Biology subjects

Hunter, M.

Publications and source records attributed to Hunter, M..

21 records · Page 2Linked to original sources

bioRxiv2020

Cell state diversity promotes metastasis through heterotypic cluster formation in melanoma

In melanoma, transcriptional profiling has revealed multiple co-existing cell states, including proliferative versus invasive sub-populations that have been posited to represent a "go or grow" tradeoff. Both of these populations are maintained in tumors, but how they physically interact to promote metastasis is unknown. We demonstrate that these subpopulations form spatially structured heterotypic clusters that cooperate in the seeding of metastasis. We unexpectedly found that INV cells were tightly adherent to each other, and formed clusters with a rim of PRO cells. Intravital imaging demonstrated cooperation between these populations, in which the INV cells facilitated the spread of less metastatic PRO cells. We identified the TFAP2 neural crest transcription factor as a master regulator of both clustering and the PRO/INV states. Our data suggest a framework for the co-existence of these two divergent cell populations, in which differing cell states form heterotypic clusters that promote metastasis via cell-cell cooperation.

cancer biology↗

bioRxiv2019

Distinctive airway microbial ecology of smokers and asthmatics

BackgroundNormal airway microbial communities play a central role in respiratory health but are poorly characterized. Cigarette smoking is the dominant global environmental influence on lung function, and asthma has become the most prevalent chronic respiratory disease worldwide. Both conditions have major microbial components that are also poorly defined. MethodsWe investigated airway bacterial communities in a general population sample of 529 Australian adults. Posterior oropharyngeal swabs were analysed by sequencing of the 16S rRNA and methionine aminopeptidase genes. The microbiota were characterised according to their prevalence, abundance, and network memberships. FindingsMicrobial communities were similar across the population and were strongly organized into co-abundance networks. Smoking associated with diversity loss, negative effects on abundant taxa, profound alterations to network structure and expansion of Streptococcus spp. By contrast, the asthmatic microbiota were selectively affected by an increase in Neisseria spp. and by reduced numbers of low abundance but prevalent organisms. InterpretationOur study shows healthy airway microbiota are contained within a highly structured ecosystem, indicating balanced relationships between the microbiome and human host factors. The marked abnormalities in smokers may be pathogenic for chronic obstructive pulmonary disease (COPD) and lung cancer. The narrow spectrum of abnormalities in asthmatics encourages investigation of damaging and protective effects of specific bacteria. FundingThe study was funded by the Asmarley Trust and a Wellcome Senior Investigator Award to WOCC and MFM (P46009). The Busselton Healthy Ageing Study is supported by the Government of Western Australia (Office of Science, Department of Health) the City of Busselton, and private donations.

ecology↗

bioRxiv2017

An analysis of EEG networks and their correlation with cognitive impairment in preschool children with epilepsy

Objective: Cognitive impairment (CI) is common in children with epilepsy and can have devastating effects on their quality of life and that of their family. Early identification of CI is a priority to improve outcomes, but the current gold standard of detection with psychometric assessment is resource intensive and not always available. This paper proposes a novel technique of network analysis using routine clinical electroencephalography (EEG) to help identify CI in children with early-onset epilepsy (CWEOE) (0-5 y.o.).\n\nMethods: We analyzed functional networks from routinely acquired EEGs of 51 newly diagnosed CWEOE from a prospective population-based study. Combinations of connectivity metrics (e.g. phase-slope index (PSI)) with sub-network analysis (e.g. cluster-span threshold (CST)) identified significant correlations between network properties and cognition scores via rank correlation analysis with Kendalls{tau} . Predictive properties were investigated using a 5-fold cross-validated K-Nearest Neighbor classification model with normal cognition, mild/moderate CI and severe CI classes.\n\nResults: Phase-dependent connectivity metrics had higher sensitivity to cognition scores, with sub-networks identifying significant functional network changes over a broad range of spectral frequencies. Approximately 70.5% of all children were appropriately classified as normal cognition, mild/moderate CI or severe CI using CST network features. CST classification predicted CI classes 55% better than chance, and reduced misclassification penalties by half.\n\nConclusions: CI in CWEOE can be detected with sensitivity at 85% (with respect to identifying either mild/moderate or severe CI) and specificity of 84%, by EEG network analysis.\n\nSignificance: This study outlines a data-driven methodology for identifying candidate biomarkers of CI in CWEOE from network features. Following additional replication, the proposed method and its use of routinely acquired EEG forms an attractive proposition for supporting clinical assessment of CI.

neuroscience↗