Application of TRIM44 expression inhibitor in preparation of drugs for cytarabine-resistant leukemia

Abstract

The invention provides application of a TRIM44 expression inhibitor in preparation of a medicine for treating cytarabine-resistant leukemia, and belongs to the technical field of bioengineering. The invention provides an application of a TRIM44 expression inhibitor in preparation of a medicine for treating cytarabine-resistant leukemia. According to the application disclosed by the invention, a key gene TRIM44 influencing AML drug resistance is identified by analyzing a single cell transcriptome data set, sinomenine can down-regulate expression of TRIM44, inhibit functions of regulatory T cells (T-reg cells) and enhance sensitivity of AML cells to chemotherapeutic drugs, shows a good synergistic treatment effect with cytarabine, reverses cytarabine drug resistance of leukemia, and has a good application prospect. And a new target site and a new molecular marker are provided for clinically predicting and improving the treatment effect of a patient.

Description

Technical Field

[0001] The invention relates to the technical field of bioengineering, and in particular to application of a TRIM44 expression inhibitor in the preparation of a cytarabine-resistant leukemia drug. Background Art

[0002] Acute myeloid leukemia (AML) is the most common acute leukemia, originating from hematopoietic stem cells and characterized by abnormal differentiation and clonal growth of myeloid progenitor cells. It is particularly common in adolescents and young adults, and the incidence increases with age. Although some progress has been made in the current immunotherapy regimen for AML, the five-year survival rate of patients is still low, showing the limitations of existing treatments. The complexity of the treatment of this disease stems from its heterogeneity at the genetic and metabolic levels, which leads to tumor immune escape, drug resistance and recurrence. The bone marrow immune microenvironment plays a vital role in the complexity of AML treatment. Tumor cells transform this environment to promote survival and immune escape. Therefore, in-depth research on the AML microenvironment is crucial to discover new therapeutic targets and improve treatment efficacy.

[0003] The cutting-edge progress of single-cell sequencing technology has greatly promoted our understanding of the microstructure of healthy tissues and malignant tumors, and generated high-resolution cell maps. Through the application of single-cell analysis technology, researchers can deeply explore the tumor microenvironment of AML patients under different pathological conditions (such as relapse and remission), identify cell populations and their heterogeneity associated with disease progression, and thus reveal the important role of cell-to-cell interactions in the AML microenvironment for tumor resistance. For example, one study analyzed bone marrow samples from AML patients and revealed the cell differentiation trajectory of leukemia stem cells and non-stem cell populations and their association with specific oncogenic factors, further promoting the understanding of AML clonal evolution. Another study focused on stem cells in the bone marrow microenvironment of AML patients and their role in drug resistance and disease relapse, emphasizing the key role of interactions between leukemia cells and the microenvironment in the formation of resistance mechanisms. These findings highlight that the bone marrow microenvironment (BMM) is not only the origin of AML stem cells, but also provides a shelter for these malignant cells and supports their clonal evolution, thereby deepening the understanding of the mechanisms of drug resistance and leukemia relapse. Multiple factors, such as adhesion of leukemia cells to the microenvironment, signal transduction, and gene expression regulation, together constitute a complex regulatory network, the key components of which may become new targets for AML treatment. Therefore, a deep understanding of the cell-cell interactions in the AML microenvironment and their impact on drug resistance is crucial for the development of therapeutic strategies targeting this complex network.

[0004] Cytarabine is an important chemotherapy drug, mainly used for induction remission and maintenance treatment of acute non-lymphocytic leukemia in adults and children, and also has therapeutic effects on leukemias such as acute lymphocytic leukemia and chronic myeloid leukemia. However, there are still many patients whose condition cannot be relieved or relapses after remission after cytarabine treatment. In addition to individual differences in patients and clinical medication regimens, drug resistance may also be part of the reason.

[0005] Traditional Chinese medicine has unique advantages and a long history in the treatment of diseases. Many Chinese medicines and their active ingredients have been shown to have significant effects in anti-tumor, immunomodulation and reversal of drug resistance. The multi-component and multi-target characteristics of Chinese medicine give it potential advantages in the treatment of complex diseases. Combining traditional Chinese medicine with modern biomedical technology is expected to provide new strategies and ideas for the treatment of malignant tumors such as AML and anti-drug resistance. Summary of the invention

[0006] The embodiment of the present invention provides an application of a TRIM44 expression inhibitor in the preparation of a drug for cytarabine-resistant leukemia. The present invention finds that the traditional Chinese medicine active ingredient sinomenine, which has the function of selectively inhibiting TRIM44, can not only synergize with cytarabine to reverse the cytarabine resistance of leukemia, but also provide a new target site and molecular marker for clinically predicting and improving the treatment effect of patients. , so as to at least solve one of the problems existing in the related art. To achieve this purpose, the present invention is implemented by the following technical scheme.

[0007] In a first aspect, the present invention provides, in an optional embodiment, the use of a TRIM44 expression inhibitor in the preparation of a cytarabine-resistant leukemia drug. The NCBI accession number of TRIM44 is Gene ID: 54765.

[0008] Further, the TRIM44 expression inhibitor includes at least one of sinomenine, α-spinasterol, mediparasite, dihydrocapsaicin and berberine. The present invention finds that sinomenine, α-spinasterol, mediparasite, dihydrocapsaicin and berberine have good molecular docking effects with TRIM44.

[0009] In a specific embodiment of the present invention, sinomenine was used as an example to conduct in vitro cell experiments and in vivo mouse experiments for verification.

[0010] Furthermore, the leukemia includes acute leukemia and / or chronic leukemia.

[0011] Furthermore, the acute leukemia includes acute myeloid leukemia and / or acute lymphocytic leukemia.

[0012] In a specific embodiment of the present invention, acute myeloid leukemia is taken as an example for experimental verification.

[0013] In a second aspect, the present invention provides a drug for preventing and / or treating leukemia, wherein the drug comprises a TRIM44 expression inhibitor and cytarabine.

[0014] Furthermore, the TRIM44 expression inhibitor includes at least one of sinomenine, α-spinasterol, meditartin, dihydrocapsaicin and berberine.

[0015] Furthermore, the present invention uses sinomenine in combination with cytarabine as an example, and finds that it has a synergistic effect.

[0016] Furthermore, the concentration of the cytarabine is 0.01-2 μM, and the concentration of the sinomenine is 1-120 μM.

[0017] Preferably, the concentration of cytarabine is 0.1 μM, and the concentration of sinomenine is 20 μM.

[0018] Furthermore, the drug also contains pharmaceutically acceptable excipients.

[0019] Furthermore, the medicine is an oral preparation or an injection preparation. The oral preparation includes capsules, tablets or granules.

[0020] In the present invention, the drug can be any conventional oral preparation or injection preparation that is pharmaceutically acceptable and prepared by conventional preparation technology, such as capsules, tablets, granules or liquids. In order to make the above oral preparations possible, pharmaceutically acceptable excipients need to be added during the preparation process, and the pharmaceutically acceptable excipients are fillers, disintegrants, lubricants, suspending agents, binders or sweeteners, etc.

[0021] The filler includes at least one of starch, lactose, microcrystalline cellulose or sucrose; the disintegrant includes at least one of starch, sodium carboxymethyl starch, pregelatinized starch, low-substituted hydroxypropyl cellulose or cross-linked sodium carboxymethyl cellulose; the lubricant includes at least one of magnesium stearate or silicon dioxide, sodium lauryl sulfate; the suspending agent includes at least one of polyvinyl pyrrolidone, sucrose or hydroxypropyl methylcellulose; the binder includes at least one of hydroxypropyl methylcellulose, starch slurry or polyvinyl pyrrolidone; the sweetener is at least one of saccharin sodium, glycyrrhetinic acid, sucrose, aspartame or cyclamate.

[0022] In order to realize the above-mentioned injection preparation, pharmaceutically acceptable excipients need to be added during the preparation process, including water for injection and / or sodium chloride solution for injection.

[0023] In a third aspect, the present invention provides the use of the drug in preparing a drug for preventing and / or treating leukemia.

[0024] Furthermore, the leukemia includes acute leukemia and / or chronic leukemia.

[0025] Furthermore, the acute leukemia includes acute myeloid leukemia and / or acute lymphocytic leukemia.

[0026] In a specific embodiment of the present invention, acute myeloid leukemia is taken as an example for experimental verification.

[0027] The embodiments of the present invention have the following beneficial effects:

[0028] In this invention, by analyzing the single-cell transcriptome datasets (GSE235063, GSE239721 and GSE235923), the changes in the composition of immune cells in the bone marrow microenvironment of AML patients and their effects on disease progression were deeply explored. Immune cells in the AML microenvironment show significant adaptability, reflecting the inherent heterogeneity of AML itself. By deeply analyzing the association between different cell phenotypes and AML resistance, focusing on key deubiquitinase (DUB) genes, TRIM44, a key gene affecting AML resistance, was identified, and its effects on AML occurrence, development and prognosis were verified.

[0029] In addition, the regulatory effect of Sinomenine, an active ingredient of traditional Chinese medicine, on TRIM44 was studied. The results showed that Sinomenine could downregulate the expression of TRIM44, inhibit the function of regulatory T cells (T-reg cells), enhance the sensitivity of AML cells to chemotherapeutic drugs, and show good synergistic therapeutic effects with cytarabine. The present invention further enriches the understanding of the AML immune microenvironment and its impact on chemotherapy resistance, provides a new perspective for AML treatment, emphasizes the importance of considering microenvironmental factors and the application of traditional Chinese medicine in disease management, and lays the foundation for the development of more effective treatment strategies.

[0030] The invention establishes the key role of TRIM44 in leukemia drug resistance and discovers the active ingredient of traditional Chinese medicine, sinomenine, which can selectively inhibit the function of TRIM44. It can not only work synergistically with cytarabine to reverse the cytarabine resistance of leukemia, but also provide new target sites and molecular markers for clinical prediction and improvement of patient treatment effects. This innovative strategy brings new hope for leukemia treatment, especially for the problem of cytarabine resistance, providing a more effective solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0032] Figure 1This is a schematic diagram of the mechanism of the synergistic treatment of leukemia by the traditional Chinese medicine monomer (sinomenine) for targeted inhibition of TRIM44 and cytarabine of the present invention.

[0033] Figure 2 The heterogeneity of the acute myeloid leukemia (AML) tumor microenvironment is displayed. A is a dot plot showing the key genes highly expressed in 13 cell clusters. The size of the dot indicates the proportion of cells with specific marker gene expression, while the color depth represents the average expression intensity of the marker gene; B is a display of the iconic genes of different AML progenitors subpopulations; C is a display of 13 major cell types identified in the AML microenvironment on t-SNE and UMAP maps; D is the four major AML progenitors cell subpopulations determined by UMAP and t-SNE analysis; E is a volcano map of differentially expressed genes in AML progenitors cells in patients with remission and relapse after treatment, and the red dots indicate genes upregulated in relapse; F is an expression heat map of DEGs in four AML progenitors cell subpopulations and their gene ontology (GO) analysis results; G is a dot plot showing the key genes highly expressed in 7 T cell clusters. The size of the dot indicates the proportion of cells expressing a specific marker gene, while the depth of the color represents the average expression intensity of the marker gene; H is the seven T cell subsets determined by UMAP and t-SNE analysis; I is the t-SNE and UMAP distribution of T cells according to different clinical stages.

[0034] Figure 3 A global view of intercellular communication analysis in AML. A is the number and intensity of communication between different cell subsets; B is the identification of important outgoing and incoming signal patterns; C is the dominant signal sending source and receiving target in two-dimensional space; D is the number of output patterns inferred by Cophenetic and Silhouette values; E is the number of input patterns inferred by Cophenetic and Silhouette values; F is the signal corresponding to the cell subsets and signal pathways for identifying output patterns and inputs, showing 5 output patterns; G is the signal corresponding to the cell subsets and signal pathways for identifying output patterns and inputs, showing 3 input patterns; H is the outgoing signal pattern of secretory cells visualized by river diagram; I is the incoming signal pattern of secretory cells visualized by river diagram; J is the communication pattern of different cell subsets as signal output sources; K is the communication pattern of different cell subsets as signal receiving sources.

[0035] Figure 4The difference in cell communication between patients with post-chemotherapy remission and post-chemotherapy relapse. Among them, A is the number of cell interactions in patients with acute myeloid leukemia who have remission and relapse after chemotherapy; B is the differential expression of the number or intensity of interactions in patients with post-chemotherapy relapse (red lines represent high expression, and blue lines represent low expression); C is the inferred number and intensity of interactions in patients with acute myeloid leukemia who have remission and relapse after chemotherapy; D is the main source and purpose of comparing patients with post-chemotherapy remission and post-chemotherapy relapse in 2D space; E is a heat map showing the number and intensity of interactions between patients with post-chemotherapy remission and post-chemotherapy relapse; F is a comparison of the outgoing signal flow associated with each cell population; G is a comparison of the incoming signal flow associated with each cell population; H is a comparison of the overall signal flow associated with each cell population; I is a comparison of the differential signal pathways between patients with post-chemotherapy remission and post-chemotherapy relapse.

[0036] Figure 5 The important regulatory role of TRIM44 in acute myeloid leukemia. Among them, A is the overlap and unique part between the relapsed gene set (Relapsed_gene), unexplored genes (Unexplored), genes with significant impact on survival (Survival) and ubiquitin-specific protease (DUB) in the Venn diagram; B is the t-SNE and UMAP diagrams showing the effect of TRIM44 on cell distribution; C is the expression density of TRIM44 on the UMAP distribution map; D is the division of cells into TRIM44 high expression and low expression groups on the UMAP and t-SNE distribution maps according to the expression level of TRIM44; E is a volcano map showing the differentially expressed genes of TRIM44; F is the results of gene ontology (GO) analysis, reflecting the enrichment of genes in biological processes (BP), cellular components (CC) and molecular functions (MF); G is the results of KEGG pathway analysis; H. The results of GSVA pathway analysis.

[0037] Figure 6 Figure 4 shows how TRIM44 reshapes the TME of acute myeloid leukemia. A is the immune infiltration of each sample; B is the cell composition ratio of all samples; C is the survival difference analysis between patients with high expression of AML cells and patients with low expression of AML cells; D is the survival difference analysis between patients with high expression of Treg and patients with low expression of Treg. E is the correlation analysis between TRIM44 and cells in the bone marrow microenvironment.

[0038] Figure 7The role of TRIM44 in regulating the evolution of T-reg cells. A is the distribution of different cell subpopulations displayed by Monocle2; B is the pseudo-sequential differentiation time of different cells displayed by Monocle2; C is the TRIM44 expression level of different cells displayed by Monocle2; D is the 3D distribution of different cell subtypes displayed by Monocle3; E is the distribution of different cell subpopulations displayed by Monocle3; F is the nodes of different cell subpopulations displayed by Monocle3; G is the pseudo-sequential differentiation time of different cells displayed by Monocle3; H is the TRIMM of different cells displayed by Monocle3 in 3D space; I is the heat map hierarchical clustering for developmental timing and overall subgroup-specific marker genes; J is the differential expression between subgroups at time point 1 of the developmental timing.

[0039] Figure 8 : TRIM44 and its relationship with multidrug resistance. A is the Spearman correlation analysis between TRIM44 expression and drug IC50 value (TCGA database). B. Spearman correlation analysis between TRIM44 expression and drug IC50 value (GSE database).

[0040] Fig. 9 This is a molecular docking analysis of TRIM44 and major leukemia therapeutic drugs. A is cytarabine; B is doxorubicin; C is mitoxantrone; D is fludarabine; E is sinomenine; F is α-spinasterol; G is meditartin; H is dihydrocapsaicin; I is berberine; and J is the molecular structure of sinomenine.

[0041] Fig.10 Figure 1 is the knockdown of TRIM44 and its expression analysis in AML resistant cell lines. A shows the effect of knocking down TRIM44 in MOLM13 / R cell lines with different siRNAs; B shows the effect of knocking down TRIM44 in MV4-11 / R cell lines with different siRNAs; C shows the protein expression of TRIM44 in knockdown cell lines by Western blot analysis; D shows the quantitative protein expression of TRIM44 in knockdown cell lines by Western blot analysis; E shows the intracellular localization and expression of TRIM44 and the apoptosis of MOLM13 / R cells after knockdown by immunofluorescence and TUNEL staining; F shows the intracellular localization and expression of TRIM44 and the apoptosis of M4-11 / R cells after knockdown by immunofluorescence and TUNEL staining. The nuclei were stained with DAPI (scale bar: 20 μm).

[0042] Fig.11The effect of TRIM44 knockdown on proliferation and apoptosis of acute myeloid leukemia cells. A is a statistical graph of CCK-8 experimental results, and the data are expressed as mean ± standard deviation (n = 3), and significant differences are marked as: *p < 0.05, **p < 0.01, ***p < 0.001; B is flow cytometry analysis of cell apoptosis; Fig.11 C is a cell cycle diagram of flow cytometry results analysis; Fig.11 D is the cell cycle quantification diagram of flow cytometry analysis results.

[0043] Fig.12 The effects of drugs on TRIM44 expression and cells. A is the effect of sinomenine, α-spinasterol, mediptertin, dihydrocapsaicin and berberine on TRIM44 expression detected by Western blot; B is the effect of sinomenine on cell apoptosis rate at different concentrations detected by flow cytometry; C is the quantitative graph of the effect of sinomenine on cell apoptosis rate at different concentrations detected by flow cytometry.

[0044] Fig.13 The effect of sinomenine on inhibiting cytarabine-resistant AML cells. A is a confidence interval diagram for the combined use of cytarabine and sinomenine; B is a synergistic effect diagram of cytarabine and sinomenine combined to treat U2OS / R cells drawn using the ZIP (I), Bliss (J), HSA (K) and Loewe (L) evaluation models. Positive synergy scores or negative synergy scores indicate synergistic effects and antagonistic effects, respectively; C is a synergistic effect measurement diagram showing the effect of 0.1 μM cytarabine combined with 20 μM sinomenine; D is based on Fig.13 The recommended concentrations in C are used to detect the effects of combined drug therapy on cell apoptosis by flow cytometry (n=5); E is a quantitative graph of the effects of combined drug therapy on cell apoptosis by flow cytometry.

[0045] Fig.14 This is an in vivo experiment of sinomenine inhibiting cytarabine-resistant mice. A is a representative image of subcutaneous tumor formation in NSG mice in the sh-TRIM44 group and shRNA-NC group; B is the growth curve of tumor growth dynamics in NSG mice in the sh-TRIM44 group and shRNA-NC group; C is a representative image of subcutaneous tumor formation in NSG mice in the Control group and sinomenine group; D is the growth curve of tumor growth dynamics in NSG mice in the Control group and sinomenine group; E is a comparative analysis of tumor mass in NSG mice in the Control group and sinomenine group DETAILED DESCRIPTION

[0046] To make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the embodiments of the present invention, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can be implemented. The division of the following embodiments is for the convenience of description, and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined and referenced with each other without contradiction.

[0047] The schematic diagram of the mechanism of the synergistic treatment of leukemia by the Chinese medicine monomer (sinomenine) of the present invention for the targeted inhibition of ternary motif-44 (TRIM44) and cytarabine is shown in Figure 1 The NCBI accession number of TRIM44 is Gene ID: 54765.

[0048] Example 1

[0049] 1. Data source and processing

[0050] Single-cell sequencing (scRNA-seq) datasets related to acute myeloid leukemia (AML) were retrieved from the Gene Expression Omnibus (GEO). Based on the clinical information of the patients from whom the samples were obtained, three scRNA-seq datasets were selected, with GSE235063, GSE239721, and GSE235923 as the core objects of the study.

[0051] At the same time, in order to deeply analyze the gene expression patterns of clinical samples, we also collected bulk RNA-seq datasets of AML populations containing survival time information from the GEO database. After careful screening, we determined that the GSE71014 and GDC TCGA Acute Myeloid Leukemia (LAML) datasets met the requirements.

[0052] The single-cell RNA sequencing (scRNA-seq) data related to acute myeloid leukemia (AML) were deeply analyzed using the Seurat package (version 4.4.3), including quality control, dimensionality reduction, and clustering of cell types. In order to screen out high-quality cells, cells with gene expression numbers less than 1000 or greater than 9000 and mitochondrial gene expression ratios greater than 10% were set as screening conditions for low-quality cells. The filtered cell data were normalized after global scaling using the "lognormalize" method, and then the FindVariableFeatures function was used to select highly variable genes as the basis for subsequent principal component analysis (PCA). The analysis process is as follows: First, dimensionality reduction was performed based on the first 10 significant principal components, and then cell types were clustered at a resolution of 1.0 using Seurat's FindClusters function. UMAP (UniformManifoldApproximation and Projection) and t-SNE (t-Distributed StochasticNeighbor Embedding) techniques were then used for further dimensionality reduction and visualization analysis. The specific marker genes of each cell cluster were identified by the FindAllMarkers function. The visualization of the cluster tree revealed the relationship between cell clusters at different resolutions. The annotation results of cell types were displayed in the form of heat maps and enrichment analysis using the ClusterGVis tool to display differentially expressed genes (DEGs) between cell subpopulations. In addition, the Nebulosa package and scCustomize package, as well as functions such as Ridgeplot, were used for the visualization of marker genes.

[0053] like Figure 2 As shown, Figure 2 A shows 11 different cell types identified by specific marker genes. These cell types include: hematopoietic stem cells (HSC), erythroid cells, acute myeloid leukemia precursor cells (AMLprogenitor), granulocyte-monocyte progenitor cells (GMP), B cells (B cells), dendritic cells (Dendriticcell), monocyte-macrophages (Mono.Mac), plasma cells (Plasma), CD4+T cells (CD4.T), early basophils (Early Basophil), natural killer cells (NK cell) and CD8+T cells (CD8.T).

[0054] Figure 2 B shows the signature genes of different AML progenitor cell subsets.

[0055] Figure 2 C shows different types of cells through t-SNE and UMAP analysis. Each point represents a single cell, and different colors represent different cell types.

[0056] Figure 2 D Four major AML progenitor cell subsets identified by UMAP and t-SNE analysis.

[0057] Figure 2 E is a volcano plot of differentially expressed genes in AML progenitor cells in patients with remission and relapse after treatment. Red dots indicate genes upregulated in relapse.

[0058] Figure 2 F is the expression heat map of differentially expressed genes in the four AML progenitor cell subsets and their gene ontology (GO) analysis results.

[0059] Figure 2 G shows the key genes highly expressed in the seven T cell clusters through a dot plot. The size of the dot indicates the proportion of cells expressing a specific marker gene, while the color depth represents the average expression intensity of the marker gene.

[0060] Figure 2 H Seven T cell subsets identified by UMAP and t-SNE analysis.

[0061] Figure 2 I is the t-SNE and UMAP distribution of T cells in different clinical stages.

[0062] 2. Analysis of intercellular communication

[0063] In order to explore the signal communication between different cell populations revealed by single-cell RNA sequencing (scRNA-seq) data, the CellChat package (version 1.6.1) in R software was used for analysis. First, a CellChat object was initialized by the "createCellChat" function according to the database not determined in 1 and the corresponding analysis results. Then, the signal network analysis was performed using the interaction database containing "SecretedSignaling". The communication probability between cells was estimated by the "computeCommunProb" function. When using the "selectK" function, we focused on the global communication pattern, and set the nPatterns parameter to 5 and 3 for the input and output of the information flow, respectively.

[0064] Figure 3 Analysis of cell-cell communication in the AML tumor microenvironment is presented. Figure 3 A shows the amount and intensity of communication between different cell subpopulations.

[0065] Figure 3 B shows the analysis of incoming and outgoing signals, showing that major histocompatibility complex class I molecules (MHC-I), CD99 antigen (CD99) and macrophage migration inhibitory factor (MIF) are the main signaling pathways for intercellular communication, emphasizing their key roles in responding to the AML tumor microenvironment.

[0066] Figure 3 C is a two-dimensional diagram of cell communication roles, with hematopoietic stem cells (HSCs), AML progenitors and granulocyte monocytic precursors (GMPs) as the main signal senders, and cytotoxic T cells (CD8+T cells), helper T cells (CD4+T cells) and natural killer cells (NK cells) as the main receivers.

[0067] Figure 3 D and Figure 3 E is the quantification result using CellChat communication mode, showing the signal output of different cell subpopulations ( Figure 3 D) and input ( Figure 3 E) mode.

[0068] Figure 3 F and Figure 3 G is the signal communication mode classification, showing 5 output and 3 input modes.

[0069] Figure 3 H and Figure 3 I is a river diagram that reveals the relationship between signal output patterns and cell populations, demonstrating the fluidity of specific signal pathways.

[0070] Figure 3 J and Figure 3 K represents the communication mode of different cell subsets as signal output sources and receivers.

[0071] Figure 4 Demonstrating differences in cellular communication between patients in remission and relapse following chemotherapy. Figure 4 A shows the number of cell interactions in patients with acute myeloid leukemia who were in remission or relapse after chemotherapy.

[0072] Figure 4 B shows the differential expression of the number or strength of interactions in relapse after chemotherapy (red lines represent high expression, blue lines represent low expression).

[0073] Figure 4 C shows the inferred number and strength of interactions for patients with acute myeloid leukemia who were in remission after chemotherapy and those who relapsed after chemotherapy.

[0074] Figure 4 D shows the main sources and destinations in 2D space comparing patients with acute myeloid leukemia who are in remission after chemotherapy and those who are in relapse after chemotherapy.

[0075] Figure 4 E Heat map showing the number and strength of interactions between patients with acute myeloid leukemia who responded to chemotherapy and those who relapsed after chemotherapy.

[0076] Figure 4 F. Figure 4 G and Figure 4 H heatmaps comparing the outflow associated with each cell population ( Figure 4 F), incoming ( Figure 4 G) and the overall signal flow ( Figure 4 H).

[0077] Figure 4 I Histogram comparing differential signaling pathways between patients with acute myeloid leukemia who responded to chemotherapy and those who relapsed after chemotherapy.

[0078] 3. Determination of deubiquitination-related genes and survival analysis

[0079] Several candidate genes were selected from the intersection of deubiquitination and related gene DEGs. Survival analysis of single genes was performed using the R package "survival" by analyzing the LAML and GSE71014 datasets. Patients were divided into high expression group and low expression group according to the cutoff value of each gene. The Log-Rank test determined the statistical significance of the survival curves, and the curves with P < 0.05 were considered different.

[0080] Figure 5 A represents the screening process of the ternary motif-44 (TRIM44) gene (the key gene TRIM44 is obtained by taking the intersection of the relapse gene set (Relapsed_gene), the unexplored gene (Unexplored), the gene set with a significant impact on survival (Survival) and the ubiquitin-specific protease (DUB) through the Venn diagram).

[0081] Figure 5 B shows the expression density of TRIM44 gene in t-SNE and UMAP graphs.

[0082] Figure 5 C shows the expression level of TRIM44 gene through UMAP graph;

[0083] Figure 5 D: According to the expression level of TRIM44 gene, the cells are divided into TRIM44 gene high expression and low expression groups on the UMAP and t-SNE distribution maps.

[0084] Figure 5 E is a volcano plot showing differentially expressed genes associated with high and low expression of TRIM44. AML progenitor cells were divided into TRIM44 high expression and TRIM44 low expression, and then the two types of cells were differentially analyzed.

[0085] 4. Enrichment analysis

[0086] Differentially expressed genes were identified by FindMarkers function, applying thresholds of Padj.P < 0.05 and log2FC > 0.25. GO and KEGG analyses were performed using cluster Profiler package, while GSEA analysis was performed using respective packages. Figure 5 F. Figure 5 G and Figure 5 H. GO, KEGG and GSVA were used to analyze the differentially expressed genes.

[0087] 5. Assessment of immune cell subtype distribution

[0088] CibersortX and the average expression values ​​of signature genes were used to analyze the infiltration of cell subtypes in the LAML clinical cohort. Spearman correlation analysis revealed the association between TRIM44 and different cell populations.

[0089] Monocle2 and Monocle3 were used to visualize the pseudo-temporal development trajectory of cells, and the plotpseudotimeheatmap function was used to display the heat map of dynamic changes in gene expression.

[0090] Figure 6 A represents the immune cell infiltration analysis of each sample.

[0091] Figure 6 B represents the distribution of various cell types in the sample.

[0092] Figure 6 C represents the Kaplan-Meier survival curve, showing the prognostic analysis of patients with high and low expression of AML progenitor cells.

[0093] Figure 6 D represents the Kaplan-Meier survival curve showing the prognostic analysis of patients with high expression of regulatory T cells (T-reg cells) and low expression of T-reg cells.

[0094] Figure 6 E represents the correlation analysis between TRIM44 expression and cells in the tumor microenvironment.

[0095] Monocle2 and Monocle3 show subpopulations ( Figure 7 A. Figure 7 D and Figure 7 E) simulation time ( Figure 7 B. Figure 7 F and Figure 7 G) and TRIM44 expression ( Figure 7C and Figure 7 H) dynamic changes. Figure 7 I and Figure 7 J for heatmap hierarchical clustering of developmental timing and subgroup-specific marker genes, Figure 7 I is the overall subgroup, Figure 7 J is the differential expression among subgroups at time point 1.

[0096] 6. Chemotherapy drug sensitivity analysis

[0097] IC values ​​of 198 drugs were obtained from the Cancer Drug Sensitivity Gene Set (GDSC; https: / / www.cancerrxgene.org / ) and the Cancer Cell Line Encyclopedia (CCLE, https: / / sites.broadinstitute.org / ccle / ) using the R package oncoPredict. 50 The drug IC was explored by Spearman analysis. 50 The correlation between the values ​​and the risk score was used to identify relevant drugs. The IC values ​​were then compared between the TRIM44 high and low expression groups. 50 The results were visualized using ggplot2 in R language to plot correlation analysis dot plots. Figure 8 A and Figure 8 B.

[0098] 7. Screening of the best active ingredients of traditional Chinese medicine

[0099] In order to identify potential traditional Chinese medicine (TCM) targets associated with the TRIM44 gene, the gene-related information was collected using the Coremine Medical database, which is an open retrieval platform that concentrates on comprehensive medical information such as herbal medicine, gene ontology, protein expression and anatomy. The screened key genes were mapped to Coremine Medical (http: / / www.coremine.com / ) respectively, and the Chinese medicines associated with the key genes were screened out, and P<0.05 was considered statistically significant. The present invention determined that the TCMs significantly associated with the TRIM44 key gene included sea cucumber, Yu Nao Shi, Nitrate Alum Nao Shi and Yu Biao Alum Ling, etc. (p<0.05). The main active ingredients of high-frequency TCMs were screened using the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), requiring oral bioavailability (OB) ≥30% and drug similarity (DL) ≥0.18. Molecular docking was then performed to verify the binding energy of all TCM monomers with TRIM44, and 20 different docking simulations were performed for each monomer. The five drugs with the lowest binding energy were identified, namely, sinomenine, α-pineasterol, meditartin, dihydrocapsaicin, and berberine.

[0100] The three-dimensional structures of TRIM44 protein and various drug molecules were obtained from PubChem database. Preparation of protein and drug structures for docking analysis included removal of water molecules and addition of nonpolar hydrogen atoms using PyMOL and AutoDock4. Subsequently, appropriate docking parameters and grid box size were established. Molecular docking simulations were performed using AutoDock4, and the results were analyzed and visualized using PyMOL.

[0101] Results Fig. 9 A to Fig. 9 I. The docking results showed that the binding energy was lower than -5.0 kcal / mol, indicating significant molecular interaction. Five drugs, including sinomenine, α-spinasterol, medipterostilbene, dihydrocapsaicin and berberine, had inhibitory effects on TRIM44, among which sinomenine had the strongest inhibitory effect. The results of molecular docking showed that sinomenine may be able to directly bind to TRIM44. Fig. 9 J shows the molecular structure of sinomenine.

[0102] Example 2 In vitro experiment

[0103] Sinomenine, α-spinasterol, meditartin, dihydrocapsaicin, and berberine were purchased from Aladdin Biochemical Technology Co., Ltd.

[0104] 1. TRIM44 gene siRNA and shRNA knockdown experiment

[0105] The construction process of cytarabine-resistant human myeloid monocytic leukemia cells MV4-11 / R and cytarabine-resistant human acute myeloid leukemia cells MOLM13 / R refers to the previous research results of the research team of the present invention (DOI: 10.1186 / s12967-023-04579-5). MV4-11 / R and MOLM13 / R cells in the logarithmic growth phase were inoculated into 6-well plates, and after the cells grew to 70% to 80% density, they were transfected with Lip3000 transfection reagent. After transfection for 6 hours, fresh culture medium was replaced, cultured for 24 hours, cells were collected, and the knockdown efficiency of siRNA was verified by Western blot and qPCR.

[0106] The siRNA sequences used are shown in Table 1.

[0107] Table 1

[0108]

[0109] In addition, in order to construct MV4-11 / R and MOLM13 / R cells with stable knockdown of TRIM44, the shRNA sequence targeting TRIM44 was designed using BLOCK-iTRNAiDesigner and cloned into the pLKO.1 vector. Subsequently, MOLM13 / R and MV4-11 / R cells were infected with viral particles, and the transduction efficiency was improved under the action of 8μg / mL polylysine. Cells were screened with 2-5μg / mL puromycin for 48-72h to obtain stable knockdown cells, and the knockdown efficiency of TRIM44 was further verified by qRT-PCR and Western blot.

[0110] The shRNA sequences used are shown in Table 2.

[0111] Table 2

[0112]

[0113] 2. Quantitative Real-time PCR Analysis

[0114] Total RNA from cells knocked down for TRIM44 in step 1 was isolated using TRIzol reagent (brand: Invitrogen; A33248), and reverse transcribed using RevertAid First Strand cDNA Synthesis Kit (brand: Thermo Fisher Scientific, USA; catalog number: K1621) to obtain cDNA. Fluorescence quantitative PCR (SYBR-Green) assay mixture was prepared, and quantitative PCR analysis was performed using a PCR instrument Roche Light Cycler 480II. To ensure the repeatability of the experiment, the number of replicate wells for each sample was 3. By comparing 2 -ΔΔCt Determine gene expression levels.

[0115] 3. Western blot

[0116] The cells were lysed with radioimmunoprecipitation buffer (RIPA, Beyotime) to obtain proteins, and the protein level was quantified using the Beyotime BCA protein assay kit (Beyotime). The proteins were separated by 10% (w / v) SDS-PAGE and transferred to a PVDF membrane to obtain a PVDF membrane with transferred proteins; in order to prevent nonspecific binding, the PVDF membrane with transferred proteins was incubated in 5% (w / v) skim milk and incubated with primary antibodies overnight on a shaker at 4°C to obtain a PVDF membrane with transferred proteins after primary antibody incubation; the PVDF membrane with transferred proteins after primary antibody incubation was incubated with HRP-labeled secondary antibodies (dilution ratio of 1:2000, Proteintech) at room temperature for 1 h, washed, and the fluorescence signal was detected using the UVP ChemStudio system (Ultraviolet Products, USA), and semi-quantitative analysis was performed using VisionWorks software (Analytik Jena, Germany). The primary antibodies used included anti-TRIM44 (dilution ratio 1:1000, Proteintech) and anti-Beta-Tubulin (dilution ratio 1:1000, Santa Cruz Biotechnology). ImageJ was used to perform semi-quantitative analysis of WB bands.

[0117] 4. TUNEL (TdT-mediated dUTPNick-End Labeling, a method for detecting cell apoptosis) and immunofluorescence double staining

[0118] To detect apoptosis and the expression of TRIM44, the cells were subjected to TUNEL and immunofluorescence double staining, using an in situ fluorescent cell death detection kit and immunofluorescence staining.

[0119] (1) Cells were seeded on appropriate culture plates and grown to a confluence of 70% to 80%. The cells were then fixed with 4% (w / v) paraformaldehyde for 15 min and washed three times with PBS, each time for 5 min.

[0120] (2) The cells treated in step (1) were permeabilized with 0.1% (v / v) Triton X-100 for 5 min and washed again with PBS three times, each time for 5 min.

[0121] (3) The cells treated in step (2) were blocked with 5% (v / v) normal goat serum for 1 h to reduce nonspecific binding.

[0122] (4) The cells treated in step (3) were incubated with a mixture of terminal deoxynucleotidyl transferase (TdT) and deoxyuridine triphosphate (dUTP), and TUNEL staining was performed at 37° C. in a humidified dark environment for 60 min.

[0123] (5) Wash the cells treated in step (4) with PBS and incubate with diluted anti-TRIM44 primary antibody at 4°C overnight. The next day, wash the cells three times with PBS for 5 min each time and then incubate with Alexa Fluor TM The secondary antibody labeled with 555 dye was incubated for 1 h at room temperature (protected from light).

[0124] (6) Wash the cells treated in step (5) three times with PBS, each time for 5 min, and seal the sections with a sealing solution containing 4',6-diamidino-2-phenylindole (DAPI).

[0125] (7) Use a fluorescence microscope to observe and photograph the cell apoptosis and TRIM44 expression after treatment in step (6).

[0126] 5. CCK-8 assay

[0127] Cell viability after treatment with relevant drugs was assessed using the Cell Counting Kit-8 (CCK-8, Sigma-Aldrich, St. Louis, MO), and cell survival was assessed by reading the optical density at a wavelength of 450 nm.

[0128] 6. Flow cytometry

[0129] Flow cytometry apoptosis: FITC Annexin V Apoptosis Detection Kit (BD, UK) was used to detect cell apoptosis.

[0130] Flow cytometry: Cell Cycle Staining Kit (MultiSciences, China) was used to detect the cell cycle.

[0131] The cells after the above treatments were detected on a flow cytometer (CytoFlex SRT, Beckman, USA), and the collected data were imported into FlowJo software (version 10.8.1) for analysis.

[0132] 7. Drug synergy visualization

[0133] The synergy of anti-AML drugs was evaluated using the synergy finder R package. In order to obtain the most reliable results possible, four different models were used: ZIP, LOEWE, BLISS, and HSA. BLISS model: Assuming that the drugs act independently, the effect is predicted by calculating the probability that each drug in the combination acts independently. HSA model: If the response of the combination is greater than the response of any single drug, it is considered a synergistic effect. LOEWE model: Considering the dose-response relationship of a single drug, the expected additive response is calculated, and a synergistic effect is considered if it is higher than expected. ZIP model: Assuming that each drug does not affect the efficacy of another drug, the combination effect is calculated. Each model analyzes the effect of drug combinations based on different assumptions and methods, thereby providing a comprehensive understanding of drug interactions.

[0134] like Fig.10 A and Fig.10 As shown in B, the effect of knocking down TRIM44 in MOLM13 / R and MV4-11 / R cell lines using different siRNAs was shown. The results showed that si-TRIM44-2 had the best knockdown efficiency in MOLM13 / R cells, while si-TRIM44-4 had the most significant knockdown effect in MV4-11 / R cells. Based on these results, cell lines with low TRIM44 expression were constructed using the corresponding siRNAs. Fig.10 C and Fig.10 As shown in D, Western Blot (WB) analysis showed that the expression of TRIM44 protein was significantly reduced in the knockdown cell line, confirming the effectiveness of siRNA. Fig.10 E and Fig.10 As shown in F, immunofluorescence further demonstrated that TRIM44 expression in the knockdown cell line was significantly reduced compared with the control group, which was consistent with the qPCR and WB results.

[0135] like Fig.11 Shown are the effects of TRIM44 knockdown on proliferation and apoptosis of acute myeloid leukemia cells. Fig.11 A is a statistical chart of the CCK-8 experimental results, and the results show that TRIM44 knockdown significantly reduced cell proliferation. Fig.11 B is the flow cytometry analysis of cell apoptosis, which is consistent with the TUNEL staining results. Flow cytometry analysis showed that cell apoptosis significantly increased after TRIM44 knockdown. Fig.11 C and Fig.11 D is the result of flow cytometry analysis of the cell cycle. Cell cycle analysis showed that after TRIM44 knockdown, cell proliferation decreased and the cell cycle arrested at the G0 / G1 phase, further highlighting the key role of TRIM44 in cell proliferation.

[0136] like Fig.12As shown in A, Western blot was used to detect the effects of sinomenine, α-spinasterol, medipterol, dihydrocapsaicin and berberine on the expression of TRIM44. The results showed that sinomenine, α-spinasterol, medipterol, dihydrocapsaicin and berberine significantly inhibited the expression of TRIM44, among which sinomenine showed the most significant effect. Fig.12 B and Fig.12 As shown in C, flow cytometry was used to detect the effect of sinomenine on cell apoptosis rate at different concentrations (10, 20 and 50 μM) (n=5). The results showed that sinomenine induced apoptosis of MV4-11 / R and MOLM13 / R cells in a dose-dependent manner.

[0137] like Fig.13 A shows the confidence interval diagram of the combined use of cytarabine and sinomenine. Cytarabine and sinomenine can kill AML.

[0138] like Fig.13 B shows the synergistic effect of cytarabine and sinomenine combined for the treatment of U2OS / R cells using the ZIP (I), Bliss (J), HSA (K) and Loewe (L) evaluation models. Positive or negative synergistic scores indicate synergistic and antagonistic effects, respectively. The average scores of ZIP, Bliss, HSA and Loewe were 18.24, 18.26, 22.55 and 22.25 ( Fig.13 B), indicating a strong synergistic effect between sinomenine and cytarabine.

[0139] like Fig.13 C shows the synergistic effect measurement diagram, which shows the effect of 0.1 μM cytarabine combined with 20 μM sinomenine. The actual inhibitory response exceeds the predicted effects of the HSA, Loewe, Bliss and ZIP models, further confirming the synergistic effect between the two drugs.

[0140] like Fig.13 D and E are shown based on Fig.13 C, flow cytometry was used to detect the effect of combined drug use on cell apoptosis (n=5), and it was found that cytarabine and sinomenine had a strong synergistic effect.

[0141] Example 3 In vivo experiment

[0142] 1. Animal experiments

[0143] The constructed female humanized NSG mice were used for in vivo animal experiments. Six 4-week-old humanized NSG mice were randomly divided into sh-NC group (n=3) and sh-TRIM44 group, and sh-TRIM44-2 (n=3) with the best knockdown effect of TRIM44 was selected. The mice were inoculated with MOLM13 / R cells (1×10 7 Each group of mice was intraperitoneally injected with cytarabine (250 mg / kg, Sigma-Aldrich, USA) for 7 consecutive days, and the tumor volume was measured every 3 days (the formula for calculating the tumor volume was: length × width × width / 2). After 21 days, the ex vivo tumor tissues of the mice were collected and weighed.

[0144] In addition, six 4-week-old humanized NSG mice were randomly divided into a sinomenine group (n=3) and a control group (n=3). The mice were orally administered 40 mg / kg of sinomenine and 40 mg / kg of normal saline, respectively. Each group of mice was intraperitoneally injected with cytarabine (250 mg / kg, brand: Sigma-Aldrich, USA) for 7 consecutive days, and then the tumor volume was measured every 3 days (the formula for calculating the tumor volume is: length × width × width / 2). After 21 days, the ex vivo tumor tissues of the mice were collected and weighed.

[0145] Fig.14 A represents representative images of subcutaneous tumor formation in NSG mice in the sh-TRIM44 group and the sh-NC group. Fig.14 B represents the tumor growth dynamics of NSG mice in the sh-TRIM44 group and the sh-NC group, which are displayed by growth curves. Knockout of TRIM44 can effectively inhibit the proliferation of AML cells and promote apoptosis, suggesting that TRIM44 is a target for AML treatment. Fig.14 C represents the representative images of subcutaneous tumor formation in NSG mice in the Control group and the Sinomenine group. Fig.14 D represents the tumor growth kinetics of NSG mice in the Control group and the Sinomenine group, displayed by growth curves. Fig.14 E represents the comparative analysis of tumor mass in NSG mice between the control group and the sinomenine group. Sinomenine effectively inhibited the malignant phenotype of cytarabine-resistant AML cells and enhanced their sensitivity to cytarabine.

[0146] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Application of TRIM44 expression inhibitors in the preparation of cytarabine-resistant leukemia drugs.

2. The use according to claim 1, characterized in that: The TRIM44 expression inhibitor includes at least one of sinomenine, α-spinasterol, meditartin, dihydrocapsaicin and berberine.

3. The use according to claim 1, characterized in that: The leukemia includes acute leukemia and / or chronic leukemia.

4. The use according to claim 3, characterized in that: The acute leukemia includes acute myeloid leukemia and / or acute lymphocytic leukemia.

5. A drug for preventing and / or treating leukemia, characterized in that: The drugs include a TRIM44 expression inhibitor and cytarabine.

6. The drug according to claim 5, characterized in that The TRIM44 expression inhibitor includes at least one of sinomenine, α-spinasterol, meditartin, dihydrocapsaicin and berberine.

7. The drug according to claim 6, characterized in that The concentration of the cytarabine is 0.01-2 μM, and the concentration of the sinomenine is 1-120 μM.

8. The drug according to claim 7, characterized in that The concentration of the cytarabine is 0.1 μM, and the concentration of the sinomenine is 20 μM.

9. The drug according to claim 8, characterized in that The drug also contains pharmaceutically acceptable excipients.

10. Use of the drug according to any one of claims 5 to 9 in the preparation of drugs for preventing and / or treating leukemia.

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