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Chew, B.

Publications and source records attributed to Chew, B..

2 recordsLinked to original sources

A neurocomputational model for intrinsic reward

Standard economic indicators provide an incomplete picture of what we value both as individuals and as a society. Furthermore, canonical macroeconomic measures, such as GDP, do not account for non-market activities (e.g., cooking, childcare) that nevertheless impact well-being. Here, we introduce a computational tool that measures the affective value of experiences (e.g., playing a musical instrument without errors). We go on to validate this tool with neural data, using fMRI to measure neural activity in male and female human subjects performing a reinforcement learning task that incorporated periodic ratings of subjective affective state. Learning performance determined level of payment (i.e., extrinsic reward). Crucially, the task also incorporated a skilled performance component (i.e., intrinsic reward) which did not influence payment. Both extrinsic and intrinsic rewards influenced affective dynamics, and their relative influence could be captured in our computational model. Individuals for whom intrinsic rewards had a greater influence on affective state than extrinsic rewards had greater ventromedial prefrontal cortex (vmPFC) activity for intrinsic than extrinsic rewards. Thus, we show that computational modelling of affective dynamics can index the subjective value of intrinsic relative to extrinsic rewards, a computational hedonometer that reflects both behavior and neural activity that quantifies the affective value of experience. SIGNIFICANCE STATEMENTTraditional economic indicators are increasingly recognized to provide an incomplete picture of what we value as a society. Standard economic approaches struggle to accurately assign values to non-market activities that nevertheless may be intrinsically rewarding, prompting a need for new tools to measure what really matters to individuals. Using a combination of neuroimaging and computational modeling, we show that despite their lack of instrumental value, intrinsic rewards influence subjective affective state and ventromedial prefrontal cortex activity. The relative degree to which extrinsic and intrinsic rewards influence affective state is predictive of their relative impacts on neural activity, confirming the utility of our approach for measuring the affective value of experiences and other non-market activities in individuals.

neuroscience↗

Deep protein methylation profiling by combined chemical and immunoaffinity approaches reveals novel PRMT1 targets

Protein methylation has been implicated in many important biological contexts including signaling, metabolism, and transcriptional control. Despite the importance of this post-translational modification, the global analysis of protein methylation by mass spectrometry-based proteomics has not been extensively studied due to the lack of robust, well-characterized techniques for methyl peptide enrichment. Here, to better investigate protein methylation, we optimized and compared two methods for methyl peptide enrichment: immunoaffinity purification (IAP) and high pH strong cation exchange (SCX). Comparison of these methods revealed that they are largely orthogonal for monomethyl arginine (MMA), suggesting that the usage of both techniques is required to provide a global view of protein methylation. Using both IAP and SCX, we investigated changes in protein methylation downstream of protein arginine methyltransferase 1 (PRMT1) and quantified [~]1,000 methylation sites on 407 proteins. Of these methylation sites, PRMT1 knockdown resulted in significant changes to 97 arginine methylation sites on 59 proteins. In contrast, zero lysine methylation sites were significantly changed upon PRMT1 knockdown. In PRMT1 knockdown cells, 84 MMA sites were either significantly downregulated or upregulated. PRMT1 knockdown also induced significant changes in both asymmetric dimethyl arginine (ADMA) and symmetric dimethyl arginine (SDMA), suggesting that loss of PRMT1 activity allows scavenging of PRMT1 substrates by other PRMTs. Using neutral loss fragmentation ions unique to ADMA and SDMA, we annotated dimethylarginines as either ADMA or SDMA. Through integrative analysis of methyl forms, we identified 12 high confidence PRMT1 substrates, 43 putative PRMT1 substrates, and 17 methylation sites that are scavenged by other non-PRMT1 arginine methyltransferases in the absence of PRMT1 activity. Taken together, our results suggest that deep protein methylation profiling by mass spectrometry requires orthogonal enrichment techniques to identify novel PRMT1 methylation targets and highlight the dynamic interplay between methyltransferases in mammalian cells.

systems biology↗