Abstract

Phylogenetic analysis by maximum likelihood (PAML) has become the standard approach to study positive selection at the molecular level, but other methods may provide complementary ways to identify amino acid replacements associated with particular conditions. Here, we compare results of the decision tree (DT) model method with ones of PAML using the key photosynthetic enzyme RuBisCO as a model system to study molecular adaptation to particular ecological conditions in oaks (Quercus). We sequenced the chloroplast rbcL gene encoding RuBisCO large subunit in 158 Quercus species, covering about a third of the global genus diversity. It has been hypothesized that RuBisCO has evolved differentially depending on the environmental conditions and leaf traits governing internal gas diffusion patterns. Here, we show, using PAML, that amino acid replacements at the residue positions 95, 145, 251, 262 and 328 of the RuBisCO large subunit have been the subject of positive selection along particular Quercus lineages associated with the leaf traits and climate characteristics. In parallel, the DT model identified amino acid replacements at sites 95, 219, 262 and 328 being associated with the leaf traits and climate characteristics, exhibiting partial overlap with the results obtained using PAML.

Highlights

  • RuBisCO is one of the best-studied enzymes and is often used as a model protein in evolutionary studies

  • The phylogenetic tree constructed for the Quercus small dataset with rbcL, matK and SSRs is well resolved with posterior probabilities > 50% (Fig 2)

  • RuBisCO of evergreen Fagales probably works at relatively low CO2 partial pressures. These results suggest that amino acid replacements at position 95 in evergreen Fagales may lead to RuBisCO with increased affinity for CO2

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Summary

Objectives

The objective of the study was to investigate molecular adaptation of Quercus RuBisCO to particular ecological conditions and to test if leaf morphological traits are associated with adaptive amino acid substitutions

Methods
Results
Discussion
Conclusion

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