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Park, M.

Publications and source records attributed to Park, M..

113 records · Page 7Linked to original sources

Molecular map of GNAO1-related disease phenotypes and reactions to treatment

The GNAO1 gene codes for the most commonly expressed G protein in the central nervous system. Pathogenic GNAO1 variants result in early-onset neurological phenotypes, sometimes with distinct epilepsy or movement disorder, and sometimes with both mani-festations in the same patient. The existing extensive knowledge about G-protein coupled receptor (GPCR) signaling provides the input needed to describe quantitatively how mutations modify the GPCR signal. This in turn allows rational interpretation of distinct phenotypes arising from mutations in GNAO1. In this work we outline a model that enables understanding of clinical phenotypes at a molecular level. The mutations affecting the catalytic pocket of GNAO1, we show, result in the improper withdrawal of the signal, and give rise to epileptic phenotypes (EPs). The converse is not true - some pure EPs are caused by mutations with no obvious impact on catalysis. Mutations close to the interface with GNAO1s downstream effector block the signal propagation in that direction, and manifest as a movement disorder phenotype without epilepsy. Quantifying the reported reaction to therapy highlights the tendency of the latter group to be unresponsive to the therapies currently in use. We argue, however, that the majority of clinically described mutations can impact several aspects of GNAO1 function at once, resulting in the continuum of phenotypes observed in patients. The reasoning based on GNAO1 signaling model provides a precision medicine paradigm to aid clinicians in selecting effective categories of medication, and in addition, can suggest pragmatic targets for future therapies.

molecular biology↗

Dethroning the Fano Factor: a flexible, model-based approach to partitioning neural variability

Neurons in many brain areas exhibit high trial-to-trial variability, with spike counts that are over-dispersed relative to a Poisson distribution. Recent work (Goris et al., 2014) has proposed to explain this variability in terms of a multiplicative interaction between a stochastic gain variable and a stimulus-dependent Poisson firing rate, which produces quadratic relationships between spike count mean and variance. Here we examine this quadratic assumption and propose a more flexible family of models that can account for a more diverse set of mean-variance relationships. Our model contains additive Gaussian noise that is transformed nonlinearly to produce a Poisson spike rate. Different choices of the nonlinear function can give rise to qualitatively different mean-variance relationships, ranging from sub-linear to linear to multiplicative. Intriguingly, a rectified squaring nonlinearity produces a linear mean-variance function, corresponding to responses with constant Fano factor. We describe a computationally efficient method for fitting this model to data, and demonstrate that a majority of neurons in a V1 population are better described by a model with non-quadratic relationship between mean and variance. Lastly, we develop an application to Bayesian adaptive stimulus selection in closed-loop neurophysiology experiments, which shows that accounting for overdispersion can lead to dramatic improvements in adaptive tuning curve estimation.

neuroscience↗

Combined social and spatial coding in a descending projection from the prefrontal cortex

Social interactions are crucial to the survival and well-being of all mammals, including humans. Although the prelimbic cortex (PL, part of medial prefrontal cortex) has been implicated in social behavior, it is not clear which neurons are relevant, nor how they contribute. We found that the PL contains anatomically and molecularly distinct subpopulations of neurons that target 3 downstream regions that have been implicated in social behavior: the nucleus accumbens (NAc), the amygdala, and the ventral tegmental area. Activation of NAc-projecting PL neurons (PL-NAc), but not the other subpopulations, decreased preference for a social target, suggesting an unique contribution of this population to social behavior. To determine what information PL-NAc neurons convey, we recorded selectively from them, and found that individual neurons were active during social investigation, but only in specific spatial locations. Spatially-specific inhibition of these neurons prevented the formation of a social-spatial association at the inhibited location. In contrast, spatially nonspecific inhibition did not affect social behavior. Thus, the unexpected combination of social and spatial information within the PL-NAc population appears to support socially motivated behavior by enabling the formation of social-spatial associations.

neuroscience↗

Multiple reference genome sequences of hot pepper reveal the massive evolution of plant disease resistance genes by retroduplication

Transposable elements (TEs) provide major evolutionary forces leading to new genome structure and species diversification. However, the role of TEs in the expansion of disease resistance gene families has been unexplored in plants. Here, we report high-quality de novo genomes for two peppers (Capsicum baccatum and C. chinense) and an improved reference genome (C. annuum). Dynamic genome rearrangements involving translocations among chromosome 3, 5 and 9 were detected in comparison between C. baccatum and the two other peppers. The amplification of athila LTR-retrotransposons, members of the gypsy superfamily, led to genome expansion in C. baccatum. In-depth genome-wide comparison of genes and repeats unveiled that the copy numbers of NLRs were greatly increased by LTR-retrotransposon-mediated retroduplication. Moreover, retroduplicated NLRs exhibited great abundance across the angiosperms, with most cases lineage-specific and thus recent events. Our study revealed that retroduplication has played key roles in the emergence of new disease-resistance genes in plants.

plant biology↗

CrosstalkNet: mining large-scale bipartite co-expression networks to characterize epi-stroma crosstalk

BackgroundOver the last several years, we have witnessed the metamorphosis of network biology from being a mere representation of molecular interactions to models enabling inference of complex biological processes. Networks provide promising tools to elucidate intercellular interactions that contribute to the functioning of key biological pathways in a cell. However, the exploration of these large-scale networks remains a challenge due to their high-dimensionality.\n\nResultsCrosstalkNet is a user friendly, web-based network visualization tool to retrieve and mine interactions in large-scale bipartite co-expression networks. In this study, we discuss the use of gene co-expression networks to explore the rewiring of interactions between tumor epithelial and stromal cells. We show how CrosstalkNet can be used to efficiently visualize, mine, and interpret large co-expression networks representing the crosstalk occurring between the tumour and its microenvironment.\n\nConclusionCrosstalkNet serves as a tool to assist biologists and clinicians in exploring complex, large interaction graphs to obtain insights into the biological processes that govern the tumor epithelial-stromal crosstalk. A comprehensive tutorial along with case studies are provided with the application.\n\nAvailabilityThe web-based application is available at the following location: http://epistroma.pmgenomics.ca/app/. The code is open-source and freely available from http://github.com/bhklab/EpiStroma-webapp.\n\nContactbhaibeka@uhnresearch.ca

systems biology↗