Biology subjects

McLoughlin, P.

Publications and source records attributed to McLoughlin, P..

4 recordsLinked to original sources

bioRxiv2024

Shear stress-induced restoration of pulmonary endothelial barrier function following ischaemia reperfusion injury requires VEGFR2 signalling

Normal physiological shear stress produced by blood flow is sensed by the vascular endothelium and required for the maintenance of both the normal structure and barrier function of the endothelium. Many common, critical illnesses are characterised by periods of abnormally reduced or absent shear stress e.g. haemorrhagic shock, myocardial infarction and pulmonary embolism and are complicated by oedema formation following restoration of normal perfusion (IRI). We tested the hypothesis that, in lungs injured by a period of ischaemia and reperfusion (IRI), reduced shear stress contributes to increased endothelial barrier permeability and oedema formation following the restoration of perfusion. Furthermore, we examined the role of VEGFR2 as a mechanosensor in the response of the pulmonary endothelium to altered shear stress in this condition. Following IRI, we perfused isolated ventilated mouse lungs with a low viscosity solution (LVS) or a higher, physiological viscosity solution (PVS) at constant flow to produce differing shear stresses on the endothelium of the intact pulmonary circulation. Lungs perfused with LVS developed pulmonary oedema due to increased endothelial permeability whereas those perfused with PVS were protected from oedema formation by reduced endothelial permeability. This effect of PVS required normal VEGFR2 tyrosine kinase activity but was unaffected by blocking VEGFA binding to the receptor. These data show for the first time that shear stress has an important role in restoring endothelial barrier function in the pulmonary circulation following injury and have important implications for the treatment of pulmonary oedema in critically ill patients following ischaemia reperfusion injury.

physiology↗

bioRxiv2023

Assessing bias and robustness of social network metrics using GPS based radio-telemetry data

O_LISocial network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, dynamical processes, and transmission events such as the spread of disease. However, the accuracy of estimated social network metrics depends on the proportion of individuals sampled, actual sample size, and frequency of observations. Robustness of network metrics derived from a sample has thus far been examined through various simulation studies. However, simulated data do not necessarily reflect the nuances of real empirical data. C_LIO_LIWe used some of the largest available GPS telemetry relocation datasets from five species of ungulates characterised by different behavioural and ecological traits and living in distinct environmental contexts to study the bias and robustness of social network metrics. We introduced novel statistical methods to quantify the uncertainty in network metrics obtained from a partial population suited to autocorrelated data such as telemetry relocations. We analysed how social network metrics respond to down-sampling from the observed data and applied pre-network data permutation techniques, a bootstrapping approach, correlation, and regression analyses to assess the stability of network metrics when based on samples of a population. C_LIO_LIWe found that global network metrics like density remain robust when the sample size is lowered, whereas some local network metrics, such as eigenvector centrality, are entirely unreliable when a large proportion of the population is not monitored. We show how to construct confidence intervals around the point estimates of these metrics representing the uncertainty as a function of the number of nodes in the network. C_LIO_LIOur uncertainty estimates enable the statistical comparison of social network metrics under different conditions, such as analysing daily and seasonal changes in the density of a network. Despite the striking differences in the ecology and sociality among the five different ungulate species, the various social network metrics behave similarly under downsampling, suggesting that our approach can be applied to a wider range of species across vertebrates. Our methods can guide methodological decisions about animal social network research (e.g., sampling design and sample sizes) and allow more accurate ecological inferences from the available data. C_LI

animal behavior and cognition↗

bioRxiv2022

Gremlin 1 is required for macrophage M2 polarization

Pro-proliferative, M2-like polarization of macrophages is a critical step in the development of fibrosis and remodeling in chronic lung diseases such as pulmonary fibrosis and pulmonary hypertension. Macrophages in healthy and diseased lungs express gremlin 1 (Grem1), a secreted glycoprotein that acts in both paracrine and autocrine manners to modulate cellular function. Increased Grem1 expression plays a central role in pulmonary fibrosis and remodelling, however, the role of Grem1 in M2-like polarization of macrophages has not previously been explored. The results reported here show that recombinant Grem1 potentiated M2-like polarization of mouse macrophages and bone marrow derived macrophages (BMDM) in response to the Th2 cytokines IL4 and IL13. Genetic depletion of Grem1 in BMDMs inhibited M2 polarization while exogenous gremlin 1 could partially rescue this effect. Taken together, these findings reveal that gremlin 1 is required for M2-like polarization of macrophages.

molecular biology↗

bioRxiv2021

Solving the Sample Size Problem for Resource Selection Analysis

O_LISample size sufficiency is a critical consideration for conducting Resource-Selection Analyses (RSAs) from GPS-based animal telemetry. Cited thresholds for sufficiency include a number of captured animals M [≥] 30 and as many relocations per animal N as possible. These thresholds render many RSA-based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. C_LIO_LIWe provide the first comprehensive solution for RSA sample size by deriving closed-form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 2 biologically meaningful quantities: habitat selection strength and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores, and herbivores living in boreal, temperate, and tropical forests, montane woodlands, swamps, and arctic tundra). C_LIO_LIOur analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results contradict conventional wisdom by demonstrating that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with far fewer data than is commonly assumed. C_LIO_LIWe identify several critical steps in implementing these equations, including (i) a priori selection of expected model coefficients, and (ii) sampling intensity for background (absence/pseudo-absence) data within a given definition of availability. We show that random sampling of background data violates the underlying mathematics of RSA, leading to incorrect values for necessary M and N and potentially incorrect RSA model outputs. We argue that these equations should be a mandatory component for all future RSA studies. C_LI

ecology↗