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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

Dot plots of DNA content (DAPI, in arbitrary fluorescence units (A.F.U.)) versus forward scatter (FSC, in A.F.U.) at different growth rates 0.1 h−1, 0.2 h−1and 0.7 h−1. The dataset of the biological replicate can be found in the Additional file 2: Figure S1. Cells of P. putida KT2440 grown at steady state conditions in chemostats were stained with DAPI and analyzed by flow cytometry. The DNA content and the forward scatter increased with increasing growth rate. The indicated gates (C1, C2, Cx) were used for sorting 5x106 cells per subpopulation for further mass spectrometric analysis.
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Figure 3: Dot plots of DNA content (DAPI, in arbitrary fluorescence units (A.F.U.)) versus forward scatter (FSC, in A.F.U.) at different growth rates 0.1 h−1, 0.2 h−1and 0.7 h−1. The dataset of the biological replicate can be found in the Additional file 2: Figure S1. Cells of P. putida KT2440 grown at steady state conditions in chemostats were stained with DAPI and analyzed by flow cytometry. The DNA content and the forward scatter increased with increasing growth rate. The indicated gates (C1, C2, Cx) were used for sorting 5x106 cells per subpopulation for further mass spectrometric analysis.

Mentions: To be able to distinguish between subpopulations, flow cytometry was proven to be a suitable tool shedding light on the dynamics of single cells within a heterogeneous microbial population (Cooper [1991]; Müller and Babel [2003]; Shapiro [2000]; Skarstad et al. [1985]). Here, the DNA content was monitored via flow cytometry in addition to forward scattering (FSC) giving relative information about cell size (Müller and Nebe-Von-Caron [2010]) (Figure 3). The dataset of the biological replicate can be found in the Additional file 2: Figure S1. The subpopulation analysis revealed that the major differential parameter was the alteration of DNA content as distinguished by flow cytometry. Three subpopulations could be identified in total: cells containing a single chromosome equivalent (C1), two chromosome equivalents (C2) and cells with more than two chromosome equivalents (Cx) (Figure 3). Population composition with respect to DNA content varied clearly as a function of growth rates. At μ = 0.1 h−1, 82.0 ± 0.3% of cells contained a single chromosome equivalent, while only 18.0 ± 0.2% contained a double chromosome equivalent content. No Cx subpopulation could be detected. On the contrary, at the high growth rate of μ = 0.7 h−1 only 1.4 ± 0.8% of cells belonged to the C1 subpopulation, 16.1 ± 0.1% of cells contained a double chromosome content and 82.5 ± 1.0% more than double.


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)

Dot plots of DNA content (DAPI, in arbitrary fluorescence units (A.F.U.)) versus forward scatter (FSC, in A.F.U.) at different growth rates 0.1 h−1, 0.2 h−1and 0.7 h−1. The dataset of the biological replicate can be found in the Additional file 2: Figure S1. Cells of P. putida KT2440 grown at steady state conditions in chemostats were stained with DAPI and analyzed by flow cytometry. The DNA content and the forward scatter increased with increasing growth rate. The indicated gates (C1, C2, Cx) were used for sorting 5x106 cells per subpopulation for further mass spectrometric analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Dot plots of DNA content (DAPI, in arbitrary fluorescence units (A.F.U.)) versus forward scatter (FSC, in A.F.U.) at different growth rates 0.1 h−1, 0.2 h−1and 0.7 h−1. The dataset of the biological replicate can be found in the Additional file 2: Figure S1. Cells of P. putida KT2440 grown at steady state conditions in chemostats were stained with DAPI and analyzed by flow cytometry. The DNA content and the forward scatter increased with increasing growth rate. The indicated gates (C1, C2, Cx) were used for sorting 5x106 cells per subpopulation for further mass spectrometric analysis.
Mentions: To be able to distinguish between subpopulations, flow cytometry was proven to be a suitable tool shedding light on the dynamics of single cells within a heterogeneous microbial population (Cooper [1991]; Müller and Babel [2003]; Shapiro [2000]; Skarstad et al. [1985]). Here, the DNA content was monitored via flow cytometry in addition to forward scattering (FSC) giving relative information about cell size (Müller and Nebe-Von-Caron [2010]) (Figure 3). The dataset of the biological replicate can be found in the Additional file 2: Figure S1. The subpopulation analysis revealed that the major differential parameter was the alteration of DNA content as distinguished by flow cytometry. Three subpopulations could be identified in total: cells containing a single chromosome equivalent (C1), two chromosome equivalents (C2) and cells with more than two chromosome equivalents (Cx) (Figure 3). Population composition with respect to DNA content varied clearly as a function of growth rates. At μ = 0.1 h−1, 82.0 ± 0.3% of cells contained a single chromosome equivalent, while only 18.0 ± 0.2% contained a double chromosome equivalent content. No Cx subpopulation could be detected. On the contrary, at the high growth rate of μ = 0.7 h−1 only 1.4 ± 0.8% of cells belonged to the C1 subpopulation, 16.1 ± 0.1% of cells contained a double chromosome content and 82.5 ± 1.0% more than double.

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