Harnessing intercellular signals to engineer the soil microbiome

Connolly, Jack; Harcombe, William R.; Smanski, Michael J.; Kinkel, Linda L.; Takano, Eriko; Breitling, Rainer

doi:10.1039/d1np00034a

Cited by 3 publications

(1 citation statement)

References 145 publications

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“…These interactions that guide community assemblage are predicated upon a metabolic economy, where the competitive community environment diversifies members into ecological niches that balance nutritional satiation and metabolic efficiency by leveraging cross-feeding [24, 25]. Metabolic cross-feeding (syntrophy) is essential to understand and engineer communities for desirable properties [26], such as bioproduction that exceeds the capabilities of monocultures [7] or sustainable biotechnology by coupling phototrophs and heterotrophs [27], yet it is difficult to experimentally measure.…”

Section: Introductionmentioning

confidence: 99%

A microbial community growth model for dynamic phenotype predictions

Freiburger

Dewey

Foflonker

et al. 2022

Preprint

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Microbial communities are increasingly recognized as key drivers in animal health, agricultural productivity, industrial operations, and ecological systems. The abundance of chemical interactions in these complex communities, however, can complicate or evade experimental studies, which hinders basic understanding and limits efforts to rationally design communities for applications in the aforementioned fields. Numerous computational approaches have been proposed to deduce these metabolic interactions -- notably including flux balance analysis (FBA) and systems of ordinary differential equations (ODEs) -- yet, these methods either fail to capture the dynamic phenotype expression of community members or lack the abstractions required to fit or explain the diverse experimental omics data that can be acquired today. We therefore developed a dynamic model (CommPhitting) that deduces phenotype abundances and growth kinetics for each community member, concurrent with metabolic concentrations, by coupling flux profiles for each phenotype with experimental growth and -omics data of the community. These data are captured as variables and coefficients within a mixed integer linear optimization problem (MILP) designed to represent the associated biological processes. This problem finds the globally optimized fit to all experimental data of a trial, thereby most accurately computing aspects of the community: (1) species and phenotype abundances over time; (2) a linearized growth kinetic constant for each phenotype; and (3) metabolite concentrations over time. We exemplify CommPhitting by applying it to study batch growth of an idealized two-member community of the model organisms (Escherichia coli and Pseudomonas flourescens) that exhibits cross-feeding in maltose media. Measurements of this community from our accompanying experimental studies -- including total biomass, species biomass, and metabolite abundances over time -- were parameterized into a CommPhitting simulation. The resultant kinetics constants and biomass proportions for each member phenotype would be difficult to ascertain experimentally, yet are important for understanding community responses to environmental perturbations and therefore engineering applications: e.g. for bioproduction. We believe that CommPhitting -- which is generalized for a diversity of data types and formats, and is further available and amply documented as a Python API -- will augment basic understanding of microbial communities and will accelerate the engineering of synthetic communities for diverse applications in medicine, agriculture, industry, and ecology.

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Section: Introductionmentioning

confidence: 99%

A microbial community growth model for dynamic phenotype predictions

Freiburger

Dewey

Foflonker

et al. 2022

Preprint

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Pseudomonas koreensis AB36 from a gold mine: genomic insights into adaptation to extreme conditions and potential plant growth promotion

Ayangbenro,

Olanrewaju,

Amoo

et al. 2024

Biologia

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The genus Pseudomonas is one of the most varied and widespread bacterial genera, with species found in most environment. They are known to degrade organic and inorganic compounds, produce secondary metabolites, and enhance plant growth. The genome of Pseudomonas koreensis AB36, a heavy metal resistant organism isolated from a gold mine was sequenced to unveil the versatile metabolic potential of the organism. The genome is a single circular chromosome of 5,902,614 bp, with G + C content of 60.1%. There are 4154 similar orthologous gene clusters shared among strain AB36 and other sequenced P. koreensis strains with 7 clusters found alone in the genome of strain AB36. Genome mining of the organism predicted 8 biosynthetic gene clusters using antiSMASH including three non-ribosomal peptide synthethase (NRPS) clusters, arylpolyene and bacteriocin. The genome contains putative genes for heavy metal transport/resistance. These results show the heavy metal resistance ability and degradation of xenobiotic compounds of strain AB36 as well as its potential to adapt to various environments.

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