Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

Bartmanski, Bartosz J.; Rocha, Miguel; Zimmermann-Kogadeeva, Maria

doi:10.1016/j.cbpa.2023.102324

Cited by 5 publications

(1 citation statement)

References 82 publications

(122 reference statements)

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“…Such approaches are suited to studies involving homogeneous sample types and sets of omics data with known functional relationships (e.g., transcriptomic—proteomic—metabolomic). Data-driven approaches on the other hand typically rely on machine learning algorithms to select relevant features from multi-omics datasets for interpretative and/or predictive purposes without a priori assumptions [ 23 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Feed Efficiency and Performance-Based Traits in Fish via Integration of Multiple Omics and Clinical Covariates

Young

Laroche

Walker

et al. 2023

Biology

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Fish aquaculture is a rapidly expanding global industry, set to support growing demands for sources of marine protein. Enhancing feed efficiency (FE) in farmed fish is required to reduce production costs and improve sector sustainability. Recognising that organisms are complex systems whose emerging phenotypes are the product of multiple interacting molecular processes, systems-based approaches are expected to deliver new biological insights into FE and growth performance. Here, we establish 14 diverse layers of multi-omics and clinical covariates to assess their capacities to predict FE and associated performance traits in a fish model (Oncorhynchus tshawytscha) and uncover the influential variables. Inter-omic relatedness between the different layers revealed several significant concordances, particularly between datasets originating from similar material/tissue and between blood indicators and some of the proteomic (liver), metabolomic (liver), and microbiomic layers. Single- and multi-layer random forest (RF) regression models showed that integration of all data layers provide greater FE prediction power than any single-layer model alone. Although FE was among the most challenging of the traits we attempted to predict, the mean accuracy of 40 different FE models in terms of root-mean square errors normalized to percentage was 30.4%, supporting RF as a feature selection tool and approach for complex trait prediction. Major contributions to the integrated FE models were derived from layers of proteomic and metabolomic data, with substantial influence also provided by the lipid composition layer. A correlation matrix of the top 27 variables in the models highlighted FE trait-associations with faecal bacteria (Serratia spp.), palmitic and nervonic acid moieties in whole body lipids, levels of free glycerol in muscle, and N-acetylglutamic acid content in liver. In summary, we identified subsets of molecular characteristics for the assessment of commercially relevant performance-based metrics in farmed Chinook salmon.

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Section: Introductionmentioning

confidence: 99%