Cargo and dynamin regulate clathrin-coated pit maturation

Abstract

Total internal reflection fluorescence microscopy (TIR-FM) has become a powerful tool for studying clathrin-mediated endocytosis. However, due to difficulties in tracking and quantifying their heterogeneous dynamic behavior, detailed analyses have been restricted to a limited number of selected clathrin-coated pits (CCPs). To identify intermediates in the formation of clathrin-coated vesicles and factors that regulate progression through these stages, we used particle-tracking software and statistical methods to establish an unbiased and complete inventory of all visible CCP trajectories. We identified three dynamically distinct CCP subpopulations: two short-lived subpopulations corresponding to aborted intermediates, and one longer-lived productive subpopulation. In a manner dependent on AP2 adaptor complexes, increasing cargo concentration significantly enhances the maturation efficiency of productive CCPs, but has only minor effects on their lifetimes. In contrast, small interfering RNA (siRNA) depletion of dynamin-2 GTPase and reintroduction of wild-type or mutant dynamin-1 revealed dynamin's role in controlling the turnover of abortive intermediates and the rate of CCP maturation. From these data, we infer the existence of an endocytic restriction or checkpoint, responsive to cargo and regulated by dynamin.

Figure 1. Automated Tracking and Statistical Analyses Detect Three Subpopulations of CCPs at the Plasma Membrane

(A) A single frame from a 10-min video of BSC1 cells stably expressing EGFP-labeled clathrin LCa-EGFP (the scale bar indicates 2 μm). (B) Overlay of all trajectories observed during the video. (C) Lifetime distribution for CCPs determined in fast- (grey) and slow-acquisition (black) TIR-FM time-lapse videos. The distribution was determined from the elapsed time between the appearance and disappearance of fluorescent structures present in the time series. n = 43,568 CCP trajectories from 23 cells. (D) Identification of three subpopulations of CCPs. Black dots and smoothed black line show distribution of all CCP lifetimes detected by TIR-FM. These data were best fit by three kinetically distinct subpopulations termed early abortive (blue line), late abortive (red line), and productive CCPs (green line). (E and F) Relative contributions and lifetimes of subpopulations. Error bars represent cell-to-cell variation; the height of the lifetime bar in (F) denotes the t50-spread of the distribution, i.e., the range around the characteristic lifetime that contains 50% of the data. Insets in (C, D, and F) show the data at different scales to emphasize short-lived CCP populations.

Figure 2. Cargo Concentration Regulates the Efficiency of CCP Maturation, but Is Not Rate-Limiting for CCV Formation

Relative contributions and lifetimes of the three CCP populations reported by LCa-EGFP (A and B) or the AP2 rat brain σ2-subunit (σ2)-EGFP (C and D), in control cells, after overexpression of TfnR (cargo o/x) and/or treatment with μ2 siRNA to reduce AP2 levels; a control experiment with nontargeting siRNA is also included. Cargo overexpression increases the relative contribution of productive CCPs at the expense of abortive structures, in an AP2-dependent manner. A single asterisk (*) and triple asterisks (***) indicate confidence levels of p < 0.05 and p < 10⁻⁴, respectively (KS-test). The number of CCP trajectories (n) and cells (k) for each condition are: LCa-EGFP: − cargo o/x (n = 43,658, k = 23); + cargo o/x (n = 23,626, k = 18); ctrl siRNA (n = 9,376, k = 10); σ2 siRNA (n = 29,106, k = 27); σ2 siRNA + cargo o/x (n = 11,071, k = 12). σ2-EGFP: − cargo o/x (n = 22,252, k = 21); + cargo o/x (n = 14,100, k = 16).

Figure 3. Cargo Overexpression in a Tetracycline (tet)-Repressed System

BSC1 cells, coinfected with adenoviruses coding for a tet-repressible transcription activator and the human transferrin receptor (TfnR), were cultured in the presence (+) or absence (−) of tet. (A) Validation of infection efficiency by adenoviruses: cells were loaded with Tfn-Alexa 568, and analyzed by epifluorescence. The scale bars represent 10 μm. (B) Whole-cell lysates were analyzed by SDS-PAGE and western blot. (C) Quantitative analysis of surface-bound TfnRs in BSC1 cells stably expressing LCa-EGFP and σ2-EGFP under control (+ tet) or cargo overexpressing (− tet) conditions. Error bars represent standard deviations of three independent experiments. (D) CCPs, visualized by LCa-EGFP and TIR-FM, in control (ctrl) and TfnR overexpressing (cargo o/x) cells. The scale bar represents 5 μm. (E) Average pit density in LCa-EGFP– and σ2-EGFP–expressing cells under control and cargo overexpressing conditions.

Figure 4. Dynamin Regulates CCP Maturation and CCV Budding

Cells were either left untreated (ctrl) or transfected with dynamin-2–specific siRNA, after which we reintroduced siRNA-resistant WT or mutant dynamin-1 as indicated. (A) The relative contributions of CCP subpopulations are largely unaffected by the treatments shown. (B) The offset fraction of very long-lived CCPs, termed “persistent,” is increased in siRNA-treated cells. (C) The lifetimes of CCP subpopulations are significantly affected by siRNA treatment and overexpression of dynamin WT and dynamin mutants. A single asterisk (*) and triple asterisks (***) indicate confidence levels of p < 0.05 and p < 10⁻⁴, respectively (KS-test). The number of CCP trajectories (n) and cells (k) for each condition are: Dyn-2 siRNA only (n = 13,410, k = 18); siRNA+dyn1WT (n = 37,280, k = 24); siRNA+dyn1K694A (n = 8,263, k = 7); siRNA+dyn1S61D (n = 28,436, k = 25); siRNA+dyn1T141A (n = 22,550, k = 18).

Figure 5. Distinct Phases in CCP Maturation Defined by Intensity Time Courses

(A) Example of typical intensity time course of an isolated productive CCP, fitted with a smoothing spline. The time course is divided into three phases—termed assembly, maturation, and departure—based on the points where the slope of the spline drops under a given threshold (20% of the maximum). (B) Bar graph of corresponding phase lengths (averaged over all extracted trajectories) for different treatments. Error bars indicate standard error of the mean. The number of CCP trajectories (n) for each condition are: control (n = 905); Dyn-2 siRNA only (n = 249); siRNA+dyn1WT (n = 907); siRNA+dyn1K694A (n = 194); siRNA+dyn1S61D (n = 140); siRNA+dyn1T141A (n = 248); empty siRNA (n = 345).

Figure 6. Model of Endocytosis Checkpoint That Controls Clathrin-Coated Pit Maturation

CCVs are formed from productive CCPs, which have undergone a maturation process. An endocytosis checkpoint gates the maturation process, and CCPs that do not progress beyond this restriction point abort by sequential disassembly of AP2 and clathrin. Progression through the restriction point is dependent on the concentration of cargo receptors, AP2 adaptors, and probably other factors. The GTP binding and hydrolysis activities of unassembled dynamin, as revealed by analysis of dynamin GTPase mutants, control the rate and extent of progression through this checkpoint. Dynamin assembly and assembly-stimulated GTPase activities required at late stages of CCV formation are not rate-limiting. See text for details.