Biology subjects

Fletcher, G.

Publications and source records attributed to Fletcher, G..

6 recordsLinked to original sources

bioRxiv2020

In-vitro evaluation of Pseudomonas fluorescens antibacterial activity against Listeria spp. isolated from New Zealand horticultural environments

Beneficial bacteria with antibacterial properties are an attractive alternative to chemical-based antibacterial or bactericidal agents. The aim of our study was to source such bacteria from horticultural produce and environments and to explore the mechanisms of their antimicrobial properties. Four strains of Pseudomonas fluorescens were isolated that possessed antibacterial activity against the pathogen Listeria monocytogenes. These strains (PFR46H06, PFR46H07, PFR46H08 and PFR46H09) were tested against L. monocytogenes (n=31), L. seeligeri (n=1) and L. innocua (n=1) isolated from seafood and horticultural sources, and two L. monocytogenes from clinical cases (Scott A and ATCC 49594, n=2). All Listeria strains were inhibited by the three strains PFR46H07, PFR46H08 and PFR46H09 with average zones of inhibition of 14.8, 15.1 and 18.2 mm, respectively, with PFR46H09 having significantly more inhibition than the other two (p<0.05). PFR46H06 showed minimal inhibition towards the L. seeligeri and L. innocua isolates and no inhibition towards most of the L. monocytogenes isolates. In order to investigate the functional protein differences between these strains, all four strains were subjected to whole cell lysate proteomics; data are available via ProteomeXchange with identifier PXD019965. We found significant differences in the peptide profiles and protein summaries between these isolates. The total number of proteins identified was 1781 in PFR46H06, 2030 in PFR46H07, 2228 in PFR46H08 and 1994 in PFR46H09. We paid special attention to secretion system proteins as these play a main role in the defense mechanisms of bacteria, particularly those of the type VI secretion system (T6SS) and found that they varied significantly (p<0.001) among the four isolates. PFR46H06 and PFR46H07 possessed the fewest secretion system structural proteins (12 and 11 respectively) while PFR46H08 and PFR46H09 each had 18. PFR46H09, which showed the greatest antimicrobial effect, had nine T6SS proteins compared to just four in the other three strains.

microbiology↗

bioRxiv2020

Diet-Induced Vitamin D Deficiency Results in Reduced Skeletal Muscle Mitochondrial Respiration in C57BL/6J Mice

Vitamin D deficiency is known to be associated with symptoms of skeletal muscle myopathy including muscle weakness and fatigue. Recently, vitamin D related metabolites have been linked to the maintenance of mitochondrial function within skeletal muscle. However, current evidence is limited to in vitro models and the effects of diet-induced vitamin D deficiency upon skeletal muscle mitochondrial function in vivo have received little attention. In order to examine the role of vitamin D in the maintenance of mitochondrial function in vivo, we utilised an established model of diet-induced vitamin D deficiency in C57BL/6J mice. Mice were fed either a control (2,200 IU/kg) or a vitamin D deplete (0 IU/kg) diet for periods of 1-, 2- and 3-months. Skeletal muscle mitochondrial function and ADP sensitivity were assessed via high-resolution respirometry and mitochondrial protein content via immunoblotting. As a result of 3-month of diet-induced vitamin D deficiency, respiration supported via CI+IIP and ETC were 35% and 37% lower when compared to vitamin D replete mice (P < 0.05). Despite functional alterations, the protein expression of electron transfer chain subunits remained unchanged in response to dietary intervention (P > 0.05). In conclusion, we report that 3-months of diet-induced vitamin D deficiency reduced skeletal muscle mitochondrial function in C57BL/6J mice. Our data, when combined with previous in vitro observations, suggests that vitamin D mediated regulation of mitochondrial function may underlie the exacerbated muscle fatigue and performance deficits observed during vitamin D deficiency.

physiology↗

bioRxiv2019

Mask family proteins ANKHD1 and ANKRD17 regulate YAP nuclear import, stability and phase separation.

The Mask family of multiple ankyrin repeat and KH domain proteins were discovered in Drosophila to promote the activity of the transcriptional coactivator Yorkie (Yki), the sole fly homolog of mammalian YAP (YAP1) and TAZ (WWTR1). The molecular function of Mask, or its mammalian homologs Mask1 (ANKHD1) and Mask2 (ANKRD17), remains unclear. Mask family proteins contain two Ankyrin repeat domains that bind Yki/YAP as well as a conserved nuclear localisation sequence (NLS) and nuclear export sequence (NES), suggesting a role in nucleo-cytoplasmic transport. Here we show that Mask acts to promote nuclear import of Yki, and that addition of an ectopic NLS to Yki is sufficient to bypass the requirement for Mask in Yki-driven tissue growth. Mammalian Mask1/2 proteins also promote nuclear import of YAP, as well as stabilising YAP and driving colloidal phase separation into large liquid droplets. Mask1/2 and YAP normally colocalise in a granular fashion in both nucleus and cytoplasm, and are co-regulated during mechanotransduction. Our results suggest that Mask family proteins promote YAP nuclear import and phase separation to regulate YAP stability and transcriptional activity.

developmental biology↗

bioRxiv2019

Apical transport of Crumbs maintains epithelial cell polarity

Crumbs (Crb in Drosophila; CRB1-3 in mammals) is a transmembrane determinant of epithelial cell polarity and a regulator of Hippo signalling. Crb is normally localized to apical cell-cell contacts, just above adherens junctions, but how apical trafficking of Crb is regulated in epithelial cells remains unclear. We use the Drosophila follicular epithelium to demonstrate that polarized trafficking of Crb is mediated by transport along microtubules by the motor protein Dynein and along actin filaments by the motor protein Myosin-V (MyoV). Blocking transport of Crb-containing vesicles by Dynein or MyoV leads to accumulation of Crb within Rab11 endosomes, rather than apical delivery. The final steps of Crb delivery and stabilisation at the plasma membrane requires the exocyst complex and three apical FERM domain proteins - Merlin, Moesin and Expanded - whose simultaneous loss disrupts apical localization of Crb. Accordingly, a knock-in deletion of the Crb FERM-binding motif (FBM) also impairs apical localization. Finally, overexpression of Crb challenges this system, creating a sensitized background to identify components involved in cytoskeletal polarization, apical membrane trafficking and stabilisation of Crb at the apical domain.

developmental biology↗

bioRxiv2018

Multicenter validation of a machine learning algorithm for 48 hour all-cause mortality prediction

PurposeThis study evaluates a machine-learning-based mortality prediction tool.\n\nMaterials and MethodsWe conducted a retrospective study with data drawn from three academic health centers. Inpatients of at least 18 years of age and with at least one observation of each vital sign were included. Predictions were made at 12, 24, and 48 hours before death. Models fit to training data from each institution were evaluated on hold-out test data from the same institution and data from the remaining institutions. Predictions were compared to those of qSOFA and MEWS using area under the receiver operating characteristic curve (AUROC).\n\nResultsFor training and testing on data from a single institution, machine learning predictions averaged AUROCs of 0.97, 0.96, and 0.95 across institutional test sets for 12-, 24-, and 48-hour predictions, respectively. When trained and tested on data from different hospitals, the algorithm achieved AUROC up to 0.95, 0.93, and 0.91, for 12-, 24-, and 48-hour predictions, respectively. MEWS and qSOFA had average 48-hour AUROCs of 0.86 and 0.82, respectively.\n\nConclusionThis algorithm may help identify patients in need of increased levels of clinical care.

bioinformatics↗

bioRxiv2018

Multicenter validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

ObjectivesWe validate a machine learning-based sepsis prediction algorithm (InSight) for detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customization to site-specific data using transfer learning, and generalizability to new settings.\n\nDesignA machine learning algorithm with gradient tree boosting. Features for prediction were created from combinations of only six vital sign measurements and their changes over time.\n\nSettingA mixed-ward retrospective data set from the University of California, San Francisco (UCSF) Medical Center (San Francisco, CA) as the primary source, an intensive care unit data set from the Beth Israel Deaconess Medical Center (Boston, MA) as a transfer learning source, and four additional institutions datasets to evaluate generalizability.\n\nParticipants684,443 total encounters, with 90,353 encounters from June 2011 to March 2016 at UCSF.\n\nInterventionsnone\n\nPrimary and secondary outcome measuresArea under the receiver operating characteristic curve (AUROC) for detection and prediction of sepsis, severe sepsis, and septic shock.\n\nResultsFor detection of sepsis and severe sepsis, InSight achieves an area under the receiver operating characteristic (AUROC) curve of 0.92 (95% CI 0.90 - 0.93) and 0.87 (95% CI 0.86 - 0.88), respectively. Four hours before onset, InSight predicts septic shock with an AUROC of 0.96 (95% CI 0.94 -0.98), and severe sepsis with an AUROC of 0.85 (95% CI 0.79 - 0.91).\n\nConclusionsInSight outperforms existing sepsis scoring systems in identifying and predicting sepsis, severe sepsis, and septic shock. This is the first sepsis screening system to exceed an AUROC of 0.90 using only vital sign inputs. InSight is robust to missing data, can be customized to novel hospital data using a small fraction of site data, and retained strong discrimination across all institutions.\n\nStrengths and limitations of this studyO_LIMachine learning is applied to the detection and prediction of three separate sepsis standards in the emergency department, general ward and intensive care settings.\nC_LIO_LIOnly six commonly measured vital signs are used as input for the algorithm.\nC_LIO_LIThe algorithm is robust to randomly missing data.\nC_LIO_LITransfer learning successfully leverages large dataset information to a target dataset.\nC_LIO_LIRetrospective nature of the study does not predict clinician reaction to information.\nC_LI

bioinformatics↗