Biology subjects

Murphy, R. A.

Publications and source records attributed to Murphy, R. A..

4 recordsLinked to original sources

bioRxiv2022

Synergistic Activity of Repurposed Peptide Drug Glatiramer Acetate with Tobramycin Against Cystic Fibrosis Pseudomonas aeruginosa

Pseudomonas aeruginosa is the most common pathogen infecting the lungs of people with cystic fibrosis (CF), causing both acute and chronic infections. Intrinsic and acquired antibiotic resistance, coupled with the physical barriers resulting from desiccated CF sputum, allow P. aeruginosa to colonise and persist in spite of antibiotic treatment. As well as the specific difficulties in eradicating P. aeruginosa from CF lungs, P. aeruginosa is also subject to the wider, global issue of antimicrobial resistance. Glatiramer acetate (GA) is a peptide drug, used in the treatment of multiple sclerosis (MS), which has been shown to have moderate anti-pseudomonal activity. Other antimicrobial peptides (AMPs) have been shown to be antibiotic resistance breakers; potentiating the activities of antibiotics when given in combination restoring and/or enhancing antibiotic efficacy. Growth, viability, minimum inhibitory concentration (MIC)-determination and synergy analysis showed that GA improved the efficacy of TOB against reference strains of P. aeruginosa, reducing TOB MICs and synergising with the aminoglycoside. This was also the case for clinical strains from people with CF. GA significantly reduced the concentration of TOB required to inhibit 50% (MIC50) of viable cells (from 1.69 [95%CI 0.26-8.97] to 0.62 [95%CI 0.15-3.94] mg/L, p=0.002) and inhibit 90% (MIC90) (from 7.00 [95%CI 1.18-26.50] to 2.20 [95%CI 0.99-15.03] mg/L, p=0.001) compared with TOB-only. Investigating mechanisms of GA activity showed that GA resulted in significant disruption of outer membranes, depolarisation of cytoplasmic membranes and permeabilisation of P. aeruginosa and was the only agent tested (including cationic AMPs) to significantly affect all three.

microbiology↗

bioRxiv2021

Uncertainty avoidance versus conditioned reinforcement: exploring paradoxical choice in rats

Paying a cost to reduce uncertainty can be adaptive, because better informed decision-makers can align their preferences to opportunities. However, birds and mammals display an appetite for information that they cannot use to functionally alter behaviour or its outcomes. We explore two putative motivational mechanisms for this paradoxical behaviour. The information hypothesis, proposes that reducing uncertainty is reinforcing per se, consistent with the concept of curiosity: a motivation to know, in the absence of instrumental benefits. In contrast, the conditioned reinforcement hypothesis sees information-seeking as a consequence of asymmetries in secondarily acquired reinforcement: responding increments caused by post-choice stimuli announcing positive outcomes (S+) exceed decrements caused by stimuli signalling absence of reward (S-). We contrast these hypotheses experimentally. Rats chose between two equally profitable options delivering food probabilistically after a fixed delay. In the informative option (Info), the outcome (food/no food) was signalled immediately after choice, whereas in the non-informative option (NoInfo) outcomes were uncertain until the delay lapsed. Subjects preferred Info when (1) outcomes were signalled by salient auditory cues, (2) only the absence of reward was signalled, and (3) only reward was signalled, though acquisition was slower when rewards were not explicitly signalled. Our results show that a salient good news signal is not required as a conditioned reinforcer to generate paradoxical preferences. Terminal preferences support the information hypothesis but the slower acquisition of Info preference when S+ is not present is consistent with the conditioning account. We conclude that both uncertainty reduction and conditioned reinforcement influence choice.

animal behavior and cognition↗

bioRxiv2021

statSuma: automated selection and performance of statistical comparisons for microbiome studies

There is a reproducibility crisis in scientific studies. Some of these crises arise from incorrect application of statistical tests to data that follow inappropriate distributions, have inconsistent equivariance, or have very small sample sizes. As determining which test is most appropriate for all data in a multicategorical study (such as comparing taxa between sites in microbiome studies), we present statsSuma, an interactive Python notebook (which can be run from any desktop computer using the Google Colaboratory web service) and does not require a user to have any programming experience. This software assesses underlying data structures in a given dataset to advise what pairwise or listwise statistical procedure would be best suited for all data. As some users may be interested in further mining specific trends, statSuma performs 5 different two-tailed pairwise tests (Students t-test, Welchs t-test, Mann-Whitney U-test, Brunner-Munzel test, and a pairwise Kruskal-Wallis H-test) and advises the best test for each comparison. This software also advises whether ANOVA or a multicategorical Kruskal-Wallis H-test is most appropriate for a given dataset and performs both procedures. A data distribution-vs-Gaussian distribution plot is produced for each taxon at each site and a variance plot between all combinations of 2 taxa at each site are produced so Gaussian tests and variance tests can be visually confirmed alongside associated statistical determinants.

microbiology↗

bioRxiv2021

uniForest: an unsupervised machine learning technique to detect outliers and restrict variance in microbiome studies

Isolation Forests is an unsupervised machine learning technique for detecting outliers in continuous datasets that does not require an underlying equivariant or Gaussian distribution and is suitable for use on small datasets. While this procedure is widely used across quantitative fields, to our knowledge, this is the first attempt to solely assess its use for microbiome datasets. Here we present uniForest, an interactive Python notebook (which can be run from any desktop computer using the Google Colaboratory web service) for the processing of microbiome outliers. We used uniForest to apply Isolation Forests to the Healthy Human Microbiome project dataset and imputed outliers with the mean of the remaining inliers to maintain sample size and assessed its prowess in variance reduction in both community structure and derived ecological statistics (-diversity). We also assessed its functionality in anatomical site differentiation (pre- and postprocessing) using principal component analysis, dissimilarity matrices, and ANOSIM. We observed a minimum variance reduction of 81.17% across the entire dataset and in alpha diversity at the Phylum level. Application of Isolation Forests also separated the dataset to an extremely high specificity, reducing variance within taxa samples by a minimum of 81.33%. It is evident that Isolation Forests are a potent tool in restricting the effect of variance in microbiome analysis and has potential for broad application in studies where high levels of microbiome variance is expected. This software allows for clean analyses of otherwise noisy datasets.

microbiology↗