MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering

<p>Abstract</p> <p>Background</p> <p>Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance.</p> <p>Results</p> <p>We present a cluster-number-based ensemble clustering algorithm, called <it>MULTI-K</it>, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple <it>k</it>-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the <it>entropy-plot </it>to control the separation of singletons or small clusters. MULTI-K, unlike the simple <it>k</it>-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets.</p> <p>Conclusion</p> <p>The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.</p

The voltage-gated potassium channel Shaker promotes sleep via thermosensitive GABA transmission

Genes and neural circuits coordinately regulate animal sleep. However, it remains elusive how these endogenous factors shape sleep upon environmental changes. Here, we demonstrate that Shaker (Sh)-expressing GABAergic neurons projecting onto dorsal fan-shaped body (dFSB) regulate temperature-adaptive sleep behaviors in Drosophila. Loss of Sh function suppressed sleep at low temperature whereas light and high temperature cooperatively gated Sh effects on sleep. Sh depletion in GABAergic neurons partially phenocopied Sh mutants. Furthermore, the ionotropic GABA receptor, Resistant to dieldrin (Rdl), in dFSB neurons acted downstream of Sh and antagonized its sleep-promoting effects. In fact, Rdl inhibited the intracellular cAMP signaling of constitutively active dopaminergic synapses onto dFSB at low temperature. High temperature silenced GABAergic synapses onto dFSB, thereby potentiating the wake-promoting dopamine transmission. We propose that temperature-dependent switching between these two synaptic transmission modalities may adaptively tune the neural property of dFSB neurons to temperature shifts and reorganize sleep architecture for animal fitness. Ji-hyung Kim and Yoonhee Ki et al. show that low temperatures suppress sleep in Drosophila by increasing GABA transmission in Shaker-expressing GABAergic neurons projecting onto the dorsal fan-shaped body, while high temperatures potentiate dopamine-induced arousal by reducing GABA transmission. This study highlights a role for Shaker in sleep modulation via a temperature-dependent switch in GABA signaling

Unleashing the full potential of Hsp90 inhibitors as cancer therapeutics through simultaneous inactivation of Hsp90, Grp94, and TRAP1

Cancer therapeutics: Extending a drug&apos;s reach A new drug that blocks heat shock proteins (HSPs), helper proteins that are co-opted by cancer cells to promote tumor growth, shows promise for cancer treatment. Several drugs have targeted HSPs, since cancer cells are known to hijack these helper proteins to shield themselves from destruction by the body. However, the drugs have had limited success. Hye-Kyung Park and Byoung Heon Kang at Ulsan National Institutes of Science and Technology in South Korea and coworkers noticed that the drugs were not absorbed into mitochondria, a key cellular compartment, and HSPs in this compartment were therefore not being blocked. They identified a new HSP inhibitor that can reach every cellular compartment and inhibit all HSPs. Testing in mice showed that this inhibitor effectively triggered death of tumor cells, and therefore shows promise for anti-cancer therapy. The Hsp90 family proteins Hsp90, Grp94, and TRAP1 are present in the cell cytoplasm, endoplasmic reticulum, and mitochondria, respectively; all play important roles in tumorigenesis by regulating protein homeostasis in response to stress. Thus, simultaneous inhibition of all Hsp90 paralogs is a reasonable strategy for cancer therapy. However, since the existing pan-Hsp90 inhibitor does not accumulate in mitochondria, the potential anticancer activity of pan-Hsp90 inhibition has not yet been fully examined in vivo. Analysis of The Cancer Genome Atlas database revealed that all Hsp90 paralogs were upregulated in prostate cancer. Inactivation of all Hsp90 paralogs induced mitochondrial dysfunction, increased cytosolic calcium, and activated calcineurin. Active calcineurin blocked prosurvival heat shock responses upon Hsp90 inhibition by preventing nuclear translocation of HSF1. The purine scaffold derivative DN401 inhibited all Hsp90 paralogs simultaneously and showed stronger anticancer activity than other Hsp90 inhibitors. Pan-Hsp90 inhibition increased cytotoxicity and suppressed mechanisms that protect cancer cells, suggesting that it is a feasible strategy for the development of potent anticancer drugs. The mitochondria-permeable drug DN401 is a newly identified in vivo pan-Hsp90 inhibitor with potent anticancer activity

GScluster: Network-weighted gene-set clustering analysis

Background: Gene-set analysis (GSA) has been commonly used to identify significantly altered pathways or functions from omics data. However, GSA often yields a long list of gene-sets, necessitating efficient post-processing for improved interpretation. Existing methods cluster the gene-sets based on the extent of their overlap to summarize the GSA results without considering interactions between gene-sets. Results: Here, we presented a novel network-weighted gene-set clustering that incorporates both the gene-set overlap and protein-protein interaction (PPI) networks. Three examples were demonstrated for microarray gene expression, GWAS summary, and RNA-sequencing data to which different GSA methods were applied. These examples as well as a global analysis show that the proposed method increases PPI densities and functional relevance of the resulting clusters. Additionally, distinct properties of gene-set distance measures were compared. The methods are implemented as an R/Shiny package GScluster that provides gene-set clustering and diverse functions for visualization of gene-sets and PPI networks. Conclusions: Network-weighted gene-set clustering provides functionally more relevant gene-set clusters and related network analysis

Performance Comparison of Two Gene Set Analysis Methods for Genome-wide Association Study Results: GSA-SNP vs i-GSEA4GWAS

Gene set analysis (GSA) is useful in interpreting a genome-wide association study (GWAS) result in terms of biological mechanism. We compared the performance of two different GSA implementations that accept GWAS p-values of single nucleotide polymorphisms (SNPs) or gene-by-gene summaries thereof, GSA-SNP and i-GSEA4GWAS, under the same settings of inputs and parameters. GSA runs were made with two sets of p-values from a Korean type 2 diabetes mellitus GWAS study: 259,188 and 1,152,947 SNPs of the original and imputed genotype datasets, respectively. When Gene Ontology terms were used as gene sets, i-GSEA4GWAS produced 283 and 1,070 hits for the unimputed and imputed datasets, respectively. On the other hand, GSA-SNP reported 94 and 38 hits, respectively, for both datasets. Similar, but to a lesser degree, trends were observed with Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets as well. The huge number of hits by i-GSEA4GWAS for the imputed dataset was probably an artifact due to the scaling step in the algorithm. The decrease in hits by GSA-SNP for the imputed dataset may be due to the fact that it relies on Z-statistics, which is sensitive to variations in the background level of associations. Judicious evaluation of the GSA outcomes, perhaps based on multiple programs, is recommended.clos

Transcriptional Regulator TonEBP Mediates Oxidative Damages in Ischemic Kidney Injury

TonEBP (tonicity-responsive enhancer binding protein) is a transcriptional regulator whose expression is elevated in response to various forms of stress including hyperglycemia, inflammation, and hypoxia. Here we investigated the role of TonEBP in acute kidney injury (AKI) using a line of TonEBP haplo-deficient mice subjected to bilateral renal ischemia followed by reperfusion (I/R). In the TonEBP haplo-deficient animals, induction of TonEBP, oxidative stress, inflammation, cell death, and functional injury in the kidney in response to I/R were all reduced. Analyses of renal transcriptome revealed that genes in several cellular pathways including peroxisome and mitochondrial inner membrane were suppressed in response to I/R, and the suppression was relieved in the TonEBP deficiency. Production of reactive oxygen species (ROS) and the cellular injury was reproduced in a renal epithelial cell line in response to hypoxia, ATP depletion, or hydrogen peroxide. The knockdown of TonEBP reduced ROS production and cellular injury in correlation with increased expression of the suppressed genes. The cellular injury was also blocked by inhibitors of necrosis. These results demonstrate that ischemic insult suppresses many genes involved in cellular metabolism leading to local oxidative stress by way of TonEBP induction. Thus, TonEBP is a promising target to prevent AKI

Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets

We present a novel approach to identify human microRNA (miRNA) regulatory modules (mRNA targets and relevant cell conditions) by biclustering a large collection of mRNA fold-change data for sequence-specific targets. Bicluster targets were assessed using validated messenger RNA (mRNA) targets and exhibited on an average 17.0% (median 19.4%) improved gain in certainty (sensitivity + specificity). The net gain was further increased up to 32.0% (median 33.4%) by incorporating functional networks of targets. We analyzed cancer-specific biclusters and found that the PI3K/Akt signaling pathway is strongly enriched with targets of a few miRNAs in breast cancer and diffuse large B-cell lymphoma. Indeed, five independent prognostic miRNAs were identified, and repression of bicluster targets and pathway activity by miR-29 was experimentally validated. In total, 29 898 biclusters for 459 human miRNAs were collected in the BiMIR database where biclusters are searchable for miRNAs, tissues, diseases, keywords and target genes

PPM1A Controls Diabetic Gene Programming through Directly Dephosphorylating PPAR?? at Ser273

Peroxisome proliferator-activated receptor gamma (PPAR gamma) is a master regulator of adipose tissue biology. In obesity, phosphorylation of PPAR gamma at Ser273 (pSer273) by cyclin-dependent kinase 5 (CDK5)/extracellular signal-regulated kinase (ERK) orchestrates diabetic gene reprogramming via dysregulation of specific gene expression. Although many recent studies have focused on the development of non-classical agonist drugs that inhibit the phosphorylation of PPAR gamma at Ser273, the molecular mechanism of PPAR gamma dephosphorylation at Ser273 is not well characterized. Here, we report that protein phosphatase Mg2+/Mn2+-dependent 1A (PPM1A) is a novel PPAR gamma phosphatase that directly dephosphorylates Ser273 and restores diabetic gene expression which is dysregulated by pSer273. The expression of PPM1A significantly decreases in two models of insulin resistance: diet-induced obese (DIO) mice and db/db mice, in which it negatively correlates with pSer273. Transcriptomic analysis using microarray and genotype-tissue expression (GTEx) data in humans shows positive correlations between PPM1A and most of the genes that are dysregulated by pSer273. These findings suggest that PPM1A dephosphorylates PPAR gamma at Ser273 and represents a potential target for the treatment of obesity-linked metabolic disorders

REGNET: Mining context-specific human transcription networks using composite genomic information

Background: Genome-wide expression profiles reflect the transcriptional networks specific to the given cell context. However, most statistical models try to estimate the average connectivity of the networks from a collection of gene expression data, and are unable to characterize the context-specific transcriptional regulations. We propose an approach for mining context-specific transcription networks from a large collection of gene expression fold-change profiles and composite gene-set information.Results: Using a composite gene-set analysis method, we combine the information of transcription factor binding sites, Gene Ontology or pathway gene sets and gene expression fold-change profiles for a variety of cell conditions. We then collected all the significant patterns and constructed a database of context-specific transcription networks for human (REGNET). As a result, context-specific roles of transcription factors as well as their functional targets are readily explored. To validate the approach, nine predicted targets of E2F1 in HeLa cells were tested using chromatin immunoprecipitation assay. Among them, five (Gadd45b, Dusp6, Mll5, Bmp2 and E2f3) were successfully bound by E2F1. c-JUN and the EMT transcription networks were also validated from literature.Conclusions: REGNET is a useful tool for exploring the ternary relationships among the transcription factors, their functional targets and the corresponding cell conditions. It is able to provide useful clues for novel cell-specific transcriptional regulations. The REGNET database is available at http://mgrc.kribb.re.kr/regnet.open0

GSA-SNP: a general approach for gene set analysis of polymorphisms

Genome-wide association (GWA) study aims to identify the genetic factors associated with the traits of interest. However, the power of GWA analysis has been seriously limited by the enormous number of markers tested. Recently, the gene set analysis (GSA) methods were introduced to GWA studies to address the association of gene sets that share common biological functions. GSA considerably increased the power of association analysis and successfully identified coordinated association patterns of gene sets. There have been several approaches in this direction with some limitations. Here, we present a general approach for GSA in GWA analysis and a stand-alone software GSA-SNP that implements three widely used GSA methods. GSA-SNP provides a fast computation and an easy-to-use interface. The software and test datasets are freely available at http://gsa.muldas.org. We provide an exemplary analysis on adult heights in a Korean population