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RNA-Seq analysis and targeted mutagenesis for improved free fatty acid production in an engineered cyanobacterium.

Ruffing AM - Biotechnol Biofuels (2013)

Bottom Line: Recent studies investigating cyanobacterial FFA production have demonstrated the potential of this process, yet FFA production was also shown to have negative physiological effects on the cyanobacterial host, ultimately limiting high yields of FFAs.Gene knockout of two porins and the overexpression of ROS-degrading proteins and hypothetical proteins reduced the toxic effects of FFA production, allowing for improved growth, physiology, and FFA yields.Comparative transcriptomics, analyzing gene expression changes associated with FFA production and other stress conditions, identified additional key genes involved in cyanobacterial stress response.

View Article: PubMed Central - HTML - PubMed

Affiliation: Sandia National Laboratories, Department of Bioenergy and Defense Technologies, MS 1413, P,O, Box 5800, 87185-1413, Albuquerque, NM, USA. aruffin@sandia.gov.

ABSTRACT

Background: High-energy-density biofuels are typically derived from the fatty acid pathway, thus establishing free fatty acids (FFAs) as important fuel precursors. FFA production using photosynthetic microorganisms like cyanobacteria allows for direct conversion of carbon dioxide into fuel precursors. Recent studies investigating cyanobacterial FFA production have demonstrated the potential of this process, yet FFA production was also shown to have negative physiological effects on the cyanobacterial host, ultimately limiting high yields of FFAs.

Results: Cyanobacterial FFA production was shown to generate reactive oxygen species (ROS) and lead to increased cell membrane permeability. To identify genetic targets that may mitigate these toxic effects, RNA-seq analysis was used to investigate the host response of Synechococcus elongatus PCC 7942. Stress response, nitrogen metabolism, photosynthesis, and protein folding genes were up-regulated during FFA production while genes involved in carbon and hydrogen metabolisms were down-regulated. Select genes were targeted for mutagenesis to confirm their role in mitigating FFA toxicity. Gene knockout of two porins and the overexpression of ROS-degrading proteins and hypothetical proteins reduced the toxic effects of FFA production, allowing for improved growth, physiology, and FFA yields. Comparative transcriptomics, analyzing gene expression changes associated with FFA production and other stress conditions, identified additional key genes involved in cyanobacterial stress response.

Conclusions: A total of 15 gene targets were identified to reduce the toxic effects of FFA production. While single-gene targeted mutagenesis led to minor increases in FFA production, the combination of these targeted mutations may yield additional improvement, advancing the development of high-energy-density fuels derived from cyanobacteria.

No MeSH data available.


Related in: MedlinePlus

Cell concentration and extracellular FFA concentration of targeted mutants relative to the control strain, SE02a. (A) relative cell concentration of mutants overexpressing hypothetical proteins; (B) relative extracellular FFA concentration of mutants overexpressing hypothetical proteins; (C) relative cell concentration of mutants overexpressing ROS-degrading proteins; (D) relative extracellular FFA concentration of mutants overexpressing ROS-degrading proteins; (E) relative cell concentration of knockout mutants; (F) relative extracellular FFA concentration of knockout mutants. Knockout mutants include one hypothetical protein mutant (Δ0444) and 4 putative transport proteins (Δ2175, Δ1224, Δ1464, and Δ1607). Data are averages of 3 biological replicates with error bars indicating the standard deviation of these measurements, with a few exceptions: Due to strain instabilities, mutants SB2632#1 and S1476#1 have only 2 biological replicates, and mutants SB2632#2 and S1656#2 have only 1 biological replicate. The dashed line at y = 1 indicates no change relative to the control, SE02a. An asterisk (*) following the mutant strain name in the legend indicates that difference between the control (SE02a) and mutant strain was determined to be statistically significant by ANOVA analysis (see Table S7 in Additional file 3 for calculated p-values).
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Figure 2: Cell concentration and extracellular FFA concentration of targeted mutants relative to the control strain, SE02a. (A) relative cell concentration of mutants overexpressing hypothetical proteins; (B) relative extracellular FFA concentration of mutants overexpressing hypothetical proteins; (C) relative cell concentration of mutants overexpressing ROS-degrading proteins; (D) relative extracellular FFA concentration of mutants overexpressing ROS-degrading proteins; (E) relative cell concentration of knockout mutants; (F) relative extracellular FFA concentration of knockout mutants. Knockout mutants include one hypothetical protein mutant (Δ0444) and 4 putative transport proteins (Δ2175, Δ1224, Δ1464, and Δ1607). Data are averages of 3 biological replicates with error bars indicating the standard deviation of these measurements, with a few exceptions: Due to strain instabilities, mutants SB2632#1 and S1476#1 have only 2 biological replicates, and mutants SB2632#2 and S1656#2 have only 1 biological replicate. The dashed line at y = 1 indicates no change relative to the control, SE02a. An asterisk (*) following the mutant strain name in the legend indicates that difference between the control (SE02a) and mutant strain was determined to be statistically significant by ANOVA analysis (see Table S7 in Additional file 3 for calculated p-values).

Mentions: Significant changes in relative cell concentration occurred most frequently for days 7, 10, and 13, following induction of the recombinant proteins (Figure 2 A, C, and E). The hypothetical protein overexpression mutants S1655#2 and #5, S0122#4, S0900#1 and #2, and S1845#1, along with the ROS-degrading protein overexpression mutants S1656#1, S0801#1, S0437#1 and #2, and S1214#1, had increased cell concentrations relative to SE02a after recombinant protein induction. Improved growth of the hypothetical protein overexpression mutants was unexpected, as these genes were repressed during FFA production. On the other hand, overexpression of ROS-degrading genes was expected to improve cellular stress resistance and overall fitness; the enhanced relative cell concentrations of the ROS-degrading mutants provide evidence to support this theory. The knockout mutants Δ2175#1 and Δ1607#3 also had statistically significant increases in relative cell concentration. As putative FFA exporters, these knockout mutants were anticipated to have higher FFA toxicity and therefore lower relative cell concentrations. While relative cell concentrations showed significant changes after induction, these differences were reduced over time, so that at the end of cultivation (day 19), only a few mutants maintained a significant deviation in relative cell concentration (Figure 2 A, C, E).


RNA-Seq analysis and targeted mutagenesis for improved free fatty acid production in an engineered cyanobacterium.

Ruffing AM - Biotechnol Biofuels (2013)

Cell concentration and extracellular FFA concentration of targeted mutants relative to the control strain, SE02a. (A) relative cell concentration of mutants overexpressing hypothetical proteins; (B) relative extracellular FFA concentration of mutants overexpressing hypothetical proteins; (C) relative cell concentration of mutants overexpressing ROS-degrading proteins; (D) relative extracellular FFA concentration of mutants overexpressing ROS-degrading proteins; (E) relative cell concentration of knockout mutants; (F) relative extracellular FFA concentration of knockout mutants. Knockout mutants include one hypothetical protein mutant (Δ0444) and 4 putative transport proteins (Δ2175, Δ1224, Δ1464, and Δ1607). Data are averages of 3 biological replicates with error bars indicating the standard deviation of these measurements, with a few exceptions: Due to strain instabilities, mutants SB2632#1 and S1476#1 have only 2 biological replicates, and mutants SB2632#2 and S1656#2 have only 1 biological replicate. The dashed line at y = 1 indicates no change relative to the control, SE02a. An asterisk (*) following the mutant strain name in the legend indicates that difference between the control (SE02a) and mutant strain was determined to be statistically significant by ANOVA analysis (see Table S7 in Additional file 3 for calculated p-values).
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Related In: Results  -  Collection

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Figure 2: Cell concentration and extracellular FFA concentration of targeted mutants relative to the control strain, SE02a. (A) relative cell concentration of mutants overexpressing hypothetical proteins; (B) relative extracellular FFA concentration of mutants overexpressing hypothetical proteins; (C) relative cell concentration of mutants overexpressing ROS-degrading proteins; (D) relative extracellular FFA concentration of mutants overexpressing ROS-degrading proteins; (E) relative cell concentration of knockout mutants; (F) relative extracellular FFA concentration of knockout mutants. Knockout mutants include one hypothetical protein mutant (Δ0444) and 4 putative transport proteins (Δ2175, Δ1224, Δ1464, and Δ1607). Data are averages of 3 biological replicates with error bars indicating the standard deviation of these measurements, with a few exceptions: Due to strain instabilities, mutants SB2632#1 and S1476#1 have only 2 biological replicates, and mutants SB2632#2 and S1656#2 have only 1 biological replicate. The dashed line at y = 1 indicates no change relative to the control, SE02a. An asterisk (*) following the mutant strain name in the legend indicates that difference between the control (SE02a) and mutant strain was determined to be statistically significant by ANOVA analysis (see Table S7 in Additional file 3 for calculated p-values).
Mentions: Significant changes in relative cell concentration occurred most frequently for days 7, 10, and 13, following induction of the recombinant proteins (Figure 2 A, C, and E). The hypothetical protein overexpression mutants S1655#2 and #5, S0122#4, S0900#1 and #2, and S1845#1, along with the ROS-degrading protein overexpression mutants S1656#1, S0801#1, S0437#1 and #2, and S1214#1, had increased cell concentrations relative to SE02a after recombinant protein induction. Improved growth of the hypothetical protein overexpression mutants was unexpected, as these genes were repressed during FFA production. On the other hand, overexpression of ROS-degrading genes was expected to improve cellular stress resistance and overall fitness; the enhanced relative cell concentrations of the ROS-degrading mutants provide evidence to support this theory. The knockout mutants Δ2175#1 and Δ1607#3 also had statistically significant increases in relative cell concentration. As putative FFA exporters, these knockout mutants were anticipated to have higher FFA toxicity and therefore lower relative cell concentrations. While relative cell concentrations showed significant changes after induction, these differences were reduced over time, so that at the end of cultivation (day 19), only a few mutants maintained a significant deviation in relative cell concentration (Figure 2 A, C, E).

Bottom Line: Recent studies investigating cyanobacterial FFA production have demonstrated the potential of this process, yet FFA production was also shown to have negative physiological effects on the cyanobacterial host, ultimately limiting high yields of FFAs.Gene knockout of two porins and the overexpression of ROS-degrading proteins and hypothetical proteins reduced the toxic effects of FFA production, allowing for improved growth, physiology, and FFA yields.Comparative transcriptomics, analyzing gene expression changes associated with FFA production and other stress conditions, identified additional key genes involved in cyanobacterial stress response.

View Article: PubMed Central - HTML - PubMed

Affiliation: Sandia National Laboratories, Department of Bioenergy and Defense Technologies, MS 1413, P,O, Box 5800, 87185-1413, Albuquerque, NM, USA. aruffin@sandia.gov.

ABSTRACT

Background: High-energy-density biofuels are typically derived from the fatty acid pathway, thus establishing free fatty acids (FFAs) as important fuel precursors. FFA production using photosynthetic microorganisms like cyanobacteria allows for direct conversion of carbon dioxide into fuel precursors. Recent studies investigating cyanobacterial FFA production have demonstrated the potential of this process, yet FFA production was also shown to have negative physiological effects on the cyanobacterial host, ultimately limiting high yields of FFAs.

Results: Cyanobacterial FFA production was shown to generate reactive oxygen species (ROS) and lead to increased cell membrane permeability. To identify genetic targets that may mitigate these toxic effects, RNA-seq analysis was used to investigate the host response of Synechococcus elongatus PCC 7942. Stress response, nitrogen metabolism, photosynthesis, and protein folding genes were up-regulated during FFA production while genes involved in carbon and hydrogen metabolisms were down-regulated. Select genes were targeted for mutagenesis to confirm their role in mitigating FFA toxicity. Gene knockout of two porins and the overexpression of ROS-degrading proteins and hypothetical proteins reduced the toxic effects of FFA production, allowing for improved growth, physiology, and FFA yields. Comparative transcriptomics, analyzing gene expression changes associated with FFA production and other stress conditions, identified additional key genes involved in cyanobacterial stress response.

Conclusions: A total of 15 gene targets were identified to reduce the toxic effects of FFA production. While single-gene targeted mutagenesis led to minor increases in FFA production, the combination of these targeted mutations may yield additional improvement, advancing the development of high-energy-density fuels derived from cyanobacteria.

No MeSH data available.


Related in: MedlinePlus