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Precise, High-throughput Analysis of Bacterial Growth
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Trait-based approach to bacterial growth efficiency.

Mario E Muscarella1,2, Xia Meng Howey1, Jay T Lennon1

  • 1Department of Biology, Indiana University, Bloomington, IN, USA.

Environmental Microbiology
|June 9, 2020
PubMed
Summary
This summary is machine-generated.

Bacterial growth efficiency (BGE) varies significantly by species and strain, not just by resource availability. Genomic and phylogenetic data can predict microbial community functions and carbon cycling in ecosystems.

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Area of Science:

  • Microbial Ecology
  • Biogeochemistry
  • Genomics

Background:

  • Bacterial growth efficiency (BGE) quantifies assimilated carbon conversion to biomass, crucial for understanding microbial energetics and ecosystem carbon cycling.
  • BGE is a variable aggregate community property, necessitating investigation into factors driving this variation.

Purpose of the Study:

  • To determine how bacterial species identity and resource type influence BGE.
  • To explore the role of metabolic trade-offs and genomic features in BGE variation.
  • To assess the predictive power of phylogenetic and genomic information for microbial functions.

Main Methods:

  • Utilized 20 bacterial isolates from Proteobacteria phylum, enriched from north temperate lakes.
  • Employed a trait-based approach integrating genomic and phenotypic data to characterize isolate metabolism.
  • Tested for trade-offs between growth rate and efficiency, and analyzed BGE variation across taxonomic levels and resource types.

Main Results:

  • Species and strain identity explained substantial BGE variation (20% at order, 58% at strain level).
  • Resource type was a minor factor across species but explained over 60% of BGE variation within species.
  • Identified a metabolic trade-off and genomic features indicating BGE is a species-specific metabolic trait.

Conclusions:

  • BGE is a species-specific metabolic property influenced by genomic and phylogenetic factors.
  • Genomic and phylogenetic information can predict aggregate microbial community functions, including BGE.
  • This understanding aids in predicting carbon fate within ecosystems.