Traditional environmental monitoring techniques are well suited to resolving acute exposure effects but lack resolution in determining subtle shifts in ecosystem functions resulting from chronic exposure(s). Surveillance with sensitive omics-based technologies could bridge this gap but, to date, most omics-based environmental studies have focused on previously degraded environments, identifying key metabolic differences resulting from anthropogenic perturbations. Here, we apply omics-based approaches to pristine environments to establish blueprints of microbial functionality within healthy estuarine sediment communities. We collected surface sediments (n = 50) from four pristine estuaries along the Western Cape York Peninsula of Far North Queensland, Australia. Sediment microbiomes were analyzed for 16S rRNA amplicon sequences, central carbon metabolism metabolites and associated secondary metabolites via targeted and untargeted metabolic profiling methods. Multivariate statistical analyses indicated heterogeneity among all the sampled estuaries, however, taxa-function relationships could be established that predicted community metabolism potential. Twenty-four correlated gene-metabolite pathways were identified and used to establish sediment microbial blueprints of essential carbon metabolism and amino acid biosynthesis that were positively correlated with community metabolic function outputs (2-oxisocapraote, tryptophan, histidine citrulline and succinic acid). In addition, an increase in the 125 KEGG genes related to metal homeostasis and metal resistance was observed, although, none of the detected metabolites related to these specific genes upon integration. However, there was a correlation between metal abundance and functional genes related to Fe and Zn metabolism. Our results establish a baseline microbial blueprint for the pristine sediment microbiome, one that drives important ecosystem services and to which future ecosurveillance monitoring can be compared.
Keywords: Community metabolomics; Ecometabolomics; Environmental metabolomics; Environmental monitoring; Multi-omics; Systems biology.
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