Tucker-Kellogg, L. | Search bioRxiv

bioRxiv2018

Bile canaliculi contract autonomously by releasing calcium into hepatocytes via mechanosensitive calcium channel

Bile canaliculi (BC) are the smallest vessels of the biliary tree. They are formed from the apical surfaces of adjoining hepatocytes, resulting in lumenal conduits for bile flow. Bile is propelled along the BC by hepatocyte contractions that arise from cyclic waves of apico-basal Ca2+, but the source and regulation of Ca2+ has been unclear. We report that BC contraction correlates with cyclic transfer of Ca2+ from BC lumen to apico-basal Ca2+ waves in adjacent hepatocytes, and does not correlate with endoplasmic reticulum Ca2+. BC contractility was triggered by ionophore A23187 and unaffected by Thapsigargin. The cycles of Ca2+ transfer could be blocked by the mechanosensitive calcium channel inhibitor GsMTx-4, resulting in cholestatic generation of BC-derived vesicles. The mechanosensitive calcium channel Piezo-1 is preferentially localized at BC membranes, and its hyper-activation by Yoda1 causes increased Ca2+ transfer and increased BC contractility. We propose that canaliculi achieve biomechanical homeostasis through the following feedback system: the pressure of accumulated bile is sensed by mechanosensitive channel, which transmit biliary calcium into adjacent hepatocytes for contraction of the BC lumen and propulsion of the bile.

cell biology↗

bioRxiv2018

System Biology Modeling with Compositional Microbiome Data Reveals Personalized Gut Microbial Dynamics and Keystone Species

A growing body of literature points to the important roles that different microbial communities play in diverse natural environments and the human body. The dynamics of these communities is driven by a range of microbial interactions from symbiosis to predator-prey relationships, the majority of which are poorly understood, making it hard to predict the response of the community to different perturbations. With the increasing availability of high-throughput sequencing based community composition data, it is now conceivable to directly learn models that explicitly define microbial interactions and explain community dynamics. The applicability of these approaches is however affected by several experimental limitations, particularly the compositional nature of sequencing data. We present a new computational approach (BEEM) that addresses this key limitation in the inference of generalised Lotka-Volterra models (gLVMs) by coupling biomass estimation and model inference in an expectation maximization like algorithm (BEEM). Surprisingly, BEEM outperforms state-of-the-art methods for inferring gLVMs, while simultaneously eliminating the need for additional experimental biomass data as input. BEEMs application to previously inaccessible public datasets (due to the lack of biomass data) allowed us for the first time to analyse microbial communities in the human gut on a per individual basis, revealing personalised dynamics and keystone species.

systems biology↗