Fig 1.
Overall flowchart of this study.
The clinical tissues and blood samples in PBC and PSC were retained in the GEO database. After in-depth mining and analysis, different information of groups was found, then difference analysis, functional enrichment and machine learning screening were performed according to the above process, the correlation analysis of immune cells was performed finally. Differentially expressed genes (DEGs) hubs related to PBC in tissue samples. Heatmap showing significantly DEGs in PBC compared HC control. Using the RNA sequencing data of the GEO cohort to screen out (|log2FC| > 1 and FDR < 0.05). Log (λ) and the error model. The 2 dashed lines in Fig 2B indicate lambda.min and lambda.1se, respectively. Lambda.min denoted the value of λ when the model error was minimal. Lambda.1se denoted the model error within a standard error range of λ. SVM-REF approach to identify signature genes in PBC compared HC samples.Venn diagram based on the intersection of the two algorithms.
Fig 2.
GSEA results showing pathways enriched in the top or bottom of the ranked list of PBC and PSC in blood samples with the corresponding to enrichment in upregulated and downregulated genes (Note: The sample size of PBC tissue was limit so it couldn’t be analyzed by GSEA).
A. Enrichment plot for the top 5 pathways in PBC-B. B. Top 5 pathways in HC control to PBC-B. C. Enrichment plot for the top 5 pathways in PSC-B. D. Top 5 pathways in HC control.
Fig 3.
The infiltration proportion of immune cell subsets in different tissues was significantly discrepancy between PBC and PSC.
A. Immune cell infiltration in tissue samples of PBC and HC control. The increase of NK cells activated, resting Mast cells, and decrease of Eosinophils and Monocytes in PBC-T. B. Correlation analysis among various immune cells in PBC-T (The closer the absolute value of the correlation coefficient is to 1, the stronger the correlation between the variables). C. Immune cell infiltration in whole blood samples of PBC and HC control. The increase of CD4 naive T cells, Treg cells, and M0 macrophages, whereas M2 macrophage levels exhibited a significant decline in PBC-B. D. Correlation analysis among various immune cells in PBC-B. E. Immune cell infiltration in whole blood samples of PSC and HC control. The extreme increase of Neutrophiles, CD4 naive T cells, the decrease of CD8 T cells and resting NK cells in PSC-T. F. Correlation analysis among various immune cells in PSC-B.
Fig 4.
The difference of X chromosome gene cluster expression in whole blood of PBC patients and PSC patients.
A&B&C. The expression of OTUD5, PQBP1, TIMM17B in the whole blood linked imbalance block gene region of PBC-B. D&E&F. Expression of SLC35A2, GRIPAP1, TIMM17B in PSC-B whole blood linkage imbalance block gene region. High-expression genes were marked with red markers, low-expression genes with green markers, and non-differential genes with black markers. P* < 0.05.
Table 1.
The predicted results of protein interaction in inBio Discover database PQBP1/ OTUD5/ TIMM17B/ PIM2/ SLC35A2.
Fig 5.
Interaction between OTUD5 and MAVS in protein network prediction database.
A. The predicted results of protein interaction in inBio Discover database PQBP1/ OTUD5/ TIMM17B/ PIM2/ SLC35A2. B. The protein network of OTUD5 and MAVS in STRING database.
Fig 6.
Over-expression of MAVS in PBC-T and positive immunohistochemical staining within the liver tissue of clinical PBC patients.
A. MAVS demonstrated a marked increase in expression levels in the GEO analysis of PBC-T. B. The comparison of phase I-III immunohistochemistry between healthy controls and PBC patients. C. The statistical results of liver tissue immunohistochemistry. Scar bar = 50 μm in the upper Fig, and Scar bar = 20μm in local magnification in the lower Fig). P* < 0.05.
Fig 7.
Over-expression of OTUD5 in whole blood samples of PBC patients and its immunofocusing with MAVS in paraffin sections of liver tissue.
A. Following the execution of RT-qPCR detection and subsequent statistical analysis, it has been determined that the over-expression of the OTUD5 gene is statistically significant in whole blood samples from PBC patients relative to healthy control. B. MAVS is positively correlated with resting mast cells and negatively correlated with eosinophils in PBC-T of GEO analysis. C. OTUD5 is positively correlated with monocyte/macrophage subsets and negatively correlated with plasma cells in PBC-B of GEO analysis. D. Over-expression of OTUD5 and MAVS in the immunofocusing of liver tissue was analyzed across PBC stages I-IV, respectively. The healthy control group served as the reference. Scale bar: 20 μm. E. The immunofocusing statistics of MAVS in liver tissues of PBC patients. P* < 0.05.
Fig 8.
PBC PBMCs single cell sequencing cell annotation results.
A. The classification annotation of major immune cells by PBC single cell sequencing, B. 22 cell subsets with a resolution of 0.5_d30 cell classification rule. C. The detailed classification analysis of monocytes, including subsets 11, 14, 15 and 21. D. The expression level of OTUD5 and MAVS in single-cell annotation of PBC and HC groups.The analysis results of differential gene expression in 11 mononuclear subsets. E. Dot size reflects the number of cells expressing differential genes. The bluer the color, the higher the average cell expression level. F. The simulated changes at different cell differentiation times with the accumulation of numbers in the pseudo time series.
Fig 9.
Results of CytoTRACE differentiation of monocyte subsets 11 and enrichment of characteristic molecules.
A. The differentially expressed characteristic genes of subpopulation 11. B. GO enrichment for mono-11. C. GSEA functional enrichment results of gene clusters. D. The horizontal coordinate is cell type, and the vertical coordinate is CytoTRACE score. The higher the score, the lower the differentiation degree and the higher the dryness of the cell type. E. The phenotype of cell type, and the CytoTRACE score, the lower the score, the higher the differentiation. F. Expression of gene markers associated with high differentiation in 11 subpopulations.
Fig 10.
Interactions between mononuclear 11 subpopulation and other related mononuclear derived differentiated cell communities and transcription factor interactions.
A. The interaction heat map shows that the redder the color, the more closely related the cell subsets are. The network diagram of direct interaction is established from the intensity of interaction. B. from the arrow direction and line thickness, it can be seen that subgroup 11 and subgroup 14 interact most closely. C. The larger the dot, the stronger the effect of the displacing factor. D. The larger the dot, the closer the effect. TGF-β1 and 3 receptors may be signaling pathways for differentiation between subgroups 11 and other subgroups. E-I. Distribution and expression characteristics of transcription factors among monocyte subsets. The higher the Auc score, the stronger the cell specificity of expression.
Fig 11.
The expression of OTUD5 knockdown RAW264.7 cell line was constructed.
A&B. The protein level and Western Blot of OTUD5-knockdown. C&D. the verification of MAVS after OTUD5 knockdown in RAW264.7. Immunofluorescence quantization of OTUD5 and MAVS protein after knockdown. E&F. The statistics of immunofluorescence respectively. P* < 0.05. G. The immunolocalization of OTUD5 and MAVS in RAW264.7 cells of OTUD5-MUS-944 and LV3-NC groups, Scar bar = 50 μm.