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Determining divergence times with a protein clock: update and reevaluation
D F Feng1, G Cho, R F Doolittle
1Center for Molecular Genetics, University of California at San Diego, La Jolla 92093-0634, USA.
Summary
New analysis of amino acid sequences suggests archaebacteria may have integrated into eukaryotes via eubacterial endosymbionts ~2 billion years ago, challenging current Tree of Life models.
Area of Science:
- Molecular Biology
- Evolutionary Biology
- Genomics
Background:
- Organismal divergence times are typically estimated using molecular sequence comparisons.
- Previous studies relied on amino acid sequence data to infer evolutionary relationships.
- Complete genome sequences offer new insights into ancient evolutionary events.
Purpose of the Study:
- To reanalyze organismal divergence times using improved methods and new genomic data.
- To investigate the phylogenetic placement of archaebacteria within the Tree of Life.
- To refine estimates of divergence times for major bacterial and archaeal lineages.
Main Methods:
- Amino acid sequence comparison with a refined distance measure accounting for substitution rate variation.
- Analysis of complete genome sequences from eubacteria and archaebacteria.
- Phylogenetic analysis to determine the relationships between different organismal groups.
Main Results:
- Archaebacterial sequences often appear as outliers or mixed with eubacterial orthologs, contradicting their usual clustering with eukaryotes.
- A significant portion of archaebacterial sequences in eukaryotes may originate from early eubacterial endosymbionts (~2 billion years ago).
- Eubacterial groups (cyanobacteria, Gram-positive, Gram-negative) and eukaryotes likely diverged around the same time, while archaebacteria and eubacteria diverged 3-4 billion years ago.
Conclusions:
- The Tree of Life may require revision regarding the placement and origin of archaebacteria.
- Endosymbiotic gene transfer from eubacteria played a crucial role in early eukaryotic evolution.
- Divergence times for early life forms can be more accurately estimated with advanced genomic and bioinformatic approaches.