Limits...
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

Circular treemaps visualizing differentially expressed functional protein categories. Proteins detected by mass spectrometry were clustered according to their pathway annotation in COG covering two levels of specificity (Tatusov et al. [1997]). The size of a sector is proportional to the number of proteins found in one specific pathway in relation to the total protein number. The color code represents the log2 mean fold change (log2 FC) of protein quantity in one pathway. The color blue codes for an underrepresentation, red for an overrepresentation of the proteins in a pathway compared to the reference population (RP, μ = 0.2 h−1). Pathways with a fold change in the range log2 FC < −0.58 and log2 FC > 0.58 are labeled with the respective pathway name. Pathways that were significantly changed using GAGE (Luo et al. [2009]) and Globaltest (Goeman et al. [2006]) gene set analysis are additionally marked (*). a. Comparison of the subpopulations C1/C2 and C2/Cx at growth rates 0.1 h−1 and 0.7 h−1, respectively. b. Comparison of the subpopulations C1 and C2 at μ = 0.1 h−1 with RP. c. Comparison of the subpopulations C2 and Cx at μ = 0.7 h−1 with RP.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4230896&req=5

Figure 4: Circular treemaps visualizing differentially expressed functional protein categories. Proteins detected by mass spectrometry were clustered according to their pathway annotation in COG covering two levels of specificity (Tatusov et al. [1997]). The size of a sector is proportional to the number of proteins found in one specific pathway in relation to the total protein number. The color code represents the log2 mean fold change (log2 FC) of protein quantity in one pathway. The color blue codes for an underrepresentation, red for an overrepresentation of the proteins in a pathway compared to the reference population (RP, μ = 0.2 h−1). Pathways with a fold change in the range log2 FC < −0.58 and log2 FC > 0.58 are labeled with the respective pathway name. Pathways that were significantly changed using GAGE (Luo et al. [2009]) and Globaltest (Goeman et al. [2006]) gene set analysis are additionally marked (*). a. Comparison of the subpopulations C1/C2 and C2/Cx at growth rates 0.1 h−1 and 0.7 h−1, respectively. b. Comparison of the subpopulations C1 and C2 at μ = 0.1 h−1 with RP. c. Comparison of the subpopulations C2 and Cx at μ = 0.7 h−1 with RP.

Mentions: As a result, at any given growth rate, the proteomic patterns of the subpopulations did not differ significantly from each other (Figure 4a). When looking at single proteins, only three were detected that comprised significantly different levels between subpopulations at growth rate μ = 0.1 h−1 and μ = 0.7 h−1, respectively. The abundance of cell division protein FtsZ was found to be 3.6 fold lower in subpopulation C1 in contrast to C2. FtsZ is a bacterial tubulin homologue self-assembling into a ring at mid-cell level and localizing the bacterial divisome machinery (Adams and Errington [2009]; Weart et al. [2007]). The two other proteins were the molecular chaperone GroEL (FC 1.7) and a P-47-like protein (PP_2007, FC 2.4). Also at high growth rate of μ = 0.7 h−1, only three proteins, the translocation protein TolB (FC 1.8), the NADH dehydrogenase subunit G (PP_4124, FC 1.51) and a succinyldiaminopimelate transaminase (PP_1588, FC 0.26) showed significant differences between the subpopulations C2 and Cx. Surprisingly, no changes in metabolic pathways could be found between subpopulations at any given growth rate.


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)

Circular treemaps visualizing differentially expressed functional protein categories. Proteins detected by mass spectrometry were clustered according to their pathway annotation in COG covering two levels of specificity (Tatusov et al. [1997]). The size of a sector is proportional to the number of proteins found in one specific pathway in relation to the total protein number. The color code represents the log2 mean fold change (log2 FC) of protein quantity in one pathway. The color blue codes for an underrepresentation, red for an overrepresentation of the proteins in a pathway compared to the reference population (RP, μ = 0.2 h−1). Pathways with a fold change in the range log2 FC < −0.58 and log2 FC > 0.58 are labeled with the respective pathway name. Pathways that were significantly changed using GAGE (Luo et al. [2009]) and Globaltest (Goeman et al. [2006]) gene set analysis are additionally marked (*). a. Comparison of the subpopulations C1/C2 and C2/Cx at growth rates 0.1 h−1 and 0.7 h−1, respectively. b. Comparison of the subpopulations C1 and C2 at μ = 0.1 h−1 with RP. c. Comparison of the subpopulations C2 and Cx at μ = 0.7 h−1 with RP.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Circular treemaps visualizing differentially expressed functional protein categories. Proteins detected by mass spectrometry were clustered according to their pathway annotation in COG covering two levels of specificity (Tatusov et al. [1997]). The size of a sector is proportional to the number of proteins found in one specific pathway in relation to the total protein number. The color code represents the log2 mean fold change (log2 FC) of protein quantity in one pathway. The color blue codes for an underrepresentation, red for an overrepresentation of the proteins in a pathway compared to the reference population (RP, μ = 0.2 h−1). Pathways with a fold change in the range log2 FC < −0.58 and log2 FC > 0.58 are labeled with the respective pathway name. Pathways that were significantly changed using GAGE (Luo et al. [2009]) and Globaltest (Goeman et al. [2006]) gene set analysis are additionally marked (*). a. Comparison of the subpopulations C1/C2 and C2/Cx at growth rates 0.1 h−1 and 0.7 h−1, respectively. b. Comparison of the subpopulations C1 and C2 at μ = 0.1 h−1 with RP. c. Comparison of the subpopulations C2 and Cx at μ = 0.7 h−1 with RP.
Mentions: As a result, at any given growth rate, the proteomic patterns of the subpopulations did not differ significantly from each other (Figure 4a). When looking at single proteins, only three were detected that comprised significantly different levels between subpopulations at growth rate μ = 0.1 h−1 and μ = 0.7 h−1, respectively. The abundance of cell division protein FtsZ was found to be 3.6 fold lower in subpopulation C1 in contrast to C2. FtsZ is a bacterial tubulin homologue self-assembling into a ring at mid-cell level and localizing the bacterial divisome machinery (Adams and Errington [2009]; Weart et al. [2007]). The two other proteins were the molecular chaperone GroEL (FC 1.7) and a P-47-like protein (PP_2007, FC 2.4). Also at high growth rate of μ = 0.7 h−1, only three proteins, the translocation protein TolB (FC 1.8), the NADH dehydrogenase subunit G (PP_4124, FC 1.51) and a succinyldiaminopimelate transaminase (PP_1588, FC 0.26) showed significant differences between the subpopulations C2 and Cx. Surprisingly, no changes in metabolic pathways could be found between subpopulations at any given growth rate.

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