Overexpression of SNX3 Decreases Amyloid-β Peptide Production by Reducing Internalization of Amyloid Precursor Protein

Xu, Shaohua; Nigam, Saket Milind; Brodin, Lennart

doi:10.1159/000486199

Cited by 11 publications

(9 citation statements)

References 43 publications

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“…Sorting nexins are a group of cytoplasmic and membrane-associated proteins involved in the regulation of intracellular trafficking 117 . SNX3 has been reported to play a role in receptor recycling and formation of multivesicular bodies 118 , and its dysregulation has been implicated in disorders of iron metabolism and the pathogenesis of some neurodegenerative diseases 119,120 .…”

Section: Resultsmentioning

confidence: 99%

The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data

Muelas

Mughal

O’Hagan³

et al. 2019

Sci Rep

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We recently introduced the Gini coefficient (GC) for assessing the expression variation of a particular gene in a dataset, as a means of selecting improved reference genes over the cohort (‘housekeeping genes’) typically used for normalisation in expression profiling studies. Those genes (transcripts) that we determined to be useable as reference genes differed greatly from previous suggestions based on hypothesis-driven approaches. A limitation of this initial study is that a single (albeit large) dataset was employed for both tissues and cell lines. We here extend this analysis to encompass seven other large datasets. Although their absolute values differ a little, the Gini values and median expression levels of the various genes are well correlated with each other between the various cell line datasets, implying that our original choice of the more ubiquitously expressed low-Gini-coefficient genes was indeed sound. In tissues, the Gini values and median expression levels of genes showed a greater variation, with the GC of genes changing with the number and types of tissues in the data sets. In all data sets, regardless of whether this was derived from tissues or cell lines, we also show that the GC is a robust measure of gene expression stability. Using the GC as a measure of expression stability we illustrate its utility to find tissue- and cell line-optimised housekeeping genes without any prior bias, that again include only a small number of previously reported housekeeping genes. We also independently confirmed this experimentally using RT-qPCR with 40 candidate GC genes in a panel of 10 cell lines. These were termed the Gini Genes. In many cases, the variation in the expression levels of classical reference genes is really quite huge (e.g. 44 fold for GAPDH in one data set), suggesting that the cure (of using them as normalising genes) may in some cases be worse than the disease (of not doing so). We recommend the present data-driven approach for the selection of reference genes by using the easy-to-calculate and robust GC.

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Section: Resultsmentioning

confidence: 99%

The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data

Muelas

Mughal

O’Hagan³

et al. 2019

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Sorting nexins are a group of cytoplasmic and membrane-associated proteins involved in the regulation of intracellular trafficking [116]. SNX3 has been reported to play a role in receptor recycling and formation of multivesicular bodies [117], and its dysregulation has been implicated in disorders of iron metabolism and the pathogenesis of some neurodegenerative diseases [118, 119].…”

Section: Resultsmentioning

confidence: 99%

The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data

Muelas

Mughal

O’Hagan

et al. 2019

Preprint

View full text Add to dashboard Cite

We recently introduced the Gini coefficient (GC) for assessing the expression variation of a particular gene in a dataset, as a means of selecting improved reference genes over the cohort (‘housekeeping genes’) typically used for normalisation in expression profiling studies. Those genes (transcripts) that we determined to be useable as reference genes differed greatly from previous suggestions based on hypothesis-driven approaches. A limitation of this initial study is that a single (albeit large) dataset was employed for both tissues and cell lines.We here extend this analysis to encompass seven other large datasets. Although their absolute values differ a little, the Gini values and median expression levels of the various genes are well correlated with each other between the various cell line datasets, implying that our original choice of the more ubiquitously expressed low-Gini-coefficient genes was indeed sound. In tissues, the Gini values and median expression levels of genes showed a greater variation, with the GC of genes changing with the number and types of tissues in the data sets. In all data sets, regardless of whether this was derived from tissues or cell lines, we also show that the GC is a robust measure of gene expression stability. Using the GC as a measure of expression stability we illustrate its utility to find tissue- and cell line-optimised housekeeping genes without any prior bias, that again include only a small number of previously reported housekeeping genes. We also independently confirmed this experimentally using RT-qPCR with 40 candidate GC genes in a panel of 10 cell lines. These were termed the Gini Genes.In many cases, the variation in the expression levels of classical reference genes is really quite huge (e.g. 44 fold for GAPDH in one data set), suggesting that the cure (of using them as normalising genes) may in some cases be worse than the disease (of not doing so). We recommend the present data-driven approach for the selection of reference genes by using the easy-to-calculate and robust GC.

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“…Variants in genes that regulate retromer function, including SNX3 , have been associated with AD [ 34 ]. Moreover, there is evidence to suggest that SNX3 regulates amyloid-beta production [ 35 ]. Retromer dysfunction has been implicated in neurodegenerative diseases including FTD [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

A comprehensive analysis of copy number variation in a Turkish dementia cohort

et al. 2021

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Background Copy number variants (CNVs) include deletions or multiplications spanning genomic regions. These regions vary in size and may span genes known to play a role in human diseases. As examples, duplications and triplications of SNCA have been shown to cause forms of Parkinson’s disease, while duplications of APP cause early onset Alzheimer’s disease (AD). Results Here, we performed a systematic analysis of CNVs in a Turkish dementia cohort in order to further characterize the genetic causes of dementia in this population. One hundred twenty-four Turkish individuals, either at risk of dementia due to family history, diagnosed with mild cognitive impairment, AD, or frontotemporal dementia, were whole-genome genotyped and CNVs were detected. We integrated family analysis with a comprehensive assessment of potentially disease-associated CNVs in this Turkish dementia cohort. We also utilized both dementia and non-dementia individuals from the UK Biobank in order to further elucidate the potential role of the identified CNVs in neurodegenerative diseases. We report CNVs overlapping the previously implicated genes ZNF804A, SNORA70B, USP34, XPO1, and a locus on chromosome 9 which includes a cluster of olfactory receptors and ABCA1. Additionally, we also describe novel CNVs potentially associated with dementia, overlapping the genes AFG1L, SNX3, VWDE, and BC039545. Conclusions Genotyping data from understudied populations can be utilized to identify copy number variation which may contribute to dementia.

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Overexpression of SNX3 Decreases Amyloid-β Peptide Production by Reducing Internalization of Amyloid Precursor Protein

Cited by 11 publications

References 43 publications

The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data

The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data

The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data

A comprehensive analysis of copy number variation in a Turkish dementia cohort

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