Animal reproductive proteins, especially those in the seminal fluid, have been shown to have higher levels of divergence than non-reproductive proteins and are often evolving adaptively. Seminal fluid proteins have been implicated in the formation of reproductive barriers between diverging lineages, and hence represent interesting candidates underlying speciation. RNA-seq was used to generate the first male reproductive transcriptome for the New Zealand tree weta species Hemideina thoracica and H. crassidens. We identified 865 putative reproductive associated proteins across both species, encompassing a diverse range of functional classes. Candidate gene sequencing of nine genes across three Hemideina, and two Deinacrida species suggests that H. thoracica has the highest levels of intraspecific genetic diversity. Non-monophyly was observed in the majority of sequenced genes indicating that either gene flow may be occurring between the species, or that reciprocal monophyly at these loci has yet to be attained. Evidence for positive selection was found for one lectin-related reproductive protein, with an overall omega of 7.65 and one site in particular being under strong positive selection. This candidate gene represents the first step in the identification of proteins underlying the evolutionary basis of weta reproduction and speciation.
Citation: Twort VG, Dennis AB, Park D, Lomas KF, Newcomb RD, Buckley TR (2017) Positive selection and comparative molecular evolution of reproductive proteins from New Zealand tree weta (Orthoptera, Hemideina). PLoS ONE 12(11): e0188147. https://doi.org/10.1371/journal.pone.0188147
Editor: J. Joe Hull, USDA Agricultural Research Service, UNITED STATES
Received: May 22, 2017; Accepted: November 1, 2017; Published: November 13, 2017
Copyright: © 2017 Twort et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Raw sequencing data are available from GenBank Short Read Archive (BioProject: PRJNA353021). Assembled transcriptomes are available from TSA (accession numbers: GFBX00000000 and GFBW00000000). Sanger sequences are available from Genbank (accession numbers: KY999988 - KY999999, MF000001 - MF000301).
Funding: Funding was provided by the Allan Wilson Centre and core funding for Crown Research Institutes from the Ministry of Business, Innovation and Employment’s Science and Innovation Group. The New Zealand Institute for Plant and Food Research Ltd provided support in the form of a salary to RDN. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The New Zealand Institute for Plant and Food Research Ltd provided support in form of a salary to RDN. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Reproductive associated proteins have been shown to have increased evolutionary rates, and diverge rapidly between related taxa [1–6]. In particular, proteins present in the seminal fluid (SFPs) have been identified as often evolving under positive selection [4, 7–9]. In insects, the synthesis and secretion of SFPs occurs within male reproductive tract secretory tissues, such as accessory glands and testis [10, 11]. SFPs encompass a diverse range of functional classes and are involved in the modulation or induction of post mating responses in females [12–18]. In addition, SFPs have been identified as playing a key role in reproductive isolation between diverging lineages [2, 19–22].
Many Drosophila SFPs show increased evolutionary rates when compared to non-seminal proteins [1, 23, 24] and show evidence of positive selection [7–9, 25, 26]. However, not all SFPs exhibit rapid evolution; some show signs of evolutionary conservation [27–30], while others exhibit both conservation and rapid evolution in different regions of a single protein [31–33]. Drosophila SFPs have been shown to play a key role in reproductive isolation through species-specific gamete use, whereby SFPs need to have a specific structure and binding affinity for successful reproduction to occur . Studies of orthopteran taxa have revealed similar patterns. In particular, studies on crickets have shown that SFPs have a higher level of divergence when compared with non-seminal proteins, with a significant proportion being under positive selection [7, 8, 25, 35]. Despite these studies in crickets, very little is known about SFP evolution in other orthopteran taxa.
Insects from the orthopteran family Anostostomatidae are collectively known in New Zealand by their Māori name, weta, and represent an important component of the native forest ecosystem . All Tree (Hemideina) and Giant (Deinacrida) weta species are endemic to New Zealand, and include both relatively widespread and threatened species. The larger Deinacrida species have limited distributions and abundance, with 10 of the 11 species being under conservation management [37–40]. There are seven species of Hemideina distributed throughout New Zealand, most of which are abundant [37–41]. Hemideina crassidens (Blanchard) has the largest distribution of all weta species, with populations distributed in the south of the North Island, as well as in the north and west coast of the South Island in New Zealand . Hemideina crassidens has two chromosomal races (15 and 19) that are morphologically identical and successfully produce offspring in laboratory crosses [43, 44]. Hemideina thoracica (White) is found in the upper three quarters of the North Island . All populations are morphologically similar despite there being eight chromosomal races with diploid chromosome numbers ranging from 11 to 24 . Individual populations have been shown to exhibit only a single karyotype, with interbreeding occurring in the narrow regions of contact [45–47]. The presence of multiple chromosomal races within these species indicates that chromosomal differences are insufficient to lead to reproductive isolation [45, 48]. Hemideina trewicki has the smallest distribution of the three species, occurring only in southern and central Hawke’s Bay. In the northern parts of the range, H. trewicki is sympatric with H. thoracica [42, 49] and has one known chromosomal race . Although much progress has been made in revealing patterns of speciation and hybridisation in tree weta, the molecular basis of mate recognition, fertilisation and other reproductive processes are not known. The present study describes the male reproductive transcriptomes for H. crassidens and H. thoracica. We identify putative male reproductive associated proteins, and investigate patterns of divergence of nine genes between three Hemideina species. We test the hypothesis that male reproductive associated proteins have elevated rates of positive selection compared to general metabolic, or housekeeping, genes.
Materials and methods
Hemideina thoracica, H. crassidens and Deinacrida mahoenui (Gibbs) specimens were collected across their known distributions between 2010 and 2012 (S1 Table), by day and night searching. All samples were collected under a permit issued by the Department of Conservation (CA-31615-OTH). Insects were transported live to Landcare Research, Auckland, and then snap frozen and stored at -80°C.
Accessory gland and testis tissue from one adult male H. thoracica and H. crassidens (S1 Table) were dissected under a dissecting microscope in 100% ethanol. Total RNA was extracted from each tissue using TRIzol RNA extraction reagent (Life Technologies) according to the manufacturer’s protocol. A further RNA clean-up was performed using the RNeasy Mini Kit (Qiagen). RNA quality and quantity was determined using a Nanodrop (ThermoScientific) and an Agilent 2100 bioanalyzer (Agilent Technologies). High quality total RNA was used to synthesise cDNA using the SMARTerTM PCR cDNA synthesis kit (Clontech) with a modified oligo-dT primer (Cap-TRSA-CV) . Double stranded cDNA was purified using AMPure beads (Agencourt). Library quality was assessed using an Agilent 2100 bioanalyzer (Agilent Technologies) and quantified with the Quanti-i TM Picogreen® assay (Life Technologies). Cleaned cDNA was fed into the Rapid Library Preparation Protocol (Roche, GS Junior Titanium Series, June 2010) at the fragment end repair step, with each tissue sample being MID barcoded. The resulting libraries were pooled by tissue and sequenced in two runs on a 454 GS Junior (Roche) at Landcare Research (Auckland).
Pre-processing, assembly and annotating RNA-seq data
Raw sequences were split by MID barcode using Geneious V5.4.6 . Reads with ambiguous bases and low quality sequences were removed using SnoWhite 1.1.14 . The primer and adaptor sequences were removed using CUTADAPT V1.1 . Poly A/T tails longer than 15 bp from either end of the reads, and reads shorter than 50 bp were removed using PRINSEQ LITE V0.16 . Cleaned reads were de novo assembled with Newbler GS de novo Assembler (V. 2.5.3), with default parameters, a minimum overlap of 25 bp and a minimum overlap identity of 95%. Redundancy in the alignment was removed using cd-hit-est V. 4.5.6 . Poor de novo assembly of the H. thoracica dataset was observed, due to a lower sequencing quality of the testis library run. Therefore, in order to obtain a more representative dataset additional assembly steps were undertaken. The cleaned, trimmed reads from both H. thoracica libraries were reference assembled against the H. crassidens transcriptome using the Roche GS reference mapper (version 2.5.3, default parameters, except for a 25 bp overlap). The purpose of this reference assembly was to obtain contigs that were unassembled in the de novo assembly due to inadequate coverage. The reference and de novo assemblies were combined and subjected to a second round of redundancy removal with cd-hit-est. Preliminary tests on the combined assembly showed similar levels of blast homology, GO annotation and assembly statistics; therefore, this assembly replaced the de novo assembly for H. thoracica in downstream analyses. Singletons (unassembled reads) were excluded from downstream analysis. The assemblies were annotated using Blastx V2.6.0  (e-value < 1e-5) against the GenBank non-redundant (nr) protein database (downloaded July 2017). Transcripts were searched for conserved protein domains with InterProScan  and GO terms were assigned using Blast2GO v2.8 . Full-length transcripts were identified using Full-Lengther .
Identification of reproductive associated, orthologous, and candidate genes
Orthologous genes were identified using a bidirectional best hit method, which has been shown to outperform more complex algorithms for orthology predication . A pair-wise reciprocal blastn approach was carried out in Geneious (e-value threshold 1e-3) with orthologues being called if the best blast hit was identical in both directions.
Putative reproductive associated genes were identified in a two-step process. First, transcripts were identified based on mapping counts of D. fallai muscle RNA-seq reads downloaded from SRA (SRA accession: SRR5965744) using RSEM . Second, transcripts unique to the reproductive transcriptomes in H. thoracica and H. crassidens were identified by mapping the D. fallai short reads to each assembly using Bowtie2  and identifying the transcripts with no counts. Within these candidates, signal peptides, cellular location and the presence of trans-membrane domains were identified with SignalP (v4.1) , ProtComp v9.0 (http://linux1.softberry.com) and TMHMM v2.0 , respectively. Transcripts were retained as putative reproductive proteins if they had one of the following: (i) signal peptide, (ii) cellular localisation as extracellular and/or plasma membrane, (iii) transmembrane helix.
Candidate genes for downstream evolutionary analysis were chosen from among the contigs identified in the reproductive and orthologous gene screen. Candidates from the reproductive gene search were chosen based on their annotation, level of similarity (cut-off of 60%) and sequence length (minimum 400 bp). Orthologous candidates were based on a minimum transcript overlap of 200 bp between H. thoracica and H. crassidens transcripts, and the level of similarity at the amino acid and nucleotide level. Lastly, general metabolic control genes were chosen based on a minimum contig length of 300 bp and their involvement in general cellular processes, thereby ensuring tissue wide expression.
Sequencing of candidate genes
For Sanger sequencing samples, total RNA extractions from testis tissue followed the methods described above for the RNA-seq samples. Contaminating DNA was removed from RNA extractions prior to cDNA synthesis using TURBO DNase (Invitrogen). The first strand cDNA synthesis used the SuperScript III First Strand Kit (Invitrogen) following the manufacturer’s protocol. cDNA libraries were subsequently amplified using 5 μL first strand cDNA, 0.8 μM random hexamer primer, 2X PCR buffer (Roche), 2.5 mM MgCl2 (Roche), 0.2 mM dNTP (Roche), 1 U FastStart Taq DNA polymerase (Roche) in a total volume of 53 μL. Amplifications were performed on a GeneAmp PCR system 9700 thermal cycler (Applied Biosystems) using the following parameters: 5 min at 95°C, 3 min at 50°C, 40 sec 72°C; 40 cycles of 40 sec at 94°C, 40 sec at 65°C and 40 sec at 72°C; and 10 min at 72°C.
Primers for nine candidate genes (COI, Protease, Sflag, EFdelta, Unk2, Tkinase, Acp3, Acp4, Acp5) were designed using Primer 3  implemented within Geneious. Primer pairs were designed to amplify products of 200–1500 bp in length, have TMs of 60°C (±3°C) and to have a GC continent of 40–60% (S2 Table). Target genes were PCR amplified with reactions consisting of approximately 5 ng DNA, 1X PCR buffer (Roche), 2 mM MgCl2 (Roche), 0.2 mM dNTP (Roche), 0.1–0.2 μM forward and reverse primers (Sigma-Aldrich), 1 U FastStart Taq DNA polymerase (Roche), in a total volume of 25 μl. Amplification were performed on a GeneAmp PCR system 9700 thermal cycler (Applied Biosystems) using the following parameters: 5 mins at 95°C; 40 cycles of 15 sec at 95°C, 30 sec at primer specific annealing temperature and 1 min 30 sec at 72°C; and 5 min at 72°C.
PCR products were sequenced using BigDye Terminator Cycle Sequencing Ready Reaction Mix v3.1 (Applied Biosystems). Cycle sequencing products were cleaned using the BigDye Xterminator Purification Kit (Applied Biosystems) and sequenced in both directions on the ABI Prism 3100 Genetic Analyzer (Applied Biosystems). Sequences were subsequently cleaned, trimmed and aligned using Geneious. In addition, the D. mahoenui and D. fallai transcriptomes (unpublished) were searched for COI and all candidate gene orthologues, respectively using a bidirectional tblastx approach, and included in downstream analysis (sequences given in S1 File).
Haplotype reconstruction for sequences that exhibited heterozygosity was performed using PHASE V. 2.1 [66, 67] prior to calculating descriptive statistics in DnaSP V. 5 . Tajima’s D  and the McDonald-Kreitman test  were calculated in DNAsp. Haplotype networks were constructed using TCS V. 1.2.1 (Clement, Posada, Crandall 200), with gaps being considered as the 5th state and a 95% connection limit. The substitution model for the COI phylogeny was selected using the corrected Akaike information criterion  generated by jModel Test v.0.0.1 [72, 73]. A maximum likelihood phylogeny was constructed in Garli v2.0  using 100 search and 1000 bootstrap replicates.
Inferring positive selection
Genes were identified for selection tests based on the number of non-synonymous changes. The three candidates with the most non-synonymous changes (Acp3, Protease, Unk2) were chosen for downstream analysis. For these three, a neighbour-joining phylogeny was generated for selection tests in Geneious. To screen for positive selection ω was estimated by maximum likelihood, using codon-based substitution models implemented in the CODEML package of PAML V. 4.5 . The models implemented (M0, M1a, M2a, M3, M7, M8, M8a) are extensively described elsewhere [76–78]. Complex models (M2a, M3, M8) allow more than one category of ω, thereby allowing individual codons to be identified as under positive selection when the average ω across the whole gene indicates purifying selection. Likelihood ration tests (LRTs) between nested models allows inference of positive selection acting on a sequence . Codons under positive selection were identified using the Bayes empirical Bayes (BEB) method under the M8 model.
Results and discussion
Transcriptome assembly and characterisation
454 sequencing of H. thoracica and H. crassidens cDNA libraries resulted in a total of 254,628 reads, of which 73,012 and 59,465 reads were from H. thoracica, and 37,384 and 84,767 from H. crassidens testis and accessory gland tissue libraries, respectively. Raw sequences have been submitted to the GenBank Short Read Archive (BioProject: PRJNA353021). After trimming to remove bases with low quality scores, adapters and MID barcodes, 97% of the data remained. De novo assemblies were generated for each species as described above. The H. crassidens assembly generated 1,759 unigenes with an N50 of 608 bp and a maximum transcript size of 2,861 bp. In comparison, the H. thoracica assembly generated 2,691 unigenes with an N50 of 576 bp and a maximum transcript size of 1,860 bp. Both assemblies have been submitted to TSA under GFBX00000000 and GFBW00000000. A total of 890 and 1,537 transcripts were identified as being full length, respectively. Approximately 45% of the unigenes present in each assembly were functionally annotated using a tblastx search against the NCBI non-redundant database, with the species distribution of top matches overlapping among the two assemblies (S1 Fig). Functional annotation (GO) was similar across the two species, with the highest number of annotated transcripts related to cellular processes (GO:0009987) and metabolic process (GO:000812) (Fig 1). All unigenes were screened against the InterPro database, from which 2,690 and 1,753 H. thoracica and H. crassidens transcripts, respectively, were identified as containing conserved protein domains. The top 20 most frequent entries are shown in Table 1. These top entries show a diverse range of predicted functions, including proteins associated with general house-keeping roles and other that have been linked to reproductive functions. Domains identified include ubiquitin (IPR000626, IPR029071) and translation protein (IPR008991) domains. Many of the genes represented in these groups are probably highly conserved genes involved in the processes of transcription and protein degradation. Other domains identified, such as proteases (IPR001254, IPR018114) and protease inhibitors (IPR00215, IPR023796), have been associated with a number of reproductive functions, such as the modifying postmating changes in females , and are frequently identified in the study of insect SFPs [9, 80, 81].
Green bars: H. crassidens and blue bars: H. thoracica.
Identification of orthologous transcripts and reproductive proteins
The bidirectional best hit method identified 113 pairs of sequences that were putatively orthologous between the two species (S3 Table). For simplicity, these genes are hereafter referred to as orthologous. Some orthologues will have been missed using this approach, as in D. melanogaster the evolutionary rate of some SFPs has been shown to be so rapid that they lack any detectable similarity with their homologues from other Drosophila species [1, 24, 82, 83].
Putative reproductive associated transcripts were identified by mapping D. fallai muscle RNA-seq reads to each transcriptome. Transcripts unique to the reproductive transcriptomes (those lacking mapped reads) were further analysed to identify putative reproductive associated proteins. Of the transcripts unique to the reproductive transcriptome, 258 and 337 meet the criteria of having a signal peptide, transmembrane helix or localisation at the plasma membrane or extracellular for H. thoracica and H. crassidens, respectively (S4 Table). Roughly 19% of these had Blast hits, indicating that those lacking homology might be novel proteins or proteins highly diverged in weta. Among the genes with Blast hits, the most common molecular function GO terms were serine-type endopeptidase activity (GO:0004252), serine-type endopeptidase inhibitor activity (GO:0004867) and ATP binding (GO:0005524) (S2 Fig). Overall, the GO categories identified in the reproductive gene search are similar to categories commonly seen when studying insect SFPs [9, 16, 81, 84]. Various peptidase and peptidase regulators are among the reproductive proteins identified, and are believed to be essential for the regulation of reproduction through proteolytic cascades . These types of proteins constitute a large proportion of the D. melanogaster , Anopheles gambiae , Aedes aegypti , Lutzomyia longipalphis  and Clitarchus hookeri  identified SFP and accessory gland proteins. The reproductive proteins identified in this screen are similar when compared with other insects, however a large proportion lack Blast hits. These unknown transcripts indicate the presence of novel or highly divergent proteins, and provide a large resource for the study of sexual reproduction and speciation in the New Zealand weta [80, 85–90].
Candidate gene identification and sequencing
To study the patterns of molecular evolution of weta reproductive proteins, alignments of the candidates generated from the transcriptome sequencing were used to design PCR primers from nine genes (Table 2). Six putative reproductive proteins (Acp3, Acp4, Acp5, Sflag, Tkinase, and Protease) were identified as interesting candidates for downstream evolutionary analysis based on their blast homologies. The contig Unk2, despite lacking significant blast homology, was included for further analysis based on the interesting amino-acid pairwise identity observed during the orthologue gene screen. In addition, one nuclear (EFdelta) and one mitochondrial (COI) gene were included as general metabolic controls due to their tissue-wide expression. All nine genes were successfully amplified and sequenced from cDNA from, 19 H. thoracica, 11 H. crassidens, 5 H. trewicki and 1 D. mahoenui (outgroup) individuals. In addition, transcripts were identified within our unpublished RNA-seq data for D. fallai for all nine genes. All sequences have been submitted to NCBI GenBank (accession numbers: KY999988—KY999999, MF000001—MF000301).
Polymorphism, divergence and molecular evolution
Sequence data was obtained for COI from the majority of Hemideina individuals, resulting in a 672 bp alignment. The maximum likelihood phylogeny (Fig 2) supports each Hemideina species as monophyletic, with H. trewicki being sister to H. crassidens. The pairing of H. crassidens with H. trewicki is consistent with previous genetic and allozyme studies [41, 91].
Bootstrap support values greater than 0.5 are indicated. Scale bar represents the number of substitutions per site.
Haplotype networks were constructed for every gene, except COI, rather than phylogenetic trees. Very little sequence divergence was present within these genes, thereby reducing the statistical power of phylogenetic reconstruction [92, 93]. Two genes (Unk2, Protease, Fig 3A and 3D) showed monophyletic groupings of alleles, whereas the remaining six genes showed the presence of shared alleles between at least two of the species sequenced (Figs 3 and 4). Generally speaking, both the reproductive and general metabolic control genes showed similar patterns. Previous work has shown that at both a genetic [39, 94] and karyotypic  level H. crassidens and H. trewicki are more genetically similar to each other than either is to H. thoracica, and hence are more likely to produce fertile hybrids. Of the 8 genes sequenced, only Protease and Unk2 show a complete lack of allele sharing among the species. The genes Tkinase, Sflag, Acp4, and Acp5, show sharing of alleles between H. thoracica and H. crassidens. The geographically restricted H. trewicki shares alleles with H. crassidens (EFdelta, Acp3, Acp5) and H. thoracica (Acp5). The two Deinacrida species are well differentiated from the three sampled Hemideina species at all loci, in agreement with previous studies [39, 91]. Overall these results demonstrate genetic differentiation among the three tree weta species, in agreement with McKean et al. .
Haplotype network of A) Unk2, B) Sflag, C) Tkinase, and D) Protease gene regions. Circles represent different haplotypes, with the circles area being proportional to the frequency of each haplotype. Lines between haplotypes represent mutational steps between sequences. The empty circles represent inferred unsampled haplotypes. Colours correspond to species: Red, H. crassidens; Blue, H. thoracica; Yellow, H. trewicki.
Haplotype network of A) Acp4, B) Acp3, C) EFdelta, and D) Acp5 gene regions. Circles represent different haplotypes, with the circles area being proportional to the frequency of each haplotype. Lines between haplotypes represent mutational steps between sequences. The empty circles represent inferred unsampled haplotypes. Colours correspond to species: Red, H. crassidens; Blue, H. thoracica; Yellow, H. trewicki.
A summary of intraspecific sequence variation is shown in Table 3. Intraspecific variation within H. thoracica was greater than that observed in H. crassidens and H. trewicki for the majority of genes examined. This is consistent with allozyme and mitochondrial DNA studies that show H. thoracica has the highest levels of intraspecific diversity of all Hemideina species [39, 41, 91]. This signature is consistent with inferences that the range of H. thoracica has recently expanded southwards [46, 95],while in comparison, little to no diversity was observed within the H. trewicki samples. This is not an unexpected result as all samples originated from a single population.
The reproductive-associated candidate genes tended to display higher levels of within species diversity than the general metabolic controls. However, in some reproductive genes, especially Acp5 and Tkinase, the observed level of genetic diversity was at the same or similar levels as the general metabolic controls. The relatively lower levels of diversity in these two reproductive genes suggest they are functionally constrained. However, this requires further investigation, as only two metabolic controls were included in this study, and only partial transcripts were sequenced. Possible explanations for the lower diversity, include the sequenced region may be in a functionally constrained region of the protein, with relaxed selection occurring upstream or downstream of the sequenced region, or these genes may be located in regions of low recombination.
Within the Acp3 alignment an allelic variant containing a 24 bp indel was identified. All H. thoracica and two H. crassidens individuals have the insertion, while the remainder of H. crassidens and all H. trewicki samples lack the insertion. The 24 bp indel, appears to be a true allelic variant rather than the effect of preferential amplification of a paralogous gene as some individuals had only one copy of either the deletion or complete variant. If the deletion variant was in fact paralogous amplification then both copies would have been expected in all individuals of H. crassidens. InterProScan analysis revealed the presence of C-lectin type domains within the coding sequence. Lectins and lectin-related proteins have been shown to be involved in carbohydrate binding and the mediation of sperm-egg interactions [88–90], suggesting that this is an interesting reproductive candidate gene family.
At the species level, Tajima’s D was significant for the serine protease gene (Protease) within H. crassidens, (D = 2.17, Table 4) which may indicate balancing selection or demographic influences. In addition, for Acp4, Acp3, Acp5, Unk2, and Tkinase Tajima’s D statistics were negative for at least one species, thereby indicating an excess of rare or recent mutations that may be due to purifying selection or a recent demographic expansion, the latter of which has been observed for H. crassidens and H. thoracica . Under the McDonald-Kreitman test no departures from neutrality were detected for any of the genes (S5 Table).
To study the patterns of molecular evolution of weta reproductive proteins, the ratio of nonsynonymous to synonymous substitution rates of protein-coding sequences (ω) was calculated for two reproductive genes (Acp3, Protease) and the unknown gene, Unk2. The candidate Unk2 was included as part of the selection tests, as initial screening identified an ORF, which showed higher numbers of nonsynonymous than synonymous substitutions (Table 3). In the case of Acp3 the mean ω ratio was >1, indicating an excess of nonsynonymous changes across the protein coding region as a whole (Table 5). In contrast, Unk2 and Protease had an ω <1 (Table 5). Omega ratios averaged over an entire protein coding region are typically <1, due to positive selection commonly acting on specific domains or residues , with evidence suggesting genes with mean ω ratios above 0.5 are experiencing episodes of adaptive evolution [1, 7, 97, 98]. Individual amino acid residues likely to have been influenced by selection were identified using site-based ω calculations, with likelihood ratio tests and chi-squared distributions used to assess the goodness of fit for a given model [77, 99, 100]. For comparison, we used six codon-substitution models to assess the mode of selection acting on each amino acid residue in our candidate genes. We found that for Acp3 and Protease there is significant among-site variation in ω, with the M3 model permitting three ω values providing a significantly better fit to the data than the M0 model (p-value<0.05; df = 4; M0:M3; Table 5, S6 Table). The M1a:M2a comparison was insignificant for all three genes. A more conservative approach for testing for positive selection is the M8:M8a comparison, under this model Acp3 was identified was being under positive selection with a mean ω of 7.9 (Tables 5 and S6).
A Bayes Empirical Bayes computation  implemented in PAML was used to assess the significance of the ω ratio at each codon position. Under the M8 model five sites were assigned to the positively selected class (ω >1), only one of which had a probability > 95% (ω = 6.366, Table 5, Fig 5). Observed changes at three of the five sites were non-conservative (S7 Table). It is acknowledged that likelihood-based methods can produce high levels of false positives [102, 103], however the alternative parsimony-based models tend to be very conservative and have low power detecting true positives, particularly in small datasets such as this one [104, 105]. The five sites presented here represent testable hypotheses for functionally important regions under selection within Acp3 that could be examined with a larger dataset and other analysis methods. However, it should be noted that the function of Acp3 is unknown, as is the exact position of these sites within the overall protein structure.
Red line represents the mean posterior omega, and the blue line represents the probability of each codon being under positive selection. The values were calculated using a Bayes Empirical Bayes analysis under the M8 site-specific model in Paml. Codon position based on full-length alignment. The annotations identified using InterProScan and the position of the 24 bp indel are shown in relation to codon position.
Here we present the first male reproductive transcriptomes for H. crassidens and H. thoracica, resulting in putative gene sets of 1,754 and 2,691 non-redundant gene sets, respectively. We identified 865 putative reproductive associated proteins, and 113 orthologs, from which nine candidates were used for downstream evolutionary analyses. Our results suggest that positive selection may be acting on some Hemideina SFPs; in contrast, we were unable to detect positive selection on the general metabolic control genes. The lectin-related Acp3 gene shows evidence for selection acting along the gene as a whole and on particular amino acids. This presents a testable hypothesis into what selection may be occurring on weta reproductive proteins. A better understanding can be achieved by incorporating functional and population genomics with candidate gene approaches to reveal the relationship between the evolution of these genes and mate recognition and speciation. In addition, the transcriptome data generated represents a first step in the identification of reproductive associated proteins in weta. These transcriptomic sequences will provide a valuable resource for further research into the evolution of reproductive proteins and speciation of New Zealand weta.
S1 Table. Sample collection details.
**Samples used for 454 sequencing.
S2 Table. Primer sequences for candidate genes.
Annotations from tblastx against non-redundant database.
S3 Table. Orthologous contigs pairs identified in the bidirectional tblastx search.
S4 Table. Contigs identified in the reproductive protein search.
S5 Table. McDonald Kreitman test results for each gene.
a Yates correction is applied to G tests.
S6 Table. Likelihood values and parameter estimates for site-specific models for each gene.
Parameters in brackets are not free, and therefore are not counted when considering difference in the number of parameters for nested model comparisons.
S7 Table. Sites identified as putatively under selection and their posterior probabilities under the M3 and M8 models.
a Amino acid site in the original alignment, including gaps; BEB, Bayes Empirical Bayes posterior probability; NEB, Naïve Empirical Bayes; **Probability > 99%.
Top-Hit species distribution for tblastx results for A) Hemideina thoracica and B) Hemideina crassidens.
Gene ontology categories identified in the reproductive gene screen for A) Hemideina thoracica and B) Hemideina crassidens.
We would like to thank George Gibbs, Robert Hoare, George Twort, Owen Twort, Ian Twort, Genesta Twort, Dave Seldon, Peter Richie, Shelley Meyers, Chrissie Painting, Luke Dunning, Robin Howarth, Kathy Howarth, Paulina Giraldo Perez, Corrine Watts and Katy Hill for assistance with field work and providing samples, the New Zealand eScience Infrastructure (NeSI) high-performance computing facilities and the staff at the Centre for eResearch at the University of Auckland, for bioinformatic support and the Department of Conservation for access to field sites. We thank Joe Hull and three anonymous reviewers for comments that improved the manuscript.
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