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Identification of thermophilic species by the amino acid compositions deduced from their genomes

Identification of thermophilic species by the amino acid compositions deduced from their genomes. David P. Kreil and Christos A. Ouzounis

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Identification of thermophilic species by the amino acid compositions deduced from their genomes

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  1. Identification of thermophilic species by the amino acid compositions deduced from their genomes David P. Kreil and Christos A. Ouzounis University of Cambridge and European Bioinformatics Institute, Computational Genomics Group, Research Programme, The European Bioinformatics Institute, EMBL Outstation, Wellcome Trust Gnome Campus, Cambridge CB10 1SD, UK Reporter: Yu Lun Kuo E-mail: sscc6991@gmail.com Date: October 26, 2006

  2. Outline • Introduction • Materials and Methods • Results • Discussion and Conclusion

  3. Introduction • The properties of thermophilic protein have been examined in the past two decades. • Thermophilic protein for particular amino acids, but general rules have not yet emerged. • Experiment is not only homologous proteins, but also protein unique to particular species.

  4. Introduction • The results for the genomes of six archaea, 19 bacteria, and the eukaryotic organisms. • Using two different approaches, several factors • Determine amino acid composition can be deduced • GC content of the coding sequences is the dominant influence on amino acid composition • Possible to identify thermophilic species

  5. Materials and Methods • Data sources and tools • Exploratory data analysis • Sensitivity analysis, sampling adequacy and significance

  6. Data Sources and Tools • Obtained from public databases • EBI (European Bioinformatics Institute) • NCBI (National Center for Biotechnology Information) • SRS – Access to multiple molecular biology databases • EPCLUST (Expression Profile data CLUSTering and analysis) • Hierarchical clustering • PCA (Principal Components Analysis)

  7. Exploratory Data Analysis • For all organisms, determined global amino acid compositions • Matrix where the rows represent the data sources list • The columns correspond to the respective percentage amino acid content

  8. Exploratory Data Analysis • Principal factors was supported two variables • GC ratio (GC counts vs. AT counts) • A binary variable (therm) • The binary variable, therm • 0 (zero) - mesophilic • 1 (one) - thermophilic

  9. Sensitivity Analysis, Sampling Adequacy and Significance • Miscellaneous clustering methods were tried • Average linkage (UPGMA) • Complete linkage (Maximum distance method) • Single linkage (Minimum distance method) • Weighted pair group method (WPGMA) • PCA was repeated to verify that this weighting did not affect any conclusions • 20 amino acids with equal weight

  10. Red – More than average Green – Less than average Results Unusually high GC ratio 57-67% 0.6 Thermophilic High GC ratio 0.2 Thermophilic 1.5

  11. Results (PCA of Amino Acid) 0-mesophile 1-thermophile • A clear separation of thermophiles and mesophiles along the second principal axis Thermophilic Archea – Red Bacteria – Green Eukaryote – Purple Outgroup - Blue

  12. Component Loadings • High Loading • Absolute component loadings > 0.6 • Component loading can be interpreted as correlation coefficients • Component 1 • Correlate with GC ratio • Component 2 • Correlate with Therm

  13. Very high factor loadings Statistical Evidence and Specific Feature of Thermophilic Species PCA factor loading for component 2 Average difference between thermo & meso Thermo & meso more or less • PCA • Starting from the distinct groups of thermophiles and mesophiles as obtained • Gln (Q) & Glu (E) • Have very high component loadings • Table 2 summarizes the results and most of the statistical evidence Strong Less – in Thermophiles < in mesophiles More - in Thermophiles > in mesophiles Low factor loadings Raw correlations with the binary variable therm

  14. Discussion and Conclusion • The results discern several underlying factors that influence amino acid composition • Completely sequenced genomes of 27 species • Employing different methods of data analysis • The two most prominent observations • Dominant effect of GC pressure • Clear identification of thermophilic species

  15. Discussion and Conclusion • PCA found GC ratio to be the most important factor • Environmental adaptations would also be expected to play a role • A pernix is found at a little distance from the other thermophiles

  16. Discussion and Conclusion • Not only true for individual proteins or groups of proteins but also for entire genomes • GC contents with a stronger influence on amino acid composition than adaptation to extreme environments (e.g., thermophily) • Interesting to extend analysis from different phyla

  17. Thanks

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