A transcriptome-based phylogenetic study of hard ticks (Ixodidae)

Abstract

Hard ticks are widely distributed across temperate regions, show strong variation in host associations, and are potential vectors of a diversity of medically important zoonoses, such as Lyme disease. To address unresolved issues with respect to the evolutionary relationships among certain species or genera, we produced novel RNA-Seq data sets for nine different Ixodes species. We combined this new data with 18 data sets obtained from public databases, both for Ixodes and non-Ixodes hard tick species, using soft ticks as an outgroup. We assembled transcriptomes (for 27 species in total), predicted coding sequences and identified single copy orthologues (SCO). Using Maximum-likelihood and Bayesian frameworks, we reconstructed a hard tick phylogeny for the nuclear genome. We also obtained a mitochondrial DNA-based phylogeny using published genome sequences and mitochondrial sequences derived from the new transcriptomes. Our results confirm previous studies showing that the Ixodes genus is monophyletic and clarify the relationships among Ixodes sub-genera. This work provides a baseline for studying the evolutionary history of ticks: we indeed found an unexpected acceleration of substitutions for mitochondrial sequences of Prostriata, and for nuclear and mitochondrial genes of two species of Rhipicephalus, which we relate with patterns of genome architecture and changes of life-cycle, respectively.

Figure 1

Single Copy Orthologues (SCO) occupancy matrix, generated after clustering with Silix. Each row represents a species (from top to bottom, species with a decreasing % of occupancy), while each column represents a SCO (presence of the gene is indicated by a black-filled cell). Different levels of shading indicate occupancy level: left (columns 1 to 30), SCO present in 100% of the species, center (columns 31 to 502), SCO present in between 75% and 100% of the species, right (columns 503 to 952), SCO present in between 50% and 75% of the species.

Figure 2

Phylogenetic tree based on nuclear genes, using the SCO50 supermatrix, mid-point rooted. Nodes with dots represent maximum support values (100% in all methods), otherwise bootstrap value and posterior probability were represented. These values are indicated for each of the SCO matrixes (occupancy level of 100%, 75%, or 50%): bootstrap values with the ML method are indicated on the top line, and posterior probabilities for the Bayesian approach on the bottom line.

Figure 3

Supernetwork based on genes from the SCO75 matrix (occupancy level of 75% among 27 tick species). This supernetwork was built from nucleotide alignments, including all codon positions. Species names abbreviated for readability (key of complete name in Supplementary Table S3).

Figure 4

Phylogenetic tree based on nine concatenated mitochondrial genes (mtAA9 data set)). Mitochondrial sequences were either derived from published mitogenomes, or if not available, from de novo transcriptome assembly (extracting contigs and translating ORFs). The tree was obtained with an ML method, with model optimization for each gene, as described in the text, with bootstrap support at the nodes.