Biology subjects

Korabel, N.

Publications and source records attributed to Korabel, N..

4 recordsLinked to original sources

bioRxiv2024

The role of Kinesin-1 in neuronal dense core vesicle transport and lifespan regulation in C. elegans

Fast axonal transport is crucial for neuronal function and is driven by kinesins and cytoplasmic dynein. We investigated the role of the kinesin-1 motor complex in dense core vesicle (DCV) transport in C. elegans, using mutants in kinesin light chains (klc-1 and klc-2) and the kinesin motor subunit (unc-116) expressing an ida-1::gfp transgene that labels DCVs in the ALA neuron. A reduced-function unc-116(rf) mutation greatly impaired DCV transport in both directions. A klc-2(rf) reduced-function mutation decreased DCV velocity in both directions and reduced the frequency of body bends during swimming. In contrast, the klc-1(-) null mutation had no effect on anterograde transport or swimming ability, but surprisingly it increased the speed of retrograde DCV transport. We also determined lifespan, finding that klc-1(-) or klc-2(rf) single mutants were wild-type whereas the unc-116(rf), ida-1::gfp and unc-116(rf); ida-1::gfp strains were short-lived. Strikingly, the ida-1::gfp transgenic synergistically interact with either klc mutant to extend lifespan compared to wild-type and parental strains. Our findings suggest that kinesin-1 not only influences anterograde and retrograde DCV transport but also plays a role in regulating lifespan.

cell biology↗

bioRxiv2019

Deciphering anomalous heterogeneous intracellular transport with neural networks

Biological intracellular transport is predominantly heterogeneous in both time and space, exhibiting varying non-Brownian behaviour. Characterisation of this movement through averaging methods over an ensemble of trajectories or over the course of a single trajectory often fails to capture this heterogeneity adequately. Here, we have developed a deep learning feedforward neural network trained on fractional Brownian motion, which provides a novel, accurate and efficient characterization method for resolving heterogeneous behaviour of intracellular transport both in space and time. Importantly, the neural network requires significantly fewer data points compared to established methods, such as mean square displacements, rescaled range analysis and sequential range analysis. This enables robust estimation of Hurst exponents for very short time series data, making possible direct, dynamic segmentation and analysis of experimental tracks of rapidly moving cellular structures such as endosomes and lysosomes. By using this analysis, we were able to interpret anomalous intracellular dynamics as fractional Brownian motion with a stochastic Hurst exponent.

cell biology↗

bioRxiv2018

A cumulative inertia phenomenon explains anomalous long-distance transport in eukaryotic cells

We demonstrate the phenomenon of cumulative inertia in intracellular transport involving multiple motor proteins in human epithelial cells by measuring the empirical survival probability of cargoes on the microtubule and their detachment rates. We found the longer a cargo moves along a microtubule, the less likely it detaches from it. As a result, the movement of cargoes is non-Markovian and involves a memory. We observe memory effects on the scale of up to 2 seconds. We provide a theoretical link between the measured detachment rate and the super-diffusive Levy walk-like cargo movement.

biophysics↗

bioRxiv2018

An In Silico Model of DNA Repair for Investigation of Mechanisms in Non-Homologous End Joining

In human cells, non-homologous end joining is the preferred process to repair radiation induced DNA double strand breaks. The complex nature of such biological systems involves many individual actions that combine to produce an overall behaviour. As such, experimentally determining the mechanisms involved, their individual roles, and how they interact is challenging. An in silico approach to radiobiology is uniquely suited for detailed exploration of these complex interactions and the unknown effects of specific mechanisms on overall behaviour. We detail the construction of a mechanistic model by combination of several, experimentally supported, hypothesised mechanisms. Compatibility of these mechanisms was tested by fitting to results reported in the literature. To avoid over fitting, individual mechanisms within this pathway were sequentially fitted. We demonstrate that using this approach the model is capable of reproducing published protein kinetics and overall repair trends. This process highlighted specific biological mechanisms which are not clearly defined experimentally, and showed that the assumed motion of individual double strand break ends plays a crucial role in determining overall system behaviour.

systems biology↗