Yeast mitochondria: an overview of mitochondrial biology and the potential of mitochondrial systems biology

Yeast mitochondria: an overview of mitochondrial biology and the potential of mitochondrial... Abstract Mitochondria are dynamic organelles of endosymbiotic origin that are essential components of eukaryal cells. They contain their own genetic machinery, have multicopy genomes and like their bacterial ancestors they consist of two membranes. However, the majority of the ancestral genome has been lost or transferred to the nuclear genome of the host, preserving only a core set of genes involved in oxidative phosphorylation. Mitochondria perform numerous biological tasks ranging from bioenergetics to production of protein co-factors, including heme and iron–sulfur clusters. Due to the importance of mitochondria in many cellular processes, mitochondrial dysfunction is implicated in a wide variety of human disorders. Much of our current knowledge on mitochondrial function and dysfunction comes from studies using Saccharomyces cerevisiae. This yeast has good fermenting capacity, rendering tolerance to mutations that inactivate oxidative phosphorylation and complete loss of mitochondrial DNA. Here, we review yeast mitochondrial metabolism and function with focus on S. cerevisiae and its contribution in understanding mitochondrial biology. We further review how systems biology studies, including mathematical modeling, has allowed gaining new insight into mitochondrial function, and argue that this approach may enable us to gain a holistic view on how mitochondrial function interacts with different cellular processes. Saccharomyces cerevisiae, mitochondria, mitochondrial metabolism, mitochondrial disorders INTRODUCTION Mitochondria are organelles found in the vast majority of all eukaryal organisms. They are remnants of an ancient α-proteobacterial endosymbiont, the genome of which has been partly lost or transferred to the nucleus throughout evolution (Gray, Burger and Lang 1999). Mitochondria consist of two membranes, the inner and outer mitochondrial membrane (IMM and OMM, respectively) surrounding the two compartments the intermembrane space (IMS) and the mitochondrial matrix. They are organized as tubular networks, the morphology of which is governed by the opposing processes of fission and fusion, allowing adaptation of mitochondrial morphology to the metabolic requirements of the cell (Westermann 2012). Shifting toward fusion results in interconnected mitochondria and toward fission results in numerous mitochondrial fragments. Interconnected mitochondria are beneficial in respiratory active cells by allowing efficient mixing of enzymes, metabolites and mitochondrial gene products throughout the entire mitochondrial network. Mitochondria are known as the powerhouses of eukaryal cells due to their role in synthesis of most of the cellular ATP, being the host of the tricarboxylic acid (TCA) cycle and oxidative phosphorylation. However, the roles of mitochondria extend to other metabolic processes including amino acid and lipid metabolism as well as synthesis of iron–sulfur clusters and heme. The scope of this review is to give an overview of mitochondrial functions, primarily metabolic, with a focus on Saccharomyces cerevisiae and how it has contributed to knowledge on mitochondrial function, as well as discuss how systems biology approaches may allow to get a broader view on how mitochondria interact with different cellular processes. Most mitochondrial functions are highly conserved across eukarya and yeast has been a powerful tool for studying mitochondrial biology and disease (Baile and Claypool 2013), and we therefore argue that advancing systems biology of yeast mitochondria may be of relevance for gaining new insight into many human diseases. MITOCHONDRIAL EVOLUTION Mitochondria are today known to have an endosymbiotic origin. The idea that mitochondria were evolved from a once free-living bacterium was first postulated by Altmann (Altmann 1890). He further proposed that mitochondria were autonomous and responsible for metabolic and genetic functions. The discovery of mitochondrial DNA (mtDNA) (Nass and Nass 1963), following the discovery of mitochondrial protein synthesis (Mclean et al.1958), initiated sequencing and studies of numerous mitochondrial genomes enabling researchers to trace the evolutionary origin of mitochondria. It is today widely accepted that the ancestor of mitochondria was a free-living bacterium, with α-proteobacteria being the closest identified relatives (Gray, Burger and Lang 1999). The role of mitochondria in the evolution of eukarya has long been disputed and a number of models for eukaryosis have been suggested over the last half a century (as reviewed by Martin et al.2001; Zimorski et al.2014). The most well known is the serial endosymbiosis theory proposed by Margulis (Sagan 1967). This theory was further developed by Cavalier-Smith, who defined the host of endosymbiosis as an archezoa, being a primitive eukaryon lacking many of the modern eukaryal features (Cavalier-Smith 1987). This hypothesis was based on the existence of early-branching eukarya lacking mitochondria, therefore being suitable as a starting point for endosymbiosis of the mitochondrial ancestor. Although at first promising, this theory was later largely abandoned after genetic and morphological studies revealed that all known archezoa possess specialized mitochondria-related organelles, called hydrogenosomes and mitosomes, formed by reductive evolution of mitochondria (Tielens et al.2002; Henze and Martin 2003; van der Giezen and Tovar 2005; van der Giezen 2009). The fall of the archezoan hypothesis paved the way for other models of eukaryal evolution, such as the hydrogen hypothesis (Martin and Müller 1998) proposing that the α-proteobacterium was taken up by a prokaryotic host, and that the complexity of eukaryal cells evolved after the acquisition of the mitochondrial ancestor. The hypothesis also accounts for a possible scenario for how both aerobic and anaerobic energy metabolism of eukarya evolved. More recent large-scale analyses also provide insight into the identity of the host cell with results pointing toward some kind of archeon (Cox et al.2008; Koonin 2010). Further arguments for the endosymbiosis between two prokaryotes as the starting point for eukaryogenesis was presented by Lane and Martin (Lane and Martin 2010). From an energetics of genome complexity point of view, they argue that the drastically increased surface area of bioenergetic membranes enabled by the endosymbiont was key to the expansion of the host cell genome, and thus also allowing increased complexity of eukaryal cells. Moreover, the loss of redundant endosymbiont genes, such as for synthesis of metabolites provided by the host, would reduce the required protein synthesis, and thus also the energy expenditure (Lane 2011). This would in turn allow the host cell to acquire new cellular features, such as a cytoskeleton at no net energy cost, and to largely expand its genome. As eukaryal characteristics evolved, further reduction of the endosymbiont genome occurred, by gene loss and transfer of mitochondrial genes to the nucleus, resulting in the specialized organelles that we call mitochondria. But what is left of this once free-living organism in terms of its genome? MITOCHONDRIAL GENOME The genome of mitochondria is highly diverse between different eukarya, both when it comes to genome size and content. The number of genes in mtDNA varies between 5, in the case of Plasmodium falciparum (Gray, Burger and Lang 1999), and 97 in the case of Reclinomonas americana, possessing one of the largest mitochondrial gene sets currently known (Lang et al.1997). The complete mitochondrial genome of the yeast S. cerevisiae was first sequenced in 1998 (Foury et al.1998). Yeast mtDNA represents on average 15% of the total cellular DNA content (Williamson 2002). It consists mostly of linear molecules of varying length ranging from ∼75 to 150 kb, but small amounts of circular DNA are also present (Williamson 2002; Westermann 2014). It has been speculated that the circular DNA acts as templates for amplification by a rolling circle mechanism in which concatemers, composed of linear arrays of several genome units, are formed (Maleszka, Skelly and Clark-Walker 1991; Shibata and Ling 2007). In S. cerevisiae, mtDNA encodes eight proteins, of which seven are subunits of the electron transport chain and oxidative phosphorylation, and one is a ribosomal protein of the small subunit (Foury et al.1998). It consists of three active replication origins (ori2, ori3 and ori5) and five replication origin-like elements. Additionally, it also contains genes for the complete set of tRNAs needed for mitochondrial translation, and 15S and 21S rRNA (Foury et al.1998). A comparison of the mitochondrial genomes of S. cerevisiae, human and R. americana can be seen in Table 1. Interestingly, with exception of the genes encoding subunits of complex I, which in S. cerevisiae, is replaced by two types of NADH dehydrogenases lacking proton-pumping ability, the ribosomal protein of the small subunit Var1 and subunit Atp9 of ATP synthase encoded in yeast mtDNA, all of the mtDNA genes are conserved between yeast and human. Table 1. Comparison of the Saccharomyces cerevisiae, human and Reclinomonas americana mitochondrial genomes. Organism S. cerevisiae Human R. americana Genome size (kb) 75 17 69 Genes 35 37 97 Protein-coding 8 13 62 Ribosomal proteins 1 – 27 tRNAs 24 22 26 rRNAs 2 2 4 Estimated proteome size 1000 1500 – Organism S. cerevisiae Human R. americana Genome size (kb) 75 17 69 Genes 35 37 97 Protein-coding 8 13 62 Ribosomal proteins 1 – 27 tRNAs 24 22 26 rRNAs 2 2 4 Estimated proteome size 1000 1500 – View Large Table 1. Comparison of the Saccharomyces cerevisiae, human and Reclinomonas americana mitochondrial genomes. Organism S. cerevisiae Human R. americana Genome size (kb) 75 17 69 Genes 35 37 97 Protein-coding 8 13 62 Ribosomal proteins 1 – 27 tRNAs 24 22 26 rRNAs 2 2 4 Estimated proteome size 1000 1500 – Organism S. cerevisiae Human R. americana Genome size (kb) 75 17 69 Genes 35 37 97 Protein-coding 8 13 62 Ribosomal proteins 1 – 27 tRNAs 24 22 26 rRNAs 2 2 4 Estimated proteome size 1000 1500 – View Large The mitochondrial genome of R. americana contains 97 genes of which 92 can be assigned a function (Lang et al.1997). The gene set comprises 18 protein-coding genes that have not been found in other mitochondrial genomes, but perhaps more interesting is the fact that it contains all of the 44 protein-coding genes previously identified in one or more other non-plant mitochondrial genomes sequenced (Lang et al.1997). It is clear that the genomes of mitochondria are highly reduced in size compared to their α-proteobacterial ancestor, as exemplified by Rickettsia prowazekii, that has been proposed to be the closest relative of the ancestor of mitochondria, with a genome of ∼1 Mbp encoding 834 proteins (Andersson et al.1998). Most of the genes encoding mitochondrial proteins reside in the nucleus, and experimental studies in yeast have shown that transfer of genes from the mitochondrial genome to the nuclear genome is the favored direction (Thorsness and Fox 1990, 1993). But if transfer of mitochondrial genes to the nucleus is favored, why were not all genes transferred? All mitochondria contain a core set of genes involved in respiration and oxidative phosphorylation, and translation (Gray, Lang and Burger 2004). A number of theories to why this relatively constant set is found in mitochondria of diverse eukarya have been suggested (Bogograd 1975; von Heijne 1986). The perhaps most compelling explanation is the co-location for redox regulation (CoRR) hypothesis (Allen 1993, 2003, 2015). This hypothesis argues that local transcription and translation of key genes, which are under regulatory control by the redox state of their gene products, within the mitochondrion is indispensable. This is because it enables a quick response to environmental changes, such as those caused by free radical leak, maintaining coupling of electron flow and oxidative phosphorylation conferring a selective advantage of keeping the redox regulated genes. The lack of selection pressure of keeping genes that are not under redox control would also explain why the majority of the essential genes of the mitochondrial ancestor have been transferred to the nuclear genome. MITOCHONDRIAL PROTEOME, PROTEIN SYNTHESIS AND IMPORT In order to advance systems biology-based analysis of mitochondria, it is important to identify their complete proteome. In the last decade, progress in mass-spectrometric methods has enabled improved characterization of the mitochondrial proteome (Gonczarowska-Jorge, Zahedi and Sickmann 2017; Palmfeldt and Bross 2017). Sickmann et al. (2003) published a large study in 2003 on the yeast mitochondrial proteome of isolated mitochondria that identified 749 unique proteins. In 2006, the proteome was expanded by Reinders et al. (2006) in a study which made use of more advanced LC-MS methods, leading to the identification of 102 additional proteins. Further efforts to characterize the proteome of the mitochondrial subcompartments have also been made (Zahedi et al.2006; Vögtle et al.2012; Morgenstern et al.2017) and in a recent study by Vögtle et al. (2017), 818 mitochondrial proteins were assigned to the four mitochondrial sub compartments. Furthermore, the study also identified 206 proteins that were not previously annotated as localized to mitochondria. As of September 2017, the total number of genes in Saccharomyces Genome Database annotated to mitochondria is 1205 (Cherry et al.2012). Interestingly, only 15% of the mitochondrial proteins are involved in energy metabolism, and ∼20% of the proteins are of unknown function (Schmidt, Pfanner and Meisinger 2010). Mitochondria possess a complete genetic machinery responsible for transcription and translation of the mitochondrial genome (for a detailed review, see Fox 2012). However, as mentioned above only eight major proteins are synthesized within mitochondria of S. cerevisiae, including subunits of the respiratory complexes III (Cytb), IV (Cox1, Cox2, Cox3) and V (Atp6, Atp8 and Atp9) as well as a ribosomal protein of the small subunit (Var1). The remaining ∼1000 proteins, encoded in the nucleus, are synthesized on cytoplasmic ribosomes and imported to their respective mitochondrial compartment. The mitochondrial proteins synthesized in the cytosol are directed to their specific location by targeting signals. Depending on where the protein is targeted, different targeting signals exist. A large fraction of the mitochondrial proteins are synthesized as precursors with N-terminal cleavable extensions (Vögtle et al.2009). The extension, often referred to as presequences, typically consist of 15–80 amino acids that form positively charged amphipathic α-helices (Herrmann and Neupert 2013). These proteins are imported through the presequence pathway (Fig. 1A), which is initiated by recognition of the presequences by receptors in the translocase of the outer membrane (TOM complex) (Yamamoto et al.2011). Upon recognition, the proteins are translocated through pores in the TOM complex and recognized by receptors of the translocase of the inner membrane 23 (TIM23). The presequence is translocated through the TIM23 complex pore driven electrophoretically by the membrane potential of the inner membrane (ΔΨ) (Martin, Mahlke and Pfanner 1991). Further translocation of the preproteins into the matrix is driven by the presequence-associated motor (PAM) through the ATP-hydrolyzing Hsp70 (Ssc1) subunit. Well inside the matrix, the presequences are cleaved off by matrix processing peptidases (Gakh, Cavadini and Isaya 2002). Some of the cleavable preproteins also contain a hydrophobic sorting signal in its sequences. Translocation of these sequences into the TIM23 complex pore leads to an arrest in translocation, followed by insertion into the IMM or release of the protein into the IMS (Glick et al.1992). Figure 1. View largeDownload slide Overview of the mitochondrial protein import machinery. (A) The presequence pathway for import of precursor proteins containing a cleavable presequence into the inner mitochondrial membrane (IMM) and matrix. (B) Import pathway for hydrophobic metabolite carrier precursors into the IMM occurs in the TOM complex, small TIM chaperones and the TIM22 complex. (C) Import of cysteine-rich precursors into the intermembrane space (IMS). Precursors are initially imported through the TOM complex and sequestered in the IMS by the mitochondrial IMS import and assembly (MIA) machinery. (D) β-barrel proteins of the outer mitochondrial membrane (OMM) translocate through the TOM complex, bind to small TIM chaperones in the IMS and are inserted into the OMM by the sorting and assembly machinery (SAM). (E) Multiple pathways for import α-helical proteins of the OMM into the membrane exist. Shown here is import via the mitochondrial import (MIM) complex. MPP, mitochondrial membrane peptidase. Figure 1. View largeDownload slide Overview of the mitochondrial protein import machinery. (A) The presequence pathway for import of precursor proteins containing a cleavable presequence into the inner mitochondrial membrane (IMM) and matrix. (B) Import pathway for hydrophobic metabolite carrier precursors into the IMM occurs in the TOM complex, small TIM chaperones and the TIM22 complex. (C) Import of cysteine-rich precursors into the intermembrane space (IMS). Precursors are initially imported through the TOM complex and sequestered in the IMS by the mitochondrial IMS import and assembly (MIA) machinery. (D) β-barrel proteins of the outer mitochondrial membrane (OMM) translocate through the TOM complex, bind to small TIM chaperones in the IMS and are inserted into the OMM by the sorting and assembly machinery (SAM). (E) Multiple pathways for import α-helical proteins of the OMM into the membrane exist. Shown here is import via the mitochondrial import (MIM) complex. MPP, mitochondrial membrane peptidase. Most metabolite carriers and other hydrophobic multispanning inner membrane proteins are synthesized without cleavable sequences but instead contain multiple targeting signals within the sequence of the mature protein (Chacinska et al.2009). These proteins are translocated through the TOM complex pore upon recognition by TOM complex receptors (Fig. 1B). Inside the IMS, these proteins are bound by Tim9–Tim10 chaperone complexes, delivered to the TIM22 insertase/translocase complex of the inner membrane and inserted in the membrane in a ΔΨ membrane-dependent manner (Rehling et al.2003). Some inner membrane proteins, such as multispanning inner membrane protein Cox18, contain cleavable presequences and are inserted into the membrane using an alternative mechanism. This pathway involves full or partial translocation of the proteins into the matrix followed by insertion into the membrane by the OXA translocase/insertase complex (Bonnefoy et al.2009). For import of proteins to the IMS, multiple mechanisms of localization exist, including covalent modifications by covalent attachment of heme and generation of internal disulfide bonds between paired cysteine residues, carried out by IMS enzymes (Fox 2012). There are at least 24 proteins containing disulfide bonds that are sequestered in the IMS by the disulfide relay system (Fig. 1C) (Koehler and Tienson 2009). The mitochondrial IMS import and assembly (MIA) machinery, responsible for the disulfide bond formation, consist of the membrane-bound subunits Mia40 and Erv1 (Herrmann and Riemer 2012). In this pathway, the pre-proteins are translocated through a pore of the TOM complex in a reduced state, followed by binding to Mia40, which functions as an oxidoreductase introducing two or more disulfide bonds into the imported protein. Upon formation of disulfide bonds, the protein is stably folded. Mia40 is reoxidized by Erv1, an FAD-dependent sulfhydryl oxidase, with the assistance of Hot13 returning it to its initial conformation. The electrons derived in the process of oxidizing imported proteins are then transferred from Erv1 to molecular oxygen via cytochrome c and cytochrome c oxidase of the respiratory chain (Bihlmaier et al.2007). All integral proteins of the OMM are synthesized in the cytoplasm and can be divided into two major classes: those containing α-helical transmembrane segments and β-barrel proteins. β-barrel proteins are imported in an unfolded state by first interacting with receptors of the TOM complex (Fig. 1D). Upon interaction, the proteins are translocated through the TOM complex pore and bound by heterohexameric chaperone complexes of the IMS, consisting of Tim9–Tim10 and Tim8–Tim13 (Krimmer et al.2001; Wiedemann et al.2004). The chaperone complexes direct the proteins to the sorting and assembly machinery complex of the OMM, which carries out the lateral insertion of the β-barrel proteins into the membrane (Wiedemann et al.2003). For insertion of α-helical integral membrane proteins in the OMM, less is known about the mechanisms and multiple pathways have been suggested, including import via the mitochondrial import machinery (MIM) complex (Becker et al.2011; Dimmer et al.2012). In this pathway, proteins with multiple membrane spanning helices first interact with the Tom70 receptor of the TOM complex, followed by insertion into the outer membrane upon interaction with the Mim1–Mim2 complex (Fig. 1E). MITOCHONDRIAL METABOLISM Mitochondria are often referred to as the powerhouses of the cell because of their major role in generating cellular energy in the form of adenosine triphosphate (ATP) through the TCA cycle and the oxidative phosphorylation. However, mitochondria are also involved in other metabolic processes, such as amino acid, lipid and intermediary metabolism, synthesis of iron–sulfur clusters and heme, and maintenance of cellular redox state. Metabolism of reactive oxygen species and reactive oxygen species-mediated regulation Reactive oxygen species (ROS) are by-products of aerobic metabolism that are formed when electrons leak from their carrier systems leading to incomplete reduction of oxygen in a non-enzymatic manner and include the superoxide (O2−), hydrogen peroxide (H2O2) and the hydroxyl radical (OH•). These species are thought to mediate the toxicity of oxygen due to their higher reactivity compared to molecular oxygen and are often associated with oxidative stress induced damage to biological molecules. However, it has recently become apparent that ROS also play a role as signaling molecules in redox biology (Schieber and Chandel 2014). Furthermore, ROS have been shown to be involved in aging, regulation of the chronological lifespan and apoptosis (Perrone, Tan and Dawes 2008; Pan 2011; Pan et al.2011; Ayer, Gourlay and Dawes 2014; Ludovico and Burhans 2014). In respiring yeast, electron leakage in the respiratory chain, mainly at complex III and to a lesser extent at complex II and IV leading to thermodynamically favorable one-electron reduction of oxygen to form superoxide, is the main source of ROS (Jastroch et al.2010; Liu 2010). Superoxide formed by complex III is released to the IMS and matrix, and by complex II and IV into the matrix, and can damage and inactivate protein by reacting with iron–sulfur clusters (Fridovich 1997; Muller, Liu and Van Remmen 2004). The superoxide dismutases Sod1 and Sod2, which rapidly convert superoxide to H2O2 and hereby ensures maintaining a low concentration of superoxide, play an important role. The two dismutases differ in localization and metal co-factors used. Sod1 is a copper-zinc superoxidide dismutase located in the cytoplasm and mitochondrial IMS, while Sod2 is a manganese superoxide dismutase localized in the mitochondrial matrix (Wallace et al.2004; Luk et al.2005). Hydrogen peroxide is not a radical in itself, but it can readily react with ferrous ions (Fe2+) to form Fe3+, a hydroxyl ion and a hydroxyl radical in the Fenton reaction (Valko, Morris and Cronin 2005). The hydroxyl radical is extremely reactive and indiscriminately oxidizes DNA, proteins and lipids causing damage and genomic instability (Dizdaroglu and Jaruga 2012). In contrast to the hydroxyl radical, hydrogen peroxide is relatively stable and therefore present in higher intracellular concentrations. To avoid formation of the highly reactive hydroxyl radical, cells have developed a number of antioxidant defenses against hydrogen peroxide, including both enzymatic and non-enzymatic (Morano, Grant and Moye-Rowley 2012). The enzymatic response includes, but is not limited to, catalases Cta1 and Ctt1, and thiol peroxidases, in both cases converting H2O2 to water and molecular oxygen, while the non-enzymatic mechanisms involve glutathione (GSH) and ascorbic acid. Apart from their role in H2O2 scavenging, it was recently shown that thiol peroxidases are involved in H2O2-mediated genome-wide regulation of gene expression in response to hydrogen peroxide (Fomenko et al.2011; Topf et al.2018). Yeast possesses in total eight thiol peroxidases, five peroxiredoxins (Tsa1, Tsa2, Ahp1, Dot5 and Prx1) and three glutathione peroxidases (Gpx1, Gpx2 and Gpx3). As for most ROS-induced regulatory pathways, signaling involving thiol peroxidases is based on the oxidation of protein-cysteine residues. The main reason for this is the high reactivity of cysteine residues as well as their ability to cycle between different oxidation states. Thiol peroxidases contain catalytic cysteine residues that are readily oxidized by H2O2 to a sulfenic acid (SOH). The oxidized enzymes then go on to oxidize regulatory proteins, resulting in regulatory responses. The perhaps most well-studied H2O2 sensor in yeast is that of glutathione peroxidase-like enzyme Grx3 (aliases Orp1 and Hyr1), basic leucine zipper (bZIP) transcription factor Yap1 and Ybp1 operating a redox relay starting with H2O2 oxidizing the catalytic Cys36 on Grx3 to a sulfenic acid (D’Autréaux and Toledano 2007; Rodrigues-Pousada, Menezes and Pimentel 2010). Grx3 then reacts with the C-terminal cysteine-rich domain of Yap1 leading to the consecutive formation of two intramolecular disulfide bonds in Yap1 (Delaunay et al.2002). This induces a conformational change, masking a nuclear export signal, thereby inhibiting Crm1-dependent export of Yap1 from the nucleus and promoting transcriptional activation (Wood, Storz and Tjandra 2004). Mitochondrial metabolite transport Mitochondria are involved in a number of different pathways that require the exchange of metabolites between the cytosol and the mitochondrial matrix. The OMM contains porins, which allow the diffusion of molecules up to 4–5 kDa in size across the membrane (Palmieri and Pierri 2010). The IMM, on the other hand, is highly impermeable and as a consequence, only small uncharged molecules, such as oxygen and carbon dioxide can diffuse freely across the membrane. Due to the compartmentation of enzymes between the mitochondrion and cytosol in various metabolic pathways, transporters are required for import of metabolites synthesized outside mitochondria and export of metabolites synthesized within mitochondria (Palmieri 2014). Metabolites imported include cofactors, such as FAD and NAD+, ADP and Pi for oxidative phosphorylation, pyruvate as well as iron for synthesis of heme and iron-sulfur clusters while examples of molecules exported are intermediates of the TCA cycle, ATP and ornithine. The transport of these cofactors, nucleotides and a variety of other metabolites is carried out by the mitochondrial carrier family (MCF). In S. cerevisiae, 35 members of the MCF have been identified and the role of the majority has also been characterized (Palmieri et al.2006; Palmieri and Pierri 2010). Characteristic for the transporters of the MCF is the presence of three tandem repeats of ∼100 amino acids, containing a characteristic conserved sequence, that each fold into two α-helices spanning the IMM. Many of the MCF transporters were first identified by the presence of the encoding gene in yeast (Rutter and Hughes 2015). The functions of many of the transporters have also been identified using yeast, further leading to identification of the substrates for human homologs (Palmieri et al.2006). Examples of members of the MCF that have been extensively studied using yeast include the ATP/ADP carrier (Pet9), dicarboxylate carrier (Dic1), coenzyme A importer (Leu5) and mitoferrins (Mrs3/4) (Palmieri et al.1999; Prohl et al.2001; Klingenberg 2008; Froschauer, Schweyen and Wiesenberger 2009). In addition to the metabolite transporters, yeast also has a number of shuttle systems, including the malate-aspartate and ethanol-acetaldehyde shuttles involved in shuttling NADH between the cytosol and mitochondrion, as well as the carnitine shuttle for transporting acetyl-CoA between the cytosol and mitochondrion (Bakker et al.2001; Palmieri et al.2006; Murray, Haynes and Tomita 2011). Use of the carnitine shuttle, however, requires supplementation of carnitine to the medium as S. cerevisiae is not able to synthesize this metabolite. Amino acid metabolism Mitochondria have an important role in central metabolism providing various metabolic intermediates that are used in biosynthetic pathways. Examples of such pathways are in synthesis of amino acid, in particular those synthesized from pyruvate and α-ketoglutarate. The pyruvate family of amino acids includes alanine and the branched-chain amino acids (BCAA) leucine, valine and isoleucine. Alanine can be synthesized directly from pyruvate, with glutamate as an ammonia donor, in a reaction catalyzed by Alt1 (García-Campusano et al.2009). In S. cerevisiae, the entire pathway for synthesis of isoleucine and valine is localized in the mitochondria, but the final step can be carried out also in the cytoplasm. The first step of isoleucine biosynthesis involves the conversion of 2-oxobutanate and pyruvate into 2-aceto-2-hydroxybutanoate, and of valine biosynthesis the conversion of pyruvate to 2-acetolactate, both reactions catalyzed by a complex consisting of Ilv2 and the regulatory subunit Ilv6 (Falco, Dumas and Livak 1985; Cullin et al.1996). The following three steps of the pathway are carried out by Ilv5, Ilv3 and the final step is catalyzed either by the mitochondrial or cytosolic BCAA transferase Bat1 or Bat2, respectively (Litske Petersen and Holmberg 1986; Velasco, Carmen and Laborda 1993; Kispal et al.1996). A complete overview of the synthesis is given in Fig. 2a. The pathway for synthesis of leucine is dually localized between mitochondria and the cytosol (Kohlhaw 2003). The initial steps of synthesis, from pyruvate to 2-oxoisovalerate, share the same set of enzymes as the isoleucine and valine pathways. This intermediate is converted into α-isopropylmalate by α-isopropylmalate synthase isozymes Leu4 and Leu9. An interesting feature of the major isozyme Leu4 is that it can exist in two forms: one containing an N-terminal signal sequence directing it to mitochondria and a shorter cytosolic variant without the N-terminal sequence. After this step, α-isopropylmalate synthesized in mitochondria is exported to the cytosol where the remaining part of leucine synthesis occurs (Kohlhaw 2003). Figure 2. View largeDownload slide Overview of yeast mitochondrial amino acid metabolism. (a) Synthesis of the branched-chain amino acids leucine, isoleucine and valine. (b) Synthesis of ornithine, an intermediate in arginine biosynthesis. Figure 2. View largeDownload slide Overview of yeast mitochondrial amino acid metabolism. (a) Synthesis of the branched-chain amino acids leucine, isoleucine and valine. (b) Synthesis of ornithine, an intermediate in arginine biosynthesis. In addition to BCAA synthesis, the synthesis of arginine and lysine partly occurs in mitochondria (Xu et al.2006; Ljungdahl and Daignan-Fornier 2012). The five steps in synthesis of ornithine, an intermediate in arginine biosynthesis, is localized to mitochondria (Jauniaux, Urrestarazu and Wiame 1978). The synthesis starts from glutamate and acetyl-CoA and involves five acetylated steps catalyzed by the enzymes Arg2, Arg5,6, Arg7 and Arg8. An overview of ornithine synthesis is given in Fig. 2b. Interestingly, N-acetylglutamyl-phosphate reductase (Arg5) and acetylglutamate kinase (Arg6) are encoded by a single gene (ARG5,6), and have been shown to form a metabolon, which is a complex formed by sequential enzymes of a metabolic pathway, with Arg2 (Abadjieva et al.2001). This metabolon catalyzes the first three steps in ornithine synthesis. In the pathway for synthesis of lysine from α-ketoglutarate, two mitochondrial enzymes are involved (Ljungdahl and Daignan-Fornier 2012). The steps from homocitrate to homoisocitrate, via homoaconitate, and from homoisocitrate to α-ketooadipate are catalyzed by the mitochondrial enzymes Lys4 and Lys12, respectively. The remaining steps of lysine biosynthesis are carried out in the cytosol. Transport of intermediates in lysine biosynthesis across the IMM involves the mitochondrial carriers Odc1, Odc2 and Ctp1 (Palmieri et al.2006). Iron–sulfur cluster biogenesis One of the essential functions of mitochondria is that of iron–sulfur (Fe/S) cluster biogenesis, illustrated in Fig. 3. Fe/S clusters are versatile co-factors with a variety of functions in cellular processes of eukarya, ranging from acting as the catalytic sites to electron transfer and sensory functions (Beinert, Holm and Münck 1997). In yeast, mitochondria are not only involved in biogenesis and assembly of mitochondrial Fe/S proteins, but are also crucial for the assembly of cytoplasmic and nuclear Fe/S proteins (Braymer and Lill 2017). Apart from ferredoxin, none of the mitochondrial Fe/S proteins, including aconitase and complexes of the respiratory chain, are known to be essential for growth of S. cerevisiae. However, some of the cytoplasmic and nuclear Fe/S proteins, such as Rli1 required for the biogenesis of cytoplasmic ribosomes, are indispensable for viability of the yeast (Kispal et al.2005). This explains why complete loss of mitochondrial Fe/S cluster biosynthesis is lethal. In yeast, the synthesis of Fe/S cluster depends on six proteins that show a high degree of conservation to their bacterial counterparts (Blanc, Gerez and Ollagnier de Choudens 2015). Iron required for the synthesis is imported by the carrier proteins Mrs3/4 of the inner membrane driven by the proton motive force (Mühlenhoff et al.2003b). Biogenesis of Fe/S clusters starts with the de novo synthesis of a [2Fe-2S] on the scaffold protein Isu1 (Mühlenhoff et al.2003a). The synthesis requires the interaction of Isu1 with a desulfurase complex consisting of Nfs1, Isd11 and Acp1 (Adam et al.2006; Van Vranken et al.2016), acting as a sulfur donor by releasing a sulfur moiety from a cysteine leading to the formation of alanine and a persulfide on a conserved cysteine on Nfs1 (Lill et al.2012). Subsequently, the sulfur is released to the scaffold Isu forming a persulfide on one of the three conserved cysteine residues on the scaffold. The persulfide is converted to a sulfide in a process that is thought to involve the electron transfer chain comprising of ferredoxin reductase (Arh1) and ferredoxin (Yah1) transferring electrons from NAD(P)H. Yeast mitochondrial ferredoxin has been shown to be essential for Fe/S cluster biogenesis (Lange et al.2000). Furthermore, Fe/S cluster synthesis requires frataxin (Yfh1) interacting with both Isu1 and Nfs1-Isd11 in an iron-dependent manner (Gerber, Mühlenhoff and Lill 2003). Yfh1 is a matrix iron chaperone that, in Fe/S cluster assembly, has the role of supplying Isu1 with iron (Ranatunga et al.2016). A recent study also identified mitochondrial acyl carrier protein (Acp1) as a subunit of the desulfurase complex, having a role in stabilizing the complex (Van Vranken et al.2016). Acp1 plays an important role in mitochondrial fatty acid metabolism (Hiltunen et al.2010). It has therefore been speculated that Acp1 may have a regulatory role linking biogenesis of respiratory functions and metabolic activities (Braymer and Lill 2017). Figure 3. View largeDownload slide An overview of yeast mitochondrial iron-sulfur (Fe/S) cluster biogenesis. Iron required for the synthesis is imported by membrane carriers Mrs3/4 driven by the proton motive force. Synthesis of [2Fe-2S] clusters occurs on the scaffold protein Isu1, requiring sulfur from the cysteine desulfurase complex Nfs1-Isd11-Acp1, and electrons from the transfer chain consisting of NAD(P)H, ferredoxin reductase and ferredoxin. The Fe/S cluster is transferred from monothiol glutaredoxin Grx5 by the action of ATP-dependent Hsp70 chaperone Ssq1, J-type co-chaperone Jac1 and nucleotide exchange factor Mge1. The [2Fe-2S] clusters are either directly inserted into target proteins or delivered to the Isa1-Isa2-Iba57 for [4Fe-4S] cluster biogenesis, followed by insertion into target proteins by targeting factors including Nfu1, Bol1 and Bol3. Figure 3. View largeDownload slide An overview of yeast mitochondrial iron-sulfur (Fe/S) cluster biogenesis. Iron required for the synthesis is imported by membrane carriers Mrs3/4 driven by the proton motive force. Synthesis of [2Fe-2S] clusters occurs on the scaffold protein Isu1, requiring sulfur from the cysteine desulfurase complex Nfs1-Isd11-Acp1, and electrons from the transfer chain consisting of NAD(P)H, ferredoxin reductase and ferredoxin. The Fe/S cluster is transferred from monothiol glutaredoxin Grx5 by the action of ATP-dependent Hsp70 chaperone Ssq1, J-type co-chaperone Jac1 and nucleotide exchange factor Mge1. The [2Fe-2S] clusters are either directly inserted into target proteins or delivered to the Isa1-Isa2-Iba57 for [4Fe-4S] cluster biogenesis, followed by insertion into target proteins by targeting factors including Nfu1, Bol1 and Bol3. After the Fe/S cluster has been synthesized, the maturation of Fe/S proteins requires a two-step process, which includes the release of the cluster from Isu1 and transfer to proteins that transiently bind it, and the insertion of the cluster into apoproteins (Lill et al.2012). The first step is carried out by a chaperone system consisting of mitochondrial Hsp70 Ssq1, J-protein Jac1, nucleotide exchange factor Mge1 and the monothiol glutaredoxin Grx5. Dutkiewicz et al. (2003) demonstrated that Jac1 and Ssq1 work cooperatively and that depletion of either of the two chaperones leads to accumulation Fe/S clusters on Isu1. Furthermore, they showed that the nucleotide exchange factor Mge1 stimulated the ATP hydrolysis activity of Ssq1 and proposed that Jac1 binds Isu1 and delivers it to the ATP-bound form of Ssq1. The working cycle of Ssq1, Jac1 and Mge1 is similar to the general mechanism of Hsp70 chaperones as described by Kampinga and Craig (2010). ATP hydrolysis by Ssq1, stimulated by interaction with Isu1 and Jac1, leads to a conformational change causing it bind to Isu1 and Grx5 (Uzarska et al.2013). This, in turn, induces a conformational change in Isu1 leading to a weakened binding of the Fe/cluster. The release of the Fe/S cluster from Isu1 is dependent on the action of monothiol glutaredoxin Grx5 (Rodriguez-Manzaneque et al.2002). Mühlenhoff et al. (2003a) further showed that depletion of Grx5 leads to an accumulation of Fe/S clusters on Isu1. More recent studies have confirmed the binding of both [2Fe-2S] and [4Fe-4S] clusters by Grx5 as well as the interaction of Grx5 with Ssq1 (Uzarska et al.2013). These results propose that Grx5 acts as a transfer protein mediating the transfer of Fe/S clusters from Isu1 to apoproteins. The chaperone cycle is completed when the ADP bound to Ssq1 is exchanged for ATP by the action of Mge1. The induced conformational change associated with nucleotide exchange causes dissociation of the Ssq1-Isu1-Grx5 complex (Uzarska et al.2013). In addition to Grx5, which has been shown to be required for maturation of mitochondrial and cytosolic Fe/S proteins, the generation of [4Fe-4S] clusters has been shown to require the action of specialized targeting components Isa1, Isa2 and Iba57 (Gelling et al.2008; Mühlenhoff et al.2011). The exact mechanism of action of the proteins has not yet been elucidated. Iba57 interacts with Isa1and Isa2, and the respective deletion mutants show similar phenotypes suggesting that the proteins act as a functional unit. Yeast strains depleted in any of the three proteins leads to deficiency of de novo Fe/S cluster assembly on proteins aconitase (Aco1) and homoaconitase (Lys4), further strengthening the role of Isa1–2 and Iba57 in Fe/S cluster maturation (Gelling et al.2008; Mühlenhoff et al.2011). More recent studies have also identified the additional targeting factors Nfu1, Bol1 and Bol3 that facilitate the transfer of [4Fe-4S] clusters from the Isa1-Isa2-Iba57 complex to target apoproteins (Melber et al.2016; Uzarska et al.2016). Heme biosynthesis and signaling The heme biosynthesis pathway in yeast, illustrated in Fig. 4, consists of eight steps and is dually localized between the mitochondrial matrix and the cytosol (Hoffman, Góra and Rytka 2003). The pathway starts with the synthesis of 5-aminolevulinic acid (ALA) from glycine and succinyl-CoA, catalyzed by Hem1, in mitochondria. ALA is transported to the cytosol where it is converted to porphobilinogen, and in four additional steps to protoporphyrinogen IX. This involves the subsequent action of enzymes Hem2, Hem3, Hem4, Hem12 and Hem13. The final two steps of heme synthesis are the conversion of protoporphyrinogen IX to protoporphyrin IX, which is then converted to heme. These two reactions are catalyzed by Hem14 and Hem15. Heme is widely known to be involved in sensing cellular oxygen levels through activation of the transcription factor Hap1, which is involved in transcriptional regulation of oxygen responsive genes (Ter Linde and Steensma 2002). Interestingly, out of the genes involved in the heme biosynthesis pathway, HEM13 is the only gene whose expression is affected by oxygen/heme. Figure 4. View largeDownload slide Overview of heme biosynthesis in yeast. Figure 4. View largeDownload slide Overview of heme biosynthesis in yeast. Mitochondrial lipids Mitochondria consist of two membranes, the inner and outer mitochondrial membrane, confining two compartments, the IMS and the matrix. The two membranes are composed mainly of phospholipids, and the phospholipid composition of yeast mitochondria has been measured (Zinser et al.1991). The two major phospholipids are posphatidylcholine (PC) and phosphatidylethanolamine (PE) constituting 40% and 27% of total mitochondrial phospholipids, respectively, but the membranes also contain phosphatidylserine (PS), phosphatidylinositol (PI), phosphatidic acid (PA) and the mitochondrion specific phospholipid cardiolipin (CL). The composition of the two membranes differs in the way that the inner membrane has a higher content of proteins, PI and CL than the outer membrane (Horvath and Daum 2013). Mitochondria are partly autonomous in synthesizing lipids, being able to synthesize PE, CL and some intermediate phospholipids (Osman, Voelker and Langer 2011; Horvath and Daum 2013). In the yeast S. cerevisiae, three different pathways for producing PE exist, localized to different cellular compartments (Birner et al.2001). PE can be synthesized de novo from the CDP-ethanolamine part of the Kennedy pathway, which also contains a branch for synthesis of PC (as reviewed by Daum et al.1998), or by the decarboxylation of PS carried out by mitochondrial Psd1 or Golgi/vacuolar Psd2 (Horvath and Daum 2013). The two decarboxylases share 19% sequence identity. The major route for PE biosynthesis is through decarboxylation of PS in the IMM by Psd1 (Fig. 5a) (Clancey, Chang and Dowhans 1993). Figure 5. View largeDownload slide Overview of lipid metabolism in yeast mitochondria. (a) Biosynthesis of phosphatidylethanolamine (PE) and cardiolipin (CL) in yeast. ERM, ER membrane; OMM, outer mitochondrial membrane; IMS, intermembrane space; IMM, inner mitochondria membrane; PA, phosphatidic acid; CDP-DAG, CDP-diacylglycerol; PGP, phosphatidylglycerolphosphate; PG, phosphatidylglycerol; MLCL, monolysocardiolipin; PS, phosphatidylserine. (b) Mitochondrial fatty acid synthesis. Figure 5. View largeDownload slide Overview of lipid metabolism in yeast mitochondria. (a) Biosynthesis of phosphatidylethanolamine (PE) and cardiolipin (CL) in yeast. ERM, ER membrane; OMM, outer mitochondrial membrane; IMS, intermembrane space; IMM, inner mitochondria membrane; PA, phosphatidic acid; CDP-DAG, CDP-diacylglycerol; PGP, phosphatidylglycerolphosphate; PG, phosphatidylglycerol; MLCL, monolysocardiolipin; PS, phosphatidylserine. (b) Mitochondrial fatty acid synthesis. The synthesis of CL, which is a dimeric lipid with four acyl chains and two PA moieties linked to glycerol, in the IMM starts with the synthesis of CDP-diacylglycerol (CDP-DAG) from PA (Fig. 5a). The reaction is catalyzed either by Cds1, which is mainly localized to the ER, but has also been detected in mitochondrial fractions (Kuchler, Daum and Paltauf 1986; Shen et al.1996), or by Tam41, which is localized to the matrix side of the IMM (Tamura et al.2013). CDP-DAG is a central intermediate in phospholipid metabolism and serves as a precursor in the biosynthesis of PI, PS and PC synthesized from PS via conversion into PE (Henry, Kohlwein and Carman 2012). The first committed and rate-limiting step of CL biosynthesis is the conversion of CDP-DAG and glycerol-3-phosphate to phosphatidylglycerol phosphate (PGP) by Pgs1 (Chang et al.1998a). The subsequent two steps include dephosphorylation of PGP by Gep4, a phosphatase on the matrix side of the IMM (Osman et al.2010), to form phosphatidylglycerol (PG), and the synthesis of CL by reacting PG with CDP-DAG catalyzed by Crd1 (Tuller et al.1998; Chang et al.1998b). Maturation of CL involves remodeling of the acyl chains by the sequential action of Cld1 and Taz1 (Gu et al.2004; Beranek et al.2009). Cld1 functions as a phospholipase, preferentially removing palmitic acid (C16:0) from premature CL, forming monolysocardiolipin (MLCL). Taz1 is an acyltransferase localized to the IMS side of the OMM that catalyzes the acylation of MLCL with unsaturated fatty acids. Since the capacity of mitochondria to synthesize phospholipids is limited to synthesis of PE and CL, mitochondrial biogenesis is strictly dependent on import of the extramitochondrially synthesized lipids. The major site of synthesis of these lipids is the subfraction of the ER known as the mitochondria associated membrane (MAM) (Gaigg et al.1995). PS is synthesized by Cho1 which is localized to the MAM, but has also recently been observed in mitochondrial fractions (Letts et al.1983; Vögtle et al.2017). The MAM has also been suggested to be responsible for transport of the synthesized phospholipids into mitochondria (Daum and Vance 1997). One of the more recently discovered functions of yeast mitochondria (and also human mitochondria) in lipid metabolism is biosynthesis of fatty acids (Hiltunen et al.2009). Mitochondrial fatty acid synthesis (FAS) occurs in an acyl carrier protein (ACP)-dependent manner that resembles bacterial type II FAS, which in contrast to eukaryal type I FAS makes use of separate enzymes that catalyze the individual reactions (Stephen et al.2005). The components of the mitochondrial FAS pathway have been identified and partially characterized in S. cerevisiae, starting with the discovery of mitochondrial ACP (Brody et al.1997), and the yeast has therefore been used as a model organism for further studies (Hiltunen et al.2009). The pathway of mitochondrial FAS is outlined in Fig. 5b. For a detailed review of the mechanisms of synthesis, see Stephen et al. (2005). The pathway involves the action of a number of enzymes with high resemblance to their bacterial homologs, namely phosphopantetheine transferase Ppt2, malonyl-CoA:ACP transferase Mct1, ketoacyl synthase Cem1 and ketoacyl reductase Kar1 (Harington et al.1993; Schneider et al.1997; Stuible et al.1998). On the other hand, hydroxyacyl-ACP hydratase Htd2 and enoyl-ACP reductase, responsible for the final steps of elongation of the acyl chain, show little resemblance to enzymes of bacterial type II FAS (Torkko et al.2001; Kastaniotis et al.2004). Furthermore, mitochondrial acetyl-CoA carboxylase encoded by HFA1 has shown to be homologous to cytosolic ACC1 (Hoja et al.2004). The major product of mitochondrial type II FAS (mtFAS) identified is octanoyl-ACP, a precursor needed for synthesis of lipoic acid, which in turn is a cofactor essential for the function of pyruvate dehydrogenase (PDH), α-ketoglutarate dehydrogenase and the glycine cleavage system (Schonauer et al.2009). Apart from acting as a route for lipoic acid synthesis, the role of mtFAS products is still largely unknown (Hiltunen et al.2009). Mitochondria with defects in mtFAS show respiratory-deficient phenotypes, resulting from low levels of lipoic acid and subsequent inactivation of PDH and α-ketoglutarate dehydrogenase. Mitochondrial fatty acids have been shown to play a role in processing of tRNA, since a strain with deletion of HTD2 showed deficiency in the 5΄ processing of mitochondrial precursor tRNAs by RNase P (Schonauer et al.2008). Furthermore, a regulatory role of mtFAS in response to pyruvate levels in yeast cells when grown on glucose has been suggested (Hiltunen et al.2009). When pyruvate levels are low, the level of acetyl-CoA going into mtFAS is also low, resulting in lower levels of lipoic acid and hence also decreased activity of PDH. MITOCHONDRIA IN DISEASE Mitochondrial function and structure is highly complex. Not only are mitochondria dynamic organelles that form tubular networks regulated by fission and fusion (Westermann 2010), but their function also depends on the interplay between the mitochondrial and nuclear genome. Due to the inherent complexity of mitochondrial structure and function, mitochondrial dysfunction has been implicated in a wide range of human disorders (Schapira 2012). Over the years, more than 150 different mitochondrial disorders have been described, with the majority of disorders associated with dysfunction of mitochondrial energetics (Lasserre et al.2015). These diseases are diverse and pleiotropic making the difficult to study. An overview of selected human mitochondrial disorders and their genetic causes is given in Table 2. Mitochondrial disorders affect at least 1 in 5000 newborns, but it can take as long as to adulthood before the symptoms present themselves (Schaefer et al.2004). Due to the dependence of mitochondrial function on two genomes, mitochondrial disorders can be divided into those caused by mutation in the nuclear DNA (nDNA) and those caused by mutations in mtDNA. Since the first discovery of a mutation in a nuclear gene causing mitochondrial disorder was discovered (Bourgeron et al.1995), more than 110 genes in nDNA have been found to cause mitochondrial diseases (Vafai and Mootha 2012). These genes are involved in various processes including assembly of complexes involved in oxidative phosphorylation, mtDNA maintenance, mitochondrial protein import, dynamics and metabolite transport. Of special interest is the POLG gene encoding the catalytic component of mitochondrial DNA polymerase gamma, for which over 150 mutations involved in human mitochondrial disorders have been identified (for a complete list, see the database https://tools.niehs.nih.gov/polg/) (Stumpf and Copeland 2011). These mutations cause defects in mitochondrial function resulting from deletions, depletion and point mutations in mtDNA. Diseases caused by point mutations in mtDNA include those in protein-coding mitochondrial genes and in mitochondrial tRNA, and result in various phenotypes. Since the protein-coding genes in human mtDNA all encode subunits of the respiratory chain and oxidative phosphorylation, mutations in the genes mainly affect the respiratory complexes they belong to. Mutations in tRNA-encoding genes lead to defects in protein synthesis causing more pleiotropic effects. Due to the high conservation between yeast and humans, mutations in tRNA can be studied using yeast as a model (Montanari et al.2008, 2010). To date, more than 200 mutations in mtDNA of humans have been identified that are involved or are suspected to be involved in disease (https://www.mitomap.org). Table 2. Examples of mitochondrial disorders. Genome Genetics Phenotype mtDNA Mutations in tRNA genes Mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome, cardiomyopathy, diabetes, deafness Single or multiple deletions Chronic progressive external ophthalmoplegia (CPEO), Kearns-Sayre syndrome, Pearson syndrome, myopathy Complex I gene mutations Leber's hereditary optic neuropathy (LHON) nDNA Mutations in DNA polymerase gamma subunit (POLG) Alper-Huttenlocher syndrome (AHS), Ataxia neuropathy spectrum (ANS), Myclonic epilepsy myopathy sensory ataxia (MEMSA), CPEO Mutations in structural subunit genes or assembly factor genes for respiratory complexes Leigh syndrome, myopathy, encephalopathy, liver disease or failure Genome Genetics Phenotype mtDNA Mutations in tRNA genes Mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome, cardiomyopathy, diabetes, deafness Single or multiple deletions Chronic progressive external ophthalmoplegia (CPEO), Kearns-Sayre syndrome, Pearson syndrome, myopathy Complex I gene mutations Leber's hereditary optic neuropathy (LHON) nDNA Mutations in DNA polymerase gamma subunit (POLG) Alper-Huttenlocher syndrome (AHS), Ataxia neuropathy spectrum (ANS), Myclonic epilepsy myopathy sensory ataxia (MEMSA), CPEO Mutations in structural subunit genes or assembly factor genes for respiratory complexes Leigh syndrome, myopathy, encephalopathy, liver disease or failure mtDNA, mitochondrial DNA; nDNA, nuclear DNA. View Large Table 2. Examples of mitochondrial disorders. Genome Genetics Phenotype mtDNA Mutations in tRNA genes Mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome, cardiomyopathy, diabetes, deafness Single or multiple deletions Chronic progressive external ophthalmoplegia (CPEO), Kearns-Sayre syndrome, Pearson syndrome, myopathy Complex I gene mutations Leber's hereditary optic neuropathy (LHON) nDNA Mutations in DNA polymerase gamma subunit (POLG) Alper-Huttenlocher syndrome (AHS), Ataxia neuropathy spectrum (ANS), Myclonic epilepsy myopathy sensory ataxia (MEMSA), CPEO Mutations in structural subunit genes or assembly factor genes for respiratory complexes Leigh syndrome, myopathy, encephalopathy, liver disease or failure Genome Genetics Phenotype mtDNA Mutations in tRNA genes Mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome, cardiomyopathy, diabetes, deafness Single or multiple deletions Chronic progressive external ophthalmoplegia (CPEO), Kearns-Sayre syndrome, Pearson syndrome, myopathy Complex I gene mutations Leber's hereditary optic neuropathy (LHON) nDNA Mutations in DNA polymerase gamma subunit (POLG) Alper-Huttenlocher syndrome (AHS), Ataxia neuropathy spectrum (ANS), Myclonic epilepsy myopathy sensory ataxia (MEMSA), CPEO Mutations in structural subunit genes or assembly factor genes for respiratory complexes Leigh syndrome, myopathy, encephalopathy, liver disease or failure mtDNA, mitochondrial DNA; nDNA, nuclear DNA. View Large Saccharomyces cerevisiae is a widely used eukaryal model, with a complete genome sequence (Goffeau et al.1996), and a well-annotated genome, and many genetic tools available, including availability of a complete gene deletion collection (Winzeler et al.1999). Yeast is a powerful model also for studying mitochondrial biology and human mitochondrial diseases (Baile and Claypool 2013; Rutter and Hughes 2015). About 40% of all genes involved in human diseases have an ortholog in yeast (Bassett, Boguski and Hieter 1996) and 70% of the nuclear genes involved in mitochondrial disease are conserved between yeast and human (Lasserre et al.2015). The role of yeast models in studying mitochondrial disorders has been reviewed in detail elsewhere (Rinaldi et al.2010; Lasserre et al.2015), but here we will briefly summarize the main advantages of yeast models and give a few examples of their uses. First, S. cerevisiae has a good fermentative capacity, being able to satisfy its energy requirements by fermentation. It can therefore tolerate mutations that disrupt oxidative phosphorylation, and even complete loss of mtDNA as long as fermentable carbon sources are present, allowing mutations causing mitochondrial dysfunction to be studied without being lethal (Chen and Clark-Walker 1999). Yeast mutants with defects in oxidative phosphorylation form small colonies when grown on plates containing a fermentable carbon source, a phenotype referred to as petite (Slonimski and Ephrussi 1949). Second, tools for manipulating the mitochondrial genome are available, allowing mutations involved in mitochondrial disorders to be reconstructed (Bonnefoy and Fox 2007). Moreover, yeast populations become homoplasmic within a few generations, meaning that all mtDNA copies have the same sequence (Shibata and Ling 2007). This allows for studying the severity and impact on mitochondrial function of point mutations that are often heteroplasmic in human mitochondria. Due to the many benefits of using yeast as a model, it has been exploited for studies of mutations in mtDNA including components of the respiratory chain complexes III (Meunier et al.2013) and IV (Kabala et al.2014). Yeast models have also been valuable for studying mutations in mitochondrial DNA polymerase gamma (Szczepanowska and Foury 2010). In contrast to human POLG, the yeast ortholog Mip1 is a single subunit. The phenotypic effect of defects in Mip1 is easily observed since defects in replication of mtDNA causing depletion or deletions result in a cytoplasmic petite phenotype, identified by the inability to form large colonies when grown on fermentable media. It is common that patients carry multiple mutations in POLG, some pathogenic and some harmless (Rinaldi et al.2010). By modeling individual mutations in Mip1, yeast models can be used to distinguish the severity of these SNPs. In addition to yeast models, mice models have also been used to study POLG. In an effort to investigate the role of mutations and deletions in mtDNA in aging, Trifunovic and co-workers generated knock-in mice with a point mutation in the catalytic subunit of mtDNA polymerase exchanging a critical aspartate residue in the second exonuclease domain with an alanine (Trifunovic et al.2004). The knock-in mice expressed a proofreading-deficient variant of the polymerase resulting in a mutator phenotype showing up to a 5-fold increase in point mutations, as well as increased amounts of deletions in mtDNA. Furthermore, the mice experienced a reduced lifespan and premature aging. MODELING OF METABOLISM Due to its central role in all living cells, a main objective of systems biology over the last decades has been to develop quantitative models of metabolism. These models incorporate bibliomic- (the entirety of information available in literature), genomic- and biochemical knowledge of the biological system in a computable format that allows for simulating the behavior of an organism under different conditions, such as genetic or environmental perturbations. Genome-scale metabolic models Genome-scale metabolic models (GEMs) are in silico reconstructions that represent the complete metabolism of a given organism. These reconstructions are built in a bottom-up approach starting from an annotated genome sequence, providing gene-protein-reaction (GPR) relationships. Using this GPR, each metabolic reaction is connected to an enzyme, and there is therefore a direct link between the genotype and metabolic phenotype. Furthermore, reconstruction of a metabolic network requires knowledge on the known capabilities of the system, stoichiometry and co-factor requirements of the reactions, as well as directionality and compartment localization. By including information collected from literature, databases and genome annotation, metabolic network reconstructions represent a knowledge base that collects extensive information about the organism. When additional information becomes available, the models can easily be expanded to include more reactions or additional cellular compartments. To use metabolic network reconstructions for simulations, the concept of flux balance analysis (FBA) can be used (Orth, Thiele and Palsson 2010). Here, mass balances around each metabolite are set-up, which provide a large number of constraints on the fluxes in the network and thus define a space of feasible solutions. By introducing an appropriate objective function, the solution space can be narrowed down and a solution can be identified using linear programming, resulting in a flux distribution for optimizing the objective. The most commonly used objective function is maximizing growth but other objectives, such as maximizing ATP production, have also been used (Schuetz, Kuepfer and Sauer 2007). A particular strength of GEMs is that, due to the presence of gene-protein-reaction relationships, they provide a framework for integrative analysis of omics data and have therefore been useful in evaluating the biological capabilities and flux states in specific conditions (Oberhardt, Palsson and Papin 2009). The constraint-based approach typically used for simulations with GEM involves a feasible solution space with a large degree of freedom. By integrating omics data, constraints are imposed on the model that allow improved predictive capabilities to gain insight into cellular phenotypes and metabolic regulation. There are many studies published where yeast GEMs have been used to integrate omics data (Sánchez and Nielsen 2015). The most widely used omics data for integration with GEMs is transcriptomics data, owing to the advancements in high-throughput sequencing techniques that have increased the speed and reduced the cost of sequencing. The main advantages of using transcriptomics data is that it provides a genome-wide coverage and that it is relatively easy to generate the data due to the highly automated process (Hoppe 2012). Integration of transcriptomics data into GEMs is based on the idea that there is a relationship between transcript level and the flux of the reaction catalyzed by the corresponding enzyme, and many methods for transcriptome data integration have been developed (Machado and Herrgård 2014). However, as there are many intermediate biological processes governing the relationship between gene expression and protein levels, including mRNA decay, protein degradation and translational efficiency, it can be difficult to directly correlate transcript levels with enzyme activity (Hoppe 2012). Therefore, in order to confer constraints on the metabolic fluxes as a consequence of enzymatic capacity, there is an increasing interest in developing modeling approaches that allow for integration of proteomics data into GEMs, which will also be advantageous for modeling of mitochondria metabolism as large proteome datasets are becoming available, as discussed above. Of particular interest are models that allow for integration of quantitative proteomics data from which enzyme levels can be directly implied. So far, proteomics datasets have mostly been indirectly used with GEMs, by correlating the protein level to the corresponding fluxes (Sánchez and Nielsen 2015). A number of different strategies have been developed to account for enzymatic limitations in metabolic models, such as flux balance analysis with molecular crowding (FBAwMC), which relies on imposing a systems-level constraint on the concentration of metabolic enzymes (Beg et al.2007). Another alternative approach to account for enzymatic limitations is genome-scale models of metabolism and gene expression (ME models) (O’Brien and Palsson 2015). These models include metabolic reactions and all processes necessary to synthesize a functional protein, starting from gene expression. Thereby, ME models allow for predictions of proteome and transcriptome allocation and limitations. In a recent study, we developed a framework referred to as GECKO, which enhances a GEM with enzymatic constraints using kinetics and omics data (Sánchez et al.2017). The framework is built on the basic constraint that any given reaction flux cannot exceed its maximum rate (vmax), which is expressed as the catalytic capacity of the enzyme (kcat) multiplied by the abundance of the corresponding enzyme. As discussed further below, this framework will be particularly well suited for modeling mitochondrial metabolism, where there are many different constraints, including proteome allocation to the different compartments. It may seem contradictory that metabolomics data are little used in metabolic modeling, but this is due to the fact that it is difficult to obtain truly quantitative data for the level of intracellular metabolites, as well demonstrated in a large interlaboratory study (Canelas et al.2010). Furthermore, it is with current technologies not possible to measure, at least in a high-throughput fashion, the metabolite levels in different compartments. Modeling mitochondria There have only been few attempts to model mitochondria metabolism. The first mitochondrion-specific model (for human cells), developed by Vo, Greenberg and Palsson (2004) based on a proteomics biochemical data, described 189 reactions accounting for energy metabolism, detoxification of reactive oxygen species, heme synthesis, as well as nitrogen and lipid metabolism of human cardiac mitochondria. The model was later used to integrate experimental data to successfully simulate mitochondrial function under normal, diabetic, ischemic and two types of diets (Thiele et al.2005). In 2011, Smith and Robinson constructed a more comprehensive model of human cardiac mitochondria named iAS253 (Smith and Robinson 2011). The model was created based on metabolite availability and was aided by the MitoMiner mitochondrial proteomics database (Smith and Robinson 2009). The database integrates experimental proteomic localization data with annotation from public resources, including Gene Ontology (Ashburner et al.2000) and UniProt (UniProt Consortium 2015), as well as metabolic pathway data from Kyoto Encyclopedia of Genes and Genomes (Kanehisa et al.2016). Since its first release, the database has been continuously updated and currently contains information for six organisms, including the yeasts S. cerevisiae and Schizosaccharomyces pombe (Smith and Robinson 2016). To demonstrate the predictive capabilities of the model, the authors used FBA to calculate the metabolic fluxes through the network under normal conditions and to simulate deficiency in fumarase, succinate dehydrogenase and α-ketoglutarate dehydrogenase, affecting the TCA cycle. In a study from 2016, an expanded version of the iAS253 model was used to simulate the metabolic effects of respiratory chain disorders, more specifically deficiency of complexes I-IV (Zieliński et al.2016). In a recent study, the model was further expanded to cover central metabolism of human cells (Smith et al.2017). The improvements of the model included an updated representation of the respiratory chain and metabolite transport with regard to the proton motive force, as well as a more accurate partitioning of reactions between the cytosol and mitochondria. The model was tested by using FBA to simulate cardiomyocyte metabolism with the objective to maximize ATP production, and was shown to produce realistic results compared to experimental data, with regard to fluxes and the use of different metabolites as energy sources. Together, these studies highlight the potential of constraint-based models of mitochondria to simulate normal physiology and disease states, providing the ability to test hypotheses and to give insights into mechanisms and possible intervention strategies of mitochondrial disorders. Saccharomyces cerevisiae was the first eukaryotic organism for which a GEM was constructed (Förster et al.2003). Since then, several groups have contributed to the expansion and development of yeast GEMs (Lopes, Rocha and Zhao Helder Lopes 2017). Although much of the current knowledge on mitochondrial biology comes from yeast, there is no mitochondrion-specific model available. Mitochondrial metabolism is integrated into the current GEMs of yeast and these models perform well for simulating central metabolism. However, when it comes to investigating mitochondria as a subsystem, a constraint-based model specific for mitochondria would come with several benefits. A reduced size and complexity of the model would allow for a more careful curation of the model, increasing reaction confidence as well as facilitating elucidation of system behavior and interpretation of simulation results. Furthermore, by including enzyme-constraints and kinetic information into the model would allow for investigating mitochondrial protein allocation as well as to integrate and analyze proteomics data. This could aid understanding of the dynamics and state of the mitochondrial proteome at different conditions, which is necessary for further understanding mitochondrial function. Since the majority of the mitochondrial proteins are synthesized by cytoplasmic ribosomes, mitochondrial function relies on translocation of these proteins into mitochondria. Incorporating protein translocation, together with protein synthesis of the intramitochondrially synthesized proteins, into a constraint-based model of mitochondria could aid the understanding of the energetic requirements of these processes as well as the interaction with other mitochondrial functions. Additionally, by adding a detailed description of the biochemical composition of mitochondria, the cost of synthesizing mitochondria in terms of energy and metabolites can be estimated. Taken together, a constraint-based model of mitochondria, accounting for protein translocation, synthesis and enzyme usage, could provide a framework for generating and testing hypotheses advancing the knowledge on mitochondrial function. PERSPECTIVES Although much progress has been made in understanding mitochondrial function and dysfunction during the recent decades, some challenges remain. Due to the inherent complexity of mitochondrial metabolism, perturbations in a small part of the network can have a large effect on the overall capabilities of mitochondria that can be difficult to predict without a systemic model. Furthermore, mitochondrial dysfunction often results in complex and non-intuitive phenotypes, making the underlying genetic cause challenging to elucidate. Here, a GEM of the mitochondrion could aid the interpretation and in unraveling the complexity, by providing a gene-protein-reaction connectivity, allowing for directly linking phenotypes to metabolic capabilities. Furthermore, GEMs allow for simulating mitochondrial behavior under different cellular conditions, changes in metabolism and modeling phenotypes as changes in metabolic fluxes. By using the recently developed GECKO framework (Sánchez et al.2017), constraints on enzymatic capacity can be included in the model, resulting in increased biological relevance of the predicted phenotypes. Additionally, such a model would give insight into enzyme usage and allow for incorporation of quantitative proteomics data, allowing the generation of context-specific models. Therefore, an enzyme-constrained model of the mitochondrion, in combination with systems biology analysis of quantitative proteomics data, may further advance our understanding of mitochondrial function by enabling the analysis of mitochondrial enzyme usage in different environments. The tools established could, together with the advantages of yeast as an experimental model for mitochondrial function, allow for gaining insight into the effects of disease-associated mutations. Acknowledgements The authors would like to thank the Novo Nordisk Foundation and the Knut and Alice Wallenberg Foundation for funding. The authors would also like to thank Francesca Di Bartolomeo for valuable input on the manuscript. Conflict of interest. None declared. REFERENCES Abadjieva A , Pauwels K , Hilven P et al. A new yeast metabolon involving at least the two first enzymes of arginine biosynthesis . J Biol Chem 2001 ; 276 : 42869 – 80 . 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This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png FEMS Yeast Research Oxford University Press

Yeast mitochondria: an overview of mitochondrial biology and the potential of mitochondrial systems biology

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© FEMS 2018.
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1567-1356
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1567-1364
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Abstract

Abstract Mitochondria are dynamic organelles of endosymbiotic origin that are essential components of eukaryal cells. They contain their own genetic machinery, have multicopy genomes and like their bacterial ancestors they consist of two membranes. However, the majority of the ancestral genome has been lost or transferred to the nuclear genome of the host, preserving only a core set of genes involved in oxidative phosphorylation. Mitochondria perform numerous biological tasks ranging from bioenergetics to production of protein co-factors, including heme and iron–sulfur clusters. Due to the importance of mitochondria in many cellular processes, mitochondrial dysfunction is implicated in a wide variety of human disorders. Much of our current knowledge on mitochondrial function and dysfunction comes from studies using Saccharomyces cerevisiae. This yeast has good fermenting capacity, rendering tolerance to mutations that inactivate oxidative phosphorylation and complete loss of mitochondrial DNA. Here, we review yeast mitochondrial metabolism and function with focus on S. cerevisiae and its contribution in understanding mitochondrial biology. We further review how systems biology studies, including mathematical modeling, has allowed gaining new insight into mitochondrial function, and argue that this approach may enable us to gain a holistic view on how mitochondrial function interacts with different cellular processes. Saccharomyces cerevisiae, mitochondria, mitochondrial metabolism, mitochondrial disorders INTRODUCTION Mitochondria are organelles found in the vast majority of all eukaryal organisms. They are remnants of an ancient α-proteobacterial endosymbiont, the genome of which has been partly lost or transferred to the nucleus throughout evolution (Gray, Burger and Lang 1999). Mitochondria consist of two membranes, the inner and outer mitochondrial membrane (IMM and OMM, respectively) surrounding the two compartments the intermembrane space (IMS) and the mitochondrial matrix. They are organized as tubular networks, the morphology of which is governed by the opposing processes of fission and fusion, allowing adaptation of mitochondrial morphology to the metabolic requirements of the cell (Westermann 2012). Shifting toward fusion results in interconnected mitochondria and toward fission results in numerous mitochondrial fragments. Interconnected mitochondria are beneficial in respiratory active cells by allowing efficient mixing of enzymes, metabolites and mitochondrial gene products throughout the entire mitochondrial network. Mitochondria are known as the powerhouses of eukaryal cells due to their role in synthesis of most of the cellular ATP, being the host of the tricarboxylic acid (TCA) cycle and oxidative phosphorylation. However, the roles of mitochondria extend to other metabolic processes including amino acid and lipid metabolism as well as synthesis of iron–sulfur clusters and heme. The scope of this review is to give an overview of mitochondrial functions, primarily metabolic, with a focus on Saccharomyces cerevisiae and how it has contributed to knowledge on mitochondrial function, as well as discuss how systems biology approaches may allow to get a broader view on how mitochondria interact with different cellular processes. Most mitochondrial functions are highly conserved across eukarya and yeast has been a powerful tool for studying mitochondrial biology and disease (Baile and Claypool 2013), and we therefore argue that advancing systems biology of yeast mitochondria may be of relevance for gaining new insight into many human diseases. MITOCHONDRIAL EVOLUTION Mitochondria are today known to have an endosymbiotic origin. The idea that mitochondria were evolved from a once free-living bacterium was first postulated by Altmann (Altmann 1890). He further proposed that mitochondria were autonomous and responsible for metabolic and genetic functions. The discovery of mitochondrial DNA (mtDNA) (Nass and Nass 1963), following the discovery of mitochondrial protein synthesis (Mclean et al.1958), initiated sequencing and studies of numerous mitochondrial genomes enabling researchers to trace the evolutionary origin of mitochondria. It is today widely accepted that the ancestor of mitochondria was a free-living bacterium, with α-proteobacteria being the closest identified relatives (Gray, Burger and Lang 1999). The role of mitochondria in the evolution of eukarya has long been disputed and a number of models for eukaryosis have been suggested over the last half a century (as reviewed by Martin et al.2001; Zimorski et al.2014). The most well known is the serial endosymbiosis theory proposed by Margulis (Sagan 1967). This theory was further developed by Cavalier-Smith, who defined the host of endosymbiosis as an archezoa, being a primitive eukaryon lacking many of the modern eukaryal features (Cavalier-Smith 1987). This hypothesis was based on the existence of early-branching eukarya lacking mitochondria, therefore being suitable as a starting point for endosymbiosis of the mitochondrial ancestor. Although at first promising, this theory was later largely abandoned after genetic and morphological studies revealed that all known archezoa possess specialized mitochondria-related organelles, called hydrogenosomes and mitosomes, formed by reductive evolution of mitochondria (Tielens et al.2002; Henze and Martin 2003; van der Giezen and Tovar 2005; van der Giezen 2009). The fall of the archezoan hypothesis paved the way for other models of eukaryal evolution, such as the hydrogen hypothesis (Martin and Müller 1998) proposing that the α-proteobacterium was taken up by a prokaryotic host, and that the complexity of eukaryal cells evolved after the acquisition of the mitochondrial ancestor. The hypothesis also accounts for a possible scenario for how both aerobic and anaerobic energy metabolism of eukarya evolved. More recent large-scale analyses also provide insight into the identity of the host cell with results pointing toward some kind of archeon (Cox et al.2008; Koonin 2010). Further arguments for the endosymbiosis between two prokaryotes as the starting point for eukaryogenesis was presented by Lane and Martin (Lane and Martin 2010). From an energetics of genome complexity point of view, they argue that the drastically increased surface area of bioenergetic membranes enabled by the endosymbiont was key to the expansion of the host cell genome, and thus also allowing increased complexity of eukaryal cells. Moreover, the loss of redundant endosymbiont genes, such as for synthesis of metabolites provided by the host, would reduce the required protein synthesis, and thus also the energy expenditure (Lane 2011). This would in turn allow the host cell to acquire new cellular features, such as a cytoskeleton at no net energy cost, and to largely expand its genome. As eukaryal characteristics evolved, further reduction of the endosymbiont genome occurred, by gene loss and transfer of mitochondrial genes to the nucleus, resulting in the specialized organelles that we call mitochondria. But what is left of this once free-living organism in terms of its genome? MITOCHONDRIAL GENOME The genome of mitochondria is highly diverse between different eukarya, both when it comes to genome size and content. The number of genes in mtDNA varies between 5, in the case of Plasmodium falciparum (Gray, Burger and Lang 1999), and 97 in the case of Reclinomonas americana, possessing one of the largest mitochondrial gene sets currently known (Lang et al.1997). The complete mitochondrial genome of the yeast S. cerevisiae was first sequenced in 1998 (Foury et al.1998). Yeast mtDNA represents on average 15% of the total cellular DNA content (Williamson 2002). It consists mostly of linear molecules of varying length ranging from ∼75 to 150 kb, but small amounts of circular DNA are also present (Williamson 2002; Westermann 2014). It has been speculated that the circular DNA acts as templates for amplification by a rolling circle mechanism in which concatemers, composed of linear arrays of several genome units, are formed (Maleszka, Skelly and Clark-Walker 1991; Shibata and Ling 2007). In S. cerevisiae, mtDNA encodes eight proteins, of which seven are subunits of the electron transport chain and oxidative phosphorylation, and one is a ribosomal protein of the small subunit (Foury et al.1998). It consists of three active replication origins (ori2, ori3 and ori5) and five replication origin-like elements. Additionally, it also contains genes for the complete set of tRNAs needed for mitochondrial translation, and 15S and 21S rRNA (Foury et al.1998). A comparison of the mitochondrial genomes of S. cerevisiae, human and R. americana can be seen in Table 1. Interestingly, with exception of the genes encoding subunits of complex I, which in S. cerevisiae, is replaced by two types of NADH dehydrogenases lacking proton-pumping ability, the ribosomal protein of the small subunit Var1 and subunit Atp9 of ATP synthase encoded in yeast mtDNA, all of the mtDNA genes are conserved between yeast and human. Table 1. Comparison of the Saccharomyces cerevisiae, human and Reclinomonas americana mitochondrial genomes. Organism S. cerevisiae Human R. americana Genome size (kb) 75 17 69 Genes 35 37 97 Protein-coding 8 13 62 Ribosomal proteins 1 – 27 tRNAs 24 22 26 rRNAs 2 2 4 Estimated proteome size 1000 1500 – Organism S. cerevisiae Human R. americana Genome size (kb) 75 17 69 Genes 35 37 97 Protein-coding 8 13 62 Ribosomal proteins 1 – 27 tRNAs 24 22 26 rRNAs 2 2 4 Estimated proteome size 1000 1500 – View Large Table 1. Comparison of the Saccharomyces cerevisiae, human and Reclinomonas americana mitochondrial genomes. Organism S. cerevisiae Human R. americana Genome size (kb) 75 17 69 Genes 35 37 97 Protein-coding 8 13 62 Ribosomal proteins 1 – 27 tRNAs 24 22 26 rRNAs 2 2 4 Estimated proteome size 1000 1500 – Organism S. cerevisiae Human R. americana Genome size (kb) 75 17 69 Genes 35 37 97 Protein-coding 8 13 62 Ribosomal proteins 1 – 27 tRNAs 24 22 26 rRNAs 2 2 4 Estimated proteome size 1000 1500 – View Large The mitochondrial genome of R. americana contains 97 genes of which 92 can be assigned a function (Lang et al.1997). The gene set comprises 18 protein-coding genes that have not been found in other mitochondrial genomes, but perhaps more interesting is the fact that it contains all of the 44 protein-coding genes previously identified in one or more other non-plant mitochondrial genomes sequenced (Lang et al.1997). It is clear that the genomes of mitochondria are highly reduced in size compared to their α-proteobacterial ancestor, as exemplified by Rickettsia prowazekii, that has been proposed to be the closest relative of the ancestor of mitochondria, with a genome of ∼1 Mbp encoding 834 proteins (Andersson et al.1998). Most of the genes encoding mitochondrial proteins reside in the nucleus, and experimental studies in yeast have shown that transfer of genes from the mitochondrial genome to the nuclear genome is the favored direction (Thorsness and Fox 1990, 1993). But if transfer of mitochondrial genes to the nucleus is favored, why were not all genes transferred? All mitochondria contain a core set of genes involved in respiration and oxidative phosphorylation, and translation (Gray, Lang and Burger 2004). A number of theories to why this relatively constant set is found in mitochondria of diverse eukarya have been suggested (Bogograd 1975; von Heijne 1986). The perhaps most compelling explanation is the co-location for redox regulation (CoRR) hypothesis (Allen 1993, 2003, 2015). This hypothesis argues that local transcription and translation of key genes, which are under regulatory control by the redox state of their gene products, within the mitochondrion is indispensable. This is because it enables a quick response to environmental changes, such as those caused by free radical leak, maintaining coupling of electron flow and oxidative phosphorylation conferring a selective advantage of keeping the redox regulated genes. The lack of selection pressure of keeping genes that are not under redox control would also explain why the majority of the essential genes of the mitochondrial ancestor have been transferred to the nuclear genome. MITOCHONDRIAL PROTEOME, PROTEIN SYNTHESIS AND IMPORT In order to advance systems biology-based analysis of mitochondria, it is important to identify their complete proteome. In the last decade, progress in mass-spectrometric methods has enabled improved characterization of the mitochondrial proteome (Gonczarowska-Jorge, Zahedi and Sickmann 2017; Palmfeldt and Bross 2017). Sickmann et al. (2003) published a large study in 2003 on the yeast mitochondrial proteome of isolated mitochondria that identified 749 unique proteins. In 2006, the proteome was expanded by Reinders et al. (2006) in a study which made use of more advanced LC-MS methods, leading to the identification of 102 additional proteins. Further efforts to characterize the proteome of the mitochondrial subcompartments have also been made (Zahedi et al.2006; Vögtle et al.2012; Morgenstern et al.2017) and in a recent study by Vögtle et al. (2017), 818 mitochondrial proteins were assigned to the four mitochondrial sub compartments. Furthermore, the study also identified 206 proteins that were not previously annotated as localized to mitochondria. As of September 2017, the total number of genes in Saccharomyces Genome Database annotated to mitochondria is 1205 (Cherry et al.2012). Interestingly, only 15% of the mitochondrial proteins are involved in energy metabolism, and ∼20% of the proteins are of unknown function (Schmidt, Pfanner and Meisinger 2010). Mitochondria possess a complete genetic machinery responsible for transcription and translation of the mitochondrial genome (for a detailed review, see Fox 2012). However, as mentioned above only eight major proteins are synthesized within mitochondria of S. cerevisiae, including subunits of the respiratory complexes III (Cytb), IV (Cox1, Cox2, Cox3) and V (Atp6, Atp8 and Atp9) as well as a ribosomal protein of the small subunit (Var1). The remaining ∼1000 proteins, encoded in the nucleus, are synthesized on cytoplasmic ribosomes and imported to their respective mitochondrial compartment. The mitochondrial proteins synthesized in the cytosol are directed to their specific location by targeting signals. Depending on where the protein is targeted, different targeting signals exist. A large fraction of the mitochondrial proteins are synthesized as precursors with N-terminal cleavable extensions (Vögtle et al.2009). The extension, often referred to as presequences, typically consist of 15–80 amino acids that form positively charged amphipathic α-helices (Herrmann and Neupert 2013). These proteins are imported through the presequence pathway (Fig. 1A), which is initiated by recognition of the presequences by receptors in the translocase of the outer membrane (TOM complex) (Yamamoto et al.2011). Upon recognition, the proteins are translocated through pores in the TOM complex and recognized by receptors of the translocase of the inner membrane 23 (TIM23). The presequence is translocated through the TIM23 complex pore driven electrophoretically by the membrane potential of the inner membrane (ΔΨ) (Martin, Mahlke and Pfanner 1991). Further translocation of the preproteins into the matrix is driven by the presequence-associated motor (PAM) through the ATP-hydrolyzing Hsp70 (Ssc1) subunit. Well inside the matrix, the presequences are cleaved off by matrix processing peptidases (Gakh, Cavadini and Isaya 2002). Some of the cleavable preproteins also contain a hydrophobic sorting signal in its sequences. Translocation of these sequences into the TIM23 complex pore leads to an arrest in translocation, followed by insertion into the IMM or release of the protein into the IMS (Glick et al.1992). Figure 1. View largeDownload slide Overview of the mitochondrial protein import machinery. (A) The presequence pathway for import of precursor proteins containing a cleavable presequence into the inner mitochondrial membrane (IMM) and matrix. (B) Import pathway for hydrophobic metabolite carrier precursors into the IMM occurs in the TOM complex, small TIM chaperones and the TIM22 complex. (C) Import of cysteine-rich precursors into the intermembrane space (IMS). Precursors are initially imported through the TOM complex and sequestered in the IMS by the mitochondrial IMS import and assembly (MIA) machinery. (D) β-barrel proteins of the outer mitochondrial membrane (OMM) translocate through the TOM complex, bind to small TIM chaperones in the IMS and are inserted into the OMM by the sorting and assembly machinery (SAM). (E) Multiple pathways for import α-helical proteins of the OMM into the membrane exist. Shown here is import via the mitochondrial import (MIM) complex. MPP, mitochondrial membrane peptidase. Figure 1. View largeDownload slide Overview of the mitochondrial protein import machinery. (A) The presequence pathway for import of precursor proteins containing a cleavable presequence into the inner mitochondrial membrane (IMM) and matrix. (B) Import pathway for hydrophobic metabolite carrier precursors into the IMM occurs in the TOM complex, small TIM chaperones and the TIM22 complex. (C) Import of cysteine-rich precursors into the intermembrane space (IMS). Precursors are initially imported through the TOM complex and sequestered in the IMS by the mitochondrial IMS import and assembly (MIA) machinery. (D) β-barrel proteins of the outer mitochondrial membrane (OMM) translocate through the TOM complex, bind to small TIM chaperones in the IMS and are inserted into the OMM by the sorting and assembly machinery (SAM). (E) Multiple pathways for import α-helical proteins of the OMM into the membrane exist. Shown here is import via the mitochondrial import (MIM) complex. MPP, mitochondrial membrane peptidase. Most metabolite carriers and other hydrophobic multispanning inner membrane proteins are synthesized without cleavable sequences but instead contain multiple targeting signals within the sequence of the mature protein (Chacinska et al.2009). These proteins are translocated through the TOM complex pore upon recognition by TOM complex receptors (Fig. 1B). Inside the IMS, these proteins are bound by Tim9–Tim10 chaperone complexes, delivered to the TIM22 insertase/translocase complex of the inner membrane and inserted in the membrane in a ΔΨ membrane-dependent manner (Rehling et al.2003). Some inner membrane proteins, such as multispanning inner membrane protein Cox18, contain cleavable presequences and are inserted into the membrane using an alternative mechanism. This pathway involves full or partial translocation of the proteins into the matrix followed by insertion into the membrane by the OXA translocase/insertase complex (Bonnefoy et al.2009). For import of proteins to the IMS, multiple mechanisms of localization exist, including covalent modifications by covalent attachment of heme and generation of internal disulfide bonds between paired cysteine residues, carried out by IMS enzymes (Fox 2012). There are at least 24 proteins containing disulfide bonds that are sequestered in the IMS by the disulfide relay system (Fig. 1C) (Koehler and Tienson 2009). The mitochondrial IMS import and assembly (MIA) machinery, responsible for the disulfide bond formation, consist of the membrane-bound subunits Mia40 and Erv1 (Herrmann and Riemer 2012). In this pathway, the pre-proteins are translocated through a pore of the TOM complex in a reduced state, followed by binding to Mia40, which functions as an oxidoreductase introducing two or more disulfide bonds into the imported protein. Upon formation of disulfide bonds, the protein is stably folded. Mia40 is reoxidized by Erv1, an FAD-dependent sulfhydryl oxidase, with the assistance of Hot13 returning it to its initial conformation. The electrons derived in the process of oxidizing imported proteins are then transferred from Erv1 to molecular oxygen via cytochrome c and cytochrome c oxidase of the respiratory chain (Bihlmaier et al.2007). All integral proteins of the OMM are synthesized in the cytoplasm and can be divided into two major classes: those containing α-helical transmembrane segments and β-barrel proteins. β-barrel proteins are imported in an unfolded state by first interacting with receptors of the TOM complex (Fig. 1D). Upon interaction, the proteins are translocated through the TOM complex pore and bound by heterohexameric chaperone complexes of the IMS, consisting of Tim9–Tim10 and Tim8–Tim13 (Krimmer et al.2001; Wiedemann et al.2004). The chaperone complexes direct the proteins to the sorting and assembly machinery complex of the OMM, which carries out the lateral insertion of the β-barrel proteins into the membrane (Wiedemann et al.2003). For insertion of α-helical integral membrane proteins in the OMM, less is known about the mechanisms and multiple pathways have been suggested, including import via the mitochondrial import machinery (MIM) complex (Becker et al.2011; Dimmer et al.2012). In this pathway, proteins with multiple membrane spanning helices first interact with the Tom70 receptor of the TOM complex, followed by insertion into the outer membrane upon interaction with the Mim1–Mim2 complex (Fig. 1E). MITOCHONDRIAL METABOLISM Mitochondria are often referred to as the powerhouses of the cell because of their major role in generating cellular energy in the form of adenosine triphosphate (ATP) through the TCA cycle and the oxidative phosphorylation. However, mitochondria are also involved in other metabolic processes, such as amino acid, lipid and intermediary metabolism, synthesis of iron–sulfur clusters and heme, and maintenance of cellular redox state. Metabolism of reactive oxygen species and reactive oxygen species-mediated regulation Reactive oxygen species (ROS) are by-products of aerobic metabolism that are formed when electrons leak from their carrier systems leading to incomplete reduction of oxygen in a non-enzymatic manner and include the superoxide (O2−), hydrogen peroxide (H2O2) and the hydroxyl radical (OH•). These species are thought to mediate the toxicity of oxygen due to their higher reactivity compared to molecular oxygen and are often associated with oxidative stress induced damage to biological molecules. However, it has recently become apparent that ROS also play a role as signaling molecules in redox biology (Schieber and Chandel 2014). Furthermore, ROS have been shown to be involved in aging, regulation of the chronological lifespan and apoptosis (Perrone, Tan and Dawes 2008; Pan 2011; Pan et al.2011; Ayer, Gourlay and Dawes 2014; Ludovico and Burhans 2014). In respiring yeast, electron leakage in the respiratory chain, mainly at complex III and to a lesser extent at complex II and IV leading to thermodynamically favorable one-electron reduction of oxygen to form superoxide, is the main source of ROS (Jastroch et al.2010; Liu 2010). Superoxide formed by complex III is released to the IMS and matrix, and by complex II and IV into the matrix, and can damage and inactivate protein by reacting with iron–sulfur clusters (Fridovich 1997; Muller, Liu and Van Remmen 2004). The superoxide dismutases Sod1 and Sod2, which rapidly convert superoxide to H2O2 and hereby ensures maintaining a low concentration of superoxide, play an important role. The two dismutases differ in localization and metal co-factors used. Sod1 is a copper-zinc superoxidide dismutase located in the cytoplasm and mitochondrial IMS, while Sod2 is a manganese superoxide dismutase localized in the mitochondrial matrix (Wallace et al.2004; Luk et al.2005). Hydrogen peroxide is not a radical in itself, but it can readily react with ferrous ions (Fe2+) to form Fe3+, a hydroxyl ion and a hydroxyl radical in the Fenton reaction (Valko, Morris and Cronin 2005). The hydroxyl radical is extremely reactive and indiscriminately oxidizes DNA, proteins and lipids causing damage and genomic instability (Dizdaroglu and Jaruga 2012). In contrast to the hydroxyl radical, hydrogen peroxide is relatively stable and therefore present in higher intracellular concentrations. To avoid formation of the highly reactive hydroxyl radical, cells have developed a number of antioxidant defenses against hydrogen peroxide, including both enzymatic and non-enzymatic (Morano, Grant and Moye-Rowley 2012). The enzymatic response includes, but is not limited to, catalases Cta1 and Ctt1, and thiol peroxidases, in both cases converting H2O2 to water and molecular oxygen, while the non-enzymatic mechanisms involve glutathione (GSH) and ascorbic acid. Apart from their role in H2O2 scavenging, it was recently shown that thiol peroxidases are involved in H2O2-mediated genome-wide regulation of gene expression in response to hydrogen peroxide (Fomenko et al.2011; Topf et al.2018). Yeast possesses in total eight thiol peroxidases, five peroxiredoxins (Tsa1, Tsa2, Ahp1, Dot5 and Prx1) and three glutathione peroxidases (Gpx1, Gpx2 and Gpx3). As for most ROS-induced regulatory pathways, signaling involving thiol peroxidases is based on the oxidation of protein-cysteine residues. The main reason for this is the high reactivity of cysteine residues as well as their ability to cycle between different oxidation states. Thiol peroxidases contain catalytic cysteine residues that are readily oxidized by H2O2 to a sulfenic acid (SOH). The oxidized enzymes then go on to oxidize regulatory proteins, resulting in regulatory responses. The perhaps most well-studied H2O2 sensor in yeast is that of glutathione peroxidase-like enzyme Grx3 (aliases Orp1 and Hyr1), basic leucine zipper (bZIP) transcription factor Yap1 and Ybp1 operating a redox relay starting with H2O2 oxidizing the catalytic Cys36 on Grx3 to a sulfenic acid (D’Autréaux and Toledano 2007; Rodrigues-Pousada, Menezes and Pimentel 2010). Grx3 then reacts with the C-terminal cysteine-rich domain of Yap1 leading to the consecutive formation of two intramolecular disulfide bonds in Yap1 (Delaunay et al.2002). This induces a conformational change, masking a nuclear export signal, thereby inhibiting Crm1-dependent export of Yap1 from the nucleus and promoting transcriptional activation (Wood, Storz and Tjandra 2004). Mitochondrial metabolite transport Mitochondria are involved in a number of different pathways that require the exchange of metabolites between the cytosol and the mitochondrial matrix. The OMM contains porins, which allow the diffusion of molecules up to 4–5 kDa in size across the membrane (Palmieri and Pierri 2010). The IMM, on the other hand, is highly impermeable and as a consequence, only small uncharged molecules, such as oxygen and carbon dioxide can diffuse freely across the membrane. Due to the compartmentation of enzymes between the mitochondrion and cytosol in various metabolic pathways, transporters are required for import of metabolites synthesized outside mitochondria and export of metabolites synthesized within mitochondria (Palmieri 2014). Metabolites imported include cofactors, such as FAD and NAD+, ADP and Pi for oxidative phosphorylation, pyruvate as well as iron for synthesis of heme and iron-sulfur clusters while examples of molecules exported are intermediates of the TCA cycle, ATP and ornithine. The transport of these cofactors, nucleotides and a variety of other metabolites is carried out by the mitochondrial carrier family (MCF). In S. cerevisiae, 35 members of the MCF have been identified and the role of the majority has also been characterized (Palmieri et al.2006; Palmieri and Pierri 2010). Characteristic for the transporters of the MCF is the presence of three tandem repeats of ∼100 amino acids, containing a characteristic conserved sequence, that each fold into two α-helices spanning the IMM. Many of the MCF transporters were first identified by the presence of the encoding gene in yeast (Rutter and Hughes 2015). The functions of many of the transporters have also been identified using yeast, further leading to identification of the substrates for human homologs (Palmieri et al.2006). Examples of members of the MCF that have been extensively studied using yeast include the ATP/ADP carrier (Pet9), dicarboxylate carrier (Dic1), coenzyme A importer (Leu5) and mitoferrins (Mrs3/4) (Palmieri et al.1999; Prohl et al.2001; Klingenberg 2008; Froschauer, Schweyen and Wiesenberger 2009). In addition to the metabolite transporters, yeast also has a number of shuttle systems, including the malate-aspartate and ethanol-acetaldehyde shuttles involved in shuttling NADH between the cytosol and mitochondrion, as well as the carnitine shuttle for transporting acetyl-CoA between the cytosol and mitochondrion (Bakker et al.2001; Palmieri et al.2006; Murray, Haynes and Tomita 2011). Use of the carnitine shuttle, however, requires supplementation of carnitine to the medium as S. cerevisiae is not able to synthesize this metabolite. Amino acid metabolism Mitochondria have an important role in central metabolism providing various metabolic intermediates that are used in biosynthetic pathways. Examples of such pathways are in synthesis of amino acid, in particular those synthesized from pyruvate and α-ketoglutarate. The pyruvate family of amino acids includes alanine and the branched-chain amino acids (BCAA) leucine, valine and isoleucine. Alanine can be synthesized directly from pyruvate, with glutamate as an ammonia donor, in a reaction catalyzed by Alt1 (García-Campusano et al.2009). In S. cerevisiae, the entire pathway for synthesis of isoleucine and valine is localized in the mitochondria, but the final step can be carried out also in the cytoplasm. The first step of isoleucine biosynthesis involves the conversion of 2-oxobutanate and pyruvate into 2-aceto-2-hydroxybutanoate, and of valine biosynthesis the conversion of pyruvate to 2-acetolactate, both reactions catalyzed by a complex consisting of Ilv2 and the regulatory subunit Ilv6 (Falco, Dumas and Livak 1985; Cullin et al.1996). The following three steps of the pathway are carried out by Ilv5, Ilv3 and the final step is catalyzed either by the mitochondrial or cytosolic BCAA transferase Bat1 or Bat2, respectively (Litske Petersen and Holmberg 1986; Velasco, Carmen and Laborda 1993; Kispal et al.1996). A complete overview of the synthesis is given in Fig. 2a. The pathway for synthesis of leucine is dually localized between mitochondria and the cytosol (Kohlhaw 2003). The initial steps of synthesis, from pyruvate to 2-oxoisovalerate, share the same set of enzymes as the isoleucine and valine pathways. This intermediate is converted into α-isopropylmalate by α-isopropylmalate synthase isozymes Leu4 and Leu9. An interesting feature of the major isozyme Leu4 is that it can exist in two forms: one containing an N-terminal signal sequence directing it to mitochondria and a shorter cytosolic variant without the N-terminal sequence. After this step, α-isopropylmalate synthesized in mitochondria is exported to the cytosol where the remaining part of leucine synthesis occurs (Kohlhaw 2003). Figure 2. View largeDownload slide Overview of yeast mitochondrial amino acid metabolism. (a) Synthesis of the branched-chain amino acids leucine, isoleucine and valine. (b) Synthesis of ornithine, an intermediate in arginine biosynthesis. Figure 2. View largeDownload slide Overview of yeast mitochondrial amino acid metabolism. (a) Synthesis of the branched-chain amino acids leucine, isoleucine and valine. (b) Synthesis of ornithine, an intermediate in arginine biosynthesis. In addition to BCAA synthesis, the synthesis of arginine and lysine partly occurs in mitochondria (Xu et al.2006; Ljungdahl and Daignan-Fornier 2012). The five steps in synthesis of ornithine, an intermediate in arginine biosynthesis, is localized to mitochondria (Jauniaux, Urrestarazu and Wiame 1978). The synthesis starts from glutamate and acetyl-CoA and involves five acetylated steps catalyzed by the enzymes Arg2, Arg5,6, Arg7 and Arg8. An overview of ornithine synthesis is given in Fig. 2b. Interestingly, N-acetylglutamyl-phosphate reductase (Arg5) and acetylglutamate kinase (Arg6) are encoded by a single gene (ARG5,6), and have been shown to form a metabolon, which is a complex formed by sequential enzymes of a metabolic pathway, with Arg2 (Abadjieva et al.2001). This metabolon catalyzes the first three steps in ornithine synthesis. In the pathway for synthesis of lysine from α-ketoglutarate, two mitochondrial enzymes are involved (Ljungdahl and Daignan-Fornier 2012). The steps from homocitrate to homoisocitrate, via homoaconitate, and from homoisocitrate to α-ketooadipate are catalyzed by the mitochondrial enzymes Lys4 and Lys12, respectively. The remaining steps of lysine biosynthesis are carried out in the cytosol. Transport of intermediates in lysine biosynthesis across the IMM involves the mitochondrial carriers Odc1, Odc2 and Ctp1 (Palmieri et al.2006). Iron–sulfur cluster biogenesis One of the essential functions of mitochondria is that of iron–sulfur (Fe/S) cluster biogenesis, illustrated in Fig. 3. Fe/S clusters are versatile co-factors with a variety of functions in cellular processes of eukarya, ranging from acting as the catalytic sites to electron transfer and sensory functions (Beinert, Holm and Münck 1997). In yeast, mitochondria are not only involved in biogenesis and assembly of mitochondrial Fe/S proteins, but are also crucial for the assembly of cytoplasmic and nuclear Fe/S proteins (Braymer and Lill 2017). Apart from ferredoxin, none of the mitochondrial Fe/S proteins, including aconitase and complexes of the respiratory chain, are known to be essential for growth of S. cerevisiae. However, some of the cytoplasmic and nuclear Fe/S proteins, such as Rli1 required for the biogenesis of cytoplasmic ribosomes, are indispensable for viability of the yeast (Kispal et al.2005). This explains why complete loss of mitochondrial Fe/S cluster biosynthesis is lethal. In yeast, the synthesis of Fe/S cluster depends on six proteins that show a high degree of conservation to their bacterial counterparts (Blanc, Gerez and Ollagnier de Choudens 2015). Iron required for the synthesis is imported by the carrier proteins Mrs3/4 of the inner membrane driven by the proton motive force (Mühlenhoff et al.2003b). Biogenesis of Fe/S clusters starts with the de novo synthesis of a [2Fe-2S] on the scaffold protein Isu1 (Mühlenhoff et al.2003a). The synthesis requires the interaction of Isu1 with a desulfurase complex consisting of Nfs1, Isd11 and Acp1 (Adam et al.2006; Van Vranken et al.2016), acting as a sulfur donor by releasing a sulfur moiety from a cysteine leading to the formation of alanine and a persulfide on a conserved cysteine on Nfs1 (Lill et al.2012). Subsequently, the sulfur is released to the scaffold Isu forming a persulfide on one of the three conserved cysteine residues on the scaffold. The persulfide is converted to a sulfide in a process that is thought to involve the electron transfer chain comprising of ferredoxin reductase (Arh1) and ferredoxin (Yah1) transferring electrons from NAD(P)H. Yeast mitochondrial ferredoxin has been shown to be essential for Fe/S cluster biogenesis (Lange et al.2000). Furthermore, Fe/S cluster synthesis requires frataxin (Yfh1) interacting with both Isu1 and Nfs1-Isd11 in an iron-dependent manner (Gerber, Mühlenhoff and Lill 2003). Yfh1 is a matrix iron chaperone that, in Fe/S cluster assembly, has the role of supplying Isu1 with iron (Ranatunga et al.2016). A recent study also identified mitochondrial acyl carrier protein (Acp1) as a subunit of the desulfurase complex, having a role in stabilizing the complex (Van Vranken et al.2016). Acp1 plays an important role in mitochondrial fatty acid metabolism (Hiltunen et al.2010). It has therefore been speculated that Acp1 may have a regulatory role linking biogenesis of respiratory functions and metabolic activities (Braymer and Lill 2017). Figure 3. View largeDownload slide An overview of yeast mitochondrial iron-sulfur (Fe/S) cluster biogenesis. Iron required for the synthesis is imported by membrane carriers Mrs3/4 driven by the proton motive force. Synthesis of [2Fe-2S] clusters occurs on the scaffold protein Isu1, requiring sulfur from the cysteine desulfurase complex Nfs1-Isd11-Acp1, and electrons from the transfer chain consisting of NAD(P)H, ferredoxin reductase and ferredoxin. The Fe/S cluster is transferred from monothiol glutaredoxin Grx5 by the action of ATP-dependent Hsp70 chaperone Ssq1, J-type co-chaperone Jac1 and nucleotide exchange factor Mge1. The [2Fe-2S] clusters are either directly inserted into target proteins or delivered to the Isa1-Isa2-Iba57 for [4Fe-4S] cluster biogenesis, followed by insertion into target proteins by targeting factors including Nfu1, Bol1 and Bol3. Figure 3. View largeDownload slide An overview of yeast mitochondrial iron-sulfur (Fe/S) cluster biogenesis. Iron required for the synthesis is imported by membrane carriers Mrs3/4 driven by the proton motive force. Synthesis of [2Fe-2S] clusters occurs on the scaffold protein Isu1, requiring sulfur from the cysteine desulfurase complex Nfs1-Isd11-Acp1, and electrons from the transfer chain consisting of NAD(P)H, ferredoxin reductase and ferredoxin. The Fe/S cluster is transferred from monothiol glutaredoxin Grx5 by the action of ATP-dependent Hsp70 chaperone Ssq1, J-type co-chaperone Jac1 and nucleotide exchange factor Mge1. The [2Fe-2S] clusters are either directly inserted into target proteins or delivered to the Isa1-Isa2-Iba57 for [4Fe-4S] cluster biogenesis, followed by insertion into target proteins by targeting factors including Nfu1, Bol1 and Bol3. After the Fe/S cluster has been synthesized, the maturation of Fe/S proteins requires a two-step process, which includes the release of the cluster from Isu1 and transfer to proteins that transiently bind it, and the insertion of the cluster into apoproteins (Lill et al.2012). The first step is carried out by a chaperone system consisting of mitochondrial Hsp70 Ssq1, J-protein Jac1, nucleotide exchange factor Mge1 and the monothiol glutaredoxin Grx5. Dutkiewicz et al. (2003) demonstrated that Jac1 and Ssq1 work cooperatively and that depletion of either of the two chaperones leads to accumulation Fe/S clusters on Isu1. Furthermore, they showed that the nucleotide exchange factor Mge1 stimulated the ATP hydrolysis activity of Ssq1 and proposed that Jac1 binds Isu1 and delivers it to the ATP-bound form of Ssq1. The working cycle of Ssq1, Jac1 and Mge1 is similar to the general mechanism of Hsp70 chaperones as described by Kampinga and Craig (2010). ATP hydrolysis by Ssq1, stimulated by interaction with Isu1 and Jac1, leads to a conformational change causing it bind to Isu1 and Grx5 (Uzarska et al.2013). This, in turn, induces a conformational change in Isu1 leading to a weakened binding of the Fe/cluster. The release of the Fe/S cluster from Isu1 is dependent on the action of monothiol glutaredoxin Grx5 (Rodriguez-Manzaneque et al.2002). Mühlenhoff et al. (2003a) further showed that depletion of Grx5 leads to an accumulation of Fe/S clusters on Isu1. More recent studies have confirmed the binding of both [2Fe-2S] and [4Fe-4S] clusters by Grx5 as well as the interaction of Grx5 with Ssq1 (Uzarska et al.2013). These results propose that Grx5 acts as a transfer protein mediating the transfer of Fe/S clusters from Isu1 to apoproteins. The chaperone cycle is completed when the ADP bound to Ssq1 is exchanged for ATP by the action of Mge1. The induced conformational change associated with nucleotide exchange causes dissociation of the Ssq1-Isu1-Grx5 complex (Uzarska et al.2013). In addition to Grx5, which has been shown to be required for maturation of mitochondrial and cytosolic Fe/S proteins, the generation of [4Fe-4S] clusters has been shown to require the action of specialized targeting components Isa1, Isa2 and Iba57 (Gelling et al.2008; Mühlenhoff et al.2011). The exact mechanism of action of the proteins has not yet been elucidated. Iba57 interacts with Isa1and Isa2, and the respective deletion mutants show similar phenotypes suggesting that the proteins act as a functional unit. Yeast strains depleted in any of the three proteins leads to deficiency of de novo Fe/S cluster assembly on proteins aconitase (Aco1) and homoaconitase (Lys4), further strengthening the role of Isa1–2 and Iba57 in Fe/S cluster maturation (Gelling et al.2008; Mühlenhoff et al.2011). More recent studies have also identified the additional targeting factors Nfu1, Bol1 and Bol3 that facilitate the transfer of [4Fe-4S] clusters from the Isa1-Isa2-Iba57 complex to target apoproteins (Melber et al.2016; Uzarska et al.2016). Heme biosynthesis and signaling The heme biosynthesis pathway in yeast, illustrated in Fig. 4, consists of eight steps and is dually localized between the mitochondrial matrix and the cytosol (Hoffman, Góra and Rytka 2003). The pathway starts with the synthesis of 5-aminolevulinic acid (ALA) from glycine and succinyl-CoA, catalyzed by Hem1, in mitochondria. ALA is transported to the cytosol where it is converted to porphobilinogen, and in four additional steps to protoporphyrinogen IX. This involves the subsequent action of enzymes Hem2, Hem3, Hem4, Hem12 and Hem13. The final two steps of heme synthesis are the conversion of protoporphyrinogen IX to protoporphyrin IX, which is then converted to heme. These two reactions are catalyzed by Hem14 and Hem15. Heme is widely known to be involved in sensing cellular oxygen levels through activation of the transcription factor Hap1, which is involved in transcriptional regulation of oxygen responsive genes (Ter Linde and Steensma 2002). Interestingly, out of the genes involved in the heme biosynthesis pathway, HEM13 is the only gene whose expression is affected by oxygen/heme. Figure 4. View largeDownload slide Overview of heme biosynthesis in yeast. Figure 4. View largeDownload slide Overview of heme biosynthesis in yeast. Mitochondrial lipids Mitochondria consist of two membranes, the inner and outer mitochondrial membrane, confining two compartments, the IMS and the matrix. The two membranes are composed mainly of phospholipids, and the phospholipid composition of yeast mitochondria has been measured (Zinser et al.1991). The two major phospholipids are posphatidylcholine (PC) and phosphatidylethanolamine (PE) constituting 40% and 27% of total mitochondrial phospholipids, respectively, but the membranes also contain phosphatidylserine (PS), phosphatidylinositol (PI), phosphatidic acid (PA) and the mitochondrion specific phospholipid cardiolipin (CL). The composition of the two membranes differs in the way that the inner membrane has a higher content of proteins, PI and CL than the outer membrane (Horvath and Daum 2013). Mitochondria are partly autonomous in synthesizing lipids, being able to synthesize PE, CL and some intermediate phospholipids (Osman, Voelker and Langer 2011; Horvath and Daum 2013). In the yeast S. cerevisiae, three different pathways for producing PE exist, localized to different cellular compartments (Birner et al.2001). PE can be synthesized de novo from the CDP-ethanolamine part of the Kennedy pathway, which also contains a branch for synthesis of PC (as reviewed by Daum et al.1998), or by the decarboxylation of PS carried out by mitochondrial Psd1 or Golgi/vacuolar Psd2 (Horvath and Daum 2013). The two decarboxylases share 19% sequence identity. The major route for PE biosynthesis is through decarboxylation of PS in the IMM by Psd1 (Fig. 5a) (Clancey, Chang and Dowhans 1993). Figure 5. View largeDownload slide Overview of lipid metabolism in yeast mitochondria. (a) Biosynthesis of phosphatidylethanolamine (PE) and cardiolipin (CL) in yeast. ERM, ER membrane; OMM, outer mitochondrial membrane; IMS, intermembrane space; IMM, inner mitochondria membrane; PA, phosphatidic acid; CDP-DAG, CDP-diacylglycerol; PGP, phosphatidylglycerolphosphate; PG, phosphatidylglycerol; MLCL, monolysocardiolipin; PS, phosphatidylserine. (b) Mitochondrial fatty acid synthesis. Figure 5. View largeDownload slide Overview of lipid metabolism in yeast mitochondria. (a) Biosynthesis of phosphatidylethanolamine (PE) and cardiolipin (CL) in yeast. ERM, ER membrane; OMM, outer mitochondrial membrane; IMS, intermembrane space; IMM, inner mitochondria membrane; PA, phosphatidic acid; CDP-DAG, CDP-diacylglycerol; PGP, phosphatidylglycerolphosphate; PG, phosphatidylglycerol; MLCL, monolysocardiolipin; PS, phosphatidylserine. (b) Mitochondrial fatty acid synthesis. The synthesis of CL, which is a dimeric lipid with four acyl chains and two PA moieties linked to glycerol, in the IMM starts with the synthesis of CDP-diacylglycerol (CDP-DAG) from PA (Fig. 5a). The reaction is catalyzed either by Cds1, which is mainly localized to the ER, but has also been detected in mitochondrial fractions (Kuchler, Daum and Paltauf 1986; Shen et al.1996), or by Tam41, which is localized to the matrix side of the IMM (Tamura et al.2013). CDP-DAG is a central intermediate in phospholipid metabolism and serves as a precursor in the biosynthesis of PI, PS and PC synthesized from PS via conversion into PE (Henry, Kohlwein and Carman 2012). The first committed and rate-limiting step of CL biosynthesis is the conversion of CDP-DAG and glycerol-3-phosphate to phosphatidylglycerol phosphate (PGP) by Pgs1 (Chang et al.1998a). The subsequent two steps include dephosphorylation of PGP by Gep4, a phosphatase on the matrix side of the IMM (Osman et al.2010), to form phosphatidylglycerol (PG), and the synthesis of CL by reacting PG with CDP-DAG catalyzed by Crd1 (Tuller et al.1998; Chang et al.1998b). Maturation of CL involves remodeling of the acyl chains by the sequential action of Cld1 and Taz1 (Gu et al.2004; Beranek et al.2009). Cld1 functions as a phospholipase, preferentially removing palmitic acid (C16:0) from premature CL, forming monolysocardiolipin (MLCL). Taz1 is an acyltransferase localized to the IMS side of the OMM that catalyzes the acylation of MLCL with unsaturated fatty acids. Since the capacity of mitochondria to synthesize phospholipids is limited to synthesis of PE and CL, mitochondrial biogenesis is strictly dependent on import of the extramitochondrially synthesized lipids. The major site of synthesis of these lipids is the subfraction of the ER known as the mitochondria associated membrane (MAM) (Gaigg et al.1995). PS is synthesized by Cho1 which is localized to the MAM, but has also recently been observed in mitochondrial fractions (Letts et al.1983; Vögtle et al.2017). The MAM has also been suggested to be responsible for transport of the synthesized phospholipids into mitochondria (Daum and Vance 1997). One of the more recently discovered functions of yeast mitochondria (and also human mitochondria) in lipid metabolism is biosynthesis of fatty acids (Hiltunen et al.2009). Mitochondrial fatty acid synthesis (FAS) occurs in an acyl carrier protein (ACP)-dependent manner that resembles bacterial type II FAS, which in contrast to eukaryal type I FAS makes use of separate enzymes that catalyze the individual reactions (Stephen et al.2005). The components of the mitochondrial FAS pathway have been identified and partially characterized in S. cerevisiae, starting with the discovery of mitochondrial ACP (Brody et al.1997), and the yeast has therefore been used as a model organism for further studies (Hiltunen et al.2009). The pathway of mitochondrial FAS is outlined in Fig. 5b. For a detailed review of the mechanisms of synthesis, see Stephen et al. (2005). The pathway involves the action of a number of enzymes with high resemblance to their bacterial homologs, namely phosphopantetheine transferase Ppt2, malonyl-CoA:ACP transferase Mct1, ketoacyl synthase Cem1 and ketoacyl reductase Kar1 (Harington et al.1993; Schneider et al.1997; Stuible et al.1998). On the other hand, hydroxyacyl-ACP hydratase Htd2 and enoyl-ACP reductase, responsible for the final steps of elongation of the acyl chain, show little resemblance to enzymes of bacterial type II FAS (Torkko et al.2001; Kastaniotis et al.2004). Furthermore, mitochondrial acetyl-CoA carboxylase encoded by HFA1 has shown to be homologous to cytosolic ACC1 (Hoja et al.2004). The major product of mitochondrial type II FAS (mtFAS) identified is octanoyl-ACP, a precursor needed for synthesis of lipoic acid, which in turn is a cofactor essential for the function of pyruvate dehydrogenase (PDH), α-ketoglutarate dehydrogenase and the glycine cleavage system (Schonauer et al.2009). Apart from acting as a route for lipoic acid synthesis, the role of mtFAS products is still largely unknown (Hiltunen et al.2009). Mitochondria with defects in mtFAS show respiratory-deficient phenotypes, resulting from low levels of lipoic acid and subsequent inactivation of PDH and α-ketoglutarate dehydrogenase. Mitochondrial fatty acids have been shown to play a role in processing of tRNA, since a strain with deletion of HTD2 showed deficiency in the 5΄ processing of mitochondrial precursor tRNAs by RNase P (Schonauer et al.2008). Furthermore, a regulatory role of mtFAS in response to pyruvate levels in yeast cells when grown on glucose has been suggested (Hiltunen et al.2009). When pyruvate levels are low, the level of acetyl-CoA going into mtFAS is also low, resulting in lower levels of lipoic acid and hence also decreased activity of PDH. MITOCHONDRIA IN DISEASE Mitochondrial function and structure is highly complex. Not only are mitochondria dynamic organelles that form tubular networks regulated by fission and fusion (Westermann 2010), but their function also depends on the interplay between the mitochondrial and nuclear genome. Due to the inherent complexity of mitochondrial structure and function, mitochondrial dysfunction has been implicated in a wide range of human disorders (Schapira 2012). Over the years, more than 150 different mitochondrial disorders have been described, with the majority of disorders associated with dysfunction of mitochondrial energetics (Lasserre et al.2015). These diseases are diverse and pleiotropic making the difficult to study. An overview of selected human mitochondrial disorders and their genetic causes is given in Table 2. Mitochondrial disorders affect at least 1 in 5000 newborns, but it can take as long as to adulthood before the symptoms present themselves (Schaefer et al.2004). Due to the dependence of mitochondrial function on two genomes, mitochondrial disorders can be divided into those caused by mutation in the nuclear DNA (nDNA) and those caused by mutations in mtDNA. Since the first discovery of a mutation in a nuclear gene causing mitochondrial disorder was discovered (Bourgeron et al.1995), more than 110 genes in nDNA have been found to cause mitochondrial diseases (Vafai and Mootha 2012). These genes are involved in various processes including assembly of complexes involved in oxidative phosphorylation, mtDNA maintenance, mitochondrial protein import, dynamics and metabolite transport. Of special interest is the POLG gene encoding the catalytic component of mitochondrial DNA polymerase gamma, for which over 150 mutations involved in human mitochondrial disorders have been identified (for a complete list, see the database https://tools.niehs.nih.gov/polg/) (Stumpf and Copeland 2011). These mutations cause defects in mitochondrial function resulting from deletions, depletion and point mutations in mtDNA. Diseases caused by point mutations in mtDNA include those in protein-coding mitochondrial genes and in mitochondrial tRNA, and result in various phenotypes. Since the protein-coding genes in human mtDNA all encode subunits of the respiratory chain and oxidative phosphorylation, mutations in the genes mainly affect the respiratory complexes they belong to. Mutations in tRNA-encoding genes lead to defects in protein synthesis causing more pleiotropic effects. Due to the high conservation between yeast and humans, mutations in tRNA can be studied using yeast as a model (Montanari et al.2008, 2010). To date, more than 200 mutations in mtDNA of humans have been identified that are involved or are suspected to be involved in disease (https://www.mitomap.org). Table 2. Examples of mitochondrial disorders. Genome Genetics Phenotype mtDNA Mutations in tRNA genes Mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome, cardiomyopathy, diabetes, deafness Single or multiple deletions Chronic progressive external ophthalmoplegia (CPEO), Kearns-Sayre syndrome, Pearson syndrome, myopathy Complex I gene mutations Leber's hereditary optic neuropathy (LHON) nDNA Mutations in DNA polymerase gamma subunit (POLG) Alper-Huttenlocher syndrome (AHS), Ataxia neuropathy spectrum (ANS), Myclonic epilepsy myopathy sensory ataxia (MEMSA), CPEO Mutations in structural subunit genes or assembly factor genes for respiratory complexes Leigh syndrome, myopathy, encephalopathy, liver disease or failure Genome Genetics Phenotype mtDNA Mutations in tRNA genes Mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome, cardiomyopathy, diabetes, deafness Single or multiple deletions Chronic progressive external ophthalmoplegia (CPEO), Kearns-Sayre syndrome, Pearson syndrome, myopathy Complex I gene mutations Leber's hereditary optic neuropathy (LHON) nDNA Mutations in DNA polymerase gamma subunit (POLG) Alper-Huttenlocher syndrome (AHS), Ataxia neuropathy spectrum (ANS), Myclonic epilepsy myopathy sensory ataxia (MEMSA), CPEO Mutations in structural subunit genes or assembly factor genes for respiratory complexes Leigh syndrome, myopathy, encephalopathy, liver disease or failure mtDNA, mitochondrial DNA; nDNA, nuclear DNA. View Large Table 2. Examples of mitochondrial disorders. Genome Genetics Phenotype mtDNA Mutations in tRNA genes Mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome, cardiomyopathy, diabetes, deafness Single or multiple deletions Chronic progressive external ophthalmoplegia (CPEO), Kearns-Sayre syndrome, Pearson syndrome, myopathy Complex I gene mutations Leber's hereditary optic neuropathy (LHON) nDNA Mutations in DNA polymerase gamma subunit (POLG) Alper-Huttenlocher syndrome (AHS), Ataxia neuropathy spectrum (ANS), Myclonic epilepsy myopathy sensory ataxia (MEMSA), CPEO Mutations in structural subunit genes or assembly factor genes for respiratory complexes Leigh syndrome, myopathy, encephalopathy, liver disease or failure Genome Genetics Phenotype mtDNA Mutations in tRNA genes Mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome, cardiomyopathy, diabetes, deafness Single or multiple deletions Chronic progressive external ophthalmoplegia (CPEO), Kearns-Sayre syndrome, Pearson syndrome, myopathy Complex I gene mutations Leber's hereditary optic neuropathy (LHON) nDNA Mutations in DNA polymerase gamma subunit (POLG) Alper-Huttenlocher syndrome (AHS), Ataxia neuropathy spectrum (ANS), Myclonic epilepsy myopathy sensory ataxia (MEMSA), CPEO Mutations in structural subunit genes or assembly factor genes for respiratory complexes Leigh syndrome, myopathy, encephalopathy, liver disease or failure mtDNA, mitochondrial DNA; nDNA, nuclear DNA. View Large Saccharomyces cerevisiae is a widely used eukaryal model, with a complete genome sequence (Goffeau et al.1996), and a well-annotated genome, and many genetic tools available, including availability of a complete gene deletion collection (Winzeler et al.1999). Yeast is a powerful model also for studying mitochondrial biology and human mitochondrial diseases (Baile and Claypool 2013; Rutter and Hughes 2015). About 40% of all genes involved in human diseases have an ortholog in yeast (Bassett, Boguski and Hieter 1996) and 70% of the nuclear genes involved in mitochondrial disease are conserved between yeast and human (Lasserre et al.2015). The role of yeast models in studying mitochondrial disorders has been reviewed in detail elsewhere (Rinaldi et al.2010; Lasserre et al.2015), but here we will briefly summarize the main advantages of yeast models and give a few examples of their uses. First, S. cerevisiae has a good fermentative capacity, being able to satisfy its energy requirements by fermentation. It can therefore tolerate mutations that disrupt oxidative phosphorylation, and even complete loss of mtDNA as long as fermentable carbon sources are present, allowing mutations causing mitochondrial dysfunction to be studied without being lethal (Chen and Clark-Walker 1999). Yeast mutants with defects in oxidative phosphorylation form small colonies when grown on plates containing a fermentable carbon source, a phenotype referred to as petite (Slonimski and Ephrussi 1949). Second, tools for manipulating the mitochondrial genome are available, allowing mutations involved in mitochondrial disorders to be reconstructed (Bonnefoy and Fox 2007). Moreover, yeast populations become homoplasmic within a few generations, meaning that all mtDNA copies have the same sequence (Shibata and Ling 2007). This allows for studying the severity and impact on mitochondrial function of point mutations that are often heteroplasmic in human mitochondria. Due to the many benefits of using yeast as a model, it has been exploited for studies of mutations in mtDNA including components of the respiratory chain complexes III (Meunier et al.2013) and IV (Kabala et al.2014). Yeast models have also been valuable for studying mutations in mitochondrial DNA polymerase gamma (Szczepanowska and Foury 2010). In contrast to human POLG, the yeast ortholog Mip1 is a single subunit. The phenotypic effect of defects in Mip1 is easily observed since defects in replication of mtDNA causing depletion or deletions result in a cytoplasmic petite phenotype, identified by the inability to form large colonies when grown on fermentable media. It is common that patients carry multiple mutations in POLG, some pathogenic and some harmless (Rinaldi et al.2010). By modeling individual mutations in Mip1, yeast models can be used to distinguish the severity of these SNPs. In addition to yeast models, mice models have also been used to study POLG. In an effort to investigate the role of mutations and deletions in mtDNA in aging, Trifunovic and co-workers generated knock-in mice with a point mutation in the catalytic subunit of mtDNA polymerase exchanging a critical aspartate residue in the second exonuclease domain with an alanine (Trifunovic et al.2004). The knock-in mice expressed a proofreading-deficient variant of the polymerase resulting in a mutator phenotype showing up to a 5-fold increase in point mutations, as well as increased amounts of deletions in mtDNA. Furthermore, the mice experienced a reduced lifespan and premature aging. MODELING OF METABOLISM Due to its central role in all living cells, a main objective of systems biology over the last decades has been to develop quantitative models of metabolism. These models incorporate bibliomic- (the entirety of information available in literature), genomic- and biochemical knowledge of the biological system in a computable format that allows for simulating the behavior of an organism under different conditions, such as genetic or environmental perturbations. Genome-scale metabolic models Genome-scale metabolic models (GEMs) are in silico reconstructions that represent the complete metabolism of a given organism. These reconstructions are built in a bottom-up approach starting from an annotated genome sequence, providing gene-protein-reaction (GPR) relationships. Using this GPR, each metabolic reaction is connected to an enzyme, and there is therefore a direct link between the genotype and metabolic phenotype. Furthermore, reconstruction of a metabolic network requires knowledge on the known capabilities of the system, stoichiometry and co-factor requirements of the reactions, as well as directionality and compartment localization. By including information collected from literature, databases and genome annotation, metabolic network reconstructions represent a knowledge base that collects extensive information about the organism. When additional information becomes available, the models can easily be expanded to include more reactions or additional cellular compartments. To use metabolic network reconstructions for simulations, the concept of flux balance analysis (FBA) can be used (Orth, Thiele and Palsson 2010). Here, mass balances around each metabolite are set-up, which provide a large number of constraints on the fluxes in the network and thus define a space of feasible solutions. By introducing an appropriate objective function, the solution space can be narrowed down and a solution can be identified using linear programming, resulting in a flux distribution for optimizing the objective. The most commonly used objective function is maximizing growth but other objectives, such as maximizing ATP production, have also been used (Schuetz, Kuepfer and Sauer 2007). A particular strength of GEMs is that, due to the presence of gene-protein-reaction relationships, they provide a framework for integrative analysis of omics data and have therefore been useful in evaluating the biological capabilities and flux states in specific conditions (Oberhardt, Palsson and Papin 2009). The constraint-based approach typically used for simulations with GEM involves a feasible solution space with a large degree of freedom. By integrating omics data, constraints are imposed on the model that allow improved predictive capabilities to gain insight into cellular phenotypes and metabolic regulation. There are many studies published where yeast GEMs have been used to integrate omics data (Sánchez and Nielsen 2015). The most widely used omics data for integration with GEMs is transcriptomics data, owing to the advancements in high-throughput sequencing techniques that have increased the speed and reduced the cost of sequencing. The main advantages of using transcriptomics data is that it provides a genome-wide coverage and that it is relatively easy to generate the data due to the highly automated process (Hoppe 2012). Integration of transcriptomics data into GEMs is based on the idea that there is a relationship between transcript level and the flux of the reaction catalyzed by the corresponding enzyme, and many methods for transcriptome data integration have been developed (Machado and Herrgård 2014). However, as there are many intermediate biological processes governing the relationship between gene expression and protein levels, including mRNA decay, protein degradation and translational efficiency, it can be difficult to directly correlate transcript levels with enzyme activity (Hoppe 2012). Therefore, in order to confer constraints on the metabolic fluxes as a consequence of enzymatic capacity, there is an increasing interest in developing modeling approaches that allow for integration of proteomics data into GEMs, which will also be advantageous for modeling of mitochondria metabolism as large proteome datasets are becoming available, as discussed above. Of particular interest are models that allow for integration of quantitative proteomics data from which enzyme levels can be directly implied. So far, proteomics datasets have mostly been indirectly used with GEMs, by correlating the protein level to the corresponding fluxes (Sánchez and Nielsen 2015). A number of different strategies have been developed to account for enzymatic limitations in metabolic models, such as flux balance analysis with molecular crowding (FBAwMC), which relies on imposing a systems-level constraint on the concentration of metabolic enzymes (Beg et al.2007). Another alternative approach to account for enzymatic limitations is genome-scale models of metabolism and gene expression (ME models) (O’Brien and Palsson 2015). These models include metabolic reactions and all processes necessary to synthesize a functional protein, starting from gene expression. Thereby, ME models allow for predictions of proteome and transcriptome allocation and limitations. In a recent study, we developed a framework referred to as GECKO, which enhances a GEM with enzymatic constraints using kinetics and omics data (Sánchez et al.2017). The framework is built on the basic constraint that any given reaction flux cannot exceed its maximum rate (vmax), which is expressed as the catalytic capacity of the enzyme (kcat) multiplied by the abundance of the corresponding enzyme. As discussed further below, this framework will be particularly well suited for modeling mitochondrial metabolism, where there are many different constraints, including proteome allocation to the different compartments. It may seem contradictory that metabolomics data are little used in metabolic modeling, but this is due to the fact that it is difficult to obtain truly quantitative data for the level of intracellular metabolites, as well demonstrated in a large interlaboratory study (Canelas et al.2010). Furthermore, it is with current technologies not possible to measure, at least in a high-throughput fashion, the metabolite levels in different compartments. Modeling mitochondria There have only been few attempts to model mitochondria metabolism. The first mitochondrion-specific model (for human cells), developed by Vo, Greenberg and Palsson (2004) based on a proteomics biochemical data, described 189 reactions accounting for energy metabolism, detoxification of reactive oxygen species, heme synthesis, as well as nitrogen and lipid metabolism of human cardiac mitochondria. The model was later used to integrate experimental data to successfully simulate mitochondrial function under normal, diabetic, ischemic and two types of diets (Thiele et al.2005). In 2011, Smith and Robinson constructed a more comprehensive model of human cardiac mitochondria named iAS253 (Smith and Robinson 2011). The model was created based on metabolite availability and was aided by the MitoMiner mitochondrial proteomics database (Smith and Robinson 2009). The database integrates experimental proteomic localization data with annotation from public resources, including Gene Ontology (Ashburner et al.2000) and UniProt (UniProt Consortium 2015), as well as metabolic pathway data from Kyoto Encyclopedia of Genes and Genomes (Kanehisa et al.2016). Since its first release, the database has been continuously updated and currently contains information for six organisms, including the yeasts S. cerevisiae and Schizosaccharomyces pombe (Smith and Robinson 2016). To demonstrate the predictive capabilities of the model, the authors used FBA to calculate the metabolic fluxes through the network under normal conditions and to simulate deficiency in fumarase, succinate dehydrogenase and α-ketoglutarate dehydrogenase, affecting the TCA cycle. In a study from 2016, an expanded version of the iAS253 model was used to simulate the metabolic effects of respiratory chain disorders, more specifically deficiency of complexes I-IV (Zieliński et al.2016). In a recent study, the model was further expanded to cover central metabolism of human cells (Smith et al.2017). The improvements of the model included an updated representation of the respiratory chain and metabolite transport with regard to the proton motive force, as well as a more accurate partitioning of reactions between the cytosol and mitochondria. The model was tested by using FBA to simulate cardiomyocyte metabolism with the objective to maximize ATP production, and was shown to produce realistic results compared to experimental data, with regard to fluxes and the use of different metabolites as energy sources. Together, these studies highlight the potential of constraint-based models of mitochondria to simulate normal physiology and disease states, providing the ability to test hypotheses and to give insights into mechanisms and possible intervention strategies of mitochondrial disorders. Saccharomyces cerevisiae was the first eukaryotic organism for which a GEM was constructed (Förster et al.2003). Since then, several groups have contributed to the expansion and development of yeast GEMs (Lopes, Rocha and Zhao Helder Lopes 2017). Although much of the current knowledge on mitochondrial biology comes from yeast, there is no mitochondrion-specific model available. Mitochondrial metabolism is integrated into the current GEMs of yeast and these models perform well for simulating central metabolism. However, when it comes to investigating mitochondria as a subsystem, a constraint-based model specific for mitochondria would come with several benefits. A reduced size and complexity of the model would allow for a more careful curation of the model, increasing reaction confidence as well as facilitating elucidation of system behavior and interpretation of simulation results. Furthermore, by including enzyme-constraints and kinetic information into the model would allow for investigating mitochondrial protein allocation as well as to integrate and analyze proteomics data. This could aid understanding of the dynamics and state of the mitochondrial proteome at different conditions, which is necessary for further understanding mitochondrial function. Since the majority of the mitochondrial proteins are synthesized by cytoplasmic ribosomes, mitochondrial function relies on translocation of these proteins into mitochondria. Incorporating protein translocation, together with protein synthesis of the intramitochondrially synthesized proteins, into a constraint-based model of mitochondria could aid the understanding of the energetic requirements of these processes as well as the interaction with other mitochondrial functions. Additionally, by adding a detailed description of the biochemical composition of mitochondria, the cost of synthesizing mitochondria in terms of energy and metabolites can be estimated. Taken together, a constraint-based model of mitochondria, accounting for protein translocation, synthesis and enzyme usage, could provide a framework for generating and testing hypotheses advancing the knowledge on mitochondrial function. PERSPECTIVES Although much progress has been made in understanding mitochondrial function and dysfunction during the recent decades, some challenges remain. Due to the inherent complexity of mitochondrial metabolism, perturbations in a small part of the network can have a large effect on the overall capabilities of mitochondria that can be difficult to predict without a systemic model. Furthermore, mitochondrial dysfunction often results in complex and non-intuitive phenotypes, making the underlying genetic cause challenging to elucidate. Here, a GEM of the mitochondrion could aid the interpretation and in unraveling the complexity, by providing a gene-protein-reaction connectivity, allowing for directly linking phenotypes to metabolic capabilities. Furthermore, GEMs allow for simulating mitochondrial behavior under different cellular conditions, changes in metabolism and modeling phenotypes as changes in metabolic fluxes. By using the recently developed GECKO framework (Sánchez et al.2017), constraints on enzymatic capacity can be included in the model, resulting in increased biological relevance of the predicted phenotypes. Additionally, such a model would give insight into enzyme usage and allow for incorporation of quantitative proteomics data, allowing the generation of context-specific models. Therefore, an enzyme-constrained model of the mitochondrion, in combination with systems biology analysis of quantitative proteomics data, may further advance our understanding of mitochondrial function by enabling the analysis of mitochondrial enzyme usage in different environments. The tools established could, together with the advantages of yeast as an experimental model for mitochondrial function, allow for gaining insight into the effects of disease-associated mutations. Acknowledgements The authors would like to thank the Novo Nordisk Foundation and the Knut and Alice Wallenberg Foundation for funding. The authors would also like to thank Francesca Di Bartolomeo for valuable input on the manuscript. Conflict of interest. None declared. REFERENCES Abadjieva A , Pauwels K , Hilven P et al. A new yeast metabolon involving at least the two first enzymes of arginine biosynthesis . J Biol Chem 2001 ; 276 : 42869 – 80 . 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FEMS Yeast ResearchOxford University Press

Published: Apr 12, 2018

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