Biomarkers for predicting onset of systemic lupus erythematosus

Abstract

The present invention relates to a biomarker composition for predicting onset of systemic lupus erythematosus, comprising, as an effective ingredient, a protein expressed in CD8+ T cells or a gene encoding the same.

Description

Technical Field

[0001] This invention relates to a biomarker for predicting the onset of systemic lupus erythematosus. Background Technology

[0002] Systemic lupus erythematosus (SLE) is a representative systemic autoimmune disease whose pathogenesis involves multiple immune cells. The clinical manifestations of SLE are highly diverse, ranging from mild skin and joint symptoms to life-threatening serious organ complications. SLE patients exhibit individualized, variable, and unpredictable disease progression, suggesting cellular and molecular heterogeneity reflecting patient-specific variations at the genetic, epigenetic, transcriptomic, and proteomic levels.

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

[0004] Because multiple types of immune cells are involved in the pathogenesis of SLE, analyzing peripheral blood mononuclear cells (PBMCs) is a powerful tool for understanding the disease trajectory and improving patient prognosis. In fact, gene expression profiling studies of immune cells as a whole in SLE patients have revealed well-defined phenotypic characteristics of patient subpopulations, which may help improve patient stratification. However, approaches that consider heterogeneous immune cells as a holistic, integrated population may fail to fully capture the complete immune environment of SLE, thus potentially overlooking the clinical significance of subtle but important treatment-relevant cell subpopulations.

[0005] To overcome these limitations and provide a detailed view of the single-cell transcriptome, single-cell RNA sequencing (scRNA-seq) has been applied to the analysis of PBMCs in SLE patients. These studies have yielded several important findings by analyzing the heterogeneity of immune cell function and behavior within and between SLE patients. For example, more than 20 distinct cell subpopulations actively involved in disease pathogenesis have been identified, the overall expression of interferon gene signatures in specific cell types has been discovered, and differences in the composition of specific cells compared to controls have been observed.

[0006] However, many existing scRNA-seq studies only provide snapshots through cross-sectional methods, and the evolving nature of immune cells during disease progression remains largely unexplored. Elucidating the cellular and molecular changes at different disease stages, particularly during flare and remission, is crucial for a comprehensive understanding of the fundamental pathogenesis of SLE.

[0007] Therefore, there is a need for a new method that can track the dynamic changes of immune cells during the disease progression of patients with systemic lupus erythematosus and elucidate the cellular and molecular changes during flare and remission.

[0008] Korean Patent No. 10-2475926, which serves as the background technology of this invention, relates to a biomarker composition for diagnosing systemic lupus erythematosus (SLE), and a method for providing information needed to diagnose SLE using the same. Summary of the Invention

[0009] The problem that the invention aims to solve The present invention aims to solve the problems in the prior art mentioned above and to provide a biomarker composition for predicting the onset of systemic lupus erythematosus.

[0010] Furthermore, the present invention provides a composition for predicting the onset of systemic lupus erythematosus.

[0011] Furthermore, the present invention provides a kit for predicting systemic lupus erythematosus flare-ups, comprising the composition for predicting systemic lupus erythematosus flare-ups.

[0012] Furthermore, the present invention provides a method for predicting the onset of systemic lupus erythematosus.

[0013] In addition, the present invention provides a method for screening therapeutic agents for systemic lupus erythematosus.

[0014] It should be understood that the technical problems to be solved by the embodiments of the present invention are not limited to the above-mentioned technical problems, and there may be other technical problems.

[0015] means for solving problems As a technical means to achieve the above-mentioned technical problem, the first aspect of the present invention provides a biomarker composition for predicting the onset of systemic lupus erythematosus, which contains a protein expressed in CD8+ T cells or a gene encoding such a protein as an active ingredient.

[0016] According to one embodiment of the present invention, the protein expressed in CD8+ T cells or the gene encoding it may include one or more 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, when the expression level of one or more of GZMH, GZMB, GZMA, GNLY and PRF1 is upregulated, it can be judged that the possibility of experiencing a systemic lupus erythematosus flare-up is high, but it is not limited thereto.

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

[0019] According to one embodiment of the present invention, the protein expressed in CD8+ T cells or the gene encoding it may include one or more 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, when the expression level of one or more of GZMH, GZMB, GZMA, GNLY and PRF1 is upregulated, it can be judged that the possibility of experiencing a systemic lupus erythematosus flare-up is high, but it is not limited thereto.

[0021] Furthermore, a third aspect of the present invention provides a kit for predicting the onset of systemic lupus erythematosus, comprising the composition for predicting the onset of systemic lupus erythematosus described in the second aspect of the present invention.

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

[0023] According to one embodiment of the present invention, the protein expressed in CD8+ T cells or the gene encoding it may include one or more 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, when the expression level of one or more of GZMH, GZMB, GZMA, GNLY and PRF1 is upregulated, it can be judged that the possibility of experiencing a systemic lupus erythematosus flare-up is high, but it is not limited thereto.

[0025] Furthermore, a fifth aspect of the present invention provides a method for screening therapeutic agents for systemic lupus erythematosus (SLE), comprising: treating CD8+ T cells with a candidate substance for a SLE therapeutic agent; observing changes in the expression levels of proteins expressed in the CD8+ T cells or genes encoding such proteins; and screening candidate substances that reduce the expression levels of proteins expressed in the CD8+ T cells or genes encoding such proteins as SLE therapeutic agents.

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

[0027] The technical means described above for solving the problem are merely illustrative and should not be construed as limiting the present invention. In addition to the exemplary embodiments described above, other embodiments may exist as shown in the accompanying drawings and detailed description of the invention.

[0028] Invention Effects The biomarkers for predicting systemic lupus erythematosus (SLE) according to the present invention can more accurately predict flares in SLE patients compared with existing biomarkers, thereby improving the monitoring of disease activity in SLE patients and contributing to the development of potential new therapeutic interventions.

[0029] However, the effects that can be obtained by the present invention are not limited to the above-described effects, and other effects may also exist. Attached Figure Description

[0030] Figure 1a This is a summary diagram of single-cell transcriptome and T-cell receptor sequence analysis for SLE. CTL represents the control group, TCR represents the T-cell receptor, and BCR represents the B-cell receptor.

[0031] Figure 1b This is a UMAP visualization of all cells in SLE, categorized by cell type (left) and disease activity (right). CD4 T represents CD4+ T cells, CD8 T represents CD8+ T cells, gdT represents γδ T cells, NK represents natural killer cells, cM represents classical monocytes, ncM represents non-classical monocytes, cDC represents conventional dendritic cells, pDC represents plasmacytoid dendritic cells, NF represents the non-ictal state, and FL represents the ictal state.

[0032] Figure 1c The normalized average expression of marker genes in 11 cell types across all immune cells in SLE is represented. The size of the dots indicates the proportion of cells expressing the gene in each cell type.

[0033] Figure 1d The left side shows the proportion of immune cell types and the right side shows the total number of immune cell types in PBMCs categorized by disease. Orange asterisks indicate a significant increase in SLE, and green indicates an increase in CTL. (**: p<0.01; ***: p<0.001; ****: p<0.0001) Figures 1e to 1i Normalized average expression of IFN-related genes (n = 100) across cell types and disease categories. The left heatmap represents the SLE dataset by Perez et al., and the right side represents this dataset. Adjacent bar charts show the log-scale fold change of DEG (Wilcoxon test) results for all cells, colored according to significance (|Log2(FC)|>log2(1.5)&p<1×10⁻⁶). IFN-related genes were clustered into 5 IFN modules, with genes on the y-axis arranged according to the fold change order within each IFN module.

[0034] Figure 1j The expression patterns of IFN modules across immune cells (top), lymphocytes (middle), and bone marrow lineages (bottom) are shown. For each IFN module, the correlation of fold change between the Perez et al. dataset and this dataset is shown (left), module score for all cells (middle), and target cell type (right). (****: p<0.0001) Figure 2a A UMAP visualization of B cells categorized by cell type (left) and disease (right). B naive represents naive B cells, ABCs represents atypical B cells, and B memory represents memory B cells.

[0035] Figure 2b This represents the normalized average expression of marker genes for five subtypes of B cells. The size of the dots indicates the proportion of cells expressing that gene in each cell type.

[0036] Figure 2c A UMAP visualization of CD20+ B cells categorized by cell type (top left) and disease (bottom left). The right panel shows abundant expression of TBX21, a marker of ABCs, in the top panel (top), and deficiency of CXCR5 in the bottom panel (bottom).

[0037] Figure 2d The inferred trajectories superimposed on the UMAP plot show two directions of development from B naive to ABCs and B memory.

[0038] Figure 2e Cell composition density aligned with the pseudo-time sequence.

[0039] Figure 2f The percentage of B-cell subtypes is categorized by disease. Orange asterisks indicate a significant increase in SLE, and green indicates an increase in CTL. (*: p<0.05; ****: p<0.0001) Figure 2g The ratio of ABCs to B memory cells in all B cells categorized by disease. (****: p<0.0001) Figure 2h This represents the normalized expression of genes related to B cell function. The size of the dot indicates the proportion of cells expressing the gene in each cell type, and the color of the dot's edge indicates significance based on B cell DEG analysis.

[0040] Figure 2i UMAP visualization of bone marrow cells categorized by cell type (left) and disease (right). cDC represents conventional dendritic cells, and ASDC represents AXL+ SIGLEC6+ dendritic cells.

[0041] Figure 2j This represents the normalized average expression of marker genes in five subtypes of bone marrow cells. The size of the dots indicates the proportion of cells expressing that gene in each cell type.

[0042] Figure 2k The percentages of bone marrow cell subtypes by disease. Orange asterisks indicate a significant increase in SLE, and green indicates an increase in CTL. (*: p<0.05; **: p<0.01; ***: p<0.001) Figure 2l The level of type I IFN production for myeloid cell subtypes classified by disease.

[0043] Figure 2m The correlation between the average type I IFN production level of pDC and the type I IFN response of ABCs.

[0044] Figure 3a UMAP visualization of αβ T cells categorized by cell type (left) and disease (right). Tcm represents central memory T cells, Tem represents effector / memory T cells, Treg represents regulatory T cells, cycling represents circulating T cells, CD4 CTL represents CD4+ cytotoxic T cells, and CD8 GZMK+ represents CD8+ GZMK+ transitional T cells.

[0045] Figure 3bThe normalized average expression of marker genes for 10 subtypes of αβT cells is given. The size of the dots represents the proportion of cells expressing the gene in each cell type.

[0046] Figure 3c The percentages of αβT cell subtypes are categorized by disease. Orange asterisks indicate a significant increase in SLE, while green indicates an increase in CTL. (***: p<0.001; ****: p<0.0001) Figure 3d The IFN module score level of lymphocytes for αβT cell subtypes classified by disease.

[0047] Figure 3e MA plot showing differentially expressed genes in SLE CD8 T cells compared to the control group. The gray dashed line represents the cutoff value for fold change (|Log2(FC)|>1.5).

[0048] Figure 3f A violin plot showing the levels of TCR signaling (left) and T cell-mediated cytotoxicity (right) in CD8 T cells categorized by disease. (****: p<0.0001) Figure 3g TCR diversity of αβT cells categorized by disease. (****: p<0.0001) Figure 3h A heatmap showing the difference in the strength of cell-cell interactions between immune cells in the control group and SLE samples.

[0049] Figure 3i The strength of different interactions from bone marrow lineage cells to CD8 T cell subtypes in MHC-I signaling (left) and Galectin signaling (right).

[0050] Figure 3j 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). Hollow dots represent insignificant interactions.

[0051] Figure 4a Designed for a seizure analysis study based on scRNA & scTCR-seq.

[0052] Figure 4b A UMAP visualization of blasting CD8 T cells, categorized by cell type (left) and disease (top right).

[0053] Figure 4c The percentage of CD8 T cells in the flare state in each CD8 T subtype.

[0054] Figure 4d MA plot showing differentially expressed genes in CD8 T cells compared to pre-onset levels. The central gray dashed line represents the cutoff value for fold change (|Log2(FC)|>1.25).

[0055] Figure 4e The inferred trajectory superimposed on the UMAP plot shows the differentiation from CD8 naive to CD8 Tem subtype.

[0056] Figure 4f To infer the correlation between pseudo-time series and CD8 T cell cytotoxicity, ridge plots on each side show the CD8 T cell density in each group, with the dashed lines of each color representing the average value.

[0057] Figure 4g The proportion of TCR clones in CD8 T cell subtypes for each patient at each time point.

[0058] Figure 4h This is a flowchart showing the percentage of the top 10 clones in CD8 T cells. Each color represents clone expansion during the acute phase.

[0059] Figure 4i A heatmap showing the overlap between samples. The patient number below the heatmap indicates the sample origin.

[0060] Figure 4j TCR clonal frequencies of P3 and P4 in patients during the first and second seizures. Dots are stained according to clonal amplification in the seizure state. Detailed Implementation

[0061] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings, so that those skilled in the art can readily implement the invention. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. Furthermore, for clarity, parts unrelated to the description have been omitted from the drawings, and the same or similar reference numerals are used throughout the specification.

[0062] In this specification, when a part is referred to as being “connected” to another part, this includes not only the case of “direct connection” but also the case of “electrical connection” indirectly through other components.

[0063] In this specification, when a component is described as being "above", "over", "upper end", "lower", "below", or "lower end" of another component, this includes not only cases where the component is in contact with the other component, but also cases where there are other components between the two components.

[0064] In this specification, when a part is described as "including" a constituent element, it does not exclude other constituent elements unless otherwise stated, but means that other constituent elements may be further included.

[0065] The degree terms "about," "substantially," etc., used in this specification, when inherent manufacturing and material tolerances exist in their meaning, are used to mean the value or close to the value, and are intended to prevent unscrupulous infringers from improperly using the disclosure of precise or absolute values ​​mentioned to aid in understanding the invention. Furthermore, in this specification, "...step" or "...step" does not mean "the step used for...".

[0066] In this specification, the term "combination of them" as used in the Markush form means a mixture or combination of one or more of the groups of constituent elements described in the Markush form, and implies that it includes one or more of the groups of said constituent elements.

[0067] In this specification, the reference to "A and / or B" means "A, B, or A and B".

[0068] The biomarker composition for predicting systemic lupus erythematosus flare-ups of the present invention will be specifically described below with reference to the embodiments, implementation methods, and accompanying drawings. However, the present invention is not limited to these embodiments, implementation methods, and accompanying drawings.

[0069] As a technical means to achieve the above-mentioned technical problem, the first aspect of the present invention provides a biomarker composition for predicting the onset of systemic lupus erythematosus, which contains a protein expressed in CD8+ T cells or a gene encoding such a protein as an active ingredient.

[0070] The biomarkers for predicting systemic lupus erythematosus (SLE) flare according to the present invention can more accurately predict flare in SLE patients compared with existing biomarkers, thereby improving the monitoring of disease activity in SLE patients and contributing to the development of potential new therapeutic interventions.

[0071] According to one embodiment of the present invention, the protein expressed in CD8+ T cells or the gene encoding it may include one or more selected from the group consisting of GZMH, GZMB, GZMA, GNLY and PRF1, but is not limited thereto.

[0072] According to one embodiment of the present invention, when the expression level of one or more of GZMH, GZMB, GZMA, GNLY and PRF1 is upregulated, it can be judged that the possibility of experiencing a systemic lupus erythematosus flare-up is high, but it is not limited thereto.

[0073] Furthermore, a second aspect of the present invention provides a composition for predicting the onset of systemic lupus erythematosus, comprising an active ingredient an agent capable of measuring the expression level of a protein or gene encoding such a protein in CD8+ T cells.

[0074] For the composition for predicting the onset of systemic lupus erythematosus as described in the second aspect of the present invention, the detailed description of the parts overlapping with the first aspect of the present invention is omitted; however, even if the description is omitted, the content described in the first aspect of the present invention can also be applied to the second aspect of the present invention.

[0075] According to one embodiment of the present invention, the protein expressed in CD8+ T cells or the gene encoding it may include one or more selected from the group consisting of GZMH, GZMB, GZMA, GNLY and PRF1, but is not limited thereto.

[0076] According to one embodiment of the present invention, when the expression level of one or more of GZMH, GZMB, GZMA, GNLY and PRF1 is upregulated, it can be judged that the possibility of experiencing a systemic lupus erythematosus flare-up is high, but it is not limited thereto.

[0077] Furthermore, a third aspect of the present invention provides a kit for predicting the onset of systemic lupus erythematosus, comprising the composition for predicting the onset of systemic lupus erythematosus described in the second aspect of the present invention.

[0078] For the kit for predicting the onset of systemic lupus erythematosus described in the third aspect of the present invention, the detailed description of the parts overlapping with the first and / or second aspects of the present invention is omitted; however, even if the description is omitted, the content described in the first and / or second aspects of the present invention can also be applied to the third aspect of the present invention.

[0079] Furthermore, a fourth aspect of the present invention provides a method for predicting the onset of systemic lupus erythematosus (SLE), comprising: isolating CD8+ T cells from a blood sample; measuring the expression level of a protein or gene encoding the protein or gene expressed in the CD8+ T cells; comparing the expression level of the protein or gene with the expression level of a control group; and predicting the onset of SLE based on the comparison result of the expression levels.

[0080] The method for predicting the onset of systemic lupus erythematosus as described in the fourth aspect of the present invention is omitted in detail from the parts overlapping with the first to third aspects of the present invention; however, even if the description is omitted, the content described in the first to third aspects of the present invention can also be applied to the fourth aspect of the present invention.

[0081] According to one embodiment of the present invention, the protein expressed in CD8+ T cells or the gene encoding it may include one or more selected from the group consisting of GZMH, GZMB, GZMA, GNLY and PRF1, but is not limited thereto.

[0082] According to one embodiment of the present invention, when the expression level of one or more of GZMH, GZMB, GZMA, GNLY and PRF1 is upregulated, it can be judged that the possibility of experiencing a systemic lupus erythematosus flare-up is high, but it is not limited thereto.

[0083] Furthermore, a fifth aspect of the present invention provides a method for screening therapeutic agents for systemic lupus erythematosus (SLE), comprising: treating CD8+ T cells with a candidate substance for a SLE therapeutic agent; observing changes in the expression levels of proteins expressed in the CD8+ T cells or genes encoding such proteins; and screening candidate substances that reduce the expression levels of proteins expressed in the CD8+ T cells or genes encoding such proteins as SLE therapeutic agents.

[0084] The method for screening systemic lupus erythematosus treatment agents described in the fifth aspect of the present invention is omitted in detail from the parts overlapping with the first to fourth aspects of the present invention; however, even if the description is omitted, the content described in the first to fourth aspects of the present invention can also be applied to the fifth aspect of the present invention.

[0085] According to one embodiment of the present invention, the protein expressed in CD8+ T cells or the gene encoding it may include one or more selected from the group consisting of GZMH, GZMB, GZMA, GNLY and PRF1, but is not limited thereto.

[0086] The present invention will be described in more detail below through embodiments, but the following embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention.

[0087] [Example 1] Analysis of the immune cell environment in SLE PBMCs were obtained from the flare-up and remission time points of 6 SLE patients (N=19) and 33 control subjects during the disease progression, and scRNA-seq and scTCR-seq data were generated. Figure 1aPBMCs were cryopreserved after separation and subsequently analyzed using the 10X Genomics Chromium Droplet platform. SLEDAI scores increased from 4–14 pre-attack to 9–19 post-attack. All patients presented with systemic symptoms of SLE, including the presence of anti-dsDNA, decreased complement levels (C3, C4, CH50), and renal impairment characterized by proteinuria and decreased eGFR. Correlation analysis revealed a significant relationship between SLEDAI score, anti-dsDNA titer, and proteinuria levels. In particular, increased proteinuria during an attack was associated with more severe renal impairment.

[0088] A total of 164,200 cells were analyzed (excluding putative double cells and low-quality cells). By integrating gene expression profiles across patient samples and performing unsupervised clustering of cells based on standard biomarker detection, 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), and 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), plasmacytoid dendritic cells (pDCs; LILRA4, PLD4, IL3RA), platelets (PPBP, PF4, NRGN) Figure 1b and Figure 1c The annotation results show reliable consistency with automatic annotations performed using the reference-based mapping tool Azimuth.

[0089] Consistent with previous PBMC scRNA-seq data reports, T cells were the most dominant, followed by NK cells, monocytes, and B cells. Figure 1d Comparative analysis of cellular composition between the control group and SLE patients revealed significant differences. In SLE samples, CD4 T cells, monocytes, cDCs, and pDCs were significantly reduced, while CD8 T cells and B cells were significantly increased. Figure 1d Lymphopenia is a frequently observed phenomenon in SLE patients. In addition, cDC and thrombocytopenia have been observed, which have been associated with interferon response in previous studies. This change in cellular composition has also been confirmed in independent cohorts.

[0090] To investigate this aspect in more detail, k-means clustering was performed on the previously identified 100 IFN-related genes to analyze expression patterns in different cell types between the control group and SLE patients. Figure 1e and Figure 1f In differential gene expression analysis across all cells, approximately 61% of IFN genes were overexpressed in SLE. Based on this, the optimal number of clusters was determined to be 5 according to the silhouette coefficient, and each cluster was named an "IFN module" based on the shared expression patterns within specific cell types. Global IFN modules (including ISG15, PSMB9, OAS1, STAT1, TAP1, IFIT1, SAMD9L, etc.) were upregulated in most immune cell types. Lymphoid IFN modules (including IFITM1, ISG20, SP100, SAMD9, GBP5, etc.) were mainly expressed in SLE T cells, NK cells, and B cells. Conversely, Myeloid IFN modules (including IFITM3, MT2A, IFI6, TNFSF10, SCO2, PLSCR1, MX2, etc.) were enriched in monocytes and cDCs. pDC IFN modules (including IRF7, BST2, LAP3, etc.) are particularly enriched in pDCs, while Platelet IFN modules (including TMEM140, IRF9, etc.) are mainly expressed in platelets and other immune cells. This finding was also observed in independent cohorts. Figure 1e ).

[0091] These IFN modules showed high consistency with the original grouping, and the gene distribution of the modules identified in previous microarray analyses showed that, compared to M3.4 and M5.12, the genes of the M1.2 module, which were not related to changes in disease state, were mainly contained in the Global and Myeloid IFN modules. This highlights the value of single-cell resolution analysis in revealing additional heterogeneity within populations that are indistinguishable between cell types.

[0092] [Example 2] Recognition of B lymphocytes and myeloid cell subsets exhibiting enhanced type I IFN response Unsupervised clustering of B lymphocytes was performed. Five clusters were found within the B cell lineage, including three B cell subtype clusters and two plasma cell subtype clusters. Figure 2a These clusters are identified through marker genes. Figure 2b Within these clusters, atypical B cells (ABCs) expressing markers such as TBX21 (T-bet) and ITGAX (CD11c) but lacking CXCR5 were identified. Figure 2b and Figure 2cPrevious studies have associated ABCs with the production of autoantibodies in conditions such as SLE and rheumatoid arthritis.

[0093] Understanding the developmental pathways of ABCs is important, especially considering their unique differentiation processes compared to conventional B cells. To gain a deeper understanding of this, Monocle3 was used 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 branch in different directions with similar levels of differentiation and varying gene expression. Figure 2d and Figure 2e This observation is consistent with previous studies that suggested ABCs mature in the extrafollicular region, while conventional B cells mature in the germinal centers of the follicular region.

[0094] This analysis showed a significant increase in plasma cells and plasmablasts, which function as antibody-secreting cells (ASCs) in SLE. Figure 2c This finding is consistent with previous reports on plasma cell proliferation in SLE. Furthermore, the ratio of ABCs to memory B cells was significantly increased (…). Figure 2e and Figure 2g This suggests that SLE promotes the conversion of B cells to ASCs through a non-traditional differentiation pathway. This observation is further supported by the increased expression ratios of IRF4 and IRF8. Figure 2h This is particularly relevant considering that high expression of IRF4 plays a crucial role in promoting plasma cell differentiation, while IRF8 counteracts this process. Furthermore, ABCs exhibited the most significant type I IFN response in the B cell population, suggesting that the aberrant behavior of ABCs may be influenced by the intrinsic high IFN stimulation in SLE. Reduced BCR clonoid diversity was detected in SLE using BCR sequencing data, indicating the dominance of amplified clones. However, this analysis failed to identify specific clonoids associated with ABCs.

[0095] Next, we investigated how bone marrow cells are complexly involved in immune dysregulation and stimulate B cells to produce autoantibodies. Bone marrow cells were re-clustered, identifying five subsets: cM (cM; CD14, S100A9), ncM (ncM; FCGR3A, TNFSF13B), cDC (CLEC10A, CD1C, FCERT1A), AXL+SIGLEC6+ DC (ASDC; AXL, SIGLEC6, CD5), and pDCs (LILRA4, PLD4). Figure 2i and Figure 2j The proportion of pDCs, which are the main type I IFN-producing cells, was observed to be decreased in SLE. Figure 2kThis reduction may be due to its migration to inflamed tissues. The enhanced type I IFN production by pDCs in SLE is consistent with the IFN response of ABCs (…). Figure 2l and Figure 2m This suggests a potential interaction between pDCs and ABCs in the pathogenesis of SLE. In summary, the results indicate that ABCs in SLE originate from the naïve B cell lineage and exhibit significantly expanded and enhanced type I IFN responses, thus confirming their important role in ABC maturation and the pathogenesis of SLE.

[0096] [Example 3] Changes in T lymphocyte behavior in the pathogenesis of SLE 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 induce tissue damage. Changes in gene expression and cytokine production caused by impaired T cell signaling regulation may ultimately affect the behavior and characteristics of T cells in SLE.

[0097] Based on gene expression profiles using established biomarkers, CD4 T cells were classified into 6 subtypes, and CD8 T cells were classified into 4 subtypes. Figure 3a and Figure 3b In SLE, the overall proportion of CD4 T cells is decreased, but the proportion of CD4 regulatory T cells (Tregs) is increased. Figure 3c However, CD4 Treg cells in SLE exhibit dysfunctional features, particularly the downregulation of CTLA4 and IL2RA, key inhibitors of T cell activation.

[0098] This pattern is further supported by low scores in the IL-2 and TGF-β signaling pathways. This suggests that the expanded CD4 Treg cell population in SLE may have reduced ability to effectively regulate other immune cells, thus potentially failing to control autoreactive immune responses.

[0099] Unlike CD4 T cells, CD8 T cells are increased in SLE, especially in naive T cells. Figure 3c Furthermore, the lymphocyte IFN module score was highest in cytotoxic T cells such as CD8 T cells or cytotoxic CD4 T cells (CD4 CTLs). Figure 3d Differential gene expression analysis showed that genes upregulated in CD8 T cells of SLE were associated with IFN genes (IFITM1, SP100, ISG20), TCR signaling (PSMB9, CD3D, CSK), and cytotoxicity (PRF1, GZMB) enriched in lymphocytes. This suggests that compared with the control group, cytotoxic CD8 T cells are overactivated in SLE. Figure 3eIn particular, increased TCR signaling and cytotoxicity were observed. Figure 3f This indicates a correlation between these gene set scores and disease. Consistent with enhanced TCR signaling, a significant decrease in TCR clonality diversity was observed in CD8 T cells. Figure 3g This indicates that T cell activation leads to the expansion of a specific CD8 T cell clonal variant.

[0100] To understand the molecular and cellular interactions between immune cells, CellChat, based on its ligand-receptor interaction knowledge base, was used to infer cell-cell communication networks from gene expression profiles. Specifically, it was confirmed that CD8 T cells engage in extensive interactions with most other immune cells. Figure 3h This significant interaction is particularly evident in its primary involvement in MHC-I and Galectin signaling between myeloid cells and other cells. Figure 3i and Figure 3j MHC-I signaling exhibited the strongest relative interaction between cM cells and CD8 T cells, 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, primarily through interaction with LGALS9, was most pronounced between pDCs and CD8 T cells. The LGALS9 gene, encoding Galectin-9, has been identified as a biomarker for SLE due to its high serum protein levels in SLE patients. Moreover, the fact that this gene is included in the myeloid IFN module suggests a previously unexplored aspect of Galectin signaling in T cell activation in the context of SLE.

[0101] In summary, cytotoxic CD8 T cells exhibit enhanced activation and strengthened interactions, while Tregs appear to have a reduced ability to effectively regulate these activities, providing insights into the unique immune perturbations observed in SLE.

[0102] [Example 4] Excessive activation of cytotoxic CD8+ T cells during an attack In SLE, a flare represents an acute exacerbation that severely impairs a patient's health. Although the exact etiological basis of flares remains largely unclear, it is presumed to be a convergence of environmental, hormonal, and genetic factors. CD8 T cells are particularly central to this inflammatory cascade during a flare. To analyze the immune dynamics during a flare, paired samples from four patients before and after a flare were analyzed, including two individuals who experienced both flares and remissions (total n=12). Figure 4aAs expected, the attacks were accompanied by a significant increase in SLEDAI scores and a concurrent increase in proteinuria compared to pre-attack baseline status. This highlights the systemic inflammatory burden during the attack.

[0103] Sub-clustering of CD8 T cells in paired samples before and after an attack revealed three clusters, including CD8 naive, CD8GZMK+, and CD8+ effector / memory (Tem) cells. Figure 4b Single-cell UMAP embeddings showed a significant increase in the CD8 Tem subset during seizures. Figure 4c At the molecular level, CD8 T cells associated with seizures exhibit a dramatic increase in genes related to cytotoxicity, such as GZMH, GZMB, GZMA, GNLY, and PRF1. Figure 4d Conversely, representative genes of the memory phenotype, including TCF1, LEF1, and CCR7, showed downregulation. These gene expression changes support cell trajectory analysis (…). Figure 4e This suggests a transition from naive T cells to Tem cells. In particular, these terminally differentiated cells are more abundant during the acute phase and are accompanied by enhanced cytotoxicity. Figure 4f ).

[0104] [Example 5] Expansion of clonal restricted cytotoxic CD8+ T cells during SLE attacks T cells undergo clonal expansion upon antigen recognition, a crucial step in their differentiation into effector cells. This specific proliferation ensures a strong immune response against recognized pathogens. Against this backdrop, the TCR sequences of CD8 T cells were analyzed, and their TCR clonities were mapped to UMAP space. The dynamic changes in CD8 T cell clonities were investigated while focusing on potential self-antigens and exploring changes during seizures. T cell expansion during seizures was detected by tracing CD8 T cell clones using TCR sequences, with particular attention paid to expanded cytotoxic CD8 T cells. Cells possessing the same TCR gene in both the α and β chains were considered to belong to the same clone. Analysis of three time points in six paired pre- and intra-seizure samples confirmed an increased proportion of over-expanded clonities (n>1%) during seizures. Figure 4g This expansion was particularly pronounced in CD8 Tem cell subsets. To track which clonoids proliferated predominantly during flare-ups, the focus was on the top 10 most abundant clonoids in CD8 T cells from each patient, and some clonoids showing more than a 2-fold change were identified. Figure 4hThis significant expansion of specific clonal types underscores increased T cell activation and suggests that specific epitopes may induce this activation during seizures. Further analysis of the frequency of the top 30 most abundant clonal types in CD8 T cells reinforced this observation, although it did not reach statistical significance due to the limited sample size, but it showed a clear trend of T cell expansion. This clonal expansion provides strong evidence for targeted immune responses against specific antigens during seizure events.

[0105] Subsequently, to infer the similarity of autoantibodies, the similarity of CD8 TCR clonal types was further investigated between patients and between seizures. As expected, TCR clonal diversity existed among patients, but similarities were found within individual patients, and were more pronounced in CD8 Tem than in CD8 naive T cells. Figure 4i Interestingly, analysis of two consecutive time points showed that some clones were already present in the early stages, but mainly amplified during specific time points. Figure 4j This suggests that the primary antigens inducing cytotoxic T-cell responses may differ between different episodic events. This dynamic nature of antigen-antigen interactions underscores the need for precise therapeutic approaches that can address the changing antigenic stimuli that activate cytotoxic T-cell responses during each episodic episode.

[0106] The above description of the present invention is merely exemplary, and those skilled in the art should understand that it can be easily modified into other specific forms without changing the technical concept or essential features of the invention. Therefore, the above embodiments should be understood in all respects as exemplary and not restrictive. For example, the constituent elements described in a single form may also be implemented separately, and similarly, the constituent elements described as separate may also be implemented in combination.

[0107] The scope of this invention should be defined by the following claims rather than the detailed description above, and it should be interpreted that all changes or modifications derived from the meaning and scope of the claims and their equivalents are included within the scope of this invention.

Claims

1. A biomarker composition for predicting the onset of systemic lupus erythematosus, characterized in that, Proteins expressed in CD8+ T cells or genes encoding them are used as active ingredients.

2. The biomarker composition for predicting systemic lupus erythematosus flare-ups according to claim 1, characterized in that, The proteins expressed in CD8+ T cells or the genes encoding them include one or more selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1.

3. The biomarker composition for predicting systemic lupus erythematosus flare-ups according to claim 2, characterized in that, When upregulation of expression levels of one or more of GZMH, GZMB, GZMA, GNLY, and PRF1 is observed, it is considered that the patient is highly likely to have experienced a systemic lupus erythematosus flare-up.

4. A composition for predicting the onset of systemic lupus erythematosus, characterized in that, The active ingredient contains a formulation capable of measuring the expression level of proteins or genes encoding them expressed in CD8+ T cells.

5. The composition for predicting systemic lupus erythematosus flare-ups according to claim 4, characterized in that, The proteins expressed in CD8+ T cells or the genes encoding them include one or more selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1.

6. The composition for predicting systemic lupus erythematosus flare-ups according to claim 5, characterized in that, When upregulation of expression levels of one or more of GZMH, GZMB, GZMA, GNLY, and PRF1 is observed, it is considered that the patient is highly likely to have experienced a systemic lupus erythematosus flare-up.

7. A kit for predicting the onset of systemic lupus erythematosus, characterized in that, The composition comprising the method for predicting the onset of systemic lupus erythematosus as described in claim 4.

8. A method for predicting the onset of systemic lupus erythematosus, characterized in that, include: The steps for isolating CD8+ T cells from a blood sample; The steps of measuring the expression level of proteins or genes encoding them expressed in the CD8+ T cells; The step of comparing the expression level of the protein or gene with the expression level of the control group; as well as The steps for predicting systemic lupus erythematosus flare-ups based on the comparison results of the expression levels.

9. The method for predicting systemic lupus erythematosus flare-ups according to claim 8, characterized in that, The proteins expressed in CD8+ T cells or the genes encoding them include one or more selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1.

10. The method for predicting systemic lupus erythematosus flare-ups according to claim 9, characterized in that, When upregulation of expression levels of one or more of GZMH, GZMB, GZMA, GNLY, and PRF1 is observed, it is considered that the patient is highly likely to have experienced a systemic lupus erythematosus flare-up.

11. A method for screening therapeutic agents for systemic lupus erythematosus, characterized in that, include: Steps for treating CD8+ T cells with candidate substances for systemic lupus erythematosus treatment; The steps for observing changes in the expression levels of proteins or genes encoding them expressed in the CD8+ T cells; as well as The step of screening candidate substances that reduce the expression level of proteins or genes encoding them in CD8+ T cells as therapeutic agents for systemic lupus erythematosus.

12. The method for screening therapeutic agents for systemic lupus erythematosus according to claim 11, characterized in that, The proteins expressed in CD8+ T cells or the genes encoding them include one or more selected from the group consisting of GZMH, GZMB, GZMA, GNLY, and PRF1.

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