Search bioRxiv⌕ Search

Biology subjects

Yoong, M.

Publications and source records attributed to Yoong, M..

2 recordsLinked to original sources

Arabidopsis GHL1 is an orthologue of Hedgehog acyl transferase but likely catalyses GPI-anchor remodelling rather than peptide acylation.

The GUP1/HHAT family of MBOAT proteins have been implicated in GPI-anchor acyl-chain remodelling in fungi and secreted peptide acylation in eumetazoans, but whether these activities are distinct or GUP1/HHAT proteins are bifunctional has not been addressed. We show that the GUP1/HHAT family form a distinct orthologous clade within eukaryotes with structural homology, suggesting a single evolutionary event for their origin and common mode of action. Arabidopsis plants homozygous for loss of GUP1/HHAT-like activity cannot be recovered suggesting that loss is lethal, and further examination suggests that there are severe effects on transmission through the male gamete. Recent work suggests that rice GUP1-like BC16 is a GPI-anchor acyl-chain remodelase, but a potential role for non-eumetazoan GUP1 and HHAT-like proteins in secreted peptide acylation has not been assessed. By reconstituting HHAT peptide acyl transferase activity towards Hedgehog-like peptides in plants we demonstrate that Arabidopsis GUP1/HHAT-like proteins likely do not possess appreciable HHAT-like peptide acyltransferase activity. However, through this work we provide a novel means for cell surface display of proteins in eukaryotic systems, demonstrating that the minimal acyl-acceptor peptide sequence of Hedgehog morphogens, when expressed alongside HHAT, allows for immobilisation of proteins in the outer leaflet of the plasma membrane via their N-terminus, rather than the C-terminus as is the case for traditional GPI-anchor mediated cell surface display.

plant biology↗

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↗