The parallel evolution in amniotes seen through the eye of functional nodal mutations

Introduction

In this article the authors describe an evolutionary convergence in mammals, birds, and reptiles, based on genomic data from NCBI. The evolution of different species and lineages is due to mutations that can appear and accumulate in organisms over time. Those mutations need a high functional potential and have to be conserved in time in order to form new species. The conservation of mutations can occur via selection pressure, mutational compensation, and/or by the separation of members from the same species by geological and environmental events.

In this comprehensive study, the authors describe, a genomic landscape of the parallel evolution by analysing functional nodal mutations (fNMs) by using different types of DNA (mitochondrial and nucleic), the thermostability of mtDNA encoding RNA genes, and the structural proximity of proteins, using the available 3D structures from PDB database. Functional nodal mutations (fNMs) can be separated in single nodal (fSNMs), recurrent nodal mutations (fRNMs), occured independently in unrelated lineages and recurrent combinations of nodal mutations (fRCNMs) recurred independently along with other nodal mutations in combinations in more than a single lineage. The recurrent ones can be taken in consideration the most when we are talking about the convergent adaptive responses, that means the parallel evolution of different species. In this study, one of the aim is to find the best candidate for this adaptive mutations that was present in the evolution of the amniotes. The compensated ones are used to identify the adaptive mutations. The main explanation for the convergent evolution is the presence of the recurrent nodal mutations. Many fNMs are in combination with potential compensatory mutations in RNA and protein-coding genes. The compensation of a functional mutation is the co-occurrence with additional mutations that are “affecting” the original function.

Results

In the article it is claimed that the evidence for parallel evolution is mainly due to the presence of a high number of uncompensated reccurent fNMs. The best candidate to show the parallel evolution is the emergence of body thermoregulation in mammals and birds, that seems to be independent.

The mtDNA, the maternal genetic information was used to identify the fNMs in the amniotes. The study is based on mtDNA from 1003 species and nDNA from 91 species. The mtDNA was used for the structure-base alignment for 24 mtDNA-encoded RNA genes (tRNA and rRNa) and 13 protein-coding gene. To this, they added 4 more mtDNA proteins with the 3D structure: CO1-3 and Cytb, as the cytochromes are highly conserved proteins across various species. The mtDNA genes are usually the same, but what seems to be different it is the order of the genes, that are changed by evolutionary rearrangements. Because of this, they first aligned the genes individually and after this, they concatenated the 37 proteins to the human mtDNA gene order.

The sequence alignment revealed a number of 25234 nodal non-synonimous and RNA gene mutations. To see the potential of this mutations, there were calculating a score that include: evolutionary conservation, physical-properties (of non-synonymous changes) and the molecular thermostability (the free estimated energy (ΔG) for the two RNA sequences was calculated before and after the mutational event). The score, from 1 to 9 is depending to the level of conservation and physico-chemical properties of the tested amino acid.After calculating the potential function score of all the nodal mutations, there were 3262 non-synonimous fNMs, mainly in RNA genes with mutations related to disease-causing.

The next step was to identify the best candidate for adaptive fNMs by studying the compensated and non-compensated mutations, but the approach chosen by the authors cannot reveal the exact order of compensation process. Meanwhile, there are some compensatory mutations that could gain lower functionality scores than the co-occurring fNMs. In the Figure 1, we can see a demonstration of the potential compensation and a possible adaptation in a protein-coding gene (COX2) through different species. The panel b shows the locations of the fNMs (S155T) and different other co-occurring compensatory mutations. The S155T mutation appears as independently re-occurrent as well as compensatory co-occurring mutations. As we can see, this approach is pure theoretical, because cannot show all the compensations, only the best ones, that got fixed in evolution. The Figure 2 shows the prevalence of different types of mutations that could be compensated or not. The predictive results reveal a high probability of fRCNMs to be compensated for RNA and protein-coding genes. Here are introduced also the information from the nDNA, that is compared with mtDNA in term of prevalence of the compensatory and non-compensatory mutations. Because there was a big difference of the number of species involved in this approach, the evolutionary resolution was reduced. So, the authors decided to analyze the same 91 species for mtDNA and nDNA and reducing the bias. Because of the reduction in the resolution, they redid the analysis by using the most ancient mutations, that occurs in deeper nodes in the case of mtDNA, but this revealed almost the same proccent as they were working with the 91 species (37% for the ancient mutations and 34% by including the younger ones) (Figure 2e & Supplementary 5b,c). So, the older mutations appear to be less compensated and this give more uncompensated mutations that are best candidates in the ancient adaptative mutations. In the supplementary Figures, the authors are using the OXPHOS complexes to compare the fNMs in mtDNA and nDNA by using 91 species. For the intra-mtDNA the albeit is less prominent (31%).

For the nDNA data is used the whole genome of the species. So, the information is much more comprehensive by the presence of a higher number of genes. In comparison with the mtDNA, the compensation prevalence is lower, having a difference of 10%, but in both case the proccent of possible compensation is higher than can be explained by the mutation rate or the chance.

In the end, to determine the best adaptive mutations over the evolution, they used the fRNMs from mtDNA, but maybe because of the low number of the samples, the result did not show any proof of the impact of non-compensated fRNMs in being the main reason for the convergent evolution. Instead, the nDNA revealed a significant pattern with highest number of potential non-compensated fRNMs shared between birds and mammals (N=51). The best candidates resulted by being the mutations in the genes related to the thermoregulation in the birds and mammals.

Conclusion

In this comprehensive study, the authors merged several information, including different types of DNA, from many species, with various physico-chemical parameters. The results of this work reveal, that the ancient functional mutation are the best for being studied, because of their possibility to overcome negative selective. The best candidates for the adaptive nodal mutations are in the end the non-compensated fNMs, that are in a higher presence in the case of old fNM. This seems to be the main helper for the evolution of the thermoregulation in birds and mammals. The protein analysis reinforces the main conclusion: for enriching the adaptative mutations, the non-compensated mutations are the best candidates.

Taken together this study provides new insights into how different lineages and species might have developed over time. It also shows a new way how to combine data from different sources. However, the authors fail in giving an adequate explanation for the fNMs, together with the fact that they lack references that describe this term makes the article difficult to understand, especially for people that are not from the field and this is in fact the contrary of how scientific writing should be done.

 

Levin & Mishmar, 2017, The genomic landscape of evolutionary convergence in mammals, birds and reptiles. Nature Ecology & Evolution 1: 0041

 

 

This entry was posted in adaptation, conservation, evolution, genomics. Bookmark the permalink.

Leave a Reply

Your email address will not be published. Required fields are marked *