The first descriptions of the microbial loop were from a “black box” perspective due to the information being restricted to a small fraction of cultivable bacteria. In recent years, the boom in DNA sequencing allowed the field of genomics to move towards the analysis of environmental samples (Tyson et al., 2004). Thus, community genomics, or metagenomics, can thoroughly detail microbial genetic diversity and, with metatranscriptomics, identify the regulation and expression of said diversity (Abram, 2015). However, these techniques cannot explain how microbial diversity relates to biogeochemical processes on a whole ecosystem level. Proteins provide the cell framework and define its function, therefore a description of the proteome can provide a description of organism function and its current state. Therefore, metaproteomics allows us to identify the functional expression of the metagenome and elucidate the metabolic activities of a community at the moment of sampling (Wilmes et al., 2006). Metaproteomics was originally defined as “the large-scale characterization of the entire protein complement of environmental microbiota at a given point in time” (Wilmes et al., 2004). The field has evolved from the identification of 3 proteins, in 2004 (Wilmes et al., 2004), to the identification of 7000 in 2015 (Hultman et al., 2015) and has been applied to various environments and microbial consortia.
1.8.1. Lessons of aquatic metaproteomics for microalgal biotechnology
The finality of metaproteomics is to describe the behaviour of the microbial community in a specific system. Therefore, this section will focus on the discoveries in marine and freshwater systems which are relevant to microalgal biotechnology.
The vast majority of the studies undertaken have focused on bacterial proteomes in marine environments. This is due to the fact that between the Global Ocean Sampling and Sargasso Sea expeditions 7.5 billion base pairs of non-redundant sequences were generated, annotated and made publicly available (Venter et al., 2004; Rusch et al., 2007). These metagenomics datasets provided a platform for several future marine studies (e.g. Dong et al., 2014). Freshwater metaproteomic analyses have also successfully described ecosystem dynamics without the support of a metagenomic database (Hanson et al., 2014). However, these studies typically rely on very large databases (e.g. NCBI non-redundant bacterial database) which can lead to problems
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with protein validation due to protein similarity between organisms. The next best approach is to determine community composition by sequencing of 16S and 18S rRNA and utilise this information to guide database assembly. This is a widely used workflow (e.g. Tang et al., 2014) that has the advantage of compensating for the inherent lack of robustness in proteomic taxonomic assignments. However, like the previous approach, it is biased towards organisms for which genome sequences are publicly available. In the case of organisms used in microalgal biotechnology many of the strains utilised have been sequenced thus providing a solid platform for metaproteomic studies.
Metaproteomics has given us a wealth of information regarding intracellular transport and, specifically, nutrient uptake. Sowell et al. (2009) was the first study to demonstrate the importance of nutrient uptake via high-affinity transporters and to identify transporters that are abundantly expressed. This study focused on the bacterioplankton community of the oligotrophic surface water of the Sargasso Sea. They found the metaproteome to be dominated by proteins belonging to ATP-binding cassette (ABC) transporters. These are high affinity transporters that utilise ATP to drive the uptake of organic (e.g. amino acids, simple sugars, phosphonates) and inorganic (e.g. iron and complexed phosphate) compounds. The synthesis of ABC transporters have a high metabolic cost therefore microbes only invest in the biosynthesis of ABC transporters that transport substrates that are limiting (Sowell et al., 2009). Determining which transporters are synthesised can also reveal the limiting substrates in high nutrient environments. Sowell et al. (2011) investigated the highly productive, nutrient-rich coastal upwelling system of the Oregon shelf (Pacific coast, USA) and found, again, a high abundance of ABC transporters. These transporters were specific for amino acids, taurine, and polyamines, which indicate that C and N were more limiting than phosphate in this environment. Another example, now regarding nutrient remineralisation, was shown in a study by Williams et al. (2012) in the Antarctic Peninsula coastal surface waters. One of the ways that NO3- becomes
bioavailable is through the process of nitrification, where NH4+ is oxidized to NO2- and
NO3-. This process has been known to be mediated by bacteria. However, Williams et
al. (2012), in a summer and winter comparison, reported that ammonia-oxidizing Archaea proteins constituted almost a third of the detected proteins in the winter and were absent in the summer. This suggests that, in some environments, Archaea play a large role in nutrient remineralisation.
Metaproteomics is also a valuable tool to uncover the coexistence dynamics in microbial communities. In a metaproteomic study, in Newcomb Bay in East Antarctica,
47 of a phytoplankton bloom, Flavobacteria were found to utilise high affinity transporters and exoenzymes (TonB-dependent transporters and glycoside hydrolases) for the uptake and breakdown of complex carbohydrates, especially microalgal exudates. The breakdown of these complex substrates released simple substrates that were made available to the community allowing for a succession of other bacterial species as the bloom progressed (Williams et al., 2013). Georges et al. (2014) reported an occurrence of resource partitioning, by two closely related clades of Gamma-proteobacteria (ARCTIC96BD-19 and SUP05), in the Northwest Atlantic Ocean (Bedford Basin). The identification of ARCTIC96BD-19 proteins involved in the transport of organic compounds indicated a heterotrophic metabolism. In contrast, the identification of sulphur oxidation proteins from SUP05 indicated the use of reduced sulphur as energy source. This metabolic differentiation potentially allows both bacterial clades to coexist in the same environment.
In two freshwater metaproteomic studies Ng et al. (2010) and Lauro et al. (2011) have described in detail the ecophysiology of a green sulfur bacterium (Chlorobiaceae), a primary producer found in Ace Lake, Antarctica. Ng et al. (2010) identified the set of proteins that allow this bacterium to thrive under cold, nutrient-limited, oxygen-limited and extremely varied annual light conditions. The presence of chlorosomes (bacteriochlorophyll structures) allows growth at extremely low light intensities and several cold adaptation mechanisms were also identified. Finally, they reported the existence of a cross-feeding relationship between green sulphur bacteria and sulphate- reducing bacteria where an exchange of sulphur compounds allows for the survival of both organisms. Lauro et al. (2011) provided a broader view of the Ace Lake ecosystem by developing a competition model based on the metaproteomic analysis of the green sulphur bacteria. This model offered several possible outcome scenarios of local competition between green sulphur bacteria and cyanobacteria and how this relationship changes with shifts in seasonal light–dark cycles and virus predation.
The metaproteomic characterisation of the strains of interest, and associated bacteria, can give precious insight into the factors limiting growth at a specific point in time. This has the potential to be integrated into the development of growth medium for different strains and communities. For microalgal biotechnology community design, the discovery of new metabolic pathways and adaptation mechanisms could provide a list of alternative organisms to fulfil the same roles under different environmental conditions. It is of interest to utilise this information to create system models in order to describe and predict relationships between abiotic and biotic factors influencing the
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microbial community. This tie between metaproteomics and modelling can be useful for predicting responses to environmental changes, biomass productivity and even community response to invasion and predation.
Over the next years further development can be expected and it remains to be seen if ecological principles, such as optimal nutrient supply and co-existence theory, can be expanded to microalgal biotechnology. If so, the utilisation of ecology within microalgal cultivation will need to tackle many challenges, including the feasible up- scaling of a production system and reduction of biomass production costs, to, ultimately, influence the future role of microalgae on the global market.
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Chapter 2: Multivariate analysis of the
response of a freshwater microbial
community under nutrient enrichment
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