Biology subjects

Lawless, C.

Publications and source records attributed to Lawless, C..

26 records · Page 2Linked to original sources

bioRxiv2020

IL-13 deficiency exacerbates lung damage and impairs epithelial-derived type 2 molecules during nematode infection

IL-13 plays a key role during protective type 2 immune responses at mucosal sites, such as during infection with nematodes. However, dysregulation of IL-13 can also contribute to the pathogenesis of atopic and fibrotic diseases such as allergic asthma. Matrix remodelling is an important component of repair processes in the lung but also a hallmark of chronic conditions involving fibrosis. Hence, understanding the role of IL-13 in tissue remodelling has important clinical implications. Since IL-13 shares receptors and signalling pathways with IL-4, disentangling the relative contributions of these type 2 cytokines has been challenging. Additionally, little is known about the singular role of IL-13 following acute tissue injury. In this study, we used Nippostrongylus brasiliensis infection as a model of acute lung tissue damage comparing responses between WT and IL-13-deficient mice, in which IL-4 signalling is intact. Importantly, we found that IL-13 played a critical role in limiting tissue injury and haemorrhaging in the lung following infection. Through proteomic and transcriptomic profiling, we identified IL-13-dependent changes in matrix and associated regulators. We further showed that IL-13 is required for the induction of epithelial-derived type 2 effector molecules such as RELM- and surfactant protein D. Pathway analyses predicted that IL-13 was heavily involved in the induction of cellular stress responses and regulation of lung epithelial cell differentiation by suppression of Foxa2 pathways. Thus, we propose that IL-13 has tissue-protective functions during lung injury and regulates epithelial cell responses during type 2 immunity in this acute setting.

immunology↗

bioRxiv2020

Increased microenvironment stiffness leads to altered aldehyde metabolism and DNA damage in mammary epithelial cells through a RhoA-dependent mechanism

Microenvironmental stiffness regulates the behaviour of both normal and cancer cells. In breast tissue, high mammographic density (HMD), which reflects greater organisation and stiffness of the periductal collagen, represents a significant risk factor for cancer. However, the mechanistic link between extracellular matrix (ECM) stiffness and increased risk of breast tumour initiation remains unclear. In particular, how increased ECM stiffness might promote genomic damage, leading to the acquisition of transforming mutations, remains to be determined. Here we determine that ECM stiffness induces changes in mammary epithelial cell (MEC) metabolism that drive genomic damage. Using a 3D-culture model, we demonstrate that genome-wide transcriptional changes in response to increased ECM stiffness impair the ability of MECs to remove reactive aldehyde species, resulting in greater accumulation of DNA damage in a RhoA-dependent manner. Together, our results provide a mechanistic link between increased ECM stiffness and the genomic damage required for breast cancer initiation.

developmental biology↗

bioRxiv2020

Quantifying variation in bacterial reproductive fitness with and without antimicrobial resistance: a high-throughput method

BackgroundTo evaluate changes in reproductive fitness of bacteria, e.g., after acquisition of antimicrobial resistance, a low-cost high-throughput method to analyse bacterial growth on agar is desirable for broad usability, including in low-resource settings. MethodIn our bacterial quantitative fitness analysis (baQFA), cultures are spotted in a predefined array on agar plates and photographed sequentially while growing. These time-lapse images are analysed using a purpose-built open source software to derive normalised image intensity values for each culture spot. Subsequently, a Gompertz growth model is fitted to these optical intensity values of each culture spot and fitness is calculated from parameters of the model. For image segmentation validation, we investigated the association between normalised intensity values and colony-forming unit (CFU) counts. To represent a range of clinically important pathogenic bacteria, we used different strains of Enterococcus faecium, Escherichia coli and Staphylococcus aureus, with and without antimicrobial resistance. Relative competitive fitness (RCF) was defined as the mean fitness ratio of two strains growing competitively on one plate. ResultsbaQFA permitted the accurate construction of growth curves from bacteria grown on semisolid agar plates and fitting of Gompertz models: Normalised image intensity values showed a strong association with the total CFU/ml count per spotted culture (p<0.001) for all strains of the three species. Bacterial QFA showed relevant reproductive fitness differences between individual strains, suggesting substantial higher fitness of methicillin-resistant S. aureus JE2 than Cowan (RCF 1.60, p<0.001). Similarly, the vancomycin-resistant E. faecium ST172b showed higher competitive fitness than susceptible E. faecium ST172 (RCF 1.72, p<0.001). ConclusionOur baQFA adaptation allows detection of fitness differences between our bacterial strains, and is likely to be applicable to other bacteria. In the future, baQFA may help to estimate epidemiological antimicrobial persistence or contribute to the prediction of clinical outcomes in severe infections at a low cost.

microbiology↗

bioRxiv2020

Definition of the fibroblast adhesome using multiplexed proximity biotinylation

Integrin adhesion complexes (IACs) bridge the extracellular matrix to the actin cytoskeleton and transduce signals in response to both chemical and mechanical cues. The composition, interactions, stoichiometry and topological organisation of proteins within IACs are not fully understood. To address this gap, we used multiplexed proximity biotinylation (BioID) to generate an in situ, proximity-dependent adhesome in mouse pancreatic fibroblasts. Integration of the interactomes of 16 IAC-associated baits revealed a network of 147 proteins with 361 proximity interactions. Candidates with underappreciated roles in adhesion were identified, in addition to established IAC components. Bioinformatic analysis revealed five clusters of IAC baits that link to common groups of prey, and which therefore may represent functional modules. The five clusters, and their spatial associations, are consistent with current models of IAC interaction networks and stratification. This study provides a resource to examine proximal relationships within IACs at a global level.

cell biology↗

bioRxiv2019

Circadian time series proteomics reveals daily dynamics in cartilage physiology

ObjectivesArticular cartilage undergoes cyclical heavy loading and low load recovery during the 24-hour day/night cycle. We investigated the daily changes of protein abundance in mouse femoral head articular cartilage by performing 24-hour time-series proteomics study.\n\nMethodsTandem mass spectrometry analysis was used to quantify proteins extracted from mouse cartilage. Bioinformatics analysis was performed to quantify rhythmic changes in protein abundance. Primary chondrocytes were isolated and cultured for independent validation of selected rhythmic proteins.\n\nResults145 rhythmic proteins were detected. Among these were key cartilage molecules including CCN2, MATN1, PAI-1 and PLOD1 & 2. Pathway analysis revealed that proteins related to protein synthesis, cytoskeleton and glucose metabolism exhibited time-of-day dependent peaks in their abundance. Meta-analysis of published proteomics datasets from articular cartilage revealed that numerous rhythmic proteins were dysregulated in osteoarthritis and/or ageing.\n\nConclusionsOur circadian proteomics study revealed that articular cartilage is a much more dynamic tissue than previously thought. Chondrocytes exhibit circadian rhythms not only in gene expression but also in protein abundance. Our results clearly call for the consideration of circadian timing in understanding cartilage biology, osteoarthritis pathogenesis, treatment strategies and biomarker detection.

physiology↗

bioRxiv2017

Modeling The Consequences Of Heterogeneity In Microbial Population Dynamics

Chapter 1The rate at which unicellular micro-organisms progress through the cell cycle is a major component of their evolutionary fitness. Measuring fitness phenotypes in a given environment or genetic background forms the basis of most quantitative assays of drug sensitivity or genetic interaction, including genome-wide assays. Growth rate is typically measured in bulk cell populations, inoculated with anything from hundreds to millions of cells sampled from purified, isogenic colonies. High-throughput microscopy reveals that striking levels of growth rate heterogeneity arise between isogenic cell lineages (Levy et al., 2012). Using published Saccharomyces cerevisiae data, I examine the implications for interpreting bulk, population scale growth rate observations, given observed levels of growth rate heterogeneity at the lineage level. I demonstrate that selection between cell lineages with a range of growth rates can give rise to an apparent lag phase at the population level, even in the absence of evidence for a lag phase at the lineage level. My simulations further predict that, given observed levels of heterogeneity, final populations should be dominated by one or a few lineages.\n\nChapter 2In order to validate and further explore the conclusions from Chapter 1, I re-analyzed high-throughput microscopy experiments carried out on Quantitative Fitness Analysis (QFA) S. cerevisiae cultures (Addinall et al., 2011), an approach referred to as QFA. To allow for precise observation of purely clonal lineages including very fast-growing lineages and non-dividing cells, I re-designed an existing image analysis tool for QFA, now available as an open source Python package. Fast-growing outliers in particular influence the extent of the lag phase apparent at the population level, making the precision of growth rate estimation a key ingredient for successfully simulating population observations. QFA data include population observations which I used to validate the population simulations generated from individual lineage data. I explored various options for modeling lineage growth curves and for carrying out growth rate parameter inference, and included the full workflow in an open source R package.\n\nContacthelena.herrmann@postgrad.manchester.ac.uk

microbiology↗

bioRxiv2017

Modelling Competition for Nutrients between MicrobialPopulations Growing on a Solid Agar Surface

MotivationGrowth rate is a major component of the evolutionary fitness of microbial organisms and an excellent surrogate for cell health. In the high-throughput procedures QFA and SGA, microbial cultures are inoculated in an array on solid agar, and growth is measured to obtain quantitative fitness estimates. Neighbouring cultures, which are often different genetic strains, consume nutrients at different rates, creating gradients in nutrient density. We believe that diffusion of nutrients between cultures is affecting growth in these experiments. Current analysis does not account for this; instead, it is assumed that cultures grow independently. I use a network model of nutrient diffusion and nutrient-dependent growth, to correct for competition, to try to improve the accuracy and precision of fitness estimates. I test the model against QFA data from studies on telomere function in Saccharomyces cerevisiae. Ultimately, the model might be used to improve the reliability of screens for genetic interaction and drug sensitivity.\n\nResultsI fit the competition model to a QFA plate from Addinall et al. (2011). Using far fewer parameters (387 vs 1152), the new model fits timecourses with similar closeness to the previous model. Fitness estimates are less precise for the fastest growing strains, but more precise for the majority of strains (36 out of 50). Fitness rankings agree in the positions of the fastest and slowest growing strains, but disagree in the middle positions. In a cross-plate validation experiment, the competition model overestimates timecourses to a similar degree that the previous model underestimates. A different method of fitting is required to find globally optimal solutions which might improve reliability.\n\nAvailability and ImplementationCANS, a Python package developed for the analysis in this paper, is freely available at https://github.com/lwlss/CANS.

microbiology↗

bioRxiv2017

Systematic analysis of the effects of the DNA damage response network in telomere defective budding yeast

Functional telomeres are critically important to eukaryotic genetic stability. Budding yeast is a powerful model organism for genetic analysis and yeast telomeres are maintained by very similar mechanisms to human telomeres. Scores of proteins and pathways are known to affect telomere function. Here, we report a series of related genome-wide genetic interaction screens performed on budding yeast cells with acute or chronic telomere defects. We examined genetic interactions in cells defective in Cdc13 and Stn1, affecting two components of CST, a single stranded DNA (ssDNA) binding complex that binds telomeric DNA. We investigated genetic interactions in cells with defects in Rfa3, affecting the major ssDNA binding protein, RPA, which has overlapping functions with CST at telomeres. We also examined genetic interactions in cells lacking EXO1 or RAD9, affecting different aspects of the DNA damage response in a cdc13-1 background. Comparing fitness profiles across the data sets allows us build up a picture of the specific responses to different types of dysfunctional telomeres. Our results show that there is no universal response to telomere defects. To help others engage with the large volumes of data we make the data available via two interactive web-based tools: Profilyzer and DIXY. Among numerous genetic interactions we found the chk1{Delta} mutation improved fitness of cdc13-1 exo1{Delta} cells more than other checkpoint mutations (ddc1{Delta}, rad9{Delta}, rad17{Delta}, rad24{Delta}), whereas in cdc13-1 cells the effects of all checkpoint mutations were similar. We find that Chk1 stimulates resection at defective telomeres, revealing a new role for Chk1 in the eukaryotic DNA damage response network.

genetics↗