scholarly journals The spatial and metabolic basis of colony size variation

2017 ◽  
Author(s):  
Jeremy Chacón ◽  
Wolfram Möbius ◽  
William Harcombe
Keyword(s):  
Colony Size ◽  
Carbon Sources ◽  
Size Variation ◽  
Colony Growth ◽  
Microbial Interactions ◽  
Spatial Proximity ◽  
Metabolic Basis ◽  
Evolutionary Consequences ◽  
Genome Scale ◽  
And Diffusion

AbstractSpatial structure impacts microbial growth and interactions, with ecological and evolutionary consequences. It is therefore important to quantitatively understand how spatial proximity affects interactions in different environments. We test how proximity influences colony size when either Escherichia coli or Salmonella enterica are grown on different carbon sources. The importance of colony location changes with species and carbon source. Spatially-explicit, genome-scale metabolic modeling predicts colony size variation, supporting the hypothesis that metabolic mechanisms and diffusion are sufficient to explain the majority of observed variation. Geometrically, individual colony sizes are best predicted by Voronoi diagrams, which identify the territory that is closest to each colony. This means that relative colony growth is largely independent of the distance to colonies beyond those that set territory boundaries. Further, the effect of location increases when colonies take-up resource quickly relative to the diffusion of limiting resources. These analyses made it apparent that the importance of location was smaller than expected for experiments with colonies growing on sugars. The accumulation of toxic byproducts appears to limit the growth of large colonies and reduce variation in colony size. Our work provides an experimentally and theoretically grounded understanding of how location interacts with metabolism and diffusion to influence microbial interactions.

scholarly journals The spatial and metabolic basis of colony size variation

2018 ◽  
Vol 12 (3) ◽  
pp. 669-680 ◽  
Author(s):  
Jeremy M. Chacón ◽  
Wolfram Möbius ◽  
William R. Harcombe
Keyword(s):  
Colony Size ◽  
Size Variation ◽  
Metabolic Basis

scholarly journals A Genome-Scale Model of Shewanella piezotolerans Simulates Mechanisms of Metabolic Diversity and Energy Conservation

mSystems ◽  
2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Keith Dufault-Thompson ◽  
Huahua Jian ◽  
Ruixue Cheng ◽  
Jiefu Li ◽  
Fengping Wang ◽  
...  
Keyword(s):  
Energy Conservation ◽  
Deep Sea ◽  
Carbon Sources ◽  
Scale Model ◽  
Anaerobic Conditions ◽  
A Genome ◽  
Group 2 ◽  
Genome Scale ◽  
Genome Scale Model ◽  
Group 1

ABSTRACT The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms. Shewanella piezotolerans strain WP3 belongs to the group 1 branch of the Shewanella genus and is a piezotolerant and psychrotolerant species isolated from the deep sea. In this study, a genome-scale model was constructed for WP3 using a combination of genome annotation, ortholog mapping, and physiological verification. The metabolic reconstruction contained 806 genes, 653 metabolites, and 922 reactions, including central metabolic functions that represented nonhomologous replacements between the group 1 and group 2 Shewanella species. Metabolic simulations with the WP3 model demonstrated consistency with existing knowledge about the physiology of the organism. A comparison of model simulations with experimental measurements verified the predicted growth profiles under increasing concentrations of carbon sources. The WP3 model was applied to study mechanisms of anaerobic respiration through investigating energy conservation, redox balancing, and the generation of proton motive force. Despite being an obligate respiratory organism, WP3 was predicted to use substrate-level phosphorylation as the primary source of energy conservation under anaerobic conditions, a trait previously identified in other Shewanella species. Further investigation of the ATP synthase activity revealed a positive correlation between the availability of reducing equivalents in the cell and the directionality of the ATP synthase reaction flux. Comparison of the WP3 model with an existing model of a group 2 species, Shewanella oneidensis MR-1, revealed that the WP3 model demonstrated greater flexibility in ATP production under the anaerobic conditions. Such flexibility could be advantageous to WP3 for its adaptation to fluctuating availability of organic carbon sources in the deep sea. IMPORTANCE The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms.

scholarly journals The influence of subcolony-scale nesting habitat on the reproductive success of Adélie penguins

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Annie E. Schmidt ◽  
Grant Ballard ◽  
Amélie Lescroël ◽  
Katie M. Dugger ◽  
Dennis Jongsomjit ◽  
...  
Keyword(s):  
Reproductive Success ◽  
Colony Size ◽  
Nest Predation ◽  
Size Variation ◽  
Area Ratio ◽  
Slope Aspect ◽  
Nesting Habitat ◽  
Terrain Attributes ◽  
Small Colony ◽  
Adélie Penguins

AbstractGroup-size variation is common in colonially breeding species, including seabirds, whose breeding colonies can vary in size by several orders of magnitude. Seabirds are some of the most threatened marine taxa and understanding the drivers of colony size variation is more important than ever. Reproductive success is an important demographic parameter that can impact colony size, and it varies in association with a number of factors, including nesting habitat quality. Within colonies, seabirds often aggregate into distinct groups or subcolonies that may vary in quality. We used data from two colonies of Adélie penguins 73 km apart on Ross Island, Antarctica, one large and one small to investigate (1) How subcolony habitat characteristics influence reproductive success and (2) How these relationships differ at a small (Cape Royds) and large (Cape Crozier) colony with different terrain characteristics. Subcolonies were characterized using terrain attributes (elevation, slope aspect, slope steepness, wind shelter, flow accumulation), as well group characteristics (area/size, perimeter-to-area ratio, and proximity to nest predators). Reproductive success was higher and less variable at the larger colony while subcolony characteristics explained more of the variance in reproductive success at the small colony. The most important variable influencing subcolony quality at both colonies was perimeter-to-area ratio, likely reflecting the importance of nest predation by south polar skuas along subcolony edges. The small colony contained a higher proportion of edge nests thus higher potential impact from skua nest predation. Stochastic environmental events may facilitate smaller colonies becoming “trapped” by nest predation: a rapid decline in the number of breeding individuals may increase the proportion of edge nests, leading to higher relative nest predation and hindering population recovery. Several terrain covariates were retained in the final models but which variables, the shapes of the relationships, and importance varied between colonies.

scholarly journals The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

2018 ◽  
Vol 35 (13) ◽  
pp. 2332-2334 ◽  
Author(s):  
Federico Baldini ◽  
Almut Heinken ◽  
Laurent Heirendt ◽  
Stefania Magnusdottir ◽  
Ronan M T Fleming ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Microbial Interactions ◽  
Microbial Genome ◽  
Metagenomic Data ◽  
Metabolic Interactions ◽  
Constraint Based Modeling ◽  
Genome Scale

Abstract Motivation The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. Results To address this gap, we created a comprehensive toolbox to model (i) microbe–microbe and host–microbe metabolic interactions, and (ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the constraint-based reconstruction and analysis toolbox. Availability and implementation The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.

scholarly journals Microbial Interactions in Oral Communities Mediate Emergent Biofilm Properties

2019 ◽  
Vol 99 (1) ◽  
pp. 18-25 ◽  
Author(s):  
P.I. Diaz ◽  
A.M. Valm
Keyword(s):  
Large Scale ◽  
Taxonomic Composition ◽  
Cell Interactions ◽  
Microbial Interactions ◽  
Spatial Proximity ◽  
Individual Species ◽  
Emergent Properties ◽  
Oral Diseases ◽  
Metabolic Cooperation ◽  
Cell Cell

Oral microbial communities are extraordinarily complex in taxonomic composition and comprise interdependent biological systems. The bacteria, archaea, fungi, and viruses that thrive within these communities engage in extensive cell-cell interactions, which are both beneficial and antagonistic. Direct physical interactions among individual cells mediate large-scale architectural biofilm arrangements and provide spatial proximity for chemical communication and metabolic cooperation. In this review, we summarize recent work in identifying specific molecular components that mediate cell-cell interactions and describe metabolic interactions, such as cross-feeding and exchange of electron acceptors and small molecules, that modify the growth and virulence of individual species. We argue, however, that although pairwise interaction models have provided useful information, complex community-like systems are needed to study the properties of oral communities. The networks of multiple synergistic and antagonistic interactions within oral biofilms give rise to the emergent properties of persistence, stability, and long-range spatial structure, with these properties mediating the dysbiotic transitions from health to oral diseases. A better understanding of the fundamental properties of interspecies networks will lead to the development of effective strategies to manipulate oral communities.

Genome-scale metabolic modeling to provide insight into the production of storage compounds during feast–famine cycles of activated sludge

2013 ◽  
Vol 67 (3) ◽  
pp. 469-476 ◽  
Author(s):  
Mohammad Tajparast ◽  
Dominic Frigon
Keyword(s):  
Activated Sludge ◽  
Carbon Sources ◽  
Value Added ◽  
Growth Cycle ◽  
Glycogen Accumulation ◽  
Storage Compounds ◽  
Metabolic Models ◽  
K 12 ◽  
Genome Scale ◽  
By Products

Studying storage metabolism during feast–famine cycles of activated sludge treatment systems provides profound insight in terms of both operational issues (e.g., foaming and bulking) and process optimization for the production of value added by-products (e.g., bioplastics). We examined the storage metabolism (including poly-β-hydroxybutyrate [PHB], glycogen, and triacylglycerols [TAGs]) during feast–famine cycles using two genome-scale metabolic models: Rhodococcus jostii RHA1 (iMT1174) and Escherichia coli K-12 (iAF1260) for growth on glucose, acetate, and succinate. The goal was to develop the proper objective function (OF) for the prediction of the main storage compound produced in activated sludge for given feast–famine cycle conditions. For the flux balance analysis, combinations of three OFs were tested. For all of them, the main OF was to maximize growth rates. Two additional sub-OFs were used: (1) minimization of biochemical fluxes, and (2) minimization of metabolic adjustments (MoMA) between the feast and famine periods. All (sub-)OFs predicted identical substrate–storage associations for the feast–famine growth of the above-mentioned metabolic models on a given substrate when glucose and acetate were set as sole carbon sources (i.e., glucose–glycogen and acetate–PHB), in agreement with experimental observations. However, in the case of succinate as substrate, the predictions depended on the network structure of the metabolic models such that the E. coli model predicted glycogen accumulation and the R. jostii model predicted PHB accumulation. While the accumulation of both PHB and glycogen was observed experimentally, PHB showed higher dynamics during an activated sludge feast–famine growth cycle with succinate as substrate. These results suggest that new modeling insights between metabolic predictions and population ecology will be necessary to properly predict metabolisms likely to emerge within the niches of activated sludge communities. Nonetheless, we believe that the development of this approach will help guide the optimization of the production of storage compounds as valuable by-products of wastewater treatment.

scholarly journals Acetate and glycerol are not uniquely suited for the evolution of cross-feeding in E. coli

2020 ◽  
Vol 16 (11) ◽  
pp. e1008433
Author(s):  
Magdalena San Roman ◽  
Andreas Wagner
Keyword(s):  
Carbon Source ◽  
Experimental Evolution ◽  
Phenotypic Diversity ◽  
Carbon Sources ◽  
Metabolic Modeling ◽  
Metabolic Changes ◽  
E Coli ◽  
Biochemical Pathways ◽  
Genome Scale ◽  
Feeding Interactions

The evolution of cross-feeding among individuals of the same species can help generate genetic and phenotypic diversity even in completely homogeneous environments. Cross-feeding Escherichia coli strains, where one strain feeds on a carbon source excreted by another strain, rapidly emerge during experimental evolution in a chemically minimal environment containing glucose as the sole carbon source. Genome-scale metabolic modeling predicts that cross-feeding of 58 carbon sources can emerge in the same environment, but only cross-feeding of acetate and glycerol has been experimentally observed. Here we use metabolic modeling to ask whether acetate and glycerol cross-feeding are especially likely to evolve, perhaps because they require less metabolic change, and thus perhaps also less genetic change than other cross-feeding interactions. However, this is not the case. The minimally required metabolic changes required for acetate and glycerol cross feeding affect dozens of chemical reactions, multiple biochemical pathways, as well as multiple operons or regulons. The complexity of these changes is consistent with experimental observations, where cross-feeding strains harbor multiple mutations. The required metabolic changes are also no less complex than those observed for multiple other of the 56 cross feeding interactions we study. We discuss possible reasons why only two cross-feeding interactions have been discovered during experimental evolution and argue that multiple new cross-feeding interactions may await discovery.

scholarly journals In silico analysis of the fast-growing thermophile Geobacillus sp. LC300 using a novel genome-scale metabolic model

2021 ◽  
Author(s):  
Emil Ljungqvist ◽  
Martin Gustavsson
Keyword(s):  
Metabolic Engineering ◽  
Carbon Sources ◽  
In Silico Analysis ◽  
High Growth ◽  
Metabolic Model ◽  
Thermophilic Microorganisms ◽  
Cell Factories ◽  
Conversion Rates ◽  
A Genome ◽  
Genome Scale

AbstractThermophilic microorganisms show high potential for use as biorefinery cell factories. Their high growth temperatures provide fast conversion rates, lower risk of contaminations, and facilitated purification of volatile products. To date, only a few thermophilic species have been utilized for microbial production purposes, and the development of production strains is impeded by the lack of metabolic engineering tools. In this study, we constructed a genome-scale metabolic model, iGEL601, of Geobacillus sp. LC300, an important part of the metabolic engineering pipeline. The model contains 601 genes, 1240 reactions and 1305 metabolites, and the reaction reversibility is based on thermodynamics at the optimum growth temperature. Using flux sampling, the model shows high similarity to experimentally determined reaction fluxes with both glucose and xylose as sole carbon sources. Furthermore, the model predicts previously unidentified by-products, closing the gap in the carbon balance for both carbon sources. Finally, iGEL601 was used to suggest metabolic engineering strategies to maximise production of five industrially relevant compounds. The suggested strategies have previously been experimentally verified in other microorganisms, and predicted production rates are on par with or higher than those previously achieved experimentally. The results highlight the biotechnological potential of LC300 and the application of iGEL601 for use as a tool in the metabolic engineering workflow.

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