Biology subjects

Brown, L. M.

Publications and source records attributed to Brown, L. M..

4 recordsLinked to original sources

bioRxiv2021

The Impact of Body Mass Index on Physical Activity and Cardiac Workload

BackgroundHaving an abnormal body mass index (BMI) adversely affects cardiac workload and level of physical activity. ObjectiveTo examine the relationship between cardiac workload, physical activity, Sex, and BMI. MethodsThe number of steps taken per day (Steps) and minutes per week spent in targeted heart rate zones were collected from primarily first and second year university students (n = 1,801; 62% female) over a standard, 15-week long semester. Other data collected included BMI, Sex, Age, and Class Standing. Sex differences in BMI, Steps, and training heart rate zone (heart rates above 50% of max) minutes (THR) were evaluated, correlations between the study parameters were analyzed, and one-way ANOVA was used to test between competing models. The values p < .05 were considered statistically significant. ResultsStatistically significant (p < .05) differences between males and females were found for Steps, THR, and BMI. Males were more physically active but spent 18% less time with heart rates above 50% of max. Students who had abnormal BMI values, both low and high, experienced greater cardiac workload (p < .05), even though they were found to be less physically active (p < .05). ConclusionOur study revealed that university students with abnormal BMI values experienced greater cardiac workload, even though they are less physically active. Thus, physical fitness and healthy lifestyle interventions should also include underweight students in addition to students who are overweight or obese.

physiology↗

bioRxiv2021

ALLSorts: a RNA-Seq classifier for B-Cell Acute Lymphoblastic Leukemia.

B-cell acute lymphoblastic leukemia (B-ALL) is the most common childhood cancer. Subtypes within B-ALL are distinguished by characteristic structural variants and mutations, which in some instances strongly correlate with responses to treatment. The World Health Organisation (WHO) recognises seven distinct classifications, or subtypes, as of 2016. However, recent studies have demonstrated that B-ALL can be segmented into 23 subtypes based on a combination of genomic features and gene expression profiles. A method to identify a patients subtype would have clear clinical utility. Despite this, no publically available classification methods using RNA-Seq exist for this purpose. Here we present ALLSorts: a publicly available method that uses RNA-Seq data to classify B-ALL samples to 18 known subtypes and five meta-subtypes. ALLSorts is the result of a hierarchical supervised machine learning algorithm applied to a training set of 1223 B-ALL samples aggregated from multiple cohorts. Validation revealed that ALLSorts can accurately attribute samples to subtypes and can attribute multiple subtypes to a sample. Furthermore, when applied to both paediatric and adult cohorts, ALLSorts was able to classify previously undefined samples into subtypes. ALLSorts is available and documented on GitHub (https://github.com/Oshlack/AllSorts/). Key PointsO_LIALLSorts is a gene expression classifier for B-cell acute lymphoblastic leukemia, which predicts 18 distinct genomic subtypes - including those designated by the World Health Organisation (WHO) and provisional entities. C_LIO_LITrained and validated on over 2300 B-ALL samples, representing each subtype and a variety of clinical features. C_LIO_LICorrectly identified subtypes in 91% of cases in a held-out dataset and between 82-93% across a newly combined cohort of paediatric and adult samples. C_LIO_LIALLSorts assigned subtypes to samples with previously unknown driver events. C_LI ALLsorts is an accurate, comprehensive and freely available classification tool that distinguishes subtypes of B-cell acute lymphoblastic leukemia from RNA-sequencing.

bioinformatics↗

bioRxiv2020

Pseudomonas aeruginosa PA14 biofilms produce R-bodies, extendable protein polymers with roles in host colonization and virulence

Pseudomonas aeruginosa PA14, an opportunistic pathogen of diverse hosts, contains genes with the potential to confer production of R-bodies (i.e., a "reb cluster"). R-bodies are large, extendable protein polymers best known for their role in killing of paramecia by the bacterium Caedibacter taeniospiralis, and genes in the reb cluster have been implicated in PA14 virulence. Here, we present evidence that PA14 expresses reb cluster genes during colonization of plant and nematode hosts. We identify products of the reb cluster that are R-body-associated and that control stochastic expression of R-body structural genes. We also show that R-body production is required for full virulence in nematodes. Analyses of nematode ribosome content and immune response indicate that R-bodies act via a mechanism involving ribosome cleavage and translational inhibition. These observations provide insight into the biology of R-body production and its consequences during P. aeruginosa infection.

microbiology↗

bioRxiv2020

Formation of biomolecular condensates in bacteria by tuning protein electrostatics

Biomolecular condensates provide a strategy for cellular organization without a physical membrane barrier while allowing for dynamic, responsive organization of the cell. To date, very few biomolecular condensates have been identified in prokaryotes, presenting an obstacle to engineering these compartments in bacteria. As a novel strategy for bacterial compartmentalization, protein supercharging and complex coacervation were employed to engineer liquid-like condensates in E. coli. A simple model for the phase separation of supercharged proteins was developed and used to predict intracellular condensate formation. Herein, we demonstrate that GFP-dense condensates formed by expressing GFP variants of sufficient charge in cells are dynamic and enrich specific nucleic acid and protein components. This study provides a fundamental characterization of intracellular phase separation in E. coli driven by protein supercharging and highlights future utility in designing functional synthetic membraneless organelles.

synthetic biology↗