Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

The Mitochondrial Genome of Arctica islandica; Phylogeny and Variation

  • Gernot Glöckner ,

    gernot.gloeckner@uni-koeln.de

    Affiliation Institute for Biochemistry I, Medical Faculty, University of Cologne, Köln and Leibniz-Institute of Freshwater Ecology and Inland Fisheries, IGB, Berlin, Germany

  • Ivonne Heinze,

    Affiliation Leibniz Institute for Age Research, Fritz-Lipmann Institute, Jena, Germany

  • Matthias Platzer,

    Affiliation Leibniz Institute for Age Research, Fritz-Lipmann Institute, Jena, Germany

  • Christoph Held,

    Affiliation Alfred Wegener Institute for Polar and Marine Research, AWI, Bremerhaven, Germany

  • Doris Abele

    Affiliation Alfred Wegener Institute for Polar and Marine Research, AWI, Bremerhaven, Germany

Abstract

Arctica islandica is known as the longest-lived non-colonial metazoan species on earth and is therefore increasingly being investigated as a new model in aging research. As the mitochondrial genome is associated with the process of aging in many species and bivalves are known to possess a peculiar mechanism of mitochondrial genome inheritance including doubly uniparental inheritance (DUI), we aimed to assess the genomic variability of the A. islandica mitochondrial DNA (mtDNA). We sequenced the complete mitochondrial genomes of A. islandica specimens from three different sites in the Western Palaearctic (Iceland, North Sea, Baltic Sea). We found the A. islandica mtDNA to fall within the normal size range (18 kb) and exhibit similar coding capacity as other animal mtDNAs. The concatenated protein sequences of all currently known Veneroidea mtDNAs were used to robustly place A. islandica in a phylogenetic framework. Analysis of the observed single nucleotide polymorphism (SNP) patterns on further specimen revealed two prevailing haplotypes. Populations in the Baltic and the North Sea are very homogenous, whereas the Icelandic population, from which exceptionally old individuals have been collected, is the most diverse one. Homogeneity in Baltic and North Sea populations point to either stronger environmental constraints or more recent colonization of the habitat. Our analysis lays the foundation for further studies on A. islandica population structures, age research with this organism, and for phylogenetic studies. Accessions for the mitochondrial genome sequences: KC197241 Iceland; KF363951 Baltic Sea; KF363952 North Sea; KF465708 to KF465758 individual amplified regions from different speciemen

Introduction

The Iceland clam Arctica islandica (Linnaeus 1767, common name „ocean quahog”) is the longest-lived non-colonial metazoan with a recorded maximum lifespan potential (MLSP) of more than 507 years reported for the subpolar population from the North Icelandic shelf [1]. Because of its outstanding longevity, A. islandica has garnered interest as a new invertebrate model of aging and minimal senescence [2]. Individual shells of this interminably growing species can reach large sizes (approaching 100mm shell length) and can also become very heavy as layers of aragonite are added to the inner shell wall. Because of its annual growth increment formation, A. islandica has been used successfully as a high-resolution marine environmental archive [3]. The species has a wide geographical distribution in the Northern hemisphere covering temperate to subarctic regions in eastern (European) and western (American) North Atlantic shelf regions. In the eastern Atlantic, stable populations exhibiting the full lifespan occur around Iceland, in the Irish Sea, and on the entire Scandinavian Atlantic shelf. In these fully marine environments, Arctica forms dense stocks between 30 and 60m water depth [4]. The German Bight and southern North Sea stocks have thinned dramatically during the past 50 years, owing mostly to the impact of intense beam trawl fishery [5]. Nearly no young premature animals can be found here and MLSP is limited to <100y [5,6]. Marginal but stable populations further exist in brackish White (WS) and Baltic (BS) seas. Low and variable salinity correlate with a dramatically decreased population-specific lifespan in the White and Baltic Sea [7]. First investigations of the transcriptome of this longevity champion indicate that specimens from low salinity environments (Baltic Sea) transcribe a number of stress genes upon exposure to anoxia, whereas individuals from Iceland and the North Sea largely shut down gene transcription under anoxia and, instead, enter a state of metabolic rate reduction in which only cellular maintenance is supported [8]. The mitochondria, their chemical composition, their energy conserving electron transport systems and their capacity to generate free radicals under different metabolic and stress conditions are being extensively studied and related to longevity of vertebrate and invertebrate species [9-14].

Bivalves are grouped into five subclasses according to a molecular phylogeny [15]. A. islandica belongs to the Heterodonta, order Veneroida, family Arcticidae. Currently 20 complete mitochondrial genomes from this subclass are available in public databases, enabling a thorough gene order comparison between them. Metazoan mitochondria have a size range of 11 to 32 kb, the latter referring to the scallop Placopecten magellanicus (NC_007234). They normally encode 13 protein coding genes and up to 22 tRNA genes together with the two mitochondrial rRNA species [16]. The number of tRNA genes is the least conserved feature in the mtDNA and can vary between 2 to nearly 30 tRNA sequences in metazoan mitochondrial genomes [16]. Cnidaria, some Porifera, and arrow worms are known to have severely reduced set of mitochondrially encoded tRNAs, but this condition is expected to have evolved independently many times throughout the phylogenetic tree [17,18]. Further, molluscan mtDNA exhibit the highest variability observed in terms of genome size, mainly due to segmental duplications with rearrangements.

Our study aiming at the deciphering and analysis of the A. islandica mitochondrial genome was motivated by three objectives: i) to establish a data set for the correct placement of A. islandica in the phylogenetic context ii) to assess its overall mt genome variability and iii) test haplotype diversity in a set of specimen.

Results and Discussion

Sampling

The three selected habitats differ considerably in their salinity and mean temperature regimes. While the salinity in the North sea (33-34‰) is like that of the Icelandic shelf (34‰), the salinity at the Baltic sea sampling site is much lower with only 25 ‰ but fluctuations are high (between 14 and 28‰), depending on episodic inflow of more saline water via the Kattegat. The mean annual water temperature around Iceland is 5°C, highest summer temperatures approach 10°C, while the Baltic and North Sea sampling sites approach a mean annual water temperature of 10 °C.

The Iceland population has a maximum reported lifespan of 507 years [1] and specimens older than 150 years have repeatedly been found [19]. Here, the animals live between 10 and 40 m water depth. Animals from the North Sea around Helgoland have a maximum observed lifespan of 100 years and the shell length approaches 100mm. Mean shell length in the population is 85mm. Baltic Sea population maximum lifespan is 30 years and maximum shell length is 50 mm. Despite the short lifespan this brakish population is comparably dense with many new recruits.

In total, we sampled 17 specimens (five from the Baltic Sea, and six from Iceland and North Sea, respectively, Table 1).

IDdate of catch  region coordinates
276614 276617 27661 27663 2766827.06.2006Baltic Sea, Kiel BayN 54°32.6´- E 10°42.1´
101ISMRD 108MRD10IS 111MRD10IS 72IS10N 86IS10N 95MRD10IS15.08.2008Iceland, NEN 66° 01.44´ - W 14° 50.91´
10MRD 24N10 33MRD10 37N10 44N10 47MRD1020.05.2008North Sea, HelgolandN 54°10.44´- E 007° 48.12´

Table 1. Sampled specimens of A. islandica.

CSV
Download CSV

Mitochondrial genome properties

Sequencing of one randomly selected individual from each sampling site/region resulted in a consensus mitochondrial sequence of 18294 bases after gap closure procedures (see Methods). In total, we obtained a consensus sequence with an average coverage of more than 40 over the whole genome (Table 2). We used this consensus sequence to close the gaps in the individuals to obtain specimen-specific mitochondrial genomes.

setreadsnumber of bases  mean read length  reads in mitochondrial contigs  assembly  no of contigs  Mean coverage
North Sea3704581467512202135182021 25
Baltic115382832942245474163124 7
Iceland144833476793240684178192 9

Table 2. Sequencing statistics.

CSV
Download CSV

Some bivalves, namely Mytiloida, freshwater mussels of the superfamily Unionoidea, and marine clams of the order Veneroida exhibit so-called doubly uniparental inheritance (DUI). The species with DUI have two different kinds of mitochondrial genomes, a male (M) and female (F) one, between which differences in gene order and sequence divergence of up to 20 % can occur [20,21]. While females have only the F genome, males are heteroplasmic and possess F and M genomes, which are differentially expressed in somatic tissues (F) and in gonads (M) [22]. Among the three complete mitochondrial genomes we report in this study we did not find comparable levels of sequence heterogeneity or gene rearrangements known from species with DUI. Further sequencing of selected regions of 17 specimens (see below) did not recover highly heterogeneous sequences either, indicating either lack of DUI in this species, sampling of only female specimens, or only slight heterogeneity between F and M type mitochondrial genomes. The data of this study thus are not sufficient to determine the presence or absence of DUI in this species.

The coding capacity of the mt genome is alike that of all other Metazoan mitochondria. As was observed in other Bivalvia, We could not find the very short atp8 gene. Presumably its similarity to known atp8 genes has degraded to levels below detectability.

tRNAs.

TRNAscan [23] with default settings did not detect more than 7 tRNA genes in the mitochondrial genome. Using the nematode specific parameters we were able to detect additional, but not a complete set of tRNAs. We thus used ARWEN [24] to predict mitochondrial tRNA genes. Most of the common tRNAs were detected by this tool but the tRNA for Glutamine was missing. A recent study showed that tRNA detection in Metazoa can be improved [25]. Using the software described there we were able to detect the tRNA for Glutamine (Table S1). Thus, the A. islandica mitochondrial genome contains tRNA genes for each amino acid. We detected two tRNA Glu genes, which is uncommon in Metazoa. They have a significantly different GC content (tRNA-Glu1 at position 8 to 81 16.2 versus 37.9 for tRNA-Glu2 at position 15409 to 15474) and sequence. However, duplication and rapid decay of tRNA genes was previously observed in a bivalve [26]. The second tRNA Glu might be a rare example of a not yet vanished gene duplication. We detected only a tRNA Ser recognizing UCN but failed to find the commonly found tRNA-Ser recognizing AGY despite the fact that these codons are used in the mt genome. We then searched for potential tRNA-like folds using Mfold [27] in intergenic regions but were not successful. It might therefore be possible that this tRNA is imported into the mitochondrion. We can, however, not exclude that a rather uncommon tRNA structure escaped our search.

Gene order and synteny.

Most bivalve mitochondria sequenced to date have all their genes on only one strand. Only a few bivalves distribute their mitochondrial genes between DNA strands, e.g. Lampsilis ornata from the Unionida [28]. A. islandica encodes all genes on the same strand with the order of genes shown in Figure 1. This specific order is not completely shared by any other currently known mitochondrial genome of Bivalvia. Gene order, or synteny, can be retained in closely related species and therefore can be utilized as a marker of relatedness in the same sense as sequence similarity. Indeed, within the superfamilies of the order Heterodonta the gene order is highly conserved, only a few synteny breaks occur (Table S5: Gene order).

thumbnail
Figure 1. Map of the mitochondrial genome of A. islandica.

The image was prepared using the CGView Server at http://stothard.afns.ualberta.ca/cgi-bin/cgview_server/. The Outer circle shows the genes, the middle circle the GC content and the inner circle the GC skew.

https://doi.org/10.1371/journal.pone.0082857.g001

Close inspection of the A. islandica gene order revealed that some genes share the same neighbors as in other bivalve species of the order Veneroida. The genes cytB, l-RNA, atp6, and nad3 occur in the same order as in A. islandica in all Paphia species of the family Veneridae. They differ, however, in that there is an insertion of a tRNA (tRNA_His) between l-RNA and atp6 in the A. islandica mitochondrial genome. Furthermore, Meretrix species and Venerupis have a similar, albeit more diversified gene cluster, only nad4 and three tRNAs are inserted between l-RNA and atp6. Thus, the tight clustering of these genes could be ancestral to these groups, whereas the location of other genes is not conserved.

Phylogeny

Mitochondrial sequences are widely used to infer phylogenetic relationships among organisms. So far, 20 species within the bivalve subclass Heterodonta have their mitochondrial genomes sequenced. We took advantage of this data resource, concatenated the translated protein sequences of each species, and aligned them using muscle [29]. The phylogenetic relationships within the Heterodonta were reconstructed based on this concatenated alignment using maximum likelihood (ML), Neighbor Joining (NJ), and Bayes based methods and rooted with Crassostrea (Bivalvia, Pteriomorphia; Figure 2). All species cluster in their respective families. We noted that the position of Sinonovacula constricta in the taxonomy provided by NCBI is different from its position in our phylogenetic reconstruction yet agrees with a previous study [30]. According to the NJ, ML, and MrBayes phylogeny, A. islandica branches at the basis of Veneroidea and is the sister group to Paphia, Meretrix, and Venerupis. This branching order is in good agreement with the observed syntenic regions shared between Veneroidea and A. islandica, further emphasizing the relationship between these families.

thumbnail
Figure 2. Molecular phylogeny of the bivalve subclass Heterodonta using concatenated protein sets from complete mitochondrial genomes.

The evolutionary history was inferred using the Neighbor-Joining (NJ) method [37], MrBayes, and Maximum likelihood (ML) analysis. The tree shows the topology of the ML tree and the values next to the branches indicate (ordered from left to right) Percentage support by ML tree/Posterior probability of MrBayes tree/) Percentage support by NJ tree. The tree was rooted using Crassostrea hongkongensis as an outgroup. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. Evolutionary analyses were conducted in MEGA5 [38] (NJ and ML) and with MrBayes [39]. The asterisk indicates that the Solenoida branch is associated with Tellinoidea in NJ and MrBayes analyses.

https://doi.org/10.1371/journal.pone.0082857.g002

Polymorphisms

We sequenced the entire mitochondrial genomes of one specimen of A. islandica from each of the three sampling sites. This gave us the opportunity to assess the distribution of polymorphisms along the mitochondrial genome. All polymorphisms detected are relatively evenly distributed over the whole genome (Table S5: polymorphisms). The 12S rRNA region is an exception where over nearly 1 kb no polymorphism could be observed (Figure 3). Furthermore, the 16S rRNA region is also comparably free of polymorphisms, indicating higher evolutionary constraints for the rRNA genes compared to protein coding genes. The two regions devoid of genes are more prone to accumulate polymorphisms than the genic regions. This is even more emphasized by the fact that most indels (4 of 5 observed) are located in these regions.

thumbnail
Figure 3. Polymorphisms in the mitochondrial genome.

Single nucleotide polymorphisms (SNPs) were counted in a sliding window of 500 with a step size of 1. All genes are depicted below the SNP frequency curve. The indel regions are indicated by vertical lines below the sliding window curve. Protein coding genes (CDS) are shown above the second line and RNA species below that same line as boxes. The CDS and the 12S and 16S RNA genes are named, while the tRNA genes are only depicted as rectangles. The bar-bell shapes indicate the amplified regions from 17 specimen.

https://doi.org/10.1371/journal.pone.0082857.g003

The Baltic Sea specimen exhibits the highest number of SNPs compared to the other two, followed by the North Sea specimen (Table 3). Later examination of further specimens showed that the Baltic and North Sea mt genomes represent the two common haplotypes, whereas the Iceland MT genome is, at the examined positions, a mixture between these two haplotypes with the majority of SNPs shared with the North Sea haplotype.

differences to consensusVariants in consensus  Total unique SNPs
Baltic16730197
North Sea401252
Iceland21223

Table 3. Polymorphisms in the completed MT genomes of A. islandica.

CSV
Download CSV

We counted transitions and transversions and calculated the proportion of these to be 9 to 1. 2.7 % of intergenic and 1.18 % of genic sites were polymorphic, respectively, with 159 coding SNPs in total (Table S2). Examining the coding SNPs revealed that the proportion of non-synonymous to synonymous SNPs (dN/dS) is 0.25 (Table S3). We searched for potential open reading frames in the regions free of canonical genes and found one at position 9196 to 9855 with very slight similarity to the nad2 gene. This ORF might be a remnant of a gene duplication. The analysis of synonymous and non-synonymous SNPs of each gene showed that this ORF has accumulated significant amounts of non-synonymous polymorphisms indicating a decaying gene (Table S4).

We wanted to obtain a better insight into SNP differences within populations by analyzing distinct parts of the mt genome in more detail in several specimen. To avoid a selection bias we chose regions relatively evenly distributed over the mt genome, but containing polymorphisms according to the complete genome sequences (Figure 3). The respective regions were amplified from a total of 17 specimens and subsequently sequenced using Sanger technology. We observed only variation employing two bases at each polymorphic position (SNPs). The results strongly suggest that only one mutational event per site led to the observed pattern. In total, we found 17 T/C, 12 A/G, and 2 G/C polymorphisms, respectively. Furthermore, two single base-pair indels contribute to the sequence variability in these regions. We also observed some individual polymorphisms not shared with other specimens potentially representing individual point mutations or markers for population substructures. In the North Sea population only one of these polymorphisms was detected, whereas the Baltic Sea and Icelandic samples contained 10 and 21, respectively (data not shown).

Investigating the DNA sequences in all three genomic regions in their entirety, we found two distinct groups of haplotypes (Table S5: SNPs in amplified regions). The two major haplotypes are present both in the Baltic Sea population (4 specimens with major haplotype 1 and 2 specimens with major haplotype 2, respectively), whereas only one of these haplotypes could be found in each of the other two populations. The North Sea Population exhibited the major haplotype 1 only, whereas the Iceland population showed major haplotype 2 in four out of six specimens. The remaining two Icelandic specimens (108MRD10IS and 72IS10N) had different haplotypes. The haplotype of 72IS10N differs from one of the major haplotypes in the third region by only one polymorphism, whereas we observed 3 differences in region 1 (out of seven sites) and 2 (out of 13 sites), respectively. The haplotype of 108MRD10IS has one difference to the other major haplotype in region 2, but appears to have inserted a portion from a different haplotype in the third region. Since it is unlikely that independent mutations led to these SNPs, this strongly suggests a potential partial mixture of haplotypes. We also performed a phylogenetic analysis of the concatenated amplicons which gave an alignment of 3025 positions or 16.5 % of the whole genome. A model test performed in MEGA 5 defined the Hasegava-Kishino-Jano model as best fitting model. Using this model the maximum likelihood calculation gave a tree with two clusters (Figure 4). The two sequences from 72IS10N and 108MRD10IS were clearly separated from these clusters as would be expected if a mixed ancestry is assumed. We also performed statistical tests using available software tools (PhiPack[31]; RDP [32]) but found no statistically significant recombination breakpoint, potentially due to the small number of examinable SNPs.

thumbnail
Figure 4. Molecular Phylogenetic analysis of the concatenated nucleotide sequences from three regions of the mitochondrial genome (2470 to 3050; 5490 to 6890; 12230 to 13380) using the Maximum Likelihood method based on the Hasegawa-Kishino-Yano model [40].

Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. Evolutionary analyses were conducted in MEGA5 [38]. Blue: Iceland; Green: Baltic Sea; Red: North Sea.

https://doi.org/10.1371/journal.pone.0082857.g004

Such apparently recombined mitochondrial haplotypes can only occur if heteroplasmy, i.e. presence of more than one type of MT genome in an individual, exists at least in some life stages [33]. Whether these mt genomes represent only slightly different F and M genomes or are variants which can just co-exist in the mitochondrion of A. islandica remains to be shown by further analysis of additional specimens.

The predominant Iceland haplotype is missing in the North Sea sampling site, which could be due to the relatively small number of sampled specimens, which are possibly closely related. Alternatively, the presence of only one haplotype at the North Sea sampling site could also be due to the increased environmental pressure from the combined effect of fisheries, eutrophication and increasing water temperatures that compromise recruitment in this population [6] leading to a population bottleneck. This is further underlined by the fact that this population has also nearly no individual variation. On the other hand the Baltic Sea population exhibits both common haplotypes and a moderate number of individual polymorphisms. Taken together, the Icelandic population has the most variable mt genomes, representing a resource for diversity whereas the more southward marginal populations are marked by more homogenous mt genomes, especially in the strongly impacted German Bight.

Conclusion

We here presented the whole mitochondrial genome sequence of Arctica islandica, a species with an outstanding potential for longevity research. The MT genome encodes only one tRNA-Ser, a feature very uncommon among most Metazoa and here firstly described for a bivalve. Polymorphisms are less frequent in structural RNAs than coding genes and accumulate preferentially in intergenic regions where also remnants of gene duplications can reside. The analysis of selected regions revealed potential haplotype mixing. The variation of haplotypes in the MT genomes could be used as good measure for population diversity and thus for assessment of the habitat quality.

Materials and Methods

Sampling

All sampling sites are not specially protected areas of the marine environment and fishing is allowed. A. islandica is not an endangered species and thus no regulations on their sampling currently apply. Sampling sites were: Baltic Sea, Kiel Bay: N 54°32.6´- E 10°42.1´; Iceland: N 66° 01`44 - W 14° 50`91; North Sea, Helgoland: N 54°10`44- E 007° 48`12.

DNA isolation and sequencing

For the determination of the mitochondrial genome sequence DNA was extracted from specimen using the Qiagen DNeasy Blood & Tissue Kit. The genomic library for the 454 flx sequencing machine was prepared according to the manufacturers protocols (Roche). The raw data from the sequencing runs were searched for mitochondria like sequences with NC_003354 (Venerupis philippinarum) using BLAST. The raw reads then were assembled using Newbler software with default values. The reads not placed into mitochondrial contigs were checked for presence of sequences indicating nuclear copies of the mt genome (NUMTs). In no case we detected fusions of mt genomic sequences to likely genomic sequences, which is likely due to the much lower coverage of genomic sequences with raw reads. We also analysed the raw reads for potential presence of an alternative mt genome indicated by sequence differences within species but found no hint for heteroplasmy in the completely sequenced specimen. Initial assembly of the North Sea specimens yielded one contig covering nearly the whole genome. The circle was closed by sequencing a PCR product resulting from amplification of mitochondrial DNA using custom primers adjacent to the remaining gap. Gaps in the assembly for specimens from the Baltic Sea and Iceland were closed using the North Sea sequence as a guide for orientation of the individual contigs yielding finally three high quality genomes. The three consensus sequences were aligned and manually curated within the STADEN package [34] and the sequence differences between the three mt genomes were extracted using custom made software tools. Subsequently, selected regions of the genome were amplified from each specimen. The resulting sequences were aligned to the combined reference sequence and the polymorphic sites counted.

Phylogeny reconstruction

The protein coding genes of the mitochondrial genomes of Veneroida species were retrieved from GenBank and aligned to their counterparts using muscle [29]. These alignments were concatenated with Scafos [35] There were a total of 3658 positions in the final dataset. The appropriate model of evolution for Maximum Likelihood calculation (Whelan And Goldman + Freq. model [36]) was selected by testing several models using the MEGA software. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 1.9354)). The The MrBayes model was a variable GTR model, and the NJ analysis used the JTT model. The trees were rooted using Crassostrea hongkongensis as an outgroup.

Supporting Information

Table S1.

Comparison of software tools for the detection of tRNAs.

https://doi.org/10.1371/journal.pone.0082857.s001

(DOCX)

Table S2.

Detected polymorphisms between the three sequenced genomes.

https://doi.org/10.1371/journal.pone.0082857.s002

(DOCX)

Table S3.

The number of synonymous versus non-synonymous exchanges in coding portions of the mitochondrial genome.

https://doi.org/10.1371/journal.pone.0082857.s003

(DOCX)

Table S4.

The SNPs in genes of the mt genome.

https://doi.org/10.1371/journal.pone.0082857.s004

(DOCX)

Table S5.

Positions and kind of all detected polymorphisms; Polymorphisms in amplified regions; Synteny between Veneroida mitochondrial genomes.

https://doi.org/10.1371/journal.pone.0082857.s005

(XLSX)

Author Contributions

Conceived and designed the experiments: GG DA. Performed the experiments: IH. Analyzed the data: GG MP CH DA. Contributed reagents/materials/analysis tools: DA. Wrote the manuscript: GG DA CH.

References

  1. 1. Butler PG, Wanamaker AD Jr, Scourse JD, Richardson CA, Reynolds DJ (2012) Variability of marine climate on the North Icelandic Shelf in a 1357-year proxy archive based on growth increments in the bivalve Arctica islandica. Palaeogeography, Palaeoclimatology, Palaeoecology.
  2. 2. Abele D, Philipp EER (2013) Environmental control and control of the environment: the basis of longevity in bivalves. Gerontology 59: 261-266. doi:https://doi.org/10.1159/000345331. PubMed: 23257622.
  3. 3. Wannamaker AD, Scourse JD, Richardson CA, Butler PG, Reynolds DJ et al. (2008) Absolute chronologies from the ocean: Records from the longest-lived, non-colonial animals on earth. Pages NEWS 16: 4-6.
  4. 4. Dahlgren TG, Weinberg JR, Halanych KM (2000) Phylogeography of the ocean quahog (Arctica islandica): influences of paleoclimate on genetic diversity and species range. Marine Biology 137: 487-495. doi:https://doi.org/10.1007/s002270000342.
  5. 5. Witbaard R, Bergman MJN (2003) The distribution and population structure of the bivalve Arctica islandica L. in the North Sea: what possible factors are involved? Journal of Sea Research 50: 11-25.
  6. 6. Strahl J, Abele D (2010) Cell turnover in tissues of the long-lived ocean quahog Arctica islandica and the short-lived scallop Aequipecten opercularis. Marine Biology (Berlin) 157: 1283-1292. doi:https://doi.org/10.1007/s00227-010-1408-6.
  7. 7. Basova L, Begum S, Strahl J, Sukhotin A, Brey T et al. (2012) Age dependent patterns of antioxidants in Arctica islandica from six regionally separate populations with different life spans. Aquatic Biology 14: 141-152. doi:https://doi.org/10.3354/ab00387.
  8. 8. Philipp EE, Wessels W, Gruber H, Strahl J, Wagner AE et al. (2012) Gene expression and physiological changes of different populations of the long-lived bivalve Arctica islandica under low oxygen conditions. PLOS ONE 7: e44621. doi:https://doi.org/10.1371/journal.pone.0044621. PubMed: 23028566.
  9. 9. Lee HC, Wei YH (2012) Mitochondria and aging. Adv Exp Med Biol 942: 311-327. doi:https://doi.org/10.1007/978-94-007-2869-1_14. PubMed: 22399429.
  10. 10. Buttemer WA, Abele D, Costantini D (2010) From bivalves to birds: oxidative stress and longevity. Functional Ecology 24: 971-983. doi:https://doi.org/10.1111/j.1365-2435.2010.01740.x.
  11. 11. Abele D, Puntarulo S (2004) Formation of reactive species and induction of antioxidant defence systems in polar and temperate marine invertebrates and fish. Comp Biochem Physiol A Mol Integr Physiol 138: 405-415. doi:https://doi.org/10.1016/j.cbpb.2004.05.013. PubMed: 15369829.
  12. 12. Barja G (2007) Mitochondrial oxygen consumption and reactive oxygen species production are independently modulated: implications for aging studies. Rejuvenation Res 10: 215-224. doi:https://doi.org/10.1089/rej.2006.0516. PubMed: 17523876.
  13. 13. Barja G, Cadenas S, Rojas C, López-Torres M, Pérez-Campo R (1994) A decrease of free radical production near critical targets as a cause of maximum longevity in animals. Comp Biochem Physiol Biochem Mol Biol 108: 501-512. doi:https://doi.org/10.1016/0305-0491(94)90103-1. PubMed: 7953069.
  14. 14. Turrens JF (2003) Mitochondrial formation of reactive oxygen species. J Physiol 552: 335-344. doi:https://doi.org/10.1113/jphysiol.2003.049478. PubMed: 14561818.
  15. 15. Plazzi F, Ceregato A, Taviani M, Passamonti M (2011) A molecular phylogeny of bivalve mollusks: ancient radiations and divergences as revealed by mitochondrial genes. PLOS ONE 6: e27147. doi:https://doi.org/10.1371/journal.pone.0027147. PubMed: 22069499.
  16. 16. Gissi C, Iannelli F, Pesole G (2008) Evolution of the mitochondrial genome of Metazoa as exemplified by comparison of congeneric species. Heredity (Edinb) 101: 301-320. doi:https://doi.org/10.1038/hdy.2008.62. PubMed: 18612321.
  17. 17. Papillon D, Perez Y, Caubit X, Le Parco Y (2004) Identification of chaetognaths as protostomes is supported by the analysis of their mitochondrial genome. Mol Biol Evol 21: 2122-2129. doi:https://doi.org/10.1093/molbev/msh229. PubMed: 15306659.
  18. 18. Schneider A (2011) Mitochondrial tRNA import and its consequences for mitochondrial translation. Annu Rev Biochem 80: 1033-1053. doi:https://doi.org/10.1146/annurev-biochem-060109-092838. PubMed: 21417719.
  19. 19. Strahl J, Philipp E, Brey T, Abele D (2007) Physiological aging in the Icelandic population of the ocean quahog Arctica islandica. Aquatic Biology 1: 77-83. doi:https://doi.org/10.3354/ab00008.
  20. 20. Breton S, Burger G, Stewart DT, Blier PU (2006) Comparative analysis of gender-associated complete mitochondrial genomes in marine mussels (Mytilus spp.). Genetics 172: 1107-1119. PubMed: 16322521.
  21. 21. Breton S, Beaupré H, Stewart DT, Hoeh WR, Blier PU (2007) The unusual system of doubly uniparental inheritance of mtDNA: isn't one enough? Trends in Genetics 23: 465-474. doi:https://doi.org/10.1016/j.tig.2007.05.011. PubMed: 17681397.
  22. 22. Jha M, Côté J, Hoeh WR, Blier PU, Stewart DT (2008) Sperm motility in Mytilus edulis in relation to mitochondrial DNA polymorphism: Implications for the evolution of doubly uniparental inheritance in bivalves. Evolution 62: 99-106. PubMed: 18039328.
  23. 23. Schattner P, Brooks AN, Lowe TM (2005) The tRNAscan-SE, snoscan and snoGPS web servers for the detection of tRNAs and snoRNAs. Nucleic Acids Res 33: W686-W689. doi:https://doi.org/10.1093/nar/gki366. PubMed: 15980563.
  24. 24. Laslett D, Canbäck B (2008) ARWEN: a program to detect tRNA genes in metazoan mitochondrial nucleotide sequences. Bioinformatics 24: 172-175. doi:https://doi.org/10.1093/bioinformatics/btm573. PubMed: 18033792.
  25. 25. Jühling F, Pütz J, Bernt M, Donath A, Middendorf M et al. (2012) Improved systematic tRNA gene annotation allows new insights into the evolution of mitochondrial tRNA structures and into the mechanisms of mitochondrial genome rearrangements. Nucleic Acids Res 40: 2833-2845. doi:https://doi.org/10.1093/nar/gkr1131. PubMed: 22139921.
  26. 26. Smith DR, Snyder M (2007) Complete mitochondrial DNA sequence of the scallop Placopecten magellanicus: evidence of transposition leading to an uncharacteristically large mitochondrial genome. J Mol Evol 65: 380-391. doi:https://doi.org/10.1007/s00239-007-9016-x. PubMed: 17922075.
  27. 27. Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31: 3406-3415. doi:https://doi.org/10.1093/nar/gkg595. PubMed: 12824337.
  28. 28. Serb JM, Lydeard C (2003) Complete mtDNA sequence of the North American freshwater mussel, Lampsilis ornata (Unionidae): an examination of the evolution and phylogenetic utility of mitochondrial genome organization in Bivalvia (Mollusca). Mol Biol Evol 20: 1854-1866. doi:https://doi.org/10.1093/molbev/msg218. PubMed: 12949150.
  29. 29. Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32: 1792-1797. doi:https://doi.org/10.1093/nar/gkh340. PubMed: 15034147.
  30. 30. Yuan Y, Li Q, Yu H, Kong L (2012) The complete mitochondrial genomes of six heterodont bivalves (Tellinoidea and Solenoidea): variable gene arrangements and phylogenetic implications. PLOS ONE 7: e32353. doi:https://doi.org/10.1371/journal.pone.0032353. PubMed: 22384227.
  31. 31. Bruen TC, Philippe H, Bryant D (2006) A simple and robust statistical test for detecting the presence of recombination. Genetics 172: 2665-2681. PubMed: 16489234.
  32. 32. Martin DP, Williamson C, Posada D (2005) RDP2: recombination detection and analysis from sequence alignments. Bioinformatics 21: 260-262. doi:https://doi.org/10.1093/bioinformatics/bth490. PubMed: 15377507.
  33. 33. Rokas A, Ladoukakis E, Zouros E (2003) Animal mitochondrial DNA recombination revisited. Trends in Ecology and Evolution 18: 411-417. doi:https://doi.org/10.1016/S0169-5347(03)00125-3.
  34. 34. Bonfield JK, Smith Kf, Staden R (1995) A new DNA sequence assembly program. Nucleic Acids Res 23: 4992-4999. doi:https://doi.org/10.1093/nar/23.24.4992. PubMed: 8559656.
  35. 35. Roure B, Rodriguez-Ezpeleta N, Philippe H (2007) SCaFoS: a tool for selection, concatenation and fusion of sequences for phylogenomics. BMC Evol Biol 7 Suppl 1: S2. doi:https://doi.org/10.1186/1471-2148-7-S1-S2. PubMed: 17288575.
  36. 36. Whelan S, Goldman N (2001) A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach Mol Biol Evol 18: 691-699. doi:https://doi.org/10.1093/oxfordjournals.molbev.a003851. PubMed: 11319253.
  37. 37. Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4: 406-425. PubMed: 3447015.
  38. 38. Tamura K, Peterson D, Peterson N, Stecher G, Nei M et al. (2011) MEGA5: Molecular Evolutionary Genetics Analysis using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods. Mol Biol Evol 28: 2731-2739. doi:https://doi.org/10.1093/molbev/msr121. PubMed: 21546353.
  39. 39. Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A et al. (2012) MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst Biol 61: 539-542. doi:https://doi.org/10.1093/sysbio/sys029. PubMed: 22357727.
  40. 40. Hasegawa M, Kishino H, Yano T (1985) Dating of the human-ape splitting by a molecular clock of mitochondrial. DNA - J Mol Evol 22: 160-174. doi:https://doi.org/10.1007/BF02101694.