2001 Progress Report: Metabolic Engineering of Solvent Tolerance in Anaerobic BacteriaEPA Grant Number: R828562
Title: Metabolic Engineering of Solvent Tolerance in Anaerobic Bacteria
Investigators: Papoutsakis, E. T. , Welker, N. E.
Institution: Northwestern University
EPA Project Officer: Richards, April
Project Period: June 1, 2000 through May 31, 2003
Project Period Covered by this Report: June 1, 2001 through May 31, 2002
Project Amount: $180,000
RFA: Technology for a Sustainable Environment (1999) RFA Text | Recipients Lists
Research Category: Sustainability , Pollution Prevention/Sustainable Development
Understanding solvent (and other toxic chemical) tolerance of microorganisms is crucial for the production of chemicals, bioremediation, and whole-cell biocatalysis. Past efforts to produce tolerant strains have relied on selection under applied pressure and chemical mutagenesis, with some favorable results, but not consistently so. This project examines whether Metabolic Engineering (ME) and genomic approaches can be used to construct more tolerant strains for bioprocessing. The accepted dogma is that toxicity is due to the chaotropic effects of solvents on the cell membrane. We have found that in Clostridium acetobutylicum, several well defined genetic modifications, not related to membrane function, impart solvent tolerance (by 40-70 percent) without strain selection. This suggests that we need to reexamine the accepted dogma. The objective of this research project is to identify genes that contribute to solvent tolerance and to use genetic modifications (involving these genes) to generate solvent tolerant strains.
Fermentations of GroESL Overexpressing Strain 824(pGROE1), Parent Strain (824), and the Plasmid Control Strain 824(pSOS95del). As previously reported, a recombinant C. acetobutylicum strain carrying a plasmid constructed to overexpress the groESL operon genes (groES and groEL) was created. A series of duplicate fermentations with the 824(pGROE1), 824(pSOS95del) control strain, and wild type 824, were conducted to examine the effects of GroES and GroEL overexpression on solvent formation, metabolic fluxes, and transcriptional and protein expression patterns. We collected supernatants for product formation analysis by High Performance Liquid Chromatography (HPLC). Cell pellets were harvested for use in Western Blot analysis, and we took RNA samples for use in microarray and Reverse Transcription-Polymerase Chain Reaction (RT-PCR) analysis. The presence of the pGROE1 plasmid had dramatic effects on product formation, particularly in the formation of acetone and butanol, when compared to both the wild type and control strains.
The 824(pGROE1) strain produced 148 mM and 231 mM of acetone and butanol, respectively, compared to 107 mM and 178 mM in the control strain, and 96 mM and 175 mM in wild type. This represents an increase in final acetone and butanol titers of 66 percent and 56 percent, respectively, relative to the 824(pSOS95del) control strain. The onset of solvent production is delayed and appears to occur in two distinct phases for both recombinant strains. This is most likely due to a frequently observed host-plasmid interaction. The 824(pGROE1) strain grew to higher optical densities than the 824(pSOS95del) control strain, but was slightly lower than the wild type strain. The doubling time for the two recombinant strains was nearly identical (2.01 and 1.99 hours), both exhibiting slower exponential growth than the wild type (1.24 hours). The specific in vivo fluxes show drastic differences between the wild type 824, 824(pSOS95del), and 824(pGROE1) strains. As previously mentioned, both recombinant strains exhibit two distinct phases, and the wild type exhibits only one. Strain 824(pGROE1) exhibited an elevated glucose utilization (rGLY1) and acetyl-CoA utilization (rTHL) relative to the control strain. The acetate and butyrate uptake rates also were higher in strain 824(pGROE1), which results in an increased acetone formation (rACETONE). For strain 824(pGROE1), acetate uptake (rACUP) appears to play a larger role in increased acetone production than butyrate uptake. Strain 824(pGROE1) also exhibited butanol (rBUOH) formation fluxes that were significantly higher than in strain 824(pSOS95del).
DNA-Microarray Analysis. Large-scale transcriptional analysis has emerged as an important tool to help better understand the differences in the global genetic programming of various cell types and growth conditions. DNA microarrys with spots representing more than 1,000 genes (approximately 25 percent of the C. acetobutylicum genome) have been printed using the TIGR protocol. One of the challenges of the massive amount of data generated by DNA array analysis is understanding how to best interpret this wealth of information. There are a number of mathematical techniques that can be used to identify patterns of gene expression. Perhaps the most common method is hierarchical clustering, whereby data points are forced into a strict hierarchy of nested subsets. An alternative method, Self Organizing Maps (SOMs), are better suited to clustering and analysis of gene expression patterns. SOMs have proven themselves as significantly more robust and accurate than hierarchical clustering and are easy to implement, are reasonably fast, and are scalable to large data sets.
Transcriptional Analysis of GroESL Overexpression. SOM analysis resulted in the identification of three gene clusters. Each cluster represents a set of genes that have elevated expression in the 824(pGROE1) culture at one of the three time points. Examination of the genes belonging to each cluster lead to several conclusions.
First, the sporulation genes (primarily spoIII and spoV family genes) show an early, increased expression level in the 824(pGROE1) strain. Sporulation in wild type C. acetobutylicum is specific to the transition into the stationary phase, and is accompanied by a shift to solvent formation, granulose accumulation, and the expression of heat shock proteins. The extent to which these processes share common control mechanisms is largely unknown. It may be possible that overexpression of the heat shock proteins GroES and GroEL is involved in the regulation, either directly or indirectly, of some of the key sporulation genes. It also is possible that overexpression of the groES and groEL genes represents a stress in addition to the presence of a plasmid. Stresses in general are thought to induce sporulation.
Second, at least three chemotaxis (Che) genes are upregulated at each of the three time points, including fliJ, cheA, cheW, and cheY.
Third, there is early and enhanced expression of the flagellar genes (fliF, fliE, fliF, fliL, fliR, and flgB), primarily at the mid to late exponential phase. Increased expression of these two sets of genes is somewhat unexpected. Many of the sporulation genes that show increased expression in the groESL overexpressing strain have been upregulated by Spo0A. However, Spo0A also has downregulated many of the chemotaxis and flaggelar genes listed above. The presence of two distinct cell populations may explain the two distinct phases of growth observed over the course of a fermentation for this and other plasmid carrying strains. Genes also showing significant upregulation include acetoacetate decarboxylase (AADC), numerous histidine kinases, glycolysis genes, electron transport genes, and pyruvate-formate lyase. Finally, both groES and groEL were upregulated at all three time points.
Transcriptional Analysis of the Host-Plasmid Interaction Effect. SOM analysis resulted in the identification of five gene clusters. Four of the clusters represent sets of genes that display elevated expression in the 824(pSOS95del) plasmid control strain, while the fifth represents a cluster of genes that are downregulated. Many of the same trends are apparent in the plasmid control strain (when compared to the wild type) as discussed above for the GroESL overexpressing strain (when compared to the plasmid control strain). Namely, many of the sporulation genes (including SpoIIAC, SpoIIIAA, SpoIIIAB, SpoIVFB, SpoVAD, SpoVB, SpoVS, SpoVT, SpoT, sigF, spore coat F) and flagellar and chemotaxis genes (including flagellin, flhA, fliL, fliJ, motA, fliK, fliD) show early and enhanced expression. In addition, many of the heat shock proteins are upregulated early, including groES, groEL, grpE, and hsp90, suggesting that the cells respond to the presence of a plasmid as they would to many other stresses. Several key metabolic genes were upregulated as well: an acid formation gene, butyrate kinase (buk); the solvent formation genes, alcohol dehydrogenase (ADH) and AADC; several glycolytic genes; and a transcriptional regulator of sugar metabolism. Many response and transcriptional regulators also are upregulated, including the SOS regulatory genes lexA, acrR, arsR, and fadR. The gene cluster representing the downregulated genes includes hrcA (HrpA is a negative transcriptional regulator of chaperonin expression), spoOJ, flgD, and fliM (flagellar genes), and the genes for beta-hydroxy butyrl-CoA dehydrogenase and butyryl-CoA dehydrogenase (primary metabolism), and pyruvate-formate lyase. The inclusion of hrcA in the downregulated gene cluster would suggest that one would expect to see an upregulation of the chaperonins, which it negatively regulates. We observe this with the presence of groES, groEL, and grpE genes in the upregulated gene clusters. Use of such a priori information provides for additional validation of the microarray data.
Heat, Ethanol, and Butanol Stress Response. C. acetobutylicum cultures were subject to one of three stresses: heat, ethanol challenge, or butanol challenge. Heat stress was induced by shifting the culture from 37°C to 45°C. Ethanol and butanol challenges involved the addition of 1.25 percent (vol/vol) of the respective alcohol at 37°C. RNA samples were taken for microarray analysis at 10, 30, and 60 minutes after initiation of shock conditions and were compared to the wild type with no stress at 37°C. The butanol stress showed the most significant growth inhibition, resulting in a 20 percent reduction in maximum optical density (A600) reading (4.94 for WT; 3.93 for butanol challenge).
Transcriptional Analysis of the Butanol Stress Response. SOM analysis resulted in the identification of three gene clusters. Each cluster represents a set of genes that have elevated expression in the 824(pGROE1) culture at one of the three time points (10, 30, or 60 minutes post-butanol challenge). Among the genes that show the earliest upregulation (10 minutes) are the heat shock proteins dnaK, dnaJ, groEL, and hsp18, and the solvent formation genes butanol dehydrogenase (bdhB) and alcohol-aldehyde dehyrogenase (aad). Other genes include genes for DHFR, nitrogenase nifK and nitroreductase (both upregulated under stress in other organisms), ADP-glucose pyrophosphorylase (stress regulated starch synthesis), and phospholipase. The second gene cluster (30 minutes post-butanol addition) contains additional heat shock genes (hsp90 and groES), as well as many of the same chemotaxis genes (cheY and cheV), late sporulation genes (spoII and spoIV family), and flagellar genes, which were upregulated in the plasmid control experiment discussed previously. The ntrC gene, responsible for regulation of polyphosphate accumulation under stress, also is included in this group. Additional genes in this second group include a number of ATPases, ATP enzymes, phosphatases, pyrophosphatases, and phosphorylases; many of which partake in energy metabolism and stress response. The final cluster, the smallest of the three with 16 genes, contains some late-stage sporulation genes.
The following will be completed in Year 3:
Overexpression GroESL and New Constructs. We will complete the DNA array analysis and prepare publications. We also will design additional constructs to take advantage of the groESL overexpression without the delayed solvent production.
Identification and Study of Other Genes that Might be Involved in Solvent Tolerance. We will manufacture larger DNA arrays (almost full C. acetobutylicum genome) to examine the effects of solvent challenges on the expression of a large number of genes. We also will implement a new protocol that helps not only identify genes affected by challenges, but also genes that are responsible for tolerance.