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Transcriptomic analysis of metabolic function in the giant kelp, M acrocystis pyrifera , across depth and season
To increase knowledge of transcript diversity for the giant kelp, M acrocystis pyrifera , and assess gene expression across naturally occurring depth gradients in light, temperature and nutrients, we sequenced four cDNA libraries created from blades collected at the sea surface and at 18 m depth dur...
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Published in: | The New phytologist 2013-04, Vol.198 (2), p.398-407 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | To increase knowledge of transcript diversity for the giant kelp,
M
acrocystis pyrifera
, and assess gene expression across naturally occurring depth gradients in light, temperature and nutrients, we sequenced four
cDNA
libraries created from blades collected at the sea surface and at 18 m depth during the winter and summer.
Comparative genomics cluster analyses revealed novel gene families (clusters) in existing brown alga expressed sequence tag data compared with other related algal groups, a pattern also seen with the addition of
M
. pyrifera
sequences.
Assembly of 228 Mbp of sequence generated
c
. 9000 isotigs and
c
. 12 000 open reading frames. Annotations were assigned using families of hidden
M
arkov models for
c
. 11% of open reading frames;
M
. pyrifera
had highest similarity to other members of the
P
haeophyceae, namely
E
ctocarpus siliculosus
and
L
aminaria digitata
.
Quantitative polymerase chain reaction of transcript targets verified depth‐related differences in gene expression; stress response and light‐harvesting transcripts, especially members of the
LI
818 (also known as
LHCSR
) family, showed high expression in the surface compared with 18 m depth, while some nitrogen acquisition transcripts (e.g. nitrite reductase) were upregulated at depth compared with the surface, supporting a conceptual biological model of depth‐dependent physiology. |
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ISSN: | 0028-646X 1469-8137 |
DOI: | 10.1111/nph.12160 |