Follow the Yellow Slick Road: Surfactant-associated Bacteria under the SML

The Sea Surface Microlayer (SML) represents the uppermost tens of microns of the air-sea interface. Previous studies have shown this layer to be distinct in terms of microbial and physiochemical composition relative to the underlying subsurface waters. As an air-sea boundary, a proper understanding of the SML’s chemistry and microbial ecology is necessary to appreciate globally-important geochemical processes such as air-sea greenhouse gas exchange and the production of marine aerosols. An influencing factor in such processes is the presence of biogenic surfactants. When present, surfactants alter the surface tension of the water in which they are dissolved, in turn influencing wave formation and the biophysics of the air-sea interface. The microbiology of marine surfactant cycling in the SML is poorly understood, as most assays are culture-dependent. Therefore, Kurata and colleagues (2016) set out to further our understanding of this phenomenon.

The authors focussed their attention on sampling ‘slicks’ (pronounced areas of concentrated surfactants) and non-slick areas of the SML off the Floridian coast. Field sampling was coordinated with satellites equipped with synthetic aperture radar (SAR) technology capable of visualising slicks from space, and the 16S rRNA signature of bacterioneuston communities from such slicks was 454 sequenced. After controlling for contamination from control filters, the study found the greatest abundance of surfactant-associated Bacteria in the subsurface waters directly below the slick SML – showing high abundance of the surfactant-producing genera Pseudomonas and Bacillus. This presence of these groups below the slick SML, as opposed to within it, drove the authors to hypothesise an updated model of slick formation. They propose that bacterial surfactant production occurs primarily in organic-rich subsurface waters and surfactants are subsequently transported to the SML by physical processes such as convection and bubble scavenging.

Overall, this paper provides a successful case study in using a novel fusion of satellite imaging and high-throughput sequencing to sample surfactant-associated SML Bacteria. While the authors humbly accept the limitations of their study and outline the work as a proof-of-concept, I believe the extrapolations of their findings beyond a case study is unwarranted. The premise of the study (using molecular data to overcome the limitations of culture-dependant identification of surfactant-associated Bacteria) falls down by functionally categorising bacteria genera into surfactant-associated roles which were initially categorised using culture-dependent methods. The 16S rRNA signature of the slick SML was classified by the authors as 97.9% ‘not associated with surfactants’, which I find hard to believe. I propose the more likely explanation is that surfactant-associated roles in these genera have not yet been classified in culture and may play a major role in surfactant production/degradation. This makes the foundation of the slick-formation model proposed by the authors unstable, therefore I would need further evidence to accept this proposal outright.

This hybrid methodology shows promise, and I would be keen to see the molecular biology of surfactant cycling further developed by using metatranscriptomics to identify genes expressed in these processes in cultured species to apply to environmental samples. It would also be exciting to see this methodology applied to the surfactant-associated eukaryotes, which may play a key predation role in slick-associated microbial food webs. Despite its limitations, this paper proposes an intriguing new method for studying microbial surfactant cycling and provides an important basis for further research into a geochemically important phenomenon.  

Reviewed Paper: Kurata, N., Vella, K., Hamilton, B., Shivji, M., Soloviev, A., Matt, S., ... & Perrie, W. (2016). Surfactant-associated bacteria in the near-surface layer of the ocean. Scientific reports, 6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4709576/