Targeting PFKFB3 to restore glucose metabolism in acute pancreatitis via nanovesicle delivery

Abstract

Background: Acute pancreatitis (AP) is a severe inflammatory disease frequently accompanied by disturbances in glucose metabolism, which further complicate the disease prognosis. This study aims to explore the role of PFKFB3, a key glycolytic enzyme, in regulating glucose metabolism in AP and assess the potential of PFKFB3 inhibition via nanovesicle delivery to mitigate metabolic dysfunction.

Methods: Transcriptomic data from Gene Expression Omnibus (GEO), including single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing, were analyzed to investigate the molecular mechanisms involved in glucose metabolism dysregulation in AP. The therapeutic effects of PFKFB3 inhibition via nanovesicle-based delivery were evaluated using both in vivo and in vitro AP models.

Results: PFKFB3 inhibition significantly restored normal glycolytic function and improved glucose metabolism in AP models. Moreover, nanovesicle-mediated delivery also alleviated both inflammation and metabolic disturbances, highlighting its promise as a therapeutic strategy for managing glucose dysfunction in AP.

Conclusion: Our findings identify PFKFB3 as a critical therapeutic target for treating glucose metabolism disorders in acute pancreatitis. Nanovesicle-based PFKFB3 inhibition may serve as an innovative approach to address metabolic complications associated with AP, offering a new direction for therapeutic interventions in inflammatory diseases.

Keywords: Acute pancreatitis; Glucose metabolism disorder; Machine learning; Nanovesicles; PFKFB3 inhibitor; Single-cell RNA sequencing.

Fig. 1

Immune Mechanisms and Enriched Signaling Pathways in AP. Note: A GSEA showing immune gene sets enriched in the control group and the AP group; B KEGG gene sets enriched in the control group and the AP group; C Violin plot of xCell immune infiltration analysis results, AP: n = 87, control: n = 32

Fig. 2

WGCNA Analysis of AP Transcriptomic Data. Note: A Scale independence, mean connectivity, and the scale-free topology fit index plot, determining the weighted value β = 20 that satisfies the scale-free network law; B Clustering dendrogram of co-expression network modules; C Correlation heatmap among the 15 modules; D Correlation analysis between modules and AP traits; E Scatter plots of module-trait relationships for brown4 and lightsteelblue1 modules with AP

Fig. 3

Differential Analysis of Bulk RNA-seq Data. Note: A Volcano plot of differential analysis, red dots represent significantly upregulated genes, gray dots represent non-significant genes, AP: n = 87, control: n = 32; B Heatmap of DEGs; C-D GO (C) and KEGG (D) enrichment analysis of DEGs; E Venn diagram showing the overlap between DEGs and WGCNA-identified AP-associated genes; F ROC diagnostic analysis of the 19 intersecting genes, with the X-axis representing 1-specificity (false positive rate, closer to zero indicates higher accuracy) and the Y-axis representing sensitivity (true positive rate, higher values indicate better accuracy); G Correlation analysis of 18 genes, with the central pie chart indicating p-values from correlation tests, * denotes p-value < 0.05. AP: n = 87, control: n = 32

Fig. 4

Cellular Differences in AP-associated scRNA-seq. Note: A UMAP-based visualization of cell annotation results by group; B Heatmap of the top 5 gene expression correlations in 14 cell types; C Proportional representation of different cell subpopulations in each sample, with cell types indicated by different colors; D Circos plot of cell communication in samples, with line thickness in the left plot indicating the number of pathways and in the right plot indicating interaction strength; E Volcano plot of differential analysis for two groups of macrophages. AP: n = 2, control: n = 2

Fig. 5

Characterization and Functionalization of Q-Ex. Note: A TEM image of EVs (×70,000), scale bar = 100 nm; B Particle size distribution of Q-Ex; C Size variation of Q-Ex stored in serum at 37 °C; D Concentration variation of quercetin in Q-Ex stored in serum at 37 °C; E Solubility of quercetin under different conditions detected by HPLC. Compared with the 25 °C group, *p < 0.05, **p < 0.01, ***p < 0.001. Cell experiments were repeated three times

Fig. 6

Cellular Uptake of Q-Ex and Its Anti-inflammatory and Anti-apoptotic Effects in vitro Note: A Fluorescence microscopy images showing the targeting ability of Q-Ex and quercetin (Q) in vitro using Cy5.5-labeled quercetin (×400), bar = 25 μm; B Cell viability assay (CCK-8) of different treatment groups; C WB analysis of PFKFB3 protein expression in various treatment groups; D WB and RT-qPCR analysis of pro-inflammatory cytokine protein levels in different groups; E Flow cytometry analysis of apoptosis levels and bar chart of apoptotic cell percentages in different groups; F TUNEL staining to observe apoptosis levels and bar chart of positive cell percentages, (×200)scale bar = 50 μm. Cell experiments were repeated three times. Compared with the DMSO + PBS group, *p < 0.05, **p < 0.01; compared with the DMSO + LPS group, #p < 0.05, ##p < 0.01

Fig. 7

Effects of Q-Ex on Glucose Metabolism and Oxidative Stress in RAW264.7 Cells. Note: A Measurement of the OCR in cells treated with quercetin using Seahorse XF Analyzer; B Measurement of the ECAR in cells treated with quercetin using Seahorse XF Analyzer; C WB and RT-qPCR analysis of key glycolysis enzyme protein levels in various treatment groups; D Flow cytometry analysis of DCFH-DA positive cells and bar chart of the number of positive cells in different groups. Cell experiments were repeated three times. Compared with the DMSO + PBS group, *p < 0.05, **p < 0.01, ***p < 0.001; compared with the DMSO + LPS group, #p < 0.05, ##p < 0.01

Fig. 8

Anti-inflammatory and Metabolic Improvement Effects of Q-Ex in a Rat Model of AP. Note: A H&E staining of pancreatic tissue showing pathological changes and histopathological scores, scale bar = 200 μm; B Immunohistochemical staining of pancreatic tissue sections showing amylase-positive levels and the percentage of positive areas, scale bar = 200 μm; C WB analysis of PFKFB3 expression levels and grayscale value statistics in pancreatic tissue; D ELISA detection of pro-inflammatory cytokine levels in rat serum; E Measurement of glycolysis rate and OCR in pancreatic tissue using Seahorse XF Analyzer; F Measurement of ECAR in pancreatic tissue using Seahorse XF Analyzer; G WB and RT-qPCR analysis of key glycolysis enzyme expression levels and grayscale value statistics in pancreatic tissue; H Immunofluorescence staining of pancreatic tissue sections showing 8-OHdG (DNA oxidative damage marker) levels and percentage of positive staining, scale bar = 100 μm. Each group consisted of 6 rats. Compared with the DMSO + NS group, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001; compared with the DMSO + AP group, #p < 0.05, ##p < 0.01, ###p < 0.001