Biology subjects

Dinner, A. R.

Publications and source records attributed to Dinner, A. R..

5 recordsLinked to original sources

bioRxiv2018

Transient intracellular acidification regulates the core transcriptional heat shock response

Heat shock induces a conserved transcriptional program regulated by heat shock factor 1 (Hsf1) in eukaryotic cells. Activation of this heat-shock response is triggered by heat-induced misfolding of newly synthesized polypeptides, and so has been thought to depend on ongoing protein synthesis. Here, using the the budding yeast Saccharomyces cerevisiae, we report the discovery that Hsf1 can be robustly activated when protein synthesis is inhibited, so long as cells undergo cytosolic acidification. Heat shock has long been known to cause transient intracellular acidification which, for reasons which have remained unclear, is associated with increased stress resistance in eukaryotes. We demonstrate that acidification is required for heat shock response induction in translationally inhibited cells, and specifically affects Hsf1 activation. Physiological heat-triggered acidification also increases population fitness and promotes cell cycle reentry following heat shock. Our results uncover a previously unknown adaptive dimension of the well-studied eukaryotic heat shock response.

cell biology↗

bioRxiv2018

Tuning molecular motor transport through cytoskeletal filament network organization

The interaction of motor proteins with intracellular filaments is required for transport processes and force generation in cells. Within a cell, crosslinking proteins organize cytoskeletal filaments both temporally and spatially to create dynamic, and structurally diverse networks. The architecture of these networks changes both the mechanics as well as the transport dynamics; however, the effects on transport are less well understood. Here, we compare the transport dynamics of myosin II motor proteins moving on model cytoskeletal networks created by common crosslinking proteins. We observe that motor dynamics change predictably based on the microstructure of the underlying networks and discuss how this can be utilized by cells to achieve specific transport goals.

biophysics↗

bioRxiv2017

Filament Rigidity And Connectivity Tune The Deformation Modes Of Active Biopolymer Networks

Molecular motors embedded within collections of actin and microtubule filaments underlie the dynamic behaviors of cytoskeletal assemblies. Understanding the physics of such motor-filament materials is critical to developing a physical model of the cytoskeleton and the design of biomimetic active materials. Here, we demonstrate through experiments and simulations that the rigidity and connectivity of filaments in active biopolymer networks regulates the anisotropy and the length scale of the underlying deformations, yielding materials with varying contractility. Semi-flexible filaments that can be compressed and bent by motor stresses undergo deformations that are predominantly biaxial. By contrast, rigid filament bundles contract via actomyosin sliding deformations that are predominantly uniaxial. Networks dominated by filament buckling are robustly contractile under a wide range of connectivities, while networks dominated by actomyosin sliding can be tuned from contractile to extensile through reduced connectivity via cross-linking. These results identify physical parameters that control the forces generated within motor-filament arrays, and provide insight into the self-organization and mechanics of cytoskeletal assemblies.

biophysics↗

bioRxiv2017

Bootstrapping and Empirical Bayes Methods Improve Rhythm Detection in Sparsely Sampled Data

MotivationThere is much interest in using genome-wide expression time series to identify circadian genes. However, the cost and effort of such measurements often limits data collection. Consequently, it is difficult to assess the experimental uncertainty in the measurements and, in turn, to detect periodic patterns with statistical confidence.\n\nResultsWe show that parametric bootstrapping and empirical Bayes methods for variance shrinkage can improve rhythm detection in genome-wide expression time series. We demonstrate these approaches by building on the empirical JTK_CYCLE method (eJTK) to formulate a method that we term BooteJTK. Our procedure rapidly and accurately detects cycling time series by combining information about measurement uncertainty with information about the rank order of the time series values. We exploit a publicly available genome-wide dataset with high time resolution to show that BooteJTK provides more consistent rhythm detection thanexisting methods at typical sampling frequencies. Then, we apply BooteJTK to genome-wide expression time series from multiple tissues and show that it reveals biologically sensible tissue relationships that eJTK misses.\n\nAvailabilityBootstrap eJTK (BooteJTK) is implemented in Python and is freely available on GitHub at https://github.com/alanlhutchison/BooteJTK.

bioinformatics↗

bioRxiv2017

Correcting for Dependent P-values in Rhythm Detection

There is much interest in using genome-wide expression time series to identify circadian genes. Several methods have been developed to test for rhythmicity in sparsely sampled time series typical of such measurements. Because these methods are statistical in nature, they rely on estimating the probabilities that patterns arise by chance (i.e., p-values). Here we show that leading methods implicitly make inappropriate assumptions of independence when estimating p-values. We show how to correct for the dependence to obtain accurate estimates for statistical significance during rhythm detection.

bioinformatics↗