The complete mitochondrial genome of the deep-sea sponge
            <i>Poecillastra laminaris</i>
            (Astrophorida, Vulcanellidae)

Zeng, Cong; Thomas, Leighton J.; Kelly, Michelle; Gardner, Jonathan P. A.

doi:10.3109/19401736.2014.958716

Cited by 7 publications

(7 citation statements)

References 5 publications

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“…Despite a few exceptions like Poecillastra laminaris (Tetractinellida: Astrophorina), where the mt genome was assembled using 454 pyrosequencing data [6], or the freshwater sponges Spongilla lacustris and Ephydatia cf. muelleri, which were assembled from Illumina (TruSeq) synthetic long-reads [7], all sponge mt genomes sequenced to date were assembled from sanger sequencing reads [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…However, among all tetractinellids, only three complete mt genomes (i.e. Poecillastra laminaris [6], Geodia neptuni [8] and Cinachyrella kuekenthali [9] have been sequenced to date, none of which are from families of desma-bearing tetractinellids. Sphaerocladina is another order of desma-bearing demosponges with a fossil record dating back to the Cambrian [15] from which no mt genome has been sequenced to date.…”

Section: Introductionmentioning

confidence: 99%

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Divergence times in demosponges (Porifera): first insights from new mitogenomes and the inclusion of fossils in a birth-death clock model

Schuster¹,

Vargas²,

Knapp

et al. 2017

Preprint

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Approximately 80% of all recent sponge species belong to the class Demospongiae. Yet, despite their diversity and importance, accurate divergence times are still unknown for most demosponge clades. The estimation of demosponge divergence time is key to answering fundamental questions like e.g. the origin of Demospongiae, their diversification and historical biogeography. Molecular sequence data alone is not informative on an absolute time scale, and therefore needs to be "calibrated" with additional data such as fossils. Here, we apply the fossilized birth-death model (FBD), which has the advantage, compared to strict node dating with the oldest fossil occurrences, that it allows for the inclusion of young and old fossils in the analysis of divergence time. We use desma-bearing sponges, a diverse group of demosponges that form rigid skeletons and have a rich and continuous fossil record dating back to the Cambrian (⇠500 Ma), aiming to date the demosponge radiation and constrain the timing of key evolutionary events, like the transition from marine to freshwater habitats. To do so, we assembled mitochondrial genomes of six desma-bearing demosponges from size-selected reducedrepresentation genomic libraries and apply a fossilized birth-death model including 30 fossils and 33 complete demosponge mitochondrial genomes to infer a dated phylogeny of Demospongiae. Our study supports a Neoproterozoic origin of Demospongiae. Novel age estimates for the split of freshwater and marine sponges dating back to the Carboniferous and the previously assumed Recent (⇠18 Ma) diversification of freshwater sponges is supported. Moreover, we provide detailed age estimates for a possible diversification of Tetractinellidae (⇠315 Ma), the Astrophorina (⇠240 Ma), the Spirophorina (⇠120 Ma) and the family Corallistidae (⇠188 Ma) all of which are considered as key groups for dating the Demospongiae, due to their extraordinary rich and continuous fossil history.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Divergence times in demosponges (Porifera): first insights from new mitogenomes and the inclusion of fossils in a birth-death clock model

Schuster¹,

Vargas²,

Knapp

et al. 2017

Preprint

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“…The nucleotide composition is A (29.9%), T (36.8%), C (12.8%) and G (20.4%) with a GC content of 33.2%. The arrangement of protein in the mitogenome of T. mismalolli was similar to the rest of tetractinellid sponges; however, it is important to emphasize that it resulted 4.8% longer than Pocillastra laminaris, 3.18% than both Geodia neptuni and Cinachyrella kuekenthali (Lavrov et al 2005(Lavrov et al , 2008Zeng et al 2016). This change in the mitogenome size is essentially due to insertions in non-coding regions.…”

Section: Introductionmentioning

confidence: 73%

“… 2005 , 2008 ; Zeng et al. 2016 ). This change in the mitogenome size is essentially due to insertions in non-coding regions.…”

Section: Introductionmentioning

confidence: 99%

The complete mitogenome of excavating spongeThoosa mismalolli(Demospongiae, Tetractinellida, Thoosidae) from Northeastern Tropical Pacific

Bautista‐Guerrero

Llera-Herrera

Carballo

et al. 2020

Mitochondrial DNA Part B

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2020) The complete mitogenome of excavating sponge Thoosamismalolli (Demospongiae, Tetractinellida, Thoosidae) from Northeastern Tropical Pacific, Mitochondrial DNA Part B, 5:1, 689-691, ABSTRACTThe complete mitogenome of Thoosa mismalolli Carballo, Cruz-Barraza & G omez, 2004 (Tetractinellida, Thoosidae) was sequenced. This is the first complete mitogenome of the suborden Thoosina and the third Tetractinellid so far. The mitochondrial genome of T. mismalolli was assembled based on reads obtained with the Illumina HiSeq platform. The length of complete mitogenome is 19,019 bp long and contained 14 protein-coding genes and 23 tRNA, with two tRNA genes. Phylogenetic reconstruction (maximum-likelihood) based on mitogenome of Tetractinellids, supports T. mismalolli as a sister group. This result is congruent with those obtained with molecular markers (CO1, 18S, and 28S), supporting the monophyletic status of Thoosa and providing additional molecular data in favor of the suborder Thoosina. ARTICLE HISTORY

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“…The purpose of Chapter 2 is to give an overview on the development of the genetic connectivity field and develop suitable hypotheses for the data testing in Chapters 5 and 6. Chapter 3 describes the mitochondrial and microsatellites markers developed for Poecillastra laminaris and corresponds to publications in the journal Mitochondrial DNA (Zeng et al, 2014a) and…”

Section: Thesis Structurementioning

confidence: 99%

Patterns of genetic connectivity in deep-sea vulnerable marine ecosystems and implications for conservation

Zeng¹

Self Cite

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<p>Knowledge about and understanding of population structure and connectivity of deep-sea fauna decreases with increasing depth, but such information is crucial for the management of vulnerable marine ecosystems (VMEs) in particular. As such, research using genetic markers, which does not require knowledge of ecological or environmental processes as a prerequisite for the analysis, is a practical method to investigate population connectivity of VME indicator taxa. However, population genetics studies are yet to be broadly conducted in the deep sea around New Zealand. To provide background information and develop hypothesises for this research, 196 population genetic studies of deep-sea fauna were reviewed and analysed. Based on the collected studies, four different patterns of spatial genetic structure were observed: global homogeneous, oceanic, regional, and fine structure. These different structures were reported that they were related to depth, topography, distance between populations, temperature and other biological factors. Quantification of the relationship between these factors and the detection of barriers to gene flow (barrier detection) showed that depth, currents and topography contributed significantly to barrier detection and depth and topography were acting as a barrier to gene flow in the deep sea. Furthermore, different sampling strategies and different genetic marker types significantly influenced genetic barrier detection. Comparison amongst different habitats suggested that different conservation strategies should be developed for different habitat types (Chapter 2). This study used different genetic markers to assess the genetic connectivity amongst VME indicator taxa Vulnerable Marine Ecosystems (VME). Seven VME indicator taxa were selected: 4 sponges (Neoaulaxinia persicum, Penares sp., Pleroma menoui and Poecillastra laminaris) and 3 corals (Goniocorella dumosa, Madrepora oculata and Solenosmilia variabilis), at different spatial scales. Due to lack of genetic information for these species, genetic markers were developed for Poecillastra laminaris (0) and S. variabilis (Chapter 4). A geographic province (northern-southern province), region (north-central-south), and geomorphic feature hierarchical testing framework was employed to examine species-specific genetic variation in mitochondrial (COI, Cytb and 12S) and nuclear markers (microsatellites) amongst populations of four deep-sea sponges within the New Zealand region. For Poecillastra laminaris, significant mitochondrial and nuclear DNA genetic differences were revealed amongst biogeographic provinces. In contrast, no significant structure was detected across the same area for Penares sp. Both Neoaulaxinia persicum and Pleroma menoui were only available from the northern province, in which Pleroma menoui showed no evidence of genetic structure, but N. persicum exhibited a geographic differentiation in 12S. No depthrelated isolation was observed for any of the four species at the mitochondrial markers, nor at the microsatellite loci for Poecillastra laminaris. Genetic connectivity in Poecillastra laminaris is likely to be influenced by oceanic sub-surface currents that generate routes for gene flow and may also act as barriers to dispersal. Although data are limited, these results suggest that the differences in patterns of genetic structure amongst the species can be attributed to differences in life history and reproductive strategies. The results are discussed in the context of existing marine protected areas, and the future design of spatial management measures for protecting VMEs in the New Zealand region (Chapter 5). To better understand the vulnerability of stony corals (Goniocorella dumosa, Madrepora oculata and Solenosmilia variabilis) to disturbance within the New Zealand region, and to guide marine protected area design, genetic structure and connectivity were determined using microsatellite loci and DNA sequencing. Analyses compared population genetic differentiation between two biogeographic provinces, amongst three sub-regions (north-central-south), and amongst geomorphic features. Population genetic differentiation varied amongst species and between marker types. For G. dumosa, genetic differentiation existed amongst regions and populations on geomorphic features, but not between provinces. For M. oculata, only a north-central-south regional structure was observed. For S. variabilis, genetic differentiation was observed between provinces, amongst regions and amongst geomorphic features based on microsatellite variation. Multivariate analyses indicated that populations on the Kermadec Ridge were genetically different from Chatham Rise populations in all three coral species. Furthermore, a significant isolation-by-depth pattern was observed for both marker types in G. dumosa, and also in ITS of M. oculata. An isolation-by-distance pattern was found in microsatellites of S. variabilis. Migrate analysis showed that medium to high self-recruitment were detected in all geomorphic feature populations, and different species presented different genetic connectivity patterns. These different patterns of population genetic structure and connectivity at a range of spatial scales indicate that flexible spatial management is required for the conservation of deep-sea corals around New Zealand (Chapter 6). Understanding the deep-sea ecological processes that shape spatial genetic patterns of species is critical for predicting evolutionary dynamics and defining significant evolutionary and/or management units. In this study, the potential role of environmental factors in shaping the genetic structure of the 7 deep-sea habit-providing study species was investigated using a seascape genetics approach. The genetic data were acquired from nuclear and mitochondrial sequences and microsatellite genotype data, and 25 environmental variables (5 topographic, 17 physiochemical and 3 biological variables). The results indicated that environmental factors affected genetic variation differently amongst the species. However, factors related to current and food source explained the north-central-south genetic structure in sponges and corals, and environmental variation in these parameters may be acting as a barrier to gene flow. At the geomorphic feature level, the DistLM and dbRDA analysis showed that factors related to the food source and topography were most related to genetic variation in microsatellites of sponge and corals. This study highlights the utility of seascape genetic studies to better understand the processes shaping the genetic structure of organisms (Chapter 7). The outcomes of this study provide vital information to assist in effective management and conservation of VME indicator taxa and contribute to an understanding of evolutionary and ecological processes in the deep sea (Chapter 8).</p>

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The complete mitochondrial genome of the deep-sea sponge Poecillastra laminaris (Astrophorida, Vulcanellidae)

Cited by 7 publications

References 5 publications

Divergence times in demosponges (Porifera): first insights from new mitogenomes and the inclusion of fossils in a birth-death clock model

Divergence times in demosponges (Porifera): first insights from new mitogenomes and the inclusion of fossils in a birth-death clock model

The complete mitogenome of excavating spongeThoosa mismalolli(Demospongiae, Tetractinellida, Thoosidae) from Northeastern Tropical Pacific

Patterns of genetic connectivity in deep-sea vulnerable marine ecosystems and implications for conservation

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