alexa Transcriptomic and proteomic shift reveals mechanisms of adaptation in larvae of Red Sea barnacle Balanusamphitrite.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Data Mining in Genomics & Proteomics

Author(s): Kondethimmanahalli H Chandram, Sarah AlAqeel, Taewoo Ryu, Huoming Zhang, Loqmane Seridi, Yanal Ghosheh, PeiYuan Qian, Timothy Ravasi

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Background

The barnacle Balanus amphitrite is widely distributed in marine shallow and tidal waters, and has significant economic and ecological importance. Nauplii, the first larval stage of most crustaceans, are extremely abundant in the marine zooplankton. However, a lack of genome information has hindered elucidation of the molecular mechanisms of development, settlement and survival strategies in extreme marine environments. We sequenced and constructed the genome dataset for nauplii to obtain comprehensive larval genetic information. We also investigated iTRAQ-based protein expression patterns to reveal the molecular basis of nauplii development, and to gain information on larval survival strategies in the Red Sea marine environment.

Results

A nauplii larval transcript dataset, containing 92,117 predicted open reading frames (ORFs), was constructed and used as a reference for the proteome analysis. Genes related to translation, oxidative phosphorylation and cytoskeletal development were highly abundant. We observed remarkable plasticity in the proteome of Red Sea larvae. The proteins associated with development, stress responses and osmoregulation showed the most significant differences between the two larval populations studied. The synergistic overexpression of heat shock and osmoregulatory proteins may facilitate larval survival in intertidal habitats or in extreme environments.

Conclusions

We presented, for the first time, comprehensive transcriptome and proteome datasets for Red Sea nauplii. The datasets provide a foundation for future investigations focused on the survival mechanisms of other crustaceans in extreme marine environments.

This article was published in BMC Genomics and referenced in Journal of Data Mining in Genomics & Proteomics

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