Biomarker for predicting systemic lupus erythematosus flare

Abstract

The present application relates to a biomarker composition for predicting systemic lupus erythematosus flare, comprising, as an active ingredient, a protein expressed in CD8 + T cells or a gene encoding same.

Description

Biomarkers for Predicting Systemic Lupus Erythematosus Flare

[0001] This paper relates to a biomarker for predicting systemic lupus erythematosus flare.

[0002] Systemic lupus erythematosus (SLE) is a representative systemic autoimmune disease in which various immune cells are involved in its pathogenesis. The clinical manifestations of SLE vary widely, ranging from mild skin and joint involvement to life-threatening organ complications. SLE patients exhibit a highly variable and unpredictable disease course, suggesting cellular and molecular heterogeneity reflecting patient-specific alterations at the genetic, epigenetic, transcriptomic, and proteomic levels.

[0003] The primary treatment goals of SLE are to achieve remission or reduce disease activity, prevent organ damage, minimize drug toxicity, and improve quality of life. However, the complex pathophysiology of SLE makes treatment challenging, and treatment responses can vary significantly among patients. Therefore, a personalized approach based on patient stratification is essential for effective disease management.

[0004] Because multiple immune cell types are involved in the pathogenesis of SLE, profiling peripheral blood mononuclear cells (PBMCs) is a powerful tool for understanding disease trajectories and improving patient outcomes. Indeed, gene expression profiling studies of bulk immune cells from SLE patients reveal distinct phenotypic characteristics of patient subpopulations, potentially improving patient stratification. However, approaches that collectively consider heterogeneous immune cells as a unified population may not fully capture the complete immune landscape of SLE, thereby overlooking the clinical implications of subtle yet important cell subpopulations relevant to treatment.

[0005] To overcome these limitations and provide a detailed view of the transcriptome of individual cells, single-cell RNA sequencing (scRNA-seq) has been applied to profile PBMCs from SLE patients. These studies have dissected the heterogeneity in the functional behavior of immune cells within and between SLE patients, yielding several important findings. These include the identification of more than 20 distinct cell subpopulations actively involved in disease pathogenesis, the global expression of interferon gene signatures in specific cell types, and differences in specific cell composition compared to controls.

[0006] However, many existing scRNA-seq studies have limited themselves to snapshots through cross-sectional approaches, and the evolving characteristics of immune cells throughout the disease process remain largely unexplored. Clarifying cellular and molecular changes across various disease stages, particularly during flare and remission, is essential for fully understanding the underlying pathogenesis of SLE.

[0007] Therefore, there is a need for new methods to track the dynamic changes in immune cells over the course of systemic lupus erythematosus and to clarify cellular and molecular changes during flare and remission periods.

[0008] The background technology of this application, Korean Patent No. 10-2475926, relates to a biomarker composition for diagnosing systemic lupus erythematosus and a method for providing information necessary for diagnosing systemic lupus erythematosus using the same.

[0009] The present invention aims to solve the problems of the above-mentioned conventional technology and provides a biomarker composition for predicting systemic lupus erythematosus flare.

[0010] Additionally, a composition for predicting systemic lupus erythematosus flare is provided.

[0011] In addition, a kit for predicting a systemic lupus erythematosus flare comprising the composition for predicting a systemic lupus erythematosus flare is provided.

[0012] Additionally, a method for predicting systemic lupus erythematosus flare is provided.

[0013] Additionally, a method for screening for a treatment agent for systemic lupus erythematosus is provided.

[0014] However, the technical tasks to be achieved by the embodiments of the present invention are not limited to the technical tasks described above, and other technical tasks may exist.

[0015] As a technical means for achieving the above-mentioned technical task, the first aspect of the present invention provides a biomarker composition for predicting systemic lupus erythematosus flare, comprising as an active ingredient a protein expressed in CD8+ T cells or a gene encoding the same.

[0016] According to one embodiment of the present invention, the protein expressed in the CD8+ T cell or the gene encoding the same may include at least one selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1, but is not limited thereto.

[0017] According to one embodiment of the present invention, if an upregulation of the expression levels of GZMH, GZMB, GZMA, GNLY, and PRF1 is observed, it may be determined that there is a high possibility of experiencing a systemic lupus erythematosus flare, but is not limited thereto.

[0018] In addition, the second aspect of the present invention provides a composition for predicting systemic lupus erythematosus flare, comprising as an active ingredient an agent capable of measuring the expression level of a protein expressed in CD8+ T cells or a gene encoding the same.

[0019] According to one embodiment of the present invention, the protein expressed in the CD8+ T cell or the gene encoding the same may include at least one selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1, but is not limited thereto.

[0020] According to one embodiment of the present invention, if an upregulation of the expression levels of GZMH, GZMB, GZMA, GNLY, and PRF1 is observed, it may be determined that there is a high possibility of experiencing a systemic lupus erythematosus flare, but is not limited thereto.

[0021] In addition, the third aspect of the present invention provides a kit for predicting a systemic lupus erythematosus flare, comprising a composition for predicting a systemic lupus erythematosus flare according to the second aspect of the present invention.

[0022] In addition, the fourth aspect of the present invention provides a method for predicting a systemic lupus erythematosus flare, comprising the steps of: isolating CD8+ T cells from a blood sample; measuring the expression level of a protein expressed in the CD8+ T cells or a gene encoding the same; comparing the expression level of the protein or the gene with the expression level of a control group; and predicting a systemic lupus erythematosus flare based on the comparison result of the expression levels.

[0023] According to one embodiment of the present invention, the protein expressed in the CD8+ T cell or the gene encoding the same may include at least one selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1, but is not limited thereto.

[0024] According to one embodiment of the present invention, if an upregulation of the expression levels of GZMH, GZMB, GZMA, GNLY, and PRF1 is observed, it may be determined that there is a high possibility of experiencing a systemic lupus erythematosus flare, but is not limited thereto.

[0025] In addition, the fifth aspect of the present invention provides a method for screening a systemic lupus erythematosus treatment agent, comprising the steps of treating CD8+ T cells with a candidate substance for the treatment of systemic lupus erythematosus; observing a change in the expression level of a protein expressed in the CD8+ T cells or a gene encoding the same; and selecting a candidate substance that reduces the expression level of the protein expressed in the CD8+ T cells or the gene encoding the same as a systemic lupus erythematosus treatment agent.

[0026] According to one embodiment of the present invention, the protein expressed in the CD8+ T cell or the gene encoding the same may include at least one selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1, but is not limited thereto.

[0027] The above-described problem-solving methods are merely exemplary and should not be construed as limiting the present invention. In addition to the exemplary embodiments described above, additional embodiments may be included in the drawings and detailed description of the invention.

[0028] The biomarker for predicting systemic lupus erythematosus (SLE) according to this invention can predict flares in SLE patients more accurately than conventional biomarkers, thereby improving monitoring of disease activity in SLE patients and contributing to the development of potential new therapeutic intervention methods.

[0029] However, the effects that can be obtained from this center are not limited to the effects described above, and other effects may exist.

[0030] Figure 1a is a schematic diagram of SLE single-cell transcriptome and T cell receptor sequence profiling. Here, CTL is the control, TCR is the T cell receptor, and BCR is the B cell receptor.

[0031] Figure 1b is a UMAP visualization of cellular composition by cell type (left) and disease activity (right) in whole cells of SLE. Here, CD4 T is CD4+ T cell, CD8 T is CD8+ T cell, gdT is gamma-delta T cell, NK is natural killer cell, cM is classical monocyte, ncM is non-classical monocyte, cDC is conventional dendritic cell, pDC is plasmacytoid dendritic cell, NF is non-flare state, and FL is flare state.

[0032] Figure 1c shows the scaled average marker gene expression of 11 cell types in the total immune cell population of SLE. Here, the size of the dots represents the proportion of cells expressing each cell type.

[0033] Figure 1d shows the proportion of immune cell types in PBMCs by disease (left) and the total number of immune cell types (right). Here, orange stars indicate a significant increase in SLE, and green indicates an increase in CTLs. (**: p < 0.01; ***: p < 0.001; ****: p < 0.0001)

[0034] Figures 1e-i show the scaled mean expression of disease-specific IFN-associated genes (n = 100) across cell types. Here, the heatmap on the left represents the SLE dataset of Perez et al., the dataset on the right, and the bar graph next to it shows the log-scaled fold change of DEGs (Wilcoxon test) results for all cells, colored by significance (|Log2(FC)| > log2(1.5) & p < 1×10-6). IFN-associated genes are clustered into five IFN modules, and genes on the y-axis are sorted by fold change within each IFN module.

[0035] Figure 1j shows global (top), lymphocyte (middle), and myeloid (bottom) IFN module expression patterns across immune cells. Each IFN module shows the fold-change correlation between the Perez et al. dataset and our dataset (left), the module score for all cells (middle), and the target cell type (right). (****: p < 0.0001)

[0036] Figure 2a is a UMAP visualization of the cell composition of B cells by cell type (left) and disease (right). Here, B naive is a naive B cell, ABCs is an atypical B cell, and B memory is a memory B cell.

[0037] Figure 2b shows the scaled average marker gene expression of five subtypes in B cells. Here, the size of the dots represents the proportion of cells expressing each cell type.

[0038] Figure 2c is a UMAP visualization of the cell composition of CD20+ B cells by cell type (top left) and disease (bottom left). Here, the right image shows abundant expression of TBX21 (top) and lack of CXCR5 (bottom) as an ABCs marker.

[0039] Figure 2d is an inferred trajectory overlaid on a UMAP diagram showing two directions from B naive to ABCs and B memory.

[0040] Figure 2e is the density of cell composition aligned with the pseudo-time.

[0041] Figure 2f shows the proportion of B cell subtypes by disease. Orange stars indicate a significant increase in SLE, and green indicates an increase in CTL. (*: p < 0.05; ****: p < 0.0001)

[0042] Figure 2g shows the ratio of ABCs: B memory cells among total B cells by disease. (****: p < 0.0001).

[0043] Figure 2h shows the scaled expression of genes associated with B cell functionality. Here, the size of the dots represents the proportion of cells expressing each cell type, and the color of the dot edges indicates the significance of the DEG analysis in B cells.

[0044] Figure 2i is a UMAP visualization of the cell composition of myeloid cells by cell type (left) and disease (right). Here, cDCs are conventional dendritic cells, and ASDCs are AXL+ SIGLEC6+ dendritic cells.

[0045] Figure 2J shows the scaled average marker gene expression of five subtypes in myeloid cells. Here, the size of the dots represents the proportion of cells expressing each cell type.

[0046] Figure 2k shows the proportion of myeloid cell subtypes by disease. Here, orange stars indicate a significant increase in SLE, and green indicates an increase in CTL. (*: p < 0.05; **: p < 0.01; ***: p < 0.001).

[0047] Figure 2l shows the level of type 1 IFN production in myeloid cell subtypes by disease.

[0048] Figure 2m shows the correlation between the average type 1 IFN production level of pDCs and the type 1 IFN response of ABCs.

[0049] Figure 3a is a UMAP visualization of the cell composition of αβT cells by cell type (left) and disease (right). Here, Tcm is a central memory T cell, Tem is an effector / memory T cell, Treg is a regulatory T cell, cycling is a circulating T cell, CD4 CTL is a CD4+ cytotoxic T cell, and CD8 GZMK+ is a CD8+ GZMK+ transitional T cell.

[0050] Figure 3b shows the scaled average marker gene expression of 10 subtypes in αβT cells. Here, the size of the dots represents the proportion of cells expressing each cell type.

[0051] Figure 3c shows the proportion of αβT cell subtypes by disease. Here, orange stars indicate a significant increase in SLE, and green stars indicate an increase in CTL. (***: p < 0.001; ****: p < 0.0001).

[0052] Figure 3d shows the lymphocyte IFN module score levels of disease-specific αβT cell subtypes.

[0053] Figure 3e is a MA plot showing genes differentially expressed in SLE CD8 T cells compared to the control group. Here, the gray dotted line indicates the cutoff for fold change (|Log2(FC)| >1.5).

[0054] Figure 3f is a violin plot showing the levels of TCR signaling (left) and T cell-mediated cytotoxicity (right) in CD8 T cells by disease (****: p < 0.0001).

[0055] Figure 3g shows the TCR diversity of αβT cells by disease. (****: p < 0.0001).

[0056] Figure 3h is a heatmap showing the difference in cell-cell interaction strength between the control and SLE samples among immune cells.

[0057] Figure 3i shows different interaction strengths from myeloid cells to CD8 T cell subtypes in MHC-I signaling (left) and Galectin signaling (right).

[0058] Figure 3J shows the ligand-receptor communication probabilities for MHC-I signaling from cM to CD8 T cell subtypes (left) and Galectin signaling from pDC to CD8 T cell subtypes (right). Empty dots indicate insignificant interactions.

[0059] Figure 4a is the study design of flare analysis by scRNA & scTCR-seq.

[0060] Figure 4b is a UMAP visualization of the cellular composition of Flare CD8 T cells by cell type (left) and disease (top right).

[0061] Figure 4c shows the percentage of CD8 T cells in the flare state in each CD8 T subtype.

[0062] Figure 4d is a MA plot showing genes differentially expressed in CD8 T cells compared to before flare. Here, the gray dashed line in the center indicates the cutoff for fold change (|Log2(FC)| >1.25).

[0063] Figure 4e is an inferred trajectory overlaid on a UMAP diagram showing differentiation from CD8 naive to CD8 Tem subtypes.

[0064] Figure 4f shows the correlation between the inferred time to response and CD8 T cell cytotoxicity. The ridge plots on each side show the CD8 T cell density in each group, and the dotted lines of each color represent the mean value.

[0065] Figure 4g shows the proportion of TCR clonotypes in CD8 T cell subtypes at each time point for each patient.

[0066] Figure 4h is a flow chart showing the percentages of the top 10 clonotypes in CD8 T cells. Each color represents clonotype expansion in the flare state.

[0067] Figure 4i is a heatmap showing the overlap rate between each sample. The patient number below the heatmap indicates the source of the sample.

[0068] Figure 4j shows the TCR clonotype frequencies in the first and second flares of patients P3 and P4. Points are colored according to clonotype expansion during the flare.

[0069] Below, with reference to the attached drawings, embodiments of the present invention are described in detail to facilitate easy implementation by those skilled in the art. However, the present invention can be implemented in various different forms and is not limited to the embodiments described herein. In the drawings, irrelevant parts have been omitted for clarity, and similar reference numerals have been used throughout the specification to indicate similar elements.

[0070] Throughout this specification, when a part is said to be "connected" to another part, this includes not only cases where it is "directly connected" but also cases where it is "electrically connected" with another element in between.

[0071] Throughout this specification, when it is said that a member is located “on,” “above,” “upper,” “lower,” “lower” or “lower” another member, this includes not only cases where the member is in contact with the other member, but also cases where another member exists between the two members.

[0072] Throughout this specification, whenever a part is said to "include" a component, this does not mean that it excludes other components, but rather that it may include other components, unless otherwise specifically stated.

[0073] The terms "about," "substantially," and the like, as used herein, are used to mean at or near the numerical value when manufacturing and material tolerances inherent to the meanings referred to are presented, and are used to prevent unscrupulous infringers from unfairly exploiting disclosures that contain precise or absolute numerical values ​​to aid understanding of the present disclosure. Furthermore, throughout the present disclosure, the terms "step of ~" or "step of ~" do not mean "step for ~."

[0074] Throughout this specification, the term "combination thereof" included in the expressions in the Makushi format means one or more mixtures or combinations selected from the group consisting of the components described in the expressions in the Makushi format, and means including one or more selected from the group consisting of said components.

[0075] Throughout this specification, references to “A and / or B” mean “A, B, or A and B.”

[0076] Hereinafter, the biomarker composition for predicting systemic lupus erythematosus flare of this invention will be described in detail with reference to embodiments, examples, and drawings. However, the invention is not limited to these embodiments, examples, and drawings.

[0077]

[0078] As a technical means for achieving the above-mentioned technical task, the first aspect of the present invention provides a biomarker composition for predicting systemic lupus erythematosus flare, comprising as an active ingredient a protein expressed in CD8+ T cells or a gene encoding the same.

[0079] The biomarker for predicting systemic lupus erythematosus (SLE) according to this invention can predict flares in SLE patients more accurately than conventional biomarkers, thereby improving monitoring of disease activity in SLE patients and contributing to the development of potential new therapeutic intervention methods.

[0080] According to one embodiment of the present invention, the protein expressed in the CD8+ T cell or the gene encoding the same may include at least one selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1, but is not limited thereto.

[0081] According to one embodiment of the present invention, if an upregulation of the expression levels of GZMH, GZMB, GZMA, GNLY, and PRF1 is observed, it may be determined that there is a high possibility of experiencing a systemic lupus erythematosus flare, but is not limited thereto.

[0082] In addition, the second aspect of the present invention provides a composition for predicting systemic lupus erythematosus flare, comprising as an active ingredient an agent capable of measuring the expression level of a protein expressed in CD8+ T cells or a gene encoding the same.

[0083] Regarding the composition for predicting systemic lupus erythematosus flare according to the second aspect of the present invention, detailed descriptions of parts overlapping with the first aspect of the present invention have been omitted, but even if the descriptions have been omitted, the contents described in the first aspect of the present invention can be equally applied to the third aspect of the present invention.

[0084] According to one embodiment of the present invention, the protein expressed in the CD8+ T cell or the gene encoding the same may include at least one selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1, but is not limited thereto.

[0085] According to one embodiment of the present invention, if an upregulation of the expression levels of GZMH, GZMB, GZMA, GNLY, and PRF1 is observed, it may be determined that there is a high possibility of experiencing a systemic lupus erythematosus flare, but is not limited thereto.

[0086] In addition, the third aspect of the present invention provides a kit for predicting a systemic lupus erythematosus flare, comprising a composition for predicting a systemic lupus erythematosus flare according to the second aspect of the present invention.

[0087] Regarding the kit for predicting systemic lupus erythematosus flare according to the third aspect of the present invention, detailed descriptions of parts overlapping with the first aspect and / or the second aspect of the present invention have been omitted, but even if the descriptions have been omitted, the contents described in the first aspect and / or the second aspect of the present invention can be equally applied to the third aspect of the present invention.

[0088] In addition, the fourth aspect of the present invention provides a method for predicting a systemic lupus erythematosus flare, comprising the steps of: isolating CD8+ T cells from a blood sample; measuring the expression level of a protein expressed in the CD8+ T cells or a gene encoding the same; comparing the expression level of the protein or the gene with the expression level of a control group; and predicting a systemic lupus erythematosus flare based on the comparison result of the expression levels.

[0089] Regarding the method for predicting a systemic lupus erythematosus flare according to the fourth aspect of the present invention, detailed descriptions of parts overlapping with the first to third aspects of the present invention have been omitted. However, even if the descriptions have been omitted, the contents described in the first to third aspects of the present invention can be equally applied to the fourth aspect of the present invention.

[0090] According to one embodiment of the present invention, the protein expressed in the CD8+ T cell or the gene encoding the same may include at least one selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1, but is not limited thereto.

[0091] According to one embodiment of the present invention, if an upregulation of the expression levels of GZMH, GZMB, GZMA, GNLY, and PRF1 is observed, it may be determined that there is a high possibility of experiencing a systemic lupus erythematosus flare, but is not limited thereto.

[0092] In addition, the fifth aspect of the present invention provides a method for screening a systemic lupus erythematosus treatment agent, comprising the steps of treating CD8+ T cells with a candidate substance for the treatment of systemic lupus erythematosus; observing a change in the expression level of a protein expressed in the CD8+ T cells or a gene encoding the same; and selecting a candidate substance that reduces the expression level of the protein expressed in the CD8+ T cells or the gene encoding the same as a systemic lupus erythematosus treatment agent.

[0093] Regarding the method for predicting a systemic lupus erythematosus flare according to the fifth aspect of the present invention, detailed descriptions of parts overlapping with the first to fourth aspects of the present invention have been omitted. However, even if the descriptions have been omitted, the contents described in the first to fourth aspects of the present invention can be equally applied to the fifth aspect of the present invention.

[0094] According to one embodiment of the present invention, the protein expressed in the CD8+ T cell or the gene encoding the same may include at least one selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1, but is not limited thereto.

[0095] The present invention will be described in more detail through the following examples; however, the following examples are for illustrative purposes only and are not intended to limit the scope of the present invention.

[0096]

[0097] [Example 1] Immune cell environment profiling in SLE

[0098] We obtained PBMCs from 6 patients with SLE (N=19) and 33 controls at flare and remission points during the disease course, and generated scRNA-seq data and scTCR-seq (Fig. 1a). PBMCs were isolated, cryopreserved, and subsequently profiled using the 10X Genomics Chromium Droplet platform. The SLEDAI score increased from 4–14 before flare to 9–19 after flare. All patients showed systemic features of SLE, including the presence of anti-dsDNA, decreased complement levels (C3, C4, CH50), and renal damage characterized by proteinuria and decreased eGFR. Correlation analysis revealed a significant relationship between SLEDAI score, anti-dsDNA titer, and proteinuria level. In particular, increased proteinuria during flare was associated with more severe renal damage.

[0099] A total of 164,200 cells were analyzed (excluding putative doublets and low-quality cells). By integrating gene expression profiles across patient samples and unsupervised clustering of cells by examining canonical markers, nine major cell types were identified and visualized in UMAP space: CD4 T cells (CD3E, CD4, CD40LG, FOXP3), CD8 T cells (CD3E, CD8A, CD8B, S100B), natural killer (NK) cells (NKG7, KLRD1, XCL1), γδ T cells (CD3E, TRDV2, TRGV9, TRDC), B cells (CD79A, CD19, MS4A1), plasma cells (MZB1, JCHAIN), classical monocytes (cM) (CD14, S100A9, CD163), non-classical monocytes (ncM) (FCGR3A, C1QA, C1QB), conventional dendritic cells (cDCs; CD1C, CLEC9A, FCER1A), and plasmacytoid dendritic cells (pDCs; LILRA4, PLD4, IL3RA), and platelets (PPBP, PF4, NRGN) (Figures 1b and 1c). These annotation results showed reliable agreement with automatic annotation using the reference-based mapping tool Azimuth.

[0100] Consistent with previous reports of PBMC scRNA-seq data, T cells were the most prevalent, followed by NK cells, monocytes, and B cells (Fig. 1d). Comparative analysis of the cellular composition between the control group and SLE patients revealed significant differences. In SLE samples, there was a significant decrease in CD4 T cells, monocytes, cDCs, and pDCs, while CD8 T cells and B cells showed a marked increase (Fig. 1d). Lymphopenia is a common finding in SLE patients. Decreases in cDCs and platelets were also observed, which have been previously associated with interferon responses. These changes in cellular composition have also been confirmed in an independent cohort.

[0101] To further investigate this aspect, we performed k-means clustering on the previously identified 100 IFN-related genes to analyze their expression patterns in various cell types between controls and SLE patients (Figures 1e and 1f). Differential gene expression analysis across all cells revealed that approximately 61% of IFN genes were overexpressed in SLE. Based on this, the optimal number of clusters was determined to be five based on the silhouette score, and each was named an “IFN module” based on its shared expression pattern within a specific cell type. The global IFN module (including ISG15, PSMB9, OAS1, STAT1, TAP1, IFIT1, and SAMD9L) was upregulated in most immune cell types. The lymphoid IFN module (including IFITM1, ISG20, SP100, SAMD9, and GBP5) was primarily expressed in SLE T cells, NK cells, and B cells. In contrast, the myeloid IFN module (including IFITM3, MT2A, IFI6, TNFSF10, SCO2, PLSCR1, and MX2) was enriched in monocytes and cDCs. The pDC IFN module (including IRF7, BST2, and LAP3) was particularly enriched in pDCs, while the platelet IFN module (including TMEM140 and IRF9) was primarily expressed in platelets and other immune cells. This finding was also observed in an independent cohort (Fig. 1e).

[0102] These IFN modules showed high concordance with the original group, and the distribution of module genes identified in previous microarray analyses revealed that genes from the M1.2 module, which is independent of disease status, were primarily included in the Global and Myeloid IFN modules, compared to the M3.4 and M5.12 modules. This highlights the value of single-cell resolution analysis in uncovering additional heterogeneity within populations that cannot be distinguished between cell types.

[0103]

[0104] [Example 2] Identification of B lymphocyte and myeloid cell subpopulations exhibiting enhanced type 1 IFN responses

[0105] Unsupervised clustering was performed on B lymphocytes. A total of five clusters emerged within the B cell lineage, comprising three B cell subsets and two plasma cell subsets (Fig. 2a). These clusters were identified by marker genes (Fig. 2b). Within these clusters, atypical B cells (ABCs) were identified, expressing markers such as TBX21 (T-bet) and ITGAX (CD11c) but lacking CXCR5 (Figs. 2b and 2c). Previous studies have linked ABCs to autoantibody production in conditions such as SLE and rheumatoid arthritis.

[0106] Understanding the developmental trajectories of ABCs is crucial, especially given their unique differentiation processes compared to conventional B cells. To gain deeper insight, we used Monocle3 to infer state trajectories, exploring dynamic differentiation states and cell transitions. This analysis revealed that both ABCs and memory B cells originate from the naïve B cell lineage, but their trajectories diverge in different directions, with similar levels of differentiation and distinct gene expression (Figures 2d and 2e). This observation is consistent with previous studies suggesting that ABCs mature in the extrafollicular region, whereas conventional B cells mature in the germinal center of the follicular region.

[0107] Our analysis revealed a marked expansion of plasma cells and plasmablasts, which function as antibody-secreting cells (ASCs), in SLE (Fig. 2c). This finding is consistent with previous reports of plasma cell proliferation in SLE. Furthermore, we observed a significant increase in the proportion of ABCs and memory B cells (Figs. 2e and 2g). This suggests that SLE exerts a role in promoting the transformation of B cells into ASCs via an unconventional differentiation pathway. This observation is further supported by the increased expression of IRF4 and IRF8 (Fig. 2h). This is particularly true given that high IRF4 expression is crucial for promoting plasma cell differentiation, whereas IRF8 counteracts this process. Furthermore, ABCs exhibited the most prominent type 1 IFN response among B cell populations, suggesting that their abnormal behavior may be influenced by the high IFN stimulation inherent in SLE. Using BCR sequencing data, we detected a decrease in BCR clonotype diversity in SLE, suggesting a predominance of expanded clones. However, the analysis did not identify specific clonotypes associated with ABCs.

[0108] Next, we investigated how myeloid cells are intricately involved in immune dysregulation and stimulate B cells to produce autoantibodies. Re-clustering of myeloid cells identified five subpopulations: cM (cM; CD14, S100A9), ncM (ncM; FCGR3A, TNFSF13B), cDCs (CLEC10A, CD1C, FCER1A), AXL+ SIGLEC6+ DCs (ASDC; AXL, SIGLEC6, CD5), and pDCs (LILRA4, PLD4) (Figures 2i and 2j). We observed a decrease in the proportion of pDCs, the major type I IFN producers, in SLE (Figure 2k). This decrease may be due to their migration to inflamed tissues. Increased type 1 IFN production by pDCs in SLE coincides with the IFN response of ABCs (Figs. 2l and 2m), suggesting a potential interaction between pDCs and ABCs in SLE pathogenesis. Taken together, our results demonstrate that ABCs differentiate from the naive B cell lineage in SLE and undergo significant expansion and enhanced type 1 IFN responses, confirming the critical role of ABC maturation in SLE pathogenesis.

[0109]

[0110] [Example 3] Altered behavior of T lymphocytes in SLE pathogenesis

[0111] T cells are essential for systemic autoimmunity and inflammation in the pathogenesis of SLE. They not only regulate B cell responses but also infiltrate target tissues and cause tissue damage. Dysregulation of T cell signaling, resulting in alterations in gene expression and cytokine production, can ultimately affect T cell behavior and characteristics in SLE.

[0112] Based on gene expression profiles based on established markers, six subtypes of CD4 T cells and four subtypes of CD8 T cells were distinguished (Figs. 3a and 3b). Although the overall proportion of CD4 T cells was reduced in SLE, CD4 regulatory T cells (Tregs) were increased (Fig. 3c). However, CD4 Treg cells in SLE exhibited dysfunctional characteristics, particularly through downregulation of CTLA4 and IL-2RA, important inhibitors of T cell activation.

[0113] This pattern was further supported by low scores for the IL-2 and TGF-β signaling pathways, suggesting that the expanded CD4 Treg cell population in SLE may be less able to effectively regulate other immune cells and thus may not be able to control autoreactive immune responses.

[0114] Unlike CD4 T cells, CD8 T cells were overexpressed in SLE, particularly in naive T cells (Fig. 3c). Furthermore, the lymphocyte IFN module score was highest in cytotoxic T cells, such as CD8 Tem and cytotoxic CD4 T cells (CD4 CTL) (Fig. 3d). Differential expression gene analysis revealed that genes upregulated in CD8 T cells from SLE were associated with lymphocyte-enriched IFN genes (IFITM1, SP100, ISG20), TCR signaling (PSMB9, CD3D, CSK), and cytotoxicity (PRF1, GZMB). This suggests hyperactivation of cytotoxic CD8 T cells in SLE compared to controls (Fig. 3e). Notably, we observed increases in both TCR signaling and cytotoxicity (Fig. 3f), indicating a correlation between these gene set scores and the disease. Consistent with enhanced TCR signaling, we observed a significant reduction in the diversity of TCR clonotypes within CD8 T cells (Fig. 3g), indicating expansion of specific CD8 T cell clonotypes by T cell activation.

[0115] To understand molecular and cellular interactions between immune cells, we used CellChat to infer cell-cell communication networks based on gene expression profiles, based on a knowledge base of ligand-receptor interactions. Specifically, we observed that CD8 T cells engage in extensive interactions with most other immune cells (Figure 3h). These prominent interactions were particularly evident in their involvement in MHC-I and galectin signaling with myeloid cells (Figures 3i and 3j). MHC-I signaling showed the highest relative interaction intensity between cM and CD8 T cells and was characterized by enhanced ligand-receptor interactions between HLA class I molecules and CD8A, CD8B, and KLRK1. This suggests enhanced CD8 T cell activation due to stronger antigen presentation. Furthermore, galectin signaling was most prominent between pDCs and CD8 T cells, primarily through the interaction with LGALS9. The LGALS9 gene, encoding galectin-9, has been identified as a biomarker of SLE due to its elevated serum protein levels in SLE patients. Moreover, the inclusion of this gene in the myeloid IFN module suggests a previously unexplored aspect of galectin signaling in T cell activation in the context of SLE.

[0116] In summary, while cytotoxic CD8 T cells exhibit increased activation and enhanced interactions, Tregs appear to be less able to effectively regulate these activities, providing insight into the unique immune derangements observed in SLE.

[0117]

[0118] [Example 4] Hyperactivation of cytotoxic CD8+ T cells during flare

[0119] A flare in SLE represents an acute exacerbation that significantly impairs the patient's well-being. The precise etiology of a flare remains largely unknown, but it is believed to involve a combination of environmental, hormonal, and genetic factors. CD8 T cells, in particular, are central to this inflammatory cascade during a flare. To analyze the immune dynamics during a flare, paired samples from four patients, two of whom experienced two flares and remissions (total n = 12) (Figure 4a). As expected, flares were associated with a marked increase in the SLEDAI score compared to the pre-flare baseline, along with a concomitant increase in proteinuria. This highlights the systemic inflammatory burden during a flare.

[0120] Subclustering of CD8 T cells in paired samples before and after flare revealed three clusters, including CD8 naive, CD8 GZMK+, and CD8+ effector / memory (Tem) cells (Fig. 4b). UMAP embedding of single cells demonstrated a marked increase in the CD8 Tem subpopulation during the flare period (Fig. 4c). At the molecular level, flare-associated CD8 T cells exhibited a surge in cytotoxicity-related genes, such as GZMH, GZMB, GZMA, GNLY, and PRF1 (Fig. 4d). Conversely, genes representative of the memory phenotype, including TCF1, LEF1, and CCR7, were downregulated. These gene expression changes supported cell trajectory analysis (Fig. 4e), suggesting a transition from naive T cells to Tem cells. Notably, these terminally differentiated cells were more abundant during the flare phase and were accompanied by enhanced cytotoxic capacity (Fig. 4f).

[0121]

[0122] [Example 5] Expansion of clonally restricted cytotoxic CD8+ T cells during SLE flare

[0123] Upon antigen recognition, T cells undergo clonal expansion, a crucial step in their differentiation into effector cells. This specialized proliferation ensures a robust immune response against recognized pathogens. In this context, we analyzed the TCR sequences of CD8 T cells and mapped their TCR clonotypes onto the UMAP space. We investigated the dynamics of CD8 T cell clonotypes, focusing on potential autoantigens and exploring changes during flares. By tracking CD8 T cell clones using TCR sequences, we detected T cell expansion during flares, focusing specifically on expanded cytotoxic CD8 T cells. Cells with identical TCR genes in both the α and β chains were considered to belong to the same clone. Analysis of three time points from six paired pre- and intra-flare samples revealed an increase in the proportion of overexpanded clonotypes (n > 1%) during flares (Figure 4g). This expansion was particularly pronounced within the CD8 Tem cell subpopulation. To track which clonotypes were predominantly expanding during flares, we focused on the top 10 abundant clonotypes in each patient's CD8 T cells and identified several clonotypes that exhibited changes greater than twofold (Figure 4h). This significant expansion of specific clonotypes highlights increased T cell activation and suggests that specific epitopes may drive this activation during flares. Further analysis of clonotype frequencies, which expanded to the top 30 abundant clonotypes in CD8 T cells, reinforced these observations and revealed a distinct trend toward T cell expansion, although this trend was not statistically significant due to limited sample size. This clonal expansion provides strong evidence for a targeted immune response to specific antigens during flare episodes.

[0124] We then further investigated the similarity of CD8 TCR clonotypes between patients and between flares to infer autoantibody similarities. As expected, TCR clonotypes varied between patients, but similarities were found within individual patients, and were more pronounced in CD8 Tem than CD8 naive T cells (Fig. 4i). Interestingly, analysis of two consecutive time points revealed that some clonotypes were present early but expanded primarily during specific time points (Fig. 4j). This suggests that the key antigens driving cytotoxic T cell responses may differ between flare episodes. The dynamic nature of these antigen interactions highlights the need for precise therapeutic approaches that can address the changing antigenic stimuli that activate cytotoxic T cell responses during each flare.

[0125]

[0126] The above description of the present invention is for illustrative purposes only, and those skilled in the art will readily appreciate that the present invention can be readily modified into other specific forms without altering the technical spirit or essential characteristics of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single entity may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined manner.

[0127] The scope of the present invention is indicated by the claims described below rather than the detailed description above, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention.

Claims

1. Containing as an active ingredient a protein expressed in CD8+ T cells or a gene encoding the same; A biomarker composition for predicting systemic lupus erythematosus flare.

2. In paragraph 1, The protein expressed in the CD8+ T cell or the gene encoding the protein comprises at least one selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1. A biomarker composition for predicting systemic lupus erythematosus flare.

3. In paragraph 2, If the expression levels of the above GZMH, GZMB, GZMA, GNLY, and PRF1 are observed to be up-regulated, it is judged that there is a high possibility of experiencing a systemic lupus erythematosus flare. A biomarker composition for predicting systemic lupus erythematosus flare.

4. A preparation containing as an active ingredient a preparation capable of measuring the expression level of a protein expressed in CD8+ T cells or a gene encoding the same, A composition for predicting systemic lupus erythematosus flare.

5. In paragraph 4, The protein expressed in the CD8+ T cell or the gene encoding the protein comprises at least one selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1. A composition for predicting systemic lupus erythematosus flare.

6. In paragraph 5, If the expression levels of the above GZMH, GZMB, GZMA, GNLY, and PRF1 are observed to be up-regulated, it is judged that there is a high possibility of experiencing a systemic lupus erythematosus flare. A composition for predicting systemic lupus erythematosus flare.

7. A kit for predicting a systemic lupus erythematosus flare, comprising a composition for predicting a systemic lupus erythematosus flare according to claim 4.

8. Step of isolating CD8+ T cells from blood samples; A step of measuring the expression level of a protein expressed in the CD8+ T cells or a gene encoding the same; A step of comparing the expression level of the protein or the gene with the expression level of the control group; and A step of predicting systemic lupus erythematosus flare through comparison results of the above expression levels; Including, A method for predicting systemic lupus erythematosus flares.

9. In paragraph 8, The protein expressed in the CD8+ T cell or the gene encoding the protein comprises at least one selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1. A method for predicting systemic lupus erythematosus flares.

10. In paragraph 9, If the expression levels of the above GZMH, GZMB, GZMA, GNLY, and PRF1 are observed to be up-regulated, it is judged that there is a high possibility of experiencing a systemic lupus erythematosus flare. A method for predicting systemic lupus erythematosus flares.

11. Step of treating CD8+ T cells with a systemic lupus erythematosus treatment candidate; A step of observing changes in the expression level of a protein expressed in the CD8+ T cells or a gene encoding the same; and A step of selecting a candidate substance that reduces the expression level of a protein expressed in the CD8+ T cells or a gene encoding the same as a treatment for systemic lupus erythematosus; Including, A method for screening for systemic lupus erythematosus therapeutics.

12. In paragraph 11, The protein expressed in the CD8+ T cell or the gene encoding the protein comprises at least one selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1. A method for screening for systemic lupus erythematosus therapeutics.

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