A comparative study of metabolic engineering anti-metabolite tolerance in Escherichia coli

ARTICLE IN PRESS

Metabolic Engineering 8 (2006) 227–239 www.elsevier.com/locate/ymben

A comparative study of metabolic engineering anti-metabolite tolerance in Escherichia coli Jeanne Bonomo, Tanya Warnecke, Patrick Hume, Alex Marizcurrena, Ryan T. Gill Department of Chemical and Biological Engineering, University of Colorado, Boulder, Campus Box 424, Boulder, CO 80309, USA Received 9 August 2005; received in revised form 15 December 2005; accepted 28 December 2005 Available online 23 February 2006

Abstract A problem in strain engineering is that mutations that benefit the expression of a phenotype in one environment may impose a cost to biological fitness in a new environment. The overall objective of this study was to improve understanding of this phenomenon within the context of a classic anti-metabolite selection strategy. We have engineered Escherichia coli using three mutagenesis techniques (chemical mutagenesis, insertional mutagenesis, and plasmid-based overexpression) and assessed the relative costs and benefits to biological fitness of mutants selected for tolerance to five amino acid analogs whose target amino acids (glutamatic acid, aspartic acid, tryptophan, glycine, and serine) differ in metabolic connectivity and biosynthetic energy requirements. Our major findings include (i) the fold increase in antimetabolite tolerance, independent of mutagenesis strategy, was much greater for aspartic acid b-hydroxamate (AAH) compared to all other tested hydroxamates, (ii) increased tolerance to glutamic acid g-hydroxamate (GAH) was not achieved using any of the mutagenesis strategies, and (iii) characteristics of the anti-metabolite, rather than those of the corresponding metabolite, were more important in determining the ability to increase tolerance. r 2006 Elsevier Inc. All rights reserved. Keywords: Metabolic engineering; Chemical mutagenesis; Insertional mutagenesis; Gene overexpression; Amino acid analog; Escherichia coli

1. Introduction Metabolic engineering involves the application of molecular genetic techniques for the purposes of redirecting metabolism and related cellular functions in a specific manner (Bailey, 1991; Stephanopoulos and Vallino, 1991; Cameron and Tong, 1993). As such, metabolic engineering builds upon classic strain selection strategies, which typically involve recursive mutagenesis and screening or selection, by implementing purposeful genetic alterations hypothesized to influence a particular trait. Here we report an investigation of the costs to biological fitness (defined as reduced growth rate throughout this study) associated with different types of mutations that result in increased tolerance to anti-metabolite compounds with targets of varying metabolic characteristics. Anti-metabolites are analogs of specific metabolites that can replace some but not all of their normal metabolic Corresponding author. Fax: +1 303 492 4341.

E-mail address: [email protected] (R.T. Gill). 1096-7176/$ - see front matter r 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.ymben.2005.12.005

functions, thus rendering them growth inhibitory (Shive and Skinner, 1963). Classic strain selection strategies utilize anti-metabolites by screening a population of mutants for members that grow more rapidly or survive at higher concentrations of the relevant anti-metabolite. This technique has been widely used to find mutants that are capable of producing more of a relevant metabolite (Demain, 1971; Yanofsky and Crawford, 1987). One challenge to such approaches is that mutations that are beneficial in one environment can have negative effects on overall growth in a new environment. Mutants identified with improved metabolite yields may grow poorly and, thus exhibit lowered productivity (Ohnishi et al., 2002). Similar issues have been observed in constructive metabolic engineering efforts aimed at improving flux through a specific metabolic pathway. For example Koffas et al. (2003) observed that overexpression of both the pyruvate carboxylase gene (pyc) and the aspartate kinase gene (ask) were needed for optimum flux through glycolysis and the anaplerotic pathways in Corynebacterium glutamicum. Overexpression of only ask resulted in increased production of lysine at a

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cost to specific growth rate and overexpression of pyc decreased specific lysine productivity but increased growth rate. Only simultaneous overexpression of both genes resulted in increased lysine productivity without a resulting decrease on host fitness. Finally, many examples of mutations conferring antibiotic resistance that result in variable costs to biological fitness have also been reported (Schrag et al., 1997; Bjorkman et al., 1998; Sander et al., 2002; Bonomo and Gill, 2005). Even with such examples, understanding of why certain mutations are costly remains limited. ‘‘Small-world’’ network theory states that changes affecting highly connected nodes will have a greater impact on overall network function compared with the effects resulting from alterations in less highly connected nodes (Watts and Strogatz, 1998; Albert et al., 2000; Jeong et al., 2001). The Escherichia coli metabolite network has been shown to be ‘‘small-world’’ because it is power law distributed, is dominated by a collection of highly connected nodes, and maintains a minimum average path length over the entire network (Jeong et al., 2000; Wagner and Fell, 2001; Ma and Zeng, 2003; Arita, 2004). Thus, we hypothesized that genetic alterations involving highly connected metabolites would be more likely to affect overall metabolic network function, and that such effects would influence overall biological fitness. Here, we sought to determine whether or not mutations conferring increased tolerance to an anti-metabolite directed at glutamate, which is the most highly connected amino acid (Wagner and Fell, 2001), had higher costs (decrease in growth rate) and/or lower benefits (increase in minimum inhibitory concentration (MIC)) compared to mutations conferring increased tolerance to anti-metabolites directed against less well connected amino acids (see Table 1). The other metabolites investigated included aspartic acid and serine, both with mid-level connectivity values of 18 and 13, and glycine and tryptophan, with low-level connectivity

values of 7 and 6. In all cases we have employed amino acid hydroxamates, which are anti-metabolites directed against individual amino acids. A complication here is that different anti-metabolites may target different pathways involving the corresponding metabolite, some of which may involve stages in metabolism that are themselves more or less highly connected (i.e., nucleotide synthesis, translation). Therefore, to delineate between metabolite characteristics and those of the anti-metabolite and its corresponding target, we have investigated two structural classes of hydroxamates. In the case of tryptophan-, serine-, and glycine-hydroxamate, the hydroxamate is located on the amino-acid backbone, thus targeting translation. For aspartate- and glutamate-hydroxamate, the hydroxamate is located on the R-group and translation is not the primary target. This investigation focuses on increasing tolerance to aspartate-, glutamate-, and tryptophan-hydroxamate. Additional studies using serine- and glycine-hydroxamate provided further insight into this investigation. A second complicating factor in these studies involves the method used to create genetic diversity. Specifically, methods for generating mutations in E. coli include random chemical or insertional mutagenesis as well as plasmid-based overexpression libraries. These techniques differ in the type of mutation they cause, the ease with which they are implemented, the amount of diversity/ sequence space they cover, and the efficiency with which the resultant mutations can be identified (Stemmer, 1994; Arnold and Moore, 1997; Moore et al., 2001; Sauer, 2001; Ohnishi et al., 2002; Jacobs et al., 2003; Kang et al., 2004; Godiska et al., 2005; Winterberg et al., 2005). We sought here to evaluate each of these techniques in our efforts to identify anti-metabolite tolerant E. coli mutants. We reasoned that the results from our anti-metabolite selections might be influenced by the specific mutagenesis strategy employed and thus confound any conclusions we

Table 1 Amino acid hydroxamate characteristics AA-Hydroxamate

MICa (mg/ml)

Kb

Energy requiredc

Relative abundanced

Metabolic precursor

Glutamate Aspartate Methioninee Serinee Alaninee Arginine Lysine Glycine Tryptophan Range of valuesf

0.04 0.01 1.28 0.32 0.64 2.56 1.28 0.04 0.70 0.01–2.56

51 18 14 13 13 8 8 7 6 6–51

15.3 12.7 34.3 11.7 11.7 27.3 30.3 11.7 74.3 11.7–74.3

83% 76% 29% 46% 100% 41% 54% 8% 8% 8–100

TCA cycle/a-ketoglutarate TCA cycle/oxaloacetate Aspartate Glycolysis/3-phosphoglycerate Glycolysis/pyruvate Glutamate Aspartate Serine Glycolysis/aromatic

a

Minimum concentration that inhibits E. coli growth. K ¼ connectivity as determined by the number of metabolic reactions involving the respective amino acid. c Energy required for synthesis (
P bonds) (Akashi and Gojobori, 2002). d Relative to the most abundant amino acid Alanine (100%) (Bailey and Ollis, 1986). e D, L mixture. f Range of values is for all of the tested amino acid hydroxamates. b

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might draw regarding any role target connectivity plays in the costs associated with certain mutations. We report here our efforts to investigate these issues along with a panel of genes for which increased dosage confers varying levels of tolerance to various amino-acid hydroxamates. 2. Materials and methods 2.1. Bacterial strains and plasmid constructs Strains of E. coli K12 (F+ l+) (ATCC), W3110 (F l IN (rrnD-rrnE)1) (ATCC), and Mach1 (F- f80(lacZ)DM15 DlacX74 hsdR(rK-mK+) DrecA1398 endA1 tonA) (Invitrogen, Carlsbad, CA) were used in this study. Plasmid pEZSeqKan (Lucigen, Middleton, WI), which contains the lac promoter, was used for overexpression library screening studies and pDM-cat was used to make an insertional mutagenesis library. The pDM-cat vector was created in our laboratory and is based on the hyperactive mini-Tn5 transposon system (Goryshin and Reznikoff, 1998). Overnight cultures from freezer stock were grown in 5 ml Miller’s Luria Broth (LB) (Fisher Scientific), which was either not supplemented (for chemical mutagenesis studies) or supplemented with 40 mg/ml kanamycin (LBK) (for overexpression studies) or 10 mg/ml chloremphenicol acetyltransferase (LBcat) (for insertional library studies). All cultures were grown at 37 1C and shaken at 225 rpm. Cell growth was determined by optical density (OD600) (UV mini spectrophotometer 1240, Shimadzu). 2.2. Amino acid analogs L-Glutamic acid g-hydroxamate (GAH), L- aspartic acid b-hydroxamate (AAH), L-tryptophan hydroxamate (TH),

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D,L-serine

hydroxamate (SH), and glycine hydroxamate (GH) were purchased from Sigma (St. Louis, MO). 2.3. Chemical mutagenesis Chemical mutagenesis using N-methyl-N0 -nitro-N-nitrosoguanidine (MNNG) was performed following the protocol in Miller (1972) with a 4 min MNNG incubation, conditions under which 50% of the cells survive. After chemical mutagenesis, cells were allowed to recover for 2 h in LB and 1 v/v% was used to inoculate 5 ml M9 minimal media (Sambrook et al., 1989). At an OD600 of 0.2 the MIC protocol was followed (see below). The contents of the well with the highest growth at 24 h were plated on LB agar plates to recover individual mutants. The MICs for individual mutants were then determined. 2.4. Alternate selection strategies for chemical mutagenesis We investigated several different selection strategies that varied in the length of the incubation with MNNG, duration of the recovery period after chemical mutagenesis, number of cell doublings under selection pressure (i.e., the presence of hydroxamate), concentration of hydroxamate during selection, and concentration of hydroxamate (0–0.08 mg/ml GAH, 0–1.28 mg/ml AAH, 0.64–2.56 mg/ ml TH) on final recovery plates. Based on the information collected from these studies, we employed a single selection strategy for the comparative studies involving chemical mutagenesis, insertional mutagenesis, or plasmid-based overexpression (see Fig. 1). Specifically, E. coli were grown for 24 h in the presence of hydroxamate (serially diluted from a starting concentration of 2.56 mg/ml in M9 minimal media), and then were plated on non-selective plates (LB, LBcat, or LBK).

Fig. 1. Selection strategy. Using the strain engineering techniques [1a] random mutagenesis, [1b] insertional mutagenesis, or [1c] plasmid based gene overexpression E. coli K12 libraries were created. We routinely evaluated greater than 106 mutants per growth selection. Next, [2] mutant strains were selected for 24 h in the presence of each hydroxamate. A sample was taken from the well with the highest concentration of hydroxamate that still exhibited cell growth and plated on non-selective plates [3]. After overnight incubation, individual colonies were evaluated for an increased MIC [4a] and changes in specific growth rate [4b]. Finally, the genetic basis for increased tolerance was determined when possible.

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2.5. Overexpression (Multi) library screening Overnight cultures of the E. coli K12 were cultivated in 150 ml of LB at 37 1C to an OD600 of 1.0. The culture was centrifuged at 5000 rpm, 4 1C for 15 min. The cell pellet was then washed in 50 ml of TES buffer (10 mM Tris–HCl, 1 mM EDTA 1.5% w/v NaCl, pH ¼ 8.0) and again centrifuged. The pellet was again resuspended in 50 ml of TES buffer. To the cell suspension 300 ml of 20 mg/ml proteinase K (Fisher) and 3 ml of 10% w/v SDS were added, which was then incubated at 55 1C for 16 h. The genomic DNA was then extracted twice with equal volumes of TE (10 mM Tris–HCl, 1 mM EDTA, pH ¼ 8.0)-saturated phenol followed by two extractions with TE-saturated phenol/chloroform/isoamyl alcohol (25:24:1). Genomic DNA was then precipitated with 1/10 volume 3 M NaOAc, pH ¼ 5.5, and 0.6 volumes of isopropanol. DNA pellets were washed with 70% ethanol and resuspended in TE buffer, pH ¼ 8.0. Six samples of 50 ng of purified genomic DNA were digested with two blunt cutters AluI and RsaI (Invitrogen), both having a four-base pair long recognition sequence. Digestion reactions, 50 ml total volume, containing four units of each enzyme plus 50 mM Tris–HCl (pH 8.0), and 10 mM MgCl2 were carried out for 10, 20 30, 40, 50, and 60 min, respectively, at 37 1C. The reactions were inactivated by heating at 70 1C for 15 min. The six restriction digestions were combined and the fragmented DNA was separated based on size using agarose gel electrophoresis. DNA fragments of 0.5, 1, 2, 4, and greater than 8 kb were excised from the gel and purified with a Gel Extraction Kit (Qiagen), according to the manufacturer’s instructions. The purity of the DNA fragments was quantified using UV absorbance with an A260/A280 absorbance ratio of 41.7 considered exceptable. Ligation of the purified, fragmented DNA into the pEZSeq Kan vector was performed according to manufacturer’s instructions (Lucigen, Middleton, WI). The ligation product was then electroporated into E. Cloni 10 G Supreme Electrocompetent Cells (Lucigen) and the transformed cells were plated on LBK. Dilution cultures were inoculated with 1/1000 volume of the original transformations and plated on LBK in order to determine transformation efficiency and transformant numbers. Three of these dilutions were plated in order to get average transformant counts. Plates were incubated overnight at 37 1C. Colonies were harvested by gently scraping the plates into TB media and incubating at 37 1C for 1 h, followed by centrifuging at 5000 rpm for 15 min. The plasmid DNA was extracted according to the manufacturer’s instructions using a HiSpeed Plasmid Midi Kit (Qiagen). In order to confirm insert sizes and transformant numbers, individual colonies from the library dilution plates were used to inoculate overnight cultures. Plasmids were purified using a Qiaprep Spin MiniPrep Kit from Qiagen. The plasmid DNA was then digested with EcoR1.

Inspection by electrophoresis showed that greater than 80% of the colonies contained an insert of the expected size. In addition, colony PCR using the SL1 (50 -CAG TCC AGT TAC GCT GGA GTC-30 ) and SR2 (50 -GGT CAG GTA TGA TTT AAA TGG TCA GT-30 ) primers was performed on ten colonies from the 0.5, 1, and 2 kb libraries. The PCR confirmed that the colonies contained an insert of the expected size and that chimeras were not present. Purified plasmid DNA from each library was introduced into MACH1TM-T1R (Invitrogen) by electroporation. MACH1TM-T1R cultures were made electrocompetent by standard glycerol washes on ice to a final concentration of 10 cells/ml (Sambrook et al., 1989). Diluted cultures were inoculated with 1/1000 volume of the original transformations and plated on LBK in order to determine transformation efficiency and transformant numbers. The original cultures were combined and used to inoculate 100 ml of M9 Minimal Media (Sambrook et al., 1989). The culture was incubated at 37 1C until an OD600 of 0.2 was reached. Following the MIC protocol, the 24 h MIC was determined and the contents of the last well with growth were plated on LBK plates. Individual colonies were picked and their MICs for the hydroxamates were determined. Plasmids were purified using Qiagen’s (Valencia, CA) plasmid mini preps and insert regions were sequenced using commercially available forward and reverse M13 primers (Macrogen, Seoul, Korea). 2.6. Insertional library screening E. coli W3110 was transformed with the pDM-cat vector and allowed to recover 1 h in TB media (under these conditions 4105 insertion clones were obtained, as confirmed by plating on LBcat plates). This vector contains the hyperactive mini-Tn5 transposon. After recovery, the insertional library was used to inoculate M9 media and grown to an OD600 of 0.2. At this time, the MIC protocol was followed (see below). The MIC for the amino acid hydroxamates at 24 h was determined and the contents of the last well with growth were plated on LBcat plates. Individual colonies were picked for MIC and growth rate studies. 2.7. Determining minimum inhibitory concentration The minimum inhibitory concentration was determined following the protocol of Balows and Hausler (1991). Briefly, overnight cultures of individual strains were grown in 5 ml LB (with antibiotic where appropriate) and then a 1v/v% was used to inoculate 5 ml M9. After the cells reached mid-exponential phase, the culture was diluted to an OD600 of 0.2. The cells were further diluted 1 to 50 and 10 ml was used to inoculate 90 ml media. For AAH, the MIC plate was made via serial dilutions with an initial concentration of hydroxamate of 2.56 mg/ml, which resulted with the following concentrations (in mg/ml)- 2.56,

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1.28. 0.64, 0.32, 016, 0.08, 0.04, 0.02, 0.01. In the case of GAH, the following hydroxamate concentrations (in mg/ml) were used- 0.16, 0.08, 0.06, 0.04, 0.035, 0.03, 0.025, 0.02, 0.01. For TH, the following hydroxamate concentrations (in mg/ml) were used 2.56, 1.28, 1.12, 0.96, 0.80, 0.64, 0.32, and 0.16. For SH and GH, the following hydroxamate concentration (in mg/ml) were used- 1.28, 0.96, 0.64, 0.48, 0.32, 0.24, 0.16, 0.12, 0.08, 0.06, 0.04, 0.02, 0.01. The MIC was determined after 24 h. 2.8. Determining specific growth rate Growth curves, performed in triplicate, were obtained using a PowerWave XS KC4 v3.1 (Biotek, Winooski, Vermont), a 96 well plate reader, incubating at 37 1C using the kinetic mode (shaking intensity medium) with readings taken every 30 min. The optical density (OD600) of the overnight culture was checked and the culture diluted to an OD600 0.250. A 20 ml aliquot of the diluted overnight culture was used to inoculate 180 ml of M9 in a flat bottom 96 well plate (Costar model 3370). Optical density measurements were recorded at 977, 900, and 600 nm, and then adjusted according to the manufacturers instructions (adjusted 600 ¼ 600/((977–900)/0.18)). The adjusted 600 nm reading was used for construction of growth curves and calculation of growth rates. 3. Results Genetic alterations impose variable costs and benefits on biological fitness that are often environmentally dependent (Bailey, 1999). In metabolic engineering efforts, the ability to assess such costs and benefits is important when attempting to prioritize among different possible strategies for manipulating the phenotype of interest. Here, we report the costs and benefits associated with mutations resulting from several commonly employed genetic engineering techniques (chemical mutagenesis, insertional mutagenesis, and plasmid based overexpression libraries) applied within a classic anti-metabolite strain engineering approach (see Table 2 and Fig. 1).

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3.1. Amino acid hydroxamate toxicity We determined the minimum inhibitory concentrations (MIC) of E. coli K12 for a range of amino acid hydroxamates (Table 1). MIC’s varied over a wide range depending upon the particular amino acid pathway targeted, which suggested that growth inhibition was due to interference with functions specific to each amino acid. Thus, we hypothesized that the costs (reduced specific growth rate) and benefits (increased MIC) of different mutations would also vary as a function of amino acid target characteristics. To test this hypothesis, we chose to engineer and select for mutants with increased tolerance to for aspartic acid b-hydroxamate (AAH), glutamic acid g-hydroxamate (GAH), tryptophan hydroxamate (TH), glycine hydroxamate (GH), or serine hydroxamate (SH) because the corresponding amino acids spanned a range of values for metabolic connectivity, energetic requirements, and relative abundance, and exhibited a MIC sufficiently low to allow for identification of mutants with increased tolerance. Several mechanisms for inhibition by these hydroxamates have been reported. Briefly, hydrolysis of AAH releases hydroxylamine, which causes mutations and cell death in bacteria (Drainas et al., 1977). Katoh et al. (1998) showed that GAH inhibits g-glutamylcysteine synthetase, the enzyme involved in the first step of glutathione synthesis, which is essential for many cellular functions. TH, SH, and GH inhibit charging of their respective tRNAs, thereby slowing protein synthesis and cell growth (Tosa and Pizer, 1971; Gao et al., 1995). It is important to note that these amino acid hydroxamates may interfere with other cellular functions in addition to those discussed above. 3.2. Evaluation of chemical mutagenesis libraries We used MNNG to mutate E. coli K12 and then selected for mutants with increased tolerance to three of the selected amino acid hydroxamates (AAH, GAH, and TH; SH and GH were not used in these initial chemical mutagenesis studies). Fig. 2 displays the benefit (increased MIC) and

Table 2 Summary of results Chemical mutagenesis

Overexpression

Insertional mutagenesis

%a

Ave (range)

%a

Benefit—fold increase in clone’s MIC AAH 3.3 (1.7–6) GAH 1.2 (1–1.5) TH 1.2 (1–1.4)

85 28 42

55 (1–213) 1 (0.95–1.1) 1.3 (0.5–2)

71 0 65

15 (8–19) 1.3 (1.2–1.6) 1.8 (1.3–2.9)

100 14 93

Cost—change in clone’s specific growth rate AAH 1.3 (0.9–2.2) GAH 1.1 (0.9–1.4) TH 0.9 (0.8–1.1)

12 0 71

1 (0.9–1.2) 1 (0.8–1.6) 1 (0.7–1.4)

93 72 93

1 (0.95–1) 0.97 (0.92–1) 0.95 (0.93–0.97)

93 79 100

Ave (range)

a

% of mutants identified with significantly increased benefit, or significantly altered cost.

Ave (range)

%a

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232 100

MIC/MICwt

10 *

*

*

*

*

*

*

*

*

*

1

*

TH 7

TH 6

TH 5

TH 4

TH 3

TH 2

TH 1

GAH 7

GAH 6

GAH 5

GAH 4

GAH 3

GAH 2

AAH 7

GAH 1

AAH 6

AAH 5

AAH 4

AAH 3

(a)

AAH 2

AAH 1

0.1

40%

*

*

TH 1

TH 2

30%

Change in specific growth rate

20% 10% 0% -10% -20% -30% -40% -50%

TH 7

TH 6

TH 5

TH 4

TH 3

GAH 7

GAH 6

GAH 5

GAH 4

GAH 3

GAH 2

GAH 1

AAH 7

AAH 6

AAH 5

AAH 4

AAH 3

AAH 2

(b)

* AAH 1

-60%

Fig. 2. Random mutagenesis results. (a) Average increased MIC (benefit) results, *clone’s MIC statistically different than wild type MIC (n ¼ 6, based upon 95% confidence interval, determined by comparing wild type’s MIC to clone’s MIC using ANOVA in excel). (b) Average cost results, percent change in clone’s specific growth, *clone’s specific growth rate statistically different compared to wild type’s specific growth rate (n ¼ 9, based upon 95% confidence interval, determined by comparing wild type to clone specific growth rates using ANOVA in excel). AAH 5 resulted in an average cost of 0%.

cost (percent change in specific growth rate) for strains obtained through the 24 h growth selection. Note that even though multiple selection strategies were evaluated, for the comparisons described here we employed identical selection strategies (see materials and methods and Fig. 1). In the case of AAH, 90% of the 43 strains evaluated exhibited an increased MIC (after one round of chemical mutagenesis). Of the seven shown in Fig. 2a, we observed an

average of 2.5-fold increased MIC and a maximum of almost 6-fold increased MIC. In contrast, only 2 of a total of 50 mutants evaluated displayed a statistically significant increase GAH tolerance (1.5-fold). Although mutants with increased TH tolerance were identified at relatively high frequencies among MNNG mutants, the improvements of 1.4-fold (average) and 1.6-fold (maximum) were modest. Interestingly, regardless of the hydroxamate investigated,

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the specific growth rates of the majority of mutants were not significantly different compared to that of the WT strain. One surprise was that the selection strategy employed for the mutants depicted in Fig. 2 did not always result in strains with increased tolerance. To investigate this observation, we examined a variety of different selection strategies and MNNG incubation times (see Materials and Methods). For example, selecting mutants for 3 h, as opposed to 24 h, resulted in the identification of mutants with an average 40-fold increase in MIC to AAH. Under the same conditions, no mutants screened exhibited increased tolerance to GAH (TH was not used in this study). We also evaluated the effect of increased MNNG mutagenesis periods. Both 8 and 10 min MNNG incubations (as opposed to 4 min used above) resulted in a mutant with a 100-fold improvement in AAH MIC. Notably, the longer MNNG incubations also resulted in mutants with reduced specific growth rates (30% average reduced growth rate). We next investigated the use of recursive chemical mutagenesis to determine if we could further improve upon tolerance, if there would be an associated biological cost, and if the results would depend on the mutant selected from round 1 of mutagenesis. Thus, AAH-tolerant mutants with either the largest or smallest increase in MIC were picked for additional mutagenesis and selection. Interestingly, the MIC against AAH did not increase after the second round of chemical mutagenesis on the high benefit mutant. In contrast, a second round of chemical mutagenesis on the low benefit mutant resulted in three mutants with an increased MIC (2-12 fold), one of which performed as well as the high benefit mutant identified in round 1. Importantly, this additional round of chemical mutagenesis did not result in the identification of any mutants with an additional increase in GAH tolerance. 3.3. Evaluation of plasmid based gene overexpression libraries Increased gene dosage is a powerful natural and laboratory mechanism for evolving new traits (Lynch and Conery, 2000). As such, we chose to investigate our overall hypothesis by performing identical growth selections as described above upon a collection of E. coli transformed with plasmid based genomic libraries of E. coli genomic DNA. Ten of the 12 overexpression strains identified through AAH growth selections (performed on overexpression libraries) exhibited increased tolerance with maximum and average increases of 200-fold and 60-fold (see Fig. 3). In the case of TH tolerance, seven of 13 growth selected strains exhibited significantly increased tolerance with maximum and average improvements of 1.8-fold and 1.3fold, respectively. In contrast, none of the strains isolated after GAH growth selections exhibited significantly increased GAH tolerance. Plasmid-based gene overexpres-

233

sion is known to result in impaired growth of the host organism (Bentley et al., 1990). Although reduced growth was observed in a few mutants, the majority of clones exhibited a higher specific growth rate than the same cells containing the vector backbone when cultured in the absence of any hydroxamate. One of the main advantages of plasmid-based library screening is the relative ease with which the genes responsible for the altered phenotype can be identified. We isolated plasmids from strains identified through growth selections and sequenced insert DNA to identify hydroxamate tolerance genes. Table 3 provides the results from these efforts. (GH and SH clones were not sequenced) Although several of the clones contained different fragments of the same set of genes, at least three different multi-gene loci were identified to provide increased tolerance to AAH. In the case of TH tolerance, six different loci were identified containing genes involved in a broad range of functions. We sequenced insert DNA from strains exhibiting the largest fold-increase in GAH tolerance (even though such fold-increases were not statistically significant). Each of the inserts contained different loci, which would be expected under for growth in the absence of significant selection. 3.4. Evaluation of insertional mutagenesis libraries Our final efforts here were directed at screening insertion mutant libraries to assess the costs and benefits of different insertional mutations selected for by growth in the presence of the three hydroxamates evaluated. As shown in Fig. 4, growth selections on the insertional mutant library produce a large number of mutants with substantially increased tolerance to AAH (average of 15-fold). For TH growth selections, gene insertion resulted in a maximum and average increase of 2.9-fold and 1.8-fold, respectively. For GAH growth selections, only two mutants showed a statistically significant increase in MIC of 1.3-fold. Although statistically significant changes in growth rate were observed, their values were small in magnitude. 3.5. Extension to additional amino-acid hydroxamates Based on these efforts, it was clear that the ability to evolve tolerance differed for each amino acid hydroxamate evaluated. However, it was not apparent if such differences were due to specifics of the anti-metabolite or to those of the targeted amino acid. To differentiate between these two issues, we performed identical selections on the insertional mutant library and the overexpression library employing SH and GH (see Fig. 5). For overexpression, we did not identify any strains (0 of 7) with an increase in GH tolerance, but did identify several (3 of 7) with a 1.5 fold increase in SH tolerance. For insertional mutagenesis, 7 of 7 selected strains exhibited a fold increase in MIC of 5.2 for SH, while strains selected for GH tolerance exhibited a maximum and average fold increase in MIC of 2 and 1.2.

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234 1000

* *

MIC/MICwt

100

* *

*

* * *

10

*

*

* 1

* * *

* *

* *

*

* *

*

*

AAH 1 AAH 2 AAH 3 AAH 4 AAH 5 AAH 6 AAH 7 AAH 8 AAH 9 AAH 10 AAH 11 AAH 12 AAH 13 GAH 1 GAH 2 GAH 3 GAH 4 GAH 5 GAH 6 GAH 7 GAH 8 GAH 9 GAH 10 GAH 11 GAH 12 GAH 13 TH 1 TH 2 TH 3 TH 4 TH 5 TH 6 TH 7 TH 8 TH 9 TH 10 TH 11 TH 12 TH 13

0.1

(a) 40%

*

30%

*

* *

*

*

* 20%

Change in specific growth rate

*

*

* * *

10%

*

*

*

*

*

*

0% * *

*

-10%

* *

-20%

*

*

*

*

*

*

* *

-30% *

(b)

* AAH 1 AAH 2 AAH 3 AAH 4 AAH 5 AAH 6 AAH 7 AAH 8 AAH 9 AAH 10 AAH 11 AAH 12 AAH 13 GAH 1 GAH 2 GAH 3 GAH 4 GAH 5 GAH 6 GAH 7 GAH 8 GAH 9 GAH 10 GAH 11 GAH 12 GAH 13 TH 1 TH 2 TH 3 TH 4 TH 5 TH 6 TH 7 TH 8 TH 9 TH 10 TH 11 TH 12 TH 13

-40%

Fig. 3. Gene overexpression results. (a) Average increased MIC (benefit) results, *clone’s MIC statistically different than wild type MIC (n ¼ 6, based upon 95% confidence interval, determined by comparing wild type’s MIC to clone’s MIC using ANOVA in excel). GAH 6 and GAH 8 did not grow in the absence of agitation. (b) Average cost results, percent change in clone’s specific growth, *clone’s specific growth rate statistically different compared to wild type’s specific growth rate (n ¼ 9, based upon 95% confidence interval, determined by comparing wild type to clone specific growth rates using ANOVA in excel).

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235

Table 3 Genetic loci identified in the overexpression studies Clone

Ben

Size (bp)

Gene regions

Functional class

AAH 2 AAH 3

149 21

2800 2700

yfbK, yfbL, yfgM, yfbN ycbW/ rpiR-rpiB

AAH 4 AAH 7

12 53

2600 2700

ybdQ, ahpF, ahpC ycbW/ rpiR-rpiB

AAH 8

5

2700

ycbW/ rpiR-rpiB

AAH 9

85

3000

moeA, ybiK, yliA

AAH 12 AAH 13

213 6

800 2700

ycbW ycbW/ rpiR-rpiB

GAH 4 GAH 7 GAH 10 TH 3 TH 4 TH 6 TH 7

1.1 1.1 1.1 0.9 1.3 1.6 1.8

500 600 1200 500 800 o50 1000

ybhN mdlA rfaP, rfaS rfaK putatitve promoter NA yegE, alkA

Hypothetical protein Ligation product of ycbW (hypothetical) and rpiR-rpiB (translation) Cell protection Ligation product of ycbW (hypothetical) and rpiR-rpiB (translation) Ligation product of ycbW (hypothetical) and rpiR-rpiB (translation) moeA-cell structure, ybiK- putative transport, yliA- putative transport Hypothetical protein Ligation product of ycbW (hypothetical) and rpiR-rpiB (translation) protein Biosynthesis of cofactors Central intermediary metabolism Cell structure putatitve promoter in 23S region

TH 8 TH 9

2 1.5

500 5000

valU rihB, yeiL, yeiM, yeiN, yeiC, fruA

TH 10

1.4

1200

ydhT, ydhU, ydhX

YegE—hypothetical protein, alkA—DNA replication and repair Promoter of valU operon yeiN—central intermediary metabolism, fruA—transport and binding, others— hypothetical protein Hypothetical protein

Ben-fold increase in MIC.

Since no trend was observed based on the characteristics of targeted metabolite presented in Table 1, it is our contention that the specific targets of the anti-metabolite are the more important consideration. Specifically, antimetabolites most often do not interfere with all aspects of the metabolism of a particular metabolite. Rather they inhibit few relevant pathways, the identity of which appears to be the more important factor. 4. Discussion We have reported the results from amino acid hydroxamate growth selections performed using libraries obtained by random chemical mutagenesis, insertional mutagenesis, and plasmid-based overexpression. Our efforts here were directed at assessing the relative costs and benefits associated with mutations conferring increased tolerance to several different anti-metabolites directed at amino acids with different metabolic characteristics. 4.1. Comparison of hydroxamate tolerance Using different strain engineering techniques we demonstrated the ability of E. coli to gain increased tolerance to five amino acid hydroxamates. Our results suggested that the target of anti-metabolite selection strategies plays an important role in the evolution and/or engineering of new

tolerance phenotypes (see Fig. 5). Specifically, regardless of the method utilized for generating mutations, which covered a range of natural evolutionary mechanisms including point mutation, increase gene dosage, and gene insertion, mutants with substantially increased tolerance to GAH were rarely identified. Of the 50 strains obtained from chemical mutagenesis followed by selection for growth in GAH, only two showed a statistically significant increase in tolerance, which was only 1.5-fold higher than that of the wild type. A second round of chemical mutagenesis did not further increase GAH tolerance. Similarly, insertional mutation studies resulted in only two strains with only a 1.3-fold increased tolerance to GAH. In contrast, all three mutagenesis techniques resulted in a large number of strains with substantially increased tolerance to AAH (18–200-fold). Mutations leading to increased TH, SH, and GH tolerance were identified more frequently than those conferring GAH tolerance. However, the maxima of 2.0–5.0-fold were modest when compared to AAH. Factors complicating our analysis include the location of the hydroxamate group on the amino acid analog, energy required to synthesize the amino acid, and metabolic precursors (see Table 1). Both GAH and AAH contain the hydroxamate group on the side chain of the amino acid. TH, SH, and GH contain the hydroxamate group on the carboxyl group of the amino acid. Glutamic acid and

ARTICLE IN PRESS J. Bonomo et al. / Metabolic Engineering 8 (2006) 227–239

236 100

* * *

* * * * *

* *

*

* *

*

MIC/MICwt

10

* *

* * * *

* *

* *

* * *

*

*

1

AAH 1 AAH 2 AAH 3 AAH 4 AAH 5 AAH 6 AAH 7 AAH 8 AAH 9 AAH 10 AAH 11 AAH 12 AAH 13 AAH 14 GAH 1 GAH 2 GAH 3 GAH 4 GAH 5 GAH 6 GAH 7 GAH 8 GAH 9 GAH 10 GAH 11 GAH 12 GAH 13 TH 1 TH 2 TH 3 TH 4 TH 5 TH 6 TH 7 TH 8 TH 9 TH 10 TH 11 TH 12 TH 13 TH 14

0

(a) 40.00%

30.00%

Change in specific growth rate

20.00%

*

10.00% *

*

* *

*

*

*

* * *

*

* *

*

*

* *

*

*

*

*

*

* *

*

*

* * *

* *

**

* *

0.00%

* -10.00%

-20.00%

-40.00%

(b)

AAH 1 AAH 2 AAH 3 AAH 4 AAH 5 AAH 6 AAH 7 AAH 8 AAH 9 AAH 10 AAH 11 AAH 12 AAH 13 AAH 14 GAH 1 GAH 2 GAH 3 GAH 4 GAH 5 GAH 6 GAH 7 GAH 8 GAH 9 GAH 10 GAH 11 GAH 12 GAH 13 TH 1 TH 2 TH 3 TH 4 TH 5 TH 6 TH 7 TH 8 TH 9 TH 10 TH 11 TH 12 TH 13 TH 14

-30.00%

Fig. 4. Insertional mutagenesis results. (a) Average increased MIC (benefit) results, *clone’s MIC statistically different than wild type MIC (n ¼ 6, based upon 95% confidence interval, determined by comparing wild type’s MIC to clone’s MIC using ANOVA in excel). (b) Average cost results, percent change in clone’s specific growth, *clone’s specific growth rate statistically different compared to wild type’s specific growth rate (n ¼ 9, based upon 95% confidence interval, determined by comparing wild type to clone specific growth rates using ANOVA in excel).

aspartic acid are similar in terms of energetic requirements and metabolic precursors. Tryptophan and serine have the same metabolic precursors but differ in energetic requirements. Glycine and serine have similar energetic require-

ments (glycine is synthesized from serine) but have different connectivity values, whereas glycine and tryptophan have similar connectivity values of 7 and 6 but differ in energetic requirements.

ARTICLE IN PRESS J. Bonomo et al. / Metabolic Engineering 8 (2006) 227–239

1000

MIC/MICwt

100

10

1

(a)

0.1

GAH

AAH

SH

GH

TH

GAH

AAH

SH

GH

TH

1000

MIC/MICwt

100

10

1

(b)

0.1

Fig. 5. Range of increased tolerance to amino acid hydroxamates. Vertical line represents range of increased tolerance ratios and represents the average increased tolerance. (a) Comparison of increased tolerance from overexpression studies. (b) Comparison of increased tolerance from insertional mutagenesis studies. Glutamic acid g-hydroxamate (GAH, connectivity (K) ¼ 51), for aspartic acid b-hydroxamate (AAH, K ¼ 18), serine hydroxamate (SH, K ¼ 13), glycine hydroxamate (GH, K ¼ 7), tryptophan hydroxamate (TH, K ¼ 6).

Based on our overall hypothesis we would predict that mutations leading to GAH tolerance would have a smaller benefit and larger cost compared to AAH and SH followed by GH and TH. This prediction was not demonstrated in our studies. A maximum fold-increase in tolerance of 204 was identified for an anti-metabolite (AAH) directed at an amino acid with a mid-level connectivity value of 18. These results suggest that target connectivity is not the sole driving force in the directed evolution of tolerance phenotypes. That is, even though tryptophan, glycine, and serine have lower connectivity values compared to aspartate, growth selections produced TH-, SH- and GHtolerant mutants at a lower frequency and with a lesser

237

increase in tolerance when compared to the more highly connected aspartate anti-metabolite studies. Therefore, the target of the anti-metabolite appeared to be more important in the engineering of hydroxamate tolerance. Hydrolysis of AAH releases hydroxylamine, causing cell death (Drainas et al., 1977). As a mutagen, hydroxylamine has the potential to disrupt a wide range of pathways. Thus, it is sensible that there may also be a wide range of tolerance mechanisms, which would suggest that tolerant strains should be identified at high frequency as reported here. SH, GH, and TH cause a stringent-like response by inhibiting charging of their respective tRNA synthetases (Tosa and Pizer, 1971; Gao et al., 1995). We observed only a moderate level of increased tolerance to this type of antimetabolite target. One known mechanism of GAH toxicity includes inhibiting g-glutamylcysteine synthetase (Katoh et al., 1998), an enzyme essential for many cellular functions. Although we may predict that high-level resistance to GAH would be difficult to achieve based upon connectivity values, an alternative explanation is that its targets, one of which is g-glutamylcysteine synthetase, are too important for cell survival to have their function interrupted. Based on the lack of clear correspondence between connectivity values and tolerance for the four additional hydroxamates studied, we suggest that interference with the synthesis of g-glutamylcysteine synthetase is the more important factor. Interestingly, robustness of biological networks has been shown through knockouts of genes integral in central carbon metabolism indicating that ‘‘cells are robust systems that are insensitive to many mutations, particularly those affecting critical ‘‘core’’ activities’’ (Bailey, 1999). Coupled together, these results suggest that directed evolution and/or metabolic engineering of tolerance phenotypes may be more difficult for essential targets. 4.2. Comparison of strain engineering techniques Plasmid based gene overexpression produced the strains with the largest increased tolerance to AAH (see Fig. 5). We observed a 200-fold increased tolerance to AAH and an average increased tolerance of 50-fold. The cost (decreased growth rate) associated with gene overexpression varied among the clones. Gene overexpression mimics natural evolutionary mechanisms that result in increased gene dosage. Such mechanisms have been suggested to account for up to 50% of new bacterial genes (Brenner et al., 1995; Teichmann et al., 1998; Lynch and Conery, 2000). Table 3 shows the genes for which overexpression result in increased tolerance to each of the hydroxamates evaluated. Nearly 33% of the genes identified in the overexpression study were hypothetical proteins (proteins with unknown function), which compares to the nearly 35% of the hypothetical proteins found on the E. coli genome. Under identical selection strategies as those used for the gene overexpression and insertional mutagenesis studies, chemical mutagenesis resulted in mutants with the smallest increase in MIC. Interestingly, longer MNNG incubation

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times and alternate selection strategies (plating on selective plates or shorter selection time in liquid media) resulted in mutants with an increased tolerance to AAH (
100-fold), which is comparable to the maximum increase observed in the gene overexpression study. These results are not surprising when considering the relative mutational mechanisms at work. MNNG incubation results primarily in A-T to G-C transitions that accumulate in a single region of the genome surrounding the replication fork (CerdaOlmedo et al., 1968; Botstein and Jones, 1969). Thus, the frequency of obtaining silent mutations, or otherwise nonbeneficial mutations, is much higher when compared to gene overexpression or insertional mutagenesis mechanisms that force changes in the expression of a particular locus. Insertional mutagenesis consistently resulted in mutants displaying similarly increased MIC’s without any major costs to biological fitness. This result was in contrast to gene overexpression or chemical mutagenesis both of which resulted in large variation in costs and benefits among selected mutants. Further investigation of the insertional mutagenesis strains revealed disruption of the same gene (pin- DNA invertase) in many cases (data not shown). This result suggests that growth selections converged upon a common mechanism for achieving AAH and GAH hydroxamate tolerance. This is a plausible result given that the hydroxamate group is at the same position on AAH and GAH but in a different position in TH. The cost of resistance (decreased growth rate) varied in the chemical mutagenesis study where, in some cases, a drastically reduced specific growth rate was observed. Insertional mutagenesis, however, resulted with strains with similar growth rates compared to the wild type. Interestingly, for gene overexpression, nearly 50% of the AAH and TH strain’s specific growth rates were higher than the wild type’s specific growth rate. This indicated that the metabolic burden associated with plasmid maintenance was not a major factor in these studies. 5. Conclusion The overall objective of this study was to improve understanding of the costs and benefits associated with different mutations conferring increased anti-metabolite tolerance. Towards this end we employed three different mutagenesis strategies to engineer E. coli mutants with increased tolerance to five different amino acid antimetabolites. We then compared the costs and benefits to biological fitness of a panel of mutants obtained by growth selections in the presence of each anti-metabolite. Overall, we observed that mutants with dramatically increased tolerance occurred more frequently for aspartic acid b-hydroxamate than for glutamic acid g-hydroxamate, tryptophan hydroxamate, serine hydroxamate, or glycine hydroxamate. Moreover, mutations leading to increased tolerance to glutamic acid g-hydroxamate were rare, the fold-increase in tolerance was relatively low, and the

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