Research

June25

Microtubule (MT) Transport by the Collective Properties of Motors: Density and Spatial Localization Effects in vitro and in vivo

Simulation from Random Walk with Drift model (Khetan and Athale, 2016, Plos Comp. Biol.)

A schematic of the microtubule filament (blue) transported by multiple surface-immobilized molecular motors (green circles).


Questions of how sub-cellular structure, dynamics, transport and localization arise can be answerered by either the biochemical or the physical approach. We are attempting to address questions relating to pulling, pushing, bending, walking and diffusion.
In previous experiments we had used DNA immobilized using surface micro-contact printing to create patterns. Using a PDMS chamber, an artificial cell was reconstituted in vitro using Xenopus egg extracts in a 100 micron high cavity.Centtrosomes flowed in with labelled MTs resulted in bright microtubule-asters, which moved centripetally towards the chromatin, which we modeled with a gradient of dynamic instability (Athale et al. (2014) Phys. Biol. 11: 016008).

The chromatinized DNA-micropatterns used to create centres of attraction for MT-asters used to study centripetal MT-aster motility (Athale et al. 2014 PB)

Simulations predicted in the absence of a gradient, the asters transition from a super-diffusive to diffusive state ofmovement solely on the basis of motor density.
We have begun to test some of these predictions in experiments using in vitro reconstitution of Microtubule gliding on surface-immobilized motors- the gliding assay.
In order to examine whether this model of aster-motility has any relevance to an in vivo scenario, we have extended the model to apply it to MTOC nucleated small microtubule asters and their convergence to the chromatin in mouse oocytes in Meiosis I. The question of how molecules find the center of a cell, with no apparent directional cue, has been one that has fascinated many cellular-biophysicists. We have used a random-walk model with drift to demonstrate, that pure-diffusion and pushing from the cell-wall alone cannot result in center-finding.

Schematic of the Random Walk with Drift model. Theta dr: directional angle, Theta df: diffusive angle (Ref: Khetan & Athale, 2016)

Extending our previous aster-motility model, we have screened multiple models and found a hybrid model of clustering by tetrameric minus-ended motors and weak-pulling gradient from the center of the cell, can produce the observed behaviour (Ref: Khetan & Athale, 2016).

DIC image of GTP-tubulin polymers

GTP-tubulin polymers from goat brain tubulin with methlycellulose in DIC microscopy (40x). Kunalika Jain.

This behaviour could have implications for

understanding both the collective behaviour of motors in general, as well as physiological conditions involving the movement of radial MT arrays.

Microtubule regulation in cells: Model of MTs in the Neuronal GC

Model of stathmin phosphorylation gradients in neuronal growth cones.

Model of stathmin phosphorylation gradients in neuronal growth cones (Mahaja & Athale 2012 BJ)

We have examined the role of reaction-diffusion gradients of stathmin and their role in regulating the polarization of microtubules, which we believe are likely to be the earliest events of MT polarization during axonal growth cone turningĀ  (Mahajan and Athale 2012). We find the reaction-diffusion network modelled works best as a simple first order system with no feedbacks (Stathmin <–> phospho-Stathmin).

Simulated MTs (cyan) inside a neuronal growth cone geometry with an initially uniform stathmin concentration (bright blue). (Mahajan & Athale 2012 BJ)

Instead the most dramatic amplification is observed when the receptor concentration is itself amplified. This suggests the “network design principle” in such systems might be to amplifying receptor signalling and not so much the cytoskeleton-regulator concentrations.

Quantifying cell size variability in bacterial populations

Image from Gangan & Athale (2017) of microfluidics of E. coli cells grown in LB at 37 deg C

Image from GanganAthale (2017) (Roy. Soc. open science) of microfluidics of E. coli cells grown in LB at 37 deg C

The role of the cytoskeleton to actively regulate cell shape involves its spatial regulation. We are using a top-down approach to estimate the statistical variability in cell shape (Athale C.A.* & Chaudhari HC (2011) Population Length Variability and Nucleoid Numbers in E. coli. Bioinformatics. 27(21): 2944-2948 [pubmed]) and attempting to connect it to molecular networks. We are using the E. coli as a system to address these questions. More recently in work published in the Royal Soc. open science, a PhD student in the lab Manasi Gangan and I have built our own mother machine based on Suckjoon Jun’s design for long-term culture of the ‘mother cell’. Using a mix of nutrients and drugs, we have used this microfluidics device to test the hypothesis of the effect of growth rate on population cell length distributions. Refer to Gangan & Athale (2017) for more details.

An image of E. coli wild type cells in (A) DIC and (B) stained with DAPI and (C) cell lenght distributions from the population.

E. coli wild type cells in (A) DIC and (B) stained with DAPI and (C) cell lenght distributions from the population.

Collective properties of cell shape

Populations of E. coli in microscopy

Populations of E. coli in microscopy: DH5a cells on an agar pad imaged in DIC (green) and hupA-GFP fluorescence (red).

Collective behaviour of cells in populations has begun to be addressed widely in cells and tissues. We are attemtping to address these issues using E. coli by a combination of stochastic modelling and experimental quantification.

Image analysis tool development

To analyse DIC microscopy data from E. coli, we have developed a MATLAB program for rod-shaped cell length analysis. We have recently added a GUI to it [Prangya Mishra]. Meanwhile Anushree Chaphalkar has developed a MATLAB tool to automate Kymography, usually done manually (e.g.: Multi-Kymography plugin for ImageJ, EMBL) analysis of time-series data [Chaphalkar et al. (submitted)].

Philosophy of the lab

Work in the lab is guided by the vision of developing a system level understanding of shape (pattern-formation/morphogenesis). Our choice of scale (cells) is driven by ease of manipulation and tractability. Self organizing systems studied in physics as well as social systems tend to show feedbacks and a high degree of connectivity- something that has increasingly become accepted as a signature of biological systems.

A lot of what we do can also be classified as Physical Biology of the Cell putting us nicely (for some) at the interface of physics and biology.


As in 2015, this year too we are planning to host the IISER Pune iGEM2017 team. The international genetically engineered machines (iGEM) contest for synthetic biology involves building networks of genes-proteins and simulating their behaviour, to both learn something new, as well as solve a real-world problem.

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