Marine GenomicsGenomicsPeer-reviewed journal article
The genome of Ectocarpus subulatus - a highly stress-tolerant brown alga
Simon M. DittamiiD, Erwan CorreiD, Loraine Brillet-GuégueniD, Agnieszka P. LipinskaiD, Noé Pontoizeau, Méziane AïteiD, Komlan AviaiD, Christophe Caron, Chung Hyun ChoiD, Jonas ColléniD, Alexandre CormieriD, Ludovic DelageiD, Sylvie Doubleau, Clémence FriouxiD, Angélique GobetiD, Irene González-Navarrete, Agnès GroisillieriD, Cécile HervéiD, Didier JollivetiD, Hetty KleinJaniD, Catherine LeblanciD, Xi Liu, Dominique MarieiD, Gabriel V. MarkoviD, André E. MinocheiD, , Thierry TononiD
Abstract
Brown algae are multicellular photosynthetic stramenopiles that colonize marine rocky shores worldwide. Ectocarpus sp. Ec32 has been established as a genomic model for brown algae. Here we present the genome and metabolic network of the closely related species, Ectocarpus subulatus Kutzing, which is characterized by high abiotic stress tolerance. Since their separation, both strains show new traces of viral sequences and the activity of large retrotransposons, which may also be related to the expansion of a family of chlorophyll-binding proteins. Further features suspected to contribute to stress tolerance include an expanded family of heat shock proteins, the reduction of genes involved in the production of halogenated defence compounds, and the presence of fewer cell wall polysaccharide-modifying enzymes. Overall, E. subulatus has mainly lost members of gene families down-regulated in low salinities, and conserved those that were up-regulated in the same condition. However, 96% of genes that differed between the two examined Ectocarpus species, as well as all genes under positive selection, were found to encode proteins of unknown function. This underlines the uniqueness of brown algal stress tolerance mechanisms as well as the significance of establishing E. subulatus as a comparative model for future functional studies.
- organism: E. coli
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- research focus: database / resource
- research focus: variant calling
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 43
- FWCI
- 2.43
- Field percentile
- top 10%
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