229
diatoms continued using iron-free versions of photosynthetic proteins (i.e. did not
replace non-iron proteins with their more efficient iron-containing counterparts),
selectively partitioning acquired iron towards nutrient assimilation rather than light
harvesting. This could offer some explanation for why diatoms dominate low iron
environments (Marchetti et al. 2012). Haptophytes, on the other hand, increased
their expression of iron-containing proteins (Marchetti et al. 2012). Thus, application of a transcriptomic approach to examine nutrient amendment is not only useful to understand lifestyle strategies between taxonomic groups but also to explore
nutrient partitioning within individual cells.
In a further example, Alexander et al. (2015) explored resource partitioning
amongst coexisting diatoms using nutrient amendment and quantitative metatranscriptomics. This approach highlighted the specific pathways for nutrient acquisition and metabolism utilised by sympatric diatoms within a natural community.
Samples for metatranscriptomes were collected from the field, and the Marine
Microbial Eukaryotic Transcriptome Sequencing Project database was used as a
reference to identify transcripts. Resource portioning was evident by the different
transcriptional responses between Skeletonema spp. and Thalassiosira rotula in the
same environment, enabling co-occurrence without competition, a first time observation. During the bloom, the dominant diatom was Skeletonema which had high
expression of growth-related genes such as those involved in carbon, nitrogen, sulfur and lipid metabolism as well as nitrate and ammonia assimilation whilst the less
abundant diatom, Thalassiosira, highly expressed genes associated with amino acid
transporters as well as transport and repair (Alexander et al. 2015). This study highlighted that despite possessing similar nitrogen and phosphorous transport and
metabolism genes, the two species differed temporally in their expression, suggesting resource partitioning.
11.4 Influence of Oceanographic Processes on Microbial
Protein Expression
Whilst transcriptomics provides valuable insights into the metabolic potential of
individual species and natural communities by pinpointing differentially expressed
genes, the observed expression does not necessarily directly translate into synthesised proteins. Since there are multiple regulatory controls on genes and posttranscriptional modifications on expressed transcripts (Williams and Cavicchioli
2014), measures of expressed proteins (i.e. products of biosynthesis) provide verification of function over metabolic potential inferred from the genome or the transcriptome (Fig. 11.1). The high-throughput ‘omic’ approach, proteomics, determines
the presence and abundance of expressed proteins, providing an additional level of
information on microbial activity and function. Metaproteomics is the application
of this technique to entire populations or communities without specific targets
(Morris et al. 2010; Williams and Cavicchioli 2014). This requires reference
genomes or metagenomic datasets to identify protein-coding genes. Complementing
11 Application of ‘Omics’ Approaches to Microbial Oceanography
diatoms continued using iron-free versions of photosynthetic proteins (i.e. did not
replace non-iron proteins with their more efficient iron-containing counterparts),
selectively partitioning acquired iron towards nutrient assimilation rather than light
harvesting. This could offer some explanation for why diatoms dominate low iron
environments (Marchetti et al. 2012). Haptophytes, on the other hand, increased
their expression of iron-containing proteins (Marchetti et al. 2012). Thus, application of a transcriptomic approach to examine nutrient amendment is not only useful to understand lifestyle strategies between taxonomic groups but also to explore
nutrient partitioning within individual cells.
In a further example, Alexander et al. (2015) explored resource partitioning
amongst coexisting diatoms using nutrient amendment and quantitative metatranscriptomics. This approach highlighted the specific pathways for nutrient acquisition and metabolism utilised by sympatric diatoms within a natural community.
Samples for metatranscriptomes were collected from the field, and the Marine
Microbial Eukaryotic Transcriptome Sequencing Project database was used as a
reference to identify transcripts. Resource portioning was evident by the different
transcriptional responses between Skeletonema spp. and Thalassiosira rotula in the
same environment, enabling co-occurrence without competition, a first time observation. During the bloom, the dominant diatom was Skeletonema which had high
expression of growth-related genes such as those involved in carbon, nitrogen, sulfur and lipid metabolism as well as nitrate and ammonia assimilation whilst the less
abundant diatom, Thalassiosira, highly expressed genes associated with amino acid
transporters as well as transport and repair (Alexander et al. 2015). This study highlighted that despite possessing similar nitrogen and phosphorous transport and
metabolism genes, the two species differed temporally in their expression, suggesting resource partitioning.
11.4 Influence of Oceanographic Processes on Microbial
Protein Expression
Whilst transcriptomics provides valuable insights into the metabolic potential of
individual species and natural communities by pinpointing differentially expressed
genes, the observed expression does not necessarily directly translate into synthesised proteins. Since there are multiple regulatory controls on genes and posttranscriptional modifications on expressed transcripts (Williams and Cavicchioli
2014), measures of expressed proteins (i.e. products of biosynthesis) provide verification of function over metabolic potential inferred from the genome or the transcriptome (Fig. 11.1). The high-throughput ‘omic’ approach, proteomics, determines
the presence and abundance of expressed proteins, providing an additional level of
information on microbial activity and function. Metaproteomics is the application
of this technique to entire populations or communities without specific targets
(Morris et al. 2010; Williams and Cavicchioli 2014). This requires reference
genomes or metagenomic datasets to identify protein-coding genes. Complementing
11 Application of ‘Omics’ Approaches to Microbial Oceanography