Identifying Aging Genes in the Aging Mouse Hypothalamus Using Gateway Node Analysis of Correlation Networks

Kathryn M. Cooper, Stephen Bonasera, Hesham Ali

2015

Abstract

High-throughput studies continue to produce volumes of data, providing a wealth of information that can be used to better guide biological research. However, models that can readily identify true biological signals from this data have not been developed at the same rate, due in part to a lack of well-developed algorithms that can handle the magnitude, variability and veracity of the data. One promising and effective solution to this complex issue is network modeling, due to its capabilities for representing biological elements and relationships en masse. In this research, we use correlation networks for analysis where genes are represented as nodes and indirect relationships (derived from expression patterns) are represented as edges. Here, we define “gateway” nodes as elements representing genes that change in co-expression and possibly co-regulation between states. We use the gateway node approach to identify critical genes in the aging mouse brain and perform a cursory investigation of the robustness of these gateway nodes according to network structure. Our results highlight the power of the gateway nodes approach and show how it can be used to limit search space and determine candidate genes for targeted studies. The novelty of this approach lies in application of the gateway node approach on novel mouse datasets, and the investigation into robustness of network structures.

References

  1. Albert, R. 2005, "Scale-free networks in cell biology", Journal of cell science, vol. 118, no. Pt 21, pp. 4947- 4957.
  2. Aoki, K.F. & Kanehisa, M. 2005, "Using the KEGG database resource", Current protocols in bioinformatics / editoral board, Andreas D.Baxevanis [et al.], vol. Chapter 1, pp. Unit 1.12.
  3. Arancio, O., Zhang, H.P., Chen, X., Lin, C., Trinchese, F., Puzzo, D., Liu, S., Hegde, A., Yan, S.F., Stern, A., Luddy, J.S., Lue, L.F., Walker, D.G., Roher, A., Buttini, M., Mucke, L., Li, W., Schmidt, A.M., Kindy, M., Hyslop, P.A., Stern, D.M. & Du Yan, S.S. 2004, "RAGE potentiates Abeta-induced perturbation of neuronal function in transgenic mice", The EMBO journal, vol. 23, no. 20, pp. 4096-4105.
  4. Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M. & Sherlock, G. 2000, "Gene ontology: tool for the unification of biology. The Gene Ontology Consortium", Nature genetics, vol. 25, no. 1, pp. 25- 29.
  5. Backes, C., Keller, A., Kuentzer, J., Kneissl, B., Comtesse, N., Elnakady, Y.A., Muller, R., Meese, E. & Lenhof, H.P. 2007, "GeneTrail--advanced gene set enrichment analysis", Nucleic acids research, vol. 35, no. Web Server issue, pp. W186-92.
  6. Bader, G.D. & Hogue, C.W. 2003, "An automated method for finding molecular complexes in large protein interaction networks", BMC bioinformatics, vol. 4, pp. 2.
  7. Barabasi, A.L. & Albert, R. 1999, "Emergence of scaling in random networks", Science (New York, N.Y.), vol. 286, no. 5439, pp. 509-512.
  8. Barabasi, A.L. & Oltvai, Z.N. 2004, "Network biology: understanding the cell's functional organization", Nature reviews.Genetics, vol. 5, no. 2, pp. 101-113.
  9. Benson, M. & Breitling, R. 2006, "Network theory to understand microarray studies of complex diseases", Current Molecular Medicine, vol. 6, no. 6, pp. 695- 701.
  10. Deane, R., Du Yan, S., Submamaryan, R.K., LaRue, B., Jovanovic, S., Hogg, E., Welch, D., Manness, L., Lin, C., Yu, J., Zhu, H., Ghiso, J., Frangione, B., Stern, A., Schmidt, A.M., Armstrong, D.L., Arnold, B., Liliensiek, B., Nawroth, P., Hofman, F., Kindy, M., Stern, D. & Zlokovic, B. 2003, "RAGE mediates amyloid-beta peptide transport across the blood-brain barrier and accumulation in brain", Nature medicine, vol. 9, no. 7, pp. 907-913.
  11. Dempsey, K., Thapa, I., Bastola, D. & Ali, H. 2012, "Functional identification in correlation networks using gene ontology edge annotation", International journal of computational biology and drug design, vol. 5, no. 3-4, pp. 222-244.
  12. Dempsey, K.M. & Ali, H.H. 2014, "Identifying agingrelated genes in mouse hippocampus using gateway nodes", BMC systems biology, vol. 8, pp. 62-0509-8- 62.
  13. Horvath, S. & Dong, J. 2008, "Geometric interpretation of gene coexpression network analysis", PLoS computational biology, vol. 4, no. 8, pp. e1000117.
  14. Jeong, H., Mason, S.P., Barabasi, A.L. & Oltvai, Z.N. 2001, "Lethality and centrality in protein networks", Nature, vol. 411, no. 6833, pp. 41-42.
  15. Kriete, A. & Mayo, K.L. 2009, "Atypical pathways of NFkappaB activation and aging", Experimental gerontology, vol. 44, no. 4, pp. 250-255.
  16. Leclerc, E., Sturchler, E., Vetter, S.W. & Heizmann, C.W. 2009, "Crosstalk between calcium, amyloid beta and the receptor for advanced glycation endproducts in Alzheimer's disease", Reviews in the neurosciences, vol. 20, no. 2, pp. 95-110.
  17. Michaut, M., Baryshnikova, A., Costanzo, M., Myers, C.L., Andrews, B.J., Boone, C. & Bader, G.D. 2011, "Protein complexes are central in the yeast genetic landscape", PLoS computational biology, vol. 7, no. 2, pp. e1001092.
  18. Reverter, A. & Chan, E.K. 2008, "Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks", Bioinformatics (Oxford, England), vol. 24, no. 21, pp. 2491-2497.
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Paper Citation


in Harvard Style

M. Cooper K., Bonasera S. and Ali H. (2015). Identifying Aging Genes in the Aging Mouse Hypothalamus Using Gateway Node Analysis of Correlation Networks . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2015) ISBN 978-989-758-070-3, pages 36-43. DOI: 10.5220/0005212600360043


in Bibtex Style

@conference{bioinformatics15,
author={Kathryn M. Cooper and Stephen Bonasera and Hesham Ali},
title={Identifying Aging Genes in the Aging Mouse Hypothalamus Using Gateway Node Analysis of Correlation Networks},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2015)},
year={2015},
pages={36-43},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005212600360043},
isbn={978-989-758-070-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2015)
TI - Identifying Aging Genes in the Aging Mouse Hypothalamus Using Gateway Node Analysis of Correlation Networks
SN - 978-989-758-070-3
AU - M. Cooper K.
AU - Bonasera S.
AU - Ali H.
PY - 2015
SP - 36
EP - 43
DO - 10.5220/0005212600360043