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Kawaguchi, R.

Publications and source records attributed to Kawaguchi, R..

4 recordsLinked to original sources

Single-cell in situ transcriptomic map of astrocyte cortical layer diversity

During organogenesis, patterns and gradients of gene expression underlie organization and diversified cell specification to generate complex tissue architecture. While the cerebral cortex is organized into six excitatory neuronal layers, it is unclear whether glial cells are diversified to mimic neuronal laminae or show distinct layering. To determine the molecular architecture of the mammalian cortex, we developed a high content pipeline that can quantify single-cell gene expression in situ. The Large-area Spatial Transcriptomic (LaST) map confirmed expected cortical neuron layer organization and also revealed a novel neuronal identity signature. Screening 46 candidate genes for astrocyte diversity across the cortex, we identified grey matter superficial, mid and deep astrocyte identities in gradient layer patterns that were distinct from neurons. Astrocyte layers formed in early postnatal cortex and mostly persisted in adult mouse and human cortex. Mutations that shifted neuronal post-mitotic identity or organization were sufficient to alter glial layering, indicating an instructive role for neuronal cues. In normal mouse cortex, astrocyte layer patterns showed area diversity between functionally distinct cortical regions. These findings indicate that excitatory neurons and astrocytes cells are organized into distinct lineage-associated laminae, which give rise to higher order neuroglial complexity of cortical architecture.

neuroscience

Selective axonal translation of prenylated Cdc42 mRNA isoform supports axon growth

The small Rho-family GTPase Cdc42 has long been known to have a role in cell motility and axon growth. The eukaryotic CDC42 gene is alternatively spliced to generate mRNAs with two different 3UTRs that encode proteins with distinct C-termini. The C-termini of these Cdc42 proteins include CAAX and CCAX motifs for post-translational prenylation and palmitoylation, respectively. Palmitoyl-Cdc42 protein was previously shown to contribute to dendrite maturation, while the prenyl-Cdc42 protein contributes to axon specification and its mRNA was detected in neurites. Here, we show that the mRNA encoding prenyl-Cdc42 isoform preferentially localizes into PNS axons and this localization selectively increases in vivo during PNS axon regeneration. Isoform specific siRNA knockdowns, rescue experiments with siRNA-resistant Cdc42 isoforms, and pharmacologically targeting Cdc42 activity indicate that prenyl-Cdc42 promotes axon growth while the palmitoyl-Cdc42 has little growth promoting activity. The growth promotion by prenyl-Cdc42 requires axonal mRNA localization with localized translation and an intact C-terminal CaaX motif for localized prenylation of the encoded protein. Together, these data show that alternative splicing of the CDC42 gene product generates an axon growth promoting locally synthesized prenyl-Cdc42 protein. SUMMARY STATEMENTAxon regeneration drives selective localization of alternatively spliced CDC42 isoform to PNS axons.

neuroscience

Obesity blocks oligodendrocyte precursor cell differentiation and impedes repair after white matter stroke

Obesity is a growing public health problem that increases rates of white matter atrophy and increases the likelihood of ischemic lesions within white matter. However, the cellular and molecular mechanisms that regulate these changes are unknown. We hypothesized that obesity may alter oligodendrocytes and myelin priming white matter for worsening injury and repair responses after ischemia. C57Bl/6 mice fed a high fat diet (60% kcal from fat) show increased numbers of oligodendrocyte precursor cells (OPCs), decreased myelin thickness with elevated g-ratios, and shorter paranodal axonal segments, indicating accelerated myelin turnover. Fate mapping of OPCs in PDGFR-CreERT;Rpl22tm1.1Psam mice demonstrated that OPC differentiation rates are enhanced by obesity. Gene expression analyses using a novel oligodendrocyte staging assay demonstrated OPC differentiation is blocked by obesity in between the pre-myelinating and myelinating stage. Using a model of subcortical white matter stroke, the number of stroke-responsive OPCs in obese mice was increased after stroke. At early time points after ischemic white matter stroke, spatial mapping of stroke-responsive OPCs indicates that obesity leads to increased OPCs at the edge of ischemic white matter lesions. At later time points, obesity results in increased OPCs within the ischemic lesion while reducing the number of GST-{pi}-positive mature oligodendrocytes in the lesion core. These data indicate that obesity disrupts normal white matter biology by blocking oligodendrocyte differentiation, leads to an exaggerated response of OPCs to white matter ischemia, and limits remyelination after stroke.\n\nSignificance StatementObesity is a growing public health crisis that specifically increases the development of white matter lesions and silent brain infarcts. This relationship implies a direct cellular effect on white matter yet direct evidence is limited. We modeled diet-induced obesity in mice and studied the effect on oligodendrocyte biology and the response to focal white matter stroke. Adult-onset obesity results in thinner myelin, compromised axonal microdomain structure, and blocks the differentiation of OPCs, leaving the white matter with increased numbers of OPCs. After focal white matter stroke in obese mice, the early OPC response to stroke is exaggerated while late reparative OPC differentiation is blocked. These results suggest that obesity specifically blocks OPC differentiation with consequence on brain repair after stroke.

neuroscience

reactIDR: Evaluation of the statistical reproducibility of high-throughput structural analyses for a robust RNA reactivity classification

MotivationRecently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures called high-throughput RNA structural (HTS) analy- sis, and dozens of different protocols were used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on experimental data generation methodology, which results in many difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods.\n\nResultsHere, we introduced a statistical framework, reactIDR, which is applicable to the experimental data obtained using multiple HTS methodologies, and it classifies the nucleotides into three structural categories, stem, loop, and unmapped. reactIDR uses the irreproducible discovery rate (IDR) with a hidden Markov model (HMM) to discriminate accurately between the true and spurious signals obtained in the replicated HTS experiments. In reactIDR, IDR and HMM parameters are efficiently optimized by using an expectation-maximization algorithm. Furthermore, if known reference structures are given, a supervised learning can be applicable in a semi-supervised manner. The results of our analyses for real HTS data showed that reactIDR achieved the highest accuracy in the classification problem of stem/loop structures of rRNA using both individual and integrated HTS datasets as well as the best correspondence with the three-dimensional structure. Because reactIDR is the first method to compare HTS datasets obtained from multiple sources in a single unified model, it has a great potential to increase the accuracy of RNA secondary structure prediction at transcriptome-wide level with further experiments performed.\n\nAvailabilityreactIDR is implemented in Python. Source code is publicly available at https://github.com/carushi/reactIDRhttps://github.com/carushi/reactIDR.\n\nContactkawaguchi-rs@aist.go.jp\n\nSupplementary informationSupplementary data are available at online.

bioinformatics