bioRxivPlant BiologyPreprint
Seagrass arabinogalactan-proteins: Are they important for adaptation to the marine environment?
Lukáš PfeiferiD, Kim L. JohnsoniD, Antony BaciciD, Thorsten B. H. ReuschiD, Carlos Manuel DuarteiD, Elizabeth A. SinclairiD, Birgit ClasseniD
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Abstract
During the late cretaceous period several lineages of angiosperm plants transitioned from land to the sea by successfully adapting to life in salt water, forming the polyphyletic group of seagrasses. Today, four seagrass families inhabit coastal systems and are deeply intertwined with health and welfare of these ecosystems. Adaptation to the ocean environment included changes in the composition of plant cell walls and associated glycoproteins. We have asked the question whether or not there is a convergent and similar arabinogalactan-protein glycan repertoire in all seagrasses, given initial findings of arabinogalactan-proteins with unique features in the well-studied eelgrass, Zostera marina. We isolated and characterized arabinogalactan-proteins from seven species covering the four major seagrass families using carbohydrate analysis and glycan immunoassays, along with a bioinformatic search for relevant gene pathways in newly published seagrass genomes and transcriptomes. Glycan parts of all seagrass arabinogalactan-proteins shared a high proportion of 1,4-linked glucuronic acids and terminal 4-O-methyl glucuronic acid residues. Trait-based dendrograms generated to inform phylogenetic-relatedness showed there was no phylogenetic signal among seagrass families and arabinogalactan-protein features. Transcriptomic datasets from Cymodocea nodosa and Thalassia hemprichii growing under hypersaline conditions showed an upregulation of enzymes involved in 4O-methylation and glucuronic acid transfer. We therefore conclude that environmental factors, especially salinity with higher monovalent ion concentration, influence seagrass arabinogalactan-proteins structure more intensely than phylogenetic history.
- organism: plant (other)
- assay: proteomics
- research focus: cell type annotation
- research focus: gene regulatory network inference
- approach: biophysical modeling
- approach: statistical model
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