A whole genome gene content phylogenetic analysis of anopheline mosquitoes

A Guide to Phylogenomic Inference

Phylogenomics aims at reconstructing the evolutionary histories of organisms taking into account whole genomes or large fractions of genomes. Phylogenomics has significant applications in fields such as evolutionary biology, systematics, comparative genomics, and conservation genetics, providing valuable insights into the origins and relationships of species and contributing to our understanding of biological diversity and evolution. This chapter surveys phylogenetic concepts and methods aimed at both gene tree and species tree reconstruction while also addressing common pitfalls, providing references to relevant computer programs. A practical phylogenomic analysis example including bacterial genomes is presented at the end of the chapter.

Systematic errors in orthology inference and their effects on evolutionary analyses

The availability of complete sets of genes from many organisms makes it possible to identify genes unique to (or lost from) certain clades. This information is used to reconstruct phylogenetic trees; identify genes involved in the evolution of clade specific novelties; and for phylostratigraphy—identifying ages of genes in a given species. These investigations rely on accurately predicted orthologs. Here we use simulation to produce sets of orthologs that experience no gains or losses. We show that errors in identifying orthologs increase with higher rates of evolution. We use the predicted sets of orthologs, with errors, to reconstruct phylogenetic trees; to count gains and losses; and for phylostratigraphy. Our simulated data, containing information only from errors in orthology prediction, closely recapitulate findings from empirical data. We suggest published downstream analyses must be informed to a large extent by errors in orthology prediction that mimic expected patterns of gene evolution.

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Presence of shared orthologs across species is used for evolutionary analyses

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We simulated realistic sets of orthologs with no gains or losses

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Errors predicting shared orthologs correlate with phylogenetic relationships

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Presence/absence datasets based on errors recapitulate findings from empirical data

Congruence Amidst Discordance between Sequence and Protein-Content Based Phylogenies of Fungi

Amid the genomic data explosion, phylogenomic analysis has resolved the tree of life of different organisms, including fungi. Genome-wide clustering has also been conducted based on gene content data that can lighten the issue of the unequal evolutionary rate of genes. In this study, using different fungal species as models, we performed phylogenomic and protein-content (PC)-based clustering analysis. The obtained sequence tree reflects the phylogenetic trajectory of examined fungal species. However, 15 PC-based trees constructed from the Pfam matrices of the whole genomes, four protein families, and ten subcellular locations largely failed to resolve the speciation relationship of cross-phylum fungal species. However, lifestyle and taxonomic associations were more or less evident between closely related fungal species from PC-based trees. Pairwise congruence tests indicated that a varied level of congruent or discordant relationships were observed between sequence- and PC-based trees, and among PC-based trees. It was intriguing to find that a few protein family and subcellular PC-based trees were more topologically similar to the phylogenomic tree than was the whole genome PC-based phylogeny. In particular, a most significant level of congruence was observed between sequence- and cell wall PC-based trees. Cophylogenetic analysis conducted in this study may benefit the prediction of the magnitude of evolutionary conservation, interactive associations, or networking between different family or subcellular proteins.

Phylogenomics

Phylogenomics aims at reconstructing the evolutionary histories of organisms taking into account whole genomes or large fractions of genomes. The abundance of genomic data for an enormous variety of organisms has enabled phylogenomic inference of many groups, and this has motivated the development of many computer programs implementing the associated methods. This chapter surveys phylogenetic concepts and methods aimed at both gene tree and species tree reconstruction while also addressing common pitfalls, providing references to relevant computer programs. A practical phylogenomic analysis example including bacterial genomes is presented at the end of the chapter.

Genome content analysis yields new insights into the relationship between the human malaria parasite Plasmodium falciparum and its anopheline vectors

Background

The persistent and growing gap between the availability of sequenced genomes and the ability to assign functions to sequenced genes led us to explore ways to maximize the information content of automated annotation for studies of anopheline mosquitos. Specifically, we use genome content analysis of a large number of previously sequenced anopheline mosquitos to follow the loss and gain of protein families over the evolutionary history of this group.

The importance of this endeavor lies in the potential for comparative genomic studies between Anopheles and closely related non-vector species to reveal ancestral genome content dynamics involved in vector competence. In addition, comparisons within Anopheles could identify genome content changes responsible for variation in the vectorial capacity of this family of important parasite vectors.

Results

The competence and capacity of P. falciparum vectors do not appear to be phylogenetically constrained within the Anophelinae. Instead, using ancestral reconstruction methods, we suggest that a previously unexamined component of vector biology, anopheline nucleotide metabolism, may contribute to the unique status of anophelines as P. falciparum vectors. While the fitness effects of nucleotide co-option by P. falciparum parasites on their anopheline hosts are not yet known, our results suggest that anopheline genome content may be responding to selection pressure from P. falciparum. Whether this response is defensive, in an attempt to redress improper nucleotide balance resulting from P. falciparum infection, or perhaps symbiotic, resulting from an as-yet-unknown mutualism between anophelines and P. falciparum, is an open question that deserves further study.

Conclusions

Clearly, there is a wealth of functional information to be gained from detailed manual genome annotation, yet the rapid increase in the number of available sequences means that most researchers will not have the time or resources to manually annotate all the sequence data they generate. We believe that efforts to maximize the amount of information obtained from automated annotation can help address the functional annotation deficit that most evolutionary biologists now face, and here demonstrate the value of such an approach.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3590-0) contains supplementary material, which is available to authorized users.