Identification of genes regulating GABAergic interneuron maturation

Fukumoto, Keita; Tamada, Kota; Toya, Tsuyoshi; Nishino, Tasuku; Yanagawa, Yuchio; Takumi, Toru

doi:10.1016/j.neures.2017.11.010

Cited by 14 publications

(14 citation statements)

References 48 publications

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“…A few of our KO lines target genes having human orthologs that share a GWAS BMI cluster with other genes linked to obesity. Genes for the kinase Dgkg 188 and the neuronal membrane protein Lrrn2 189 have no publicly available KO data or published links to obesity despite having increased body fat in our HTS ( Table 7 ). DGKG and ETV5 (ETS variant transcription factor 5) are the only genes sharing a BMI cluster on human chromosome 3, while LRRN2 and MDM4 (MDM4 regulator of p53) are the only genes sharing a BMI cluster on human chromosome 1 ( Table 8 ); the low body fat observed in Etv5 KO mice 190 and Mdmx KO mice 191 suggests that one or both genes in each of these two BMI clusters may contribute to the GWAS signal.…”

Section: Resultsmentioning

confidence: 99%

High-Throughput Screening of Mouse Gene Knockouts Identifies Established and Novel High Body Fat Phenotypes

Powell¹,

Revelli²,

Doree³

et al. 2021

DMSO

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Purpose Obesity is a major public health problem. Understanding which genes contribute to obesity may better predict individual risk and allow development of new therapies. Because obesity of a mouse gene knockout (KO) line predicts an association of the orthologous human gene with obesity, we reviewed data from the Lexicon Genome5000 TM high throughput phenotypic screen (HTS) of mouse gene KOs to identify KO lines with high body fat. Materials and Methods KO lines were generated using homologous recombination or gene trapping technologies. HTS body composition analyses were performed on adult wild-type and homozygous KO littermate mice from 3758 druggable mouse genes having a human ortholog. Body composition was measured by either DXA or QMR on chow-fed cohorts from all 3758 KO lines and was measured by QMR on independent high fat diet-fed cohorts from 2488 of these KO lines. Where possible, comparisons were made to HTS data from the International Mouse Phenotyping Consortium (IMPC). Results Body fat data are presented for 75 KO lines. Of 46 KO lines where independent external published and/or IMPC KO lines are reported as obese, 43 had increased body fat. For the remaining 29 novel high body fat KO lines, Ksr2 and G2e3 are supported by data from additional independent KO cohorts, 6 ( Asnsd1, Srpk2, Dpp8, Cxxc4, Tenm3 and Kiss1 ) are supported by data from additional internal cohorts, and the remaining 21 including Tle4, Ak5, Ntm, Tusc3, Ankk1, Mfap3l, Prok2 and Prokr2 were studied with HTS cohorts only. Conclusion These data support the finding of high body fat in 43 independent external published and/or IMPC KO lines. A novel obese phenotype was identified in 29 additional KO lines, with 27 still lacking the external confirmation now provided for Ksr2 and G2e3 KO mice. Undoubtedly, many mammalian obesity genes remain to be identified and characterized.

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Section: Resultsmentioning

confidence: 99%

High-Throughput Screening of Mouse Gene Knockouts Identifies Established and Novel High Body Fat Phenotypes

Powell¹,

Revelli²,

Doree³

et al. 2021

DMSO

View full text Add to dashboard Cite

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“…The differential splicing and over-expression of an ADP-ribosylation factor-like protein 4D (ARL4D) isoform (Figure 2) in MIA relative to control weaned males (27.9%) is aligned with the over-expression of ARL4D in the cerebral cortex GABAergic neurons of mice heterozygous for GAD67-GFP knock-in line that presents ADHD and ASD-like behaviors [99]. Likewise, the differential alternative splicing detected in neurocan (NCAN) could be connected with mutations in this gene associated with SSD and bipolar disorder [100].…”

Section: Differential Alternative Splicing Associated With Maternal I...mentioning

confidence: 92%

Disruption of Alternative Splicing in the Amygdala of Pigs Exposed to Maternal Immune Activation

Southey

Keever-Keigher

Rymut

et al. 2021

Immuno

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The inflammatory response of gestating females to infection or stress can disrupt gene expression in the offspring’s amygdala, resulting in lasting neurodevelopmental, physiological, and behavioral disorders. The effects of maternal immune activation (MIA) can be impacted by the offspring’s sex and exposure to additional stressors later in life. The objectives of this study were to investigate the disruption of alternative splicing patterns associated with MIA in the offspring’s amygdala and characterize this disruption in the context of the second stress of weaning and sex. Differential alternative splicing was tested on the RNA-seq profiles of a pig model of viral-induced MIA. Compared to controls, MIA was associated with the differential alternative splicing (FDR-adjusted p-value < 0.1) of 292 and 240 genes in weaned females and males, respectively, whereas 132 and 176 genes were differentially spliced in control nursed female and male, respectively. The majority of the differentially spliced (FDR-adjusted p-value < 0.001) genes (e.g., SHANK1, ZNF672, KCNA6) and many associated enriched pathways (e.g., Fc gamma R-mediated phagocytosis, non-alcoholic fatty liver disease, and cGMP-PKG signaling) have been reported in MIA-related disorders including autism and schizophrenia in humans. Differential alternative splicing associated with MIA was detected in the gene MAG across all sex-stress groups except for unstressed males and SLC2A11 across all groups except unstressed females. Precise understanding of the effect of MIA across second stressors and sexes necessitates the consideration of splicing isoform profiles.

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“…Ezh2 has been suggested as a relevant TF for oligodendrocytes, and the expression of Ezh2 in OPCs (oligodendrocytes precursor cells), even up to the stage of pre-myelinating immature oligodendrocytes, remains high ( Copray et al, 2009 ) ( Figure 6C ). Furthermore, the MultiCapsNet found that Rpp25 is strongly associated with interneurons which SCENIC did not, and Rpp25 has been reported up-regulated in GABAergic interneuron ( Fukumoto et al, 2018 ).…”

Section: Resultsmentioning

confidence: 99%

MultiCapsNet: A General Framework for Data Integration and Interpretable Classification

et al. 2022

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The latest progresses of experimental biology have generated a large number of data with different formats and lengths. Deep learning is an ideal tool to deal with complex datasets, but its inherent “black box” nature needs more interpretability. At the same time, traditional interpretable machine learning methods, such as linear regression or random forest, could only deal with numerical features instead of modular features often encountered in the biological field. Here, we present MultiCapsNet (https://github.com/wanglf19/MultiCapsNet), a new deep learning model built on CapsNet and scCapsNet, which possesses the merits such as easy data integration and high model interpretability. To demonstrate the ability of this model as an interpretable classifier to deal with modular inputs, we test MultiCapsNet on three datasets with different data type and application scenarios. Firstly, on the labeled variant call dataset, MultiCapsNet shows a similar classification performance with neural network model, and provides importance scores for data sources directly without an extra importance determination step required by the neural network model. The importance scores generated by these two models are highly correlated. Secondly, on single cell RNA sequence (scRNA-seq) dataset, MultiCapsNet integrates information about protein-protein interaction (PPI), and protein-DNA interaction (PDI). The classification accuracy of MultiCapsNet is comparable to the neural network and random forest model. Meanwhile, MultiCapsNet reveals how each transcription factor (TF) or PPI cluster node contributes to classification of cell type. Thirdly, we made a comparison between MultiCapsNet and SCENIC. The results show several cell type relevant TFs identified by both methods, further proving the validity and interpretability of the MultiCapsNet.

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Identification of genes regulating GABAergic interneuron maturation

Cited by 14 publications

References 48 publications

High-Throughput Screening of Mouse Gene Knockouts Identifies Established and Novel High Body Fat Phenotypes

High-Throughput Screening of Mouse Gene Knockouts Identifies Established and Novel High Body Fat Phenotypes

Disruption of Alternative Splicing in the Amygdala of Pigs Exposed to Maternal Immune Activation

MultiCapsNet: A General Framework for Data Integration and Interpretable Classification

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