Generation of a Mitochondrial Protein Compendium in Dictyostelium Discoideum

Generation of a Mitochondrial Protein Compendium in Dictyostelium Discoideum | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Generation of a Mitochondrial Protein Compendium in Dictyostelium Discoideum Anna V Freitas, Jake T Herb, Miao Pan, Yong Cheng, Marjan Gucek, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1054199/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 May, 2022 Read the published version in iScience → Version 1 posted You are reading this latest preprint version Abstract The social amoeba Dictyostelium discoideum is a well-established model to study numerous cellular processes including cell motility, chemotaxis, and differentiation. As energy metabolism is involved in these processes, mitochondrial genetics and bioenergetics are of interest, though many features of Dictyostelium mitochondria differ from metazoans. A comprehensive inventory of mitochondrial proteins is critical to understanding mitochondrial processes and their involvement in various cellular pathways. Here, we utilized high-throughput multiplexed protein quantitation and homology analyses to generate a high-confidence mitochondrial protein compendium. Our proteomic approach, which utilizes quantitative mass spectrometry in combination with mathematical modeling, was validated through mitochondrial targeting sequence prediction and live-cell imaging. Our final compendium consists of 1082 proteins. Within our D. discoideum mitochondrial proteome, we identify many proteins that are not present in humans, yeasts, or the ancestral alpha-proteobacteria, which can serve as a foundation for future investigations into the unique mitochondria of Dictyostelium . Additionally, we leverage our compendium to highlight the complexity of metabolic reprogramming during starvation-induced development. Our compendium lays a foundation to investigate mitochondrial processes that are unique in protists, as well as for future studies to understand the functions of conserved mitochondrial proteins in health and diseases using D. discoideum as the model. General Cell Biology & Physiology Computational Biology Bioinformatics Developmental Biology Systems Biology Dictyostelium discoideum mitochondria metabolism MitoCarta proteomic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Dictyostelium discoideum , a social amoeba, is a well-established model organism to study eukaryotic cellular processes such as cell motility, chemotaxis, and differentiation (Bozzaro, 2013). Under normal nutrient conditions, D. discoideum grows axenically through binary fission (Kessin, 2001). However, upon starvation, amoebae secrete cAMP, which attracts neighboring cells to aggregate together and form a multicellular mound. Cells in a mound move collectively as a slug toward light, heat, or humidity to find a suitable environment. The slug eventually matures into a fruiting body consisting of two major types of differentiated cells, spore cells that will start a new life cycle and stalk cells that form a stalk to hold the spore aloft (Kay, 1982). As many of the aforementioned biological processes are intertwined with cellular energetics, investigation of mitochondrial biogenesis and functions is an emerging area in D. discoideum research (Francione et al. , 2011; Pearce et al. , 2019). The D. discoideum mitochondrial genome is ~56 kb, circular, double-stranded DNA that encodes two ribosomal RNAs, 18 transfer RNAs (tRNAs), five open reading frames without annotated function, and 38 proteins including 18 subunits of the electron transport chain complexes and 15 ribosomal proteins (Ogawa et al. , 2000). Phylogenetic studies reveal that Amoebazoa diverged before Opisthokonta, but after the divergence of Plantae (Baldauf and Doolittle, 1997), and are more closely related to animals than plants. Notably, the Dictyostelium mitochondrial genetic system possesses a few differences from metazoans (Pearce et al. , 2019). D. discoideum mitochondrial DNA (mtDNA) has four introns in cox1/2 genes and utilizes universal codons (Angata et al. , 1995; Ogawa et al. , 2000), a common feature of most plants’ mitochondria (Jukes and Osawa, 1990; Cho et al. , 1998). The universal genetic code and the lack of a full set of tRNA genes on the Dictyostelium mitochondrial genome indicate that some nuclear-encoded tRNAs are likely imported into mitochondria to support the organellar translation. Additionally, the electron transport chain in Dictyostelium contain an additional component compared to its metazoan counterparts: an alternative oxidase (AOX) (Pearce et al. , 2019), which is found across eukaryotic clades besides animals (McDonald et al ., 2008). AOX is highly expressed during vegetative growth, but its expression level is markedly reduced upon starvation, suggesting a potential metabolic reprogramming occurs during starvation-induced development (Jarmuszkiewicz et al. , 2002). Interestingly, either reduction of mtDNA content or disruption of the rps4 locus (encoding mt-ribosomal protein S4) on mtDNA impairs aggregation and slug phototaxis but has no impact on vegetative growth (Chida, 2004; Chida et al. , 2008), suggesting that mtDNA, and most likely an intact oxidative phosphorylation system is essential to initiate the development program. On the other hand, pharmacological inhibitions of either Complex I or Complex V can induce aggregation, even though mitochondrial respiration appears to increase at the beginning of starvation (Kelly et al. , 2021). Therefore, the interplay between mitochondrial function and Dictyostelium development remains to be explored. Despite the growing interest in using D. discoideum as a model organism to study many conserved mitochondrial processes and some unique biology, a comprehensive list of the mitochondrial proteins has yet to be established. A recent proteomic study detected 294 proteins in D. discoideum mitochondria (Mazur et al. , 2021), which we believe is far from complete. Nuclear-encoded mitochondrial proteins, which constitute over 90% of the total mitochondrial proteome, are synthesized in the cytoplasm and then imported to mitochondria. It is estimated that the import of ~60% of these proteins relies on a positively charged, N-terminal mitochondrial targeting sequence (MTS) (Vögtle et al. , 2009). Computational approaches that integrate machine learning and known biological data are frequently used to predict mitochondrial targeting based on the presence of an MTS (Almagro Armenteros et al. , 2019). However, this method is insufficient to capture all mitochondrial proteins, as most proteins on the outer membrane and in the inner membrane space, and some inner membrane proteins rely on alternate translocation mechanisms. An alternative computational approach leverages sequence homology to known mitochondrial protein compendiums that were generated using mass spectrometry (MS)-based proteomic discovery (Pagliarini et al. , 2008; Morgenstern et al. , 2017). However, to compensate for the rapid evolution of the mitochondrial genome, nuclear-encoded mitochondrial proteins evolve faster than other nuclear-encoded proteins (Cole et al. , 1995; Sloan et al. , 2014; Havird et al. , 2015; Li et al. , 2017; Yan et al. , 2019). Thus, some Dictyostelium mitochondrial proteins may escape the homology search, and protist-specific mitochondrial proteins will certainly be missed. In this study, we combined quantitative proteomics and mathematical modeling to identify over 900 high-confidence mitochondrial proteins, which were validated through both in silico and fluorescent microscopy analyses. We further complemented the proteomics-based mitochondrial protein discovery with bioinformatic approaches to create a compendium of 1082 D. discoideum mitochondrial proteins. We also discuss conserved D. discoideum mitochondrial proteins that may be used as the basis of validating mitochondrial proteins in other organisms, as well as unique features of the mitochondrial proteome in D. discoideum. Results And Discussion Mitochondrial protein discovery using quantitative proteomics To identify putative D. discoideum mitochondrial proteins, we searched for Dictyostelium homologs of 1136 human mitochondrial proteins listed in the Human MitoCarta 3.0 (Morgenstern et al. , 2017; Rath et al. , 2021), and retrieved 616 proteins (Figure 1 A, Table S1). This number is much less than known mitochondrial proteins in humans (1136) and baker’s yeast (901) (Pagliarini et al. , 2008; Rath et al. , 2021). We posited that mitochondrial proteome might be highly divergent between D. discoideum and humans, and many Dictyostelium mitochondrial proteins might be missed from this bioinformatic curation. We, therefore, took a proteomic approach to directly identify mitochondrial proteins in D. discoideum (Figure 1 A). From AX2 axenic cultures, we prepared mitochondria isolates—both crude and highly purified—through Percoll gradient ultracentrifugation. We performed tandem mass tag (TMT) liquid chromatography-mass spectrometry (LC-MS) on both mitochondria isolates and included AX2 whole-cell lysate as the control. A total of 6,892 proteins were captured in all samples (Table S2). A limitation of identifying organellar proteins from their subcellular fractions alone is that high-abundance contaminants are often co-purified and result in false-positive hits. To address this issue, we assessed the probability of a protein localizing to mitochondria by comparing its relative enrichment in mitochondrial preparations to a list of 47 authentic mitochondrial proteins that includes components of electron transport chain complexes and conserved enzymes in citrate cycles (Table S3). We first calculated the ratio of a protein’s abundance in the mitochondria isolates, both crude and highly purified, versus its abundance in the whole-cell lysate. The resulting value, indicating its enrichment in mitochondrial preparations, was further normalized to the average enrichment ratio of the 47 reference mitochondrial proteins, to compute the relative enrichment ratio (RER). Overall, a protein’s RER in crude mitochondria isolate is in accordance with that in purified mitochondria (Figure 2 A). However, the distribution of RERs appears continuous in crude mitochondria (Figure 1 B), but clusters into two distinct populations in purified mitochondria (Figure 1 B), which allowed us to determine a proper threshold of RER for mitochondrial proteins using mathematical modeling. Thus, we proceeded to analyze the RER for purified mitochondria only. In principle, a true mitochondrial protein would be co-purified with the reference mitochondrial proteins in pure mitochondrial isolates, and its RER should be 1.0. However, the RER distribution of these 47 reference proteins (Figure 2 B) appears as a normal curve centered around 1.0, suggesting that many mitochondrial proteins may have an RER below 1.0. Among all proteins profiled using TMT-based LC-MS, only 259 have an RER higher than 1.0 in purified mitochondria (Figure 2 C). We posit that different mitochondria proteins might be degraded to different extents, based on their intrinsic stability, during the procedure of mitochondrial purification, which involves overnight ultracentrifugation. Therefore, it is necessary to determine a proper RER value to differentiate mitochondrial proteins from non-mitochondrial proteins. We applied the expectation-maximization (EM) approach to a gaussian mixture model (GMM) to bin all proteins into two clusters: non-mitochondrial and mitochondrial proteins based on their RER values (Figure 2 D). We chose an RER cutoff of 0.343 (Figure 2 D) and assigned a total of 908 proteins having an RER higher than 0.343 as putative mitochondrial proteins (Table S4). GMM predicts that less than 17% proteins in the mitochondrial cluster, and only 0.1% proteins in the non-mitochondrial cluster would spill over to the other group (Figure 2 D), which corresponds to an 83% recovery rate and 7% false discovery rate, respectively. Validation of mitochondrial protein discovery based on quantitative proteomics To validate the accuracy of RER-based mitochondrial protein discovery, we first assessed the recovery rate of putative mitochondrial proteins in silico. Many mitochondrial proteins possess an N-terminus mitochondrial targeting sequence (MTS) that directs the import of nuclear-encoded mitochondrial proteins into the mitochondrial matrix (Backers, 2017). Overall, 24% of all proteins retrieved in the proteomics discovery experiment contain an MTS (Table S4). Importantly, 94% of these MTS-bearing proteins had an RER greater than 0.343 (Figure 3 A). On the contrary, 96% of proteins that were destined to other organelles such as the ER, Golgi, lysosomes, vacuoles, or secretory pathway had an RER less than 0.343. These analyses demonstrate that a cutoff value of an RER at 0.343 effectively separates mitochondrial proteins from non-mitochondrial proteins. We also surveyed the localization of 81 proteins recovered in LC-MS (Table S5), using fluorescent microscopy. These proteins were selected on the basis that their subcellular localization has not been annotated previously as mitochondrial, and their RERs are randomly distributed from 0.1 to 1.5. Each protein was tagged with GFP at its C-terminus and co-expressed with an MTS-mCherry fusion protein, which marks mitochondria in D. discoideum AX2 cells. Among the 81 proteins, 90% of proteins with an RER higher than 0.343 showed complete or partial mitochondrial localization (Figure 3 B, 3 D), whereas only 5% of proteins with an RER less than 0.343 showed mitochondrial localization (Figure 3 B, 3 D), demonstrating a strong positive correlation between RER value and probability of mitochondrial localization (Figure 3 B). Moreover, logistic regression analysis on the localization pattern of these 81 proteins predicts that a protein has more than a 78% probability of localizing to the mitochondria if its RER is higher than 0.343 (Figure 3 C). A comprehensive mitochondrial protein compendium in D. discoideum. To further improve the coverage and accuracy of the mitochondrial protein discovery, we revised the list based on the in vivo microscopy validation by removing four non-mitochondrial localizing proteins and adding two mitochondrial localizing proteins. We also integrated three sets of mitochondrial protein discovery: the aforementioned list of mitochondrial proteins identified from quantitative proteomics analyses, those retrieved from homology detection, and those retrieved during a gene ontology search for mitochondrial genes. Among the 616 D. discoideum homologs of human mitochondrial proteins (Table S1), 352 proteins have an RER higher than 0.343 and hence were already included in the list, 223 proteins have an RER lower than 0.343, and 41 proteins were not captured in LC-MS. Among the 264 proteins that were not included in the list, 113 proteins do not have a predicted MTS (Table S1), whereas their human homologs have MTSs, suggesting these proteins might localize to other cellular compartments in D. discoideum . An exception is ribosomal protein S14 (O21035), which is encoded in the nuclear genome in humans but is encoded in the mitochondrial genome in D. discoideum , and thus contains an MTS in human cells but lacks one in D. discoideum . We added those remaining 152 proteins to the list, as well as 32 proteins with mitochondrial gene ontologies that had not emerged during the proteomic or homology analysis. The final compendium consists of 1082 high-confidence mitochondrial proteins in D. discoideum (Table S6). Characterization of the D. discoideum mitochondrial proteome Out of the 1082 D. discoideum mitochondrial proteins, there are 627 and 458 proteins that have homologs in the mitochondrial proteome of human and Saccharomyces cerevisiae , respectively (Figure 4 A), indicating that D. discoideum mitochondria are more closely related to mitochondria in metazoans than fungi. Only 324 D. discoideum mitochondrial proteins have homologs in Rickettsia prowazekii (Figure 4 A), an α-proteobacteria that is closely related to the mitochondrial ancestor. Overall, a total of 313 proteins, representing 28.9% of the D. discoideum mitochondrial proteome, have no homologs in the whole proteome of humans, S. cerevisiae or R. prowazekii (Figure 4 A, Table S6), indicating that a large fraction of D. discoideum mitochondrial proteins was evolved de novo after the divergence of Amoebozoa. Moreover, 75 D. discoideum mitochondrial proteins (6.9%) have no homologs in D. purpureum (Figure 4 A), a closely related species of social amoeba, further substantiating the fast-evolving nature of the amoeba mitochondrial proteome. There are 89 D. discoideum mitochondrial proteins (8.2%) with human homologs that had not been annotated as mitochondrial proteins (Figure 4 B, Table S6). Among these 89 proteins, 74 were also not annotated as mitochondrial proteins in yeast, including 32 that had homologs in S. cerevisiae . Given the estimated false-discovery rate of our compendium, the localization of these proteins needs to be experimentally accessed. Nonetheless, there are a few examples, such as the RNB domain-containing protein (DDB_G0288469) and tRNA-binding domain-containing protein (DDB_G0349377), both of which have predicted MTSs and are likely targeted to the mitochondrial matrix. The human homologs of DDB_G0288469, DIS3-like exonuclease 2, and DDB_G0349377, rhomboid-related protein 4, were not included in the human compendium (Rath et al. , 2021), despite evidence that the yeast homolog of DIS3-like exonuclease 2 localizes to the mitochondria (Pagliarini et al. , 2008), and that rhomboid-related protein 4 has been partially shown to localize to the mitochondria. The mitochondrial localization of their D. discoideum homologs substantiates these two proteins might indeed localize to the mitochondria and indicates that our compendium can complement previous studies toward a more comprehensive discovery of mitochondrial proteins in other organisms. Additionally, we categorized the D. discoideum mitochondrial proteome using PANTHER biological function or protein family classifications (Figure 4 B, Table S6). Proteins involved in mitochondrial gene expression and metabolism comprise the largest fractions of all mitochondrial proteins, over 20% for each category. Other proteins are involved in mitochondrial protein homeostasis, the electron transport chain, redox signaling and metabolism, and regulation of mitochondrial morphology and dynamics. A large fraction of D. discoideum mitochondrial proteins, approximately 15%, have no classified functions based on PANTHER analyses (Figure 4 B). D. discoideum -specific mitochondrial proteins Proteins involved in gene expression consisted of a large fraction of D. discoideum specific mitochondrial proteome (Figure 4 B), reflecting that the D. discoideum mitochondrial genome is more complex than human mtDNA. On the contrary, few metabolism proteins emerged in the list (Figure 4 B), suggesting that metabolic processes are highly conserved between D. discoideum and metazoans. Here, we expand upon a few of the unique features of the D. discoideum mitochondrial protein compendium. Mosaic nature of mitochondrial ribosomes Mitochondrial ribosomes (mitoribosomes), ribosomal assembly factors, and other proteins involved in translation represented 8.9% and 9.3% of the overall and unique mitochondrial protein compendium, respectively. While mitoribosomes are thought to be evolved from bacterial ribosomes, these two differ greatly with regards to their structure, function, as well as their composition of proteins and RNAs. We identified 51 proteins that are predicted to be mitoribosomal proteins, including 13 proteins that did not share significant homology with any H. sapiens , S. cerevisiae , or R. prowazekii proteins (Table S7). Interestingly, D. discoideum mitoribosomal proteins belong to families across several taxonomic groups: 35 proteins belong to mammalian mitoribosomal protein families (28s and 39s), 2 belong to eukaryotic cytosolic ribosomal protein families (60s), 9 belong to yeast mitoribosomal protein families (37s and 54s), 2 belong to chloroplast or bacterial ribosomal protein families (30s and 50s), 1 is from archaea, and 2 are universally conserved among prokaryotes and eukaryotes. It has previously been shown that cytosolic ribosomes tether to the mitochondrial outer membrane. Hence, the recovery of the 60S ribosomal protein L22 could be the result of the association of cytoplasmic ribosome with the mitochondrial outer membrane rather than its localization in the matrix (Gold et al. , 2017). Nonetheless, the presence of proteins representing multiple mitoribosome lineages suggests that there may be D. discoideum or protist-specific mechanisms to process mitochondrial transcripts and to regulate mitochondrial translation. Further validation of these findings is necessary as the composition and structure of the D. discoideum mitoribosome have yet to be resolved. Mitochondrial DNA and RNA processing factors Among the list of unique proteins are 24 candidate mtDNA and mtRNA processing factors including five endonucleases and a pentatricopeptide repeat (PPR)-containing protein A (PtcA). Bioinformatic analysis suggests that PtcA belongs to the mitochondrial group I intron splicing family. PPR proteins, defined by tandem PPR domains, are implicated in several different mitochondrial gene expression processes including translation initiation, and ribosomal stabilization (Manna, 2015). The number of PPR proteins that are encoded in an organism varies greatly: terrestrial plants, such as Arabidopsis thaliana , have upwards of 450 PPR proteins, while humans have 7 (Lurin et al. , 2004; Lightowlers and Chrzanowska-Lightowlers, 2013). D. discoideum has 12 PPR-domain containing peptides, including PtcA, reflecting a greater complexity of D. discoideum’s mitochondrial genome compared to that of metazoans (Manna et al. , 2013). Divergent evolution path of lipid biosynthesis The mevalonate pathway, which produces five-carbon blocks for the synthesis of diverse biomolecules such as cholesterol and coenzyme Q10, is an essential and highly conserved process in eukaryotes, archaea, and some bacteria. In animals and fungi, the mevalonate pathway takes place in ER, and 3-hydroxy-3-methylglutaryl (HMG)-coenzyme A (CoA) reductase (HMGR), a key enzyme in this pathway that converts HMG-CoA to mevalonate, localizes in the ER and peroxisomes (Chin et al. , 1984; Keller et al. , 1986; Burg and Espenshade, 2011). HMGR2, one of two HMG reductases in D. discoideum , is recovered in our compendium and contains a predicted MTS, suggesting that it likely localizes to the mitochondrial matrix. Additionally, HGSA, one of the two HMG-CoA synthases, also emerged as a mitochondrial protein. Our mitochondrial protein discovery suggests that mevalonate metabolism may take place in mitochondria in D. discoideum , highlighting the evolutionary divergence of some metabolic pathways that originated from the common mitochondrial ancestor. Implication of mitochondrial function in multicellular development D. discoideum with reduced mtDNA or a disruption of the gene encoding mt-ribosomal protein S4 display no defect in vegetative growth but have impaired starvation-induced development, suggesting that mitochondrial respiration is necessary for multicellularity (Chida, 2004; Chida et al. , 2008). However, contrasting evidence has demonstrated a significant decrease in mitochondrial respiration after respiration, and accordingly, a decreasing expression of many respiration complexes (Kelly et al. , 2021). To understand potential regulations of mitochondrial function in multicellular development, we retrieved RNA sequencing data using the Dictyostelium gene expression database, dictyExpress (Parikh et al. , 2010; Stajdohar et al. , 2017). Overall, there was a decrease in the expression of mitochondrial genes within our compendium over the 24-hr development time course (Figure 5 A). A similar pattern is observed in proteins that are involved in mitochondrial DNA maintenance and gene expression. Interestingly, despite the decrease in gene expression machinery (Figure 5 B), over half of the mitochondria-encoded genes in the dataset (19 of 35) were upregulated (log2FC ≥ 1) after starvation induction (Figure 5 C). Further, in examining all respiratory chain complexes, 12 nuclear-encoded ETC subunits had a higher expression level (log2FC ≥ 1) at or after 12 hours of starvation (Figure 5 D), besides the 10 nuclear or mitochondrial-encoded subunits that show a burst of expression in the first 4 hours after the starvation (Figure 5 D). The complex pattern of mitochondrial gene expression, particularly the upregulation of electron transport chain complex subunits during the development suggests potential roles of mitochondrial respiration in Dictyostelium development, and that both nuclear and mitochondrial-encoded proteins are likely implicated in these processes. Conclusion Here, we generated the most comprehensive list of mitochondrial proteins in D. discoideum to date. Our compendium lays the foundation for future studies to understand the functions of conserved mitochondrial proteins in health and diseases using D. discoideum as the model. It also provides an entry to study many fascinating mitochondrial processes that are unique in protists. Additionally, thorough comparative genomics, our compendium will complement mitochondrial protein discovery in other organisms and may shed light on the evolution of mitochondrial proteome and processes. Materials And Methods Cell culture and transformation Dictyostelium discoideum AX2 cultures were maintained in HL5 medium at 22°C (Fey et al. , 2007). Transformants were generated via electroporation as previously described (Gaudet et al. , 2007), with modifications. After electroporation (BioRad Genepulser), cells were incubated on ice for 10 minutes. Subsequently, cells were transferred from the cuvette with 2 mL of HL5 and plated onto 12-well tissue culture plates. After 24 hours, transformants were selected with Genectin and/or Blasticidin S (Thermo Fisher, 10 µg/mL each) in HL5. Protein mass spectrometry Mitochondrial isolation Cells were harvested at a concentration of 1-3 x 10 6 cells/mL and resuspended at 2 x 10 7 cells/mL in 800 µL of Reagent A from the Mitochondrial Isolation Kit for Cultured Cells (Thermo Fisher #89874) on ice. Cell lysis and crude mitochondrial preparation were performed as previously described with modifications (Graham, 1999; Glancy and Balaban, 2011). Cells were lysed with 35 strokes of a Dounce homogenizer followed by the addition of an equal volume of Reagent C. Whole-cell lysate samples were stored at -80°C or were centrifuged three times (700 x g, 10 minutes, 4°C) to purify the mitochondria. For each centrifugation step, the supernatant was transferred to a fresh 1.5 mL tube. The crude mitochondrial lysate was used immediately for purification or was stored at -80°C. To generate purified mitochondrial isolates, Percoll gradient centrifugation was performed as follows. Lysis suspension (1-2 mL) was added to the top of a Percoll (Cytiva) and Development Buffer (DB) (5 mM Na 2 HPO 4 , 5 mM KH 2 PO 4 , 1 mM CaCl 2 , 2 mM MgCl 2 , pH = 6.5) solution (8 mL, 30% Percoll) in a 10 mL ultracentrifuge tube. Ultracentrifugation (68,000 x g, 40 minutes) yielded three distinct layers. The top layer, containing contaminants, was discarded. The middle layer, containing mitochondria, was transferred into a fresh 2 mL tube. Aliquots of the mitochondrial suspension were topped off with 500 µL of DB, then centrifuged (13,000 x g, 10 minutes, 4°C). Following centrifugation, the supernatant was aspirated, and the mitochondria-containing pellet was maintained on ice. To lyse the mitochondria, the pellet was washed with 2 mL of DB, centrifuged (10,000 x g, 10 minutes, 4°C), and resuspended in 2 mL of DB with urea (8M). Protein yield was quantified via Bradford Assay (BioRad) according to the manufacturer’s protocol. Relative protein quantification Resuspended cell pellets were lysed via pulsed sonication, then sequentially reduced, alkylated, and digested overnight with trypsin. The protein digests were labeled with 10-plex Tandem Mass Tag (TMT) reagents (Thermo Fisher Scientific) (Dayon et al. , 2008), then were pooled and desalted. To separate the peptide mixtures into 24 fractions, high pH reversed-phase liquid chromatography was performed (Yang et al. , 2012). Each fraction was analyzed on an Orbitrap Lumos (Thermo Fisher Scientific) nanoLCMS system. Peptide and proteins were identified as described in He et al. , (2020). In brief, the resulting LCMS raw data were searched against a database downloaded from dictybase.org using the Sequest HT algorithm on the Proteome Discoverer 2.4 platform (Thermo Fisher Scientific). Three groups of samples were normalized to 47 reference mitochondrial proteins. The relative enrichment (RE) was defined as the ratio of a protein’s enrichment from a crude or purified mitochondria sample over its enrichment from a whole-cell lysate sample. To calculate the relative enrichment ratio (RER), REs were normalized such that the average RER of 47 known mitochondrial (TCA cycle, ETC, or OXPHOS) proteins is 1 (Table S3). The RER presented is a median value of three biological replicates. Mathematical modeling To classify proteins as mitochondrial or non-mitochondrial based on their RER, the RER distribution of isolated proteins was fit to a Gaussian mixture model (GMM) using the Expectation-Maximization (EM) algorithm in R (Benaglia et al. , 2009). The cutoff value (RER = 0.343) was four times the standard deviation plus the mean of curve 1 (representing non-mitochondrial proteins), such that 99.9% of the proteins below the cutoff were contained within curve 1. Bioinformatic analyses Homology analyses Protein sequence homology was established by BlastP expect 40 (Pearson, 2013), or by HMMER sequence e-value < 0.01. Subcellular localization was predicted using TargetP-2.0 (Almagro Armenteros et al. , 2019). Biological functions for all proteins in the D. discoideum and human mitochondrial proteome were manually categorized from biological function or protein family classifications provided from PANTHER (The Gene Ontology Consortium et al. , 2021). In silico dataset correction Two strategies were implemented to supplement the mitochondrial protein discovery. The top D. discoideum homolog of human mitochondrial proteins (Table S1, Rath et al. , 2021) were curated. Additionally, D. discoideum proteins annotated with the gene ontology term “mitochond*” on AmiGO were selected (Carbon et al. , 2009). Proteins within these lists were integrated into the final mitochondrial compendium so long as they had a predicted mitochondrial targeting sequence if their human homolog also had a predicted mitochondrial targeting sequence, except in cases where the D. discoideum protein was mitochondrial-encoded. RNA sequencing data visualization Normalized RNA-seq data from Parikh et al. (2010) was retrieved using dictyExpress (Stajdohar et al. , 2017). For the 1082 proteins in the mitochondrial compendium, only 1075 corresponding genes were present in the dataset. To compare the gene profiles, data were scaled to a mean of 0 and standard deviation of 1 using the scale function in R. For the overall mitochondrial expression profile, genes and timepoints were ordered using hierarchical clustering (heatmaps.2). For the profiles of individual biological processes, scaled data (Table S8) were imported into the matrix visualization software Morpheus ( https://software.broadinstitute.org/morpheus ) and ordered via hierarchical clustering with one minus Pearson’s correlation as the distance metric and average as the linkage method. Gene upregulation was determined by a log2 fold-change ≥ 1. Library generation for imaging verification To evaluate the efficacy of our mitochondrial protein identification, 98 proteins were selected to be GFP tagged so that their localization could be assessed via fluorescence microscopy (Table S5). None of the proteins selected had a gene ontology annotation that indicated mitochondrial localization. Four proteins selected for verification had homologs listed in the human mitochondrial proteome. All genes were synthesized by Gene Universal. Of the 98 genes submitted for synthesis, 85 were generated as inserts in pDM323, a D. discoideum extrachromosomal expression vector with G418 resistance and a C-terminal GFP tag (Veltman et al. , 2009); 6 genes were generated as inserts in the shuttle vector puC57 and were subsequently cloned into pDM323 between BglII and SpeI sites using the In-Fusion® HD Cloning Kit (Takara Bio USA) and confirmed by sequencing. The other 7 genes were unable to be synthesized, such that only 92 proteins were screened. Of these 92 genes, only 81 were successfully expressed in D. discoideum. The RER of the proteins that were verified were as follows: 14 proteins with a RER > .75, 11 proteins with a RER = 0.75-0.5, 24 proteins with a RER = .5-0.25, and 32 proteins with RER < 0.25. To observe mitochondrial localization, the mitochondrial targeting sequence of respiratory cytochrome oxidase c subunit IV fused with mCherry (CoxIV-mCherry) was cloned into pDM326, a D. discoideum extrachromosomal expression vector with Blasticidin S resistance (Veltman et al. , 2009). All primers for cloning are listed in Table S9. Live-cell imaging To image cells in the axenic phase, cells (200 µl) were transferred to 8-well glass chambers 7 to 10 days post-transformation. Cells were allowed to adhere to the bottom of the chamber for 30 minutes before the media was aspirated. The media was replaced with 1x PBS after three washes (200 µl for all). Confocal images were collected on a PerkinElmer Ultraview system (Zeiss Plan-apochromat 63x/1.4 oil lens, Volocity acquisition software, Hamamatsu Digital Camera C10600 ORCA-R2, Immersol immersion oil). Images (0.5 µm z-step) were analyzed with ImageJ (National Institutes of Health) and formatted in Adobe Photoshop. Code and data availability Proteomics data are deposited at ProteomXchange (PXD029101). R code is available from Anna Freitas’s GitHub repo https://github.com/freitasav/DD-mitoproteome . Quantification and Statistical Analyses All data were presented as the mean ± SD unless otherwise indicated. P values were calculated in R using a one-way analysis of variance (ANOVA) followed by Tukey’s post-hoc test to test for the effect of RER on mitochondrial localization. Statistical significance of difference was considered when p < 0.05. To predict the probability of localization based on RER, outliers were identified and removed from the microscopy validation dataset based on the interquartile method (median + 1.5 SD). Data were analyzed using a binomial logistic regression (glm function in R) with excluded from the mitochondria as the reference level, and partial mitochondrial localization , mitochondrial localization , or combined (in which the partial and mitochondrial outcomes are collapsed) as the outcome levels. Predicted probabilities and 95% confidence intervals were calculated (predict function in R) to compare outcomes. Declarations ACKNOWLEDGEMENTS We thank Dr. Edward Korn for his advice and reagents on D. discoideum culturing; Dr. Raúl Covian Garcia for his advice on mitochondria purification; and dictyBase for various plasmids. This work was supported by the Intramural Research Program of National Heart, Lung, and Blood Institute. DECLARATION OF INTERESTS The authors declare no competing interests. References Almagro Armenteros, JJ, Salvatore, M, Emanuelsson, O, Winther, O, von Heijne, G, Elofsson, A, and Nielsen, H (2019). Detecting sequence signals in targeting peptides using deep learning. Life Sci Alliance 2, e201900429. Angata, K, Kuroe, K, Yanagisawa, K, and Tanaka, Y (1995). Codon usage, genetic code and phylogeny of Dictyostelium discoideum mitochondrial DNA as deduced from a 7.3-kb region. Curr Genet 27, 249–256. Benaglia, T, Chauveau, D, Hunter, DR, and Young, D (2009). mixtools: An R Package for Analyzing Finite Mixture Models. J Stat Soft 32. Bozzaro, S (2013). The Model Organism Dictyostelium discoideum. In: Dictyostelium Discoideum Protocols, ed. L Eichinger, and F Rivero, Totowa, NJ: Humana Press, 17–37. Burg, JS, and Espenshade, PJ (2011). Regulation of HMG-CoA reductase in mammals and yeast. Progress in Lipid Research 50, 403–410. Carbon, S, Ireland, A, Mungall, CJ, Shu, S, Marshall, B, Lewis, S, the AmiGO Hub, and the Web Presence Working Group (2009). AmiGO: online access to ontology and annotation data. Bioinformatics 25, 288–289. Chida, J (2004). 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FEBS Letters 532, 459–464. Jukes, TH, and Osawa, S (1990). The genetic code in mitochondria and chloroplasts. Experientia 46, 1117–1126. Kay, RR (1982). cAMP and spore differentiation in Dictyostelium discoideum. Proceedings of the National Academy of Sciences 79, 3228–3231. Keller, GA, Pazirandeh, M, and Krisans, S (1986). 3-Hydroxy-3-methylglutaryl coenzyme A reductase localization in rat liver peroxisomes and microsomes of control and cholestyramine-treated animals: quantitative biochemical and immunoelectron microscopical analyses. Journal of Cell Biology 103, 875–886. Kelly, B et al. (2021). Sulfur sequestration promotes multicellularity during nutrient limitation. Nature 591, 471–476. Kessin, RH (2001). Dictyostelium: Evolution, Cell Biology, and the Development of Multicellularity, Cambridge: Cambridge University Press. Li, Y, Zhang, R, Liu, S, Donath, A, Peters, RS, Ware, J, Misof, B, Niehuis, O, Pfrender, ME, and Zhou, X (2017). 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Morgenstern, M et al. (2017). Definition of a High-Confidence Mitochondrial Proteome at Quantitative Scale. Cell Reports 19, 2836–2852. Ogawa, S et al. (2000). The mitochondrial DNA of Dictyostelium discoideum: complete sequence, gene content and genome organization. Mol Gen Genet 263, 514–519. Pagliarini, DJ et al. (2008). A Mitochondrial Protein Compendium Elucidates Complex I Disease Biology. Cell 134, 112–123. Parikh, A et al. (2010). Conserved developmental transcriptomes in evolutionarily divergent species. Genome Biol 11, R35. Pearce, XG, Annesley, SJ, and Fisher, PR (2019). The Dictyostelium model for mitochondrial biology and disease. Int J Dev Biol 63, 497–508. Pearson, WR (2013). An Introduction to Sequence Similarity (“Homology”) Searching. Current Protocols in Bioinformatics 42. Rath, S et al. (2021). MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations. Nucleic Acids Research 49, D1541–D1547. Sloan, DB, Triant, DA, Wu, M, and Taylor, DR (2014). Cytonuclear Interactions and Relaxed Selection Accelerate Sequence Evolution in Organelle Ribosomes. Molecular Biology and Evolution 31, 673–682. Stajdohar, M, Rosengarten, RD, Kokosar, J, Jeran, L, Blenkus, D, Shaulsky, G, and Zupan, B (2017). dictyExpress: a web-based platform for sequence data management and analytics in Dictyostelium and beyond. BMC Bioinformatics 18, 291. The Gene Ontology Consortium et al. (2021). The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Research 49, D325–D334. Veltman, DM, Akar, G, Bosgraaf, L, and Van Haastert, PJM (2009). A new set of small, extrachromosomal expression vectors for Dictyostelium discoideum. Plasmid 61, 110–118. Vögtle, F-N et al. (2009). Global Analysis of the Mitochondrial N-Proteome Identifies a Processing Peptidase Critical for Protein Stability. Cell 139, 428–439. Yan, Z, Ye, G, and Werren, JH (2019). Evolutionary Rate Correlation between Mitochondrial-Encoded and Mitochondria-Associated Nuclear-Encoded Proteins in Insects. Molecular Biology and Evolution 36, 1022–1036. Yang, F, Shen, Y, Camp, DG, and Smith, RD (2012). High-pH reversed-phase chromatography with fraction concatenation for 2D proteomic analysis. Expert Review of Proteomics 9, 129–134. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xlsx Cite Share Download PDF Status: Published Journal Publication published 01 May, 2022 Read the published version in iScience → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1054199","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":63473770,"identity":"bf09af24-de78-4c61-8722-2f0eb2c0d9f1","order_by":0,"name":"Anna V Freitas","email":"","orcid":"","institution":"National Institutes of Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anna","middleName":"V","lastName":"Freitas","suffix":""},{"id":63473774,"identity":"dcec863c-15ac-4572-8968-89f8a3d4e054","order_by":1,"name":"Jake T Herb","email":"","orcid":"","institution":"National Institutes of Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jake","middleName":"T","lastName":"Herb","suffix":""},{"id":63473778,"identity":"18f245e4-87ba-4765-b5ff-2b6afd788dcc","order_by":2,"name":"Miao Pan","email":"","orcid":"","institution":"National Institutes of Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Pan","suffix":""},{"id":63473781,"identity":"cb433e3a-c0f2-4a38-ba50-6b22f5e6e3be","order_by":3,"name":"Yong Cheng","email":"","orcid":"","institution":"National Institutes of Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Cheng","suffix":""},{"id":63473784,"identity":"55296b64-e8eb-4b74-bace-d855cc66b1b9","order_by":4,"name":"Marjan Gucek","email":"","orcid":"","institution":"National Institutes of Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marjan","middleName":"","lastName":"Gucek","suffix":""},{"id":63473788,"identity":"2d647dbd-43f8-4fc1-a19a-c29e4c052423","order_by":5,"name":"Tian Jin","email":"","orcid":"","institution":"National Institutes of Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tian","middleName":"","lastName":"Jin","suffix":""},{"id":63473791,"identity":"5c956ed9-23c8-4068-910e-af886b063290","order_by":6,"name":"Hong Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYBACAygtx8DMQ6IWY9K1JDYwEKvFnL338AvGtsPp29l5DzB+qThMWItlz7k0C4Yzh3N3NvMlMMucIUKLwY0cMwOGisO5Gw7zGDBLtqURq8XgcLoBKVqMHwBtSQBpYfzYZkOEljNnzBgSzqQbghx2mOEMMVqO9xh/+NhmLW9w/ozhwx8VEoS1AAGbRAKUdZjYqGH+AGMx/iBSyygYBaNgFIwsAACuezdOgXffrQAAAABJRU5ErkJggg==","orcid":"","institution":"National Institutes of Health","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2021-11-05 17:44:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1054199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1054199/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.isci.2022.104332","type":"published","date":"2022-05-01T20:27:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":15638471,"identity":"2b65b91d-be0d-4126-b12c-6a94177a5c95","added_by":"auto","created_at":"2021-11-17 16:09:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":112469,"visible":true,"origin":"","legend":"Curation of a comprehensive mitochondrial proteome in Dictyostelium discoideum. (A) A homology search against human mitochondrial protein sequences yielded a list of 616 putative D. discoideum mitochondrial proteins. To identify additional mitochondrial proteins, we performed quantitative MS for proteins identified in the whole-cell lysate, as well as in crude and purified mitochondrial samples. Microscopy was used to validate MS and enrichment analyses. After correcting the proteomic dataset based on microscopy results, we incorporated the homology and proteomic analyses to yield a comprehensive mitochondrial protein compendium. (B) Volcano plots displaying the −log10 (p-value) versus log2 (relative enrichment ratios) of proteins in crude mitochondrial or pure mitochondrial versus whole-cell lysate samples. Data are presented as means (n = 3).","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1054199/v1/6761d55633977ea128ec2596.jpg"},{"id":15638888,"identity":"b9bebb84-2a2e-4914-a5be-280f12e05e6c","added_by":"auto","created_at":"2021-11-17 16:12:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94546,"visible":true,"origin":"","legend":"Prediction of mitochondrial localization based on relative enrichment analysis. (A) Crude mitochondrial RER versus pure mitochondrial RER for all samples quantified in the proteomic discovery experiment. The dashed line represents equal crude and pure RERs. Data are presented as means (n = 3). (B) Summary table of pure mitochondrial relative enrichment ratios for all proteins in the protein discovery experiment. (C) Distribution of core D. discoideum mitochondrial ETC, OXPHOS, and TCA cycle proteins (n = 47) based on RER. (D) The distribution of mitochondrial (n = 908) and non-mitochondrial proteins (n = 5984) based on RER follow a Gaussian mixture model, in which a cutoff value (red, RER = 0.343) separates two normal curves.","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1054199/v1/f7a6e15081d03c35fdb89328.jpg"},{"id":15638975,"identity":"cace0100-8bf5-4ab9-b3c2-6bb72faceeb9","added_by":"auto","created_at":"2021-11-17 16:15:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125074,"visible":true,"origin":"","legend":"Validation of proteomic discovery and enrichment analyses. (A) Distribution of all proteins quantified with a predicted mitochondrial targeting sequence (MTS) or signal peptide sequence. The dashed line represents the RER cutoff for predicting mitochondrial localization (0.343). (B) Dot plot with interquartile ranges of proteins that had mitochondrial localization (green), partial mitochondrial localization (gray), or were excluded from the mitochondria (red). *, p ≤ 5 x 10-5. (C) Predicted probabilities and confidence intervals as analyzed by logistic regression for mitochondrial (green), partial mitochondrial (gray), and either mitochondrial or partial (blue) localization. The dashed line represents the RER cutoff for predicting mitochondrial localization (0.343). (D) Representative live-cell confocal images of axenic stage D. discoideum expressing GFP-tagged genes of interest leveled with cytochrome oxidase c subunit IV tagged with mCherry (Mito-mCherry). Arrowheads denote areas of partial localization.","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1054199/v1/c976d83edc1958e36853399b.jpg"},{"id":15638472,"identity":"254a232f-8638-4608-a443-27ef713e92cc","added_by":"auto","created_at":"2021-11-17 16:09:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":125856,"visible":true,"origin":"","legend":"Categorization of the D. discoideum mitochondrial protein compendium. (A) D. discoideum mitochondrial proteins with homologs in H. sapiens or S. cerevisiae mitochondrial proteomes, or the complete proteomes of the alphaproteobacteria R. prowazekii or D. purpureum. (B) The functional categorization of all D. discoideum mitochondrial proteins (overall, n = 1082), those that lack human homologs (unique, n = 367), and those that have human homologs, but homologs were not annotated as mitochondrial proteins (diff. localized, n = 89).","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1054199/v1/77b62ade2055b6dcac7b5828.jpg"},{"id":15638473,"identity":"0f7df965-afa4-47fa-be75-314c4443d6b7","added_by":"auto","created_at":"2021-11-17 16:09:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":275245,"visible":true,"origin":"","legend":"Expression profile of mitochondrial genes during D. discoideum development. Heatmaps showing normalized RNA-seq analysis values of (A) all mitochondrial proteins within the compendium and (B-D) specific mitochondrial proteins during the 24-hour starvation-induced development cycle. Each column of heat maps represents the time in hours after developmental induction. Rows are clustered by similarity in gene expression profiles. Boxes outline ETC subunits showing increased expression 4 hours after starvation (solid) and 12 hours after starvation (dashed). ","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1054199/v1/fa3d021f513186fc56e1e9a5.jpg"},{"id":59296227,"identity":"356f8dda-65e4-4735-9c87-ea4143f82d93","added_by":"auto","created_at":"2024-06-28 20:27:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1344444,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1054199/v1/b9710fa3-d859-4c1b-ad93-3dea9019886d.pdf"},{"id":15638476,"identity":"14c8e228-40bc-4329-92ae-695e995dcb25","added_by":"auto","created_at":"2021-11-17 16:09:31","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1446556,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1054199/v1/3eb4cc9ec22eceb58f799ec2.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eGeneration of a Mitochondrial Protein Compendium in \u003cem\u003eDictyostelium Discoideum\u003c/em\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eDictyostelium discoideum\u003c/em\u003e, a social amoeba, is a well-established model organism to study eukaryotic cellular processes such as cell motility, chemotaxis, and differentiation (Bozzaro, 2013). Under normal nutrient conditions, \u003cem\u003eD. discoideum\u003c/em\u003e grows axenically through binary fission (Kessin, 2001). However, upon starvation, amoebae secrete cAMP, which attracts neighboring cells to aggregate together and form a multicellular mound. Cells in a mound move collectively as a slug toward light, heat, or humidity to find a suitable environment. The slug eventually matures into a fruiting body consisting of two major types of differentiated cells, spore cells that will start a new life cycle and stalk cells that form a stalk to hold the spore aloft (Kay, 1982). As many of the aforementioned biological processes are intertwined with cellular energetics, investigation of mitochondrial biogenesis and functions is an emerging area in \u003cem\u003eD. discoideum\u003c/em\u003e research (Francione \u003cem\u003eet al.\u003c/em\u003e, 2011; Pearce \u003cem\u003eet al.\u003c/em\u003e, 2019).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial genome is ~56 kb, circular, double-stranded DNA that encodes two ribosomal RNAs, 18 transfer RNAs (tRNAs), five open reading frames without annotated function, and 38 proteins including 18 subunits of the electron transport chain complexes and 15 ribosomal proteins (Ogawa \u003cem\u003eet al.\u003c/em\u003e, 2000). Phylogenetic studies reveal that Amoebazoa diverged before Opisthokonta, but after the divergence of Plantae (Baldauf and Doolittle, 1997), and are more closely related to animals than plants. Notably, the \u003cem\u003eDictyostelium\u003c/em\u003e mitochondrial genetic system possesses a few differences from metazoans (Pearce \u003cem\u003eet al.\u003c/em\u003e, 2019). \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial DNA (mtDNA) has four introns in \u003cem\u003ecox1/2\u003c/em\u003e genes and utilizes universal codons (Angata \u003cem\u003eet al.\u003c/em\u003e, 1995; Ogawa \u003cem\u003eet al.\u003c/em\u003e, 2000), a common feature of most plants\u0026rsquo; mitochondria (Jukes and Osawa, 1990; Cho \u003cem\u003eet al.\u003c/em\u003e, 1998). The universal genetic code and the lack of a full set of tRNA genes on the \u003cem\u003eDictyostelium\u003c/em\u003e mitochondrial genome indicate that some nuclear-encoded tRNAs are likely imported into mitochondria to support the organellar translation. Additionally, the electron transport chain in \u003cem\u003eDictyostelium\u003c/em\u003e contain an additional component compared to its metazoan counterparts: an alternative oxidase (AOX) (Pearce \u003cem\u003eet al.\u003c/em\u003e, 2019), which is found across eukaryotic clades besides animals (McDonald \u003cem\u003eet al\u003c/em\u003e., 2008). AOX is highly expressed during vegetative growth, but its expression level is markedly reduced upon starvation, suggesting a potential metabolic reprogramming occurs during starvation-induced development (Jarmuszkiewicz \u003cem\u003eet al.\u003c/em\u003e, 2002). Interestingly, either reduction of mtDNA content or disruption of the \u003cem\u003erps4\u003c/em\u003e locus (encoding mt-ribosomal protein S4) on mtDNA impairs aggregation and slug phototaxis but has no impact on vegetative growth (Chida, 2004; Chida \u003cem\u003eet al.\u003c/em\u003e, 2008), suggesting that mtDNA, and most likely an intact oxidative phosphorylation system is essential to initiate the development program. On the other hand, pharmacological inhibitions of either Complex I or Complex V can induce aggregation, even though mitochondrial respiration appears to increase at the beginning of starvation (Kelly \u003cem\u003eet al.\u003c/em\u003e, 2021). Therefore, the interplay between mitochondrial function and \u003cem\u003eDictyostelium\u003c/em\u003e development remains to be explored.\u003c/p\u003e \u003cp\u003eDespite the growing interest in using \u003cem\u003eD. discoideum\u003c/em\u003e as a model organism to study many conserved mitochondrial processes and some unique biology, a comprehensive list of the mitochondrial proteins has yet to be established. A recent proteomic study detected 294 proteins in \u003cem\u003eD. discoideum\u003c/em\u003e mitochondria (Mazur \u003cem\u003eet al.\u003c/em\u003e, 2021), which we believe is far from complete. Nuclear-encoded mitochondrial proteins, which constitute over 90% of the total mitochondrial proteome, are synthesized in the cytoplasm and then imported to mitochondria. It is estimated that the import of ~60% of these proteins relies on a positively charged, N-terminal mitochondrial targeting sequence (MTS) (V\u0026ouml;gtle \u003cem\u003eet al.\u003c/em\u003e, 2009). Computational approaches that integrate machine learning and known biological data are frequently used to predict mitochondrial targeting based on the presence of an MTS (Almagro Armenteros \u003cem\u003eet al.\u003c/em\u003e, 2019). However, this method is insufficient to capture all mitochondrial proteins, as most proteins on the outer membrane and in the inner membrane space, and some inner membrane proteins rely on alternate translocation mechanisms. An alternative computational approach leverages sequence homology to known mitochondrial protein compendiums that were generated using mass spectrometry (MS)-based proteomic discovery (Pagliarini \u003cem\u003eet al.\u003c/em\u003e, 2008; Morgenstern \u003cem\u003eet al.\u003c/em\u003e, 2017). However, to compensate for the rapid evolution of the mitochondrial genome, nuclear-encoded mitochondrial proteins evolve faster than other nuclear-encoded proteins (Cole \u003cem\u003eet al.\u003c/em\u003e, 1995; Sloan \u003cem\u003eet al.\u003c/em\u003e, 2014; Havird \u003cem\u003eet al.\u003c/em\u003e, 2015; Li \u003cem\u003eet al.\u003c/em\u003e, 2017; Yan \u003cem\u003eet al.\u003c/em\u003e, 2019). Thus, some \u003cem\u003eDictyostelium\u003c/em\u003e mitochondrial proteins may escape the homology search, and protist-specific mitochondrial proteins will certainly be missed.\u003c/p\u003e \u003cp\u003eIn this study, we combined quantitative proteomics and mathematical modeling to identify over 900 high-confidence mitochondrial proteins, which were validated through both \u003cem\u003ein silico\u003c/em\u003e and fluorescent microscopy analyses. We further complemented the proteomics-based mitochondrial protein discovery with bioinformatic approaches to create a compendium of 1082 \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial proteins. We also discuss conserved \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial proteins that may be used as the basis of validating mitochondrial proteins in other organisms, as well as unique features of the mitochondrial proteome in \u003cem\u003eD. discoideum.\u003c/em\u003e\u003c/p\u003e"},{"header":"Results And Discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMitochondrial protein discovery using quantitative proteomics\u003c/h2\u003e \u003cp\u003eTo identify putative \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial proteins, we searched for \u003cem\u003eDictyostelium\u003c/em\u003e homologs of 1136 human mitochondrial proteins listed in the Human MitoCarta 3.0 (Morgenstern \u003cem\u003eet al.\u003c/em\u003e, 2017; Rath \u003cem\u003eet al.\u003c/em\u003e, 2021), and retrieved 616 proteins (Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, Table S1). This number is much less than known mitochondrial proteins in humans (1136) and baker\u0026rsquo;s yeast (901) (Pagliarini \u003cem\u003eet al.\u003c/em\u003e, 2008; Rath \u003cem\u003eet al.\u003c/em\u003e, 2021). We posited that mitochondrial proteome might be highly divergent between \u003cem\u003eD. discoideum\u003c/em\u003e and humans, and many \u003cem\u003eDictyostelium\u003c/em\u003e mitochondrial proteins might be missed from this bioinformatic curation. We, therefore, took a proteomic approach to directly identify mitochondrial proteins in \u003cem\u003eD. discoideum\u003c/em\u003e (Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). From AX2 axenic cultures, we prepared mitochondria isolates\u0026mdash;both crude and highly purified\u0026mdash;through Percoll gradient ultracentrifugation. We performed tandem mass tag (TMT) liquid chromatography-mass spectrometry (LC-MS) on both mitochondria isolates and included AX2 whole-cell lysate as the control. A total of 6,892 proteins were captured in all samples (Table S2).\u003c/p\u003e \u003cp\u003eA limitation of identifying organellar proteins from their subcellular fractions alone is that high-abundance contaminants are often co-purified and result in false-positive hits. To address this issue, we assessed the probability of a protein localizing to mitochondria by comparing its relative enrichment in mitochondrial preparations to a list of 47 authentic mitochondrial proteins that includes components of electron transport chain complexes and conserved enzymes in citrate cycles (Table S3). We first calculated the ratio of a protein\u0026rsquo;s abundance in the mitochondria isolates, both crude and highly purified, versus its abundance in the whole-cell lysate. The resulting value, indicating its enrichment in mitochondrial preparations, was further normalized to the average enrichment ratio of the 47 reference mitochondrial proteins, to compute the relative enrichment ratio (RER). Overall, a protein\u0026rsquo;s RER in crude mitochondria isolate is in accordance with that in purified mitochondria (Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). However, the distribution of RERs appears continuous in crude mitochondria (Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), but clusters into two distinct populations in purified mitochondria (Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), which allowed us to determine a proper threshold of RER for mitochondrial proteins using mathematical modeling. Thus, we proceeded to analyze the RER for purified mitochondria only.\u003c/p\u003e\u003cp\u003eIn principle, a true mitochondrial protein would be co-purified with the reference mitochondrial proteins in pure mitochondrial isolates, and its RER should be 1.0. However, the RER distribution of these 47 reference proteins (Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) appears as a normal curve centered around 1.0, suggesting that many mitochondrial proteins may have an RER below 1.0. Among all proteins profiled using TMT-based LC-MS, only 259 have an RER higher than 1.0 in purified mitochondria (Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). We posit that different mitochondria proteins might be degraded to different extents, based on their intrinsic stability, during the procedure of mitochondrial purification, which involves overnight ultracentrifugation. Therefore, it is necessary to determine a proper RER value to differentiate mitochondrial proteins from non-mitochondrial proteins. We applied the expectation-maximization (EM) approach to a gaussian mixture model (GMM) to bin all proteins into two clusters: non-mitochondrial and mitochondrial proteins based on their RER values (Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). We chose an RER cutoff of 0.343 (Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) and assigned a total of 908 proteins having an RER higher than 0.343 as putative mitochondrial proteins (Table S4). GMM predicts that less than 17% proteins in the mitochondrial cluster, and only 0.1% proteins in the non-mitochondrial cluster would spill over to the other group (Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), which corresponds to an 83% recovery rate and 7% false discovery rate, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eValidation of mitochondrial protein discovery based on quantitative proteomics\u003c/h2\u003e \u003cp\u003eTo validate the accuracy of RER-based mitochondrial protein discovery, we first assessed the recovery rate of putative mitochondrial proteins \u003cem\u003ein silico.\u003c/em\u003e Many mitochondrial proteins possess an N-terminus mitochondrial targeting sequence (MTS) that directs the import of nuclear-encoded mitochondrial proteins into the mitochondrial matrix (Backers, 2017). Overall, 24% of all proteins retrieved in the proteomics discovery experiment contain an MTS (Table S4). Importantly, 94% of these MTS-bearing proteins had an RER greater than 0.343 (Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). On the contrary, 96% of proteins that were destined to other organelles such as the ER, Golgi, lysosomes, vacuoles, or secretory pathway had an RER less than 0.343. These analyses demonstrate that a cutoff value of an RER at 0.343 effectively separates mitochondrial proteins from non-mitochondrial proteins.\u003c/p\u003e\u003cp\u003eWe also surveyed the localization of 81 proteins recovered in LC-MS (Table S5), using fluorescent microscopy. These proteins were selected on the basis that their subcellular localization has not been annotated previously as mitochondrial, and their RERs are randomly distributed from 0.1 to 1.5. Each protein was tagged with GFP at its C-terminus and co-expressed with an MTS-mCherry fusion protein, which marks mitochondria in \u003cem\u003eD. discoideum\u003c/em\u003e AX2 cells. Among the 81 proteins, 90% of proteins with an RER higher than 0.343 showed complete or partial mitochondrial localization (Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), whereas only 5% of proteins with an RER less than 0.343 showed mitochondrial localization (Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), demonstrating a strong positive correlation between RER value and probability of mitochondrial localization (Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Moreover, logistic regression analysis on the localization pattern of these 81 proteins predicts that a protein has more than a 78% probability of localizing to the mitochondria if its RER is higher than 0.343 (Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003cb\u003eA comprehensive mitochondrial protein compendium in\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eD. discoideum.\u003c/span\u003e\u003c/p\u003e \u003cp\u003eTo further improve the coverage and accuracy of the mitochondrial protein discovery, we revised the list based on the \u003cem\u003ein vivo\u003c/em\u003e microscopy validation by removing four non-mitochondrial localizing proteins and adding two mitochondrial localizing proteins. We also integrated three sets of mitochondrial protein discovery: the aforementioned list of mitochondrial proteins identified from quantitative proteomics analyses, those retrieved from homology detection, and those retrieved during a gene ontology search for mitochondrial genes. Among the 616 \u003cem\u003eD. discoideum\u003c/em\u003e homologs of human mitochondrial proteins (Table S1), 352 proteins have an RER higher than 0.343 and hence were already included in the list, 223 proteins have an RER lower than 0.343, and 41 proteins were not captured in LC-MS. Among the 264 proteins that were not included in the list, 113 proteins do not have a predicted MTS (Table S1), whereas their human homologs have MTSs, suggesting these proteins might localize to other cellular compartments in \u003cem\u003eD. discoideum\u003c/em\u003e. An exception is ribosomal protein S14 (O21035), which is encoded in the nuclear genome in humans but is encoded in the mitochondrial genome in \u003cem\u003eD. discoideum\u003c/em\u003e, and thus contains an MTS in human cells but lacks one in \u003cem\u003eD. discoideum\u003c/em\u003e. We added those remaining 152 proteins to the list, as well as 32 proteins with mitochondrial gene ontologies that had not emerged during the proteomic or homology analysis. The final compendium consists of 1082 high-confidence mitochondrial proteins in \u003cem\u003eD. discoideum\u003c/em\u003e (Table S6).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCharacterization of the\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eD. discoideum\u003c/span\u003e \u003cb\u003emitochondrial proteome\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOut of the 1082 \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial proteins, there are 627 and 458 proteins that have homologs in the mitochondrial proteome of human and \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e, respectively (Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), indicating that \u003cem\u003eD. discoideum\u003c/em\u003e mitochondria are more closely related to mitochondria in metazoans than fungi. Only 324 \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial proteins have homologs in \u003cem\u003eRickettsia prowazekii\u003c/em\u003e (Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), an α-proteobacteria that is closely related to the mitochondrial ancestor. Overall, a total of 313 proteins, representing 28.9% of the \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial proteome, have no homologs in the whole proteome of humans, \u003cem\u003eS. cerevisiae\u003c/em\u003e or \u003cem\u003eR. prowazekii\u003c/em\u003e (Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Table S6), indicating that a large fraction of \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial proteins was evolved \u003cem\u003ede novo\u003c/em\u003e after the divergence of Amoebozoa. Moreover, 75 \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial proteins (6.9%) have no homologs in \u003cem\u003eD. purpureum\u003c/em\u003e (Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), a closely related species of social amoeba, further substantiating the fast-evolving nature of the amoeba mitochondrial proteome.\u003c/p\u003e\u003cp\u003eThere are 89 \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial proteins (8.2%) with human homologs that had not been annotated as mitochondrial proteins (Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Table S6). Among these 89 proteins, 74 were also not annotated as mitochondrial proteins in yeast, including 32 that had homologs in \u003cem\u003eS. cerevisiae\u003c/em\u003e. Given the estimated false-discovery rate of our compendium, the localization of these proteins needs to be experimentally accessed. Nonetheless, there are a few examples, such as the RNB domain-containing protein (DDB_G0288469) and tRNA-binding domain-containing protein (DDB_G0349377), both of which have predicted MTSs and are likely targeted to the mitochondrial matrix. The human homologs of DDB_G0288469, DIS3-like exonuclease 2, and DDB_G0349377, rhomboid-related protein 4, were not included in the human compendium (Rath \u003cem\u003eet al.\u003c/em\u003e, 2021), despite evidence that the yeast homolog of DIS3-like exonuclease 2 localizes to the mitochondria (Pagliarini \u003cem\u003eet al.\u003c/em\u003e, 2008), and that rhomboid-related protein 4 has been partially shown to localize to the mitochondria. The mitochondrial localization of their \u003cem\u003eD. discoideum\u003c/em\u003e homologs substantiates these two proteins might indeed localize to the mitochondria and indicates that our compendium can complement previous studies toward a more comprehensive discovery of mitochondrial proteins in other organisms.\u003c/p\u003e \u003cp\u003eAdditionally, we categorized the \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial proteome using PANTHER biological function or protein family classifications (Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Table S6). Proteins involved in mitochondrial gene expression and metabolism comprise the largest fractions of all mitochondrial proteins, over 20% for each category. Other proteins are involved in mitochondrial protein homeostasis, the electron transport chain, redox signaling and metabolism, and regulation of mitochondrial morphology and dynamics. A large fraction of \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial proteins, approximately 15%, have no classified functions based on PANTHER analyses (Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eD. discoideum\u003c/span\u003e \u003cb\u003e-specific mitochondrial proteins\u003c/b\u003e \u003c/p\u003e \u003cp\u003eProteins involved in gene expression consisted of a large fraction of \u003cem\u003eD. discoideum\u003c/em\u003e specific mitochondrial proteome (Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), reflecting that the \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial genome is more complex than human mtDNA. On the contrary, few metabolism proteins emerged in the list (Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), suggesting that metabolic processes are highly conserved between \u003cem\u003eD. discoideum\u003c/em\u003e and metazoans. Here, we expand upon a few of the unique features of the \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial protein compendium.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eMosaic nature of mitochondrial ribosomes\u003c/h2\u003e \u003cp\u003eMitochondrial ribosomes (mitoribosomes), ribosomal assembly factors, and other proteins involved in translation represented 8.9% and 9.3% of the overall and unique mitochondrial protein compendium, respectively. While mitoribosomes are thought to be evolved from bacterial ribosomes, these two differ greatly with regards to their structure, function, as well as their composition of proteins and RNAs. We identified 51 proteins that are predicted to be mitoribosomal proteins, including 13 proteins that did not share significant homology with any \u003cem\u003eH. sapiens\u003c/em\u003e, \u003cem\u003eS. cerevisiae\u003c/em\u003e, or \u003cem\u003eR. prowazekii\u003c/em\u003e proteins (Table S7). Interestingly, \u003cem\u003eD. discoideum\u003c/em\u003e mitoribosomal proteins belong to families across several taxonomic groups: 35 proteins belong to mammalian mitoribosomal protein families (28s and 39s), 2 belong to eukaryotic cytosolic ribosomal protein families (60s), 9 belong to yeast mitoribosomal protein families (37s and 54s), 2 belong to chloroplast or bacterial ribosomal protein families (30s and 50s), 1 is from archaea, and 2 are universally conserved among prokaryotes and eukaryotes. It has previously been shown that cytosolic ribosomes tether to the mitochondrial outer membrane. Hence, the recovery of the 60S ribosomal protein L22 could be the result of the association of cytoplasmic ribosome with the mitochondrial outer membrane rather than its localization in the matrix (Gold \u003cem\u003eet al.\u003c/em\u003e, 2017). Nonetheless, the presence of proteins representing multiple mitoribosome lineages suggests that there may be \u003cem\u003eD. discoideum\u003c/em\u003e or protist-specific mechanisms to process mitochondrial transcripts and to regulate mitochondrial translation. Further validation of these findings is necessary as the composition and structure of the \u003cem\u003eD. discoideum\u003c/em\u003e mitoribosome have yet to be resolved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eMitochondrial DNA and RNA processing factors\u003c/h2\u003e \u003cp\u003eAmong the list of unique proteins are 24 candidate mtDNA and mtRNA processing factors including five endonucleases and a pentatricopeptide repeat (PPR)-containing protein A (PtcA). Bioinformatic analysis suggests that PtcA belongs to the mitochondrial group I intron splicing family. PPR proteins, defined by tandem PPR domains, are implicated in several different mitochondrial gene expression processes including translation initiation, and ribosomal stabilization (Manna, 2015). The number of PPR proteins that are encoded in an organism varies greatly: terrestrial plants, such as \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, have upwards of 450 PPR proteins, while humans have 7 (Lurin \u003cem\u003eet al.\u003c/em\u003e, 2004; Lightowlers and Chrzanowska-Lightowlers, 2013). \u003cem\u003eD. discoideum\u003c/em\u003e has 12 PPR-domain containing peptides, including PtcA, reflecting a greater complexity of \u003cem\u003eD. discoideum\u0026rsquo;s\u003c/em\u003e mitochondrial genome compared to that of metazoans (Manna \u003cem\u003eet al.\u003c/em\u003e, 2013).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eDivergent evolution path of lipid biosynthesis\u003c/h2\u003e \u003cp\u003eThe mevalonate pathway, which produces five-carbon blocks for the synthesis of diverse biomolecules such as cholesterol and coenzyme Q10, is an essential and highly conserved process in eukaryotes, archaea, and some bacteria. In animals and fungi, the mevalonate pathway takes place in ER, and 3-hydroxy-3-methylglutaryl (HMG)-coenzyme A (CoA) reductase (HMGR), a key enzyme in this pathway that converts HMG-CoA to mevalonate, localizes in the ER and peroxisomes (Chin \u003cem\u003eet al.\u003c/em\u003e, 1984; Keller \u003cem\u003eet al.\u003c/em\u003e, 1986; Burg and Espenshade, 2011). HMGR2, one of two HMG reductases in \u003cem\u003eD. discoideum\u003c/em\u003e, is recovered in our compendium and contains a predicted MTS, suggesting that it likely localizes to the mitochondrial matrix. Additionally, HGSA, one of the two HMG-CoA synthases, also emerged as a mitochondrial protein. Our mitochondrial protein discovery suggests that mevalonate metabolism may take place in mitochondria in \u003cem\u003eD. discoideum\u003c/em\u003e, highlighting the evolutionary divergence of some metabolic pathways that originated from the common mitochondrial ancestor.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImplication of mitochondrial function in multicellular development\u003c/h2\u003e \u003cp\u003e \u003cem\u003eD. discoideum\u003c/em\u003e with reduced mtDNA or a disruption of the gene encoding mt-ribosomal protein S4 display no defect in vegetative growth but have impaired starvation-induced development, suggesting that mitochondrial respiration is necessary for multicellularity (Chida, 2004; Chida \u003cem\u003eet al.\u003c/em\u003e, 2008). However, contrasting evidence has demonstrated a significant decrease in mitochondrial respiration after respiration, and accordingly, a decreasing expression of many respiration complexes (Kelly \u003cem\u003eet al.\u003c/em\u003e, 2021). To understand potential regulations of mitochondrial function in multicellular development, we retrieved RNA sequencing data using the \u003cem\u003eDictyostelium\u003c/em\u003e gene expression database, dictyExpress (Parikh \u003cem\u003eet al.\u003c/em\u003e, 2010; Stajdohar \u003cem\u003eet al.\u003c/em\u003e, 2017).\u003c/p\u003e \u003cp\u003eOverall, there was a decrease in the expression of mitochondrial genes within our compendium over the 24-hr development time course (Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). A similar pattern is observed in proteins that are involved in mitochondrial DNA maintenance and gene expression. Interestingly, despite the decrease in gene expression machinery (Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), over half of the mitochondria-encoded genes in the dataset (19 of 35) were upregulated (log2FC \u0026ge; 1) after starvation induction (Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Further, in examining all respiratory chain complexes, 12 nuclear-encoded ETC subunits had a higher expression level (log2FC \u0026ge; 1) at or after 12 hours of starvation (Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), besides the 10 nuclear or mitochondrial-encoded subunits that show a burst of expression in the first 4 hours after the starvation (Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The complex pattern of mitochondrial gene expression, particularly the upregulation of electron transport chain complex subunits during the development suggests potential roles of mitochondrial respiration in \u003cem\u003eDictyostelium\u003c/em\u003e development, and that both nuclear and mitochondrial-encoded proteins are likely implicated in these processes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHere, we generated the most comprehensive list of mitochondrial proteins in \u003cem\u003eD. discoideum\u003c/em\u003e to date. Our compendium lays the foundation for future studies to understand the functions of conserved mitochondrial proteins in health and diseases using \u003cem\u003eD. discoideum\u003c/em\u003e as the model. It also provides an entry to study many fascinating mitochondrial processes that are unique in protists. Additionally, thorough comparative genomics, our compendium will complement mitochondrial protein discovery in other organisms and may shed light on the evolution of mitochondrial proteome and processes.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and transformation\u003c/h2\u003e \u003cp\u003e \u003cem\u003eDictyostelium discoideum\u003c/em\u003e AX2 cultures were maintained in HL5 medium at 22\u0026deg;C (Fey \u003cem\u003eet al.\u003c/em\u003e, 2007). Transformants were generated via electroporation as previously described (Gaudet \u003cem\u003eet al.\u003c/em\u003e, 2007), with modifications. After electroporation (BioRad Genepulser), cells were incubated on ice for 10 minutes. Subsequently, cells were transferred from the cuvette with 2 mL of HL5 and plated onto 12-well tissue culture plates. After 24 hours, transformants were selected with Genectin and/or Blasticidin S (Thermo Fisher, 10 \u0026micro;g/mL each) in HL5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eProtein mass spectrometry\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eMitochondrial isolation\u003c/h2\u003e \u003cp\u003eCells were harvested at a concentration of 1-3 x 10\u003csup\u003e6\u003c/sup\u003e cells/mL and resuspended at 2 x 10\u003csup\u003e7\u003c/sup\u003e cells/mL in 800 \u0026micro;L of Reagent A from the Mitochondrial Isolation Kit for Cultured Cells (Thermo Fisher #89874) on ice. Cell lysis and crude mitochondrial preparation were performed as previously described with modifications (Graham, 1999; Glancy and Balaban, 2011). Cells were lysed with 35 strokes of a Dounce homogenizer followed by the addition of an equal volume of Reagent C. Whole-cell lysate samples were stored at -80\u0026deg;C or were centrifuged three times (700 x g, 10 minutes, 4\u0026deg;C) to purify the mitochondria. For each centrifugation step, the supernatant was transferred to a fresh 1.5 mL tube. The crude mitochondrial lysate was used immediately for purification or was stored at -80\u0026deg;C.\u003c/p\u003e \u003cp\u003eTo generate purified mitochondrial isolates, Percoll gradient centrifugation was performed as follows. Lysis suspension (1-2 mL) was added to the top of a Percoll (Cytiva) and Development Buffer (DB) (5 mM Na\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e, 5 mM KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, 1 mM CaCl\u003csub\u003e2\u003c/sub\u003e, 2 mM MgCl\u003csub\u003e2\u003c/sub\u003e, pH = 6.5) solution (8 mL, 30% Percoll) in a 10 mL ultracentrifuge tube. Ultracentrifugation (68,000 x g, 40 minutes) yielded three distinct layers. The top layer, containing contaminants, was discarded. The middle layer, containing mitochondria, was transferred into a fresh 2 mL tube. Aliquots of the mitochondrial suspension were topped off with 500 \u0026micro;L of DB, then centrifuged (13,000 x g, 10 minutes, 4\u0026deg;C). Following centrifugation, the supernatant was aspirated, and the mitochondria-containing pellet was maintained on ice. To lyse the mitochondria, the pellet was washed with 2 mL of DB, centrifuged (10,000 x g, 10 minutes, 4\u0026deg;C), and resuspended in 2 mL of DB with urea (8M). Protein yield was quantified via Bradford Assay (BioRad) according to the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eRelative protein quantification\u003c/h2\u003e \u003cp\u003eResuspended cell pellets were lysed via pulsed sonication, then sequentially reduced, alkylated, and digested overnight with trypsin. The protein digests were labeled with 10-plex Tandem Mass Tag (TMT) reagents (Thermo Fisher Scientific) (Dayon \u003cem\u003eet al.\u003c/em\u003e, 2008), then were pooled and desalted. To separate the peptide mixtures into 24 fractions, high pH reversed-phase liquid chromatography was performed (Yang \u003cem\u003eet al.\u003c/em\u003e, 2012). Each fraction was analyzed on an Orbitrap Lumos (Thermo Fisher Scientific) nanoLCMS system.\u003c/p\u003e \u003cp\u003ePeptide and proteins were identified as described in He \u003cem\u003eet al.\u003c/em\u003e, (2020). In brief, the resulting LCMS raw data were searched against a database downloaded from dictybase.org using the Sequest HT algorithm on the Proteome Discoverer 2.4 platform (Thermo Fisher Scientific). Three groups of samples were normalized to 47 reference mitochondrial proteins.\u003c/p\u003e \u003cp\u003eThe relative enrichment (RE) was defined as the ratio of a protein\u0026rsquo;s enrichment from a crude or purified mitochondria sample over its enrichment from a whole-cell lysate sample. To calculate the relative enrichment ratio (RER), REs were normalized such that the average RER of 47 known mitochondrial (TCA cycle, ETC, or OXPHOS) proteins is 1 (Table S3). The RER presented is a median value of three biological replicates.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMathematical modeling\u003c/h2\u003e \u003cp\u003eTo classify proteins as mitochondrial or non-mitochondrial based on their RER, the RER distribution of isolated proteins was fit to a Gaussian mixture model (GMM) using the Expectation-Maximization (EM) algorithm in R (Benaglia \u003cem\u003eet al.\u003c/em\u003e, 2009). The cutoff value (RER = 0.343) was four times the standard deviation plus the mean of curve 1 (representing non-mitochondrial proteins), such that 99.9% of the proteins below the cutoff were contained within curve 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatic analyses\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eHomology analyses\u003c/h2\u003e \u003cp\u003eProtein sequence homology was established by BlastP expect \u0026lt; 0.001 and bit-score \u0026gt; 40 (Pearson, 2013), or by HMMER sequence e-value \u0026lt; 0.01. Subcellular localization was predicted using TargetP-2.0 (Almagro Armenteros \u003cem\u003eet al.\u003c/em\u003e, 2019). Biological functions for all proteins in the \u003cem\u003eD. discoideum\u003c/em\u003e and human mitochondrial proteome were manually categorized from biological function or protein family classifications provided from PANTHER (The Gene Ontology Consortium \u003cem\u003eet al.\u003c/em\u003e, 2021).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eIn silico\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003edataset correction\u003c/span\u003e\u003c/p\u003e \u003cp\u003eTwo strategies were implemented to supplement the mitochondrial protein discovery. The top \u003cem\u003eD. discoideum\u003c/em\u003e homolog of human mitochondrial proteins (Table S1, Rath \u003cem\u003eet al.\u003c/em\u003e, 2021) were curated. Additionally, \u003cem\u003eD. discoideum\u003c/em\u003e proteins annotated with the gene ontology term \u0026ldquo;mitochond*\u0026rdquo; on AmiGO were selected (Carbon \u003cem\u003eet al.\u003c/em\u003e, 2009). Proteins within these lists were integrated into the final mitochondrial compendium so long as they had a predicted mitochondrial targeting sequence if their human homolog also had a predicted mitochondrial targeting sequence, except in cases where the \u003cem\u003eD. discoideum\u003c/em\u003e protein was mitochondrial-encoded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003eRNA sequencing data visualization\u003c/h2\u003e \u003cp\u003eNormalized RNA-seq data from Parikh et al. (2010) was retrieved using dictyExpress (Stajdohar \u003cem\u003eet al.\u003c/em\u003e, 2017). For the 1082 proteins in the mitochondrial compendium, only 1075 corresponding genes were present in the dataset. To compare the gene profiles, data were scaled to a mean of 0 and standard deviation of 1 using the scale function in R. For the overall mitochondrial expression profile, genes and timepoints were ordered using hierarchical clustering (heatmaps.2). For the profiles of individual biological processes, scaled data (Table S8) were imported into the matrix visualization software Morpheus (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://software.broadinstitute.org/morpheus\u003c/span\u003e\u003c/span\u003e) and ordered via hierarchical clustering with one minus Pearson\u0026rsquo;s correlation as the distance metric and average as the linkage method. Gene upregulation was determined by a log2 fold-change \u0026ge; 1.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLibrary generation for imaging verification\u003c/h2\u003e \u003cp\u003eTo evaluate the efficacy of our mitochondrial protein identification, 98 proteins were selected to be GFP tagged so that their localization could be assessed via fluorescence microscopy (Table S5). None of the proteins selected had a gene ontology annotation that indicated mitochondrial localization. Four proteins selected for verification had homologs listed in the human mitochondrial proteome.\u003c/p\u003e \u003cp\u003eAll genes were synthesized by Gene Universal. Of the 98 genes submitted for synthesis, 85 were generated as inserts in pDM323, a \u003cem\u003eD. discoideum\u003c/em\u003e extrachromosomal expression vector with G418 resistance and a C-terminal GFP tag (Veltman \u003cem\u003eet al.\u003c/em\u003e, 2009); 6 genes were generated as inserts in the shuttle vector puC57 and were subsequently cloned into pDM323 between BglII and SpeI sites using the In-Fusion\u0026reg; HD Cloning Kit (Takara Bio USA) and confirmed by sequencing. The other 7 genes were unable to be synthesized, such that only 92 proteins were screened. Of these 92 genes, only 81 were successfully expressed in \u003cem\u003eD. discoideum.\u003c/em\u003e The RER of the proteins that were verified were as follows: 14 proteins with a RER \u0026gt; .75, 11 proteins with a RER = 0.75-0.5, 24 proteins with a RER = .5-0.25, and 32 proteins with RER \u0026lt; 0.25. To observe mitochondrial localization, the mitochondrial targeting sequence of respiratory cytochrome oxidase c subunit IV fused with mCherry (CoxIV-mCherry) was cloned into pDM326, a \u003cem\u003eD. discoideum\u003c/em\u003e extrachromosomal expression vector with Blasticidin S resistance (Veltman \u003cem\u003eet al.\u003c/em\u003e, 2009). All primers for cloning are listed in Table S9.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLive-cell imaging\u003c/h2\u003e \u003cp\u003eTo image cells in the axenic phase, cells (200 \u0026micro;l) were transferred to 8-well glass chambers 7 to 10 days post-transformation. Cells were allowed to adhere to the bottom of the chamber for 30 minutes before the media was aspirated. The media was replaced with 1x PBS after three washes (200 \u0026micro;l for all). Confocal images were collected on a PerkinElmer Ultraview system (Zeiss Plan-apochromat 63x/1.4 oil lens, Volocity acquisition software, Hamamatsu Digital Camera C10600 ORCA-R2, Immersol immersion oil). Images (0.5 \u0026micro;m z-step) were analyzed with ImageJ (National Institutes of Health) and formatted in Adobe Photoshop.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCode and data availability\u003c/h2\u003e \u003cp\u003eProteomics data are deposited at ProteomXchange (PXD029101). R code is available from Anna Freitas\u0026rsquo;s GitHub repo \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/freitasav/DD-mitoproteome\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eQuantification and Statistical Analyses\u003c/h2\u003e \u003cp\u003eAll data were presented as the mean \u0026plusmn; SD unless otherwise indicated. P values were calculated in R using a one-way analysis of variance (ANOVA) followed by Tukey\u0026rsquo;s post-hoc test to test for the effect of RER on mitochondrial localization. Statistical significance of difference was considered when p \u0026lt; 0.05.\u003c/p\u003e \u003cp\u003eTo predict the probability of localization based on RER, outliers were identified and removed from the microscopy validation dataset based on the interquartile method (median + 1.5 SD). Data were analyzed using a binomial logistic regression (glm function in R) with \u003cem\u003eexcluded from the mitochondria\u003c/em\u003e as the reference level, and \u003cem\u003epartial mitochondrial localization\u003c/em\u003e, \u003cem\u003emitochondrial localization\u003c/em\u003e, or \u003cem\u003ecombined\u003c/em\u003e (in which the partial and mitochondrial outcomes are collapsed) as the outcome levels. Predicted probabilities and 95% confidence intervals were calculated (predict function in R) to compare outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Edward Korn for his advice and reagents on \u003cem\u003eD. discoideum\u003c/em\u003e culturing; Dr. Ra\u0026uacute;l Covian Garcia for his advice on mitochondria purification; and dictyBase for various plasmids. This work was supported by the Intramural Research Program of National Heart, Lung, and Blood Institute.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDECLARATION OF INTERESTS\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlmagro Armenteros, JJ, Salvatore, M, Emanuelsson, O, Winther, O, von Heijne, G, Elofsson, A, and Nielsen, H (2019). Detecting sequence signals in targeting peptides using deep learning. Life Sci Alliance 2, e201900429.\u003c/li\u003e\n \u003cli\u003eAngata, K, Kuroe, K, Yanagisawa, K, and Tanaka, Y (1995). Codon usage, genetic code and phylogeny of Dictyostelium discoideum mitochondrial DNA as deduced from a 7.3-kb region. Curr Genet 27, 249\u0026ndash;256.\u003c/li\u003e\n \u003cli\u003eBenaglia, T, Chauveau, D, Hunter, DR, and Young, D (2009). mixtools: An R Package for Analyzing Finite Mixture Models. 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Evolution 69, 3069\u0026ndash;3081.\u003c/li\u003e\n \u003cli\u003eHe, Y, Chen, Y, Morris, DL, Lee, D-Y, and Tjandra, N (2020). Bax expression is optimal at low oxygen tension and constant agitation. Protein Expression and Purification 165, 105501.\u003c/li\u003e\n \u003cli\u003eJarmuszkiewicz, W, Behrendt, M, Navet, R, and Sluse, FE (2002). Uncoupling protein and alternative oxidase of \u003cem\u003eDictyostelium discoideum\u003c/em\u003e : occurrence, properties and protein expression during vegetative life and starvation-induced early development. FEBS Letters 532, 459\u0026ndash;464.\u003c/li\u003e\n \u003cli\u003eJukes, TH, and Osawa, S (1990). The genetic code in mitochondria and chloroplasts. Experientia 46, 1117\u0026ndash;1126.\u003c/li\u003e\n \u003cli\u003eKay, RR (1982). cAMP and spore differentiation in Dictyostelium discoideum. Proceedings of the National Academy of Sciences 79, 3228\u0026ndash;3231.\u003c/li\u003e\n \u003cli\u003eKeller, GA, Pazirandeh, M, and Krisans, S (1986). 3-Hydroxy-3-methylglutaryl coenzyme A reductase localization in rat liver peroxisomes and microsomes of control and cholestyramine-treated animals: quantitative biochemical and immunoelectron microscopical analyses. Journal of Cell Biology 103, 875\u0026ndash;886.\u003c/li\u003e\n \u003cli\u003eKelly, B et al. (2021). Sulfur sequestration promotes multicellularity during nutrient limitation. Nature 591, 471\u0026ndash;476.\u003c/li\u003e\n \u003cli\u003eKessin, RH (2001). Dictyostelium: Evolution, Cell Biology, and the Development of Multicellularity, Cambridge: Cambridge University Press.\u003c/li\u003e\n \u003cli\u003eLi, Y, Zhang, R, Liu, S, Donath, A, Peters, RS, Ware, J, Misof, B, Niehuis, O, Pfrender, ME, and Zhou, X (2017). The molecular evolutionary dynamics of oxidative phosphorylation (OXPHOS) genes in Hymenoptera. BMC Evol Biol 17, 269.\u003c/li\u003e\n \u003cli\u003eLightowlers, RN, and Chrzanowska-Lightowlers, ZM (2013). Human pentatricopeptide proteins: Only a few and what do they do? RNA Biology 10, 1433\u0026ndash;1438.\u003c/li\u003e\n \u003cli\u003eLurin, C et al. (2004). Genome-Wide Analysis of Arabidopsis Pentatricopeptide Repeat Proteins Reveals Their Essential Role in Organelle Biogenesis[W]. The Plant Cell 16, 2089\u0026ndash;2103.\u003c/li\u003e\n \u003cli\u003eManna, S (2015). An overview of pentatricopeptide repeat proteins and their applications. Biochimie 113, 93\u0026ndash;99.\u003c/li\u003e\n \u003cli\u003eManna, S, Brewster, J, and Barth, C (2013). Identification of Pentatricopeptide Repeat Proteins in the Model Organism \u003cem\u003eDictyostelium discoideum\u003c/em\u003e. International Journal of Genomics 2013, 1\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eMazur, M, Wojciechowska, D, Sitkiewicz, E, Malinowska, A, Świderska, B, Kmita, H, and Wojtkowska, M (2021). Mitochondrial Processes during Early Development of Dictyostelium discoideum: From Bioenergetic to Proteomic Studies. Genes 12, 638.\u003c/li\u003e\n \u003cli\u003eMorgenstern, M et al. (2017). Definition of a High-Confidence Mitochondrial Proteome at Quantitative Scale. Cell Reports 19, 2836\u0026ndash;2852.\u003c/li\u003e\n \u003cli\u003eOgawa, S et al. (2000). The mitochondrial DNA of Dictyostelium discoideum: complete sequence, gene content and genome organization. Mol Gen Genet 263, 514\u0026ndash;519.\u003c/li\u003e\n \u003cli\u003ePagliarini, DJ et al. (2008). A Mitochondrial Protein Compendium Elucidates Complex I Disease Biology. Cell 134, 112\u0026ndash;123.\u003c/li\u003e\n \u003cli\u003eParikh, A et al. (2010). Conserved developmental transcriptomes in evolutionarily divergent species. Genome Biol 11, R35.\u003c/li\u003e\n \u003cli\u003ePearce, XG, Annesley, SJ, and Fisher, PR (2019). The Dictyostelium model for mitochondrial biology and disease. Int J Dev Biol 63, 497\u0026ndash;508.\u003c/li\u003e\n \u003cli\u003ePearson, WR (2013). An Introduction to Sequence Similarity (\u0026ldquo;Homology\u0026rdquo;) Searching. Current Protocols in Bioinformatics 42.\u003c/li\u003e\n \u003cli\u003eRath, S et al. (2021). MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations. Nucleic Acids Research 49, D1541\u0026ndash;D1547.\u003c/li\u003e\n \u003cli\u003eSloan, DB, Triant, DA, Wu, M, and Taylor, DR (2014). Cytonuclear Interactions and Relaxed Selection Accelerate Sequence Evolution in Organelle Ribosomes. Molecular Biology and Evolution 31, 673\u0026ndash;682.\u003c/li\u003e\n \u003cli\u003eStajdohar, M, Rosengarten, RD, Kokosar, J, Jeran, L, Blenkus, D, Shaulsky, G, and Zupan, B (2017). dictyExpress: a web-based platform for sequence data management and analytics in Dictyostelium and beyond. BMC Bioinformatics 18, 291.\u003c/li\u003e\n \u003cli\u003eThe Gene Ontology Consortium et al. (2021). The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Research 49, D325\u0026ndash;D334.\u003c/li\u003e\n \u003cli\u003eVeltman, DM, Akar, G, Bosgraaf, L, and Van Haastert, PJM (2009). A new set of small, extrachromosomal expression vectors for Dictyostelium discoideum. Plasmid 61, 110\u0026ndash;118.\u003c/li\u003e\n \u003cli\u003eV\u0026ouml;gtle, F-N et al. (2009). Global Analysis of the Mitochondrial N-Proteome Identifies a Processing Peptidase Critical for Protein Stability. Cell 139, 428\u0026ndash;439.\u003c/li\u003e\n \u003cli\u003eYan, Z, Ye, G, and Werren, JH (2019). Evolutionary Rate Correlation between Mitochondrial-Encoded and Mitochondria-Associated Nuclear-Encoded Proteins in Insects. Molecular Biology and Evolution 36, 1022\u0026ndash;1036.\u003c/li\u003e\n \u003cli\u003eYang, F, Shen, Y, Camp, DG, and Smith, RD (2012). High-pH reversed-phase chromatography with fraction concatenation for 2D proteomic analysis. Expert Review of Proteomics 9, 129\u0026ndash;134.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dictyostelium discoideum, mitochondria, metabolism, MitoCarta, proteomic","lastPublishedDoi":"10.21203/rs.3.rs-1054199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1054199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe social amoeba \u003cem\u003eDictyostelium discoideum\u003c/em\u003e is a well-established model to study numerous cellular processes including cell motility, chemotaxis, and differentiation. As energy metabolism is involved in these processes, mitochondrial genetics and bioenergetics are of interest, though many features of \u003cem\u003eDictyostelium\u003c/em\u003e mitochondria differ from metazoans. A comprehensive inventory of mitochondrial proteins is critical to understanding mitochondrial processes and their involvement in various cellular pathways. Here, we utilized high-throughput multiplexed protein quantitation and homology analyses to generate a high-confidence mitochondrial protein compendium. Our proteomic approach, which utilizes quantitative mass spectrometry in combination with mathematical modeling, was validated through mitochondrial targeting sequence prediction and live-cell imaging. Our final compendium consists of 1082 proteins. Within our \u003cem\u003eD. discoideum\u003c/em\u003e mitochondrial proteome, we identify many proteins that are not present in humans, yeasts, or the ancestral alpha-proteobacteria, which can serve as a foundation for future investigations into the unique mitochondria of \u003cem\u003eDictyostelium\u003c/em\u003e. Additionally, we leverage our compendium to highlight the complexity of metabolic reprogramming during starvation-induced development. Our compendium lays a foundation to investigate mitochondrial processes that are unique in protists, as well as for future studies to understand the functions of conserved mitochondrial proteins in health and diseases using \u003cem\u003eD. discoideum\u003c/em\u003e as the model.\u003c/p\u003e","manuscriptTitle":"Generation of a Mitochondrial Protein Compendium in Dictyostelium Discoideum","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-11-17 16:09:28","doi":"10.21203/rs.3.rs-1054199/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b21ba12a-9911-4baa-a07f-6781cc4e4311","owner":[],"postedDate":"November 17th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":8563514,"name":"General Cell Biology \u0026 Physiology"},{"id":8563515,"name":"Computational Biology"},{"id":8563516,"name":"Bioinformatics"},{"id":8563517,"name":"Developmental Biology"},{"id":8563518,"name":"Systems Biology"}],"tags":[],"updatedAt":"2024-06-28T20:27:09+00:00","versionOfRecord":{"articleIdentity":"rs-1054199","link":"https://doi.org/10.1016/j.isci.2022.104332","journal":{"identity":"iscience","isVorOnly":true,"title":"iScience"},"publishedOn":"2022-05-01 20:27:09","publishedOnDateReadable":"May 1st, 2022"},"versionCreatedAt":"2021-11-17 16:09:28","video":"","vorDoi":"10.1016/j.isci.2022.104332","vorDoiUrl":"https://doi.org/10.1016/j.isci.2022.104332","workflowStages":[]},"version":"v1","identity":"rs-1054199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1054199","identity":"rs-1054199","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}