Application of pro-inflammatory macrophage subpopulation Cc16 + IM and NK cells in preparation of medicine for treating sepsis-induced acute kidney injury

Abstract

The invention relates to the field of biological medicine, in particular to application of pro-inflammatory macrophage subpopulation Cc16 + IM and NK cells in preparation of a medicine for treating sepsis-induced acute kidney injury. According to the invention, single cell sequencing and spatial transcriptomics research find that proinflammatory macrophage subgroup Ccl6 + IM exists in a sepsis process, and the proinflammatory macrophage subgroup Ccl6 + IM recruits and activates NK cells through a Ccl6-Ccr2 ligand receptor pair. The medicine can activate signal channels such as TGF-BETA and the like by promoting Ccl6 + IM to highly express CCL6, or regulate a Ccl6-Ccr2 ligand receptor pair to play a role. The key effect of Cc16 + IM in sepsis recovery is disclosed for the first time, a new macrophage-NK cell interaction mechanism is clarified, multi-dimensional analysis of a functional pathway and a spatial interaction network is realized by adopting multi-omics integration analysis, and a new target spot and a technical thought are provided for treatment of sepsis-induced acute kidney injury.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine technology, and in particular to a pro-inflammatory macrophage subset Ccl6 + Application of IM and NK cells in the preparation of drugs for treating sepsis-induced acute kidney injury. Background Art

[0002] Acute kidney injury (AKI) is a clinical condition characterized by acute impairment of renal function and a high risk of mortality. It is precipitated by a variety of factors, including sepsis. In recent years, AKI has accounted for a significant proportion of multi-organ damage caused by emergencies and natural disasters worldwide. The pathophysiology of acute kidney injury during sepsis is complex. Although the contribution of different immune cell types to the pathogenesis of sepsis has been extensively studied, the precise mechanistic details remain elusive. Therefore, understanding the etiology and mechanisms of sepsis-induced acute kidney injury is crucial.

[0003] Existing studies have shown that macrophages participate in the inflammatory response to sepsis by secreting cytokines and chemokines, but their subset-specific functions are not yet fully understood, and the functions of different subsets vary significantly. Natural killer (NK) cells, as innate lymphocytes, possess cytotoxic and immunomodulatory functions, but their recruitment and activation mechanisms in sepsis remain unclear. Therefore, identifying new therapeutic targets and mechanisms of action is crucial for the treatment of sepsis-induced AKI. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a pro-inflammatory macrophage subset Ccl6 + The application of IM and NK cells in the preparation of drugs for treating sepsis-induced acute kidney injury. The present invention found that there is a unique pro-inflammatory macrophage subset Ccl6 in the process of sepsis. + IM regulates NK cell function through a unique mechanism, providing a new target for the treatment of sepsis-induced AKI.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows: The present invention provides a pro-inflammatory macrophage subpopulation Ccl6 + Use of IM in the preparation of drugs for treating sepsis-induced acute kidney injury.

[0006] Preferably, the drug is a drug that promotes the expression of pro-inflammatory macrophage subpopulation Ccl6 + IM drugs that overexpress CCL6.

[0007] Preferably, the drug is a drug that activates the following signaling pathways: TGF-BETA-SIGNALING, IL2-STAT5-SIGNALING, TNFA-SIGNALING-VIA-NFKB, INFLAMMATORY-RESPONSE, IL6-JAK-STAT3-SIGNALING.

[0008] The present invention also provides an application of NK cells in the preparation of a medicine for treating sepsis-induced acute kidney injury.

[0009] Preferably, the drug is a drug that promotes NK cell recruitment and activation by regulating the Ccl6-Ccr2 ligand receptor pair.

[0010] The present invention also provides a pro-inflammatory macrophage subset Ccl6 + Application of IM combined with NK cells in the preparation of drugs for the treatment of sepsis-induced acute kidney injury.

[0011] Preferably, the drug is a pro-inflammatory macrophage subset Ccl6 + IM is a drug that recruits and activates NK cells through the Ccl6-Ccr2 ligand receptor pair.

[0012] Preferably, the administration of the drug includes at least one of oral administration, intravenous injection and intraperitoneal injection.

[0013] Preferably, the drug is in any pharmaceutically acceptable dosage form, including at least one of tablets, capsules, injections, granules, suspensions and solutions.

[0014] The present invention discloses the use of pro-inflammatory macrophage subsets and NK cells in the preparation of a drug for treating sepsis-induced acute kidney injury, which has many significant technical effects, as follows: 1. This study reveals for the first time the critical role of the Ccl6+ IM macrophage subset in the recovery of sepsis-induced acute kidney injury (SAKI). This subset proliferates significantly in the late stages of SAKI injury, highly expressing the Ccl6 gene and activating multiple immune and inflammatory pathways, such as TGF-β-signaling and IL2-STAT5-signaling, significantly contributing to proinflammatory responses.

[0015] 2. The present invention elucidates a new mechanism of macrophage-NK cell interaction mediated by the Ccl6-Ccr2 ligand receptor pair. + IM recruits and activates NK cells through this ligand-receptor pair, promoting NK cells to exert cell killing and cytotoxic functions in the late stage of SAKI, which is beneficial to SAKI recovery.

[0016] 3. This invention adopts a multi-omics integrated analysis method. Compared with traditional single-omics research, it integrates single-cell sequencing, spatial transcriptomics and cell communication analysis, and for the first time realizes the multi-dimensional analysis of the dynamics of macrophage subpopulation functional pathways and spatial interaction networks, providing new technical ideas and methods for the study of the mechanism of sepsis-induced AKI. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 Figure 3: ScRNA-seq identification of various renal cell populations; A is the integrated UMAP map of renal cell clusters at 0, 1, 4, 16, 27, 36, and 48 hours after LPS injection; B is a bubble map of representative marker genes in each renal cell cluster, where the closer the bubble color is to yellow, the more significant the expression, and the larger the bubble, the higher the expression ratio; C is the integrated UMAP map of renal cell types after manual annotation of marker genes; D is the expression of typical marker genes in UMAP clusters, where purple indicates expression and gray indicates no expression, and the darker the color, the more significant the expression; E is a bar graph of the changes in the proportion of different types of renal cells at different time points.

[0019] Figure 2 The following are diagrams showing the functional changes of different types of immune cells in the kidney tissue of LPS mice; A is a diagram showing the proportion changes of different types of immune cells at different time points; B is a mountain diagram showing the enrichment evaluation of four types of immune cells in inflammatory response, with the vertical axis being the immune cell type and the horizontal axis being the SSGSEA enrichment analysis score; C is a heat map showing the functional enrichment analysis of different immune cell types based on the SSGSEA algorithm.

[0020] Figure 3Figure 3 is a graph of macrophage subsets characterized by ScRNA-seq; A is a UMAP graph of 12 different macrophage clusters obtained by unbiased cluster analysis; B is a bubble graph of representative marker genes for each macrophage cluster, where the size of the bubbles indicates the proportion of cells expressing the marker genes in each cluster, and the color depth indicates the level of the marker genes; C is an integrated UMAP graph of macrophage subsets annotated by known methods; D is an expression graph of representative markers of monocyte-derived macrophages (IMs) on UMAP clusters; E is a violin plot of the expression of different types of monocyte-derived macrophage marker genes in each macrophage cluster, namely Ccl6, Ly6c1, Pglyrp1, S100a8, and Umod; IM: monocyte-derived macrophages, KRM: kidney-resident macrophages, Mac: macrophages that cannot be defined as IM or KRM.

[0021] Figure 4 Functional variation diagram of various macrophage subsets during SAKI; A is a functional enrichment heat map of each macrophage subset based on the SSGSEA algorithm; B is a density heat map showing the up-regulated pathways; C is a bar chart showing the number and probability of up-regulation, down-regulation and no significant results of four algorithms (AUCell, UCell, SSGSEA, JASMINE); data are shown as mean ± standard deviation *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

[0022] Figure 5 Analyzing the CCL signaling network for cell communication is beneficial to restoring the SAKI diagram; among them: A is a heat map of the number of cell communications at 0h, 1h, 4h, 16h, 27h, 36h, and 48h; B is a bar graph comparing the number and intensity of cell communication at different time points; C is a heat map of the contribution of 0h, 1h, 4h, 27h, 36h, and 48h signals to the outgoing signals of different immune cell populations. The top colored bar graph shows the total signal intensity of the cell group by summarizing all the signal pathways shown in the heat map, and the gray bar graph on the right shows the total signal intensity of the signal pathway by summarizing all the cell groups shown in the heat map; D is a heat map of the contribution of 0h, 1h, 4h, 27h, 36h, and 48h signals to the received signals of different immune cell populations.

[0023] Figure 6 Determine Ccl6 for pseudo-timing + IM subpopulation cell trajectory diagram; A is the construction of pseudo-timeline trajectory and the arrangement of cells in pseudo-time, time goes from left to right, and the left is the starting point; B is the trajectory diagram arranged by cell type along the pseudo-timeline; C is the cell trajectory diagram of different types of cells arranged along the pseudo-timeline; D is the Ccl6 at different time points +Integration diagram of IM on UMAP; E is the gene expression change diagram of Ccl6 in different types of cells along the pseudo timeline; F is the violin diagram of Ccl6 expression at different time points; G is the cell number change diagram of four types of immune cells at different time points.

[0024] Figure 7 It is a mapping diagram of immune cells in the spatial transcriptome; among them: A is the unsupervised clustering diagram of spatial transcriptomics of mouse kidney; B is the UMAP integration diagram of immune cells; C is the extended clustering of immune cells after spatial transcriptomics integration, the darker the color, the greater the probability of the cell appearing in the spatial transcriptome spot; D is the UMAP integration diagram of unsupervised clustering of kidney cells in spatial transcriptomics; E is the distribution diagram of different kidney cell populations in spatial transcriptomics; F is the extended clustering diagram of different types of immune cells after spatial transcriptomics integration at 0h, 1h, and 4h; G is the extended clustering diagram of different types of immune cells after spatial transcriptomics integration at 16h; H is the extended clustering diagram of different types of immune cells after spatial transcriptomics integration at 27h, 36h, and 48h.

[0025] Figure 8 It is an interaction diagram between cell groups in the spatial transcriptome; among them: A is the chord diagram of the cell communication quantity between different clusters obtained by spatial transcriptomics clustering; B is the heat map of the cell communication quantity between different clusters obtained by spatial transcriptomics clustering; C is the chord diagram of the CCL signaling network in spatial transcriptomics; D is the hierarchical diagram of cell communication between different clusters obtained by spatial transcriptomics clustering; E is the chord diagram of the interaction between different cell clusters based on the CCL signaling network.

[0026] Figure 9 Validation of Ccl6 for cell communication and enrichment analysis + Figure 3: IM induces NK cell proliferation and inhibits SAKI; A is a bubble diagram of signal ligand receptor pairs communicating between macrophage subsets and neutrophils at 27h, 36h, and 48h. The darker the red, the higher the probability of communication. Bubbles represent p<0.01. B is a bar graph of the contribution value of ligand receptor pairs of the CCL signaling pathway at 27h, 36h, and 48h. C is a chord diagram of the communication between macrophages and immune cells in the CCL signaling pathway. D is the recognition, sending, or The cell population with significant changes in receiving signals, the horizontal axis is the signal intensity, and the vertical axis is the received signal intensity; E is the expression diagram of the Ccr2 gene in the NK cell UMAP cluster; the expression status of seven different periods of 0h, 1h, 4h, 16h, 27h, 36h, and 48h are displayed in turn; F is a violin plot of the expression changes of Ccr2 at different times; G is a GO enrichment analysis diagram of NK cells at six different time points of 1h, 4h, 16h, 27h, 36h, and 48h. DETAILED DESCRIPTION

[0027] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely below. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer are used. Where the manufacturer of the reagents or instruments is not specified, all are conventional products that can be purchased commercially.

[0028] 1. Experimental Methods 1. Data Collection The mouse scRNA-seq database was obtained from the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information (NCBI). The sequencing platform was GPL24247 Illumina NovaSeq 6000, with accession number GSE151658. Specifically, the data includes GSM4587730 (LPS0h), GSM4587731 (LPS1h), GSM4587732 (LPS4h), GSM4587733 (LPS16h), GSM4587734 (LPS27h), GSM4587735 (LPS36hh), and GSM4587736 (LPS48h).

[0029] 2. Quality Control and Cell Type Identification scRNA-seq data were analyzed using the Seurat R-package. Seurat objects were created for both non-integrated and integrated (including all time points) using the following filtering criteria: gene counts were set between 200 and 3000 and the percentage of mitochondrial genes was less than 50 to exclude doublets and poor-quality cells. Gene counts were log-transformed and scaled to 10. 4 After data normalization for each sample using the NormalizeData function, unsupervised cluster analysis was performed based on the first 20 principal components (PCA). Unsupervised dimensionality reduction (UMAP) was used (resolution 4.0) to generate 23 clusters for visualization. Seurat was used to identify differentially expressed genes within each cluster using p < 0.05 and |log2Foldchange| > 0.25. Cell types were annotated and grouped based on classical marker genes from the literature.

[0030] 3. Functional Enrichment Analysis of Cell Clusters Functional enrichment analysis of clusters was performed using the irGSEA R-package (https: / / github.com / chuiqin / irGSEA / ). Enrichment scores for different pathways within each cell cluster were derived using the AUCell algorithm. The top 50 pathways, determined by their enrichment scores, were visually represented in a heatmap. Four algorithms, including AUCell, UCell, ssgsea, and JASMINE, were used to compare the activated pathways within each cluster. Furthermore, ridge plots were used to illustrate the enrichment scores for specific pathways within each immune cell cluster.

[0031] 4. Collection of Macrophage DEGs The DESeq R-package was used to identify DEGs in macrophages between the control and experimental groups. The thresholds p < 0.05 and |log2Foldchange| > 1 were set to screen out highly significant differentially expressed genes. As a result, 168 significantly upregulated differentially expressed genes and 93 significantly downregulated differentially expressed genes were selected for macrophages, and then visualized using the ggplot2 R-package.

[0032] 5. Analysis of Intercellular Communication To examine interactions between macrophage clusters and other immune cells, cell communication analysis was performed using the CellChat R-package for samples at each time point and across integrated samples. To visualize cellular crosstalk, a circle plot was used to display the number and strength of interactions between any two cell clusters, while a layered plot was used to visualize source-target interactions within a specific pathway. Furthermore, a bar chart was used to visualize the contribution of each ligand-receptor pair within a specific pathway.

[0033] 6. Spatial transcriptomics Using Seurat 4.4.0, we identified anchor points between the integrated single-cell objects and the spatial transcriptomics dataset and used them to transfer clustering data from single cells to spatial transcriptomics. For each spatial transcriptomics point, this transfer assigned a score to each single-cell cluster. We selected the cluster with the highest score at each point to represent its single-cell associated cluster.

[0034] 7. Statistical Analysis No blinding was used in the animal experiments. All data were analyzed using the R software package, and relevant statistics are provided in the Results, Methods, and figure legends. A p value < 0.05 was considered statistically significant.

[0035] Example 1 ScRNA-seq identification of different types of kidney cells In the present invention, a total of 63,287 renal cells were harvested at 0, 1, 4, 16, 27, 36, and 48 hours after endotoxin (LPS) administration. After quality control, a total of 53,366 cells (7109 LPS 0h, 8042 1h, 8231 4h, 4960 16h, 5953 27h, 8557 36h, and 10514 48h) were retained and integrated into a normalized and unbatched dataset. Principal component analysis (PCA) was then performed, and unsupervised clustering was performed based on the first 20 principal components. The dimensionality reduction was visualized by UMAP ( Figure 1 A). Using known classical phenotypic markers, 13 major cell types were identified based on the relative expression of marker genes within each cluster ( Figure 1 B- Figure 1 C). Figure 1 D shows the expression of typical marker genes of each cell type on the UMAP cluster diagram. For example, Slc13a1 is significantly expressed in proximal renal tubular epithelial cells, while macrophages highly express its classic marker Ctss ( Figure 1 E). Observations revealed that at 16 hours, the number of monocytes was much higher than that of other cell types. Compared with proximal tubular epithelial cells and other parenchymal cells, immune cells dominated at this time point ( Figure 1 F), which may have certain reference significance for the later study of the impact of immune cells on the pathogenesis of SAKI.

[0036] Example 2 Changes in immune cell function in the kidneys of LPS mice By studying the relative proportions and functional changes of various immune cells in renal tissue after LPS treatment, we found the specific immune cell population that causes SAKI immune response. Figure 2 As shown in A, after LPS treatment for 0, 1, 4, 27, 36, and 48 hours, the number of macrophages increased significantly, while NK cells accounted for a large proportion of all immune cells. After 16 hours of LPS treatment, the number of macrophages and NK cells decreased sharply, while the number of monocytes increased sharply. Over time, the number of NK cells and macrophages gradually recovered.

[0037] like Figure 2 As shown in B, the mountain plot shows the enrichment scores of the four immune cells in the inflammatory response. The vertical axis is the immune cell type, and the horizontal axis is the SSGSEA enrichment analysis score. It can be seen that macrophages, neutrophils, and natural killer cells have higher inflammatory response scores than monocytes. The functional variation of different types of immune cells was studied using the SSGSEA algorithm in irGSEA ( Figure 2 B, Figure 2C). The present invention discovered that macrophages and neutrophils activated multiple immune response and inflammation-related pathways, including TGF-β signaling, inflammatory response pathways, IL2-STAT5 signaling, IL6-JAK-STAT3 signaling, TNFA-SIGNALING-VIA-NFKB, INTERFERON-GAMMA-RESPONSE, and INTERFERON-ALPHA-RESPONSE. These macrophages and neutrophils also activated pathways associated with cell proliferation and differentiation, such as KRAS-SIGNALING-UP, NOTCH-SIGNALING, and PI3K-AKT-MTOR-SIGNALING. Furthermore, NK cells activated several immune response and inflammation-related pathways, such as HALLMARK-IL2-STAT5-SIGNALING. The present invention's research demonstrates that during SAKI, macrophages, neutrophils, and NK cells play a critical role in the immune and inflammatory responses of the kidney.

[0038] Example 3 ScRNA-seq characterization of macrophage subsets To gain a deeper understanding of the changes in macrophages during SAKI, we used unbiased cluster analysis to perform a more detailed subdivision of macrophages ( Figure 3 A), 12 different macrophage clusters were obtained. The present invention divides the 12 clusters into 10 different subgroups ( Figure 3 B- Figure 3 C) 1, 4, and 5 were identified as kidney-resident macrophages (KRMs), and 0, 2, 3, 9, and 10 were defined as monocyte-derived macrophages (IMs). Cells that were neither KRMs nor IMs were named using their marker genes, resulting in four undefined macrophage subsets (Ccl5). + Mac, Fscn1 + Mac, Il1rn1 + Mac, Rsad2 + Mac ( Figure 3 D).

[0039] Violin plots of marker genes of different types of monocyte-derived macrophage subsets expressed in each macrophage subset revealed that Ccl6 + IM only highly expresses Ccl6 gene, which is different from other monocyte-derived macrophage subsets. Interestingly, Ccl6 gene is expressed in IMs and its expression level is relatively high, while it is relatively low in KRM, Fscn1+Mac, and Il1rn1+Mac. + Mac, Rsad2+ Mac does not express ( Figure 3 E). Further analysis revealed that the three macrophage clusters with the highest Ccl6 expression levels showed an adjacent relationship in the UMAP clustering, that is, these three cell clusters were close to each other and separated from other clusters ( Figure 3 D). The present invention can speculate that Ccl6 + IM and Ccl6 genes play an important role in SAKI.

[0040] Example 4 Ccl6 + IM is strongly proinflammatory during SAKI By deeply studying the functions and effects of each macrophage cluster, we can explore the collective mechanism of SAKI. The present invention uses four different algorithms, AUCell, UCell, ssgsea, and JASMINE, to perform enrichment analysis on different macrophage subsets. The present invention found that when the ssgsea enrichment score was implemented for macrophage subsets, a large number of immune response and inflammatory response-related pathways were expressed in Ccl6 + IM was significantly activated ( Figure 4 A), including but not limited to TGF-BETA-SIGNALING, IL2-STAT5-SIGNALING, TNFA-SIGNALING-VIA-NFKB, INFLAMMATORY-RESPONSE, IL6-JAK-STAT3-SIGNALING, and these pathways are not fully activated in other macrophage subsets. Figure 4 A indicates Ccl6 + IM is specific for immune and inflammatory responses. Figure 4 B shows that Ccl6 + The expression levels of genes related to the four pathways of IM and INFLAMMATORY−RESPONSE, IL6−JAK−STAT3−SIGNALING, TGF-BETA-SIGNALING, and TNFA−SIGNALING−VIA−NFKB were all high. Figure 4 C shows that the results of the four algorithms are basically consistent. These findings strongly suggest that Ccl6 + IM makes a significant contribution to pro-inflammatory macrophages during SAKI.

[0041] Example 5 Cell Communication Analysis Reveals Ccl6 + IMs play an important role in cell interactions Figure 5 A shows that the intensity of cell interaction increased at 27h, 36h, and 48h, and it is worth noting that the special macrophage subset Ccl6 +IMs are more active than other cell types, both as signal sources and target cells, suggesting that this macrophage subset plays an important role in cell interactions. Figure 5 B shows that global cell communication failure occurred at 16h, which is an important time point. To prove this point, we further analyzed the relationship between signal emission and signal reception at 6 different time points and different cell types. Figure 5 C. Figure 5 As shown in D. It can be seen that after 16 hours, the cell communication intensity has been greatly improved, and Ccl6 + IM accounts for the highest proportion of signals. Interestingly, the statistical analysis of the emission signals found that + Among all the signal networks related to IM, CCL signal network consistently occupies the highest proportion, and the intensity change of CCL signal network is similar to that of Ccl6. + The changes in the intensity of IM interactions remain consistent. In summary, it is speculated that Ccl6 + At 27h, 36h, and 48h, IM secretes a large number of ligands through the CCL signaling network to act on other cells and thus affect SAKI.

[0042] Example 6: Pseudo-sequential analysis to determine Ccl6 + IM differentiation trajectory Figure 6 A. Figure 6 B. Figure 6 C indicates the location of different types of cells. + IM is at the end of the timeline, and Ccl6 appears at 27h, 36h, and 48h. + IM and increased dramatically ( Figure 6 D), and KRM is in the entire false timeline, so it is speculated that Ccl6 + IM differentiates from KRM. This is accompanied by an increase in Ccl6 expression in the later stages ( Figure 6 E. Figure 6 F). In order to more clearly analyze the interaction between different types of macrophage subsets and other immune cells, the changes in the number of four types of immune cells at different time points were obtained ( Figure 6 G). The present invention found that NK cells proliferate more significantly than other immune cells except macrophages, and their proportion is second only to macrophages, showing rapid proliferation in the later stage. + There may be a close interaction between IM and NK cells.

[0043] Example 7 Spatial transcriptomics localization of different types of immune cells in the kidney Spatial transcriptomics of mouse kidney revealed clustering of cells into eight clusters ( Figure 7A), named C1, C2, C3, C4, C5, C6, C7, and C8 respectively. The present invention performs label transfer through the sctransform normalization method. This process outputs the probability classification of each scRNA-seq data for each point, and obtains the spatial transcriptomic distribution of different types of immune cells. It is not difficult to find that NK cells are mainly concentrated in the periphery, while macrophages are mainly concentrated in the interior ( Figure 7 B. Figure 7 C). UMAP integration obtained after unsupervised clustering of spatial transcriptomics kidney cells Figure 7 D. And shows its distribution in space ( Figure 7 E). The status shows that C5 and C7 are located inside the spatial transcriptomics, extending outward to C3 and C6, and C1, C2, C4, and C8 are located in the periphery. In order to gain a deeper understanding of how the distribution of various immune cells changes over time, the present invention performs sctransform normalization on three different time periods (0h, 1h, 4h, 16h, 27h, 36h, and 48h) to obtain three different spatial transcriptomics distributions. The probability of NK cells initially concentrating inside is the highest. At the 16h time point, there is no obvious distribution of NK cells. Finally, in the 27h, 36h, and 48h time periods, NK cells are found to be mainly distributed in the periphery. Interestingly, the changing trends of macrophages and NK cells are opposite, which is a noteworthy point, and may indicate that there is a certain relationship between macrophages and NK cells ( Figure 3 F. Figure 3 G. Figure 3 H).

[0044] Example 8: Spatial transcriptomics reveals the interaction between different types of immune cells in the kidney The present invention hopes that spatial transcriptomics can be combined with the aforementioned cell communication, so the present invention establishes cell interactions in spatial transcriptomics. By observing the interactions between different clusters, it can be seen that there is a strong interaction between C3, C5, C6, C7, and C8 ( Figure 8 A). The heat map also clearly shows this point. From the heat map, we can see that the cell communication intensity between C5 and C5, between C5 and C7, and between C8 and C8 is extremely high ( Figure 8 B). The CCL signaling network mentioned above was introduced and mapped on spatial transcriptomics, and it was found that C1, C2, C3, C4, and C8 were relatively active in this signaling network ( Figure 8 C). Figure 8 D and Figure 8E both demonstrated the interaction between different cell populations in the CCL signaling network, and both obtained consistent information, namely, C6 and C3 send signals, and C8 receives signals. In terms of spatial transcriptomics, it is shown that the internal cell population is the source of the signal and sends signals outward. At the same time, C1, C2, and C4 in the periphery also send strong CCL signals to C8. It is worth noting that the region with the highest probability of receiving signals as target cells is NK cells ( Figure 7 C). In summary, the cellular interactions in the spatial transcriptomic region where macrophages and NK cells reside are strong, and the CCL signaling network is highly intense both outside the macrophages and inside the NK cells. Therefore, the present invention speculates that NK cells and macrophages may influence SAKI through the CCL signaling network. At the same time, NK cells migrate to the periphery at a later stage, indicating that the 27h, 36h, and 48h time periods are particularly important for NK cells to exert their effects.

[0045] Example 9 Ccl6+ recruits and activates NK cells via Ccl6-Ccr2 in the late IM phase Figure 9 Figure A shows that the receptor ligand pair with the highest probability of Ccl6-Ccr2 is obtained when NK cells are used as target cells and different types of macrophage subsets are used as signal sources. Each receptor ligand pair in the CCL signaling network is sorted by contribution value. The results show that the receptor ligand pair with the highest contribution value is also Ccl6-Ccr2 ( Figure 9 B). In order to fully verify the specificity of the receptor-ligand pair, the signal network related to the receptor-ligand pair was drawn into a circle diagram ( Figure 9 C), analysis showed that the two cell populations with the highest interaction intensity through this receptor ligand pair are NK cells and Ccl6 + IM. Figure 9 D is the cell population that identified significant changes in the sending or receiving signals in the CCL signaling pathway at three different periods, from the acute phase of SAKI (LPS 1h, 4h, 16h) to the recovery phase of SAKI (LPS 27h, 36h, 48h), the main source of which was Ccl5 + Mac converted to Ccl6 + IM, NK cells have been added as the main target, which further illustrates that Ccl6 + The importance of IM to SAKI and NK cells, Figure 9 E and Figure 9 F shows that the expression of Ccr2 gene increased sharply at 27h, 36h and 48h. In order to further analyze the functional changes of NK cells at different time points, Figure 9G shows the GO enrichment results. It is not difficult to find that the main functions of NK cells at 27h, 36h, and 48h are cell killing and cytotoxicity, which indicates that NK cells play an important role in SAKI during this time period, possibly clearing abnormal cells and thus facilitating the recovery of SAKI.

[0046] In this study, the present invention revealed the specific mechanism of its influence on SAKI by analyzing the specific monocyte-derived macrophage subpopulation during SAKI. The present invention's observations found that in the late stage of SAKI, both macrophages and NK cells showed significant proliferation and macrophages activated a large number of immune response and inflammatory response-related pathways compared to other types of immune cells. These findings indicate that macrophages and NK cells play an important role in the later stage, which is consistent with previous reports. Through further analysis, the present invention discovered a monocyte-derived macrophage cluster Ccl6 that has an important influence on immune response and inflammatory response. + IM. Ccl6 shows significant variability between normal and disease, and Ccl6 + IM only exists in the late stage of SAKI, which may indicate that Ccl6 + IM plays an important role in the recovery of SAKI. + To understand the specific mechanism by which IM affects SAKI, the present invention further studies the interaction between macrophage subsets and different types of immune cells, and combines spatial transcriptomics to reveal the spatial interaction between different types of immune cells. + IM strongly acts on NK cells through the Ccl6-Ccr2 ligand receptor pair, affecting the CCL signaling pathway and NK cell recruitment and activation. Furthermore, Ccl6 has been shown to be abundantly released during SAKI.

[0047] Previous studies have emphasized that macrophages play a key role in coordinating the systemic response to SAKI by secreting cytokines and chemokines. The present study also reconfirmed this finding. It is worth noting that the pathway scoring results analysis based on SSGSEA showed that during SAKI, macrophages significantly activated pathways related to immune response and inflammatory response, including key cytokines such as IL-2, IL-6, TNF-α and TGF-β, as well as pathways related to proliferation and differentiation. Therefore, it can be shown that macrophages play an immune effect as the main immune cell type during SAKI.

[0048] By analyzing macrophage subpopulations, specific pro-inflammatory macrophage clusters were discovered. In order to gain a deeper understanding of the impact of macrophages on SAKI, the present invention further subdivides macrophages. Advances in scRNA-seq technology have enabled researchers to further stratify macrophages into more refined subpopulations. Using known classification methods, macrophages in the kidneys can be roughly divided into two main types according to their origin: KRM and IMs ADDIN EN.CITE ADDIN EN.CITE.DATA. However, it should be noted that some macrophage clusters belong neither to KRM nor to IMs. The present invention defines this as a newly emerged unknown macrophage cluster. Enrichment analysis showed that Ccl6 + IM is significantly pro-inflammatory and activates a large number of pathways related to immune response and inflammatory response.

[0049] To further explore Ccl6 + The specific mechanism of IM in SAKI was analyzed by intercellular communication and confirmed that Ccl6 + IM interacts more strongly with other different types of immune cells and indicates that CCL may be an important signaling network. + The intensity of IM cell communication in the later stage is much higher than that of other cell types. The statistical analysis of the emission signal found that the Ccl6 + Among all the signal networks related to IM, CCL signal network consistently accounts for the highest proportion, especially in the later stage, the change of CCL signal intensity is closely related to Ccl6 + The interaction intensity of IM remains basically consistent.

[0050] Pseudo-sequential analysis showed that Ccl6 + IM exists in the late stage of SAKI and is accompanied by a significant increase in Ccl6 expression. Pseudo-time series analysis of macrophage subsets using Monocle2 R-package showed that Ccl6 + IM proliferates in large numbers in the later stage, which suggests that this macrophage subset is differentiated from KRM. + IM only exists in the late stage of SAKI, and the expression level of Ccl6 increases significantly during this period, indicating that Ccl6 + IM may play an important role during this period. These findings strongly suggest that Ccl6 + IM regulates the pathogenesis of SAKI through the chemokine Ccl6.

[0051] In the study of the present invention, the present invention uses scRNA-seq data sets to map to kidney tissue through spatial transcriptomics and carry out cell communication based on spatial transcriptomics. The present invention found that the position of NK cells in kidney tissue changed in the late stage of SAKI. In the early stage, most of the NK cells were located inside the kidney, and in the later stage they were recruited to the peripheral area near the kidney. The pattern of macrophages was exactly the opposite of that of NK cells, and they were transferred from the initial kidney periphery to the inside of the kidney tissue. At the same time, the NK cells and macrophages had strong cell communication in the renal tissue area where the macrophages were located, and the CCL signaling network showed significant interaction between NK cells and the peripheral area of ​​macrophages. This further illustrates that macrophages may play an important role by recruiting NK cells.

[0052] The results of communication between macrophage subsets and different types of immune cells showed that Ccl6 + IM recruits and activates NK cells through Ccl6-Ccr2 ligand receptors, and exerts its cell killing and cytotoxic functions, thereby facilitating the recovery of SAKI. By analyzing the communication results between different types of immune cells at different time points, the present invention found that global cell communication failure occurred 16 hours after LPS treatment. After this time point, NK cells and Ccl6 + The number of IM increased dramatically, which may be an important time point. At the same time, the results of the interaction between macrophage subsets and NK cells showed that Ccl6 + IM has the highest intensity of action on NK cells through the Ccl6-Ccr2 ligand receptor pair in the CCL signaling pathway, and during this period, the Ccl6-Ccr2 ligand receptor pair has the greatest contribution to the CCL signaling network. GO enrichment analysis of NK cells found that they exhibit cell killing and cytotoxicity in the later stage, which are not significant in the early stage. The above results strongly indicate that Ccl6 + IM can recruit and activate NK cells and play an important role during SAKI recovery.

[0053] In summary, the present study reveals the pathogenesis of SAKI and confirms that Ccl6 + IM has an extremely important impact on the pathogenesis of SAKI. In general, the present invention studied the specific monocyte-derived macrophage cluster Ccl6 by scRNA-seq. + The specific role of IM during SAKI, combined with spatial transcriptomics, revealed the interaction between macrophages and NK cells, indicating that Ccl6 + IM has significant proinflammatory properties and recruits and activates NK cells in the later stage, thereby contributing to the recovery of SAKI. These information provides valuable insights into further elucidating the underlying mechanisms of SAKI.

[0054] The above are merely preferred embodiments of the present invention. It should be noted that the above preferred embodiments should not be construed as limiting the present invention, and the scope of protection of the present invention should be determined by the scope defined in the claims. Persons skilled in the art will appreciate that improvements and modifications may be made without departing from the spirit and scope of the present invention, and such improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. Pro-inflammatory macrophage subset Ccl6 + Use of IM in the preparation of drugs for treating sepsis-induced acute kidney injury.

2. The use according to claim 1, characterized in that The drug promotes the pro-inflammatory macrophage subpopulation Ccl6 + IM drugs that overexpress CCL6.

3. The use according to claim 1, characterized in that The drug is a drug that activates the following signaling pathways: TGF-BETA-SIGNALING, IL2−STAT5−SIGNALING, TNFA−SIGNALING−VIA−NFKB, INFLAMMATORY−RESPONSE, and IL6−JAK−STAT3−SIGNALING.

4. Application of NK cells in the preparation of drugs for the treatment of sepsis-induced acute kidney injury.

5. The use according to claim 4, characterized in that The drug is a drug that promotes NK cell recruitment and activation by regulating the Ccl6-Ccr2 ligand receptor pair.

6. Pro-inflammatory macrophage subset Ccl6 + Application of IM combined with NK cells in the preparation of drugs for the treatment of sepsis-induced acute kidney injury.

7. The use according to claim 6, characterized in that The drug utilizes the pro-inflammatory macrophage subset Ccl6 + IM is a drug that recruits and activates NK cells through the Ccl6-Ccr2 ligand receptor pair.

8. The use according to claim 1, 4 or 6, characterized in that The administration method of the drug includes at least one of oral administration, intravenous injection and intraperitoneal injection.

9. The use according to claim 1, 4 or 6, characterized in that The drug is in any pharmaceutically acceptable dosage form, including at least one of tablets, capsules, injections, granules, suspensions and solutions.

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