Biology subjects

Park, J.

Publications and source records attributed to Park, J..

39 records · Page 3Linked to original sources

bioRxiv2017

Excessive ERK-dependent synaptic clustering with enhanced motor learning in the MECP2 duplication syndrome mouse model of autism

Autism-associated genetic mutations may produce altered learning abilities by perturbing the balance between stability and plasticity of synaptic connections in the brain. Here we report an increase in the stabilization of dendritic spines formed during repetitive motor learning in the mouse model of MECP2-duplication syndrome, a high-penetrance form of syndromic autism. This increased stabilization is mediated entirely by spines that form cooperatively in clusters. The number of clusters formed and stabilized predicts the mutants enhanced motor learning and memory phenotype, reminiscent of savant-like behaviors occasionally associated with autism.\n\nThe ERK signaling pathway, which promotes cooperative plasticity between spines, was found to be hyperactive in MECP2-duplication motor cortex specifically after training. Inhibition of ERK signaling normalizes clustered spine stabilization and rescues motor learning behavior in mutants. We conclude that learning-associated dendritic spine clustering stabilized by hyperactive ERK signaling drives abnormal motor learning and memory consolidation in this model of syndromic autism.

neuroscience↗

bioRxiv2016

Label-free high-resolution 3-D imaging of gold nanoparticles inside live cells using optical diffraction tomography

Delivery of gold nanoparticles (GNPs) into live cells has high potentials, ranging from molecular-specific imaging, photodiagnostics, to photothermal therapy. However, studying the long-term dynamics of cells with GNPs using conventional fluorescence techniques suffers from phototoxicity and photobleaching. Here, we present a method for 3-D imaging of GNPs inside live cells exploiting refractive index (RI) as imaging contrast. Employing optical diffraction tomography, 3-D RI tomograms of live cells with GNPs are precisely measured for an extended period with sub-micrometer resolution. The locations and contents of GNPs in live cells are precisely addressed and quantified due to their distinctly high RI values, which was validated by confocal fluorescence imaging of fluorescent dye conjugated GNPs. In addition, we perform quantitative imaging analysis including the segmentations of GNPs in the cytosol, the volume distributions of aggregated GNPs, and the temporal evolution of GNPs contents in HeLa and 4T1 cells.\n\nAbbreviations

biophysics↗

bioRxiv2016

Deep sequencing of natural and experimental populations of Drosophila melanogaster reveals biases in the spectrum of new mutations

Mutations provide the raw material of evolution, and thus our ability to study evolution depends fundamentally on whether we have precise measurements of mutational rates and patterns. Here we explore the rates and patterns of mutations using i) de novo mutations from Drosophila melanogaster mutation accumulation lines and ii) polymorphisms segregating at extremely low frequencies. The first, mutation accumulation (MA) lines, are the product of maintaining flies in tiny populations for many generations, therefore rendering natural selection ineffective and allowing new mutations to accrue in the genome. In addition to generating a novel dataset of sequenced MA lines, we perform a meta-analysis of all published MA studies in D. melanogaster, which allows more precise estimates of mutational patterns across the genome. In the second half of this work, we identify polymorphisms segregating at extremely low frequencies using several publicly available population genomic data sets from natural populations of D. melanogaster. Extremely rare polymorphisms are difficult to detect with high confidence due to the problem of distinguishing them from sequencing error, however a dataset of true rare polymorphisms would allow the quantification of mutational patterns. This is due to the fact that rare polymorphisms, much like de novo mutations, are on average younger and also relatively unaffected by the filter of natural selection. We identify a high quality set of ~70,000 rare polymorphisms, fully validated with resequencing, and use this dataset to measure mutational patterns in the genome. This includes identifying a high rate of multi-nucleotide mutation events at both short (~5bp) and long (~1kb) genomic distances, showing that mutation drives GC content lower in already GC-poor regions, and finding that the context-dependency of the mutation spectrum predicts long-term evolutionary patterns at four-fold synonymous sites. We also show that de novo mutations from independent mutation accumulation experiments display similar patterns of single nucleotide mutation, and match well the patterns of mutation found in natural populations.

evolutionary biology↗