CHAPTER 5. DISCUSSION
5.3 Outlook
In this work, two distinct docking states of LDCV were proposed: morphological and functional. The precise molecular mechanisms and constituents of the minimal docking machinery are not yet uncovered and the models existing in the literature remain controversial. The next steps in delineation of the docking mechanisms may be the investigation of the role of Synaptotagmin isoforms and their binding to SNAREs and the investigation of the possible role of mammalian exocyst complex components. The methods developed in this work for quantitative analysis of TIRFM imaging data can be successfully used for these purposes. Additional experiments using approaches of molecular biology and ultrastructural morphology will be necessary for the refinement of the model obtained from live imaging.
Another line of research can employ live TIRFM imaging using fluorescently tagged versions of putative key players in the docking machinery (like Munc18, Syntaxin, Synaptotagmin etc.), bearing specific mutations that affect their interactions. Live fluorescent imaging techniques with high spatial resolution and single molecule sensitivity (like TIRFM) can be used to study the dynamics of these proteins and their complexes, the putative ‘docking platforms’, simultaneously with real-time monitoring of vesicle docking. Spatiotemporal correlation of vesicle docking events and ‘docking platform’ dynamics could provide further insight into the molecular mechanisms of vesicle docking/priming in living cells.
Summary
Secretory vesicles dock at the plasma membrane and proceed through several steps in preparation for Ca2+-triggered fusion. The molecular events between the first morphological contact with the membrane and primed state are poorly understood. In this work, total internal reflection microscopy (TIRFM) was used for real-time imaging of single fluorescently labeled large dense core vesicles (LDCVs) beneath the plasma membrane of intact adrenal chromaffin cells. TIRFM imaging was combined with the single particle tracking, correlation and residency time analysis, and assisted by stochastic modeling, to better characterize molecular events in vesicle docking. Cells from munc18-1 null mutant mice were chosen as a model system since this t-SNARE Syntaxin-munc18-1a interacting protein is known to be essential for vesicle docking and fusion.
Footprint density of NPY-Venus labeled LDCVs, measured with TIRFM, was found to reflect alterations in morphological docking, confirming EM ultrastructural differences in munc18-1 null (M18 KO) vs. wildtype (WT) and null+Munc18-1 (Rescue) cells. Analysis of the ‘jittering’ movement of docked LDCV in axial direction by the velocity autocorrelation function revealed a distinct negative autocorrelation component at small τ ~0.5-1 s, which was 4-5 times smaller in M18 KO cells than in WT. The negative autocorrelation amplitude (NPA) was restored in Rescue cells and partially recovered by overexpressing mutated Munc18-1D34N;M38V with low affinity for Syntaxin-1a or Munc18-2. Phorbol ester PMA rescued docking and secretion in M18 KO cells, but the NPA remained small. Computer simulations of LDCV movement showed that the NPA is determined by restrictions applied to the free diffusion model. The small NPA can be attributed to either strongly restricted movement by encagement or tethering forces, or nearly free diffusion with weak restrictions. Since the tethering could be provided by the actin cytomatrix, thickened in the M18 KO cells, the effect of pharmacological actin depolymerization on the NPA was examined. Actin cortex removal led to restoration of morphological docking in M18 KO cells without altering the NPA or secretion, suggesting weak and non-functional tethering/docking of vesicles in M18 KO cells. The role of SNAREs in docking was probed in SNAP-25A null mutant cells and by neurotoxin-mediated cleavage of SNAREs: only BoNT-C1 light chain overexpression reduced the NPA. By developing and using an automated analysis of vesicle residency time at the membrane, approaching vesicles were shown to infrequently dock with only some ‘visitors’ being captured by at least two different tethering modes, low and high-affinity. Munc18-1 increased the population of the latter state as well as the overall vesicle delivery rate.
In conclusion, three distinct docking states were identified where docking vesicles either undock immediately or are captured by minimal docking/tethering principles and converted into a Munc18-1/Syntaxin-dependent, tightly tethered, fusion competent state.
Supplementary Tables
Table S.1. Proposed roles for Munc18/Sec1 proteins in secretion Role as a docking
factor
Interactions with SNAREs before/at
the priming stage
Role in the late fusion steps
Other interactions implicated in effects
on secretion LDCVs in
neurosecretory cells (Voets et al., 2001;
Korteweg et al., 2005)
Structural and conformational
evidences: positive regulator of secretion (Dulubova et al., 1999;
Misura et al., 2000)
SNARE-independent
mechanisms via interaction with Mints (Ciufo et al., 2004; Schutz et al., 2005)
Yeast secretion: positive regulator (Carr et al., 1999; Scott et al., 2004)
Syntaxin trafficking to the plasma membrane (Rowe et al., 2001)
Modulation of actin cytoskeleton via Cdk5 (Shetty et al., 1995;
Veeranna et al., 1997) Vesicles in C. elegance
NMJ (Weimer et al., 2003)
D. melanogaster Rop:
negative regulator (Schulze et al., 1994;
Wu et al., 2001)
Relieve of Ca2+ channels inactivation by Syntaxin (Gladycheva et al., 2004;
Mitchell and Ryan, 2005) Possible modulation
pathways of Munc18-1
PKC-mediated phosphorylation at residues Ser313, Ser306 (Fujita et al., 1996)
Cdk5-mediated phosphorylation at Thr574 (Fletcher et al., 1999)
Table S.2. Mean square displacement analysis summary for LDCV lateral (XY) movement. Data are mean ± SEM.
Average MSD fitting parameters Diffusion type frequency
Table S.3. Mean square displacement analysis summary for LDCV axial (Z) movement. Data are mean ± SEM.
Average MSD fitting parameters Diffusion type frequency
Rcage Din cage Dcage Dfree Cage Free
Total Phenotype
nm (×10-4)
μm2/s (×10-5) μm2/s % ves
WT 8.82±0.55 4.77±0.97 0.40±0.09 26.9±7.36 64 33 254
M18 KO 10.4±0.8 9.49+1.94 2.24±0.56 8.76±1.99 32 64 137 KO+M18-1 15.1±1.7 20.1±6.3 5.20±1.52 80.0±21.7 36 58 128 KO+
M18-1D34N;M38V 11.0±1.1 10.8±2.9 2.07±0.46 72.8±14.5 44 48 178 WT+LatrA 8.81±0.62 9.09±2.99 1.01±0.21 26.2±8.21 54 44 115 M18 KO+
LatrA 10.1±0.7 7.88±2.21 1.92±0.53 13.0±6.4 50 35 122 M18 KO+
PMA 5.65±0.34 2.20±0.46 0.60±0.13 44.0±11.6 53 44 169
M18 KO+
4a-PMA 8.72±0.80 5.97±1.36 1.35±0.35 20.2±7.7 47 48 163 WT+PMA 8.63±0.92 12.2±5.1 2.79±1.33 26.4±4.7 48 44 157 TeTx 16.2±0.7 33.1±8.7 2.05±0.40 48.1±15.4 81 18 144 BoNT-C1 13.5±0.7 11.6±2.8 1.39±0.40 16.4±4.4 67 32 142 Fixed 4.71±0.44 2.26±0.54 0.15±0.03 0.62±0.17 24 76 113 Beads 5.15±0.26 0.52±0.07 0.19±0.03 0.92±0.12 20 80 127
Table S.4. Kinetic parameters of the first-order stochastic tethers used in the random walk simulations of particle diffusion
Assigned
color kon, s-1 koff, s-1 Steady-state
activation τactive, s τinactive, s kon/koff
Purple 5 100 0.04762 0.01 0.2 0.05
Green 50 400 0.1111 0.0025 0.02 0.125
Yellow 5 20 0.2 0.05 0.2 0.25
Red 50 100 0.3333 0.01 0.02 0.5
Blue 5 10 0.3333 0.1 0.2 0.5
Black 0.5 1 0.3333 1 2 0.5
Supplementary Figures
Fig. S.1. Simulation of the effective diffusion space restriction with stochastic tethers. (a) Mean square displacement from simulated Z-trajectories of particle movement in the space with stochastic tethers (Ntotal=4, ς=0.25) of different kinetics (color assignment is in accordance with the Suppl. Table S.3). (b-d) Samples of the four-tether ensemble activity during 10 s simulation time (33 ms sampling rate). Tethers fluctuations between the active and inactive states as well as the average lifetimes and state occupancy (Suppl. Table S.3) are governed by tether’s kinetics:
kon=0.5, koff =1 s-1 (b), kon=5, koff=10 s-1 (c), kon=50, koff =100 s-1 (d). Number of active tethers 4
3 Number of active tethers 4
3 Number of active tethers 4
3
Appendix A
Dialog windows designed for controlling the new TIRFM setup under DaVis 6.2