Dynamic assembly of malate dehydrogenase–citrate synthase multienzyme complex in the mitochondria

Enzymes catalyzing sequential reactions can interact to form a multienzyme complex, often called a ‘metabolon’, which channels the metabolic intermediate within the complex. Metabolite channeling can mediate pathway reactions by concentrating the reaction intermediates near the enzyme active site and sequestering them from competing reactions (Obata, 2020; Spivey and Ovádi, 1999; Wheeldon et al., 2016). Thus, the dynamic assembly of multienzyme complex is believed to quickly regulate cellular metabolic flux by changing their degree of association and dissociation without involving time-consuming and resource-demanding protein synthesis, degradation, and modification (Halper and Srere, 1977; Morgunov and Srere, 1998; Obata, 2019; Omini et al., 2024; Srere, 1985; Sumegi et al., 1993). However, limited experimental evidence shows metabolon dynamics and the mechanisms regulating its association.

The tricarboxylic acid (TCA) cycle multienzyme complex composed of malate dehydrogenase (MDH) and citrate synthase (CS) is conserved in all organisms analyzed so far, including animals, bacteria, yeast, and plants (Meyer et al., 2011; Morgunov and Srere, 1998; Robinson et al., 1987; Zhang et al., 2017). MDH and CS catalyze the key steps in respiratory metabolism as their reactions comprise the carbon entry steps from the glycolytic processes as malate and acetyl-CoA, respectively (Sweetlove et al., 2010). MDH–CS complex is considered a dynamic protein complex (Bulutoglu et al., 2016; Omini et al., 2021) that protects the channeled intermediate, oxaloacetate, from degradation and competing pathways in the bulk phase of the cell (Bulutoglu et al., 2016). Importantly, MDH–CS complex formation and oxaloacetate channeling are considered essential for the forward TCA cycle flux to occur since MDH forward reaction to synthesize oxaloacetate is thermodynamically unfavorable in physiological conditions (Fukuda et al., 2008). These findings suggest that the dynamic association and dissociation of the MDH–CS multienzyme complex play a role in coordinating the TCA cycle and associated metabolic pathways. To address this hypothesis, this study focuses on demonstrating the in vivo dynamics of the MDH–CS complex in relation to respiratory states, a crucial aspect of achieving metabolic regulation. Another key requirement, the direct impacts of the metabolon on pathway flux, will be addressed in a separate study.

This study utilized the yeast mitochondrial MDH (MDH1)–CS (CIT1) multienzyme complex as the model. Budding yeast, Saccharomyces cerevisiae, dramatically rearranges its central carbon metabolism in response to changes in nutrient availability and stress conditions (Causton et al., 2001; Rep et al., 1999; Rolland et al., 2002). Remarkably, yeast respiratory metabolism, involving aerobic respiration and fermentation, is highly adaptable to substrate availability (Pfeiffer and Morley, 2014). When oxygen and non-fermentable respiratory substrates, such as acetate, are abundant, aerobic respiration is upregulated, with increased carbon flux through the TCA cycle (Gerstmeir et al., 2003; Lee et al., 2011). In the presence of fermentable sugars, such as glucose and fructose, the fermentation pathway is upregulated, and aerobic respiration is repressed, even when oxygen is available (Zhang et al., 2022). Furthermore, aerobic respiration and fermentation cooperate in the presence of a poorly fermentable carbon source like raffinose (Guaragnella et al., 2013). Application of fermentable substrates to respiring yeast cells induces a substantial shift from aerobic respiration to fermentation, known as the ‘Crabtree effect’, involving a massive transcriptional rearrangement of enzyme genes related to aerobic respiration and fermentation (Gancedo, 1998; Ronne, 1995; Thierie and Penninckx, 2010). This inducible metabolic shift in yeast makes it an ideal model for investigating the dynamic relationship between the MDH1–CIT1 multienzyme complex and TCA cycle flux. Understanding the mechanisms of the Crabtree effect is crucial for metabolic engineering applications to enhance the supply of TCA cycle intermediates for desired product synthesis (Yin et al., 2015) and for gaining insights into metabolic regulation in other eukaryotic systems, including cancer cells, which exhibit metabolic shifts similar to the Crabtree effect (Diaz-Ruiz et al., 2011).

Various allosteric regulators and environmental factors, including pH and redox state, affect MDH and CS enzyme activities (Beeckmans, 1984; Williamson and Cooper, 1980). These factors alter protein conformations, influencing the MDH–CS complex affinities. Previous in vitro studies have demonstrated that NAD⁺, malate, succinate, acetyl-CoA, α-ketoglutarate, and acidic pH enhance the MDH–CS interaction, while NADH, citrate, and basic pH weaken it (Omini et al., 2024; Omini et al., 2021; Tompa et al., 1987; Wu and Minteer, 2015). These allosteric regulators promote the MDH–CS interaction when the TCA cycle substrates are abundant and products are limited, indicating the role of the MDH–CS multienzyme complex in the TCA cycle feedback regulation. The electron transport chain (ETC) activity affects the mitochondrial matrix pH, redox state, and ATP content and is closely related to cellular respiratory flux distributions (Santo-Domingo and Demaurex, 2012). Therefore, the MDH–CS multienzyme complex may associate and dissociate according to the microenvironment in the mitochondrial matrix, such as metabolite concentrations, pH, redox state, and energy levels.

We adopted a NanoBiT protein–protein interaction assay system (Dixon et al., 2016) to monitor real-time multienzyme complex interactions in living yeast cells using continuous microplate readings. The NanoBiT split-luciferase system is based on the 18 kDa NanoLUC luciferase derived from deep-sea shrimp. The small size of this reporter is designed to minimize structural and behavioral interference with the target proteins, while offering a dynamic range and brightness approximately 150-fold greater than those of conventional luciferases, enabling detection of weak, transient interactions. The NanoBiT subunits exhibit low intrinsic affinity (K_d = 190 µM) and rapid association and dissociation kinetics (k_on = 500 M^–1 s^–1, k_off = 0.2 s^–1), ensuring that the luminescent complex formation is dictated by the interaction of the fused proteins rather than the affinity of the tags themselves (Dixon et al., 2016). While other fluorescence-based methods, such as FLIM-FRET and BRET, are widely used for in vivo protein–protein interaction studies, they are limited in yeast microplate assays due to high cellular autofluorescence. The NanoBiT system overcomes these constraints, providing semi-quantitative monitoring of relative fluctuations in protein interaction levels over time across a diverse range of metabolic conditions.

In this study, the NanoBiT system examined the MDH1–CIT1 multienzyme complex association in the presence of various carbon sources and respiratory inhibitors. The results showed the relationship between respiratory activity and the dynamic MDH1–CIT1 complex assembly. Analysis of the mitochondrial matrix microenvironment using fluorescent biosensors and cellular metabolite profiles revealed crosstalk among cellular respiratory status, the MDH1–CIT1 interaction, and the mitochondrial matrix microenvironment, including pH. These findings indicate the functional dynamics of the MDH–CS multienzyme complex and how they coordinate with the metabolic states of the TCA cycle and adjacent pathways.

Here, we report the relationships between respiratory activity and mitochondrial MDH1–CIT1 multienzyme complex interaction in living yeast cells. MDH1–CIT1 multienzyme complex dynamically associated and dissociated in response to respiratory status. This study is the first in vivo evidence in any organism showing the dynamic association and dissociation of the TCA cycle metabolon in real time. The MDH–CS metabolon, specifically, is considered essential for the forward TCA cycle flux due to its ability to overcome the unfavorable thermodynamics of MDH reaction under physiological oxaloacetate concentrations (Beeckmans and Kanarek, 1981; Huang et al., 2018). The results of this study demonstrate that the MDH1–CIT1 multienzyme complex associates when the TCA cycle is active and dissociates when it is suppressed, supporting the idea that metabolon dynamics are related to pathway regulation. Given that the MDH–CS metabolon is proposed to enhance pathway reactions (Bulutoglu et al., 2016; Fukuda et al., 2008), its dynamic assembly and disassembly would influence pathway activity. Together, these observations align with the proposed theory that metabolon dynamics act as a regulatory mechanism of metabolic flux (Noor et al., 2014; Obata, 2020).

The MDH1–CIT1 complex dissociated under conditions suppressing the TCA cycle. TCA cycle inhibition by arsenite and AOA-induced dissociation of the MDH1–CIT1 complex (Figure 3B, C). In a physiological condition, the MDH1–CIT1 complex dissociated when Crabtree-inducing fermentable sugars were supplied to the media (Figures 1 and 2, Figure 2—figure supplements 1–3). The Crabtree effect redistributes respiratory flux to stimulate fermentation and repress aerobic respiratory pathways, including the TCA cycle (Gancedo, 1998; Ronne, 1995). The metabolic shift by the Crabtree effect is a concerted action of multiple mechanisms, such as the suppression of enzyme gene expressions and the inhibition of mitochondrial transporters (Ahmadzadeh et al., 1996). Interestingly, glucose-induced rapid repression of mitochondrial respiration occurs at a rate that is too great to be explained only by the inhibition of enzyme synthesis, and additional forms of regulation are anticipated to be involved (Ahmadzadeh et al., 1996). The dissociation of the TCA cycle metabolon coincided with this repression (Figure 2A), highlighting metabolon dynamics as a potential additional mechanism to swiftly downregulate mitochondrial respiration during the Crabtree effect.

On the other hand, the MDH1–CIT1 complex interaction was enhanced under conditions that activate the TCA cycle. An increase in TCA cycle activity by the addition of acetate (Orlandi et al., 2014; Vilela-Moura et al., 2011) enhanced the MDH1–CIT1 complex interaction (Figure 3A), suggesting the role of the MDH–CS metabolon in facilitating TCA cycle activity.

However, ETC inhibitions also increased MDH1–CIT1 interaction (Figure 4A–C), despite their inhibitory effects on oxidative respiration (Figure 4—figure supplement 1) and the TCA cycle. This apparent contradiction can be explained by the transient nature of the response. The rise in MDH1–CIT1 interaction following ETC inhibition was transient, suggesting that it does not represent steady-state changes in respiratory activity. Instead, it is more likely linked to acute changes in the mitochondrial matrix microenvironment. Notably, the timing of the increased interaction coincided with changes in matrix pH, supporting a role for pH-dependent regulation (see below). These findings align with our model, which posits that the MDH1–CIT1 interaction is controlled by microenvironmental changes accompanying shifts in respiration. In the case of ETC inhibition, transient perturbations of the matrix microenvironment appear to be disconnected from steady-state respiratory output, thereby promoting MDH1–CIT1 interaction despite overall inhibition of respiration.

We also investigated the molecular mechanisms regulating MDH1–CIT1 complex dynamics. We previously reported that protein conformation changes induced by the solution pH and allosteric regulators affect porcine MDH–CS complex interaction in vitro (Omini et al., 2021). The recombinant yeast MDH1 and CIT1 showed similar responses to the porcine enzymes; MDH1–CIT1 affinity was higher in low pH and in the presence of upstream TCA cycle intermediates (malate and fumarate) but disrupted in the presence of reaction product (citrate; Figure 5). These factors can affect the TCA cycle metabolon formation in vivo. The relationship between these factors and the MDH1–CIT1 interaction observed in this study is summarized in Figure 6A.

Figure 6

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We observed an enhanced MDH1–CIT1 interaction under conditions that lower mitochondrial matrix pH in vivo. The mitochondrial matrix pH decreased after adding acetate, corresponding to the enhanced MDH1–CIT1 interaction (Figure 3A, D). The time courses of the temporal increase in MDH1–CIT1 interaction and pH decrease were inversely related following the complex III and IV inhibitions (Figures 4B, C, E, F, 6A). The decrease in the pH from 7.5 to 6.0 ranges is remarkable since the K_d of MDH1–CIT1 association decreases by an order of magnitude within this pH range (Figure 5A). The mitochondrial matrix acidification under respiratory conditions likely favors the MDH1–CIT1 interaction. On the other hand, the mitochondria matrix pH was maintained around 7.2 in the presence of glucose (Figure 2D), which likely weakens the MDH1–CIT1 complex interaction. These results indicate that mitochondrial matrix pH, which is related to proton transport activity by the ETC, can stabilize or destabilize the MDH1–CIT1 complex, thereby regulating the MDH1–CIT1 interaction in response to respiratory activity (Figure 6B). Enhancing the MDH1–CIT1 interaction with the uncoupler CCCP (Figure 4—figure supplement 4) also supports the prominent effect of pH.

Nevertheless, mitochondrial pH may influence the MDH1–CIT1 association, but it is not always the predominant factor regulating the interaction. The observed changes in pH and interaction do not consistently occur simultaneously following the Crabtree induction and acetate supplementation (Figures 2 and 3). TCA cycle inhibitions reduce the MDH1–CIT1 interaction without significantly affecting mitochondrial matrix pH (Figure 3B, C, E, F). These findings suggest that mitochondrial matrix pH can be a contributing factor, either favoring or disfavoring the interaction, rather than the primary regulator, which appears to be the direct induction and dissociation of the MDH1–CIT1 complex.

Little evidence in this study supports a primary role for mitochondrial matrix redox state or ATP levels in controlling MDH1–CIT1 interaction. Our in vitro analyses demonstrate that NAD(H) and ATP can modulate MDH1–CIT1 interaction (Figure 5—figure supplement 1B; Omini et al., 2021), suggesting that these factors may contribute to complex dynamics. Although the lower matrix ATP level following glucose supplementation (Figure 2D) and matrix reduction upon arsenite and AOA-induced TCA cycle inhibition (Figure 3H, I) associated with decreased interaction (Figures 2A and 3B and C), these relationships were not consistently observed across conditions (Figure 6A), indicating that neither redox state nor ATP levels predict complex association in vivo. However, we cannot rule out their contribution under specific metabolic contexts not captured in the current study.

The TCA cycle intermediates and cofactors can also regulate the MDH1–CIT1 interaction, considering their effects on the interaction in vitro (Omini et al., 2021). The yeast enzymes showed responses to malate, α-ketoglutarate, succinate, and citrate (Figure 5B, Figure 5—figure supplement 1) similar to the enzymes of other organisms (Omini et al., 2021; Tompa et al., 1987; Wu et al., 2015), while fumarate is newly identified as an effector of the MDH–CS interaction. Especially, fumarate, malate, and citrate showed significant influences on yeast enzymes (Figure 5B). Cellular levels of these effector metabolites significantly altered under the conditions tested in this study (Figures 2E, 3M). Increased and decreased levels of malate and fumarate following the TCA cycle and ETC inhibitions (Figures 3M and 4M), respectively, are likely related to the MDH1–CIT1 interaction since malate enhances the interaction (Figure 5D; Omini et al., 2021). These results indicate the involvement of metabolite effectors, such as malate and fumarate, in the regulation of the MDH1–CIT1 interaction (Figure 6). However, their precise effects must be evaluated through site-specific, time-dependent perturbation and quantification of metabolite levels in the mitochondrial matrix, as a whole-cell metabolite profile may not reflect the metabolite concentrations that directly influence the complex association.

Aside from MDH1–CIT1 interaction dynamics, our results highlight the complex regulation of TCA cycle metabolism. The activation and inhibition of respiratory activity resulted in diverse metabolic phenotypes (Figure 6A, Source data 1), where intermediate levels did not simply reflect overall pathway activity. This complexity stems from the distinct mechanisms of each inhibitor, such as arsenite affecting α-ketoglutarate dehydrogenase and AOA disrupting the malate-aspartate shuttle (Cavero et al., 2003; Eto et al., 1999; Lee et al., 2011; Liu and Butow, 2006), off-target effects of the inhibitors, and from the adaptive reorganization of intersecting metabolic networks to bypass local blockades (Herrgård et al., 2008; Lehtinen et al., 2013; Rogers et al., 2021). These diverse metabolic phenotypes allow us to assess the relationships between metabolites and metabolon assembly independently of respiratory activity.

This study demonstrates that the TCA cycle MDH1–CIT1 multienzyme complex dynamically interacts in response to the cellular respiratory status. Cues of cellular respiratory state may be transmitted to the multienzyme complex, at least in part, by mitochondrial matrix microenvironment and metabolite levels. In particular, mitochondrial matrix pH and malate and fumarate levels likely have significant effects on the stability of the MDH1–CIT1 complex, since these factors strongly influence complex affinity, and their in vivo changes coincide with complex dynamics (Figure 6). However, none of these factors consistently correlates with the MDH1–CIT1 interaction (Figure 6A), suggesting that none of them is a predominant regulator, but multiple factors work together in a cooperative manner to regulate the formation of the multienzyme complex. This coordinated regulation probably serves to fine-tune the flux of the TCA cycle, allowing it to adapt efficiently to varying metabolic demands and maintain cellular homeostasis. Although we focused on allosteric regulators in this study, further factors are potentially involved in the MDH1–CIT1 complex regulation. For example, 44 and 33 post-translational modifications have been identified in CIT1 and MDH1, respectively (Bhagwat et al., 2021; Henriksen et al., 2012; Holt et al., 2009; Lanz et al., 2021; Reinders et al., 2007; Swaney et al., 2013; Weinert et al., 2013), some of which likely affect the MDH1–CIT1 complex affinity. Various scaffolding molecules, such as long noncoding RNAs, lipid layers, and scaffolding proteins, have been shown to stabilize the multienzyme complexes in other systems (Zhu et al., 2022). Future studies should investigate the effects of these factors to understand the regulatory mechanisms for MDH1–CIT1 interaction.

Changes in environmental conditions and nutrient availability occur swiftly, and a corresponding change in metabolic flux must occur at a similar rate for the successful adaptation of living cells. Dynamic metabolons can be a system to regulate and fine-tune metabolic network flux quickly, allowing cells to maintain metabolic homeostasis in rapidly fluctuating environments (Obata, 2019). Considering the thermodynamic unfavorability of the forward MDH reaction and that substrate channeling overcomes this thermodynamic barrier in vitro, the MDH1–CIT1 complex formation likely enhances the forward TCA cycle flux (Bulutoglu et al., 2016; Fukuda et al., 2008; Sweetlove et al., 2010). The dynamic assembly of the metabolon probably provides fine-tuning of TCA cycle activity in response to respiratory demand. While the present study delineates the environmental and metabolic factors governing this dynamic assembly, future studies utilizing targeted genetic tools to independently modulate specific variables will be critical for dissecting the contributions of individual regulatory factors in vivo. Further studies assessing the impacts of metabolon formation on metabolic pathway flux in living cells are essential to understand the functions of metabolons in metabolic network regulation. Elucidating the regulatory system of the TCA cycle metabolon can lead to a novel strategy to manipulate the TCA cycle flux in metabolic engineering to achieve efficient industrial production of various molecules requiring TCA cycle intermediates as substrates or to control the Warburg effect to suppress cancer cells.

All data generated or analyzed during this study are included in the manuscript and supporting files. The dataset for the metabolite profiling is provided in Source data 1.

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Author details

1. Joy Omini

   Department of Biochemistry and Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, United States

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

2. Inga Krassovskaya

   Department of Biochemistry and Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, United States

   Contribution

   Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

3. Taiwo Adeolu Dele-Osibanjo

   Department of Biochemistry and Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, United States

   Contribution

   Validation, Investigation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2537-6998

4. Connor Pedersen

   Department of Biochemistry and Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Toshihiro Obata

   Department of Biochemistry and Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, United States

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Visualization, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   tobata2@unl.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8931-7722

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