Fig 1: Mapping of the interaction between APP’s intracellular domain and the PIKfyve complex. a Sequences of the APP intracellular domain AICD and truncations utilised. b Proteo-liposomes displaying cysteine (negative control), AICD or truncations of AICD. AICD and the N-terminal AICD-Tr.4 recruited PIKfyve and Vac14 of the PIKfyve complex from pig brain cytosol while the negative control and all C-terminal AICD truncations failed to do so. c, d Quantification of Vac14 and PIKfyve bound to proteo-liposomes displaying AICD and AICD truncation mutants (n ≥ 3). Significant differences (ANOVA test followed by Tukey’s post hoc analysis with α = 0.05) are indicated. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001. Error bars are s.e.m. e Double-point mutations in the highly conserved YENPTY motif and their impact on AICD’s ability to interact with PIKfyve or Vac14. AICD mutations AANPTY and YENPAA fully abolished its binding to Vac14 while YEAATY strongly reduced the interaction. f, g Quantification of YENPTY mutants and their interaction with Vac14 and PIKfyve (ANOVA test followed by Tukey’s post hoc analysis with α = 0.05. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001. Error bars are s.e.m.). h AICD bound purified Vac14 in protein recruitments while the negative control, cysteine did not. i Quantification of binding of purified Vac14 to AICD proteo-liposomes. Analysed by Student’s t test, *p ≤ 0.05, n = 3. j MBP pull-downs showed that AICD and AICD-Tr.4 interact with purified Vac14 while the MBP (negative control) or AICD truncations 1–3 did not, confirming that AICD binds Vac14 directly. k Protein sequence alignment of the intracellular domains (ICD) of APP gene family members, highlighting the high degree of homology of the intracellular domains. l APLP1 and, to a lesser extent, APLP2 are both able to bind Vac14 and PIKfyve in proteo-liposome recruitments compared to a cysteine control. m, n Quantification of Vac14 and PIKfyve binding to APP, APLP1 and APLP2 (ANOVA test followed by Dunett’s post hoc analysis with α = 0.05. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001. Error bars are s.e.m., n = 3)
Fig 2: Inhibition of PIKfyve disrupts endosomal sorting of APP. a APP-GFP localisation in the absence or presence of the PIKfyve inhibitor Apilimod (100 nM, 4 h), co-labelled with early endosomal marker EEA1, late endosomal/lysosomal marker LampI and Golgi marker GM130. Upon PIKfyve inhibition, APP accumulated in EEA1 positive vacuoles, while little overlap with LampI was detected. PIKfyve inhibition also reduced APP label detectable in the Golgi area. b Quantification of APP-GFP label in EEA1, LampI and GM130 structures. EEA1, LampI or GM130 staining was used to define a mask and the percentage of cellular APP label contained inside the mask was measured in three independent experiments and expressed as a percentage of total APP-GFP label. PIKfyve inhibition led to a marked increase of APP in EEA1 positive structures, while APP label in LampI and GM130 positive structures was significantly decreased as tested using unpaired Student’s t test (**p < 0.01, ***p < 0.001, error bars are s.e.m.). Total number of cells analysed per condition is indicated in each bar in the diagram. Between 10 and 12 stacks were collected from three independent experiments and pooled. c APP accumulated in cells upon overnight inhibition of PIKfyve using 4 µM YM201636 as analysed by western blotting. The total of all APP species was increased approximately fivefold (n = 6). Particularly the 135 kDa form was affected by PIKfyve inhibition. d No major change was detected for APLP2
Fig 3: APP and APLP-2 suppression disrupts PIKfyve-dependent processes. a siRNA duplex sequences utilised and percent of HeLa cells in which vacuoles were observed after suppression of APP, APLP2 or double suppression. Incidence of vacuoles was increased upon single or joint suppression of both APP and APLP2. b Confirmation of successful knock-down of APP and APLP2 by western blotting. c Joint suppression of APP and APLP2 by RNAi and automated detection of ML1Nx2 positive vesicles. Single or joint suppression of APP or APLP2 resulted in a significant reduction of ML1Nx2 positive structures compared to the Luciferase negative control duplex (ANOVA test followed by Tukey’s post hoc analysis, n = 3). d, e RNAi suppression of APP and APLP2 combined with limited PIKfyve inhibition (1 µM, 45 min) and quantification of the number of apparent number of vacuoles. Under these conditions only few, small vacuoles (arrows) were detected in the Luciferase control, while suppression of APLP2 using duplex I and double suppression of both APP and APLP2 significantly increases vacuole number (quantified in e), suggesting that joint suppression of APP and APLP2 sensitises HeLa cells for PIKfyve inhibition. c, e Statistical test using ANOVA followed by Tukey’s post hoc analysis. α = 0.05, only significant differences are indicated, *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001. Number of cells analysed per condition are indicated in each bar in the diagram. Error bars are s.e.m., n = 3)
Fig 4: Examination of Pathway Enrichment Associated with Significant Prognostic Indicators. (A) APLP2; (B) SERPING1; (C) CAT; (D) PDIA6; (E) SLC2A2; (F) C1S; (G) CFB: GSEA and pathway enrichment assessment
Fig 5: Pseudotime trajectory analysis reveals dynamic cellular state transitions and gene expression programs.(A) Pseudotime trajectory of cells inferred by Monocle analysis. Each dot represents a single cell, and the color gradient indicates pseudotime progression from early to late cellular states. Black nodes indicate branch points along the inferred developmental trajectory.(B) Distribution of cells along the trajectory colored by distinct Monocle-defined states. Different colors represent different cell states, indicating that cells are organized into multiple transitional and terminal branches.(C) Distribution of normal and tumor cell clusters along the pseudotime trajectory. Cells from the normal cluster and tumor cluster are shown in different colors, revealing distinct enrichment patterns of tumor-associated cells within specific trajectory branches.(D) Heatmap showing dynamically expressed genes along pseudotime. Rows represent genes and columns represent cells ordered by pseudotime. The color scale indicates relative gene expression levels, with red representing higher expression and blue representing lower expression. Genes were grouped according to their temporal expression patterns, suggesting coordinated transcriptional programs associated with cell state transition.(E)Expression dynamics of representative genes along pseudotime, including APLP2, C1S, CAT, CTB, PDIA5, SERPINC1, and SLC2A2. Scatter plots show single-cell expression levels, and fitted curves indicate smoothed expression trends across pseudotime. Cells are colored according to cluster identity, Monocle state, or pseudotime value, respectively. These results indicate that key genes exhibit distinct temporal expression patterns during the progression of cellular state transition.
Supplier Page from Abcam for Anti-APLP2 antibody [EPR5938(2)]