Lupus-like kidney disease in mice deficient in the Src family tyrosine kinases Lyn and Fyn

Yu, Calvin C.K.; Yen, T. S. Benedict; Lowell, Clifford A.; DeFranco, Anthony L.

doi:10.1016/s0960-9822(00)00024-5

Cited by 111 publications

(83 citation statements)

References 11 publications

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“…Interestingly, a group of 12 genes known to be localized in mitochondria (HSPA9B, COX8A, COX7C, AKAP1, BCKDHA, CLPP, FDX1, AMT, DBT, ATP5G3, AK2, and NME4) are identified as significant, and their expression level uniformly declines in older age. A number of age-regulated genes are also known to be involved in renal diseases, including C1QA, CCR1, LYN, PTPRC, and TNFSF13B, which all show increasing expression with age (27)(28)(29)(30)(31). The expression of TRPM6 (a transient receptor potential cation channel) negatively correlates with age, and mutations of this gene cause hypomagnesemia with secondary hypocalcemia (32).…”

Section: Analysis Of Systemic Inflammatory Response Induced By Lpsmentioning

confidence: 99%

Significance analysis of time course microarray experiments

Storey

Xiao

Leek

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

543

593

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Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes. This method is applied to two studies on humans. In one study, genes are identified that show differential expression over time in response to in vivo endotoxin administration. By using our method, 7,409 genes are called significant at a 1% false-discovery rate level, whereas several existing approaches fail to identify any genes. In another study, 417 genes are identified at a 10% false-discovery rate level that show expression changing with age in the kidney cortex. Here it is also shown that as many as 47% of the genes change with age in a manner more complex than simple exponential growth or decay. The methodology proposed here has been implemented in the freely distributed and open-source EDGE software package.T he identification of genes that show changes in expression between varying biological conditions is a frequent goal in microarray experiments (1). Differential expression can be studied from a static or temporal viewpoint. In a static experiment, the arrays are obtained irrespective of time, capturing only a single moment of gene expression. In a temporal experiment the arrays are collected over a time course, allowing one to study the dynamic behavior of gene expression. A large amount of work has been done on the problem of identifying differentially expressed genes in static experiments (2). Because the regulation of gene expression is a dynamic process, it is also important to identify and characterize changes in gene expression over time. Here we present a general statistical method that identifies genes differentially expressed over time.Several clustering methods have been applied to time course microarray data, including hierarchical clustering (3, 4), principal components-based clustering (5), Bayesian model-based clustering (6), and K-means clustering of curves (7,8). None of these clustering methods is directly applicable to identifying genes that show statistically significant changes in expression over time. The K-means clustering method has been modified to compare expression over time between two groups (9), but this method can only be applied to a few hundred genes at a time because of the computational cost of fitting a single model to all genes simultaneously (7,8). This approach also requires that the statistical significance be calculated under the assumption that the clustering model estimated for one of the groups is true, which is nonstandard and potentially problematic.The method that we propose draws on ideas from the extensive statistical literature on time course data analysis (10, 11), particularly spline-based methods (12-16). It is applicable to detecting changes in exp...

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Section: Analysis Of Systemic Inflammatory Response Induced By Lpsmentioning

confidence: 99%

Significance analysis of time course microarray experiments

Storey

Xiao

Leek

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

543

593

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“…So far, it is unclear which kinases are involved in regulating these events, but the genetic inactivation of the src family kinase fyn caused proteinuria in mice (90). Interestingly, as a double-acylated molecule, fyn, has a high affinity for lipid rafts (91,92).…”

Section: Involvement Of Lipid Rafts In Functional Organization Of the Sdmentioning

confidence: 99%

Podocyte Biology and Response to Injury

Mündel¹,

Shankland²

2002

Journal of the American Society of Nephrology

595

541

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The visceral glomerular epithelial cell, also called podocyte, is a terminally differentiated cell that lines the outer aspect of the glomerular basement membrane (GBM). It therefore forms the final barrier to protein loss, which explains why podocyte injury is typically associated with marked proteinuria. Indeed, all forms of nephrotic syndrome are characterized by abnormalities in the podocyte. In this review, we will provide an update of the known functions of recent podocyte-specific proteins and focus on the slit diaphragm (SD) and the mechanisms underlying foot process (FP) flattening and how the podocyte responds to injury. Molecular Anatomy of the Podocyte FP CytoskeletonPodocyte FP are not static, but rather contain a contractile system similar to that seen in pericytes. This contractile apparatus is composed of actin, myosin-II, ␣-actinin-4, talin, and vinculin (1). The actin filament bundles form arches between adjacent FP of the same podocyte (2). Figure 1 is a schema of our current understanding of the molecular composition of the cytoskeleton in podocyte FP. Importantly, the actin filaments are connected to the underlying GBM at focal contacts via an ␣3␤1 integrin complex (3,4). The bends of the actin filament arches appear to be connected directly to the microtubules of the major processes (own unpublished results). FP are anchored to the GBM via ␣3␤1 integrin (5) and dystroglycans (6,7). Neighboring FP are connected by a cell-cell junction, the glomerular SD, which represents the main size selective filter barrier in the kidney (8 -10). The SD is thought to be a modified adherens junction (11) that is composed of a growing number of proteins, including nephrin (12-14), P-cadherin, CD2AP (15-18), , FAT (20), podocin (16,21), and possibly Neph1 (see reference 22 and below). In addition to the contractile proteins described above, we have reported the association of synaptopodin with the actin filaments in FP (23). Synaptopodin is the first member of a novel class of prolinerich proteins (24) and, like ␣-actinin-4, interacts with the tight junction protein MAGI-1 (25) that is also expressed in podocytes (26). Four Major Causes of FP EffacementThe basolateral portion of the foot processes represents the center of podocyte function and is defined by three membrane domains: the apical membrane domain, the SD protein complex, and the basal membrane domain or sole plate (27). The submembranous regions of all compartments are connected to the FP actin cytoskeleton, e.g., on the apical membrane domain, podocalyxin associates with the actin cytoskeleton through interactions with ezrin and the actin cytoskeleton via Na ϩ /H ϩ -exchanger regulatory factor 2 (NHERF2), a scaffold protein containing two PDZ (PSD-95/Dlg/ZO-1) domains and an ERM-binding region (28,29). The FP actin cytoskeleton is highly dynamic and ultimately determines the structural maintenance of the filtration slits as demonstrated in the acute PS/heparin model by several groups (30 -32). Interference with one of the three domains eventually ...

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“…The importance of the Rho pathway in mediating cytoskeletal rearrangements again points to a disregulated cytokeleton as the cause of this phenotype. Mice deficient in Fyn, a member of the Src family of tyrosine kinases, develop a lymphocyte-independent form of proteinuria (79). Mice with an interruption in the MPV17 gene, which encodes a preoxisomal protein that resulates MMP2 production, develop FSGS (80 -82).…”

Section: The Possible and The Actual: Animal Modelsmentioning

confidence: 99%

Inherited Podocytopathies

Pollak¹

2002

Journal of the American Society of Nephrology

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Lupus-like kidney disease in mice deficient in the Src family tyrosine kinases Lyn and Fyn

Cited by 111 publications

References 11 publications

Significance analysis of time course microarray experiments

Significance analysis of time course microarray experiments

Podocyte Biology and Response to Injury

Inherited Podocytopathies

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