Introduction
Metabolism is the basis of all cellular processes. Metabolic systems
must be optimized to maintain function, regardless of the environmental
conditions experienced. Plants are exposed to environments which
fluctuate over timescales varying from seconds to decades, with
environmental parameters such as light and temperature directly
impacting metabolism. As such, plants provide an excellent example in
which to understand how metabolic systems can be optimized to tolerate
environmental change. To understand such optimization, we can use
approaches taking form systems engineering.
In systems processes, the quality of a function or product is referred
to as its reliability. The quantity, on the other hand, is dependent on
the system’s robustness (King and Jewett, 2010). As an analogy, we can
consider a business process where the aim of the process is to produce
as many high-quality products as possible, despite fluctuations in
operating conditions. A reliable process ensures that the quality of the
products is consistently high, whereas a robust process ensures that as
many products as possible are being produced. If all low-quality
products are discarded then, to make a profit, the system must first be
reliable and then robust (Bakera et al. , 2008; King and Jewett,
2010). This is comparable to plants optimizing seed production (Sadras
2007). Only viable seeds will germinate. Thus, features of reliability,
which ensure that seeds germinate, are of primary concern. Features of
robustness, which ensure that as many seeds as possible are produced,
are secondary. In fact, in the model plant species Arabidopsis
thaliana the trade-off between seed size and number is minimal (Gnanet al. , 2014), suggesting that these traits may be selected for
independently.
This distinction between robustness and reliability is considered
crucial in many disciplines, including systems engineering and product
development (Yassine, 2007; King and Jewett, 2010). With regards to
metabolism, the two concepts are rarely distinguished, and the
reliability of metabolic networks has not been formally quantified.
However, reliability engineering frameworks exist which can be applied
to metabolic networks.
Failure mode and effect analysis (FMEA) is a framework that is commonly
applied to study the reliability of a product of a process, and is
considered to be one of the most effective ways of conducting a failure
analysis (i.e. collecting data on why a system might fail). FMEA breaks
the system down into individual components or single processes and
quantifies the risk that each of them poses to the system (Stamatis,
1995; Rausanda and Øienb, 1996). The risk of each component (or process)
is assessed by studying the potential causes of failure and effects that
these would have on the system. To quantify this risk, each component is
assigned a probability of failure (P ) and a severity score in the
event of failure (S ). The risk (R ) of each component is
then calculated such that R = P × S . Components which are
essential for the system to function are considered high-risk. A
well-engineered system has control mechanisms to regulate the
functioning of the high-risk components, to minimize the impact of
failures. Such control mechanisms can be inherent features of the system
(e.g. the closing of one valve automatically opens another) or can be
sensed and regulated (e.g. a sensor perceives that one valve is closed
and opens another).
The benefit of the FMEA framework is that it can be adapted to conduct a
failure analysis of any system, making it also suitable for studies of
metabolic systems. We can think of individual metabolites as the
components that constitute a system. The metabolic system may represent
the whole of metabolism or a subset of metabolites, depending on the
processes of interest. The components (i.e. the metabolites) need to be
present within specific concentration ranges, or the system will not
function correctly. Failure of the system might mean a failure to
produce certain products at an appropriate rate (e.g. a failure to
synthesise sufficient ATP to support cell maintenance) or the
accumulation of metabolites to toxic concentrations. We would expect the
concentration of high-risk metabolites to be tightly regulated to avoid
systems failures. The probability of failure of an individual
metabolite, i.e. the probability that its concentration falls outside an
acceptable range, could be considered a function of upstream metabolites
and the severity of failure as a function of downstream metabolites.
Thus, the risk score for each metabolite arises from the underlying
metabolic network structure, the highest risk metabolites being those
which are heavily dependent on the production of other metabolites
whilst at the same time supporting the production of many others.
Reliability engineering has potential in trying to understand how
metabolic systems respond to environmental stress. For example, it is
predicted that, due to anthropogenic climate change, plants will
increasingly be exposed to extreme weather events, including both high
and low temperature periods (Changnon et al ., 2000; Trenberth,
2012). These extreme conditions will affect crops yield, threatening
food security for a growing world population (Lesk et al. , 2016;
Powell and Reinhard, 2016). Thus, the identification of traits which
determine the reliability of key acclimation processes becomes a
priority. In this context, plant responses to single temperature
stresses have been well characterized (e.g. Hikosaka et al. ,
2006; Yamori et al. , 2014; Ding et al. , 2020). A few
studies have also compared the effects of warm and cold temperature
stresses on Arabidopsis leaves, showing that the two processes share
common features (Kaplan et al. , 2004; Zhang et al. , 2019).
Nevertheless, how metabolism adjusts across the entire physiological
temperature range of a plant is still largely unknown.
When exposed to short term environmental changes, plants canregulate enzymes to ensure metabolic function is maintained. Over
longer periods, plants acclimate to their environment, altering
the concentration of different enzymes to match the prevailing
conditions (Webber, 1994; Stitt and Hurry, 2002). For example, when a
plant is transferred from warm to cold conditions, enzyme activities are
slowed, reducing metabolic activity. This would be reflected, for
example, in a reduction in the light-saturated rate of photosynthesis.
Following acclimation, plants transferred to the cold typically increase
the concentration of key enzymes, allowing them to achieve a similar
rate of photosynthesis at low temperature to that previously seen in the
warm (Hurry et al. , 2000; Strand et al. , 2003; Dysonet al. , 2016). This results in an increase in the photosynthetic
capacity (Pmax ) (Athanasiou et al. , 2010;
Dyson et al. , 2016). Conversely, in response to increased
temperature, plants decrease their Pmax , allowing
them to reallocate resources to other processes (Herrmann et al. ,
2019b).
During the day, plants fix atmospheric carbon through photosynthesis. A
large proportion of that fixed carbon is accumulated in the leaf during
the photoperiod. Overnight this is then remobilised to support growth
and cell maintenance. Under different environmental conditions, plants
vary the form in which photoassimilates are stored. Arabidopsis stores
carbon primarily in the form of the polysaccharide starch and the
organic acids fumarate and malate (Chia et al ., 2000; Smith and
Stitt, 2007; Zell et al ., 2010; Pracharoenwattana et al .,
2010). Starch accumulation and degradation have been studied extensively
as this is the major non-structural carbohydrate in plants (Smith and
Stitt, 2007; Streb and Zeeman, 2012). Less is known about malate and
fumarate. Both metabolites are intermediates of the tricarboxylic acid
cycle in the mitochondria; however, it has also been shown that they
accumulate in the vacuole diurnally (Kovermann et al ., 2007;
Fernie and Martinoia, 2009). In the cytosol, malate is made from or
converted to oxaloacetate, citrate, isocitrate, cis-aconitate, fumarate,
α-ketaglutarate, glutamic acid and pyruvate (Arnold and Nikoloski,
2014). It provides carbon skeletons for various biosynthetic pathways,
including amino acid synthesis (Zell et al. , 2010). In the
cytosol, fumarate is synthesised by a cytosolic isoform of the enzyme
fumarase, FUM2 (Pracharoenwattana et al ., 2010). It is made from
malate during the day and is assumed to be converted back to malate
during the night (Pracharoenwattana et
al ., 2010; Zellet al ., 2010; Dyson et al. , 2016). Fumarate accumulation
in the cytosol has previously been linked to growth on high nitrogen
(Pracharoenwattana et al ., 2010) and photosynthetic acclimation
to cold (Dyson et al. , 2016); however, its role in these
processes is unclear (Chia et al. , 2000).
The fum2 Arabidopsis mutant is a cytosolic fumarase knockout
(Pracharoenwattana et al ., 2010). Previously, we showed that
fumarate and malate accumulation increase in response to cold, and that
the fum2 knockout mutants, which do not accumulate fumarate, are
unable to acclimate photosynthetic capacity in response to low
temperature (Dyson et al ., 2016). The mutant accumulates higher
levels of both malate and starch under cold stress. It has also been
shown that plants which accumulate more fumarate accumulate less starch
(Chia et al ., 2000; Pracharoenwattana et al ., 2010; Dysonet al., 2016) and that fumarate accumulation is proportional to
biomass production at low temperature (Scott et al., 2014).
Therefore, fumarate accumulation participates in the cold acclimation
response, although its specific role in this remains to be elucidated.
In this study, we demonstrate that FUM2 activity is essential not only
for low temperature acclimation but also for high temperature responses.
Using three genotypes, with varying capacities to accumulate fumarate,
we have examined acclimation across the physiological temperature range.
We have measured photosynthetic and metabolic responses of the Col-0
wild-type, which accumulates high levels of fumarate, a fum2knockout, which does not accumulate fumarate (Pracharoenwattana et
al ., 2010), and the C24 wild-type, which accumulates intermediate
levels of fumarate compared to Col-0 (Riewe et al ., 2016). To
understand their responses to temperature treatment, we combine the
novel application of FMEA with a more widely used kinetic modelling
approach (Saa and Nielsen, 2017; Herrmann et al., 2019a), identifying
cytosolic malate as a high-risk component and its metabolic neighbour,
cytosolic fumarate, as a low-risk one. We propose that fumarate
synthesis acts as a fail-safe, maintaining malate concentrations within
safe limits and ensuring that metabolic functions continue across a wide
range of conditions. We discuss that FMEA is a valuable approach to
understanding metabolic system with possible widespread applications in
biotechnology and systems biology.