(410d) Species Comparison Analysis Based On Metabolic Pathways | AIChE

(410d) Species Comparison Analysis Based On Metabolic Pathways


Ovacik, M. A. - Presenter, Rutgers University
Androulakis, I. - Presenter, Rutgers University, the State University of New Jersey

Certain fundamental remain invariable from bacteria to eukarya, therefore cross-species comparison often provides insights into underlying laws behind complex biological phenomena [1]. The general outline for the comparative analysis among species includes any similarity measure, distance matrix evaluation based on the similarity measure and phylogenic tree construction. The studies based on this approach include multiple sequence alignment of certain characteristic sequences: sequence of a single protein, single rRNA or single gene from each organism. The general standard is comparing the obtained phylogenic tree is with NCBI taxonomy [2]. The main concern about individual characteristic comparison is expressed by Felsenstein [3] that many of the conclusions derived from these studies are based on sub-parts of the phylogenetic tree and their dependability should be judged in this respect. Similarity analysis between i.e. single gene sequences cannot represent the complex relationships between species. Therefore, the whole genome is used in similarity analysis between different species. Yet, comparing genome sequences may not represent the higher level organization of organisms such as metabolic pathways and protein interaction networks.

Comparative analysis of metabolic pathways allows examining the totality of biological processes rather than the individual elements. Attempts to compare pathways include reaction content [4] and enzyme presence [5] as well as a combination of multiple features such as enzyme presence and sequence information of the enzymes in a given pathway [6-7]. The comparative analyses of pathways, however, only include individual pathways such as electron transfer, amino acid biosynthesis and TCA cycle over limited species or simpler organisms.

In this study, we expand the pathway similarity algorithm proposed by Forst et al. [7], and integrate the gene promoter similarity. We evaluate the similarity analysis for both individual pathways and over all pathways along different animal models such as rat, mouse, zebrafish, worm as well as simpler model organisms such as arabidopsis and yeast. We use the KEGG database for the pathway and the enzyme sequence information. The promoter regions are retrieved from TRANSFAC®. Over all pathways along different species, the phylogenetic relations based on our similarity measure are comparable with NCBI taxonomy; however individual pathways may differ in their own evolution context that of the organism. Our results demonstrate the potential for such an extensive bioinformatics analysis to identify appropriate surrogate species in experimental design.

[1]Wachtershauser G.(1990)Proc Natl Acad Sci U S A, 87: 200-4.

[2]Sayers EW et al. (2009) Nucleic Acids Res. D5-15.

[3]Felsenstein, J.( 1988) Annu Rev Genet, 22: 521-65.

[4]Hong, S.H. et al.(2004) Appl Microbiol Biotechnol, 65(2): 203-10

[5]Heymans, M. et al. (2003) Bioinformatics,19 Suppl 1: p. i138-46.

[6]Forst, C.V. et al. (1999) Comput Biol, 6 :343-60.

[7]Forst, C.V. et al.(2001) J Mol Evol,52(6): p. 471-89.