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Deyneko, I. V.

Publications and source records attributed to Deyneko, I. V..

2 recordsLinked to original sources

Comparing synaptic proteomes across seven mouse models for autism reveals molecular subtypes and deficits in Rho GTPase signaling

Impaired synaptic function is a common phenotype in animal models for autism spectrum disorder (ASD), and ASD risk genes are enriched for synaptic function. Here we leverage the availability of multiple ASD mouse models exhibiting synaptic deficits and behavioral correlates of ASD and use quantitative mass spectrometry with isobaric tandem mass tagging (TMT) to compare the hippocampal synaptic proteomes from 7 mouse models. We identified common altered cellular and molecular pathways at the synapse, including changes in Rho family small GTPase signaling, suggesting that it may be a point of convergence in ASD. Comparative analyses also revealed clusters of synaptic profiles, with similarities observed among models for Fragile X syndrome (Fmr1 knockout), PTEN hamartoma tumor syndrome (Pten haploinsufficiency), and the BTBR+ model of idiopathic ASD. Opposing changes were found in models for cortical dysplasia focal epilepsy syndrome (Cntnap2 knockout), Phelan McDermid syndrome (Shank3 InsG3680), Timothy syndrome (Cacna1c G406R), and ANKS1B syndrome (Anks1b haploinsufficiency), which were similar to each other. We propose that these clusters of synaptic profiles form the basis for molecular subtypes that explain genetic heterogeneity in ASD despite a common clinical diagnosis. Drawn from an internally controlled survey of the synaptic proteome across animal models, our findings support the notion that synaptic dysfunction in the hippocampus is a shared mechanism of disease in ASD, and that Rho GTPase signaling may be an important pathway leading to disease phenotypes in autism.

neuroscience

Dataset-specific thresholds significantly improve detection of low transcribed regulatory genes in polysome profiling experiments

MotivationPolysome profiling is novel, and yet has proved to be an effective approach to detect mRNAs with differential ribosomal load and explore the regulatory mechanisms driving efficient translation. Genes encoding regulatory proteins, having a great influence of the organism, usually reveal moderate to low transcriptional levels, compared, for example, to genes of house-keeping machinery. This complicates the reliable detection of such genes in the presence of technical and/or biological noise. ResultsIn this work we investigate how cleaning of polysome profiling data on Arabidopsis thaliana influences the ability to detect genes with low level of total mRNA, but with a highly differential ribosomal load, i.e. genes translationally active. Suggested data modelling approach to identify a background level of mRNA counts individually for each dataset, shows higher power in detection of low transcribed genes, compared to the use of thresholds for the minimal required mRNA counts or the use of raw data. The significant increase in detected number of regulation-related genes was demonstrated. The described approach is applicable to a wide variety of RNA-seq data. All identified and classified mRNAs with high and low translation status are made available in supplementary material.

plant biology