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Genomic basis of the convergent evolution of electric organs

December 1, 2014December 1, 2014 - by - Leave a Comment

Electric organs in fish have evolved independently in six lineages and are an interesting example of convergent evolution. However, the genetic basis underlying the convergence of this trait is poorly understood. By sequencing and assembling the transcriptomes from the electric organ (EO) and skeletal muscle of three of those lineages of electric fish, Galant et al. showed the presence of shared patters of gene expression in pathways related to differentiation from muscle cell, increased cell size, reduced contractility and increased excitability.

Paper summary

Electric organs allow fish to communicate, navigate and cope with predators and preys. They have evolved rather recently: less than 100 million years ago in the Cenozoic (as shown in Figure 1A). Importantly they have evolved independently in at least six taxonomically diverse lineages, constituting a clear example of convergent evolution.

Electrocytes are thought to be developmentally derived from myogenic precursors and are morphologically very different among fish lineages. This is illustrated in Figure 1B, where the authors show micrographs of electrocytes in two lineages of electric fish: gymnotiformes, such as Electrophorus electricus and Sternopygus macrurus, present electrocytes devoid of sarcomere, the contractile unit of muscle cells. In contrast, in mormiroids like Paramormyrops kingsleyae a disorganized and non-functional sarcomere can be found in electrocytes.

In order to understand the genetic programme that led to the common function of electrocytes in such morphologically different cells among lineages, Gallant et al. assembled the genome of the gymnotiform E. electricus and use RNA-seq reads from eight tissues for gene prediction, giving rise to 29,363 gene models. Genes co-expressed between tissues in E. electricus were subjected to k-means clustering analysis to reveal groups of genes that are either up-regulated (211 genes) or down-regulated (186 genes) in the EO as compared to skeletal muscle (Figure S1).

They sequenced and assembled the transcriptomes from EO and skeletal muscle of two other gymnotiforms (S. macrurus and E. virescens) and two species with an independently-evolved electric organ: M. electricus and the mormyroid B. brachyistius. In this four species they looked for the orthologs of those transcripts found up/donw-regulated in the cluster analysis of E. electricus to detect shared patterns of gene expression. Finally they focused on genes that might explain the convergent features of electrocytes versus muscle cells by selecting pathways related to down-regulation of muscle differentiation, increased excitability and insulation, decreased contractility and larger cell size.

A summary of the results is presented in figure 2A. Consistent with their idea that electrocytes derived from muscle cells, they found that transcription factors typically down-regulated in mature muscle cells are highly expressed in electrocytes (e.g. six2a, hey1), together with the down-regulation of transcription factors specifically involved in muscle cell differentiation (e.g. six4b, myogenin), except in S. macrurus.

They also showed that the increased excitability of electrocytes compared to muscle cells could be explained by the enhanced expression of certain genes involved in ion pumps and transporters (e.g. atp1a2a, scn4aa), with the notable exception of atp1a3a in E.electricus.

Similarly, they found a general down-regulation of genes related to the assembly of sarcomeres (e.g. smyd1a. cacna1sa), again with milder results in the EO of S. macrurus., that would account for the lack of functional sarcomeres and thus reduced contractility of these cells as opposed to muscle cells.

Finally, they found a general enhancement of the insulin-like growth factor (IGF) signalling pathway (e.g. igf2b, net-37, further illustrated in figure 2B), which would contribute to the larger cell size of electrocytes. An overview of the combined contribution of the studied pathways to the characteristic phenotype of electrocytes is proposed in figure 2C.

Personal comment

In the present study, Gallant et al. use transcriptome sequencing to elucidate the genetic basis of the convergent function of independently-evolved and morphologically diverse EO. However, in spite of this transcription-wide approach, the authors focused on a very selected and relatively small number of genes and transcription factors, potentially ignoring other genetic contributions that could be provided with the rich and large dataset generated.

Based on the notion that electrocytes are derived from muscle cells, they first selected genes that are up/down-regulated in the EO compared to skeletal muscle solely in E. electricus, disregarding the implications of genes that do not show differential expression, or genes up/down-regulated in EO of the other species but not in E. electricus.

Secondly, they selected certain pathways that they considered a priori to be likely responsible for the distinct phenotype of electrocytes, and for each of those pathway they presented in the main text results for only 5 genes that strongly supports their hypothesis. However in the supplementary figures S2-4 they extended their findings with other genes within the selected pathways that show more variable and unconserved patterns among species and that are nor further discussed. One particular case, atp1a3a shown in figure 2A, do not follow the pattern of enhanced expression in EO of E. electricus as they claim in the text for ion transporters responsible for increased excitability, but the unexpected result is not justified.

The presence of vestiges of disarrayed and non-functional sarcomeres in the electrocytes of the mormiroid S. macrurus that nevertheless might be energetically expensive to keep could suggest a more recent evolution of electric organs in this species. Consistent with this idea would be the milder up/down-regulation reported for some of the studied genes in S. macrurus compared to the other electric fish.

However it is important to notice the lack of biological and/or technical replicates in the study, a caveat that weakens its conclusions and questions its presence in a high-impact journal like Science. Given that the used animals were not wild but commercially obtained, increasing the sample size to a minimum of 3 animals per lineage studied could be feasible and would eliminate potential undesired technical or biological variability and provide more robust and conclusive results.

Reference

Gallant, J., Traeger, L., Volkening, J., Moffett, H., Chen, P., Novina, C., Phillips, G., Anand, R., Wells, G., Pinch, M., Guth, R., Unguez, G., Albert, J., Zakon, H., Samanta, M., & Sussman, M. (2014). Genomic basis for the convergent evolution of electric organs Science, 344 (6191), 1522-1525 DOI: 10.1126/science.1254432

Genome-wide signatures of convergent evolution in echolocating mammals (Parker et al., 2013)

February 27, 2014March 13, 2014 - by - Leave a Comment

Phenotype convergence is a fascinating topic in evolution. Usually species evolve by divergence, starting from a common ancestor and then developing different genomic changes that lead to different phenotypes. which are then selected by the environment. Nevertheless, it has been observed in several examples that two or more different species, even very far-related in the phylogenetic tree, appear to have developed, after their divergence, similar phenotypic traits in order to adapt to the environment, therefore leading to an apparent convergence of their branches.

The aim of this work is to investigate the hypothesis according to which convergent phenotypes are not just a lucky coincidence produced by different point-mutations occurred in different species, but rather that a convergent phenotype is associated with the same mutation in all the species involved, and that these mutations are not happen by chance but are pushed by adaptation to the environment.

In order to do it, this group analysed sequence identities in the genomes of species that developed independently echolocation, certainly a very complex feature that it’s hard to believe it has developed in different species just by chance.

The first step was building the gene set to work on. Therefore, they sequenced the genome of four different bat species (both echolocating and non-echolocating), and acquired online the coding gene sequences (CDSs) of other bat species, a dolphin species and other non-echolocating animals (dog, cow, horse, mouse and human). In order to remove potential error sources, all ambiguous sites/codons were removed, and they selected only those genes that had no missing data and whose homologous were present in at least six of the investigated species. The final gene pool was about 2000 CDSs.

In order to evaluate the genetic convergence of these genes, they considered three different phylogenetic tree hypothesis.

H₀: the real phylogenetic tree

H₁: a fake phylogenetic tree in which all echolocating bats (in brown) are in the same branch

H₂: a fake phylogenetic tree in which all echolocating animals (including dolphins) are in the same branch.

Then they proceeded with the alignment of all the CDSs in these species, and measured their SSLS

SSLS: site-wise log-likelihood support. A score that, based on the alignment of each amino acid in each gene of the investigated species, tells how much that alignment fits to a tree hypothesis.

If echolocating animals really have a sequence convergence in one or more genes, the alignment of these genes in echolocating animals will be better than expected, so it will fit to the fake phylogenetic trees (H₁-H₂) better than to the real tree.

The fitness (SSLS) to the three trees is evaluated, and ?SSLS of each gene is calculated.

?SSLS: difference between SSLS to H₀ and the SSLS to one of the fake trees. If the alignment of a gene fits more to H₁ than to H₀(sequence convergence), then ?SSLS(H₁) = SSLS(H0) – SSLS(H₁) of that specific gene will be a negative number. This calculation is applied to all the genes of the pool. Then they checked the scores of those genes which were already found to be involved in echolocation.

All the hearing genes known to be involved in echolocation show negative ?SSLS, as well as other genes involved in hearing (green) and vision (blue), supporting the hypothesis of genetic convergence. With H₂, the ?SSLS scores are still negative, but not so much, because the species involved are very distant and the hypothesis is more stringent.

In order to evaluate whether or not these converging mutation were pushed by adaptation, they used the ? ratio as a score of the influence of selection for each site.

If ? > 1, it means that there is adaptation.

For each site, the ? score was correlated with ?SSLS. For instance, if in a gene ?SSLS and ? are inversely correlated (positive ? and negative ?SSLS) it means that adaptation has pushed those particular sites towards the same mutations in the different echolocating species.

As expectable, after screening all the gene pool, all possible kinds of correlations were found. What is interesting is that some genes involved in hearing and vision had a strong correlation between ? and ?SSLS that supports the hypothesis of convergence by adaptation (bottom left in the picture).

However, it’s not really specified if all the genes involved in echolocation showed this correlation.

The paper shows a very interesting approach for correlating genetic paths in evolution in different species, and some of the results strongly suggest the validity of the hypothesis of genetic convergence by adaptation. Nevertheless, it seems that the authors are trying to prove their point skipping some elements that would need to be further investigated.

First of all, it seems that negative ?SSLS is present also in many genes that haven’t shown, so far, any association with echolocation, a fact that might bring some doubts about how much the ?SSLS information is actually important in this context.

Moreover, in the second part of the paper, they show that one of the proteins with the best ? score is Cdk1, saying that it supports their hypothesis because this protein is important for the development of hair cells in the inner ear. But Cdk1 is a ubiquitary protein, necessary in the cell cycle of every type of cells, so it’s not a protein specifically involved in hearing.

Parker, J., Tsagkogeorga, G., Cotton, J., Liu, Y., Provero, P., Stupka, E., & Rossiter, S. (2013). Genome-wide signatures of convergent evolution in echolocating mammals Nature, 502 (7470), 228-231 DOI: 10.1038/nature12511