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New technologies for studying how microbial communities change over time, and which groups of organisms predominate under particular environmental conditions, have increasingly offered opportunities to anticipate adverse outcomes within system components and thus lead to the design of better sensors and tests for the effective monitoring of microbial communities in fish or plant cultures. For instance, various 'omics' technologies — metagenomics, metatranscriptomics, community proteomics, metabolomics — are increasingly enabling researchers to study the diversity of microbiota in RAS, biofilters, hydroponics and sludge digestor systems where sampling includes whole microbial assemblages instead of a given genome. Analysis of prokaryotic diversity in particular, has been helped enormously in recent decades by metagenomic and metatranscriptomic techniques. In particular, amplification and sequence analysis of the 16S rRNA gene, based on intraspecific conservation of neutral gene sequences flanking ribosomal operons in bacterial DNA, has been considered the 'gold standard' for taxonomic classification and identification of bacterial species. Such data is also used in microbiology to track epidemics and geographical distributions and study bacterial populations and phylogenies (Bouchet et al. 2008). The methodology can be labour-intensive and expensive, but recent automated systems, whilst not necessarily discriminatory at the species and strain level, offer opportunities for application in aquaponics settings (Schmautz et al. 2017). Recent reviews summarize applications of 16S rRNA as they pertain to RAS (Martínez-Porchas and Vargas-Albores 2017; Munguia-Fragozo et al. 2015; Rurangwa and Verdegem 2015). Advances in metagenomics of microbes other than bacteria found in RAS and hydroponics rely on similar methodologies but use 18S (eukaryotes), 26S (fungi) and 16S in combination with 26S (yeasts) rRNA clone libraries to characterize these microbiota (Martínez-Porchas and VargasAlbores 2017). Detailed rRNA libraries, for instance, have also been used in hydroponics to characterize microbial communities in the rhizosphere (Oburger and Schmidt 2016). Such libraries can be particularly useful in aquaponics, given that they can examine assemblage of microorganisms such as bacteria, archaea, protozoans and fungi and provide feedback on changes within the system.
The development of automated next-generation sequencing (NGS) has also enabled data analysis of genomes from population samples (metagenomics) that can be used to characterize microbiota, reveal temporal-spatial phylogenetic changes and trace pathogens. Applications in RAS include tracking certain bacterial strains amongst cultured fish and eliminating populations that carry virulent strains, whilst preserving carriers of other strains (review: (Bayliss et al. 2017). Metagenomic approaches can be culture- and amplification-independent, which allows previously unculturable species to become known and investigated for their possible effects (Martínez-Porchas and Vargas-Albores 2017). Next-generation sequencing techniques are commonly used in plant microbiology along with follow-up metatranscriptomics analyses. An excellent example is the first whole-plant study of microbial communities in the rhizosphere, wherein root exudates were shown to correlate with developmental stages (Knief 2014).
Proteomics is most useful when studying a particular bacterial species or strain under specific environmental conditions in order to describe its pathogenicity or possible role in symbiosis. Nevertheless, there are advances in community proteomics that build on prior metagenomic studies and use various biochemical techniques to identify, for example, secreted proteins associated with commensal or symbiotic microbial communities, and further possibilities abound as the capability of NGS technologies advance rapidly (review: (Knief et al. 2011).
Metabolomics characterizes the functions of genes, but the techniques are not organism-specific or sequence-dependent and thus can reveal the wide range of metabolites that are end-products of cellular biochemistry in organisms, tissues, cells or cell compartment (depending on which samples are analysed). Nevertheless, knowledge about the metabolome of microbial communities under particular environmental conditions (microcosms) reveals a great deal about the biogeochemical cycling of nutrients and the effects of perturbations. Such knowledge characterizes various metabolic pathways and the range of metabolites present in samples. Subsequent biochemical and statistical analyses can point to physiological states that can in turn be correlated with environmental parameters which may not be evident from genomic or proteomic approaches. Nevertheless, combining metabolomics with gene function studies has tremendous potential in furthering aquaponics research; see review (van Dam and Bouwmeester 2016).