Thermal proteome profiling in bacteria: probing protein state in vivo
2018; Springer Nature; Volume: 14; Issue: 7 Linguagem: Inglês
10.15252/msb.20188242
ISSN1744-4292
AutoresAndré Mateus, Jacob Bobonis, Nils Kurzawa, Frank Stein, Dominic Helm, Johannes F. Hevler, Athanasios Typas, Mikhail M. Savitski,
Tópico(s)Bacterial Genetics and Biotechnology
ResumoArticle24 July 2018Open Access Transparent process Thermal proteome profiling in bacteria: probing protein state in vivo André Mateus André Mateus orcid.org/0000-0001-6870-0677 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Jacob Bobonis Jacob Bobonis Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Faculty of Biosciences, Heidelberg University, Heidelberg, Germany Search for more papers by this author Nils Kurzawa Nils Kurzawa orcid.org/0000-0002-7846-2817 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Faculty of Biosciences, Heidelberg University, Heidelberg, Germany Search for more papers by this author Frank Stein Frank Stein Proteomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Dominic Helm Dominic Helm Proteomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Johannes Hevler Johannes Hevler Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Athanasios Typas Corresponding Author Athanasios Typas [email protected] orcid.org/0000-0002-0797-9018 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Mikhail M Savitski Corresponding Author Mikhail M Savitski [email protected] orcid.org/0000-0003-2011-9247 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author André Mateus André Mateus orcid.org/0000-0001-6870-0677 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Jacob Bobonis Jacob Bobonis Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Faculty of Biosciences, Heidelberg University, Heidelberg, Germany Search for more papers by this author Nils Kurzawa Nils Kurzawa orcid.org/0000-0002-7846-2817 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Faculty of Biosciences, Heidelberg University, Heidelberg, Germany Search for more papers by this author Frank Stein Frank Stein Proteomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Dominic Helm Dominic Helm Proteomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Johannes Hevler Johannes Hevler Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Athanasios Typas Corresponding Author Athanasios Typas [email protected] orcid.org/0000-0002-0797-9018 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Mikhail M Savitski Corresponding Author Mikhail M Savitski [email protected] orcid.org/0000-0003-2011-9247 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Author Information André Mateus1, Jacob Bobonis1,2, Nils Kurzawa1,2, Frank Stein3, Dominic Helm3, Johannes Hevler1, Athanasios Typas *,1 and Mikhail M Savitski *,1 1Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany 2Faculty of Biosciences, Heidelberg University, Heidelberg, Germany 3Proteomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany *Corresponding author. Tel: +49 6221 387 8156; E-mail: [email protected] *Corresponding author. Tel: +49 6221 387 8560; E-mail: [email protected] Molecular Systems Biology (2018)14:e8242https://doi.org/10.15252/msb.20188242 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Increasing antibiotic resistance urges for new technologies for studying microbes and antimicrobial mechanism of action. We adapted thermal proteome profiling (TPP) to probe the thermostability of Escherichia coli proteins in vivo. E. coli had a more thermostable proteome than human cells, with protein thermostability depending on subcellular location—forming a high-to-low gradient from the cell surface to the cytoplasm. While subunits of protein complexes residing in one compartment melted similarly, protein complexes spanning compartments often had their subunits melting in a location-wise manner. Monitoring the E. coli meltome and proteome at different growth phases captured changes in metabolism. Cells lacking TolC, a component of multiple efflux pumps, exhibited major physiological changes, including differential thermostability and levels of its interaction partners, signaling cascades, and periplasmic quality control. Finally, we combined in vitro and in vivo TPP to identify targets of known antimicrobial drugs and to map their downstream effects. In conclusion, we demonstrate that TPP can be used in bacteria to probe protein complex architecture, metabolic pathways, and intracellular drug target engagement. Synopsis Thermal proteome profiling is adapted to Escherichia coli to probe the thermostability of proteins in vivo, yielding insights into protein complex architecture, protein activity, cellular metabolic state, intracellular drug target engagement and drug downstream effects. The E. coli proteome is more thermostable than the human one, with protein thermostability depending on protein subcellular location. Subunits of protein complexes residing in one compartment melt similarly, while protein complexes spanning compartments often have their subunits melting in a location-wise manner. Knockout of tolC led to the thermal destabilization of its interaction partners, the downregulation of a major porin (OmpF), and increased periplasmic stress. Introduction In the past decade, microbial research has regained traction, with a particular focus on the human microbiome diversity and its importance in health, and on difficult-to-treat infections by multidrug-resistant pathogens. While the microbiome may offer new opportunities for monitoring health and interventions in the long run (Zeller et al, 2014; Bullman et al, 2017; Maier et al, 2018), the shortage of effective antibiotics poses an imminent threat to public health (Brown & Wright, 2016; Tacconelli et al, 2018). Discovery of new antibiotics is urgently needed, but developing new drugs is a lengthy, costly, and often unsuccessful process. Interestingly, one of the major bottlenecks at early discovery stages remains the lack of tools for identification of the mode of action (MoA) of antibiotics. At the same time, even for antibiotics used for decades, there is an open debate on how target engagement leads to cell death or arrest (Kohanski et al, 2007; Ezraty et al, 2013; Keren et al, 2013; Liu & Imlay, 2013; Dwyer et al, 2014), there is little known about how much off-target effects may play a role in their MoA, and our knowledge on resistance mechanisms is incomplete. On the other hand, studying the physiology of commensal, pathobiont, or pathogenic bacteria is equally important for assessing microbiome-related questions and devising intervention tools. We have recently introduced a novel technology for detecting protein–drug interactions in situ on a proteome-wide scale, termed thermal proteome profiling (TPP; Savitski et al, 2014). TPP combines the principle of the cellular thermal shift assay (Martinez Molina et al, 2013) with multiplexed quantitative mass spectrometry (MS) using tandem mass tags (TMT; McAlister et al, 2012; Werner et al, 2012, 2014). In TPP, and in the recently introduced more sensitive 2D-TPP (Becher et al, 2016), cells are heated to a range of temperatures and the soluble component of the proteome is interrogated quantitatively at each temperature. Therefore, we can record the melting behavior of thousands of proteins. Protein–drug interactions typically increase the thermal tolerance of proteins, resulting in higher apparent melting points. Thus, comparison of proteome-wide thermostability of drug-treated and drug-untreated cells can lead to identification of drug targets (Savitski et al, 2014; Franken et al, 2015; Huber et al, 2015; Becher et al, 2016). In addition to detecting protein–drug interactions, TPP provides a powerful tool for detecting a wide range of physiological changes in protein state: protein–metabolite interactions, post-translational modifications, protein–protein interactions, protein–DNA interactions, and chaperone–client interactions (Savitski et al, 2014, 2018; Reinhard et al, 2015; Becher et al, 2018; Tan et al, 2018). Thus, applying TPP at different growth/cell cycle stages, in different nutritional environments, or upon chemical perturbations can yield a unique insight into the cellular physiology and the underlying adaptations taking place (Mateus et al, 2017). We reasoned that adapting TPP to bacteria would hold great promise for deconvoluting the MoA of new compounds with antimicrobial activity and for furthering our understanding of established antibiotics and their downstream effects. More importantly, it would provide a novel and orthogonal way of systematically phenotyping the cell, thereby improving our understanding of basic bacterial biology. Here, we adapt and apply TPP to Escherichia coli, and illustrate how it can be used to gain insights into drug–protein interactions, resistance mechanisms, metabolic activity, and protein complex formation. Results The Escherichia coli meltome To study the melting behavior of the E. coli (strain BW25113) proteome, we adapted the TPP protocol (Savitski et al, 2014; Franken et al, 2015; Reinhard et al, 2015) by optimizing the lysis conditions and the temperature range to be compatible with gram-negative bacteria (Fig 1A; see "Materials and Methods" for details). We identified 1,831 proteins (with at least two unique peptides in at least two replicates; Fig EV1A; Dataset EV1), which largely overlapped with recent proteomics datasets obtained from E. coli (Wisniewski & Rakus, 2014; Schmidt et al, 2016; Fig EV1B). We confirmed that the use of a mild detergent (NP-40) did not affect the solubilization and extraction of membrane proteins—as no bias in quantification was observed when compared with a strong detergent, SDS (Fig EV1C; Dataset EV2). Figure 1. Thermal proteome profiling in Escherichia coli Thermal proteome profiling protocol overview. After cells are grown to a specified optical density (OD578), aliquots are heated to a range of temperatures, lysed, and the remaining soluble fraction of the proteome is collected. Mass spectrometry-based proteomics (using tandem mass tags, TMT) is then used to quantify the amount of protein at each condition, and melting curves are plotted for each protein. Melting curves for E. coli proteins. The average melting curve for each cellular compartment is shown. Distribution of melting temperatures (Tm) of the E. coli and the human proteomes. Distribution of melting temperatures (Tm) of the E. coli and the human proteomes according to selected gene ontology terms. Line represents the median, box represents the interquartile range, and whiskers of the box plots represent the 5th and 95th percentiles. Distribution of melting temperatures (Tm) of the E. coli proteome according to their cellular compartment. Box plots are plotted as panel (D). Correlation of melting points in lysate determined by TPP (this study) with melting points determined by limited proteolysis coupled to mass spectrometry (Leuenberger et al, 2017). For the results from Leuenberger et al (2017), the median melting point of the reported peptides for each protein was used. Only proteins with at least two identified peptides were compared. Red dots represent ribosomal proteins, which generally appear less thermostable in TPP. Correlation of melting points in lysate determined by TPP upon addition of 10 mM MgCl2 (this study) with melting points determined by limited proteolysis coupled to mass spectrometry (Leuenberger et al, 2017). For the results from Leuenberger et al (2017), the median melting point of the reported peptides for each protein was used. Only proteins with at least two identified peptides were compared. Red dots represent ribosomal proteins. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Melting behavior of proteins identified in the Escherichia coli meltome and their properties A. Reproducibility of identified proteins in each replicate of E. coli meltome analysis. B. Overlap of identified proteins with previously published proteomics datasets obtained from E. coli. C. Distribution of differences between protein abundance after being extracted with NP-40 or with SDS. D, E. Correlation of melting point with (D) protein abundance (r = 0.06, P = 0.015, as measured by the top3 intensity corresponding to the lowest temperature) and (E) molecular weight (r = −0.08, P = 0.0009). F. Correlation of melting point in living cells with melting point in lysate—both from TPP (r = 0.82, P < 0.0001). G. Melting curves for E. coli outer membrane proteins. The average melting curve for each class of outer membrane proteins is shown. H. Distribution of melting temperatures (Tm) of the E. coli proteome according to their cellular compartment. I. Fraction of proteins with Tm > 87°C (the highest temperature tested) in each cellular compartment. Download figure Download PowerPoint We calculated apparent melting points (Tm) for 1,738 of the identified proteins (Fig 1B). Overall, the E. coli proteome was more thermostable than the human proteome (P < 0.0001, Mann–Whitney test; Fig 1C), which is consistent with the ability of this organism to grow regularly at temperatures up to 45°C or up to 49°C after evolutionary adaptation (Fotadar et al, 2005; Rudolph et al, 2010; Blaby et al, 2012; Deatherage et al, 2017). As previously observed for human proteins (Savitski et al, 2014), the Tm correlated very weakly with protein abundance (r = 0.06, P = 0.015; Fig EV1D) and molecular weight (r = −0.08, P = 0.0009; Fig EV1E). Proteins with low Tm included, for example, TypA (BipA) and DeaD, pivotal proteins for growth at low temperatures (Pfennig & Flower, 2001; Charollais et al, 2004); GatZ, which has been described to be sensitive to high temperatures (Brinkkotter et al, 2002); or the RNA polymerase sigma D factor, RpoD, which is known to lose its function upon heat shock (Blaszczak et al, 1995). In addition, multiple essential proteins also melted at low temperatures (Tm < 52°C), including topoisomerases (GyrB, ParC, ParE, TopA), proteins involved in DNA replication (DnaA, DnaE) and in cell shape (FtsA, FtsE, FtsI, MrdA, MreB, MukB), as well as multiple components of the small subunit of the ribosome (RpsB, RpsC, RpsD, RpsE, RpsJ, RpsL). In contrast to the overall higher melting temperature of E. coli proteins, the small ribosomal subunit melted in a similar temperature range as its human counterpart (Fig 1D). At the opposite side of the spectrum, a number of E. coli proteins did not melt in the tested temperature range (up to 87°C, n = 93; Fig EV1I); these were enriched in the Tat translocation system (TatA, TatB, TatE) and outer membrane proteins (e.g., BamC, OmpA, TolC; Dataset EV1). Protein thermostability increased from the interior to the exterior of the bacterial cell (interquartile range of Tm: cytosol (53.3–59°C) < inner membrane (55.5–61.7°C) ≈ periplasm (54.5–61.2°C) < outer membrane (55.7–68.9°C); P < 0.0001, Kruskal–Wallis test, with a Dunn's multiple comparison test showing P < 0.0001 for all comparisons except inner membrane and periplasm; Figs 1B and E, and EV1H). The most stable periplasmic proteins (Tm > 65°C) included all the superoxide dismutases (SodA, SodB, SodC), chaperones (FkpA, Skp, Spy, DsbC, HdeA, HdeB), and ligand-binding subunits of ATP-binding cassette (ABC) transporters (such as, PotD, PstS, MlaD, LolA, DppA, or ArtI), all engaged in cargo or ligand binding, suggesting that TPP may capture active periplasmic processes. In the outer membrane, integral membrane proteins were generally more thermostable than lipoproteins (P = 0.031, Mann–Whitney test; Fig EV1G). Outer membrane porins (OMPs) are known to be particularly stable once assembled and even resistant to denaturation (Ureta et al, 2007; Burgess et al, 2008; Stanley & Fleming, 2008; Roman & Gonzalez Flecha, 2014). Compatible with this, we observed that multiple OMPs showed a biphasic melting behavior, with a fraction melting at lower temperature and another fraction being stable up to 87°C (Fig 1B). A fraction of the proteins in the outer membrane (mostly integral proteins) showed an increase in solubility at approximately 55°C (Fig EV1G), which could be linked to a reported disorganization of the outer membrane at this temperature (Tsuchido et al, 1985). To compare our results to a complementary approach probing protein thermal unfolding based on limited proteolysis (LiP-MS; Leuenberger et al, 2017), we further determined the Tm of proteins in an E. coli lysate (by lysing the cells prior to heat treatment). Despite a good agreement between our lysate and living cell experiments (r = 0.82, P < 0.0001; Fig EV1F; Dataset EV3), we observed only a moderate correlation with the limited proteolysis approach (r = 0.45, P < 0.0001; Fig 1F); particularly, the ribosome was considerably less thermostable in our experiments. Since ribosomal thermostability is affected by the presence of magnesium ions (Friedman et al, 1967; Piazza et al, 2018), we repeated our lysate experiments with a similar concentration of MgCl2 (10 mM) to that described in Leuenberger et al (2017). This resulted in a much improved correlation (r = 0.65, P < 0.0001; Fig 1G; Dataset EV3), due to the overall increased stability of the ribosome. Interestingly, RplA, RplJ, RplL, and RpsA were not affected by increased magnesium concentration. These are structurally distinct from the other ribosomal proteins, as they are part of the lateral stalk (RplJ and RplL; Choi et al, 2015), the L1 stalk (RplA; Reblova et al, 2012), or known not to always be associated with the 30S subunit (RpsA; Duval et al, 2013). The TPP data showed on average higher Tm than the LiP-MS approach, which probably reflects the differences between the two approaches. These include technical differences—a longer heat-treatment time in LiP-MS that might lead to more extensive unfolding—and conceptual differences—LiP-MS detects local events in protein unfolding, while TPP studies protein aggregation that might require the unfolding of a substantial fraction of the protein. In summary, the E. coli proteome is more thermostable than the human one, thermostability of proteins correlates with their subcellular localization, and proteins with low temperature-related functions, as well as several essential proteins, have low thermostability. Impact of growth phase on protein thermostability and abundance We tested the impact of growth phase on the thermostability of the proteome, since we expected differences in protein activity to be reflected in thermostability (Savitski et al, 2014; Reinhard et al, 2015)—for example, proteins might be stabilized by substrates (indicating flux through the pathway), products (indicating inhibition), or allosteric regulators. For this, we harvested cells growing in LB medium in exponential phase (OD578 ≈ 0.1) and in the transition to stationary phase (OD578 ≈ 2; Fig 1A). We identified 39 proteins with significant differences in thermostability (P < 0.001, non-parametric analysis of response curves (see "Materials and Methods"; Fig 2A; Dataset EV4). Among these were lactate dehydrogenase (LldD), and some of the members of the respiratory complex I (NuoA and NuoL) and II (SdhB and SdhD), which were stabilized in the transition to stationary phase (all the other members of these complexes were also stabilized, albeit not significantly). This is consistent with stronger respiratory activity in stationary phase, as observed by the higher levels of triphenylformazan formed from the reduction in triphenyltetrazolium chloride (P = 0.0027, Student's t-test; Fig 2C). Accordingly, the glycerol kinase (GlpK), which converts glycerol to sn-glycerol 3-phosphate, and the sn-glycerol 3-phosphate uptake transporter (GlpT) were also stabilized at this growth phase; glycerol can only be used through respiration. Furthermore, YjiY, a novel specific transporter of pyruvate, which is induced exactly in the transition to stationary phase to import pyruvate (Kristoficova et al, 2017), was also more thermostable at this phase. Conversely, the peptide transporters, MppA and OppA, and tryptophanase (TnaA) were destabilized in the transition to stationary phase, suggesting that the cell had exhausted small peptides and had started producing indole. TnaA has been shown to form a single inactive focus in the cell pole during exponential phase, but disperse and gain activity in stationary phase (Li & Young, 2015). The decrease in Tm is presumably reflective of the shift from the focal conformation to free active tetramers (Li & Young, 2015). Figure 2. Impact of growth phase on the Escherichia coli meltome and proteome Melting temperatures (Tm) of proteins in exponential and transition to stationary growth phases. Proteins highlighted in orange indicate significantly different melting behavior. Protein abundance in exponential and transition to stationary growth phases, as measured by the top3 intensity corresponding to the lowest temperature (see "Materials and Methods"). Proteins highlighted in orange indicate significantly different levels. Proteins were considered not detectable (n.d.) in one condition, if absent in three replicates in that condition, but detectable by at least three unique peptides in at least two replicates in the other condition. Respiratory activity in exponential and stationary cells determined as the conversion of triphenyltetrazolium chloride to triphenylformazan during the same time and normalized by OD (˜number of cells). n = 3; error bars represent standard deviation; **P < 0.01, Student's t-test. Download figure Download PowerPoint In addition to protein thermostability, we could estimate protein abundances at the two growth phases—as traditional proteomics approaches (Aebersold & Mann, 2016)—by comparing the intensities of the signal at the lowest temperature (37°C), since the vast majority of proteins have not yet started to melt (Fig 1B). We found 17 proteins exclusively expressed in exponential phase (identified in at least two replicates by at least three unique peptides, while not being detectable in three replicates of stationary phase) and 57 proteins only expressed in the transition to stationary phase. In addition, five proteins were down-regulated in the transition to stationary phase and 53 were up-regulated [P < 0.01, using a linear model to assess differential expression (see "Materials and Methods"); Fig 2B; Dataset EV4]. Proteins that were absent in this growth phase included the iron (III) hydroxamate transporter (FhuC, FhuD). Proteins with exclusive or higher abundance included the sigma factor S (RpoS)—note that its regulon is activated later in stationary phase (Weber et al, 2005)—and multiple proteins involved in metabolic processes, the majority of them being regulated by cAMP receptor protein (CRP)—known to be dominant at this growth stage in rich media. These included proteins mediating fatty acid beta-oxidation (FadA, FadI, FadJ); the transport of maltose (MalF, MalK) or sorbitol (SrlA, SrlB, SrlD, and SrlE); and the catabolism of threonine (TdcB, TdcE), glycerol (GlpB, GlpC), glucarate (GarL, GudD), N-acetylneuraminate (NanE, NanK), and glycine (GcvP, GcvT). Members of respiratory complexes and proteins involved in glycerol, lactate, pyruvate, and tryptophan metabolism had affected not only their thermostability (as described above), but also their abundance. In summary, we observe thermostability changes for many metabolic enzymes and complexes during the transition to stationary phase, many of which are consistent with changes in their protein activity, and the cell using less efficient energy sources and slowing down growth at this stage. For most of these proteins, the increase in thermostability coincided with increase in abundance. The only prominent outlier, TnaA, which had lower thermostability, is also known to have higher activity at this growth phase (Li & Young, 2015). Melting behavior of protein complexes Next, we evaluated the thermostability behavior of protein complexes. We expected that proteins of the same complex would co-melt, since the melting of one of the components can destabilize the remaining complex members—causing them to have similar melting behavior, a phenomenon recently called thermal proximity coaggregation, TPCA (Tan et al, 2018). To test this, we calculated the average Euclidean distance between melting curves of proteins of the same complex in E. coli and in human cells for comparison (see "Materials and Methods"; Dataset EV5). A large proportion of protein complexes melted coherently for both human and E. coli protein complexes (Fig 3A; Dataset EV6), but the bacterial complexes had a more variable melting behavior (i.e., higher Euclidean distance). Figure 3. Melting behavior of protein complexes A. Melting behavior of protein complexes from human and Escherichia coli was measured by the average Euclidean distance between the melting curves of proteins from each complex. Line represents the median, box represents the interquartile range, and whiskers of the box plots represent the 10th and 90th percentiles. Pie charts represent the fraction of protein complexes that melt coherently (compared with a distribution of 10,000 random complexes; P < 0.05). B. Comparison of the melting behavior of protein complexes located in a single cellular compartment or in multiple compartments. Line represents the median, box represents the interquartile range, and whiskers of the box plots represent the 10th and 90th percentiles. Pie charts represent the fraction of protein complexes that melt coherently (compared with a distribution of 10,000 random complexes; P < 0.05). C. Schematic representation of complexes located in a single cellular compartment or in multiple compartments. D–H. Melting curves for examples of non-co-melting complexes located in the same cellular compartment: (D) ClpP protease complex, (E) Ruv DNA repair complex, (F) Uvr DNA repair complex, (G) Suf Fe-S biogenesis complex, and (H) Bam outer membrane porin assembly complex. P indicates the probability that the complex melts coherently (compared with a distribution of 10,000 random complexes). Download figure Download PowerPoint Interestingly, most of the non-co-melting bacterial complexes spanned different cellular compartments (Fig 3B and C), as for example, multidrug efflux complexes that are composed of inner membrane, periplasmic, and outer membrane components. This suggests that protein localization overrides the effect of stabilization by other complex members and that presumably many of these across-compartment complexes consist of more stable co-localized subcomplexes. For example, the inner-membrane-bound subunits of efflux pumps melted very similarly indicating that they form separate subcomplexes, which is consistent with the differential localization and dynamics of AcrB in the presence or absence of TolC (Bergmiller et al, 2017). Nevertheless, such protein complexes still have the thermostability of their members linked, as evidenced by the destabilization of all inner membrane complex components upon removing their outer membrane counterpart, TolC (see below "Effect of knocking out tolC on proteome thermostability and abundance"). Complexes in the same compartment with non-co-melting behavior could generally be attributed to the formation of previously observed subcomplexes. For example, these included the following: proteases, in which the catalytic subunit (ClpP or HslV) was more thermostable than the also singly acting chaperonin (ClpA, ClpX, or HslU; Rohrwild et al, 1996; Ortega et al, 2004; Fig 3D); DNA repair complexes (Ruv and Uvr), in which the respective more thermostable DNA binding components (RuvA and UvrA) can also act on their own (Van Houten et al, 1987; Shiba et al, 1991; Fig 3E and F); the Suf complex, in which SufBD is known to form the core of the complex (Hirabayashi et al, 2015) and was more thermostable than SufC (Fig 3G); and the Bam complex, the machinery for outer membrane porin insertion, in which three subcomplexes were apparent (BamAB, BamDE, and BamC; Fig 3H; Noinaj et al, 2017). BamC was completely thermostable, which is consistent with its enigmatic topology, i.e., high flexibility in crystal structures and appearing surface-exposed in in vivo experiments (Bakelar et al, 2016; Gu et al, 2016; Han et al, 2016; Iadanza et al, 2016; Noinaj et al, 2017). An overview of the co-melting behavior of all E. coli protein complexes detected is available in Fig EV2. Click here to expand this figure. Figure EV2. Melting behavior of protein complexesEuclidean distance between all the pairs of melting curves of proteins from each complex—collected from EcoCyc v.21.1 (https://ecocyc.org/; Keseler et al, 2017). Only proteins detected with at least two unique peptides in at least two replicates are shown. Node color represents protein location, and edge c
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