Alternative oxidase

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Physiologia Plantarum 137: 371–382. 2009 Copyright © Physiologia Plantarum 2009, ISSN 0031-9317
REVIEW
Alternative oxidase: a defence against metabolic
fluctuations?
Allan G. Rasmussona,∗, Alisdair R. Fernieb and Joost T. van Dongenb
aDepartment of Cell and Organism Biology, Lund University, Sölvegatan 35B, SE-223 62 Lund, Sweden
bMax-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
Correspondence
*Corresponding author,
e-mail: allan.rasmusson@cob.lu.se
Received 1 March 2009; revised 7 May
2009
doi: 10.1111/j.1399-3054.2009.01252.x
An increasing number of oscillating or fluctuating cellular systems have been
recently described following the adaptation of fluorescent technology. In
diverse organisms, these variously involve signalling factors, heat production,
central metabolism and reactive oxygen species (ROS). In response to many
plant stresses and primarily via the influence of ROS, changes in mRNA and
protein levels or in vivo activity of alternative oxidase are often observed.
However, in several investigations, a lack of correlation between the mRNA,
protein and in vivo activity has been evident. This discrepancy has made it
questionable whether the induction of alternative oxidase has importance in
regulating alternative pathway activity in vivo, or being diagnostic for a role of
alternative oxidase in stress tolerance and ROS avoidance. Here, we suggest a
role of alternative oxidase in counteracting deleterious short-term metabolic
fluctuations, especially under stress conditions. This model emphasizes the
importance of peak activity for establishing protein levels and allows an
amalgamation of the present status of physiological, cellular and molecular
knowledge.
Introduction
Plant mitochondria contain several proteins that
decrease the coupling of carbon catabolism to ATP
synthesis (Fig. 1). Alternative NAD(P)H dehydrogenases,
alternative oxidases (AOXs) and uncoupling proteins
(UCPs) bypass energy conservation by complex I, com-
plex III and IV, and complex V, respectively (Clifton
et al. 2006, Fernie et al. 2004, Rasmusson et al. 2004,
2008, Vanlerberghe and McIntosh 1997, Vercesi et al.
2006). The AOX, in particular, has received considerable
research attention and resultant advances in understand-
ing. This is, at least in part, due to its large impact
on energy conservation, its absence in mammals, its
early molecular characterisation and the possibility to
measure the in vivo AOX activity by oxygen isotope
Abbreviations – AOX, alternative oxidase; GABA, gamma-aminobutyric acid; ROS, reactive oxygen species; TCA, tricarboxylic
acid; UCP, uncoupling protein; UQ, oxidised ubiquinone; UQH2, reduced ubiquinone.
partitioning (Elthon et al. 1989, Guy et al. 1989, Vanler-
berghe and McIntosh 1997).
Dicot plants contain AOX1 and AOX2 genes, whereas
only AOX1 genes are present in monocots (Considine
et al. 2002, Karpova et al. 2002). As an example,
Arabidopsis contains four AOX1 genes (a–d) and one
AOX2. The Arabidopsis AOX genes are differently
expressed through the plant and its life cycle, and
differently responsive to external stimuli (Clifton et al.
2006). In Arabidopsis, especially AOX1a is induced
by stress treatments (Clifton et al. 2005), and overall,
AOX capacity, protein and RNA have been shown to
be induced by abiotic and biotic stresses in several
species (Djajanegara et al. 2002, Lennon et al. 1997,
Sieger et al. 2005, Simons et al. 1999, Vanlerberghe
and McIntosh 1992). The physiological rationale of
Physiol. Plant. 137, 2009 371
Fig. 1. Oxidative phosphorylation and energy bypasses in plant
mitochondria. Proteins and protein complexes present in both plants
and mammals are denoted in grey, whereas the AOX and NAD(P)H
dehydrogenases absent in mammals are denoted in green. The present
uncertainty on the exact enzymatic function of NDA2, NDB3 and NDC1
is denoted by question marks. DH, dehydrogenase.
this induction has been explained by that increased
levels of reactive oxygen species (ROS) during stress
will induce AOX gene expression. Mitochondria are an
important source of ROS production mainly at the site of
complex I and III of the electron transport chain (Noctor
et al. 2007). Not surprisingly, a specific anti-oxidative
ascorbate-glutathione cycle exists in plant mitochondria
to decrease toxic effects of ROS within the matrix (Chew
et al. 2003), and other ROS-removal systems present in
mammalian mitochondria may also exist in plants, as
previously reviewed (Møller 2001, Rhoads et al. 2006).
The induction of AOX gene expression upon ROS-
inducing stress conditions was explained by a role of
AOX to avoid ROS formation by lowering the ubiquinone
(UQ) reduction level (Maxwell et al. 1999, Møller 2001,
Purvis and Shewfelt 1993, Rhoads and Subbaiah 2007).
Furthermore, it has been suggested that AOX activity
could reduce the level of molecular oxygen within
the mitochondria thus reducing the production of ROS
(Gupta et al. this issue).
The importance of AOX gene expression changes
during stress has, however, become questioned by
measurements of in vivo AOX activity using oxygen
isotope partitioning. In many different experiments in
which the AOX protein and/or capacity levels were
elevated, e.g. in response to salicylic acid in tobacco,
chilling in mung bean, overexpression of an AOX gene
in tobacco, or as a consequence of the cytoplasmic
male sterile II mutation in Nicotiana sylvestris, the in
vivo activity of AOX did not increase (Gonzàlez-Meler
et al. 1999, Guy and Vanlerberghe 2005, Lennon et al.
1997, Vidal et al. 2007). Further, increased in vivo AOX
activity in response to drought in soybeans was not
associated with increased AOX protein levels (Ribas-
Carbo et al. 2005). Therefore, it is clear that there
are several conditions where AOX protein exists in
surplus, and that AOX protein level does not generally
regulate the in vivo activity (Millenaar and Lambers
2003). However, a very important issue is whether the
discrepancy between protein level and in vivo activity
indicates that changes in gene expression cannot be used
diagnostically for function of the AOX during diverse
physiological conditions. AOX is one of the very few
proteins whose in vivo activity can readily be determined
at steady state by fully non-invasive methodology.
Therefore, it serves as a model for other proteins, whose
function is predicted from changes in expression using
global RNA analyses or quantitative proteomics.
In this article, we discuss whether short-term temporal
activity fluctuations at the cellular or sub-cellular level
may hold an explanation for the discrepancies reported
for AOX protein level and activity. We suggest that a
regulated overcapacity of AOX is needed for certifying
adequate activity during peaks of substrate supply
after metabolic fluctuations have been induced by
changes in the plant environment. Thus, by ameliorating
metabolic transitions, AOX can play a critical role in cell
reprogramming under stress.
Fluctuations and oscillations exist in gene
regulatory and metabolic systems
In recent years, the improved technology for fluorescent
detection of proteins has made it evident that gene
expression can be intrinsically noisy in response
to fluctuations in cellular components (Elowitz et al.
2002). Rapid fluctuations have also been reported in
protein dynamics, for example, as transient burst in
nuclear localisation of transcription factors induced by
cellular stress (Cai et al. 2008). Owing to a lack of
appropriate methodology, less is known about short-
term fluctuations in smaller molecules. An exception is
the signalling molecule Ca2+. Availability of in vivo
probes has allowed in-depth analyses of periodical
transients in free Ca2+ concentrations in the cell in
response to excitation by external signals in many
types of organisms (Berridge et al. 2000, Charpentier
et al. 2008, Dolmetsch et al. 1998, Knight et al. 1991,
Trewavas and Malho 1998). These transients display
372 Physiol. Plant.137, 2009specificity of period and duration in response to different
stimuli. However, the likely progression of the Ca2+
changes, via Ca2+-regulated enzymes, to variations in
metabolite fluxes and concentrations have so far not
been possible to study. This is generally as a result of a
lack of probes that can detect rapid fluctuations in vivo.
While this fact may be ameliorated in the near future
by the development of fluorescence-based metabolite
sensors, it should be noted that the number of sensors
with application in plant cells proves rather limited to
date (Lalonde et al. 2005, Okumoto et al. 2008).
Yeast glycolysis displays feedback loop-driven oscil-
lations in the metabolites around the phosphofruc-
tokinase reaction step, involving frequencies with
periods in both minute and hour ranges, and under
both anaerobic and aerobic conditions (Richard 2003).
Phosphofructokinase-derived glycolytic oscillations also
exist in pancreatic beta-cells, possibly linked to the
periodical exocytotic release of insulin (Bergsten 2002,
Westermark and Lansner 2003), and in cardiomy-
ocytes (Yang et al. 2008). The recorded glycolytic oscil-
lations involve, apart from fructose-6-phosphate and
fructose-1,6-bisphosphate, several other compounds that
interact with the oxidative phosphorylation system
(e.g. ADP, ATP and NAD(P)H). The slower oscilla-
tions in yeast have also been shown to synchronously
affect gene expression, including genes for respiratory
metabolism (Tu et al. 2005).
Glycolytic oscillations have, as yet, not been demon-
strated in plants. However, NAD(P)H oscillations, in
the 20-s period range, have been registered in lily
pollen tubes. In this study, troughs in NAD(P)H fluo-
rescence were observed to progress like waves from
the tube apex and precede the period of maximal
growth (Cardenas et al. 2006). Owing to the background
of different nucleotide pools and differences in fluores-
cence yield for bound and free nucleotides, an estimate
of the magnitude of oscillations can unfortunately not
be made. However, the report clearly demonstrates
the presence of short-term variations in a major sub-
strate for mitochondrial oxygen consumption in plants.
The NAD(P)H oscillations could be an effect of the
Ca2+ oscillations that also take place (Cardenas et al.
2006), which could periodically activate external mito-
chondrial NADH and NADPH dehydrogenases (Geisler
et al. 2007, Michalecka et al. 2004). Alternatively, the
NAD(P)H oscillations may be induced by other mecha-
nisms, yet could promote oscillatory changes in oxygen
consumption rate by the mitochondrial electron trans-
port chain. In a different syndrome connected to plant
respiration, oscillations have, additionally, been regis-
tered in whole plant tissues. The temperature in thermo-
genic skunk cabbage spadix was found to oscillate with
a period of 137 min over several hours after a shift in
outside temperature (Ito et al. 2004). The heat evolution
in spadices is driven by respiratory electron transport,
and therefore this observation additionally strongly indi-
cates the likelihood of oscillations in respiratory electron
transport flux. Similar examples, which are yet more
intimately related to metabolic oscillations, include the
diurnal regulation of components involved in photosyn-
thetic metabolism (Dodd et al. 2005, Gibon et al. 2006,
Smith et al. 2004, Urbanczyk-Wochniak et al. 2005),
and responsive regulation of dual pathways leading to
a common end point, such as succinate production by
early reactions of the tricarboxylic acid (TCA) cycle and
the gamma-aminobutyric acid (GABA) shunt (Studart-
Guimaraes et al. 2007) or the de novo and salvage
pathways of pyrimidine synthesis (Geigenberger et al.
2005, Jung et al. 2009).
In general, the joint mass of reports detailed above
strongly suggests that oscillations and fluctuations of two
types exist in plants as in other organisms. Controlled,
persistent beneficial oscillations exist as a means of
carrying out complex processes vital for the function of
the cell and organism. An example is the synchronisation
of exocytosis in pollen tube tip growth (Cardenas et al.
2006, 2008), consistent with an analogous function for
exocytosis during insulin release in pancreatic beta-
cells (Bergsten 2002). In a separate category, reported
transient fluctuations are induced by rapid changes in
external conditions (Ito et al. 2004, Luo et al. 2009).
Such environmental perturbations are likely to induce
undesired variation in the metabolic fluxes, which could
ultimately lead to a chaotic deregulation of the entire
metabolic system, unless dampened.
Postulates
Central metabolism is controlled by intrinsic buffer
mechanisms that dampen fluctuations
A perturbation inflicted on a complex system will induce
fluctuations in conversion rates and concentrations of
system constituents. We must therefore assume that
plant metabolism must suffer stress-induced fluctuations
in metabolic components, and consequently must
also possess mechanisms by which to minimise their
amplitude, duration and consequences. In general,
fluctuations will be more marked for molecular
species with rapid turnover and small pool sizes (e.g.
oxaloacetate, NAD(H) and UQ(H2)), as compared with
molecules with a large cellular pool that can buffer
fluctuations in rates of anabolism and catabolism (e.g.
malate and glutamate).
The most obvious oscillatory changes in plants are
governed by the regular shift between day and night.
Physiol. Plant.137, 2009 373
When metabolism is considered across a diurnal cycle,
clear oscillatory behaviour is observed at the levels
of transcript, protein and metabolite abundance (Dodd
et al. 2005, Gibon et al. 2006, Smith et al. 2004,
Urbanczyk-Wochniak et al. 2005). Such changes can
be seen for components of the photosynthetic and res-
piratory machineries, as well as numerous associated
metabolic processes. For example, diurnal cycling of
mRNA but not protein levels is a general picture for pho-
torespiratory enzymes (Rasmusson and Escobar 2007)
and also associated with starch degradation (Smith et al.
2004). The cyclic behaviours support the timing of bio-
genesis of photosynthetically associated proteins and
mediate a cyclic build-up and degradation of starch.
The latter, in turn, mediates a relatively constant car-
bon output from the leaf, and thus supply for the plant,
over the diurnal cycle (Geiger and Servaites 1994). This
can be regarded as a stabiliser of plant metabolism to
dampen the differences between day and night. On
a short timescale, substantial fluctuations in concen-
trations of carbon dioxide, oxygen, ATP and Pi have
recently been suggested for photosynthetic metabolism
in chloroplasts based on a modelling approach known
as minimization of metabolic adjustment dynamic flux
balance analysis (Luo et al. 2009). In this model, fluctu-
ations occurred as transients following perturbation of
carbon dioxide or water status, and were suggested to
be minimised by cooperative regulation of the enzymes
involved. The capability of metabolic pathways to neu-
tralise inflicted changes in substrate input is also highly
visible in other recent studies. Firstly, glutamate decar-
boxylase, one of the reactions catalysing the GABA
shunt (which can bypass the 2-oxoglutarate dehydroge-
nase and succinyl CoA ligase reactions of the TCA cycle),
is regulated by Ca2+-calmodulin and as such by Ca2+
oscillations (Baum et al. 1996). GABA itself is reported to
accumulate under conditions of stress (Sweetlove et al.
2007), and the GABA shunt is furthermore able to
fully support respiration in the near absence of succinyl
CoA ligase activity (Studart-Guimaraes et al. 2007). The
underlying principle is additionally illustrated on analy-
sis of metabolic engineering strategies that were intended
for enhancing crop yield (Morandini and Salamini 2003).
Surprisingly, many genetic manipulations that were
taken in this vein did not lead to the expected yield
benefits, apparently because the metabolic dynamics of
the pathwayneutralised effects of the transgenic mani-
pulation. For example, increasing sucrolysis resulted
in an increased sink strength, as evidenced by the
higher glycolytic flux in the transformants, but not the
anticipated increase in starch yield (Fernie et al. 2004,
Sonnewald et al. 1997, Trethewey et al. 1999). Appar-
ently, the complex combination of interacting metabolic
pathways is able to buffer the increased flux of carbohy-
drates into a given sector of central metabolism.
Further evidence for the auto-regulatory capacity of
metabolism is given by a recent extensive investigation
of steady-state in vivo metabolic fluxes and maximum
catalytic activities of 22 different enzymes of the
investigated pathways (Junker et al. 2007). This study
revealed that the enzymes exhibited a much higher
capacity than that required, to maintain the observed
fluxes. The implication of this observation is that changes
in the flux through the metabolic network do not
necessarily require dedicated regulatory mechanisms
for modulating enzyme activities over and above the
simple fluctuations in the ratio of substrate and product
concentrations of any given enzyme within the pathway.
However, it is worth noting that this only holds true under
conditions in which the in vivo activity of the enzymes
involved remains well below their total capacity. This
overcapacity of enzymes is thus likely to contribute to
the robustness of metabolic systems.
AOX neutralises stress-inflicted respiratory activity
fluctuations
Several observations strongly suggest that fluctuations
and defence mechanisms against them should exist in
the respiratory electron transport flux in plants. Insult
to mammalian cells by toxins or ischemia/reperfusion
can lead to ROS-induced ROS release, where threshold
levels of ROS lead to opening of mitochondrial channels
that lead to additional release of ROS (Zorov et al.
2000). By positive feedback, this leads to oscillations
in the membrane potential and a consequential
decrease in mitochondrial integrity and cell survival.
Propagation can also occur between closely spaced
mitochondria (Zorov et al. 2006). It is of interest to note,
that this potentially deleterious cycle takes place in an
organism that lacks AOX. This raises the issue of whether
AOX activity may disallow the insult-evoked primary
metabolic perturbation to cause ROS formation from the
mitochondrial electron transport chain, or at least not
to reach beyond the capacities of direct ROS removal
in the proximal compartments. Because knowledge on
ROS removal in plant mitochondria is incomplete,
relative ROS threshold levels in mammals and plants
can at present not be speculated on. Nevertheless,
such a function of AOX would be consistent with
the general correlation of ROS occurrence and AOX
induction (Amirsadeghi et al. 2006, Maxwell et al.
1999) induced by changes in external conditions (e.g.
stress), and with the antiapoptotic property shown for
AOX (Vanlerberghe et al. 2002).
374 Physiol. Plant.137, 2009
Fig. 2. A model for how fluctuations may spread through redox
pathways. Fluctuations (dashed red arrows) are initiated by changes
in environment (e.g. light, temperature or calcium oscillation-inducing
signals) or derived from endogenous oscillators (e.g. glycolysis), and
spread to cytosolic NAD(P)H pools. The fluctuations may be relayed
by external NAD(P)H dehydrogenases (NDex) to UQ. Fluctuations in
UQ reduction level may lead to transient ROS formation (1), that in
mammals induce ROS-dependent ROS formation. Further propagation
of a fluctuation via the cytochrome pathway (2) may lead to fluctuations
in proton motive force (pmf) that may trigger ROS fluctuations. A
rapid variation in AOX in response to fluctuations in UQ reduction
level would mediate the fluctuation (3) to an endpoint, O2. The joint
action of external NAD(P)H dehydrogenases and AOX would thus
provide the cytosolic metabolism with a redox fluctuation sink, without
inducing deleterious ROS-formation effects. Another potential sink for
fluctuations could be the UCP, in case it can respond rapidly to changes
in pmf. By analogy, putative matrix fluctuations may be mediated by
internal NAD(P)H dehydrogenases (NDin).
We suggest that AOX buffers the effect of induced
respiratory fluctuations that would otherwise disturb
or even destruct metabolic coordination and lead to
oxidative stress (Fig. 2). By allowing transfer of electrons
from reduced ubiquinone (UQH2) to oxygen, peaks of
reduction level within the UQ pool can be dissipated
into a terminal sink, avoiding ROS production that is
associated with high UQH2/UQ ratios (Møller 2001). To
carry out a buffering function adequately, it is required
that the activity of AOX can respond rapidly to any
change in the activity of the mitochondrial electron
transport chain. UQ is centrally located in the chain,
and the UQH2/UQ ratio will be immediately influenced
by changes in individual activities. AOX will respond to
changes in UQH2/UQ (Hoefnagel et al. 1995) and will
therefore instantly adjust its UQH2 oxidation activity to
electron transport fluctuations.
For rapid AOX rate changes to occur, an overcapacity
must be present, yet the capacity must be adjusted to
the reigning physiological conditions and be modified in
response to changes. This can take place on longer
term via gene expression, but for faster changes in
capacity, several regulatory mechanisms have been
described. Most important are the activation by
ketoacids, especially pyruvate (Hoefnagel et al. 1997,
Millar et al. 1996), and the activation by thioredoxin-
mediated reductive breakage of a disulphide cross-link
between the monomers of the AOX dimer (Gelhaye et al.
2004, Umbach et al. 2006, Vanlerberghe and McIntosh
1997). A cysteine residue in the N-terminal domain
mediates the cross-link, and is in its reduced form, the
target of the keto-acid binding. A second, downstream
cysteine can bind glyoxylate in vitro, but an in vivo
relevance of this binding has not been clarified (Umbach
et al. 2006). A variation exists among plant homologues
with respect to these residues. For example, the first
cysteine in tomato AOX1b is replaced by a serine, which
conveys stimulation by succinate and excludes redox
regulation (Holtzapffel et al. 2003, Umbach et al. 2006).
However, in most known homologues the cysteines
are invariant. This includes all five homologues in
Arabidopsis, which are thus expected to be regulated by
redox and stimulated by pyruvate. A transgenic approach
in which pyruvate kinase was strongly silenced in potato
tubers showed that also in intact tissue both the level of
AOX protein and the enzyme capacity depend on the
cell internal pyruvate concentration (Oliver et al. 2008).
In an independent approach, cytosolic pyruvate kinase
was reduced in tobacco leaves, yet overall respiratory
oxygen consumption was unaffected (Gottlob-McHugh
et al. 1992). Jointly, this is consistent with an in vivo
separation of activity and capacity regulation.
The inactivation by covalent disulphide bonds
allows for the presence of an inactive pool of AOX
that can be mobilised during sudden changes in
external conditions. This idea is supported by several
experimental observations. When rice plants were
treated with the fungicide SSF126 which inhibits the
cytochrome pathway, disulphide reduction of the AOX
protein was observed concomitant with a significant
increase of the AOX capacity within few hours
after treatment (Mizutani et al. 1998). Down-regulation
of the capacity of AOX has also been observed
when the availability of oxygen for mitochondrial
respiration decreased gradually from ambient oxygen
concentrations to complete anoxia within 90 min (Gupta
et al. 2009, Zabalza et al. 2009). Under such conditions,
the capacity of AOX (as well as cytochrome c oxidase)
declined accordingly. The inactivation in response to
hypoxia was not due to a limitation of oxygen at substrate
Physiol. Plant.137, 2009 375
level, as adding pyruvate to the incubation buffer lead to
an increaseof the respiratory oxygen consumption even
under near-anoxic conditions.
Despite the ability of the AOX enzyme to dramatically
increase its capacity via post-translational regulation
mechanisms, AOX protein levels increase in response
to stress (Djajanegara et al. 2002, Lennon et al. 1997,
Sieger et al. 2005, Simons et al. 1999, Vanlerberghe and
McIntosh 1992). This induction can be interpreted as
being in cellular anticipation of stress-induced metabolic
fluctuations. The increased capacity thus matches an
expected increase in peak activity in individual cells
and organelles during short transients. Analysis of
the gene expression data that are collected in the
GeneVestigator database shows that from about 165
experimental treatments (collected under the category
’stimulus’) four treatments lead to a more than two-
fold reduction of the expression of AOX1a, whereas
in more than 46 treatments, the level of expression
increased more than two-fold. Examples of experiments
that lead to a reduction of AOX1a expression include
a treatment of cold (4◦C) in combination with abscisic
acid, whereas in other experiments the photoperiod
was reduced from 16 to 8 h. Examples of conditions
that lead to an increased gene expression are salt
stress, low nitrate concentration in the nutrient solution,
photosynthesis at low CO2, and changing the light
quality using different UV-filters. A similar tendency
was shown for all Arabidopsis AOX homologues (Fig. 3).
This observation is in agreement with earlier studies
from which responsiveness of AOX to external triggers
was concluded. Especially AOX1a and to a lesser extent
AOX1d were concluded stress-induced, whereas AOX2
were responsive only to particular treatments (Clifton
et al. 2006).
Another important issue to discuss is whether the
cytochrome pathway in conjunction with UCP may
allow buffering of stress-induced metabolic fluctuations,
and thus exclude the necessity of an AOX-mediated
buffering. Plant UCP has been associated with ROS
metabolism and has been shown to be essential for
photosynthetic metabolism (Brandalise et al. 2003, Con-
sidine et al. 2003, Kowaltowski et al. 1998, Sweetlove
et al. 2006). Yet, as described above, mammalian cells,
which contain UCP proteins but not AOX, allow large
fluctuations in ROS levels (Zorov et al. 2006). A mecha-
nistic explanation for the difference is that UCP should be
more slowly responsive to UQH2/UQ shifts than AOX is,
the latter being a direct alternative electron sink whereas
UCP depends on the cytochrome pathway activity. UCP
is controlled in vitro by fatty acids, superoxide and
free nucleotides (Vercesi et al. 2006). As changes in the
concentrations in these components will be indirect
Fig. 3. AOX gene expression is generally up-regulated upon various
stresses. Log2-expression ratios of AOX genes from Arabidopsis
plants that were treated with various biotic and abiotic stresses
were compiled from approximately 165 Affymetrix micro-arrays as
collected in the GeneVestigator database under the category ’stimulus’
(http://www.genevestigator.com). The variation in gene expression as
induced by various treatments was visualised using a box plot showing
the median, lower and upper quartile, and the sample minimum and
maximum. For AOX1a, b and d, a skewed distribution can be observed
with more treatments leading to up-regulation of AOX gene expression
than to down-regulation.
consequences of, among others, changes in UQH2/UQ,
regulation of UCP activity by these parameters will be
slower than a direct substrate (UQH2) regulation of AOX.
Thus, AOX can buffer rapid fluctuations in UQH2/UQ
ratios. On the other hand, UCP may be involved in
buffering slower fluctuations and ameliorating chronic
oxidative stress, i.e. extreme situations where ROS pro-
duction has become larger than the capacity of the ROS
scavenging systems and oxidation products may inacti-
vate AOX (Rhoads et al. 2006, Winger et al. 2005). This
is consistent with the previously suggested partitioning
of cellular redox maintenance work between AOX and
UCP (Fernie et al. 2004).
In summary, AOX capacity is regulated by several
mechanisms at protein as well as transcript level.
The massive responsiveness of especially AOX1a to
many different stresses indicates that a substantial set
of gene regulatory pathways are involved, consistent
with the prediction of a range of cis-acting regulatory
elements (Ho et al. 2007). In the light of that the
AOX activity in vivo is relatively independent of
AOX protein levels and capacity, the magnitude of
376 Physiol. Plant.137, 2009
regulation involved in determining capacity clearly
points at a demand for capacity control that is
independent from activity control. Our suggestion is
therefore that the capacity inductions upon stress
treatment are a mechanism to increase robustness
of the metabolic system in anticipation of metabolic
fluctuations. Increased fluctuations would not by
necessity increase tissue average activity, as measured
in vivo over longer times and in large tissue samples
using isotope partitioning (Guy et al. 1989). Thus AOX
capacity and in vivo activity should be considered
separate parameters each individually having diagnostic
value for predicting roles of AOX in various physiological
situations. This is in analogy with the diagnostic value of
diurnal cycling in RNA levels, but not protein levels, for
photosynthetically associated genes (Gibon et al. 2006,
Rasmusson and Escobar 2007, Smith et al. 2004, Turner
et al. 1993).
A role of NADH dehydrogenases in mediating
fluctuations to AOXs
In the sub-cellular microenvironment, AOX is suggested
to have a major function in ameliorating damages due
to rapid fluctuations in metabolic flux and metabolite
levels. The UQ reduction level fluctuation may originate
from metabolic imbalances in the cytosol or the
chloroplast, and be relayed to UQ by external NADH
dehydrogenase (Fig. 2). This scenario would suggest
cooperative roles for external NADH dehydrogenases
and AOX in ameliorating fluctuation damages. In this
vein, it is of interest that mitochondria from higher
animals lack both alternative NAD(P)H dehydrogenases
and AOXs. The consequence of lacking an external
NADH dehydrogenase is that fluctuations in cytosolic
NADH reduction will be less readily propagated to
the UQ pool. In the absence of external NADH
dehydrogenases, propagation of fluctuations may still be
possible via redox shuttles (Krämer and Palmieri 1989,
Rasmusson et al. 2008, Rigoulet et al. 2004), but the
pool sizes and the demand for transport of the shuttle
intermediates (malate, aspartate and triose phosphate)
may substantially decrease the responsivity towards
fluctuations. Another indication for the cooperation
of NADH dehydrogenases and AOX comes from that
individual NADH dehydrogenase genes has been shown
to correlate at mRNA level to specific AOX genes
in Arabidopsis. In a set of articles, a correlation
in treatment responses has displayed a pair-wise
expression of NDB2 with AOX1a (Clifton et al. 2005)
and NDB4 with AOX1c (Ho et al. 2007). The metabolic
or enzymatic mechanistic background to the co-
regulation is, however, presently all but clear. Both
NDB2 and NDB4 specifically oxidise NADH, but NDB2
is calcium-regulated and NDB4 is independent of
calcium (Geisler et al. 2007), indicating a separation
in metabolic function between the two proteins.
In addition to the previously reported co-expression
pairs (Clifton et al. 2005, Ho et al. 2007), we found
that also the two remaining AOX1 genes co-express
with individual NADH dehydrogenase genes over
a series of tissue types and developmental stages
(Fig. 4). By means of a gene expression tree, co-
expression was analysed for Arabidopsis genes encoding
known and putative alternative components of the
mitochondrial electron transport chain (like alternative
NAD(P)H dehydrogenases, AOXs, UCPs, and various
dehydrogenases directly oxidising carbon metabolites;
see Rasmusson et al. (2008) for review) as well as
subunits of complex Ithat make up its NADH-
binding domain. Clearly, each AOX1 gene is co-
regulated with a specific gene encoding an alternative
NADH dehydrogenase. This co-regulation cannot be
explained by a general tendency of all genes encoding
mitochondrial proteins, as a distinct variation in
expression patterns could be observed among the
different genes that were analysed.
Possible reasons for pair-wise expression of NADH
dehydrogenases and AOXs may include pair-wise
protein interactions, or a pair-wise fit of kinetic
parameters that could adapt an expressed enzyme pair
to cell-specific levels in substrates and affectors. Thus,
the presence of specific NADH dehydrogenase/AOX
pairs may mediate increased electron flux from cytosolic
NADH to oxygen. At the present status of biochemical
knowledge for the individual homologues, including the
conservation of potentially regulatory cysteines in all
Arabidopsis AOXs, it is not possible to speculate further
on distinct functions of the NADH dehydrogenase/AOX
pairs. Nevertheless, the expressional affiliation of each
AOX1 gene with an individual NADH dehydrogenase,
whereas the expression of AOX2 does not correlate to
any other gene investigated, demands a physiological
explanation. AOX1b and AOX1d are very lowly
expressed in most tissues. However, the gene pair
AOX1b/NDB3 is specifically and harmoniously highly
expressed in male organs. Likewise, AOX1d and
NDA2 show distinct similarities in the distribution
over all tissues except seeds, with obvious specifically
elevated signals seen in sepals and senescing leaves
(Fig. 4B, C). NDB3 and NDA2 are external and internal
enzymes (Elhafez et al. 2006), respectively, and both
have been predicted to oxidise NADH (Michalecka et al.
2004). In the suggested scenario of AOX having a
role in ameliorating hazardous metabolic fluctuations,
a catalytic cooperation between individually fitted
Physiol. Plant.137, 2009 377
Fig. 4. Co-expression analysis of Arabidopsis genes encoding alternative components of the mitochondrial electron transport chain. A clear pair-wise
co-expression of genes encoding AOX1 proteins and individual NADH dehydrogenases with elevated expression levels in the same tissues was
consistently observed. The analysis was performed using the expression browser tool at the Bioarray Resource (BAR) at www.bar.utoronto.ca (Toufighi
et al. 2005). (A) Gene expression tree with a heat map of the corresponding gene expression data. The conditions tested are according to the ’extended
tissues series’ dataset defined by BAR including both a developmental series and a wide variety of tissue types. However, guard and mesophyll
cell-specific expression data were excluded. Annotations in blue indicate previously described co-expression pairs, whereas names in red indicate
newly recognised co-expression patterns. The genes analysed encode AOX and NDA-C proteins, UCPs [PUMP1-6 nomenclature according to Vercesi
(2006)], electron transfer flavoprotein: quinone oxidoreductase (ETFQO), putative glycolate dehydrogenases (GlyDH), FAD-glycerol-3-phosphate
dehydrogenase (G3PDH), proline dehydrogenase (ProDH) and galactono-gamma-lactone dehydrogenase (GLDH). The NADH-binding domain subunits
(24, 55 and 76 kDa) of complex I were included for comparison. (B) The distribution of mRNA levels for AOX1b and NDB3 are shown in more detail
for the same tissue profile as in panel A. In both cases, expression was highest in stamen, pollen and embryo. (C) Detailed overview of mRNA levels
for AOX1d and NDA2. Highest levels were observed in sepals and senescent leaves.
external NADH dehydrogenases and AOXs should be
functionally advantageous. Fluctuations derived from
the metabolic systems in the mitochondrial matrix
would by analogy be mediated by internal NADH
dehydrogenases. However, though fluctuations in matrix
metabolism are likely to exist (e.g. owing to the action of
glutamate carboxylase, as described above), they have
not been demonstrated yet.
Perspectives
The hypothesis presented here is a further development
of especially a previous hypothesis where AOX was
postulated to ameliorate UQH2/UQ redox imbalances
and thus avoid ROS formation (Purvis and Shewfelt
1993). This hypothesis has been very successful in
explaining the AOX gene expression effects upon
stress treatments, yet has not been able to explain
the observed inconsistency between in vivo activity
measurements and gene expression analyses. The novel
hypothesis presented here introduces dynamic aspects
of metabolic fluctuations into the functional dissection
of AOX, in order to account for observations at all
complexity levels. The resulting model, that AOX
may counteract metabolic fluctuation, complements
other potential modes of metabolic stabilisation, for
example, the model of cooperative regulations that was
suggested to balance stromal metabolism in plastids (Luo
378 Physiol. Plant.137, 2009
et al. 2009). Also, considering the energy-dissipating
nature of AOX, an induction of AOX to counteract
stress-imposed metabolic fluctuation is in line with a
postulated intrinsic trade-off between robustness and
performance (e.g. growth) in biological systems (Kitano
2004).
As discussed in section Introduction, AOX is one of
the few enzymes which allows in vivo measurement
of its activity because of its discrimination against
heavy stable isotopes of oxygen (Guy et al. 1989). Of
course, this methodology has opened many doors to
obtain a better understanding of the role of AOX in
the regulation of respiration. Nevertheless, owing to its
demands for relatively large tissue amounts and for long
integrations times, the presently available in vivo activity
measurement technology using 18O partition cannot
address all aspects of AOX function in vivo. Currently, it
is not possible to detect any rapid temporary changes in
peak activities directly following a stress application. The
technology would greatly benefit from integration into
a setup of technologies for time-resolved single cell O2,
metabolite and flux measurements, and gene expression
analyses.
An important outcome of the reasoning here relates to
the interpretation of global analyses of RNA and protein
levels. Simplified assumptions of correlation between
in vivo activities and protein and RNA levels must be
refrained from, but at the same time, a too strong focus
on regulation of average flux should be avoided. As a
consequence, the importance of regulation of capacity
per se should be taken into consideration.
Further developments in the understanding of
metabolic systems demand an improvement of time-
resolved in vivo probes for a set of potentially rapidly
fluctuating intermediates (e.g. NADH). These should
be combined with more quantitative extraction-based
methods. We predict that for a stress condition that
leads to increased AOX protein levels, but not in
vivo AOX activity, elevated oscillations or fluctua-
tions should be observed for the membrane potential,
NAD(H) reduction level, UQ reduction level and/or
local, mitochondrial oxygen concentrations. The fluctu-
ations should be detectable shortly after the application
of the stress, and disappear after induction of AOX has
taken place.
Acknowledgements – Work on respiration in the Rasmus-
son laboratory is financially supported by grants from the
Swedish Research Council while that of the van Dongen
and Fernie laboratories are supported by a shared SFB grant
of the Deutsche Forschungs Gemeinschaft SFB 429 and the
Max Planck Society.
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