Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria
2015; Springer Nature; Volume: 11; Issue: 2 Linguagem: Inglês
10.15252/msb.20145697
ISSN1744-4292
AutoresSheng Hui, Josh M. Silverman, Stephen S. Chen, David W. Erickson, Markus Basan, Jilong Wang, Terence Hwa, James R. Williamson,
Tópico(s)Bacterial Genetics and Biotechnology
ResumoArticle12 February 2015Open Access Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria Sheng Hui Sheng Hui Department of Physics, University of California at San Diego, La Jolla, CA, USA Search for more papers by this author Josh M Silverman Josh M Silverman Department of Integrative Structural and Computational Biology, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA Department of Chemistry, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA Search for more papers by this author Stephen S Chen Stephen S Chen Department of Integrative Structural and Computational Biology, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA Department of Chemistry, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA Search for more papers by this author David W Erickson David W Erickson Department of Physics, University of California at San Diego, La Jolla, CA, USA Search for more papers by this author Markus Basan Markus Basan Department of Physics, University of California at San Diego, La Jolla, CA, USA Search for more papers by this author Jilong Wang Jilong Wang Department of Physics, University of California at San Diego, La Jolla, CA, USA Search for more papers by this author Terence Hwa Corresponding Author Terence Hwa Department of Physics, University of California at San Diego, La Jolla, CA, USA Section of Molecular Biology, Division of Biological Sciences, University of California at San Diego, La Jolla, CA, USA Search for more papers by this author James R Williamson Corresponding Author James R Williamson Department of Integrative Structural and Computational Biology, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA Department of Chemistry, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA Search for more papers by this author Sheng Hui Sheng Hui Department of Physics, University of California at San Diego, La Jolla, CA, USA Search for more papers by this author Josh M Silverman Josh M Silverman Department of Integrative Structural and Computational Biology, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA Department of Chemistry, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA Search for more papers by this author Stephen S Chen Stephen S Chen Department of Integrative Structural and Computational Biology, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA Department of Chemistry, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA Search for more papers by this author David W Erickson David W Erickson Department of Physics, University of California at San Diego, La Jolla, CA, USA Search for more papers by this author Markus Basan Markus Basan Department of Physics, University of California at San Diego, La Jolla, CA, USA Search for more papers by this author Jilong Wang Jilong Wang Department of Physics, University of California at San Diego, La Jolla, CA, USA Search for more papers by this author Terence Hwa Corresponding Author Terence Hwa Department of Physics, University of California at San Diego, La Jolla, CA, USA Section of Molecular Biology, Division of Biological Sciences, University of California at San Diego, La Jolla, CA, USA Search for more papers by this author James R Williamson Corresponding Author James R Williamson Department of Integrative Structural and Computational Biology, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA Department of Chemistry, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA Search for more papers by this author Author Information Sheng Hui1, Josh M Silverman2,3, Stephen S Chen2,3, David W Erickson1, Markus Basan1, Jilong Wang1, Terence Hwa 1,4 and James R Williamson 2,3 1Department of Physics, University of California at San Diego, La Jolla, CA, USA 2Department of Integrative Structural and Computational Biology, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA 3Department of Chemistry, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA 4Section of Molecular Biology, Division of Biological Sciences, University of California at San Diego, La Jolla, CA, USA *Corresponding author. Tel: +1 858 534 7263; E-mail: [email protected] *Corresponding author. Tel: +1 858 784 8740; E-mail: [email protected] Molecular Systems Biology (2015)11:784https://doi.org/10.15252/msb.20145697 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 A central aim of cell biology was to understand the strategy of gene expression in response to the environment. Here, we study gene expression response to metabolic challenges in exponentially growing Escherichia coli using mass spectrometry. Despite enormous complexity in the details of the underlying regulatory network, we find that the proteome partitions into several coarse-grained sectors, with each sector's total mass abundance exhibiting positive or negative linear relations with the growth rate. The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters. The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell. This strategy of global gene regulation should serve as a basis for future studies on gene expression and constructing synthetic biological circuits. Coarse graining may be an effective approach to derive predictive phenomenological models for other 'omics' studies. Synopsis Quantitative relative and absolute protein abundance data allow the use of coarse-graining analysis to reveal strategies of resource allocation by E. coli. A predictive, mathematical model of the proteome is constructed requiring only a few parameters. Coarse-graining procedure makes proteomics data amenable to quantitative analysis. Five functionally distinct proteome sectors each exhibit linear relations with the growth rate. A simple flux model captures proteome-wide responses accurately with few parameters. Proteome economy is shown to be a principle governing global gene regulation. Introduction One of the most extensively studied questions in biology is how cells alter gene expression to deal with changes in their environment. A widely held view, supported by a mountain of observations, is the idea that cells handle challenges to growth-limiting perturbations, for example, nutrient limitation, by increasing the amount of enzymes devoted to overcoming the limited process, in analogy with 'supply and demand' (Hofmeyr & Cornish-Bowden, 2000). This qualitative picture has been widely articulated in conceptual models from early on (Hinshelwood, 1944) to the present and is supported by analyses of 2D gel experiments (O'Farrell, 1975), microarrays (Brown & Botstein, 1999), deep sequencing (Ingolia et al, 2009), mass spectrometry (Aebersold & Mann, 2003), and other high-throughput measurements of gene expression (Ghaemmaghami et al, 2003; Taniguchi et al, 2010). For example, cells grown in minimal media increase the level of amino acid synthesis enzymes compared to rich media, and cells grown in the presence of translation inhibitors increase the synthesis of ribosomes (Dennis, 1976; Tao et al, 1999; Boer et al, 2003). Despite these results, there is little in the way of a quantitative understanding of resource allocation even in the simplest cells (Chubukov & Sauer, 2014). Recently, it was shown that simple genetic circuits respond to changes in the physiological state of a cell in different ways, based upon the details of their defined regulation (Klumpp et al, 2009). At the molecular level, a cell's response to an applied limitation is the outcome of a complex interaction of metabolites, transcription factors, promoters, and other factors, conspiring to produce the observed pattern of gene expression. It is therefore unclear how the behavior of single genes under defined and specific regulation can be generalized to shifts in global gene expression arising from environmental changes. Many elementary questions remain unaddressed. In response to a growth-limiting perturbation, by how much does the cell adjust its composition to deal with the limiting process(es)? Does the cell handle limitation in the supply of a given nutrient by adjusting operons related to the specific shortage, or is gene expression organized according to some higher schema? Can the effect of different types of growth limitations be meaningfully compared? From the perspective of analysis, can cellular response, with changes in thousands of quantities as revealed by 'omics' experiments, be summarized by simple quantitative measures beyond statistical analysis? In characterizing the state of a gas, useful quantitative measures are macroscopic quantities such as pressure and temperature, not the statistical clustering of the trajectories of molecules in the gas. In systems biology, might similar measures exist to provide meaningful quantitative characterization of cellular responses? Early studies of bacterial physiology identified a number of relations between the cell growth rate and quantities such as chromosome copy number, cell mass, and ribosome content (Schaechter et al, 1958; Bremer & Dennis, 2009). Despite the incredible complexity of ribosome biogenesis and its regulation, the proportion of translational machinery among all proteins can be captured by a simple linear relation with the cell growth rate (Bennett & Maaloe, 1974). These observations hint to a quantitative framework underlying the intuitive 'supply and demand' picture. The hint is the balance between the flux of amino acids synthesized into proteins by ribosomes and the flux of molecular building blocks from catabolic and biosynthetic reactions culminating in amino acids that are consumed by the ribosomes. This highlights an attractive possibility. If enzymes are regulated as subsets according to their shared purpose, as the hundred or so genes involved in translation are, it may be possible to capture their collective behavior quantitatively as is possible for the translational machinery. Rather than focusing upon the molecular details of hundreds of enzymes as they facilitate myriad reactions, the enzymes of a functional group might instead be profitably viewed as a single effective coarse-grained enzyme that catalyzes interconversion between major metabolic pools, such as carbon precursors to amino acids. In this view, proteome-wide response to nutrient limitations may be characterized quantitatively as adjustments to the concentrations of coarse-grained enzymes. This coarse-grained view of the proteome yields a simple picture that is amenable to mathematical analysis. Recently, the coarse-graining approach has been used to address the effects of protein overexpression (Scott et al, 2010), cAMP-mediated catabolite repression (You et al, 2013), growth bistability in response to antibiotics (Deris et al, 2013), and methionine biosynthesis (Li et al, 2014). But these studies focused on the expression of only a few genes, declared to be proxies for hundreds of proteins (Scott et al, 2010; You et al, 2013), or isolated in a backdrop of changing proteome (Deris et al, 2013; Li et al, 2014). There has been no study of its global applicability and, indeed, no work to predict quantitative proteome composition from physiological state. Toward this end, it is our aim to quantitatively characterize global gene expression under various modes of growth limitation and to interrogate the intuitive ideas regarding resource allocation quantitatively. Samples were collected for E. coli cells growing exponentially in a variety of growth conditions: under titration of carbon import and nitrogen assimilation, and in the presence of varying amounts of translation inhibitor. Using quantitative mass spectrometry, the relative concentrations of ~1,000 enzymes were measured across the set of growth-limiting conditions. Analysis of the enzyme concentrations reveals six groups of enzymes with distinct modes of gene expression in response to the applied limitations. An enrichment analysis of gene ontology terms appearing in these groups shows that each group consists of enzymes with uniform purpose, such as translation and catabolism. The cell up-regulates relevant groups to counteract the imposed limitation, confirming the qualitative expectations based on supply and demand. A key to this analysis is the concept of an 'effective concentration' for each coarse-grained enzyme, obtained as the fractional abundance of the sum of all its enzyme components among all expressed proteins in each condition. The concentration of the coarse-grained enzymes was estimated using coarse-grained spectral counts as a proxy for protein abundance (Malmström et al, 2009). Strikingly, the concentrations of these coarse-grained enzymes correlated linearly with the growth rate. These data, together with the intrinsic constraints of finite resource allocation, led to the construction of a self-consistent, flux-matching model of the proteome that not only quantitatively accounts for all the observed data but also predicts proteome composition in novel environments involving combinatorial modes of growth limitation. Results Growth limitations To probe gene expression, cell growth was perturbed by imposing three different modes of growth limitation at crucial bottlenecks in the metabolic network. A coarse-grained metabolic flow diagram for protein production by E. coli growing in minimal medium is shown in Fig 1. Four metabolic sections act in concert to convert external carbon sources to proteins, incorporating nitrogen and sulfur elements during the process. Following the work of You et al (You et al, 2013), growth limitation was imposed on three of the four metabolic sections. The limitation imposed on the catabolic section (C-limitation or C-lim) was implemented by titrating the expression of lactose permease for cells growing on lactose (Supplementary Fig S1). The limitation on the anabolic section (A-limitation or A-lim) was realized by titrating a key enzyme (GOGAT) in the ammonia assimilation pathway (Supplementary Fig S2). Such 'titratable uptake systems' have been characterized in detail and found comparable to other modes of growth limitations such as those derived from continuous culture or microfluidic devices (You et al, 2013). To impose growth limitation on the polymerization sections, sublethal amounts of a translation inhibitor antibiotic, chloramphenicol, were supplied to the growth medium to inhibit translation by ribosomes (R-limitation or R-lim). The collective response of the E. coli proteome to these applied growth limitations was monitored using quantitative mass spectroscopy. Figure 1. Coarse-grained metabolic flow of protein production and the three modes of growth limitationThrough the (carbon) catabolic section, the cells take up external carbon sources and break them down into the set of standard carbon skeletons (pyruvate, oxaloacetate, etc.). The carbon skeletons are interconvertible through the central metabolism section. The anabolic section synthesizes amino acids from the carbon skeletons and other necessary elements such as ammonia and sulfur. The amino acids are then assembled into proteins by the polymerization section. The three modes of growth limitation were imposed on the metabolic sections as shown. The C-limitation (C-lim) and A-limitation (A-lim) were carried out with strains constructed for titrating the catabolic and anabolic flux, respectively; see Supplementary Figs S1 and S2, and Supplementary Table S1. The R-limitation (R-lim) was realized for the WT strain by supplying the growth medium with various levels of an antibiotic, chloramphenicol. Download figure Download PowerPoint Quantitative proteomic mass spectrometry Proteomic mass spectrometry is a powerful tool for quantifying changes in global protein expression patterns (Aebersold & Mann, 2003; Ong & Mann, 2005; Bantscheff et al, 2007; Han et al, 2008). As shown below, mass spectrometry also has the advantage of reliably detecting small changes in protein levels, with precision comparable to that of enzymatic assays. Metabolic labeling with 15N (Oda et al, 1999) provides relative quantitation of unlabeled proteins with respect to labeled proteins across growth conditions of interest. Each experimental sample in a series is mixed in equal amount with a known labeled standard sample as reference, and the relative change of protein expression in the experimental sample is obtained for each protein. Accuracy and precision The accuracy and precision of quantifying relative protein expression levels was determined from a standard curve using samples of unlabeled and 15N-labeled purified ribosomes. The observed relative levels, measured by ratios of the labeled to the unlabeled ribosomal proteins (or 15N/14N), agree extremely well with the expected values over a range of about two orders of magnitude (Fig 2A). To assess the accuracy and precision for a whole-cell lysate with a much more complex proteome, labeled and unlabeled cells were mixed in fixed ratios and measured with quantitative mass spectrometry. The relative changes in protein levels can be precisely determined over the range of ratios from 0.1 to 10, as shown in Fig 2B. The effective precision of relative protein quantification is ±18%, based on analysis of the 1:1 sample (Supplementary Fig S3). Thus, subtle changes in proteome composition that are much < 2-fold can be precisely determined. Furthermore, the relative quantitation using quantitative mass spectrometry agrees extremely well with a traditional biochemical measurement of ribosome content (Supplementary Fig S4A) and also with quantitation of LacZ using a β-galactosidase assay (Supplementary Fig S4B). Figure 2. The quantitative protein mass spectrometry Observed values versus real values for ratios of 15N ribosomal proteins to 14N ribosomal proteins. Black dots are the mean values, with error bars representing the range of the values for all ribosomal proteins. The dashed line represents perfect agreement between the observed values and real values. Observed values versus real values for ratios of 15N proteins to 14N proteins from whole-cell lysates. Black dots are the median values for more than 600 proteins. The error bar for each median value indicates the quartiles. The dashed line represents perfect agreement between the observed values and real values. Additional characterizations are shown in Supplementary Figs S3 and S4. The expression matrix and clustering results. The matrix is composed of 1,053 proteins (rows) and 14 conditions (columns); see Supplementary Table S2. The first five columns are for C-limitation, the next five columns for A-limitation, and the last four columns for R-limitation. For each mode of growth limitation, the growth rate increases from left to right. The matrix is log2-transformed, with expression values at the standard condition as zero (see 4), represented as black color. Red color indicates negative values, green color positive values, and gray color missing entries. A dendrogram generated by clustering analysis is shown on the left of the expression matrix (see 4), with the five major clusters shown on the right side of the matrix. The data are estimated to cover ˜80% of the proteome; see Supplementary Fig S5. Download figure Download PowerPoint Datasets and protein coverage For the C-, A-, and R-limitations, a series of cultures were prepared with varying growth rates. For the C-limitation series, controlled inducible expression of the lacY gene gave doubling times from 40 to 92 min (five conditions), for the A-limitation series, controlled expression of GOGAT gave doubling times from 43 to 91 min (five conditions), and for the R-limitation series, inhibition of protein synthesis with chloramphenicol gave doubling times from 42 to 147 min (four conditions), as detailed in Supplementary Table S1. Samples from each of the fourteen cultures were collected, and the relative protein levels were determined using mass spectrometry, as described in the Materials and Methods. For C-, A-, and R-limitations, the numbers of proteins with reliable expression data are 856, 898, and 756, respectively. Most proteins present in one dataset are present in others, with 616 proteins shared in all three datasets and a total of 1,053 unique proteins in any dataset. Due to a highly non-uniform distribution of protein abundance, our experiments are estimated to cover ~80% of the total proteome by mass and are validated using absolute abundance estimated by a recent experiment using ribosome profiling (Li et al, 2014); see Supplementary Fig S5. For data analysis, the combined datasets were represented as a matrix of 1,053 proteins across the 14 growth conditions (Supplementary Table S2), graphically shown in Fig 2C. Clustering analysis of protein expression trends A qualitative global analysis of the data was performed with hierarchical clustering using the Pearson correlation as a distance metric (4), and the resulting dendrogram is shown on the expression matrix in Fig 2C. Five major clusters are apparent, characterized by different trends in the three limitation series. The cluster where protein levels increase as growth rate is reduced under C-limitation, but decrease under A- and R-limitations, represents proteins that specifically respond to C-limitation and is designated as the C-cluster. The A-cluster is defined by increased protein levels under A-limitation, but decreased levels under C- and R-limitations, responding specifically to A-limitation. Similarly, the cluster where proteins levels increase in response to R-limitation, but decrease under C- and A-limitations, specifically respond to R-limitation and is designated as the R-cluster. The S-cluster is defined by protein levels that increase under both A- and C-limitations. Finally, the cluster for proteins that generally do not respond specifically to any of the three modes of growth limitation is designated as the U-cluster. The clustering analysis is useful for providing an overview of the trends in the proteomic data, and revealing the qualitative responses of proteins to the different modes of growth limitation: Most proteins respond specifically to a single mode of growth limitation with the exception of the S-cluster. These clusters suggest that proteome levels are strongly coordinated based on the environmental stress and that the response of the proteome to the environment might be amenable to a quantitative coarse-graining analysis. Coarse-grained proteome sectors Extensive analysis of a number of exemplary reporters of catabolic and biosynthetic gene expression revealed strikingly linear growth rate dependence in the expression of these genes (You et al, 2013). The prevalence of linear growth rate dependence has been described in omics studies of both proteins (Pedersen et al, 1978) and mRNAs (Brauer et al, 2008). Visual inspection of the expression data of individual proteins in Fig 2C (see Supplementary Dataset S1 for individual plots) suggested that many exhibited a linear trend, and the coefficient of determination (R2) for the expression of each protein was calculated for each mode of growth limitations. The cumulative distribution of R2 for each mode of growth limitation shows that linear dependence on growth rate is widespread in our data (Supplementary Fig S6A), and is further supported by comparison with a quadratic fit of the data (Supplementary Fig S6B). Possible causes for the occurrence of low R2 values include limited method precision (Supplementary Fig S7A) and weak growth rate dependence for some genes (Supplementary Fig S7B and C). The approximate linear nature of the protein abundance data suggests that the results may be simplified using a coarse-grained analysis, by summing over the absolute abundance of individual proteins in a cluster (since the sum of linear functions is still linear). For a protein exhibiting linear growth rate dependence, a negative slope corresponds to a higher expression level at slower growth rate, referred to as the 'upward' response (↑), while a positive slope corresponds to a lower expression level at slower growth rate, referred to as the 'downward' response (↓). Given that a protein has either upward or downward response under each of the three modes of growth limitation (C-, A-, and R-limitation), it has to belong to one of the 23 = 8 groups: C↑A↓R↓, C↑A↑R↓, C↓A↑R↓, C↓A↑R↑, C↓A↓R↑, C↑A↓R↑, C↑A↑R↑, and C↓A↓R↓, where the group names are indicated by the upward or downward response under each of the three modes of growth limitation. For example, the C↑A↓R↓ group consists of proteins that have upward response under C-limitation and downward responses under both the A- and R-limitation. The membership of proteins in the resulting eight groups is given in Supplementary Table S2 and graphically shown in Supplementary Fig S8. Due to the precision limitations of the method, proteins exhibiting small change under a specific growth limitation are subject to misclassification. To examine the effect of this misclassification on our results, we carried out a probabilistic classification, by assigning a protein to one of the eight groups according to a probability (see Supplementary Text S2 for details). The analysis shows a very limited effect misclassification has on the binary classification. The collective behavior of a protein group can be approximated by coarse graining, effectively summing the absolute protein abundance of proteins in the same group. Among the methods for quantifying absolute protein abundance from proteomic mass spectrometry data (Beynon et al, 2005; Ishihama et al, 2005, 2008; Lu et al, 2006; Silva et al, 2006; Vogel & Marcotte, 2008; Schmidt et al, 2011; Muntel et al, 2014), the method of spectral counting takes the number of peptides recorded for each protein as proxy for the absolute abundance of the protein (Malmström et al, 2009). While spectral counting provides a crude estimate of the absolute protein abundance for individual proteins (Bantscheff et al, 2007), it gives a much more reliable approximation for groups of proteins. For a protein group comprising more than ~5% of the total proteome, spectral counting produces estimates with < 20% error (Supplementary Fig S9A). The comparison of spectral counting data for ribosomal proteins with estimates based on biochemical measurements and the ribosome profiling results (Li et al, 2014) is in good agreement (Supplementary Fig S9B). By applying the spectral counting method, the proteome fractions for the nine protein groups defined in Supplementary Table S2 were determined for each of the three series of growth limitations (Supplementary Fig S10). It is clear from Supplementary Fig S10 that some groups occupy significant fractions of the proteome while others are minor constituents. Ranked by the extent the fraction varies (indicated by the difference between the maximal and minimal intercepts on the y-axis), the top three groups are C↑A↓R↓, C↓A↑R↓, and C↓A↓R↑. These consist of proteins that only respond upward to the C-, A-, and R-limitation and are referred to as the C-, A-, and R-sector, respectively (Fig 3A–C). The C↓A↓R↓ group includes proteins that are uninduced by any of the three applied limitations, and is referred to as the U-sector (Fig 3D). Another significant protein sector is the C↑A↑R↓ group, which is composed of proteins that have upward response to both the A- and C-limitations, and referred to as the S-sector for general starvation; see Fig 3E. The three remaining groups (i.e., C↑A↑R↑, C↑A↓R↑, and C↓A↑R↑ groups) are small, with most of the data at or below 5% of the proteome, below the accuracy of the spectral counting method (Supplementary Fig S9A). The three small groups were placed together into the O-sector (Fig 3F). In summary, the proteome is coarse-grained into 6 'sectors': C-, A-, R-, U-, S-, and O-sectors with distinct growth rate dependences as shown in Fig 3, with complete data for all fractions shown in Supplementary Table S3. In contrast, the results obtained for randomly shuffled expression matrices do not show significant growth rate dependence (Supplementary Fig S11). Figure 3. The coarse-grained proteome sectors A–F. Coarse-grained responses of the C-, A-, R-, U-, S-, and O-sectors to the three modes of growth limitation. As indicated in (A), the red symbols in each panel are for C-limitation, the blue for A-limitation, and the green for R-limitation. The error bars indicate the standard deviation of triplicate mass spectrometry runs. Error bars smaller than the corresponding symbols are not shown (see Supplementary Fig S10 on the different degrees of variability associated with different sectors.) On each plot, the number in the title indicates the number of proteins in that se
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