Albumin uptake in human podocytes: a possible role for the cubilin-amnionless (CUBAM) complex

Abstract Albumin re-uptake is a receptor-mediated pathway located in renal proximal tubuli. There is increasing evidence of glomerular protein handling by podocytes, but little is known about the mechanism behind this process. In this study, we found that human podocytes in vitro are committed to internalizing albumin through a receptor-mediated mechanism even after exposure to low doses of albumin. We show that these cells express cubilin, megalin, ClC-5, amnionless and Dab2, which are partners in the tubular machinery. Exposing human podocytes to albumin overload prompted an increase in CUBILIN, AMNIONLESS and CLCN5 gene expression. Inhibiting cubilin led to a reduction in albumin uptake, highlighting its importance in this mechanism. We demonstrated that human podocytes are committed to performing endocytosis via a receptor-mediated mechanism even in the presence of low doses of albumin. We also disclosed that protein overload first acts on the expression of the cubilin-amnionless (CUBAM) complex in these cells, then involves the ClC-5 channel, providing the first evidence for a possible role of the CUBAM complex in albumin endocytosis in human podocytes

Spontaneous calcification process in primary renal cells from a medullary sponge kidney patient harbouring a GDNF mutation.

Medullary nephrocalcinosis is a hallmark of medullary sponge kidney (MSK). We had the opportunity to study a spontaneous calcification process in vitro by utilizing the renal cells of a patient with MSK who was heterozygous for the c.-27 + 18G>A variant in the GDNF gene encoding glial cell-derived neurotrophic factor. The cells were obtained by collagenase digestion of papillary tissues from the MSK patient and from two patients who had no MSK or nephrocalcinosis. These cells were typed by immunocytochemistry, and the presence of mineral deposits was studied using von Kossa staining, scanning electron microscopy analysis and an ALP assay. Osteoblastic lineage markers were studied using immunocytochemistry and RT-PCR. Staminality markers were also analysed using flow cytometry, magnetic cell separation technology, immunocytochemistry and RT-PCR. Starting from p2, MSK and control cells formed nodules with a behaviour similar to that of calcifying pericytes; however, Ca2PO4 was only found in the MSK cultures. The MSK cells had morphologies and immunophenotypes resembling those of pericytes or stromal stem cells and were positive for vimentin, ZO1, aSMA and CD146. In addition, the MSK cells expressed osteocalcin and osteonectin, indicating an osteoblast-like phenotype. In contrast to the control cells, GDNF was down-regulated in the MSK cells. Stable GDNF knockdown was established in the HK2 cell line and was found to promote Ca2PO4 deposition when the cells were incubated with calcifying medium by regulating the osteonectin/osteopontin ratio in favour of osteonectin. Our data indicate that the human papilla may be a perivascular niche in which pericyte/stromal-like cells can undergo osteogenic differentiation under particular conditions and suggest that GDNF down-regulation may have influenced the observed phenomenon

Human parietal epithelial cells (PECs) and proteinuria in lupus nephritis: a role for ClC-5, megalin, and cubilin?

Background: Parietal epithelial cells are a heterogeneous population of cells located on Bowman’s capsule. These cells are known to internalize albumin with a still undetermined mechanism, although albumin has been shown to induce phenotypic changes in parietal epithelial cells. Proximal tubular cells are the main actors in albumin handling via the macromolecular complex composed by ClC-5, megalin, and cubilin. This study investigated the role of ClC-5, megalin, and cubilin in the parietal epithelial cells of kidney biopsies from proteinuric lupus nephritis patients and control subjects and identified phenotypical changes occurring in the pathological milieu. Methods: Immunohistochemistry and immunofluorescence analyses for ClC-5, megalin, cubilin, ANXA3, podocalyxin, CD24, CD44, HSA, and LTA marker were performed on 23 kidney biopsies from patients with Lupus Nephritis and 9 control biopsies (obtained from nephrectomies for renal cancer). Results: Two sub-populations of hypertrophic parietal epithelial cells ANXA3+/Podocalyxin−/CD44−, both expressing ClC-5, megalin, and cubilin and located at the tubular pole, were identified and characterized: the first one, CD24+/HSA−/LTA− had characteristics of human adult parietal epithelial multipotent progenitors, the second one, CD24−/LTA+/HSA+ committed to become phenotypically proximal tubular cells. The number of glomeruli presenting hypertrophic parietal epithelial cells positive for ClC-5, megalin, and cubilin were significantly higher in lupus nephritis patients than in controls. Conclusions: Our results may provide further insight into the role of hypertrophic parietal epithelial cells located at the tubular pole and their possible involvement in protein endocytosis in lupus nephritis patients. These data also suggest that the presence of hypertrophic parietal epithelial cells in Bowman's capsule represents a potential resource for responding to protein overload observed in other glomerulonephritis

3did: identification and classification of domain-based interactions of known three-dimensional structure

The database of three-dimensional interacting domains (3did) is a collection of protein interactions for which high-resolution three-dimensional structures are known. 3did exploits the availability of structural data to provide molecular details on interactions between two globular domains as well as novel domain–peptide interactions, derived using a recently published method from our lab. The interface residues are presented for each interaction type individually, plus global domain interfaces at which one or more partners (domains or peptides) bind. The 3did web server at http://3did.irbbarcelona.org visualizes these interfaces along with atomic details of individual interactions using Jmol. The complete contents are also available for download

iRefR: an R package to manipulate the iRefIndex consolidated protein interaction database

<p>Abstract</p> <p>Background</p> <p>The iRefIndex addresses the need to consolidate protein interaction data into a single uniform data resource. iRefR provides the user with access to this data source from an R environment.</p> <p>Results</p> <p>The iRefR package includes tools for selecting specific subsets of interest from the iRefIndex by criteria such as organism, source database, experimental method, protein accessions and publication identifier. Data may be converted between three representations (MITAB, edgeList and graph) for use with other R packages such as igraph, graph and RBGL.</p> <p>The user may choose between different methods for resolving redundancies in interaction data and how n-ary data is represented. In addition, we describe a function to identify binary interaction records that possibly represent protein complexes. We show that the user choice of data selection, redundancy resolution and n-ary data representation all have an impact on graphical analysis.</p> <p>Conclusions</p> <p>The package allows the user to control how these issues are dealt with and communicate them via an R-script written using the iRefR package - this will facilitate communication of methods, reproducibility of network analyses and further modification and comparison of methods by researchers.</p

Inferring PDZ Domain Multi-Mutant Binding Preferences from Single-Mutant Data

Many important cellular protein interactions are mediated by peptide recognition domains. The ability to predict a domain's binding specificity directly from its primary sequence is essential to understanding the complexity of protein-protein interaction networks. One such recognition domain is the PDZ domain, functioning in scaffold proteins that facilitate formation of signaling networks. Predicting the PDZ domain's binding specificity was a part of the DREAM4 Peptide Recognition Domain challenge, the goal of which was to describe, as position weight matrices, the specificity profiles of five multi-mutant ERBB2IP-1 domains. We developed a method that derives multi-mutant binding preferences by generalizing the effects of single point mutations on the wild type domain's binding specificities. Our approach, trained on publicly available ERBB2IP-1 single-mutant phage display data, combined linear regression-based prediction for ligand positions whose specificity is determined by few PDZ positions, and single-mutant position weight matrix averaging for all other ligand columns. The success of our method as the winning entry of the DREAM4 competition, as well as its superior performance over a general PDZ-ligand binding model, demonstrates the advantages of training a model on a well-selected domain-specific data set

Ligand-activated BMP signaling inhibits cell differentiation and death to promote melanoma

Oncogenomic studies indicate that copy number variation (CNV) alters genes involved in tumor progression; however, identification of specific driver genes affected by CNV has been difficult, as these rearrangements are often contained in large chromosomal intervals among several bystander genes. Here, we addressed this problem and identified a CNV-targeted oncogene by performing comparative oncogenomics of human and zebrafish melanomas. We determined that the gene encoding growth differentiation factor 6 (GDF6), which is the ligand for the BMP family, is recurrently amplified and transcriptionally upregulated in melanoma. GDF6-induced BMP signaling maintained a trunk neural crest gene signature in melanomas. Additionally, GDF6 repressed the melanocyte differentiation gene MITF and the proapoptotic factor SOX9, thereby preventing differentiation, inhibiting cell death, and promoting tumor growth. GDF6 was specifically expressed in melanomas but not melanocytes. Moreover, GDF6 expression levels in melanomas were inversely correlated with patient survival. Our study has identified a fundamental role for GDF6 and BMP signaling in governing an embryonic cell gene signature to promote melanoma progression, thus providing potential opportunities for targeted therapy to treat GDF6-positive cancers

MINE: Module Identification in Networks

<p>Abstract</p> <p>Background</p> <p>Graphical models of network associations are useful for both visualizing and integrating multiple types of association data. Identifying modules, or groups of functionally related gene products, is an important challenge in analyzing biological networks. However, existing tools to identify modules are insufficient when applied to dense networks of experimentally derived interaction data. To address this problem, we have developed an agglomerative clustering method that is able to identify highly modular sets of gene products within highly interconnected molecular interaction networks.</p> <p>Results</p> <p>MINE outperforms MCODE, CFinder, NEMO, SPICi, and MCL in identifying non-exclusive, high modularity clusters when applied to the <it>C. elegans </it>protein-protein interaction network. The algorithm generally achieves superior geometric accuracy and modularity for annotated functional categories. In comparison with the most closely related algorithm, MCODE, the top clusters identified by MINE are consistently of higher density and MINE is less likely to designate overlapping modules as a single unit. MINE offers a high level of granularity with a small number of adjustable parameters, enabling users to fine-tune cluster results for input networks with differing topological properties.</p> <p>Conclusions</p> <p>MINE was created in response to the challenge of discovering high quality modules of gene products within highly interconnected biological networks. The algorithm allows a high degree of flexibility and user-customisation of results with few adjustable parameters. MINE outperforms several popular clustering algorithms in identifying modules with high modularity and obtains good overall recall and precision of functional annotations in protein-protein interaction networks from both <it>S. cerevisiae </it>and <it>C. elegans</it>.</p