Co-regulation of Gremlin and Notch signalling in diabetic nephropathy

Diabetic nephropathy is currently the leading cause of end-stage renal disease worldwide, and occurs in approximately one third of all diabetic patients. The molecular pathogenesis of diabetic nephropathy has not been fully characterized and novel mediators and drivers of the disease are still being described. Previous data from our laboratory has identified the developmentally regulated gene Gremlin as a novel target implicated in diabetic nephropathy in vitro and in vivo. We used bioinformatic analysis to examine whether Gremlin gene sequence and structure could be used to identify other genes implicated in diabetic nephropathy. The Notch ligand Jagged1 and its downstream effector, hairy enhancer of split-1 (Hes1), were identified as genes with significant similarity to Gremlin in terms of promoter structure and predicted microRNA binding elements. This led us to discover that transforming growth factor-beta (TGFbeta1), a primary driver of cellular changes in the kidney during nephropathy, increased Gremlin, Jagged1 and Hes1 expression in human kidney epithelial cells. Elevated levels of Gremlin, Jagged1 and Hes1 were also detected in extracts from renal biopsies from diabetic nephropathy patients, but not in control living donors. In situ hybridization identified specific upregulation and co-expression of Gremlin, Jagged1 and Hes1 in the same tubuli of kidneys from diabetic nephropathy patients, but not controls. Finally, Notch pathway gene clustering showed that samples from diabetic nephropathy patients grouped together, distinct from both control living donors and patients with minimal change disease. Together, these data suggest that Notch pathway gene expression is elevated in diabetic nephropathy, co-incident with Gremlin, and may contribute to the pathogenesis of this disease.     

Activation of the Nlrp3 inflammasome by mitochondrial reactive oxygen species: A novel mechanism of albumin-induced tubulointerstitial inflammation

Albuminuria is not only an important marker of chronic kidney disease but also a crucial contributor to tubulointerstitial inflammation (TIF). In this study, we determined whether activation of the Nlrp3 inflammasome is involved in albuminuria induced-TIF and the underlying mechanisms of inflammasome activation by mitochondrial reactive oxygen species (mROS). We established an albumin-overload induced rat nephropathy model characterised by albuminuria, renal infiltration of inflammatory cells, tubular dilation and atrophy. The renal expression levels of the Nlrp3 inflammasome, IL-1β and IL-18 were significantly increased in this animal model. In vitro, albumin time- and dose-dependently increased the expression levels of the Nlrp3 inflammasome, IL-1β and IL18. Moreover, the silencing of the Nlrp3 gene or the use of the caspase-1 inhibitor Z-VAD-fmk significantly attenuated the albumin-induced increase in IL-1β and IL-18 expression in HK2 cells. In addition, mROS generation was elevated by albumin stimulation, whereas the ROS scavenger N-acetyl-l-cysteine (NAC) inhibited Nlrp3 expression and the release of IL-1β and IL-18. In kidney biopsy specimens obtained from patients with IgA nephropathy, Nlrp3 expression was localised to the proximal tubular epithelial cells, and this result is closely correlated with the extent of proteinuria and TIF. In summary, this study demonstrates that albuminuria may serve as an endogenous danger-associated molecular pattern (DAMP) that stimulates TIF via the mROS-mediated activation of the cytoplasmic Nlrp3 inflammasome.     

Clustering gene expression data based on predicted differential effects of GV interaction

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV （gene by variety） interaction using the adjusted unbiased prediction （AUP） method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.     

Clustering by soft-constraint affinity propagation: applications to gene-expression data

MOTIVATION: Similarity-measure-based clustering is a crucial problem appearing throughout scientific data analysis. Recently, a powerful new algorithm called Affinity Propagation (AP) based on message-passing techniques was proposed by Frey and Dueck (2007a). In AP, each cluster is identified by a common exemplar all other data points of the same cluster refer to, and exemplars have to refer to themselves. Albeit its proved power, AP in its present form suffers from a number of drawbacks. The hard constraint of having exactly one exemplar per cluster restricts AP to classes of regularly shaped clusters, and leads to suboptimal performance, e.g. in analyzing gene expression data. RESULTS: This limitation can be overcome by relaxing the AP hard constraints. A new parameter controls the importance of the constraints compared to the aim of maximizing the overall similarity, and allows to interpolate between the simple case where each data point selects its closest neighbor as an exemplar and the original AP. The resulting soft-constraint affinity propagation (SCAP) becomes more informative, accurate and leads to more stable clustering. Even though a new a priori free parameter is introduced, the overall dependence of the algorithm on external tuning is reduced, as robustness is increased and an optimal strategy for parameter selection emerges more naturally. SCAP is tested on biological benchmark data, including in particular microarray data related to various cancer types. We show that the algorithm efficiently unveils the hierarchical cluster structure present in the data sets. Further on, it allows to extract sparse gene expression signatures for each cluster.     

Graph-based consensus clustering for class discovery from gene expression data

MOTIVATION: Consensus clustering, also known as cluster ensemble, is one of the important techniques for microarray data analysis, and is particularly useful for class discovery from microarray data. Compared with traditional clustering algorithms, consensus clustering approaches have the ability to integrate multiple partitions from different cluster solutions to improve the robustness, stability, scalability and parallelization of the clustering algorithms. By consensus clustering, one can discover the underlying classes of the samples in gene expression data. RESULTS: In addition to exploring a graph-based consensus clustering (GCC) algorithm to estimate the underlying classes of the samples in microarray data, we also design a new validation index to determine the number of classes in microarray data. To our knowledge, this is the first time in which GCC is applied to class discovery for microarray data. Given a pre specified maximum number of classes (denoted as K(max) in this article), our algorithm can discover the true number of classes for the samples in microarray data according to a new cluster validation index called the Modified Rand Index. Experiments on gene expression data indicate that our new algorithm can (i) outperform most of the existing algorithms, (ii) identify the number of classes correctly in real cancer datasets, and (iii) discover the classes of samples with biological meaning. AVAILABILITY: Matlab source code for the GCC algorithm is available upon request from Zhiwen Yu.     

Gene expression profiling of the synergy of 5-aza-2'-deoxycytidine and paclitaxel against renal cell carcinoma

ABSTRACT: We previously demonstrated that 5-Aza-2'-deoxycytidine (DAC) could significantly increase the susceptibility of renal cell carcinoma (RCC) cells to Paclitaxel (PTX) treatment in vitro, and showed the synergy of DAC and PTX against RCC. However, the gene expression profiling and the pathways involved in the synergy of these two agents remain unknown. In this study, we performed cDNA microarray, which was coupled with real-time PCR, to identify critical genes in the synergistic mechanism of the both agents against RCC cells. Various patterns of gene expression were observed by cluster analysis, and results indicated that lymphoid enhancer-binding factor 1 (LEF1), transforming growth factor beta-induced (TGFBI), C-X-C motif ligand 5 (CXCL5) and myelocytomatosis viral related oncogene (c-myc) may play a pivotal role in the synergy of DAC and PTX. In addition, network analysis using IPA software, suggested that the PI3K/Akt pathway and some other pathways associated with cyclins, DNA replication and cell cycle/mitotic regulation were also associated with the synergy of DAC and PTX against RCC.     

cluML

cluML is a new markup language for microarray data clustering and cluster validity assessment. The XML-based format has been designed to address some of the limitations observed in traditional formats, such as inability to store multiple clustering (including biclustering) and validation results within a dataset. cluML is an effective tool to support biomedical knowledge representation in gene expression data analysis. Although cluML was developed for DNA microarray analysis applications, it can be effectively used for the representation of clustering and for the validation of other biomedical and physical data that has no limitations.     

Noise-robust soft clustering of gene expression time-course data

Clustering is an important tool in microarray data analysis. This unsupervised learning technique is commonly used to reveal structures hidden in large gene expression data sets. The vast majority of clustering algorithms applied so far produce hard partitions of the data, i.e. each gene is assigned exactly to one cluster. Hard clustering is favourable if clusters are well separated. However, this is generally not the case for microarray time-course data, where gene clusters frequently overlap. Additionally, hard clustering algorithms are often highly sensitive to noise. To overcome the limitations of hard clustering, we applied soft clustering which offers several advantages for researchers. First, it generates accessible internal cluster structures, i.e. it indicates how well corresponding clusters represent genes. This can be used for the more targeted search for regulatory elements. Second, the overall relation between clusters, and thus a global clustering structure, can be defined. Additionally, soft clustering is more noise robust and a priori pre-filtering of genes can be avoided. This prevents the exclusion of biologically relevant genes from the data analysis. Soft clustering was implemented here using the fuzzy c-means algorithm. Procedures to find optimal clustering parameters were developed. A software package for soft clustering has been developed based on the open-source statistical language R. The package called Mfuzz is freely available.     

Antagonistic regulation of EMT by TIF1γ and Smad4 in mammary epithelial cells

TGF-β is a potent inducer of epithelial-to-mesenchymal transition (EMT), a process involved in tumour invasion. TIF1γ participates in TGF-β signalling. To understand the role of TIF1γ in TGF-β signalling and its requirement for EMT, we analysed the TGF-β1 response of human mammary epithelial cell lines. A strong EMT increase was observed in TIF1γ-silenced cells after TGF-β1 treatment, whereas Smad4 inactivation completely blocked this process. Accordingly, the functions of several TIF1γ target genes can be linked to EMT, as shown by microarray analysis. As a negative regulator of Smad4, TIF1γ could be crucial for the regulation of TGF-β signalling. Furthermore, TIF1γ binds to and represses the plasminogen activator inhibitor 1 promoter, demonstrating a direct role of TIF1γ in TGF-β-dependent gene expression. This study shows the molecular relationship between TIF1γ and Smad4 in TGF-β signalling and EMT.