Protein structure, amino acid composition and sequence determine proteome vulnerability to oxidation‐induced damage
2020; Springer Nature; Volume: 39; Issue: 23 Linguagem: Inglês
10.15252/embj.2020104523
ISSN1460-2075
AutoresRoger L. Chang, Julian A. Stanley, Matthew C. Robinson, Joel W. Sher, Zhanwen Li, Yujia A. Chan, Ashton Omdahl, Ruddy Wattiez, Adam Godzik, Sabine Matallana‐Surget,
Tópico(s)Redox biology and oxidative stress
ResumoArticle19 October 2020Open Access Transparent process Protein structure, amino acid composition and sequence determine proteome vulnerability to oxidation-induced damage Roger L Chang Corresponding Author Roger L Chang [email protected] orcid.org/0000-0003-1630-6584 Department of Systems Biology, Blavatnik Institute at Harvard Medical School, Boston, MA, USA Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA Search for more papers by this author Julian A Stanley Julian A Stanley Department of Systems Biology, Blavatnik Institute at Harvard Medical School, Boston, MA, USA Search for more papers by this author Matthew C Robinson Matthew C Robinson Department of Systems Biology, Blavatnik Institute at Harvard Medical School, Boston, MA, USA Search for more papers by this author Joel W Sher Joel W Sher Department of Systems Biology, Blavatnik Institute at Harvard Medical School, Boston, MA, USA Search for more papers by this author Zhanwen Li Zhanwen Li Division of Biomedical Sciences, University of California Riverside School of Medicine, Riverside, CA, USA Search for more papers by this author Yujia A Chan Yujia A Chan Department of Systems Biology, Blavatnik Institute at Harvard Medical School, Boston, MA, USA Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA Search for more papers by this author Ashton R Omdahl Ashton R Omdahl Department of Systems Biology, Blavatnik Institute at Harvard Medical School, Boston, MA, USA Search for more papers by this author Ruddy Wattiez Ruddy Wattiez Department of Proteomics and Microbiology, Research Institute for Biosciences, University of Mons, Mons, Belgium Search for more papers by this author Adam Godzik Adam Godzik Division of Biomedical Sciences, University of California Riverside School of Medicine, Riverside, CA, USA Search for more papers by this author Sabine Matallana-Surget Corresponding Author Sabine Matallana-Surget [email protected] orcid.org/0000-0002-6023-3215 Division of Biological and Environmental Sciences, Faculty of Natural Sciences, University of Stirling, Stirling, UK Search for more papers by this author Roger L Chang Corresponding Author Roger L Chang [email protected] orcid.org/0000-0003-1630-6584 Department of Systems Biology, Blavatnik Institute at Harvard Medical School, Boston, MA, USA Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA Search for more papers by this author Julian A Stanley Julian A Stanley Department of Systems Biology, Blavatnik Institute at Harvard Medical School, Boston, MA, USA Search for more papers by this author Matthew C Robinson Matthew C Robinson Department of Systems Biology, Blavatnik Institute at Harvard Medical School, Boston, MA, USA Search for more papers by this author Joel W Sher Joel W Sher Department of Systems Biology, Blavatnik Institute at Harvard Medical School, Boston, MA, USA Search for more papers by this author Zhanwen Li Zhanwen Li Division of Biomedical Sciences, University of California Riverside School of Medicine, Riverside, CA, USA Search for more papers by this author Yujia A Chan Yujia A Chan Department of Systems Biology, Blavatnik Institute at Harvard Medical School, Boston, MA, USA Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA Search for more papers by this author Ashton R Omdahl Ashton R Omdahl Department of Systems Biology, Blavatnik Institute at Harvard Medical School, Boston, MA, USA Search for more papers by this author Ruddy Wattiez Ruddy Wattiez Department of Proteomics and Microbiology, Research Institute for Biosciences, University of Mons, Mons, Belgium Search for more papers by this author Adam Godzik Adam Godzik Division of Biomedical Sciences, University of California Riverside School of Medicine, Riverside, CA, USA Search for more papers by this author Sabine Matallana-Surget Corresponding Author Sabine Matallana-Surget [email protected] orcid.org/0000-0002-6023-3215 Division of Biological and Environmental Sciences, Faculty of Natural Sciences, University of Stirling, Stirling, UK Search for more papers by this author Author Information Roger L Chang *,1,2, Julian A Stanley1, Matthew C Robinson1, Joel W Sher1, Zhanwen Li3, Yujia A Chan1,2, Ashton R Omdahl1, Ruddy Wattiez4, Adam Godzik3 and Sabine Matallana-Surget *,5 1Department of Systems Biology, Blavatnik Institute at Harvard Medical School, Boston, MA, USA 2Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA 3Division of Biomedical Sciences, University of California Riverside School of Medicine, Riverside, CA, USA 4Department of Proteomics and Microbiology, Research Institute for Biosciences, University of Mons, Mons, Belgium 5Division of Biological and Environmental Sciences, Faculty of Natural Sciences, University of Stirling, Stirling, UK *Corresponding author. E-mail: [email protected] *Corresponding author. E-mail: [email protected] The EMBO Journal (2020)39:e104523https://doi.org/10.15252/embj.2020104523 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 Oxidative stress alters cell viability, from microorganism irradiation sensitivity to human aging and neurodegeneration. Deleterious effects of protein carbonylation by reactive oxygen species (ROS) make understanding molecular properties determining ROS susceptibility essential. The radiation-resistant bacterium Deinococcus radiodurans accumulates less carbonylation than sensitive organisms, making it a key model for deciphering properties governing oxidative stress resistance. We integrated shotgun redox proteomics, structural systems biology, and machine learning to resolve properties determining protein damage by γ-irradiation in Escherichia coli and D. radiodurans at multiple scales. Local accessibility, charge, and lysine enrichment accurately predict ROS susceptibility. Lysine, methionine, and cysteine usage also contribute to ROS resistance of the D. radiodurans proteome. Our model predicts proteome maintenance machinery, and proteins protecting against ROS are more resistant in D. radiodurans. Our findings substantiate that protein-intrinsic protection impacts oxidative stress resistance, identifying causal molecular properties. Synopsis Proteins differ in intrinsic vulnerability to carbonylation, an adaptation for oxidative stress tolerance. Here, an integrated proteomics, protein structure, and machine learning approach unravels properties determining damage by γ-irradiation in two bacterial species at multiple biological scales. γ-irradiation causes more-targeted oxidative degradation of proteins in D. radiodurans than in E. coli, suggesting the evolution of protein-specific protection mechanisms against oxidative stress. D. radiodurans proteins enriched in methionine and cysteine but depleted in lysine are more resistant to oxidative degradation. Protein-intrinsic molecular properties including local solvent accessibility, electrostatic charge, and lysine enrichment predict site-specific vulnerability to protein carbonylation by reactive oxygen species in E. coli and D. radiodurans. D. radiodurans ribosomal proteins, chaperones, and proteins involved in response to and detoxification of reactive oxygen species are more resistant to carbonylation than their E. coli orthologs. Introduction Proteome oxidation caused by reactive oxygen species (ROS) is a primary determinant of cellular sensitivity to desiccation and irradiation (Daly et al, 2007; Krisko & Radman, 2010) and is involved in the progression of age-related human diseases (Krisko & Radman, 2019), including neurodegeneration and cancer (Hohn et al, 2017). ROS toxicity is a common antibiotic mechanism (Belenky et al, 2015) and presents challenges in biotechnology including metabolic engineering (Ruenwai et al, 2011; Chin et al, 2017; Sun et al, 2018) and synthetic systems involving the high expression of fluorescent proteins (Ganini et al, 2017). Prior to the previous decade, the dogma surrounding biological sensitivity to ionizing radiation focused primarily on DNA damage, but this changed as key experiments substantiated the role of protection from protein oxidation in the extreme radioresistance of the bacterium Deinococcus radiodurans (Daly, 2006). Deinococcus is a crucial model for investigating resistance to ROS because of its notorious tolerance of extreme oxidative stress, even prolonged cosmic doses of γ-radiation (Yamagishi et al, 2018). This tolerance stems from the evolution of D. radiodurans to tolerate desiccation, which also induces oxidative stress (Slade & Radman, 2011). D. radiodurans accumulates less protein oxidation than more sensitive species such as Escherichia coli (Krisko & Radman, 2010). Resistance in D. radiodurans is due partly to highly active ROS-detoxifying systems providing protein-extrinsic protection against ROS (i.e., not a property of the oxidation targets themselves; Daly et al, 2004, 2007). Foundational work hypothesized that differential rates of protein oxidation and subsequent degradation also play a key role in stress response phenotypes (Stadtman, 1986) and broadly established that bacteria exhibit protein-specific patterns of susceptibility to oxidation under oxidative conditions leading to cellular senescence (Dukan & Nystrom, 1998). More recently, it was observed that pathogenic bacteria, which have evolved mechanisms to combat host immune responses that utilize ROS, are less sensitive to protein oxidation than non-pathogenic species and that certain physicochemical properties broadly differentiate the proteomes of pathogenic versus non-pathogenic bacteria, hypothesizing a causal link to susceptibility to protein oxidation (Vidovic et al, 2014). However, the extent to which protein-intrinsic properties (i.e., specific to individual protein species) contribute to ROS resistance and how such properties are distributed across distinct protein species has not been well-established. This comparative study of D. radiodurans and E. coli proteomes reveals proteins with distinguished vulnerability to ROS, thereby discovering mechanisms that contribute to the survival of oxidative stress following irradiation. Reactive oxygen species damage proteins by the oxidation of side chains and backbones generally resulting in loss of function due to misfolding, aggregation, and proteolysis. Several types of protein oxidation can result upon reaction with ROS (Stadtman & Levine, 2003). In this study, we have focused exclusively on protein carbonylation, which has also been the focus of most experimental methods and foundational work on protein oxidation to date. Protein carbonyl sites (CS) on arginine, lysine, proline, and threonine (RKPT) sidechains (Appendix Fig S1) are seen as the most severe oxidative damage due to their irreversibility and frequency of occurrence. Furthermore, these carbonyls themselves are also highly reactive leading subsequently to additional damaging downstream reactions, such as non-enzymatic backbone cleavage via the proline oxidation pathway (Uchida et al, 1990; Cabiscol et al, 2000; Nystrom, 2005). In this way, RKPT carbonylation can be thought of as a committed step initiating a cascade of protein damage. Site-specific susceptibility to carbonylation differs across amino acid types and structural location, extending to the whole-molecule scale to distinguish ROS vulnerability across protein species (Fig 1A). However, specific molecular properties responsible for this vulnerability remain poorly understood. Figure 1. Study concept and workflow Relationship between carbonylation site distribution, protein vulnerability to reactive oxygen species, and stress phenotypes. Structural systems biology workflow for proteome-wide carbonyl site prediction. Red circles = carbonyl sites (CS); black circles = non-oxidized RKPT residues; gray protein regions = non-RKPT residues. Download figure Download PowerPoint Previous work provided evidence that there is a difference in carbonylation susceptibility between distinct protein species in bacteria through observation of banding patterns on carbonyl assay gels (Daly, 2009), but this work did not provide protein identification, quantification, nor residue specificity of carbonylation events. Identification of proteins prone to carbonylation and their specific sites is vital to understanding the molecular manifestation of deleterious oxidative stress phenotypes. This goal has motivated the development of mass spectrometry for direct proteome-wide CS identification and concomitant relative abundance changes, termed shotgun redox proteomics (Matallana-Surget et al, 2013). However, these experiments provide limited coverage of modified sites, a common problem in proteomics of post-translational modifications. Chemical derivatization during these experiments helps to stabilize the inherently transient, highly reactive protein carbonyls to promote their detection, but interference from derivatized adducts with proteolytic sites can also limit CS sampling capabilities. Computational methods for CS prediction are intended to learn shared features across modified sites in redox proteomic datasets and generalize to unknown sites on other proteins. Existing methods (Maisonneuve et al, 2009; Lv et al, 2014; Weng et al, 2017) are not ideal because they rely on linear sequence motifs and local homology; such a correlative basis for predicting structure–function relationships can require a very large number of example sequences before very strong predictors can be trained (Kamisetty et al, 2013), which are not yet available in the context of redox proteomic data. Furthermore, the exclusion of molecular structure features beyond simple sequence motifs provides limited understanding of causal mechanisms for protein carbonylation. In response to the limitations of conventional techniques, the field of structural systems biology offers approaches based on protein 3D molecular properties to investigate multi-scale proteomic questions, including mechanisms of physicochemical stress (Chang et al, 2013a,b). These approaches are empowered by the expansion of experimentally determined protein structures and advances in protein fold prediction (Yang et al, 2015). Our robust experimental design combined for the first time redox proteomics performed on cells exposed to an acute dose of γ-radiation with structural systems biology and machine learning (Fig 1B), generating a predictive model for protein carbonylation. This interdisciplinary workflow enabled proteome-wide characterization of susceptibility to carbonylation in E. coli and D. radiodurans, identifying phenotypically important protein targets, providing molecular explanations for target susceptibility, and supporting the role of protein-intrinsic properties in the survival of extreme oxidative stress. Results Gamma-irradiation causes more targeted protein damage in D. radiodurans than E. coli To investigate oxidative damage to bacterial proteins, cultures were exposed to an acute dose of γ-radiation (6.7 kGy) lethal to E. coli but yielding 55–70% survival of D. radiodurans, and protein carbonyls and relative abundance changes were measured by mass spectrometry (Figs 1B, 2, and EV1). Based on previous work (Krisko & Radman, 2010), a dosage of radiation lethal to E. coli is required in order to observe any deleterious impact on D. radiodurans survival. Furthermore, our selected dosage approximates the highest reported dosage (7 kGy) used in bulk protein carbonylation measurements from whole cell lysate and dialyzed samples from both species (Krisko & Radman, 2010), providing a basis to model the impact of extrinsic protection of proteins by small molecule antioxidants. In order to limit de novo protein synthesis throughout and following irradiation, bacterial cultures were maintained near 0°C using a custom rack design (Dataset EV1 and EV2). Importantly, this resulted in differential relative protein abundances due specifically to oxidative damage (Materials and Methods), distinguishing our results from previous proteomic studies. Protein concentrations upon extraction were similar regardless of irradiation for each species (Appendix Table S1), and SDS–PAGE banding patterns were also qualitatively similar across protein samples extracted from the same species (Appendix Fig S2). Altogether, these results suggest that cell membrane integrity was preserved upon radiation. Figure 2. Summary of shotgun redox proteomic data Total carbonyl-bearing proteins detected by shotgun redox proteomic measurement in three biological replicates each of E. coli and D. radiodurans with and without irradiation. The left axis is the number of sequence-unique proteins detected as carbonylated. The right axis is the number of sites in total detected as carbonylated (red) or not oxidized (black) in peptides bearing at least one carbonyl. Stripes indicate carbonylated proteins and carbonylatable sites detected only in irradiated samples. See also Appendix Fig S1. Volcano plots for relative protein abundance changes measured by mass spectrometry in E. coli (left) and D. radiodurans (right) after irradiation using the same biological replicates as in Fig 2A. Black-circled points are those proteins with significant changes (paired, 2-sided t-test P-value < 0.05) of > 2-fold or < 0.5-fold. Red points are proteins with at least one carbonylated peptide detected. Fold change and P-value cutoffs considered for significance are indicated by dashed lines. See also Fig EV1. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Survival and carbonyl site sampling limits for proteomic experiments, related to Figs 2 and 3 Survival rates (based on CFU counts) of irradiated E. coli and D. radiodurans corresponding to biological triplicate samples from which proteomic data were acquired. Absolutely no colonies were recovered from E. coli cultures that had been irradiated, even without diluting the samples before plating. Carbonyl site measurement saturation curves for biological triplicate shotgun redox proteomic measurements in E. coli and D. radiodurans. Exponential saturation functions were fit by minimizing the sum of squared errors with the triplicate data points; the bolded term in each function is the estimated number of total non-redundant carbonyl sites in our samples. Download figure Download PowerPoint As expected (Krisko & Radman, 2010), we observed carbonylation of more proteins in E. coli (~700 CS in 102 of 1,373 identified proteins) than in D. radiodurans (~400 CS in 70 of 1,264 identified proteins) under either unirradiated or irradiated conditions (Fig 2A and Table EV1). D. radiodurans showed similar detection rates to that in Photobacterium angustum exposed to UVB (62 carbonylated proteins of 1,221 identified) using the same redox proteomic technique (Matallana-Surget et al, 2013). The lesser total protein carbonylation in D. radiodurans was likely due to its effective ROS detoxification mechanisms (Slade & Radman, 2011). CS saturation curves suggest the fewer detected carbonylation events in D. radiodurans account for a greater percent coverage of all in vivo events than is the case for E. coli (85 and 27%, respectively; Fig EV1B), in agreement with the difference in oxidative stress sensitivity between these species. Slightly more unique proteins were detected as carbonylated in a radiation-dependent manner in D. radiodurans (25) than in E. coli (20; Fig 2A). Based on the much lower estimated coverage of all in vivo carbonylation in E. coli, we suggest that extensive damage to the E. coli proteome—leading to more degraded and aggregated proteins—hindered identification of some carbonylated peptides by mass spectrometry. Relative protein quantification provided clear evidence of contrasting differential protein damage distinguishing these organisms (Fig 2B and Table EV2). Although in E. coli only six proteins showed significant > 2-fold differential relative abundance (paired t-test P-value < 0.05), 163 proteins overall showed > 2-fold changes albeit with higher variability across replicates. In D. radiodurans, 81 proteins significantly changed in relative abundance by > 2-fold; the magnitude of change was greater on average with lower variability than in E. coli. Proteins for which we detected at least one CS decreased in relative abundance more than other proteins in D. radiodurans (unpaired t-test P-value = 0.031), illustrating the expected relationship between carbonylation and degree of protein degradation. However, this relationship was less prominent in our E. coli data (unpaired t-test P-value = 0.104). Hence, although E. coli accumulated more protein carbonyls overall, their distribution is broader across distinct protein species, providing evidence of more protein-specific mechanisms for protection against ROS in D. radiodurans that are absent in E. coli. Analogous relative peptide quantification was also performed. For D. radiodurans, 148 peptides representing 134 unique proteins significantly increased in relative abundance (fold change > 2, satisfying Benjamini–Hochberg criteria with false discovery rate of 0.05) after irradiation, and one peptide significantly decreased (fold change < 0.5, satisfying Benjamini-Hochberg criteria). For E. coli, 26 peptides representing 25 unique proteins significantly decreased in relative abundance after irradiation, and no peptides significantly increased. No individual carbonylated peptides significantly changed in relative abundance in either species. These observations generally parallel the anticipated contrasting response upon irradiation of these species. However, greater statistical power is achieved when pooling peptides to evaluate abundance changes at the whole-protein level. This is partly because stochastically missed tryptic sites and post-translational modifications lead to imperfect peptide identity when quantifying at the peptide level. Broad functional characterization of proteins with substantial relative abundance change ( 2-fold) was carried out by Gene Ontology (GO) biological process term enrichment analysis with protein abundance correction (Scholz et al, 2015). These proteins in E. coli exhibited no significantly over- or underrepresented GO annotations. In contrast, D. radiodurans proteins with > 2-fold relative increase were overrepresented by proteins involved in translation and broader protein metabolism (Table 1), including many ribosomal subunits. Additionally, D. radiodurans proteins with < 0.5-fold change underrepresented proteins involved in nitrogen compound biosynthesis, indirectly implicating the importance of amino acid and nucleotide synthesis. Therefore, resistance to protein oxidation in D. radiodurans preferentially protects the critical process of proteome regeneration under oxidative stress. Table 1. Gene Ontology terms enriched among D. radiodurans proteins with high relative abundance change Retained or lost GO ID Over-/underrepresented % foreground % background Fold enrichment Foreground count Background count P-value GO biological process Retained > 2-fold GO:0006412 O 16.67 7.58 2.20 22 10 0.037 Translation GO:0006518 O 19.70 9.09 2.17 26 12 0.022 Peptide metabolic process GO:0044267 O 21.97 11.36 1.93 29 15 0.031 Cellular protein metabolic process GO:0009059 O 24.24 12.88 1.88 32 17 0.026 Macromolecule biosynthetic process GO:0019538 O 28.03 15.91 1.76 37 21 0.025 Protein metabolic process GO:0009987 O 73.49 61.36 1.20 97 81 0.049 Cellular process Lost < 0.5-fold GO:0044271 U 12.12 27.27 0.44 8 18 0.048 Cellular nitrogen compound biosynthetic process Amino acid composition protects against oxidative damage Although the relative frequency of carbonylated RKTP residues generally confirmed previous studies (Rao & Moller, 2011; Matallana-Surget et al, 2013), we found lysine to be as susceptible as proline to carbonylation under γ-irradiation (Fig 3A) in D. radiodurans (ratio 1.77 versus 1.66) and to a lesser extent in E. coli (ratio 1.17 versus 1.43). Protein carbonylation by natively generated ROS in eukaryotes (Rao & Moller, 2011) and UV irradiation in P. angustum (Matallana-Surget et al, 2013) both indicated proline as the most ROS susceptible of RKPT and lysine as not especially or least susceptible, respectively. Proline carbonylation often leads to polypeptide self-cleavage, which may explain the relatively low proline content of bacterial ribosomal versus non-ribosomal proteins (Lott et al, 2013), an evolutionary adaptation contributing to protection of translation against oxidative stress. In contrast, lysine, found incorporated into proteins much more frequently, lacks a similar mechanism for self-cleavage upon carbonylation. The more complex role of lysine in oxidative stress is discussed below. Figure 3. Amino acid prevalence in proteomic data before and after irradiation Prevalence of individual RKPT residues and prevalence of carbonylated form in experimentally measured peptides combining all three biological replicates of both conditions for each organism. Ratios are given above each pair of bars. All proportions are significantly different between each RKPT and their respective carbonylation state by two-tailed z-test of two proportions (P-values < 0.01; see Materials and Methods), and meaning carbonylated proportions are not determined simply by relative prevalence of RKPT. See also Appendix Fig S1. Prevalence of all canonical amino acids before irradiation of E. coli and D. radiodurans, combining all three biological replicates for each condition. Ratios are given above each pair of bars. All proportions are significantly different between species by two-tailed z-test of two proportions (P-values < 0.01). See also Figs EV1 and EV2. Download figure Download PowerPoint Selective amino acid composition is a major adaptation organisms have evolved to thrive in diverse environmental niches (Brbic et al, 2015). Comparing compositions between expressed proteomes of E. coli and D. radiodurans under permissive conditions (Fig 3B) revealed significant differences among oxidizable amino acids. Lysine and arginine, both positively charged at physiological pH, differ in ROS susceptibility and exhibited significant usage differences. While highly susceptible lysine was found to be less frequently used in D. radiodurans, less susceptible arginine was overrepresented instead (0.71-fold and 1.57-fold, respectively). Reversibly oxidizable sulfur-containing amino acids, cysteine and methionine, were rare in both species, but significantly less prevalent in D. radiodurans under permissive conditions (0.53-fold and 0.17-fold, respectively). Surface methionines and cysteines help protect proteins from oxidative damage in many organisms due to their own reversible oxidation (Stadtman & Levine, 2003). However, cysteine and methionine are metabolically expensive (i.e., stoichiometrically consume the most ATP) for bacterial synthesis (Kaleta et al, 2013), and D. radiodurans is auxotrophic for methionine (Zhou et al, 2017), which may explain their significantly lower prevalence in slower-growing D. radiodurans despite expected benefits for resistance. Tryptophan and tyrosine, two metabolically inexpensive amino acids that function as integrated antioxidants in some proteins (Moosmann & Behl, 2000), were significantly more abundant in D. radiodurans than in E. coli (both ~1.3-fold). To evaluate the impact of oxidative stress on amino acid prevalence in identified proteins, we compared changes in amino acid composition after γ-irradiation of E. coli and D. radiodurans (Fig EV2). While only seven amino acids significantly changed in E. coli, 16 significantly changed in D. radiodurans and to a greater magnitude. The greatest decrease among RKPT was lysine in both species, further supporting that incorporated lysine is an important mediator of protein oxidative damage under γ-irradiation. Lysine can sometimes be exchanged for histidine in proteins and still preserve protein function as shown in synthetic mutational studies (Yampolsky & Stoltzfus, 2005). Notably, relative histidine prevalence increased modestly (+2%) in E. coli and significantly (+11%) in D. radiodurans after irradiation, suggesting that D. radiodurans has evolved proteins that are more composed of non-carbonylatable histidine rather than lysine as another protein-intrinsic protection mechanism. Indeed, across sequences of functional orthologs and isozymes in these species (Appendix Fig S3) we found 10% greater histidine composition in D. radiodurans than in E. coli as a fraction of total histidine and lysine (paired t-test P-value < 6 × 10−60). Following irradiation, tyrosine prevalence significantly increased in E. coli (+4%) and in D. radiodurans (+8%), and cysteine increased significantly (+18%) only in D. radiodurans. The most significant decrease in E. coli (−13%) and increase in D. radiodurans (+45%) was for methionine. This contrast suggests a more efficient methionine sulfoxide reductase system under oxidative stress in D. radiodurans. All together, these results establish that protein-intrinsic properties, even in primary structure, differ between E. coli and D. radiodurans and affect which proteins withstand the onslaught of ROS-induced oxidative damage. Click here to expand this figure. Figure EV2. Canonical amino acid prevalence change f
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