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Systems biology of nuclear hormone receptors in Caenorhabditis
POSTERS ABSTRACTS 1
to each type of tumour. For instance, the gonadotropin-releasing hormone pathway is particularly linked to colorectal cancers, the ABC transporter family may have a special role in brain tumours, and many proteins containing a laminin G protein domain are mutated in pancreatic cancers. Interestingly, some of these associations have not been previously described in the literature and demonstrate the value of a scaled-up the analysis at the level of cellular functions. doi:10.1016/j.nbt.2010.01.070
[P1.64] Systems biology of nuclear hormone receptors in Caenorhabditis M. Kostrouchova ∗ , Z. Kostrouch Charles University in Prague, Czech Republic
Nematode model organisms from the genus Caenorhabditis are very efficient biological models. The community of Caenorhabditis elegans pioneered the open access complex informatics for sharing experimental data. The relatively compact genome and high accuracy sequencing, together with the experimental data, allow efficient predictions and functional modeling. Nuclear hormone receptors (nhrs) are transcription factors found in all metazoan species studied to date. In some Caenorhabditis species, the nhr gene family includes 200–300 genes distributed unevenly in the genomes. They include genes that show high degree of conservation during metazoan evolution and approximately 150 genes that arose by successive duplications and sequence diversification. We studied the distribution of the nhrs in the genome of C. elegans. Our analysis shows that nhrs form gene clusters on chromosomes I, IV, V and X. Experimental data obtained by functional studies of 24 nhrs and the data available on C. elegans database indicate that nhr genes acquired new properties by diversification in gene sequence and in gene expression. Our study indicates that nhrs are highly variable and versatile genes that are prone to adopt new species specific regulatory functions. This work was supported by grants 304/08/0970 and 304/07/0529 from the Czech Science Foundation and by the grant 0021620806 from the Ministry of Education, Youth and Sports of the Czech Republic. doi:10.1016/j.nbt.2010.01.071
[P1.65] Reconstruction of a genome-wide protein–protein functional linkage map: a computational approach to study cellular physiology V.Y. Muley ∗ , A. Ranjan Centre for DNA Fingerprinting and Diagnostics, India
Cellular responses to environmental conditions are governed by the fine tuning of functional and physical interactions in the S46
www.elsevier.com/locate/nbt
New Biotechnology · Volume 27S · April 2010
proteome. A number of methods have been proposed for the prediction of these protein–protein interactions (PPI), which are based on genomic contexts (includes co-evolution, chromosomal proximity, gene-order conservation) and expression similarity. Integration of the above methods boost the performance and machine learning methods (MLMs) provides a straight forward solution for integration. In this study, we derived the whole genome protein functional linkage map of Escherichia coli K12 using a combination of seven MLMs. Integration of six features (extracted using five genomic context based methods and ESM) and high-quality gold standard dataset for training of MLMs resulted in combined average balanced accuracy, sensitivity and specificity of over 93%, 87% and 99% respectively during a 10-fold validation run. The 59 054 functional linkages containing 3994 proteins which were predicted by a combination of more than four methods were considered as positive interactions. We believe that the combination of four MLMs captured the dominant structure of data considering the redundancy and similarity among some of the genomic features while reducing the false positives. The predicted PPIs promises to be a rich source of physiological insights encoded in E. coli K12. As a proof of principle, we able to reconstruct the whole pathway of purine catabolism using predicted PPI and further analyzed using the expression correlation values of the protein pairs involved which shows the inverse regulatory effect of purine catabolism on pyrimidine and arginine biosynthesis. Similarly, we show the importance of SdiA, cyclic-diGMP signalling proteins in the regulation of cell division, biofilms, and motility and also link them to membrane components. Analysis of predicted interaction networks in context of expression correlation in various conditions reveals the dynamic changes associated with a number of biological processes.
Figure 1 The performance of machine learning methods on gold standard data using combination of genomic context method and ESM features. DT: decision trees; RF: random forest; NB: naïve bayes; BN: bayesian network; NN: neural network; LR: logistic regression; and SVM: support vector machine.
to each type of tumour. For instance, the gonadotropin-releasing hormone pathway is particularly linked to colorectal cancers, the ABC transporter family may have a special role in brain tumours, and many proteins containing a laminin G protein domain are mutated in pancreatic cancers. Interestingly, some of these associations have not been previously described in the literature and demonstrate the value of a scaled-up the analysis at the level of cellular functions. doi:10.1016/j.nbt.2010.01.070
[P1.64] Systems biology of nuclear hormone receptors in Caenorhabditis M. Kostrouchova ∗ , Z. Kostrouch Charles University in Prague, Czech Republic
Nematode model organisms from the genus Caenorhabditis are very efficient biological models. The community of Caenorhabditis elegans pioneered the open access complex informatics for sharing experimental data. The relatively compact genome and high accuracy sequencing, together with the experimental data, allow efficient predictions and functional modeling. Nuclear hormone receptors (nhrs) are transcription factors found in all metazoan species studied to date. In some Caenorhabditis species, the nhr gene family includes 200–300 genes distributed unevenly in the genomes. They include genes that show high degree of conservation during metazoan evolution and approximately 150 genes that arose by successive duplications and sequence diversification. We studied the distribution of the nhrs in the genome of C. elegans. Our analysis shows that nhrs form gene clusters on chromosomes I, IV, V and X. Experimental data obtained by functional studies of 24 nhrs and the data available on C. elegans database indicate that nhr genes acquired new properties by diversification in gene sequence and in gene expression. Our study indicates that nhrs are highly variable and versatile genes that are prone to adopt new species specific regulatory functions. This work was supported by grants 304/08/0970 and 304/07/0529 from the Czech Science Foundation and by the grant 0021620806 from the Ministry of Education, Youth and Sports of the Czech Republic. doi:10.1016/j.nbt.2010.01.071
[P1.65] Reconstruction of a genome-wide protein–protein functional linkage map: a computational approach to study cellular physiology V.Y. Muley ∗ , A. Ranjan Centre for DNA Fingerprinting and Diagnostics, India
Cellular responses to environmental conditions are governed by the fine tuning of functional and physical interactions in the S46
www.elsevier.com/locate/nbt
New Biotechnology · Volume 27S · April 2010
proteome. A number of methods have been proposed for the prediction of these protein–protein interactions (PPI), which are based on genomic contexts (includes co-evolution, chromosomal proximity, gene-order conservation) and expression similarity. Integration of the above methods boost the performance and machine learning methods (MLMs) provides a straight forward solution for integration. In this study, we derived the whole genome protein functional linkage map of Escherichia coli K12 using a combination of seven MLMs. Integration of six features (extracted using five genomic context based methods and ESM) and high-quality gold standard dataset for training of MLMs resulted in combined average balanced accuracy, sensitivity and specificity of over 93%, 87% and 99% respectively during a 10-fold validation run. The 59 054 functional linkages containing 3994 proteins which were predicted by a combination of more than four methods were considered as positive interactions. We believe that the combination of four MLMs captured the dominant structure of data considering the redundancy and similarity among some of the genomic features while reducing the false positives. The predicted PPIs promises to be a rich source of physiological insights encoded in E. coli K12. As a proof of principle, we able to reconstruct the whole pathway of purine catabolism using predicted PPI and further analyzed using the expression correlation values of the protein pairs involved which shows the inverse regulatory effect of purine catabolism on pyrimidine and arginine biosynthesis. Similarly, we show the importance of SdiA, cyclic-diGMP signalling proteins in the regulation of cell division, biofilms, and motility and also link them to membrane components. Analysis of predicted interaction networks in context of expression correlation in various conditions reveals the dynamic changes associated with a number of biological processes.
Figure 1 The performance of machine learning methods on gold standard data using combination of genomic context method and ESM features. DT: decision trees; RF: random forest; NB: naïve bayes; BN: bayesian network; NN: neural network; LR: logistic regression; and SVM: support vector machine.