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

Valba OV, Nechaev SK, Sterken MG, Snoek LB, Kammenga JE, Vasieva OO - BioData Min (2015)

Bottom Line: 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.Several algorithms were compared in respect to their predictive potential in different network connectivity contexts.The modularity of less continuous genetic interaction network does not correspond to modularity of the dense network comprised by gene co-expression interactions.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Nematology, Wageningen University, Wageninge, Netherlands.

ABSTRACT

Background: Connectivity 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.

Methods: eQTL 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.

Results: We 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).

Conclusions: This 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.

No MeSH data available.


Network reconstructed from the C.elegans genes with an adult life span phenotype from WormBase 220. Three main distinguished clusters can be seen: in the center — ribosomal, top left —metabolic, top right — proteosome and exosome functions. Blue circles indicate the test Cluster K1 genes. Orange-predicted regulators, dashed borders — functionally associated regulators discussed in the manuscript. (Not all aging-related functions related to the Cluster K1 are shown on this figure)
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Fig3: Network reconstructed from the C.elegans genes with an adult life span phenotype from WormBase 220. Three main distinguished clusters can be seen: in the center — ribosomal, top left —metabolic, top right — proteosome and exosome functions. Blue circles indicate the test Cluster K1 genes. Orange-predicted regulators, dashed borders — functionally associated regulators discussed in the manuscript. (Not all aging-related functions related to the Cluster K1 are shown on this figure)

Mentions: The test co-expression gene Cluster K1 mainly contained genes involved in the translational machinery. Our analysis highlighted its primary association with insulin-dependent pathway via such suggested regulators as daf-2 and insulin-regulated mRNA decay functions iff-1 and bir-2 [35]. The insulin pathway has an established role in the regulation of translation [36, 37]. As it is involved in regulation of the aging process, and iff-1 was shown to have a longevity phenotype, we investigated the genes of cluster K1 for association with longevity phenotypes (Fig. 3). The analysis has produced a supportive outcome. Predicted K1 connections, iff-1 and tin-9.2, are associated in a network with a ribosome maturation protein SBDS [38, 39], which is required for the longevity phenotype of daf-2 [40]. Interestingly, the transcription factors predicted for Cluster K1 by the SPF method were also found to be involved in regulation of longevity. The genes cgh-1 [41], dao-5 [42], hel-1 [43] were already linked to aging processes downstream of daf-2, daf-16, and in case of dao-5, to a daf-16 independent pathway associated with determination of the adult life span GO-term in WormBase database.Fig. 3


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

Valba OV, Nechaev SK, Sterken MG, Snoek LB, Kammenga JE, Vasieva OO - BioData Min (2015)

Network reconstructed from the C.elegans genes with an adult life span phenotype from WormBase 220. Three main distinguished clusters can be seen: in the center — ribosomal, top left —metabolic, top right — proteosome and exosome functions. Blue circles indicate the test Cluster K1 genes. Orange-predicted regulators, dashed borders — functionally associated regulators discussed in the manuscript. (Not all aging-related functions related to the Cluster K1 are shown on this figure)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4631084&req=5

Fig3: Network reconstructed from the C.elegans genes with an adult life span phenotype from WormBase 220. Three main distinguished clusters can be seen: in the center — ribosomal, top left —metabolic, top right — proteosome and exosome functions. Blue circles indicate the test Cluster K1 genes. Orange-predicted regulators, dashed borders — functionally associated regulators discussed in the manuscript. (Not all aging-related functions related to the Cluster K1 are shown on this figure)
Mentions: The test co-expression gene Cluster K1 mainly contained genes involved in the translational machinery. Our analysis highlighted its primary association with insulin-dependent pathway via such suggested regulators as daf-2 and insulin-regulated mRNA decay functions iff-1 and bir-2 [35]. The insulin pathway has an established role in the regulation of translation [36, 37]. As it is involved in regulation of the aging process, and iff-1 was shown to have a longevity phenotype, we investigated the genes of cluster K1 for association with longevity phenotypes (Fig. 3). The analysis has produced a supportive outcome. Predicted K1 connections, iff-1 and tin-9.2, are associated in a network with a ribosome maturation protein SBDS [38, 39], which is required for the longevity phenotype of daf-2 [40]. Interestingly, the transcription factors predicted for Cluster K1 by the SPF method were also found to be involved in regulation of longevity. The genes cgh-1 [41], dao-5 [42], hel-1 [43] were already linked to aging processes downstream of daf-2, daf-16, and in case of dao-5, to a daf-16 independent pathway associated with determination of the adult life span GO-term in WormBase database.Fig. 3

Bottom Line: 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.Several algorithms were compared in respect to their predictive potential in different network connectivity contexts.The modularity of less continuous genetic interaction network does not correspond to modularity of the dense network comprised by gene co-expression interactions.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Nematology, Wageningen University, Wageninge, Netherlands.

ABSTRACT

Background: Connectivity 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.

Methods: eQTL 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.

Results: We 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).

Conclusions: This 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.

No MeSH data available.