Genome‐scale metabolic reconstruction and constraint‐based modelling of the Antarctic bacterium <scp><i>P</i></scp><i>seudoalteromonas haloplanktis</i> <scp>TAC</scp>125

Fondi, Marco; Maida, Isabel; Perrin, Elena; Mellera, Alessandra; Mocali, Stefano; Parrilli, Ermenegilda; Tutino, Maria Luisa; Lió, Píetro; Fani, Renato

doi:10.1111/1462-2920.12513

Cited by 50 publications

(57 citation statements)

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“…This prompted a comprehensive characterization of this strains, through the analysis of its complete genome [7], its proteome in different conditions [14, 15], detailed growth phenotypes in complex media [9, 11]. The integration of genomic and expression data together with detailed physiological data allowed for the formulation of a genome scale metabolic model [16], that can be used to predict phenotypes and design experiments aimed at the optimization of Ph TAC125 metabolic capabilities.…”

Section: Introductionmentioning

confidence: 99%

The pangenome of (Antarctic) Pseudoalteromonas bacteria: evolutionary and functional insights

et al. 2017

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Background Pseudoalteromonas is a genus of ubiquitous marine bacteria used as model organisms to study the biological mechanisms involved in the adaptation to cold conditions. A remarkable feature shared by these bacteria is their ability to produce secondary metabolites with a strong antimicrobial and antitumor activity. Despite their biotechnological relevance, representatives of this genus are still lacking (with few exceptions) an extensive genomic characterization, including features involved in the evolution of secondary metabolites production. Indeed, biotechnological applications would greatly benefit from such analysis.ResultsHere, we analyzed the genomes of 38 strains belonging to different Pseudoalteromonas species and isolated from diverse ecological niches, including extreme ones (i.e. Antarctica). These sequences were used to reconstruct the largest Pseudoalteromonas pangenome computed so far, including also the two main groups of Pseudoalteromonas strains (pigmented and not pigmented strains). The downstream analyses were conducted to describe the genomic diversity, both at genus and group levels. This allowed highlighting a remarkable genomic heterogeneity, even for closely related strains. We drafted all the main evolutionary steps that led to the current structure and gene content of Pseudoalteromonas representatives. These, most likely, included an extensive genome reduction and a strong contribution of Horizontal Gene Transfer (HGT), which affected biotechnologically relevant gene sets and occurred in a strain-specific fashion. Furthermore, this study also identified the genomic determinants related to some of the most interesting features of the Pseudoalteromonas representatives, such as the production of secondary metabolites, the adaptation to cold temperatures and the resistance to abiotic compounds.ConclusionsThis study poses the bases for a comprehensive understanding of the evolutionary trajectories followed in time by this peculiar bacterial genus and for a focused exploitation of their biotechnological potential.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3382-y) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

The pangenome of (Antarctic) Pseudoalteromonas bacteria: evolutionary and functional insights

et al. 2017

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“…We have recently reconstructed a genome-scale metabolic model of Ph TAC125, using it for inferring the metabolic adjustments of this bacterium induced by changes in gene expression following a temperature downgrade in this bacterium [17]. Here we used this model to investigate the metabolic rearrangements occurring during growth in a nutritionally rich medium.…”

Section: Resultsmentioning

confidence: 99%

“…The metabolic model used in this manuscript has been described in Fondi et al [17] and can be found in Additional file 1, together with the codes used to produce the results reported herein. The physiological data of Ph TAC125 growth in soy-peptone complex medium [14] was used to identify time steps corresponding to amino acid depletions.…”

Section: Methodsmentioning

confidence: 99%

“…In silico growth was simulated for each phase using FBA (see below) with salts uptake as defined as in Fondi et al [17] and adding amino acid uptake reactions with lower bound equal to the uptake rate estimated from growth data as described above.…”

Section: Methodsmentioning

confidence: 99%

“…It is capable of thriving in a wide temperature range and it has been suggested as an alternative host for the soluble overproduction of heterologous proteins, given its ability to rapidly multiply at low temperatures [13–16]. A genome-scale metabolic reconstruction of Ph TAC125 has been recently published by our group [17], allowing a system-level representation of its phenotypic landscape and boosting a holistic comprehension of its metabolic features. Interestingly, the growth curve of Ph TAC125 in a complex medium (peptone) is characterised by a number of metabolic switches among the amino acids available in the medium [14].…”

Section: Introductionmentioning

confidence: 99%

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Modelling microbial metabolic rewiring during growth in a complex medium

et al. 2016

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

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