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Modelling microbial metabolic rewiring during growth in a complex medium

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ABSTRACT

Background: In their natural environment, bacteria face a wide range of environmental conditions that change over time and that impose continuous rearrangements at all the cellular levels (e.g. gene expression, metabolism). When facing a nutritionally rich environment, for example, microbes first use the preferred compound(s) and only later start metabolizing the other one(s). A systemic re-organization of the overall microbial metabolic network in response to a variation in the composition/concentration of the surrounding nutrients has been suggested, although the range and the entity of such modifications in organisms other than a few model microbes has been scarcely described up to now.

Results: We used multi-step constraint-based metabolic modelling to simulate the growth in a complex medium over several time steps of the Antarctic model organism Pseudoalteromonas haloplanktis TAC125. As each of these phases is characterized by a specific set of amino acids to be used as carbon and energy source our modelling framework describes the major consequences of nutrients switching at the system level. The model predicts that a deep metabolic reprogramming might be required to achieve optimal biomass production in different stages of growth (different medium composition), with at least half of the cellular metabolic network involved (more than 50% of the metabolic genes). Additionally, we show that our modelling framework is able to capture metabolic functional association and/or common regulatory features of the genes embedded in our reconstruction (e.g. the presence of common regulatory motifs).

Results: Finally, to explore the possibility of a sub-optimal biomass objective function (i.e. that cells use resources in alternative metabolic processes at the expense of optimal growth) we have implemented a MOMA-based approach (called nutritional-MOMA) and compared the outcomes with those obtained with Flux Balance Analysis (FBA). Growth simulations under this scenario revealed the deep impact of choosing among alternative objective functions on the resulting predictions of fluxes distribution.

Conclusions: Here we provide a time-resolved, systems-level scheme of PhTAC125 metabolic re-wiring as a consequence of carbon source switching in a nutritionally complex medium. Our analyses suggest the presence of a potential efficient metabolic reprogramming machinery to continuously and promptly adapt to this nutritionally changing environment, consistent with adaptation to fast growth in a fairly, but probably inconstant and highly competitive, environment. Also, we show i) how functional partnership and co-regulation features can be predicted by integrating multi-step constraint-based metabolic modelling with fed-batch growth data and ii) that performing simulations under a sub-optimal objective function may lead to different flux distributions in respect to canonical FBA.

Electronic supplementary material: The online version of this article (doi:10.1186/s12864-016-3311-0) contains supplementary material, which is available to authorized users.

No MeSH data available.


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a. Co-varying reactions clusters identification. Common flux trends (expressed as the normalized difference between the absolute value of fluxes across each growth phase and the following one) for the reactions embedded in each of the 28 clusters. b. The distribution of STRING combined scores among all the genes embedded in each cluster of genes (primary y axis) and the number of genes embedded by each cluster (secondary y axis, red line). The grey line represents the median of the combined score computed for each possible pair of genes in the model
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Fig6: a. Co-varying reactions clusters identification. Common flux trends (expressed as the normalized difference between the absolute value of fluxes across each growth phase and the following one) for the reactions embedded in each of the 28 clusters. b. The distribution of STRING combined scores among all the genes embedded in each cluster of genes (primary y axis) and the number of genes embedded by each cluster (secondary y axis, red line). The grey line represents the median of the combined score computed for each possible pair of genes in the model

Mentions: Overall, our method led to the identified 28 different clusters Fig. 6a, comprising 203 reactions. According to the GPR of our model, these reactions were encoded by 223 genes.Fig. 6


Modelling microbial metabolic rewiring during growth in a complex medium
a. Co-varying reactions clusters identification. Common flux trends (expressed as the normalized difference between the absolute value of fluxes across each growth phase and the following one) for the reactions embedded in each of the 28 clusters. b. The distribution of STRING combined scores among all the genes embedded in each cluster of genes (primary y axis) and the number of genes embedded by each cluster (secondary y axis, red line). The grey line represents the median of the combined score computed for each possible pair of genes in the model
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5121958&req=5

Fig6: a. Co-varying reactions clusters identification. Common flux trends (expressed as the normalized difference between the absolute value of fluxes across each growth phase and the following one) for the reactions embedded in each of the 28 clusters. b. The distribution of STRING combined scores among all the genes embedded in each cluster of genes (primary y axis) and the number of genes embedded by each cluster (secondary y axis, red line). The grey line represents the median of the combined score computed for each possible pair of genes in the model
Mentions: Overall, our method led to the identified 28 different clusters Fig. 6a, comprising 203 reactions. According to the GPR of our model, these reactions were encoded by 223 genes.Fig. 6

View Article: PubMed Central - PubMed

ABSTRACT

Background: In their natural environment, bacteria face a wide range of environmental conditions that change over time and that impose continuous rearrangements at all the cellular levels (e.g. gene expression, metabolism). When facing a nutritionally rich environment, for example, microbes first use the preferred compound(s) and only later start metabolizing the other one(s). A systemic re-organization of the overall microbial metabolic network in response to a variation in the composition/concentration of the surrounding nutrients has been suggested, although the range and the entity of such modifications in organisms other than a few model microbes has been scarcely described up to now.

Results: We used multi-step constraint-based metabolic modelling to simulate the growth in a complex medium over several time steps of the Antarctic model organism Pseudoalteromonas haloplanktis TAC125. As each of these phases is characterized by a specific set of amino acids to be used as carbon and energy source our modelling framework describes the major consequences of nutrients switching at the system level. The model predicts that a deep metabolic reprogramming might be required to achieve optimal biomass production in different stages of growth (different medium composition), with at least half of the cellular metabolic network involved (more than 50% of the metabolic genes). Additionally, we show that our modelling framework is able to capture metabolic functional association and/or common regulatory features of the genes embedded in our reconstruction (e.g. the presence of common regulatory motifs).

Results: Finally, to explore the possibility of a sub-optimal biomass objective function (i.e. that cells use resources in alternative metabolic processes at the expense of optimal growth) we have implemented a MOMA-based approach (called nutritional-MOMA) and compared the outcomes with those obtained with Flux Balance Analysis (FBA). Growth simulations under this scenario revealed the deep impact of choosing among alternative objective functions on the resulting predictions of fluxes distribution.

Conclusions: Here we provide a time-resolved, systems-level scheme of PhTAC125 metabolic re-wiring as a consequence of carbon source switching in a nutritionally complex medium. Our analyses suggest the presence of a potential efficient metabolic reprogramming machinery to continuously and promptly adapt to this nutritionally changing environment, consistent with adaptation to fast growth in a fairly, but probably inconstant and highly competitive, environment. Also, we show i) how functional partnership and co-regulation features can be predicted by integrating multi-step constraint-based metabolic modelling with fed-batch growth data and ii) that performing simulations under a sub-optimal objective function may lead to different flux distributions in respect to canonical FBA.

Electronic supplementary material: The online version of this article (doi:10.1186/s12864-016-3311-0) contains supplementary material, which is available to authorized users.

No MeSH data available.


Related in: MedlinePlus