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Paul, E. M.

Publications and source records attributed to Paul, E. M..

2 recordsLinked to original sources

Neuropeptide-dependent spike time precision and plasticity in circadian output neurons

Circadian rhythms influence various physiological and behavioral processes such as sleep-wake cycles, hormone secretion, and metabolism. In Drosophila, an important set of circadian output neurons are called pars intercerebralis (PI) neurons, which receive input from specific clock neurons called DN1. These DN1 neurons can further be subdivided into functionally and anatomically distinctive anterior (DN1a) and posterior (DN1p) clusters. The neuropeptide diuretic hormones 31 (Dh31) and 44 (Dh44) are the insect neuropeptides known to activate PI neurons to control activity rhythms. However, the neurophysiological basis of how Dh31 and Dh44 affect circadian clock neural coding mechanisms underlying sleep in Drosophila is not well understood. Here, we identify Dh31/Dh44-dependent spike time precision and plasticity in PI neurons. We first find that a mixture of Dh31 and Dh44 enhanced the firing of PI neurons, compared to the application of Dh31 alone and Dh44 alone. We next find that the application of synthesized Dh31 and Dh44 affects membrane potential dynamics of PI neurons in the precise timing of the neuronal firing through their synergistic interaction, possibly mediated by calcium-activated potassium channel conductance. Further, we characterize that Dh31/Dh44 enhances postsynaptic potentials in PI neurons. Together, these results suggest multiplexed neuropeptide-dependent spike time precision and plasticity as circadian clock neural coding mechanisms underlying sleep in Drosophila.

neuroscience↗

Diagnosis of Osteoarthritis Subtypes with Blood Biomarkers

ObjectiveTo identity osteoarthritis(OA) subtypes with gene expression of peripheral blood mononuclear cells.\n\nMethodsGene expression data (GSE48556) of Genetics osteoARthritis and Progression (GARP) study was downloaded from Gene Expression Omnibus. Principal component analysis and unsupervised clustering were analyzed to identify subtypes of OA and compare major KEGG pathways and cell type enrichment using GSEA and xCell. Classification of subtypes were explored by the utilization of support vector machine.\n\nResultsUnsupervised clustering identified two distinct OA subtypes: Group A comprised of 60 patients (56.6%) and Group B had 46 patients (43.3%). A classifier including nine genes and CD4+ T cell and Regulatory T cell flow cytometry could accurately distinguish patients from each group (area under the curve of 0.99 with gene expression). Group A is typical degenerative OA with glycosaminoglycan biosynthesis and apoptosis. Group B is related to Graft versus host disease and antigen processing and presentation, which indicated OA has a new type of \"Antigen processing and presentation\" similarly as that of RA.\n\nConclusionOA can be clearly classified into two distinguished subtypes with blood transcriptome, which have important significance on the development of precise OA therapeutics.

bioinformatics↗