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  • Oxford University Press (OUP)  (2)
  • Hsu, Chih-Wei  (2)
  • 1
    In: Human Molecular Genetics, Oxford University Press (OUP), Vol. 29, No. 13 ( 2020-08-03), p. 2171-2184
    Abstract: Lysinuric protein intolerance (LPI) is an inborn error of cationic amino acid (arginine, lysine, ornithine) transport caused by biallelic pathogenic variants in SLC7A7, which encodes the light subunit of the y+LAT1 transporter. Treatments for the complications of LPI, including growth failure, renal disease, pulmonary alveolar proteinosis, autoimmune disorders and osteoporosis, are limited. Given the early lethality of the only published global Slc7a7 knockout mouse model, a viable animal model to investigate global SLC7A7 deficiency is needed. Hence, we generated two mouse models with global Slc7a7 deficiency (Slc7a7em1Lbu/em1Lbu; Slc7a7Lbu/Lbu and Slc7a7em1(IMPC)Bay/em1(IMPC)Bay; Slc7a7Bay/Bay) using CRISPR/Cas9 technology by introducing a deletion of exons 3 and 4. Perinatal lethality was observed in Slc7a7Lbu/Lbu and Slc7a7Bay/Bay mice on the C57BL/6 and C57BL/6NJ inbred genetic backgrounds, respectively. We noted improved survival of Slc7a7Lbu/Lbu mice on the 129 Sv/Ev × C57BL/6 F2 background, but postnatal growth failure occurred. Consistent with human LPI, these Slc7a7Lbu/Lbu mice exhibited reduced plasma and increased urinary concentrations of the cationic amino acids. Histopathological assessment revealed loss of brush border and lipid vacuolation in the renal cortex of Slc7a7Lbu/Lbu mice, which combined with aminoaciduria suggests proximal tubular dysfunction. Micro-computed tomography of L4 vertebrae and skeletal radiographs showed delayed skeletal development and suggested decreased mineralization in Slc7a7Lbu/Lbu mice, respectively. In addition to delayed skeletal development and delayed development in the kidneys, the lungs and liver were observed based on histopathological assessment. Overall, our Slc7a7Lbu/Lbu mouse model on the F2 mixed background recapitulates multiple human LPI phenotypes and may be useful for future studies of LPI pathology.
    Type of Medium: Online Resource
    ISSN: 0964-6906 , 1460-2083
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 1474816-2
    SSG: 12
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  • 2
    In: Bioinformatics, Oxford University Press (OUP), Vol. 36, No. 5 ( 2020-03-01), p. 1492-1500
    Abstract: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. Results Here we introduce ‘soft windowing’, a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype–phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. Availability and implementation The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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