Temporal Dynamics of the Saccharopolyspora erythraea Phosphoproteome
2014; Elsevier BV; Volume: 13; Issue: 5 Linguagem: Inglês
10.1074/mcp.m113.033951
ISSN1535-9484
AutoresCuauhtémoc Licona‐Cassani, Sooa Lim, Esteban Marcellin, Lars K. Nielsen,
Tópico(s)Fungal Biology and Applications
ResumoActinomycetes undergo a dramatic reorganization of metabolic and cellular machinery during a brief period of growth arrest ("metabolic switch") preceding mycelia differentiation and the onset of secondary metabolite biosynthesis. This study explores the role of phosphorylation in coordinating the metabolic switch in the industrial actinomycete Saccharopolyspora erythraea. A total of 109 phosphopeptides from 88 proteins were detected across a 150-h fermentation using open-profile two-dimensional LC-MS proteomics and TiO2 enrichment. Quantitative analysis of the phosphopeptides and their unphosphorylated cognates was possible for 20 pairs that also displayed constant total protein expression. Enzymes from central carbon metabolism such as putative acetyl-coenzyme A carboxylase, isocitrate lyase, and 2-oxoglutarate dehydrogenase changed dramatically in the degree of phosphorylation during the stationary phase, suggesting metabolic rearrangement for the reutilization of substrates and the production of polyketide precursors. In addition, an enzyme involved in cellular response to environmental stress, trypsin-like serine protease (SACE_6340/NC_009142_6216), decreased in phosphorylation during the growth arrest stage. More important, enzymes related to the regulation of protein synthesis underwent rapid phosphorylation changes during this stage. Whereas the degree of phosphorylation of ribonuclease Rne/Rng (SACE_1406/NC_009142_1388) increased during the metabolic switch, that of two ribosomal proteins, S6 (SACE_7351/NC_009142_7233) and S32 (SACE_6101/NC_009142_5981), dramatically decreased during this stage of the fermentation, supporting the hypothesis that ribosome subpopulations differentially regulate translation before and after the metabolic switch. Overall, we show the great potential of phosphoproteomic studies to explain microbial physiology and specifically provide evidence of dynamic protein phosphorylation events across the developmental cycle of actinomycetes. Actinomycetes undergo a dramatic reorganization of metabolic and cellular machinery during a brief period of growth arrest ("metabolic switch") preceding mycelia differentiation and the onset of secondary metabolite biosynthesis. This study explores the role of phosphorylation in coordinating the metabolic switch in the industrial actinomycete Saccharopolyspora erythraea. A total of 109 phosphopeptides from 88 proteins were detected across a 150-h fermentation using open-profile two-dimensional LC-MS proteomics and TiO2 enrichment. Quantitative analysis of the phosphopeptides and their unphosphorylated cognates was possible for 20 pairs that also displayed constant total protein expression. Enzymes from central carbon metabolism such as putative acetyl-coenzyme A carboxylase, isocitrate lyase, and 2-oxoglutarate dehydrogenase changed dramatically in the degree of phosphorylation during the stationary phase, suggesting metabolic rearrangement for the reutilization of substrates and the production of polyketide precursors. In addition, an enzyme involved in cellular response to environmental stress, trypsin-like serine protease (SACE_6340/NC_009142_6216), decreased in phosphorylation during the growth arrest stage. More important, enzymes related to the regulation of protein synthesis underwent rapid phosphorylation changes during this stage. Whereas the degree of phosphorylation of ribonuclease Rne/Rng (SACE_1406/NC_009142_1388) increased during the metabolic switch, that of two ribosomal proteins, S6 (SACE_7351/NC_009142_7233) and S32 (SACE_6101/NC_009142_5981), dramatically decreased during this stage of the fermentation, supporting the hypothesis that ribosome subpopulations differentially regulate translation before and after the metabolic switch. Overall, we show the great potential of phosphoproteomic studies to explain microbial physiology and specifically provide evidence of dynamic protein phosphorylation events across the developmental cycle of actinomycetes. Microorganisms have evolved mechanisms that enable them to grow and rapidly adapt to changing environmental conditions. Regulation of protein activity can occur at transcriptional, translational, and/or post-translational levels. Transcriptional and translational control are slow and have high energy costs due to de novo synthesis of proteins (i.e. transcription, translation, and protein-folding processes). Conversely, protein post-translational modifications drive adaptive cellular responses more efficiently by adding or removing functional groups from specific protein residues (1.Deribe Y.L. Pawson T. Dikic I. Post-translational modifications in signal integration.Nat. Struct. Mol. Biol. 2010; 17: 666-672Crossref PubMed Scopus (533) Google Scholar). Among the post-translational modifications that regulate protein functionality, phosphorylation is by far the most studied in bacteria (2.Mijakovic I. Protein phosphorylation in bacteria.Microbe. 2010; 5: 21-25Google Scholar, 3.Kobir A. 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Recently, in vivo phosphorylation controlling enzyme functionality was studied in E. coli and Streptomyces coelicolor developmental cycles (16.Manteca A. Ye J. Sanchez J. Jensen O.N. Phosphoproteome analysis of Streptomyces development reveals extensive protein phosphorylation accompanying bacterial differentiation.J. Proteome Res. 2011; 10: 5481-5492Crossref PubMed Scopus (61) Google Scholar, 17.Soares N.C. Spät P. Krug K. Macek B. Global dynamics of the Escherichia coli proteome and phosphoproteome during growth in minimal medium.J. Proteome Res. 2013; 12: 2611-2621Crossref PubMed Scopus (88) Google Scholar), showing the yet poorly explored effect of dynamic protein phosphorylation in microbial physiology. Actinomycetes produce a large variety of secondary metabolites, including approximately half the antibiotics in current use (18.Berdy J. Bioactive microbial metabolites.J. Antibiotics. 2005; 58: 1-26Crossref PubMed Scopus (1702) Google Scholar). The production of secondary metabolites occurs after a critical culture transition known as the "metabolic switch" (19.Nieselt K. Battke F. Herbig A. Bruheim P. Wentzel A. Jakobsen O. Sletta H. Alam M. Merlo M. Moore J. Omara W. Morrissey E. Juarez-Hermosillo M. Rodriguez-Garcia A. Nentwich M. Thomas L. Iqbal M. Legaie R. Gaze W. Challis G. Jansen R. Dijkhuizen L. Rand D. Wild D. Bonin M. Reuther J. Wohlleben W. Smith M. Burroughs N. Martin J. The dynamic architecture of the metabolic switch in Streptomyces coelicolor.BMC Genomics. 2010; 11: 10Crossref PubMed Scopus (136) Google Scholar), believed to be triggered by nutrient limitation or oxidative stress. Understanding the regulatory mechanisms underpinning the reorganization of the metabolic and cellular machinery at the metabolic switch (20.Marcellin E. Mercer T. Licona-Cassani C. Palfreyman R. Dinger M. Steen J. Mattick J. Nielsen L. Saccharopolyspora erythraea's genome is organised in high-order transcriptional regions mediated by targeted degradation at the metabolic switch.BMC Genomics. 2013; 14: 15Crossref PubMed Scopus (18) Google Scholar) is of both fundamental and practical importance, as efficient induction is essential for high-level production. Although some cellular regulatory mechanisms have been explained with regard to transcription (19.Nieselt K. Battke F. Herbig A. Bruheim P. Wentzel A. Jakobsen O. Sletta H. Alam M. Merlo M. Moore J. Omara W. Morrissey E. Juarez-Hermosillo M. Rodriguez-Garcia A. Nentwich M. Thomas L. Iqbal M. Legaie R. Gaze W. Challis G. Jansen R. Dijkhuizen L. Rand D. Wild D. Bonin M. Reuther J. Wohlleben W. Smith M. Burroughs N. Martin J. The dynamic architecture of the metabolic switch in Streptomyces coelicolor.BMC Genomics. 2010; 11: 10Crossref PubMed Scopus (136) Google Scholar, 20.Marcellin E. Mercer T. Licona-Cassani C. Palfreyman R. Dinger M. Steen J. Mattick J. Nielsen L. Saccharopolyspora erythraea's genome is organised in high-order transcriptional regions mediated by targeted degradation at the metabolic switch.BMC Genomics. 2013; 14: 15Crossref PubMed Scopus (18) Google Scholar), in vivo protein phosphorylation has not been yet explored at the global level and promises to fill an important gap in the understanding of the biology of actinomycetes. Saccharopolyspora erythraea is a soil-dwelling actinomycete from the Pseudonocaridaceae family. This soil bacterium contains within its 8.29-Mb genome the machinery required for the synthesis of more than 25 different secondary metabolites, including erythromycin, the first clinically used macrolide antibiotic (21.Oliynyk M. Samborskyy M. Lester J.B. Mironenko T. Scott N. Dickens S. Haydock S.F. Leadlay P.F. Complete genome sequence of the erythromycin-producing bacterium Saccharopolyspora erythraea NRRL23338.Nat. Biotechnol. 2007; 25: 447-453Crossref PubMed Scopus (321) Google Scholar). 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These investigations show that regulation of the erythromycin gene cluster is complex and may be regulated at the post-translational level. Here, we present a dynamic phosphoproteomic study of the erythromycin-producing actinomycete S. erythraea. Using samples taken across a fermentation time course, a total of 109 phosphorylation sites were identified in discovery mode, before the degree of phosphorylation at each site was monitored using multiple reaction monitoring (MRM). 1The abbreviations used are: MRM, multiple reaction monitoring; CoA, coenzyme A; ES, enrichment score; HtrA, high temperature requirement A; TCA, tricarboxylic acid. 1The abbreviations used are: MRM, multiple reaction monitoring; CoA, coenzyme A; ES, enrichment score; HtrA, high temperature requirement A; TCA, tricarboxylic acid. Quantitatively significant changes in phosphorylation were observed for many proteins during the metabolic switch and the stationary phase, and several of these events can be directly linked to known metabolic effects. We thus present a time-resolved dynamic study of protein phosphorylation in S. erythraea that specifically provides new insights into the physiology of actinomycetes. S. erythraea strain NRRL23338 was purchased from the American Type Culture Collection (ATCC number 11635TM). Unless otherwise specified, all chemicals were purchased from Sigma. Medium ISP 2 (yeast extract, 4 g/l; malt extract, 10 g/l; dextrose, 4 g/l; agar, 20 g/l) was used for spore germination and seed cultures. Medium MM-101 used in the bioreactors contained (per liter) 7 g of NH4Cl, 3 g of KH2PO4, 7 g of K2HPO4, 0.25 g of MgSO4·7 H2O, 0.0138 g of CaCl2·2 H2O, 40 g of glucose, and 4 ml of trace solution element. The trace solution composition (per liter) was 40 mg of ZnCl2, 200 mg of FeCl3·6 H2O, 10 mg of CuCl2·2 H2O, 10 mg of MnCl2·4 H2O, 10 mg of Na2B4O7·10 H2O, and 10 mg of (NH4)6Mo7O24·4 H2O. Samples were extracted from two different fermentations in 2-l Applikon reactors (Applikon Biosciences, Schiedam, The Netherlands) operated at working volumes of 1.4 l. The temperature and pH remained constant at 30 °C and 7, respectively, throughout the fermentation; the pH was controlled by the addition of 20% sodium hydroxide (NaOH) or 10.9% hydrochloric acid (HCl). Dissolved oxygen was maintained at 45% to 60% saturation by increasing the air flow and reactor mixing. Carbon dioxide production was measured using an HPR20 QIC mass spectrometer (Hiden Analytical Ltd., Warrington, UK) attached to the bioreactor's condensers. To characterize and quantitate the phosphoproteome of S. erythraea, we used monitoring-initiated detection and sequencing with information-dependant acquisition. Samples were extracted at six time points across the fermentation: one time point during the exponential phase (34 h), three around the metabolic switch (48, 49, and 51 h), and finally two during the stationary phase (78 and 98 h). Proteins were extracted according to Ref. 4.Macek B. Gnad F. Soufi B. Kumar C. Olsen J.V. Mijakovic I. Mann M. Phosphoproteome analysis of E. coli reveals evolutionary conservation of bacterial Ser/Thr/Tyr phosphorylation.Mol. Cell. Proteomics. 2008; 7: 299-307Abstract Full Text Full Text PDF PubMed Scopus (347) Google Scholar, incorporating some modifications. Cell pellets (100 ml of culture broth) were harvested from the bioreactor, pelleted (4 °C at 5000 rpm) using an Allegra X-15R centrifuge (Beckman Coulter), washed in 100 mm NaCl, 25 mm Tris-HCl (pH 7.5), and resuspended in lysis buffer (50 mm Tris-HCl, 5 mg/ml lysozyme, and 5 mm of each of the following phosphatase inhibitors: sodium fluoride, 2-glycerol phosphate, sodium vanadate, and sodium pyrophosphate). After 10 min of incubation at room temperature, N-octylglucoside was added at a final concentration of 1% for the solubilization of membrane proteins. Enhanced cell disruption was achieved by homogenizing cell lysates with glass beads for 5 min at 4800 rpm using a Mini-bead beater (Extech Equipment Pty Ltd, Wantirna South, Australia). Cellular debris was removed by centrifuging (10 min, 4 °C, 13,000 rpm) using a Microfuge (22R, Beckman Coulter). To remove nucleic acids, samples were incubated with DNase I (100 μg/ml; Fermentas, Vilnius, Lithuania) and RNase A (100 μg/ml; Fermentas) for 10 min at 37 °C. Finally, protein extracts were dialyzed for 16 h in deionized water using 3.5 molecular weight cutoff Side-A-Lyser dialysis cassettes (Thermo Scientific). The total protein content was quantified using a 2D Quant assay kit (GE Healthcare). Protein extract aliquots (10, 5, and 1 mg and 500, 200, 100, and 50 μg) were freeze-dried using Alpha 1–4 LSC (John Morris Scientific Pty Ltd, Willoughby, Australia) for 16 h and stored at −20 °C. Trypsin digestion was performed as described in Ref. 4.Macek B. Gnad F. Soufi B. Kumar C. Olsen J.V. Mijakovic I. Mann M. Phosphoproteome analysis of E. coli reveals evolutionary conservation of bacterial Ser/Thr/Tyr phosphorylation.Mol. Cell. Proteomics. 2008; 7: 299-307Abstract Full Text Full Text PDF PubMed Scopus (347) Google Scholar. Briefly, 10 mg of dried protein was resuspended in 6 m urea, 2 m thiourea, and 2% CHAPS for protein denaturation and incubated for 45 min at room temperature with 1 mm DTT. Iodoacetamide (2.5 mm final concentration) was added, and samples were incubated for 45 min at room temperature in the dark. Samples were then diluted with 25 mm ammonium bicarbonate to obtain a final concentration of urea of <800 mm. Trypsin (Promega Gold, MS grade) was added at a ratio of 1/100 (trypsin/protein), and samples were incubated for 16 h at 37 °C. Reversed-phase chromatography was used to clean the digested peptide mixtures using C-18 cartridges (Sep-Pak tC18, Waters, Milford, MA) eluting with 60% acetonitrile (v/v). Residual acetonitrile was removed by vacuum centrifugation (Eppendorf, Hamburg, Germany) and resuspended in 0.1% formic acid prior to analysis. All samples were processed in parallel. A total of three 10-mg samples (time points 34 and 48 h, time points 49 and 51 h, and time points 78 and 98 h) were fractionated (16 fractions) using strong cation exchange chromatography as described in Ref. 5.Macek B. Mijakovic I. Olsen J.V. Gnad F. Kumar C. Jensen P.R. Mann M. The serine/threonine/tyrosine phosphoproteome of the model bacterium Bacillus subtilis.Mol. Cell. Proteomics. 2007; 6: 697-707Abstract Full Text Full Text PDF PubMed Scopus (319) Google Scholar. Briefly, samples were loaded onto a 1-ml Resource S column (GE Healthcare) in solvent A (5 mm KH2PO4, 30% acetonitrile, 0.1% trifluoroacetic acid, pH 2.7) at 1 ml/min. Sample elutions were collected in 16 2-ml fractions using a 0%–30% gradient of solvent B (5 mm KH2PO4, 30% acetonitrile, 350 mm KCl, 0.1% trifluoroacetic acid, pH 2.7) over 30 min. Each fraction was desalted using reversed-phase chromatography, concentrated by vacuum centrifugation, and pH adjusted (<2.7) with 0.1% formic acid. The resulting 48 fractions were enriched for phosphorylated peptides using titanium dioxide (TiO2) chromatography with Phos-TiO2 (GL Sciences Inc., Tokyo, Japan) according to the manufacturer's instructions. Peptide identification was performed in an LC MS/MS QSTAR Elite (AB Sciex, Ontario, Canada). The LC system was equipped with a Vydac MS C18 300-Å, 150 mm × 0.3 mm column (Grace Davison Discovery Sciences, Deerfield, IL) operated at 30 °C with a 0%–80% acetonitrile gradient (in 0.1% formic acid) for 105 min at a flow rate of 3 μl/min. All MS/MS raw data are available online through the University of Queensland website (S. erythraea phosphoproteomic data (385 MB)). Proteins were identified via advanced information-dependent acquisition of the fragmentation spectra of one to five charged peptides with a precursor selection window of m/z 100–1800 using enhanced pulsed extraction of fragments (using Analyst 1.5.2; AB Sciex), employing specific features such as "Smart Collision" and "Smart Exit" (fragment intensity multiplier set to 2.0 and maximum accumulation time of 1.5 s) to obtain MS/MS spectra. Tandem mass spectra were acquired for 1 s, and fragmented peptides were selected for sequencing for 12 s in positive mode. The Paragon search algorithm (27.Shilov I.V. Seymour S.L. Patel A.A. Loboda A. Tang W.H. Keating S.P. Hunter C.L. Nuwaysir L.M. Schaeffer D.A. The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra.Mol. Cell. Proteomics. 2007; 6: 1638-1655Abstract Full Text Full Text PDF PubMed Scopus (1059) Google Scholar) from Protein Pilot 4.0 software (Applied Biosystems, Foster City, CA) was used to identify all phosphoproteins. The mass tolerance values for precursor ions and fragment ions were set to the default values of the Paragon search algorithm. Trypsin was specified as the digesting protease, the iodoacetamide derivative of cysteine (carboxyamidomethylcysteine) was specified as the fixed modification, and urea denaturation and phosphorylation were specified as special factors. The sequence database used was taken from the S. erythraea Genome Project website (release version 15/03/2007). A false discovery rate analysis was performed for all searches. Hits were considered positive when at least two peptides with more than six residues and 90% confidence were detected. A monitoring-initiated detection and sequencing workflow was used for the relative quantification of peptide phosphorylation (28.Unwin R.D. Griffiths J.R. Leverentz M.K. Grallert A. Hagan I.M. Whetton A.D. Multiple reaction monitoring to identify sites of protein phosphorylation with high sensitivity.Mol. Cell. Proteomics. 2005; 4: 1134-1144Abstract Full Text Full Text PDF PubMed Scopus (186) Google Scholar). MRM assays for all the identified phosphopeptides and their unphosphorylated counterparts were developed and optimized from TiO2-enriched samples with MRMPilotTM 2.0 software (AB Sciex) according to the software manual. All quantitative experiments were performed from total protein extracts (without TiO2 enrichment or chromatographic fractionation) using a triple quadrupole mass spectrometer (QTRAPTM 4000, Applied Biosystems) with an electrospray ion source configured in positive mode. LC was performed using a 100 mm × 2.1 mm 2.6-μm, 100-Å Kinetix C18 column (Phenomenex, Torrance, CA) running a gradient of 2%–80% acetonitrile (in 0.1% formic acid) for 100 min. Mobile phase A was an aqueous solution of 2% acetonitrile and 0.1% formic acid, and solvent B was 2% MilliQ water and 0.1% formic acid in 100% acetonitrile. Every peptide and phosphorylated cognate was monitored by at least two transition ions overlapping in retention time in the LC chromatogram (supplemental Table S7). The MS scan was performed with the following parameters: ion source voltage, 5400 V; temperature, 350 °C; curtain gas, 20 psi; collisionally activated dissociation gas, high; TurboIonSpray nebulizer gas or Atmospheric-pressure chemical ionization nebulizer gas (Gas 1), 60 psi; TurboIonSpray heater gas (Gas 2), 60 psi; orifice (differential pressure), 80. To minimize technical variations, all injections were performed in triplicate. NetPhos (29.Blom N. Gammeltoft S. Brunak S. Sequence and structure-based prediction of eukaryotic protein phosphorylation sites.J. Mol. Biol. 1999; 294: 1351-1362Crossref PubMed Scopus (2514) Google Scholar) and NetPhos-Bac (30.Miller M.L. Soufi B. Jers C. Blom N. Macek B. Mijakovic I. NetPhosBac—a predictor for Ser/Thr phosphorylation sites in bacterial proteins.Proteomics. 2009; 9: 116-125Crossref PubMed Scopus (57) Google Scholar) were used as primary sources for identifying phosphorylation sites. PhosCalc (31.MacLean D. Burrell M. Studholme D. Jones A. PhosCalc: a tool for evaluating the sites of peptide phosphorylation from mass spectrometer data.BMC Res. Notes. 2008; 1: 30Crossref PubMed Scopus (47) Google Scholar) was used to assign probabilities to the potential phosphorylation sites on the identified peptides. All possible phosphorylation positions, namely, tyrosine, threonine, serine, histidine, and aspartic acid, were evaluated. DAVID Bioinformatics Resources 6.7 (32.Huang D.W. Sherman B.T. Lempicki R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.Nat. Protoc. 2008; 4: 44-57Crossref Scopus (25338) Google Scholar) was used to perform gene-enrichment and functional annotation analysis. For all MRM experiments, automatic quantification methods were built in Analyst 1.5.2 (AB Sciex). All peaks were manually verified by visualizing their shape and correct elution time. This procedure was performed by two different people for two biological replicates in order to avoid bias in the analysis. The abundance for each peptide and its phosphorylated cognate was estimated as the average peak area of three technical replicates. The phosphorylation ratio was estimated from the area of the phosphorylated and unphosphorylated peptides. The phosphorylation ratios were log transformed to satisfy the linear model assumption of residual normality and to stabilize variances (phosphorylation ratio range of −3 to +3). The total protein amount was estimated from the logged sum of peak areas for the phosphorylated and nonphosphorylated peptide cognates. Cluster 3.0 was used for all hierarchical cluster analyses. Complete linkage clustering was used to compute distances between groups of phosphoproteins. Dendrograms were made using Java TreeView 1.1.5r2 (33.Saldanha A.J. Java Treeview—extensible visualization of microarray data.Bioinformatics. 2004; 20: 3246-3248Crossref PubMed Scopus (2334) Google Scholar). Similarities between biological replicates were evaluated according to the Spearman rank correlation coefficient using a coefficient cutoff of 0.7 and p values < 0.2. The statistical significance (p value) for the correlation coefficients was estimated using R. Cell growth was monitored by measuring A450 and by determining the cell dry weight during the fermentation. The cell dry weight was quantified by filtering 15 ml of broth using 22-μm nylon membranes (Millipore, Bedford, MA) and drying the samples at 60 °C for 8 h. The concentration of glucose was determined via high-performance liquid chromatography (Agilent 1200 HPLC system) (as described in Ref. 34.Chen W.Y. Marcellin E. Hung J. Nielsen L.K. Hyaluronan molecular weight is controlled by UDP-N-acetylglucosamine concentration i
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