A Systems Biology Approach Uncovers Cellular
Strategies Used by Methylobacterium extorquens AM1
During the Switch from Multi- to Single-Carbon Growth
Elizabeth Skovran1*, Gregory J. Crowther1¤a, Xiaofeng Guo1¤b, Song Yang1, Mary E. Lidstrom1,2
1Department of Chemical Engineering, University of Washington, Seattle, Washington, United States of America, 2Department of Microbiology, University of
Washington, Seattle, Washington, United States of America
Background: When organisms experience environmental change, how does their metabolic network reset and adapt to the
new condition? Methylobacterium extorquens is a bacterium capable of growth on both multi- and single-carbon
compounds. These different modes of growth utilize dramatically different central metabolic pathways with limited
Methodology/Principal Findings: This study focused on the mechanisms of metabolic adaptation occurring during the
transition from succinate growth (predicted to be energy-limited) to methanol growth (predicted to be reducing-power-
limited), analyzing changes in carbon flux, gene expression, metabolites and enzymatic activities over time. Initially, cells
experienced metabolic imbalance with excretion of metabolites, changes in nucleotide levels and cessation of cell growth.
Though assimilatory pathways were induced rapidly, a transient block in carbon flow to biomass synthesis occurred, and
enzymatic assays suggested methylene tetrahydrofolate dehydrogenase as one control point. This ‘‘downstream priming’’
mechanism ensures that significant carbon flux through these pathways does not occur until they are fully induced,
precluding the buildup of toxic intermediates. Most metabolites that are required for growth on both carbon sources did
not change significantly, even though transcripts and enzymatic activities required for their production changed radically,
underscoring the concept of metabolic setpoints.
Conclusions/Significance: This multi-level approach has resulted in new insights into the metabolic strategies carried out to
effect this shift between two dramatically different modes of growth and identified a number of potential flux control and
regulatory check points as a further step toward understanding metabolic adaptation and the cellular strategies employed
to maintain metabolic setpoints.
Citation: Skovran E, Crowther GJ, Guo X, Yang S, Lidstrom ME (2010) A Systems Biology Approach Uncovers Cellular Strategies Used by Methylobacterium
extorquens AM1 During the Switch from Multi- to Single-Carbon Growth. PLoS ONE 5(11): e14091. doi:10.1371/journal.pone.0014091
Editor: Rodolfo Aramayo, Texas A&M University, United States of America
Received June 29, 2010; Accepted October 18, 2010; Published November 24, 2010
Copyright: ? 2010 Skovran et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded by grants from National Institute of General Medical Sciences (GM58933 to MEL) and the Ruth Kirschstein National Research
Service Award, 08FGM78835A to ES, and 5F32GM070297 to GJC. The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org.
¤a Current address: Department of Medicine, University of Washington, Seattle, Washington, United States of America
¤b Current address: Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
Methylobacterium extorquens AM1 is a facultative methylotrophic
bacterium capable of growth on single-carbon compounds such as
methanol, methylamine and formate, and multicarbon com-
pounds such as pyruvate and succinate [1–3]. In addition to their
role in the global carbon cycle , methylotrophs are of interest
for their potential in the biotechnological production of valued-
added chemicals from methanol, which is an inexpensive and
abundant source of carbon . These organisms are ubiquitous in
nature and are often associated with the leaf surfaces of plants
where methanol is released when the stomata open in the morning
[6–7]. To accommodate these bursts of methanol, methylotrophs
must be poised to quickly adapt, capturing available methanol
while preventing buildup of its subsequent toxic oxidation product,
formaldehyde, as well as further downstream toxic metabolites
such as glyoxylate and glycine [8–9]. Because of the high flux of
toxic metabolites produced during methylotrophic growth, this
metabolic mode of growth presents an interesting case study
regarding the regulation of and balance between production and
consumption of these toxic metabolic products.
M. extorquens AM1 has been studied for decades (reviewed in
) and is a model organism for understanding the metabolic
components required for methylotrophic growth. The most
broadly studied physiological condition has been the comparison
of growth on methanol vs. succinate. When growing on multi-
carbon substrates, M. extorquens AM1 uses pathways that are
common to many heterotrophs, including the TCA cycle, the
pentose-phosphate pathway, parts of anapleurotic pathways and
gluconeogenesis, and an electron transport chain involving NADH
PLoS ONE | www.plosone.org1November 2010 | Volume 5 | Issue 11 | e14091
dehydrogenase [1,11]. In contrast, growth on single-carbon
compounds involves specific metabolic pathways for both energy
metabolism and assimilation, and requires over 100 additional
gene products for central metabolism . These two modes of
growth are dramatically different, with methanol growth predicted
to be limited by reducing power (NAD(P)H) while succinate
growth is predicted to be limited by energy (ATP) [12–13].
Global approaches have been developed for M. extorquens AM1
to study multiple layers in the metabolic hierarchy including
transcriptomic , metabolomic [15–17] and proteomic [18–19]
approaches, as well as analysis of enzymes and fluxes [13,20–21].
These individual studies have shown that the majority of genes
known to function in C1 and multicarbon metabolism are
differentially expressed and produced according to their function
in these two different modes of growth. Most of the amino acids,
biosynthetic precursors and intermediates involved in the serine
cycle (the main methylotrophic assimilatory pathway) and the
TCA cycle are at similar concentrations for methanol growth and
succinate growth, while intermediates of the methylotrophy-
specific ethylmalonyl-CoA (EMC) pathway are highly elevated in
methanol cultures (up to 24-fold differences) [13,17].
of growth conditions, but these individually-gathered datasets have
not been fully integrated to provide a systems biology view of
metabolism, which is crucial to understanding methylotrophic
growth and predicting metabolic manipulations for industrial
benefit. In addition, the dynamics of the response during the
transition between growth substrates has not been studied at a global
level, and that approach has great potential to provide insights into
how the metabolic network is reset to allow methylotrophic growth
as well as to uncover general cellular strategies for adaption to
environmental change and metabolic stress.
In this study, we address how metabolism responds and adapts
under perturbation as a system of interconnected metabolic
pathways by adding methanol to a succinate-limited chemostat
culture and following the transition of the cells from succinate
growth to methanol growth, obtaining data at multiple time points
for different layers in the metabolic hierarchy: transcription,
enzyme activity, metabolites and fluxes. This integrated analysis
provides information about the transient imbalance and the
response to carbon starvation the cells experience when they are
shifted out of the multi-carbon metabolic mode, the flux
redistribution that occurs while the cells are adapting to the new
C1 metabolic mode, and the relationships between transcript,
enzyme activity, metabolite and flux during this transition.
In addition, we show that unlike in other organisms, [22–23],
M. extorquens AM1 induces its metabolic pathways in a reverse
order (biomass cycles first before the pathways that generate the
biomass precursor metabolites), which may be a cellular strategy to
prevent the buildup of downstream toxic metabolites. Finally, we
show that for those genes/enzymes common to both modes of
growth, expression and activity change greatly during the
transition, yet metabolite concentrations remain relatively constant
underscoring the concept of metabolic setpoints .
Parameters of the Transition Experiment and Growth
Chemostat cultures were chosen as the starting point for the
substrate transition experiments due to the ability to maintain
reproducible initial growth conditions. Succinate-limited chemo-
stat cultures of M. extorquens AM1 were grown to steady state (OD
,0.63), samples were taken as a zero time point, and then 50 mM
methanol was added. Cells were harvested at multiple time points
after the transition, up to 6 h, and cellular response was assessed
via cell growth, carbon flux measurements, selected enzymatic
activity assays, global gene expression profiling and measurement
of a suite of targeted intracellular and excreted metabolites. Upon
cessation of succinate addition to the growth medium and the
subsequent addition of methanol, it can be estimated that the
carbon flux to biomass dropped from ,30 nmol C min21ml of
culture21(calculated from the dilution rate, see Methods) to
,0.2 nmol C min21ml of culture21, which is the measured flux of
methanol and CO2carbon to biomass in succinate-grown cells
exposed to methanol in batch culture . This drop in flux of
over two orders of magnitude would be expected to plunge the
culture into carbon starvation initially, until the induction of the
methylotrophic assimilatory pathways allowed increased flux to
biomass from those routes. In keeping with this expectation, the
culture underwent a 2 h lag phase, demonstrated by the relatively
unchanged optical density (OD600) (Figure 1A), then transitioned
to log phase indicating that the culture had started to actively grow
on methanol. The methanol concentration in the medium began
to drop between 15–30 min post methanol addition, with a
concentration of ,30 mM left at 6 h post transition. Note that
since the culture was initially at steady-state under succinate
limitation, this protocol effected a transition from succinate to
methanol growth without the need to wash the starting culture.
Global Gene Expression Response to Carbon Source
To gain insights into the global cellular response upon carbon
source switch, gene expression data were analyzed to determine
the cellular processes (outside of central metabolism) that showed
the largest changes in their initial response to methanol addition
(Figure 2) and were compared to a set of carbon-starvation
response arrays as controls (Table S1). Several classes of genes
responded similarly in both conditions, in keeping with the
prediction that the cells would experience carbon starvation at the
onset of the transition. These included genes whose expression
increased in both cases, including predicted stress response genes,
predicted genes involved in NADH homeostasis, cytochrome bd
ubiquinol oxidase genes, and a predicted bacterioferritin (iron
storage) gene. In addition, many classes of genes showed a
transient decrease in expression in both conditions, either within
the first 30 min or after a transient increase, and then a recovery.
This pattern included genes predicted to be involved in synthesis
of proteins, fatty acids, lipopolysaccharides, ATP, flagella, cell
walls and nucleotide synthesis and salvage, along with cytochrome
c oxidase genes and several genes predicted for cell division,
chemotaxis, glycosylation, and protease activity. A similar pattern
of decrease and recovery was also observed for several methylo-
trophy genes, discussed below. Genes encoding transhydrogenase,
which interconverts NADH and NADPH , were significantly
down-regulated (15–20 fold) in the methanol addition condition
but not in the starvation control, and remain repressed during the
time period of methylotrophic growth (Figure 2), consistent with
previous steady state studies . Cytochrome d terminal oxidase
genes increased independently of the carbon starvation control
while the cyotchrome o ubiquinol oxidase genes increased in
opposition to the carbon starvation control, where expression
decreased ,5 fold.
Overview of Central Metabolic Carbon Flow
In M. extorquens AM1, methanol is first oxidized to the toxic
metabolite, formaldehyde, then to formate. Formate serves as a
branch point and can either be oxidized to CO2 producing
Carbon Switch in M. extorquens
PLoS ONE | www.plosone.org2 November 2010 | Volume 5 | Issue 11 | e14091
reducing power, or be converted to methylene-H4F, which enters
the central metabolic carbon assimilation pathways including the
serine cycle, the EMC pathway, the PHB cycle and a portion of
the TCA cycle, where essential intermediates are produced for cell
growth [1–2,21] (Figure 3). These pathways were analyzed in
detail along with two core pathways involved in multi-carbon
growth, the TCA cycle and the pentose-phosphate pathway. After
methanol was added to succinate-grown cultures, distribution of
carbon flow was assessed using 4 methods: growth curve analysis,
measurement of flux to CO2 using
measurement of methanol, formaldehyde and formate in the
culture supernatant and measurement of assimilatory pathway
metabolites (described below). Growth did not occur until between
1–2 h post transition (Figure 1A), yet flux of methanol to CO2
increased significantly within the first hour, prior to growth
(Figure 1B). Formaldehyde and formate concentrations in the
supernatant increased until about 2 h, when levels began to
decrease (Figure 1C), suggesting that before active growth
occurred, both compounds were excreted and then as the cells
began to divide, excretion decreased. For the first hour,
approximately 1/3 of the carbon from methanol oxidation was
in these two pools, the remainder in CO2. Note that the peak
concentrations, 50 and 450 mM, respectively, are not toxic for
M. extorquens AM1 [26–28]. By 2 h, the total flux to formaldehyde,
formate, and CO2 of the culture (,32 nmol min21[ml at 1
OD]21) was about L of the full flux measured in methanol-grown
cells (41.5 nmol min21[ml at 1 OD]21; ). These data suggest
that the lag in growth was due to a block in formate assimilation,
not production (summarized in Figure 3).
Visualization of Hierarchical Changes for Central
With the introduction of global ‘‘omics’’ level tools, it has recently
become possible to investigate multiple layers of an organism’s
metabolic network during a condition of study. However, the power
of these tools can also be a detriment, generating large amounts of
data that can often be difficult to integrate and understand as a
whole. To facilitate insights and infer meaning regarding the multi-
leveled changes and adaptations that the metabolic network of M.
extorquens AM1 undergoes during the transition from succinate- to
methanol-growth, diagrams were constructed that visually compile
and summarize each level of data obtained in relation to central
metabolism. The initial response to methanol addition (time =10–
30 min) is shown in Figure 4A with 4B serving as a legend.
Diagrams depicting the metabolic state prior to methanol addition
(time=0 min), response just prior to/at the start of cell growth (1–
2 h) and during log phase cell growth (3–6 h) are included in Figure
S1. These diagrams depict information about gene expression
intensities (arrow thickness) and fold changes (number of arrow
heads), changes in measured metabolite concentrations (color
shadings), and enzymatic activities (color shadings of boxed protein
names), providing insight into both the metabolic changes
themselves and the level at which those changes occurred. While
only semi-quantitative due to possible differing labeling and
hybridization efficiencies that could occur during microarray
experiments, information on gene intensities is provided since the
intensity data aid in pathway interpretation of possible carbon flow.
Analysis of fold changes alone can be misleading if for example, no
change in expression occurs for a gene, yet expression of that gene is
high, or if a gene has a significant change in expression but
expression is extremely low.
Response of Measured Nucleotide Pools
The adenine and pyridine nucleotide pools reflect the metabolic
state of the cell. As expected for a growth downshift experiment,
these compounds all showed significant changes, with most values
changing by 50–100% during the time course (Figure 5). Consistent
with the prediction that methanol growth is reducing power limited
and succinate growth is energy limited [12–13], NADPH increased
immediately, in keeping with an initial major downshift in biomass
synthesis, peaked at the 30 min timepoint, then decreased by 6 h to
a lower value than the initial value. ATP decreased immediately, in
keeping with the downshift in carbon flow through the TCA cycle,
then increased, then decreased, and after 1 h, slowly increased to a
final value greater than that for succinate growth. The other
nucleotides all decreased immediately, then rose and either stayed
constant or decreased slightly, a pattern reminiscent of the
starvation-induced gene expression data.
Figure 1. Optical density (OD), rates of carbon flux to CO2and
external metabolite measurements before and during the
transition from succinate to methanol growth. (A) Change of
optical density (filled squares) and methanol concentrations
(open squares). (B) Carbon flux to CO2measured using14C-methanol.
(C) Formaldehyde (squares) and formate (circles) concentrations in the
culture supernatant. Standard deviations are shown as error bars.
Carbon Switch in M. extorquens
PLoS ONE | www.plosone.org3 November 2010 | Volume 5 | Issue 11 | e14091
carbohydrates. Samples were taken at 0 min (before methanol
addition), 10 min, 20 min, 30 min, 1 h, 2 h and 3 h after
methanol addition, and were analyzed by GC6GC-TOFMS as
described . Numbers are averages of nine replicates (three
extractions with 3 biological replicate injections for each
Fisher Ratio analysis of cell extracts
Fisher ratio analysis was used to assess metabolite differences
between two conditions as previously described . Numbers are
averages of nine replicates (three extractions with 3 biological
replicate injections for each extraction).
changes that occurred in measured metabolites, gene expression,
and enzymatic activities for central metabolism after cells were
transitioned from succinate to methanol growth. A boxed gene/
protein name indicates the activity of this enzyme was measured.
Red lettering for the protein designation indicates an increase in
activity; green, decrease; black, no change. Metabolites appearing
more than once are connected by gray lines. Diagrams are shown
for the following time periods: (A) pre-methanol addition, time
=0 min; (B) initial response, time =10–30 min post methanol
addition; (C) just prior to/at the start of cell growth, time =1–2 h
post methanol addition; and (D) exponential cell growth, time
=3–6 h post methanol addition with (E) serving as a legend. Gene
expression intensities are represented by arrow thickness while
gene expression fold changes are depicted by arrowhead number.
Abbreviations and reaction descriptions are included in Supple-
mentary Table II along with gene expression intensities, Log-
Pathway schematics depicting
Ratios, fold changes and p-values. Mesaconyl-CoA, ethylmalonyl-
CoA, methylsuccinyl-CoA were measured as free acids. **Color at
T=0 represents the concentration before methanol was added for
all metabolites except for methanol which was calculated as
50 mM for the initial T=0 value.
Found at: doi:10.1371/journal.pone.0014091.s001 (0.21 MB
Supplementary Table S1
processes that significantly changed during the transition from
succinate to methanol growth.
Found at: doi:10.1371/journal.pone.0014091.s002 (0.96 MB
Gene expression data for cellular
Supplementary Table S2
metabolic pathways during the transition from succinate to
Found at: doi:10.1371/journal.pone.0014091.s003 (0.40 MB
Gene expression data for central
We thank Mila Chistoserdova, Marina Kalyuzhnaya and Norma Cecilia
Martinez-Gomez for critical reading of this manuscript, Bo Hu for his aid
in producing and maintaining chemostat grown cells and Julia Vorholt for
initial enzyme characterization.
Conceived and designed the experiments: ECS GJC XG SY MEL.
Performed the experiments: ECS GJC XG SY. Analyzed the data: ECS
GJC XG SY MEL. Contributed reagents/materials/analysis tools: ECS
XG SY. Wrote the paper: ECS MEL.
1. Anthony C (1982) The biochemistry of methylotrophs. New YorkNY: Academic
2. Green P (1991) The genus Methylobacterium. In: Balows A, Tru ¨per HG,
Dworkin M, Harder W, Schleifer KH, eds. The Prokaryotes, 2 edn Springer:
Berlin, . pp 2342–2349.
3. Lidstrom ME (2001) Aerobic methylotrophic prokaryotes. In: Stackebrandt, ed.
The Prokaryotes, 3 edn. New York, NY: Springer-Verlag. pp 223–244.
4. Chistoserdova L, Lapidus A, Han C, Goodwin L, Saunders L, et al. (2007)
Genome of Methylobacillus flagellatus, molecular basis for obligate methylotrophy.
J Bacteriol 189: 4020–7.
5. Schrader J, Schilling M, Holtmann D, Sell D, Filho MV, et al. (2009) Methanol-
based industrial biotechnology: current status and future perspectives of
methylotrophic bacteria. Trends Biotechnol 2: 107–15.
6. Corpe WA, Rheem S (1989) Ecology of the methylotrophic bacteria on living
leaf surfaces. FEMS Microbiol Ecol 62: 243–50.
7. Hu ¨ve K, Christ MM, Kleist E, Uerlings R, Niinemets U, et al. (2007)
Simultaneous growth and emission measurements demonstrate an interactive
control of methanol release by leaf expansion and stomata. J Exp Bot 58:
8. Salem, AR, Hacking AJ, Quayle JR (1976) Lack of malyl-CoA lyase in a mutant
of Pseudomonas AM1. J Gen Microbiol 81: 525–7.
9. Harder W, Quayle JR (1971) The biosynthesis of serine and glycine in
Pseudomonas AM1 with special reference to growth on carbon sources other than
C1 compounds. Biochem J 121: 753–62.
10. Chistoserdova L, Chen SW, Lapidus A, Lidstrom ME (2003) Methylotrophy in
Methylobacterium extorquens AM1 from a genomic point of view. J Bacteriol 185:
11. Van Dien SJ, Okubo Y, Hough MT, Korotkova N, Taitano T, et al. (2003)
Reconstruction of C3 and C4 metabolism in Methylobacterium extorquens AM1
using transposon mutagenesis. Microbiology 149: 601–9.
12. Van Dien SJ, Lidstrom ME (2002) Stoichiometric model for evaluating the
metabolic capabilities of the facultative methylotroph Methylobacterium extorquens
AM1, with application to reconstruction of C(3) and C(4) metabolism.
Biotechnol Bioeng 78: 296–312.
13. Guo X, Lidstrom ME (2006) Physiological analysis of M. extorquens AM1 grown
in continuous and batch cultures. Arch Microbiol 186: 139–49.
14. Okubo Y, Skovran E, Guo X, Sivam D, Lidstrom ME (2007) Implementation of
microarrays for Methylobacterium extorquens AM1. Omics 11: 325–40.
15. Guo X, Lidstrom ME (2008) Metabolite profiling analysis of Methylobacterium
extorquens AM1 by comprehensive two-dimensional gas chromatography coupled
with time-of flight mass spectrometry. Biotechnol Bioeng 99: 929–40.
16. Kiefer P, Portais JC, Vorholt JA (2008) Quantitative metabolome analysis using
liquid chromatography-high-resolution mass spectrometry. Anal Biochem 382:
17. Peyraud R, Kiefer P, Christen P, Massou S, Portais JC, et al. (2009)
Demonstration of the ethylmalonyl-CoA pathway by using 13C metabolomics.
Proc Natl Acad Sci U S A 106: 4846–51.
18. Laukel M, Rossignol M, Borderies G, Vo ¨lker U, Vorholt JA (2004) Comparison
of the proteome of Methylobacterium extorquens AM1 grown under methylotrophic
and nonmethylotrophic conditions. Proteomics 4: 1247–64.
19. Bosch G, Skovran E, Xia Q, Wang T, Taub F, et al. (2008) Comprehensive
proteomics of Methylobacterium extorquens AM1 metabolism under single carbon
and nonmethylotrophic conditions. Proteomics 8: 3494–505.
20. Marx CJ, Van Dien SJ, Lidstrom ME (2005) Flux analysis uncovers key role of
functional redundancy in formaldehyde metabolism. PLoS Biol 3: e16.
21. Crowther GJ, Kosa ´ly G, Lidstrom ME (2008) Formate as the main branch point
for methylotrophic metabolism in Methylobacterium extorquens AM1. J Bacteriol
22. Kro ¨mer JO, Sorgenfrei O, Klopprogge K, Heinzle E, Wittmann C (2004) In-
depth profiling of lysine-producing Corynebacterium glutamicum by combined
analysis of the transcriptome, metabolome, and fluxome. J Bacteriol 186:
23. Zaslaver A, Mayo AE, Rosenberg R, Bashkin P, Sberro H, et al. (2004) Just-in-
time transcription program in metabolic pathways. Nat Genet 36: 486–91.
24. Ishii N, Nakahigashi K, Baba T, Robert M, Soga T, et al. (2007) Multiple high-
throughput analyses monitor the response of E. coli to perturbations. Science
25. Sauer U, Canonaco F, Heri S, Perrenoud A, Fischer E (2004) The soluble and
membrane-bound transhydrogenases UdhA and PntAB have divergent
functions in NADPH metabolism of Escherichia coli. J boil chem 279: 6613–9.
26. Chistoserdova L, Laukel M, Portais JC, Vorholt JA, Lidstrom ME (2004)
Multiple formate dehydrogenase enzymes in the facultative methylotroph
Methylobacterium extorquens AM1 are dispensable for growth on methanol.
J Bacteriol 186: 22–8.
27. Chistoserdova L, Crowther GJ, Vorholt JA, Skovran E, Portais JC, et al. (2007)
Identification of a fourth formate dehydrogenase in Methylobacterium extorquens
Carbon Switch in M. extorquens
PLoS ONE | www.plosone.org15 November 2010 | Volume 5 | Issue 11 | e14091
AM1 and confirmation of the essential role of formate oxidation in
methylotrophy. J Bacteriol 189: 9076–81.
28. Miller JA (March 2009) Formaldehyde stress response in Methylobacterium
extorquens AM1. Doctoral thesis University of Washington, Seattle WA.
29. Vorholt JA, Marx CJ, Lidstrom ME, Thauer RK (2000) Novel formaldehyde-
activating enzyme in Methylobacterium extorquens AM1 required for growth on
methanol. J Bacteriol 182: 6645–50.
30. Marx CJ, Lidstrom ME (2004) Development of an insertional expression vector
system for Methylobacterium extorquens AM1 and generation of null mutants lacking
mtdA and/or fch. Microbiology 150: 9–19.
31. Harder W, Quayle JR (1971) Aspects of glycine and serine biosynthesis during
growth of Pseudomonas putida I. Synthesis of enzymes by the wild type. Biochem J
32. Hepinstall J, Quayle JR (1970) Pathways leading to and from serine during
growth of Pseudomonas AM1 on C1 compounds or succinate. Biochem J 117:
33. McNerney T, O’Connor ML (1980) Regulation of enzymes associated with C-1
metabolism in three facultative methylotrophs. Appl Environ Microbiol 40:
34. Korotkova N, Chistoserdova L, Kuska B, Lidstrom ME (2002) Glyoxylate
regeneration pathway in the methylotroph Methylobacterium extorquens AM1.
J Bacterial 184: 1750–8.
35. Korotkova N, Lidstrom ME (2001) A connection between poly-b-hydroxybu-
tyrate biosynthesis and growth on C1and C2compounds in the methylotroph
Methylobacterium extorquens AM1. J Bacteriol 183: 1038–46.
36. Korotkova N, Chistoserdova L, Lidstrom ME (2002) Poly-b-hydroxybutyrate
biosynthesis in the facultative methylotroph Methylobacterium extorquens AM1:
identification and mutation of gap11, gap20, and phaR. J Bacteriol 184: 6174–81.
37. Lee IY, Kim MK, Park YH, Lee SY (1996) Regulatory effects of cellular
nicotinamide nucleotides and enzyme activities on poly(3-hydroxybutyrate)
synthesis in recombinant Escherichia coli. Biotechnol Bioeng 52: 707–12.
38. Lee YH, Kim TW, Park JS, Huh TL (1996) Effect of the supplementation of
metabolites on cell growth and poly-b-hydroxybutyrate biosynthesis of Alcaligenes
eutrophus. J Microbiol Biotechnol 6: 120–7.
39. Mangos TJ, Haas MJ (1997) A spectrophotometric assay for the enzymatic
demethoxylation of pectins and the determination of pectinesterase activity. Anal
Biochem 244: 357–66.
40. Nash T (1953) The colorimetric estimation of formaldehyde by means of the
Hantzsch reaction. Biochem J 55: 416–21.
41. Stoscheck CM (1990) Quantitation of protein. Methods Enzymol 182: 50–69.
42. Vorholt JA, Chistoserdova L, Lidstrom ME, Thauer (1998) The NADP-
dependent methylene tetrahydromethanopterin dehydrogenase in Methylobacter-
ium extorquens AM1. J Bacteriol 180: 5351–6.
43. Gruer MJ, Guest JR (1994) Two genetically-distinct and differentially-regulated
aconitases (AcnA and AcnB) in Escherichia coli. Microbiology 140: 2531–41.
44. Erb TJ, Berg IA, Brecht V, Mu ¨ller M, Fuchs G, et al. (2007) Synthesis of C5-
dicarboxylic acids from C2-units involving crotonyl-CoA carboxylase/reductase:
The ethylmalonyl-CoA pathway, Proc Natl Acad Sci U S A 104: 10631–6.
45. Spencer ME, Guest JR (1973) Isolation and properties of fumarate reductase
mutants of Escherichia coli. J Bacteriol 114: 563–70.
46. Skovran E, Downs DM (2002) Metabolic defects caused by mutations in the isc
gene cluster in Salmonella enterica serovar Typhimurium, implications for thiamine
synthesis. J Bacteriol 182: 3896–903.
47. Bergmeyer HU, Gawehn K, Grassl M (1974) Methods of enzymatic analysis, 2
edn. New YorkNY: Academic Press. pp 501–3.
48. Yang S, Sadilek M, Synovec RE, Lidstrom ME (2009) Liquid chromatography-
tandem quadrupole mass spectrometry and two-dimensional gas chromatogra-
phy-time-of-flight mass spectrometry measurement of targeted metabolites of
Methylobacterium extorquens AM1 grown on two different carbon sources.
J Chromatogr A 1216: 3280–9.
49. Goldberg I, Rock JS, Ben-Bassat A, Mateles RI (1976) Bacterial yields on
methanol, methylamine, formaldehyde, and formate. Biotechnol Bioeng 18:
50. Marx CJ, Chistoserdova L, Lidstrom ME (2003) Formaldehyde-detoxifying role
of the tetrahydromethanopterin-linked pathway in Methylobacterium extorquens
AM1. J Bacteriol 185: 7160–8.
51. Large PJ, Quayle JR (1963) Microbial growth on C(1) compounds. 5. Enzyme
activities in extracts of Pseudomonas AM1. Biochem J 87: 386–96.
Carbon Switch in M. extorquens
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