Neurodevelopmental disorders offer insight into synaptic mechanisms. I-J) No effect of glutamatergic-specific sea RNAi (VGlut>CS=38, sea RNAi=40, VGlut>RNAi= 44 animals) p values were estimated with the Kolmogorov–Smirnov test. G-H) The number of sleep bouts is decreased in catecholaminergic-specific sea RNAi animals. C-H) Probability plots of sleep parameters per 24 hours (C, D and G) or 12 hours light/dark periods (E, F and H) from animals depicted in B. Each column is one zeitgeber hour and each row is one animal. B) Heat maps of sleep-wake activity (gray and teal, respectively) in Ddc driver control (Ddc>CS, n=56), sea RNAi control (n=40), and catecholaminergic-specific sea RNAi animals (Ddc>RNAi, n=40) depict activity for each animal averaged across one hour bins. A) Individual hypnograms of Canton S control, sea RNAi controls, and catecholaminergic-specific sea RNAi (Ddc>RNAi) flies (n=2 each) illustrates sleep-wake activity patterns across the 12:12 hour light (zeitgeber times ZT1 to 12) and dark (zeitgeber times ZT12 to 24) periods. Download Figure 2-2, TIF fileĭrosophila SLC25A1 Orthologue Sea is Required in Catecholaminergic Neurons for Sleep. C) Cellular Component gene ontology analysis (GO CC) was performed with the ENRICHR engine using the Wesseling Df(16)A-/+ brain proteome dataset either by itself, or in combination with our 22q11.2 proteome, or with 1500 (1x) or 3000 (2x) randomly generated genes. Random gene list was generated with the engine RandomGeneSetGenerator. B) Cellular Component gene ontology analysis of GO CC generated with the ENRICHR engine using the Wesseling Df(16)A-/+ brain proteome dataset and a similarly sized random mouse gene dataset. The Wesseling Df(16)A-/+ brain proteome and the mouse Mitocarta 2.0 dataset. The Wesseling Df(16)A-/+ brain proteome and our 22q11.2 proteome. A) Venn diagrams depict from top to bottom: a comparison of common hits between our Df(16)A-/+ brain proteome and the Df(16)A-/+ brain proteome reported by Wesseling et al. Download Figure 1-1, TIF fileĬomparative Bioinformatics of the 22q11.2 Proteome and Two Independent Df(16)A-/+ Brain Proteomes. Individual pedigree and collective bioinformatics data can be found in Fig. D) Shows gene ontology terms obtained by pooling into one dataset the proteomes from all pedigrees in B).
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C) Represents GO:CC term tiles overlapping among pedigrees in B).
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B) Comparison of GO:CC terms obtained by genealogical proteomics from three pedigrees. Overlap of GO:CC terms is presented as yellow. Other canvases in A show experiments comparing TMT with two independent SILAC experiments and combinations of TMT, LFQ and SILAC. A) First canvas to the left depicts a comparison for genealogical proteomes obtained in one pedigree by TMT (green) and to Label Free Quantification (LFQ, red). Data are depicted as canvases where every tile is occupied by an individual GO category whose p value significance is depicted by color intensity. A-D) Cellular Component Gene ontologies (GO:CC) obtained using the ENRICHR engine.
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22q11.2 Microdeletion Genealogical Proteome Comparisons Among Pedigrees and Mass Spectrometry Quantitation Strategies.