Biology subjects

Majumder, S.

Publications and source records attributed to Majumder, S..

3 recordsLinked to original sources

bioRxiv2018

A synthetic biology platform for the reconstitution and mechanistic dissection of LINC complex assembly

The linker of nucleoskeleton and cytoskeleton (LINC) is a conserved nuclear envelope-spanning molecular bridge that is responsible for the mechanical integration of the nucleus with the cytoskeleton. LINC complexes are formed by a transluminal interaction between the outer and inner nuclear membrane KASH and SUN proteins, respectively. Despite recent structural insights, our mechanistic understanding of LINC complex assembly remains limited by the lack of an experimental system for its in vitro reconstitution and manipulation. Here, we describe artificial nuclear membranes (ANMs) as a synthetic biology platform based on mammalian cell-free expression for the rapid reconstitution of SUN proteins in supported lipid bilayers. We demonstrate that SUN1 and SUN2 are oriented in ANMs with solvent-exposed C-terminal KASH-binding SUN domains. We also find that SUN2 possesses a single transmembrane domain, while SUN1 possesses three. Finally, SUN protein-containing ANMs bind synthetic KASH peptides, thereby reconstituting the LINC complex core. This work represents the first in vitro reconstitution of KASH-binding SUN proteins in supported lipid bilayers using cell-free expression, which will be invaluable for testing proposed models of LINC complex assembly and its regulation.

synthetic biology↗

bioRxiv2017

Patchiness and scale-free correlations: characterising criticality in ecosystems.

I.O_LIIn diverse ecosystems, organisms cluster together in such a manner that the frequency distribution of cluster sizes is a power-law function. Spatially-explicit models of ecosystems suggest that loss of such power-law clustering may indicate loss of ecosystem resilience. Hence, it is hypothesised that spatial clustering properties in ecosystems - which can be readily measured using remotely-sensed high-resolution data - can help infer proximity to ecosystem thresholds and may even provide early warning signals of ecosystem collapse. Recent empirical and simulation studies, however, dont find consistent relationships between spatial clustering and ecosystem resilience. Furthermore, how spatial clustering metrics relate to other well-known early warning signals of ecosystems collapse, specifically the phenomenon of critical slowing down (CSD), remains unclear. C_LIO_LIWe synthesize the literature on cluster sizes in empirical and theoretical studies that show how local interactions (especially, positive feedback) among organisms can cause power-law clustering. In addition, we analyse a minimal spatial model of ecosystem transitions that allows us to disentangle the role of environmental stressor and positive feedback on spatial patterns and ecosystem resilience. C_LIO_LIOur literature synthesis reveals that empirically observed power-law clustering in ecosystems is parsimoniously explained by local positive feedback. Our synthesis together with model analysis demonstrates that, depending on the strength of positive feedback, emergence of power-law clustering can occur at any distance from the critical threshold of ecosystem collapse. In fact, we find that for systems with strong positive feedbacks, which are most likely to exhibit abrupt transitions, loss of power-law clustering may not even occur prior to ecosystem thresholds. We also argue that cluster-size distributions are unrelated to the phenomenon of CSD. C_LIO_LIWe demonstrate that, due to CSD, a power-law feature does occur near critical thresholds but in a different quantity; specifically, a power-law decay of spatial correlations of ecosystem state. C_LIO_LIWe conclude that loss of power-law clustering cannot be used as a reliable indicator of ecosystem resilience. Our synthesis and model analyses highlights links between local positive feedback, emergent spatial properties and how they may be used to interpret ecosystem resilience. C_LI

ecology↗

bioRxiv2017

Inferring critical points of ecosystem transitions from spatial data

Ecosystems can undergo abrupt transitions from one state to an alternative stable state when the driver crosses a threshold or a critical point. Dynamical systems theory suggests that systems take long to recover from perturbations near such transitions. This leads to characteristic changes in the dynamics of the system, which can be used as early warning signals of imminent transitions. However, these signals are qualitative and cannot quantify the critical points. Here, we propose a method to estimate critical points quantitatively from spatial data. We employ a spatial model of vegetation that shows a transition from vegetated to bare state. We show that the critical point can be estimated as the ecosystem state and the driver values at which spatial variance and autocorrelation are maximum. We demonstrate the validity of this method by analysing spatial data from regions of Africa and Australia that exhibit alternative vegetation biomes.

ecology↗