Journal
MARINE ENVIRONMENTAL RESEARCH
Volume 177, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.marenvres.2022.105622
Keywords
Picocyanobacteria; Phylogenetic diversity; Pigment type; Niche division; Ecological function
Funding
- National Key Research and Development Program of China [2018YFD0901102]
- National Natural Science Foundation of China [42176131]
- Key Deployment Project of Centre for Ocean Mega-Research of Science, Chinese Academy of Science [COMS2020Q09]
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Marine Synechococcus, a main group of picocyanobacteria, exhibits high phylogenetical and phenotypical diversity, and understanding its genetic variation, niche division, and potential functions is crucial for gaining a comprehensive understanding of marine ecosystems.
Marine Synechococcus, a main group of picocyanobacteria, has been ubiquitously observed across the global oceans. Synechococcus exhibits high phylogenetical and phenotypical diversity, and horizontal gene transfer makes its genetic evolution much more intricate. With the development of measurement technologies and analysis methods, the genomic information and niche partition of each Synechococcus lineage tend to be precisely described, but the global analysis is still lacking. Therefore, it is necessary to summarize existing studies and integrate published data to gain a comprehensive understanding of Synechococcus on genetic variation, niche division, and potential functions. In this review, the maximum likelihood trees are constructed based on existing sequence data, including both phylogenetic and pigmentary gene markers. The global distribution characteristics of abundance, lineages, and pigment types are concluded through pooled analysis of more than 700 samples obtained from approximately 50 scientific research cruises. The potential functions of Synechococcus are explored in element cycles and biological interactions. Future work on Synechococcus is suggested to focus on not only elucidating the nature of Synechococcus biodiversity but also demonstrating its interactions with the ecosystem by combining bioinformatics and macroscopic isotope-labeled environmental parameters.
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