In neurons, mitochondria are transported to distal regions for supplying energy and buffer calcium. Mitochondrial transport is mediated by Miro and TRAK adaptors that recruit kinesin and dynein-dynactin. To understand how mitochondria are transported by these opposing motors and stalled at regions with elevated calcium levels, we reconstituted the mitochondrial transport machinery in vitro. We show that the coiled-coil domain of TRAK activates dynein-dynactin motility, but kinesin requires an additional factor to efficiently transport Miro/TRAK. Unexpectedly, TRAK adaptors that recruit both motors move towards the plus-end, whereas kinesin is excluded from binding TRAK transported by dynein-dynactin. The assembly and motility of the transport machinery are not affected by calcium. Instead, the mitochondrial docking protein syntaphilin is sufficient to oppose the forces generated by kinesin and stall the motility. Our results provide mechanistic insight into how mitochondria are transported by the coordinated action of motors and statically anchored to regions with high neuronal activity.

Mitochondrial transport along microtubules is mediated by Miro1 and TRAK adaptors that recruit kinesin-1 and dynein-dynactin. To understand how these opposing motors are regulated during mitochondrial transport, we reconstitute the bidirectional transport of Miro1/TRAK along microtubules in vitro. We show that the coiled-coil domain of TRAK activates dynein-dynactin and enhances the motility of kinesin-1 activated by its cofactor MAP7. We find that TRAK adaptors that recruit both motors move towards kinesin-1’s direction, whereas kinesin-1 is excluded from binding TRAK transported by dynein-dynactin, avoiding motor tug-of-war. We also test the predictions of the models that explain how mitochondrial transport stalls in regions with elevated Ca2+. Transport of Miro1/TRAK by kinesin-1 is not affected by Ca2+. Instead, we demonstrate that the microtubule docking protein syntaphilin induces resistive forces that stall kinesin-1 and dynein-driven motility. Our results suggest that mitochondrial transport stalls by Ca2+-mediated recruitment of syntaphilin to the mitochondrial membrane, not by disruption of the transport machinery.

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Single particle tracking (SPT) is a powerful class of techniques for exploring the dynamics of single molecules moving inside living cells. In order to extract information about the biophysical processes under study, one desires both the trajectories of the tracked particles as well as the parameters of their motions, such as diffusion coefficients, confinement lengths, and similar values. Typically this information is determined in two steps, first through trajectory estimation from the image data and then parameter estimation from the trajectories In prior work, we have introduced a general algorithm known as Sequential Monte Carlo-Expectation Maximization (SMC-EM) that leverages nonlinear system identification tools using particle filters and particle smoothers to handle nonlinear models of motion as well as nonlinear models of observation, allowing us to incorporate camera models, varying point spread functions, and other experimental realities. These scheme does not separate the estimation of trajectory and motion parameters. SMC-EM is in fact a family of algorithms described by the choice of filter and smoother. In this work, we undertake a systematic study of the effect the choice of these different sub-algorithms, focusing on diffusion models as well as the simplest setting that is biologically relevant. The algorithms are compared with respect to a variety of measures, including speed of convergence, computational complexity, trajectory estimation accuracy, parameter estimation accuracy, and robustness to noise. We present a Bayesian approach to fluorescence correlation spectroscopy (FCS) parameter estimation. Traditionally, FCS data are analyzed by leastsquare fitting the autocorrelation function of the fluorescent intensity signal. We show by simulation that a least-square analysis of FCS autocorrelation functions is problematic both in the sense that the analysis results in an order-of-magnitude overestimation of confidence in the fitting parameters, but more importantly in systematic shifts of parameters away from the true value. This effect is more pronounced the shorter the data set is. Motivated by this result, we developed a Bayesian framework for the analysis of FCS data that takes single photon arrival times as input. So far, we have solved the time-independent probability distribution of photon arrival times for a 3d Gaussian beam shape and we are in the process of comparing our method to traditional photon counting histograms in FCS. In the future, we will incorporate the dynamics of fluorescent molecule diffusion into the model. 685-PosExperimental Dissection of Excluded Volume Effects from Quinary Interactions in Macromolecular Crowding . Current models of diffusion are, at best, approximations of what happens in vivo. Fick's Law and Stokes-Einstein approximate dilute solution behavior, but they do not account for in vivo environmental factors that modulate particle motion such as electrostatic forces, hydrophobic interactions, and excluded volume. Such factors represent the contributions f...