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Biology subjects

McGill, A.

Publications and source records attributed to McGill, A..

2 recordsLinked to original sources

In vitro and in vivo analysis of microvesicle-mediated metastasis using a bright, red-shifted bioluminescent reporter protein of extracellular vesicles

Cancer cells produce heterogeneous extracellular vesicles (EVs) as mediators of intercellular communication. Our study focused on a novel method to image EV subtypes and their biodistribution in vivo. Regardless of injection routes, we established that reporter EVs isolated from murine mammary carcinoma cells expressing PalmReNL, which utilizes bioluminescence resonance energy transfer (BRET), localized to the lungs. This new EV reporter allowed highly sensitive EV tracking in vitro and in vivo and enabled us to begin studies to understand the commonalities and functional differences of the EV subtypes. We demonstrated the early appearance of metastatic foci in the lungs of mammary tumor-bearing mice following multiple injections of the microvesicle (MV)-enriched fraction derived from mammary carcinoma cells. In addition, the results we present here show that tumor cell-derived MVs act on distant tissues through upregulating LC3 expression within the lung.

cancer biology↗

Phenology supports the eco-environmental hypothesis for Ebola spillover events

Ebola virus disease outbreaks in animals (including humans and great apes) start with sporadic host switches from unknown reservoir species. The factors leading to such spillover events are little explored. Filoviridae viruses have a wide range of natural hosts and are unstable once outside hosts. Spillover events, which involve the physical transfer of viral particles across species, could therefore be directly promoted by conditions of host ecology and environment. In this report we outline a proof of concept that temporal fluctuations of a set of ecological and environmental variables describing the dynamics of the host ecosystem are able to predict such events of Ebola virus spillover to humans and animals. We compiled a dataset of climate and plant phenology variables and Ebola virus disease spillovers in humans and animals. We identified critical biotic and abiotic conditions for spillovers via multiple regression and neural networks based time series regression. Phenology variables proved to be overall better predictors than climate variables. African phenology variables are not yet available as a comprehensive online resource. Given the likely importance of phenology for forecasting the likelihood of future Ebola spillover events, our results highlight the need for cost-effective transect surveys to supply phenology data for predictive modelling efforts.

epidemiology↗