Dynamic covariation between gene expression and proteome characteristics

Sharabiani, Mansour Taghavi Azar; Siermala, Markku; Lehtinen, Tommi O; Vihinen, Mauno

doi:10.1186/1471-2105-6-215

Cited by 9 publications

(1 citation statement)

References 61 publications

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“…Integrative analysis of different types of data is widely applied to construct regulatory gene networks [ 2 , 3 ], overall improving the predictive power of such networks [ 4 ]. It has been shown that indirect indications of functional relevance between genes such as gene co-expression and genome co-localization are largely complementary and correlate well with ontology-based protein groupings [ 5 , 6 ]. However, not all interactions correlate well: genetic and protein interactions barely overlap [ 7 ], that causes some obvious challenges in retrieving useful information from a reconstructed species-specific network.…”

Section: Introductionmentioning

confidence: 99%

On predicting regulatory genes by analysis of functional networks in C. elegans

et al. 2015

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BackgroundConnectivity networks, which reflect multiple interactions between genes and proteins, possess not only a descriptive but also a predictive value, as new connections can be extrapolated and tested by means of computational analysis. Integration of different types of connectivity data (such as co-expression and genetic interactions) in one network has proven to benefit ‘guilt by association’ analysis. However predictive values of connectives of different types, that had their specific functional meaning and topological characteristics were not obvious, and have been addressed in this analysis.MethodseQTL data for 3 experimental C.elegans age groups were retrieved from WormQTL. WormNet has been used to obtain pair-wise gene interactions. The Shortest Path Function (SPF) has been adopted for statistical validation of the co-expressed gene clusters and for computational prediction of their potential gene expression regulators from a network context. A new SPF-based algorithm has been applied to genetic interactions sub-networks adjacent to the clusters of co-expressed genes for ranking the most likely gene expression regulators causal to eQTLs.ResultsWe have demonstrated that known co-expression and genetic interactions between C. elegans genes can be complementary in predicting gene expression regulators. Several algorithms were compared in respect to their predictive potential in different network connectivity contexts. We found that genes associated with eQTLs are highly clustered in a C. elegans co-expression sub-network, and their adjacent genetic interactions provide the optimal functional connectivity environment for application of the new SPF-based algorithm. It was successfully tested in the reverse-prediction analysis on groups of genes with known regulators and applied to co-expressed genes and experimentally observed expression quantitative trait loci (eQTLs).ConclusionsThis analysis demonstrates differences in topology and connectivity of co-expression and genetic interactions sub-networks in WormNet. The modularity of less continuous genetic interaction network does not correspond to modularity of the dense network comprised by gene co-expression interactions. However the genetic interaction network can be used much more efficiently with the SPF method in prediction of potential regulators of gene expression. The developed method can be used for validation of functional significance of suggested eQTLs and a discovery of new regulatory modules.

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Section: Introductionmentioning

confidence: 99%

On predicting regulatory genes by analysis of functional networks in C. elegans

et al. 2015

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Human Blood-Brain Differential Gene-Expression Correlates with Dipeptide Frequency of Gene Products

Ahmad¹

Bioinformatics Research and Applications

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Chemotherapy sorting can be used to identify cancer stem cell populations

Shao

et al. 2012

Mol Biol Rep

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Cancer stem cells (CSCs) of bladder transitional cell cancers (BTCC) had not been identified by the reported common methods. According to the phenomenon that CSCs were resistant to chemotherapy, BTCC cell lines T24 and 5637 were cultured with mitomycin C respectively. Cell inhibition assay revealed an increased population of drug resistant cancer cells with a concentration gradient of mitomycin C. The maximal and minimal cell inhibition rate in cell line T24 was 92.5 % ± 1.0 versus 64.1 % ± 1.4 (P < 0.001), and in cell line 5637 was 90.2 % ± 2.5 versus 55.1 % ± 1.8 (P < 0.001), respectively. There is no significant difference between these two groups. Drug resistant cells just comprised approximately 7.5 % (T24) versus 9.8 % (5637) of the total cells. Compared with control cells, cell cycle analysis demonstrated that more drug resistant cells were at G0G1 phase and fewer were at S phase with the concentration gradient of mitomycin C in both cell lines, which is in accord with the stem cell theory that most stem cells maintain in a quiescent condition. Importantly, we found that embryonic stem cell markers (OCT-4 and NANOG) were highly expressed in both gene and protein level in BTCC cell line T24 and 5637 after 24-h chemotherapy exposure. Interestingly, the drug concentration gradient was in accord with OCT-4 and NANOG expression, suggesting that chemotherapy sorting might be a feasible method for BTCC CSCs identification.

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Dynamic covariation between gene expression and proteome characteristics

Cited by 9 publications

References 61 publications

On predicting regulatory genes by analysis of functional networks in C. elegans

On predicting regulatory genes by analysis of functional networks in C. elegans

Human Blood-Brain Differential Gene-Expression Correlates with Dipeptide Frequency of Gene Products

Chemotherapy sorting can be used to identify cancer stem cell populations

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