Hiromitsu Araki

Gene network inference and visualization tools for biologists: application to new human transcriptome datasets

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions.

The Transcriptional Targets of Mutant FOXL2 in Granulosa Cell Tumours

Background Despite their distinct biology, granulosa cell tumours (GCTs) are treated the same as other ovarian tumours. Intriguingly, a recurring somatic mutation in the transcription factor Forkhead Box L2 (FOXL2) 402C>G has been found in nearly all GCTs examined. This investigation aims to identify the pathogenicity of mutant FOXL2 by studying its altered transcriptional targets. Methods The expression of mutant FOXL2 was reduced in the GCT cell line KGN, and wildtype and mutant FOXL2 were overexpressed in the GCT cell line COV434. Total RNA was hybridised to Affymetrix U133 Plus 2 microarrays. Comparisons were made between the transcriptomes of control cells and cells altered by FOXL2 knockdown and overexpression, to detect potential transcriptional targets of mutant FOXL2. Results The overexpression of wildtype and mutant FOXL2 in COV434, and the silencing of mutant FOXL2 expression in KGN, has shown that mutant FOXL2 is able to differentially regulate the expression of many genes, including two well known FOXL2 targets, StAR and CYP19A. We have shown that many of the genes regulated by mutant FOXL2 are clustered into functional annotations of cell death, proliferation, and tumourigenesis. Furthermore, TGF-β signalling was found to be enriched when using the gene annotation tools GATHER and GeneSetDB. This enrichment was still significant after performing a robust permutation analysis. Conclusion Given that many of the transcriptional targets of mutant FOXL2 are known TGF-β signalling genes, we suggest that deregulation of this key antiproliferative pathway is one way mutant FOXL2 contributes to the pathogenesis of adult-type GCTs. We believe this pathway should be a target for future therapeutic interventions, if outcomes for women with GCTs are to improve.

GeneSetDB: A comprehensive meta‐database, statistical and visualisation framework for gene set analysis

Most “omics” experiments require comprehensive interpretation of the biological meaning of gene lists. To address this requirement, a number of gene set analysis (GSA) tools have been developed. Although the biological value of GSA is strictly limited by the breadth of the gene sets used, very few methods exist for simultaneously analysing multiple publically available gene set databases. Therefore, we constructed GeneSetDB (http://genesetdb.auckland.ac.nz/haeremai.html), a comprehensive meta‐database, which integrates 26 public databases containing diverse biological information with a particular focus on human disease and pharmacology. GeneSetDB enables users to search for gene sets containing a gene identifier or keyword, generate their own gene sets, or statistically test for enrichment of an uploaded gene list across all gene sets, and visualise gene set enrichment and overlap using a clustered heat map.

Estimating Genome-Wide Gene Networks Using Nonparametric Bayesian Network Models on Massively Parallel Computers

We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures, existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the small network, as well as others that could not be captured during the small network estimation. The results also revealed master-regulator genes that are not in the small network but that control many of the genes in the small network. These analyses were impossible to realize without our proposed algorithm.

Understanding endothelial cell apoptosis: what can the transcriptome, glycome and proteome reveal?

Endothelial cell (EC) apoptosis may play an important role in blood vessel development, homeostasis and remodelling. In support of this concept, EC apoptosis has been detected within remodelling vessels in vivo, and inactivation of EC apoptosis regulators has caused dramatic vascular phenotypes. EC apoptosis has also been associated with cardiovascular pathologies. Therefore, understanding the regulation of EC apoptosis, with the goal of intervening in this process, has become a current research focus. The protein-based signalling and cleavage cascades that regulate EC apoptosis are well known. However, the possibility that programmed transcriptome and glycome changes contribute to EC apoptosis has only recently been explored. Traditional bioinformatic techniques have allowed simultaneous study of thousands of molecular signals during the process of EC apoptosis. However, to progress further, we now need to understand the complex cause and effect relationships among these signals. In this article, we will first review current knowledge about the function and regulation of EC apoptosis including the roles of the proteome transcriptome and glycome. Then, we assess the potential for further bioinformatic analysis to advance our understanding of EC apoptosis, including the limitations of current technologies and the potential of emerging technologies such as gene regulatory networks.

Analysis of PPARα-dependent and PPARα-independent transcript regulation following fenofibrate treatment of human endothelial cells

Fenofibrate is a synthetic ligand for the nuclear receptor peroxisome proliferator-activated receptor (PPAR) alpha and has been widely used in the treatment of metabolic disorders, especially hyperlipemia, due to its lipid-lowering effect. The molecular mechanism of lipid-lowering is relatively well defined: an activated PPARα forms a PPAR–RXR heterodimer and this regulates the transcription of genes involved in energy metabolism by binding to PPAR response elements in their promoter regions, so-called “trans-activation”. In addition, fenofibrate also has anti-inflammatory and anti-athrogenic effects in vascular endothelial and smooth muscle cells. We have limited information about the anti-inflammatory mechanism of fenofibrate; however, “trans-repression” which suppresses production of inflammatory cytokines and adhesion molecules probably contributes to this mechanism. Furthermore, there are reports that fenofibrate affects endothelial cells in a PPARα-independent manner. In order to identify PPARα-dependently and PPARα-independently regulated transcripts, we generated microarray data from human endothelial cells treated with fenofibrate, and with and without siRNA-mediated knock-down of PPARα. We also constructed dynamic Bayesian transcriptome networks to reveal PPARα-dependent and -independent pathways. Our transcriptome network analysis identified growth differentiation factor 15 (GDF15) as a hub gene having PPARα-independently regulated transcripts as its direct downstream children. This result suggests that GDF15 may be PPARα-independent master-regulator of fenofibrate action in human endothelial cells.

Computational gene network analysis reveals TNF-induced angiogenesis

BackgroundTNF (Tumor Necrosis Factor-α) induces HUVEC (Human Umbilical Vein Endothelial Cells) to proliferate and form new blood vessels. This TNF-induced angiogenesis plays a key role in cancer and rheumatic disease. However, the molecular system that underlies TNF-induced angiogenesis is largely unknown.MethodsWe analyzed the gene expression changes stimulated by TNF in HUVEC over a time course using microarrays to reveal the molecular system underlying TNF-induced angiogenesis. Traditional k-means clustering analysis was performed to identify informative temporal gene expression patterns buried in the time course data. Functional enrichment analysis using DAVID was then performed for each cluster. The genes that belonged to informative clusters were then used as the input for gene network analysis using a Bayesian network and nonparametric regression method. Based on this TNF-induced gene network, we searched for sub-networks related to angiogenesis by integrating existing biological knowledge.Resultsk-means clustering of the TNF stimulated time course microarray gene expression data, followed by functional enrichment analysis identified three biologically informative clusters related to apoptosis, cellular proliferation and angiogenesis. These three clusters included 648 genes in total, which were used to estimate dynamic Bayesian networks. Based on the estimated TNF-induced gene networks, we hypothesized that a sub-network including IL6 and IL8 inhibits apoptosis and promotes TNF-induced angiogenesis. More particularly, IL6 promotes TNF-induced angiogenesis by inducing NF-κB and IL8, which are strong cell growth factors.ConclusionsComputational gene network analysis revealed a novel molecular system that may play an important role in the TNF-induced angiogenesis seen in cancer and rheumatic disease. This analysis suggests that Bayesian network analysis linked to functional annotation may be a powerful tool to provide insight into disease.

Cell cycle gene networks are associated with melanoma prognosis.

Background Our understanding of the molecular pathways that underlie melanoma remains incomplete. Although several published microarray studies of clinical melanomas have provided valuable information, we found only limited concordance between these studies. Therefore, we took an in vitro functional genomics approach to understand melanoma molecular pathways. Methodology/Principal Findings Affymetrix microarray data were generated from A375 melanoma cells treated in vitro with siRNAs against 45 transcription factors and signaling molecules. Analysis of this data using unsupervised hierarchical clustering and Bayesian gene networks identified proliferation-association RNA clusters, which were co-ordinately expressed across the A375 cells and also across melanomas from patients. The abundance in metastatic melanomas of these cellular proliferation clusters and their putative upstream regulators was significantly associated with patient prognosis. An 8-gene classifier derived from gene network hub genes correctly classified the prognosis of 23/26 metastatic melanoma patients in a cross-validation study. Unlike the RNA clusters associated with cellular proliferation described above, co-ordinately expressed RNA clusters associated with immune response were clearly identified across melanoma tumours from patients but not across the siRNA-treated A375 cells, in which immune responses are not active. Three uncharacterised genes, which the gene networks predicted to be upstream of apoptosis- or cellular proliferation-associated RNAs, were found to significantly alter apoptosis and cell number when over-expressed in vitro. Conclusions/Significance This analysis identified co-expression of RNAs that encode functionally-related proteins, in particular, proliferation-associated RNA clusters that are linked to melanoma patient prognosis. Our analysis suggests that A375 cells in vitro may be valid models in which to study the gene expression modules that underlie some melanoma biological processes (e.g., proliferation) but not others (e.g., immune response). The gene expression modules identified here, and the RNAs predicted by Bayesian network inference to be upstream of these modules, are potential prognostic biomarkers and drug targets.

Unraveling dynamic activities of autocrine pathways that control drug-response transcriptome networks

Some drugs affect secretion of secreted proteins (e.g. cytokines) released from target cells, but it remains unclear whether these proteins act in an autocrine manner and directly effect the cells on which the drugs act. In this study, we propose a computational method for testing a biological hypothesis: there exist autocrine signaling pathways that are dynamically regulated by drug response transcriptome networks and control them simultaneously. If such pathways are identified, they could be useful for revealing drug mode-of-action and identifying novel drug targets. By the node-set separation method proposed, dynamic structural changes can be embedded in transcriptome networks that enable us to find master-regulator genes or critical paths at each observed time. We then combine the protein-protein interaction network with the estimated dynamic transcriptome network to discover drug-affected autocrine pathways if they exist. The statistical significance (p-values) of the pathways are evaluated by the meta-analysis technique. The dynamics of the interactions between the transcriptome networks and the signaling pathways will be shown in this framework. We illustrate our strategy by an application using anti-hyperlipidemia drug, Fenofibrate. From over one million protein-protein interaction pathways, we extracted significant 23 autocrine-like pathways with the Bonferroni correction, including VEGF-NRP1-GIPC1-PRKCA-PPARalpha, that is one of the most significant ones and contains PPARalpha, a target of Fenofibrate.

Enhanced stomatal conductance by a spontaneous Arabidopsis tetraploid, Me-0, results from increased stomatal size and greater stomatal aperture

The Arabidopsis tetraploid ecotype, Me-0, overcomes the handicap of stomatal opening that is typical for tetraploid plants and achieves a high stomatal conductance. The rate of gas exchange in plants is regulated mainly by stomatal size and density. Generally, higher densities of smaller stomata are advantageous for gas exchange; however, it is unclear what the effect of an extraordinary change in stomatal size might have on a plant’s gas-exchange capacity. We investigated the stomatal responses to CO2 concentration changes among 374 Arabidopsis (Arabidopsis thaliana) ecotypes and discovered that Mechtshausen (Me-0), a natural tetraploid ecotype, has significantly larger stomata and can achieve a high stomatal conductance. We surmised that the cause of the increased stomatal conductance is tetraploidization; however, the stomatal conductance of another tetraploid accession, tetraploid Columbia (Col), was not as high as that in Me-0. One difference between these two accessions was the size of their stomatal apertures. Analyses of abscisic acid sensitivity, ion balance, and gene expression profiles suggested that physiological or genetic factors restrict the stomatal opening in tetraploid Col but not in Me-0. Our results show that Me-0 overcomes the handicap of stomatal opening that is typical for tetraploids and achieves higher stomatal conductance compared with the closely related tetraploid Col on account of larger stomatal apertures. This study provides evidence for whether larger stomatal size in tetraploids of higher plants can improve stomatal conductance.