Members of the order Haemosporida are protist parasites that infect mammals, reptiles and birds. This group includes the causal agents of malaria, Plasmodium parasites, the genera Leucocytozoon and Fallisia, as well as the species rich genus Haemoproteus with its two subgenera Haemoproteus and Parahaemoproteus. Some species of Haemoproteus cause severe disease in avian hosts, and these parasites display high levels of diversity worldwide. This diversity emphasizes the need for accurate evolutionary information. Most molecular studies of wildlife haemosporidians use a bar coding approach by sequencing a fragment of the mitochondrial cytochrome b gene. This method is efficient at differentiating parasite lineages but insufficient for accurate phylogenetic inferences in highly diverse taxa such as haemosporidians. Recent studies have utilized multiple mitochondrial genes (cyt b, cox1 and cox3), sometimes combined with a few apicoplast and nuclear genes. These studies have been highly successful with one notable exception: the evolutionary relationships of the genus Haemoproteus remain unresolved. Here we describe the transcriptome of Haemoproteus columbae and investigate its phylogenetic position recovered from a multi-gene dataset (600 genes). This genomic approach restricts the taxon sampling to 18 species of apicomplexan parasites. We employed Bayesian inference and maximum likelihood methods of phylogenetic analyses and found H. columbae and a representative from the subgenus Parahaemoproteus to be sister taxa. This result strengthens the hypothesis of genus Haemoproteus being monophyletic; however, resolving this question will require sequences of orthologs from, in particular, representatives of Leucocytozoon species.

Parasites of the genus Plasmodium infect a wide array of hosts, causing malaria in all major groups of terrestrial vertebrates including primates, reptiles, and birds. Molecular mechanisms explaining why some Plasmodium species are virulent, while other closely related malaria pathogens are relatively benign in the same hosts, remain unclear. Here, we present the RNA sequencing and subsequent transcriptome assembly of two avian Plasmodium parasites which can eventually be used to better understand the genetic mechanisms underlying Plasmodium species pathogenicity in an avian host. Plasmodium homocircumflexum, a cryptic, pathogenic species that often causes mortality and Plasmodium delichoni, a newly described, relatively benign malaria parasite that does not kill its hosts, were used to experimentally infect two Eurasian siskins (Carduelis spinus). RNA extractions were performed and RNA sequencing was completed using high throughput Illumina sequencing. Using established bioinformatics pipelines, the sequencing data from both species were used to generate transcriptomes using published Plasmodium species genomes as a scaffold. The finalized transcriptome of P. homocircumflexum contained 21,612 total contigs while that of P. delichoni contained 12,048 contigs. We were able to identify many genes implicated in erythrocyte invasion actively expressed in both P. homocircumflexum and P. delichoni, including the well described vaccine candidates Apical Membrane Antigen-1 (AMA-1) and Merozoite Surface Protein 1 (MSP1). This work acts as a stepping stone to increase available data on avian Plasmodium parasites, thus enabling future research into the evolution of pathogenicity in malaria.

No abstract

High‐throughput sequencing has become commonplace in evolutionary studies. Large, rapidly collected genomic datasets are used to capture biodiversity and for monitoring global and national scale disease transmission patterns, among many other applications. Updating homologous sequence datasets with new samples is cumbersome, requiring excessive program runtimes and data processing. We describe Extensiphy, a bioinformatics tool to efficiently update multiple sequence alignments with whole‐genome short‐read data. Extensiphy performs reference based sequence assembly and alignment in one process while maintaining the alignment length of the original alignment. Input data‐types for Extensiphy are any multiple sequence alignment in fasta format and whole‐genome, short‐read fastq sequences. To validate Extensiphy, we compared its results to those produced by two other methods that construct whole‐genome scale multiple sequence alignments. We measured our comparisons by analysing program runtimes, base‐call accuracy, dataset retention in the presence of missing data and phylogenetic accuracy. We found that Extensiphy rapidly produces high‐quality updated sequence alignments while preventing alignment shrinkage due to missing data. Phylogenies estimated from alignments produced by Extensiphy show similar accuracy to other commonly used alignment construction methods. Extensiphy is suitable for updating large sequence alignments and is ideal for studies of biodiversity, ecology and epidemiological monitoring efforts.

No abstract