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Subpopulation-proteomics reveal growth rate, but not cell cycling, as a major impact on protein composition in Pseudomonas putida KT2440.

Lieder S, Jahn M, Seifert J, von Bergen M, Müller S, Takors R - AMB Express (2014)

Bottom Line: The proteome of separated subpopulations at given growth rates was found to be highly similar, while different growth rates caused major changes of the protein inventory with respect to e.g. carbon storage, motility, lipid metabolism and the translational machinery.In conclusion, cells in various cell cycle stages at the same growth rate were found to have similar to identical proteome profiles showing no significant population heterogeneity on the proteome level.In contrast, the growth rate clearly determines the protein composition and therefore the metabolic strategy of the cells.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Biochemical Engineering, University of Stuttgart, Allmandring 31, Stuttgart, Germany.

ABSTRACT
Population heterogeneity occurring in industrial microbial bioprocesses is regarded as a putative effector causing performance loss in large scale. While the existence of subpopulations is a commonly accepted fact, their appearance and impact on process performance still remains rather unclear. During cell cycling, distinct subpopulations differing in cell division state and DNA content appear which contribute individually to the efficiency of the bioprocess. To identify stressed or impaired subpopulations, we analyzed the interplay of growth rate, cell cycle and phenotypic profile of subpopulations by using flow cytometry and cell sorting in conjunction with mass spectrometry based global proteomics. Adjusting distinct growth rates in chemostats with the model strain Pseudomonas putida KT2440, cells were differentiated by DNA content reflecting different cell cycle stages. The proteome of separated subpopulations at given growth rates was found to be highly similar, while different growth rates caused major changes of the protein inventory with respect to e.g. carbon storage, motility, lipid metabolism and the translational machinery. In conclusion, cells in various cell cycle stages at the same growth rate were found to have similar to identical proteome profiles showing no significant population heterogeneity on the proteome level. In contrast, the growth rate clearly determines the protein composition and therefore the metabolic strategy of the cells.

No MeSH data available.


Related in: MedlinePlus

Summary of the physiological state of the average population. The specific glucose uptake rate (qs, gGLCgCDWh−1, black bars), the adenylate energy charge (AEC, dark grey bars) and the biomass yield (Yx/s, gCDWgGLC−1, light grey bars) were measured at steady state conditions for different growth rates μ (h−1). The growth rate was stepwise increased until a wash-out of the cells was monitored. Concentrations of cell dry weight (CDW), glucose (GLC) and the AEC were measured offline, sampling after 5 residence times of one specific growth rate (0.1 ≤ μ (h−1) ≤ 0.7). Error bars show the standard deviation between three biological replicate cultivations.
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Figure 2: Summary of the physiological state of the average population. The specific glucose uptake rate (qs, gGLCgCDWh−1, black bars), the adenylate energy charge (AEC, dark grey bars) and the biomass yield (Yx/s, gCDWgGLC−1, light grey bars) were measured at steady state conditions for different growth rates μ (h−1). The growth rate was stepwise increased until a wash-out of the cells was monitored. Concentrations of cell dry weight (CDW), glucose (GLC) and the AEC were measured offline, sampling after 5 residence times of one specific growth rate (0.1 ≤ μ (h−1) ≤ 0.7). Error bars show the standard deviation between three biological replicate cultivations.

Mentions: Subpopulation dynamics of P. putida KT2440 were analyzed in a wide range from slow growth rates starting at μ = 0.1 h−1 to high growth rates of up to μ = 0.7 h−1. At growth rates higher than μ = 0.7 h−1, wash out of the culture was observed, meaning that the maximal growth rate was exceeded and cells could not reproduce fast enough to keep the population density constant. For this reason, μ = 0.7 h−1 was the highest growth rate investigated in this study. The physiological and the energetic state of the averaged cell population was analyzed by biomass/substrate yield (Yx/s), biomass specific substrate uptake rates (qs), and adenylate energy charge measurements (AEC), each measured at steady state growth conditions (Figure 2). Observed stable carbon dioxide emission rates served as the criterion to qualify the achievement of steady-state cultivation conditions.


Subpopulation-proteomics reveal growth rate, but not cell cycling, as a major impact on protein composition in Pseudomonas putida KT2440.

Lieder S, Jahn M, Seifert J, von Bergen M, Müller S, Takors R - AMB Express (2014)

Summary of the physiological state of the average population. The specific glucose uptake rate (qs, gGLCgCDWh−1, black bars), the adenylate energy charge (AEC, dark grey bars) and the biomass yield (Yx/s, gCDWgGLC−1, light grey bars) were measured at steady state conditions for different growth rates μ (h−1). The growth rate was stepwise increased until a wash-out of the cells was monitored. Concentrations of cell dry weight (CDW), glucose (GLC) and the AEC were measured offline, sampling after 5 residence times of one specific growth rate (0.1 ≤ μ (h−1) ≤ 0.7). Error bars show the standard deviation between three biological replicate cultivations.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230896&req=5

Figure 2: Summary of the physiological state of the average population. The specific glucose uptake rate (qs, gGLCgCDWh−1, black bars), the adenylate energy charge (AEC, dark grey bars) and the biomass yield (Yx/s, gCDWgGLC−1, light grey bars) were measured at steady state conditions for different growth rates μ (h−1). The growth rate was stepwise increased until a wash-out of the cells was monitored. Concentrations of cell dry weight (CDW), glucose (GLC) and the AEC were measured offline, sampling after 5 residence times of one specific growth rate (0.1 ≤ μ (h−1) ≤ 0.7). Error bars show the standard deviation between three biological replicate cultivations.
Mentions: Subpopulation dynamics of P. putida KT2440 were analyzed in a wide range from slow growth rates starting at μ = 0.1 h−1 to high growth rates of up to μ = 0.7 h−1. At growth rates higher than μ = 0.7 h−1, wash out of the culture was observed, meaning that the maximal growth rate was exceeded and cells could not reproduce fast enough to keep the population density constant. For this reason, μ = 0.7 h−1 was the highest growth rate investigated in this study. The physiological and the energetic state of the averaged cell population was analyzed by biomass/substrate yield (Yx/s), biomass specific substrate uptake rates (qs), and adenylate energy charge measurements (AEC), each measured at steady state growth conditions (Figure 2). Observed stable carbon dioxide emission rates served as the criterion to qualify the achievement of steady-state cultivation conditions.

Bottom Line: The proteome of separated subpopulations at given growth rates was found to be highly similar, while different growth rates caused major changes of the protein inventory with respect to e.g. carbon storage, motility, lipid metabolism and the translational machinery.In conclusion, cells in various cell cycle stages at the same growth rate were found to have similar to identical proteome profiles showing no significant population heterogeneity on the proteome level.In contrast, the growth rate clearly determines the protein composition and therefore the metabolic strategy of the cells.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Biochemical Engineering, University of Stuttgart, Allmandring 31, Stuttgart, Germany.

ABSTRACT
Population heterogeneity occurring in industrial microbial bioprocesses is regarded as a putative effector causing performance loss in large scale. While the existence of subpopulations is a commonly accepted fact, their appearance and impact on process performance still remains rather unclear. During cell cycling, distinct subpopulations differing in cell division state and DNA content appear which contribute individually to the efficiency of the bioprocess. To identify stressed or impaired subpopulations, we analyzed the interplay of growth rate, cell cycle and phenotypic profile of subpopulations by using flow cytometry and cell sorting in conjunction with mass spectrometry based global proteomics. Adjusting distinct growth rates in chemostats with the model strain Pseudomonas putida KT2440, cells were differentiated by DNA content reflecting different cell cycle stages. The proteome of separated subpopulations at given growth rates was found to be highly similar, while different growth rates caused major changes of the protein inventory with respect to e.g. carbon storage, motility, lipid metabolism and the translational machinery. In conclusion, cells in various cell cycle stages at the same growth rate were found to have similar to identical proteome profiles showing no significant population heterogeneity on the proteome level. In contrast, the growth rate clearly determines the protein composition and therefore the metabolic strategy of the cells.

No MeSH data available.


Related in: MedlinePlus