Agmatine is the product of arginine decarboxylation and can be hydrolyzed by agmatinase to putrescine, the precursor for biosynthesis of higher polyamines, spermidine, and spermine. Besides being an intermediate in polyamine metabolism, recent findings indicate that agmatine may play important regulatory roles in mammals. Agmatinase is a binuclear manganese metalloenzyme and belongs to the ureohydrolase superfamily that includes arginase, formiminoglutamase, and proclavaminate amidinohydrolase. Compared with a wealth of structural information available for arginases, no threedimensional structure of agmatinase has been reported. Agmatinase from Deinococcus radiodurans, a 304-residue protein, shows ϳ33% of sequence identity to human mitochondrial agmatinase. Here we report the crystal structure of D. radiodurans agmatinase in Mn 2؉ -free, Mn 2؉ -bound, and Mn 2؉ -inhibitor-bound forms, representing the first structure of agmatinase. It reveals the conservation as well as variation in folding, oligomerization, and the active site of the ureohydrolase superfamily. D. radiodurans agmatinase exists as a compact homohexamer of 32 symmetry. Its binuclear manganese cluster is highly similar but not identical to the clusters of arginase and proclavaminate amidinohydrolase. The structure of the inhibited complex reveals that inhibition by 1,6-diaminohexane arises from the displacement of the metal-bridging water.
Heterogeneity in the etiopathology of autism spectrum disorders (ASD) limits the development of generic remedies, requires individualistic and patient-specific research. Recent progress in human-induced pluripotent stem cell (iPSC) technology provides a novel platform for modeling ASDs for studying complex neuronal phenotypes. In this study, we generated telencephalic induced neuronal (iN) cells from iPSCs derived from an ASD patient with a heterozygous point mutation in the DSCAM gene. The mRNA of DSCAM and the density of DSCAM in dendrites were significantly decreased in ASD compared to control iN cells. RNA sequencing analysis revealed that several synaptic function-related genes including NMDA receptor subunits were downregulated in ASD iN cells. Moreover, NMDA receptor (R)-mediated currents were significantly reduced in ASD compared to control iN cells. Normal NMDA-R-mediated current levels were rescued by expressing wild-type DSCAM in ASD iN cells, and reduced currents were observed by truncated DSCAM expression in control iN cells. shRNA-mediated DSCAM knockdown in control iN cells resulted in the downregulation of an NMDA-R subunit, which was rescued by the overexpression of shRNA-resistant DSCAM. Furthermore, DSCAM was co-localized with NMDA-R components in the dendritic spines of iN cells whereas their co-localizations were significantly reduced in ASD iN cells. Levels of phospho-ERK1/2 were significantly lower in ASD iN cells, suggesting a potential mechanism. A neural stem cell-specific Dscam heterozygous knockout mouse model, showing deficits in social interaction and social memory with reduced NMDA-R currents. These data suggest that DSCAM mutation causes pathological symptoms of ASD by dysregulating NMDA-R function.
Astrocytes directly participate in learning and memory. However, the structural association between astrocytes and memory-encoding engram neurons after learning remains to be elucidated. We developed astrocyte-enhanced green fluorescent protein reconstitution across synaptic partners (eGRASP) to examine tripartite synapses between astrocytes and engram neurons. Using astrocyte-eGRASP, we found that astrocytes had increased connections to engram neurons after learning. Dendritic spines with astrocytic contacts showed enhanced morphology. Live-cell imaging of astrocyte-eGRASP revealed that astrocytic connections are stabilized by neuronal activity. These results indicate that astrocytes distinguish contact between engram neurons and generate engram-specific contact patterns during learning.
Cities are highly industrialized and populated areas and major sources of greenhouse gas emissions. For carbon neutrality, examining the correlation between urban characteristics and greenhouse gas emissions is necessary. This study aimed to analyze the characteristics of each city from a carbon neutrality perspective. As such, we conducted a carbon-neutral city analysis. First, the physical environmental variables of 250 municipal, county, and district local governments were collected and constructed and then reduced and purified through factor analysis. Second, the type was derived by performing cluster analysis on the reduced factor variables and carbon emissions by analysis unit. Finally, the characteristics of each type were analyzed, and the carbon-neutral city planning and applicable carbon-neutral technology fields were proposed according to the characteristics. After the categorization of carbon-neutral cities throughout Korea, six cluster types were derived; cities in each cluster had similar characteristics. This study suggests that solutions for carbon reduction should be applied by comprehensively considering the social, economic, and environmental characteristics of each city. It concludes that regional physical environmental indicators and energy consumption statistics can be used comprehensively to establish effective policies and apply technologies and techniques at the local government level.
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