Non-linear dimensionality reduction of signaling networks

Ivakhno, Sergii; Armstrong, J. Douglas

doi:10.1186/1752-0509-1-27

Cited by 17 publications

(9 citation statements)

References 30 publications

(44 reference statements)

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“…, 2007; Hibbs et al. , 2007; Ivakhno and Armstrong, 2007). Moreover, comparisons of data across multiple laboratories have shown that the data sets for each laboratory tend to be more similar to each other than they are to those of other laboratories (data not shown).…”

Section: Resultsmentioning

confidence: 99%

The AtGenExpress hormone and chemical treatment data set: experimental design, data evaluation, model data analysis and data access

et al. 2008

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SummaryWe analyzed global gene expression in Arabidopsis in response to various hormones and in related experiments as part of the AtGenExpress project. The experimental agents included seven basic phytohormones (auxin, cytokinin, gibberellin, brassinosteroid, abscisic acid, jasmonate and ethylene) and their inhibitors. In addition, gene expression was investigated in hormone-related mutants and during seed germination and sulfate starvation. Hormone-inducible genes were identified from the hormone response data. The effects of each hormone and the relevance of the gene lists were verified by comparing expression profiles for the hormone treatments and related experiments using Pearson's correlation coefficient. This approach was also used to analyze the relationships among expression profiles for hormone responses and those included in the AtGenExpress stress-response data set. The expected correlations were observed, indicating that this approach is useful to monitor the hormonal status in the stress-related samples. Global interactions among hormones-inducible genes were analyzed in a pairwise fashion, and several known and novel hormone interactions were detected. Genome-wide transcriptional gene-to-gene correlations, analyzed by hierarchical cluster analysis (HCA), indicated that our data set is useful for identification of clusters of co-expressed genes, and to predict the functions of unknown genes, even if a gene's function is not directly related to the experiments included in AtGenExpress. Our data are available online from AtGenExpressJapan; the results of genome-wide HCA are available from PRIMe. The data set presented here will be a versatile resource for future hormone studies, and constitutes a reference for genome-wide gene expression in Arabidopsis.

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Section: Resultsmentioning

confidence: 99%

The AtGenExpress hormone and chemical treatment data set: experimental design, data evaluation, model data analysis and data access

et al. 2008

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“…Developing additional high-throughput technologies like genome-wide phosphorylation assays as well as novel methods for incorporating these data with existing data sets would significantly enable systems biology efforts to construct models that predict complex system behavior (Olsen et al 2006;Dong et al 2007;Sahin et al 2007). The availability of these new high-throughput data types is on the horizon and will enable efforts to gain much deeper insights into complex living systems (Yeang et al 2004;Janes and Yaffe 2006;Ivakhno and Armstrong 2007;Martin et al 2007;Ourfali et al 2007;Villen et al 2007). Lastly, although we have generated a general picture for the network underlying insulin signaling, it was derived from a mouse cell line.…”

Section: Discussionmentioning

confidence: 99%

Integrating siRNA and protein–protein interaction data to identify an expanded insulin signaling network

Tu¹,

Argmann²,

Wong³

et al. 2009

Genome Res.

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Insulin resistance is one of the dominant symptoms of type 2 diabetes (T2D). Although the molecular mechanisms leading to this resistance are largely unknown, experimental data support that the insulin signaling pathway is impaired in patients who are insulin resistant. To identify novel components/modulators of the insulin signaling pathway, we designed siRNAs targeting over 300 genes and tested the effects of knocking down these genes in an insulin-dependent, anti-lipolysis assay in 3T3-L1 adipocytes. For 126 genes, significant changes in free fatty acid release were observed. However, due to off-target effects (in addition to other limitations), high-throughput RNAi-based screens in cell-based systems generate significant amounts of noise. Therefore, to obtain a more reliable set of genes from the siRNA hits in our screen, we developed and applied a novel network-based approach that elucidates the mechanisms of action for the true positive siRNA hits. Our analysis results in the identification of a core network underlying the insulin signaling pathway that is more significantly enriched for genes previously associated with insulin resistance than the set of genes annotated in the KEGG database as belonging to the insulin signaling pathway. We experimentally validated one of the predictions, S1pr2, as a novel candidate gene for T2D.

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“…By further development of non‐linear multivariate statistical methods, it may be possible that the non‐linear responses in transcript abundance and metabolite levels could be better integrated into a combined data matrix to uncover important relationships with temperature‐stress tolerance that can be linked with regulation of gene expression through the major cold stress regulatory mechanisms. Recently, it was shown that non‐linear relationships between cellular components could be more important than previously thought (Ivakhno and Armstrong 2007).…”

Section: Low‐temperature Stressmentioning

confidence: 99%

Metabolomics of temperature stress

Guy

Kaplan

Kopka

et al. 2007

Physiologia Plantarum

455

314

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Plants possess inducible tolerance mechanisms that extend the temperature range for survival during acute temperature stress. The inducible mechanisms of cold acclimation and acquired thermotolerance involve highly complex processes. These include perception and signal transduction of non-optimal temperatures or their physical consequences on cellular components that program extensive modification of the transcriptome, proteome, metabolome and composition and physical structure of the cytoplasm, membranes and cell walls. Therefore, a systems biology approach will be necessary to advance the understanding of plant stress responses and tolerance mechanisms. One promise of systems biology is that it will greatly enhance our understanding of individual and collective functions and thereby provide a more holistic view of plant stress responses. Past studies have found that several metabolites that could functionally contribute to induced stress tolerance have been associated with stress responses. Recent metabolite-profiling studies have refocused attention on these and other potentially important components found in the 'temperature-stress metabolome'. These metabolomic studies have demonstrated that active reconfiguration of the metabolome is regulated in part by changes in gene expression initiated by temperature-stress-activated signaling and stress-related transcription factors. One aspect of metabolism that is consistent across all of the temperature-stress metabolomic studies to date is the prominent role of central carbohydrate metabolism, which seems to be a major feature of the reprogramming of the metabolome during temperature stress. Future metabolomic studies of plant temperature-stress responses should reveal additional metabolic pathways that have important functions in temperature-stress tolerance mechanisms.

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Non-linear dimensionality reduction of signaling networks

Cited by 17 publications

References 30 publications

The AtGenExpress hormone and chemical treatment data set: experimental design, data evaluation, model data analysis and data access

The AtGenExpress hormone and chemical treatment data set: experimental design, data evaluation, model data analysis and data access

Integrating siRNA and protein–protein interaction data to identify an expanded insulin signaling network

Metabolomics of temperature stress

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