± 7.27	16.49 ± 7.15	0.050
T cells CD4 naive	0.07 ± 0.28	0.39 ± 1.14	0.071
T cells CD4 memory resting	6.06 ± 6.54	3.94 ± 7.31	0.054
T cells CD4 memory activated	0.05 ± 0.39	1.45 ± 2.45	< 0.001
T cells follicular helper	1.28 ± 1.95	4.23 ± 3.70	< 0.001
T cells regulatory (Tregs)	3.88 ± 2.51	3.74 ± 2.46	0.855
T cells gamma delta	1.49 ± 2.55	5.40 ± 4.69	< 0.001
NK cells resting	1.31 ± 2.00	0.44 ± 0.87	0.018
NK cells activated	2.69 ± 2.34	1.45 ± 2.07	0.002
Monocytes	6.04 ± 3.87	3.71 ± 2.92	< 0.001
Macrophages M0	0.00 ± 0.00	1.92 ± 6.94	0.001
Macrophages M1	14.35 ± 4.97	16.67 ± 5.95	0.083
Macrophages M2	14.75 ± 3.56	14.96 ± 6.46	0.534
Dendritic cells resting	2.64 ± 1.45	2.46 ± 2.75	0.026
Dendritic cells activated	0.04 ± 0.20	0.00 ± 0.02	0.557
Mast cells resting	4.55 ± 3.51	3.84 ± 2.48	0.500
Mast cells activated	0.41 ± 1.15	0.22 ± 1.19	0.099
Eosinophils	0.03 ± 0.21	0.05 ± 0.21	0.657
Neutrophils	0.58 ± 0.94	0.72 ± 0.94	0.280

2.2. Results of GSEA Analysis

To further explore the different roles of immune cell infiltration between ABMR and TCMR groups. Pathways related to the activation of immune cells and other immune-related signaling pathways were performed by GSEA. GSEA results showed that Th1 and Th2 cell differentiation, Th17 cell differentiation were significantly enriched in the TCMR group, and plasma cell-related pathways were significantly enriched in the ABMR group (Fig. 5).

2.3. Validation of Immune Cell Infiltration

To confirm our results, the GSE48581 datasets were used to perform immune infiltration analysis (Fig. 6). These results were similar to our previous results. Finally, we confirmed that Plasma cells, resting NK cells, activated NK cells, and resting dendritic cells were increased in ABMR. In TCMR, a significant increase in CD4 + activated memory T cells, follicular helper T cells, gamma delta (GD) T cells was also observed. However, there was no significant difference in the proportion of monocytes and M0 macrophages between the two groups.

2.4. Discriminative values of Infiltrating Immune Cells

To assess the value of those cells in discriminating ABMR from TCMR. Receiver operating characteristic (ROC) curve analysis was performed. As shown in Fig. 7, in the GSE36059 dataset, CD4 + activated memory T cells had an AUC of 0.725 (95% CI: 0.638–0.813), follicular helper T cells had an AUC of 0.751 (95% CI: 0.639–0.862), gamma delta (GD) T cells had an AUC of 0.751 (95% CI: 0.644–0.858), plasma cells had an AUC of 0.709 (95% CI: 0.591–0.826), resting NK cells had an AUC of 0.638 (95% CI: 0.534–0.743), activated NK cells had an AUC of 0.695 (95% CI: 0.579–0.811), resting dendritic cells had an AUC of 0.643 (95% CI: 0.510–0.776). The combined analysis of these infiltrating immune cells presented excellent value in discriminating ABMR from TCMR with an AUC of 0.839 (95% CI: 0.753–0.924) in the GSE36059 dataset (Fig. 6) and an AUC of 0.899 (95% CI: 0.823–0.975) in the validation cohort (Fig. 8).

In this study, infiltrating immune cells in ABMR and TCMR were described, and the differences in the abundance of immune cells between the two groups were analyzed. We observed significant differences in immune cell composition between the two groups. We found that TCMR groups had a significant increase in CD4 + activated memory T cells, follicular helper T cells, gamma delta (GD) T cells. In the ABMR group, we found that plasma cells, resting NK cells, activated NK cells, and resting dendritic cells were increased. Similar results were also confirmed in the validation cohort. Our GSEA data also confirmed that some pathways related to T cell subset were significantly enriched in the TCMR group, and some pathways related to plasma cells were significantly enriched in the ABMR group. Then, Receiver operating characteristic (ROC) curve analysis was generated to assess the value of the infiltrating immune cells in discriminating ABMR from TCMR. We found that plasma cells, resting NK cells, activated NK cells, resting dendritic cells, CD4 + activated memory T cells, follicular helper T cells combined with gamma delta T cells presented excellent value in discriminating ABMR from TCMR both in the GSE36059 datasets and the validation cohort.

In the ABMR group, we found an increase in the proportion of resting NK cells and activated NK cells. In recent years, the increase in the number of NK cells in ABMR is an important breakthrough and they have gained specific interest in kidney transplantation[19–21]. NK cell induction is the main feature of ABMR after kidney and heart transplantation. It has been reported that NK cell was one of the main molecular signatures of cardiac ABMR[22]. Some previous animal studies have shown that NK cells are associated with cardiac and kidney ABMR[23–25]. In fact, the activation of NK cells is essential for mediating acute ABMR within kidney allografts. An involvement of NK cells in ABMR was also proved by differential gene expression analysis in graft biopsies [26].

NK cells constitute only a minority of infiltrating leukocytes. Our results also confirmed this. Therefore, it is reasonable to speculate that NK cells in ABMR are not that important. However, NK cells are capable of producing cytokines and chemokines and interacting with other immune and non-immune cells. It is likely that they play a much more important role than their number. Till now, it has not been determined which subsets of NK cells are associated with ABMR after kidney transplantation. Jung et al found that CD56⁺CD57⁺ NK cells were the most predominant subset in ABMR and NK cell infiltration was related to poor death-censored graft survival in kidney allografts[27]. Patients with DSA were associated with higher proportions of the NKG2A⁺and CD56^bright subsets[28]. In fact, NK cells may play an important role in long-term allograft tolerance[29]. Prior results indicate that NK cells have both regulatory and pathologic effects depending on the nature of the allograft itself and the timing of cellular interactions[30, 31]. These results raise questions about the complex role of NK cells in kidney injury. Therefore, further studies need to address the question of which NK cell subset was associated with ABMR and the potential mechanisms to develop more specific drugs.

Dendritic cells (DCs), as a kind of key antigen-presenting cells, are capable of linking innate immunity and adaptive immunity. Under inflammatory conditions, DCs are powerful potent immunogens. However, in the steady state, DCs are very important for the induction and maintenance of self-tolerance. The role of DCs in promoting the immune response to the transplanted organ is well described. Some previous studies demonstrated that DCs were involved in graft versus host disease (GVHD) after alloHCT[32, 33]. The donor DCs could accompany the graft move to secondary lymphoid organs of the host and act as APCs at the time of transplantation[34]. Some clinical data from kidney recipients also supported that densities of DCs in allograft biopsies were associated with poor allograft outcomes[35]. Far fewer studies investigated the role of resting DCs in allograft biopsies. Some studies showed that resting DCs have a regulatory role to protect against autologous T-cell-mediated autoimmune damage[36]. Our results demonstrated that dendritic cells were increased in the ABMR group compared with that in the TCMR group. One possible reason may be related to resting NK cells. It has been demonstrated that resting NK cells could activate DCs and enhance produce pro-inflammatory cytokines[37, 38]. More samples and studies are needed to elucidate the role of resting DCs in transplantation.

In the ABMR group, an increase in the proportion of plasma cells was observed in our results. Plasma cells, known as antibody-secreting cells, are vital for the maintenance of humoral immunity. Despite their crucial role in immune balance, plasma cell activity and Alloantibodies remain a vexing barrier to successful transplantation. plasma cells play a pivotal role in mediating transplant rejection through the cellular and humoral arms of the immune system and lead to chronic graft failure through the secretion of large amounts of antibodies[39, 40]. Ab mediated rejection (AMR) is a leading cause of allograft failure, which accounts for more than one quarter of the acute graft dysfunction cases and is associated with chronic rejection episodes[41]. In fact, there is a type of allograft rejection called plasma cell-rich rejection (PCRR), which is characterized by the presence of mature plasma cells. Martin et al found that plasma cell infiltration in kidney allograft was associated with the appearance of DSA and C4d deposition[42]. It was also reported that late-onset allograft rejections had relatively more plasma cell infiltration and poor outcomes and PCRR might act as a characteristic type of late-onset rejection[43]. Some PCRR cases are indeed associated with ABMR, plasma cells can produce DSA, but the relationship between PCRR and DSA is poorly understood.

Recently, Long-lived plasma cells (LLPCs) receive increasing attention in kidney transplantation. Long-lived plasma cells are the main and long-term source of the antibodies, which play a crucial role in ABMR, accounting for more than 50% of kidney graft loss[44]. In general, LLPCs mediate graft injury through the generation of DSAs, such as preformed antibodies in sensitized patients and de novo antibodies post-transplantation. More than 50% of highly sensitized recipients have preexisted alloantibodies and LLPCs are the main sources of panel-reactive antibodies (PRAs), including preformed Donor-specific antibodies (DSAs). De novo DSAs in kidney recipients are the main reason for the dysfunction and failure of allograft[45]. The allo-antigens from donors can trigger the differentiation of LLPCs, resulting in long-term producing antibodies. Compared to the short-lived plasma cells, it is believed that the differentiation of LLPCs is independent of B cell precursors[40]. Therefore, LLPCs may act as a promising therapeutic target for recipients with ABMR. A full understanding of the roles of LLPCs in ABMR is crucial to improve allograft survival. Why some plasma cells can persist for decades some plasma cells are short-lived remains shortly understood. There is still a long way to go.

The recent visions of the Banff classification have acknowledged the limited accuracy and sensitivity of microscopy and the use of molecular markers as alternative diagnostic criteria for ABMR was encouraged. The estimation of immune cell infiltration between AMBR and TCMR by using the CIBERSORT method has been rarely reported. In this study, by using the CIBERSORT method we provide a full landscape of immune cell infiltration in ABMR and TCMR. Some significant immune cells were identified, and our results were also confirmed in the validation cohort and GSEA results. These results may provide an effective supplement for the diagnosis of rejection.

Immune cell infiltration play crucial roles in renal rejection. Using the CIBERSORT analysis, we could evaluate the expression of immune cell subsets and obtain their proportions from samples with gene expression data. These immune cell subsets could be used as biomarkers in discriminating ABMR from TCMR, which holds some promise in the future of rejection diagnosis and help use better understand the mechanisms of kidney allograft rejection. The results based on CIBERSORT algorithm combine with some other methods including single-cell RNA sequencing, which may provide a more detailed description of Immune cell infiltration. Large samples are need to confirm this finding.

4.1. Microarray Data

The data used in this study were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). The GSE36059 was based on the GPL570 platform of Affymetrix Human Genome U133 Plus 2.0 Array, which is consisted of transcript profiles of kidney tissues from 403 samples and 8 controls (nephrectomies). 53 ABMR and 34 TCMR kidney tissues were selected for further analysis. The GSE48581 dataset was used as a validation cohort. The GSE48581 was also based on the GPL570 platform of Affymetrix Human Genome U133 Plus 2.0 Array, including 35 ABMR and 30 TCMR samples.

4.2. Evaluation of Infiltrating Immune Cells

A bioinformatics algorithm called CIBERSORT was applied to analyze the selected expression data. The percentage of each immune cell type in the samples with significant enrichment was calculated and shown in a bar plot. The R package “pheatmap” was used to construct the heat map of the 22 immune cells. We used the R package “vioplot” to compare the levels of 22 immune cells between ABMR and TCMR groups in the GSE36059 dataset. The correlation heatmap was drawn to visualize the correlations between 22 types of infiltrated immune cells by using the R package “corrplot”. The R package “ggplot2” was used to visualize the immune cell percentage in the validation cohort.

4.3. Gene Set Enrichment Analysis (GSEA)

The R package “clusterProfiler” package was used to perform the Gene Set Enrichment Analysis (GSEA). p < 0.05 and a false discovery rate (FDR) < 0.25 were considered significant enrichment. Besides, some gene set databases used in this process were downloaded from the Molecular Signatures Database https://www.gsea-msigdb.org/gsea/msigdb/index.jsp

4.4. ROC Curve for Discriminating ABMR from TCMR

Receiver operating characteristic (ROC) curve analysis was generated to assess the value of the infiltrating immune cells in discriminating ABMR from TCMR. The discriminative values of these infiltrating immune cells were assessed using the area under the curve (AUC) obtained from the ROC curve analysis. ROC curves were performed using the R package “PROC”.

4.5. Statistical Analysis

All statistical analyses were conducted using R software (version 4.0.2). Student’s t-test for normally distributed variables and Mann–Whitney U test for abnormally distributed variables were conducted to compare the differences between the two groups. The value of p < 0.05 was regarded statistically significant.

The detection of the ratio of immune cells allows us to better understand the different immune statuses of different kidney rejections in patients. These cells might provide some useful markers for the diagnosis of rejection in kidney transplantation and the development of new therapies to improve clinical outcomes after kidney transplantation. For further clinical implementation, Further evaluation and validation are still warranted.

Supplementary Materials: Supplementary materials are available.

Author Contributions: Bingxuan Zheng and Meng Dou performed the bioinformatic analysis and wrote the manuscript. Ge Deng, Wei Tian, and Zejiaxin Niu: data analysis. Chenguang Ding and Wujun Xue: protocol development, manuscript editing. Pu-xun Tian: protocol development, manuscript writing and editing. All authors approved the final submitted manuscript.

Funding: This work was supported by the National Nature Science Foundation of China (No.82270791 and No. 81870514).

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: The data sets included in this study are available at the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/).

Acknowledgments: All authors are grateful to the GEO database for providing high quality data.

Conflicts of Interest: The authors declare no conflict of interest

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No competing interests reported.

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Immune Cell Infiltration is Discriminative for ABMR and TCMR

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Abstract

Figures

1. Introduction

2. Results

2.1. Immune Cell Infiltration

2.2. Results of GSEA Analysis

2.3. Validation of Immune Cell Infiltration

2.4. Discriminative values of Infiltrating Immune Cells

3. Discussion

4. Materials and Methods

4.1. Microarray Data

4.2. Evaluation of Infiltrating Immune Cells

4.3. Gene Set Enrichment Analysis (GSEA)

4.4. ROC Curve for Discriminating ABMR from TCMR

4.5. Statistical Analysis

5. Conclusions

Declarations

References

Additional Declarations

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