Photosynth Res DOI 10.1007/s11120-015-0140-y

REVIEW

Toward the complete proteome of Synechocystis sp. PCC 6803 Liyan Gao1 • Jinlong Wang1 • Haitao Ge2 • Longfa Fang1 Yuanya Zhang1 • Xiahe Huang1 • Yingchun Wang1



Received: 31 January 2015 / Accepted: 2 April 2015 Ó Springer Science+Business Media Dordrecht 2015

Abstract The proteome of the photosynthetic model organism Synechocystis sp. PCC 6803 has been extensively analyzed in the last 15 years for the purpose of identifying proteins specifically expressed in subcellular compartments or differentially expressed in different environmental or internal conditions. This review summarizes the progress achieved so far with the emphasis on the impact of different techniques, both in sample preparation and protein identification, on the increasing coverage of proteome identification. In addition, this review evaluates the current completeness of proteome identification, and provides insights on the potential factors that could affect the complete identification of the Synechocystis proteome. Keywords Synechocystis  Proteomics  Membrane  Proteome  Proteome coverage

Electronic supplementary material The online version of this article (doi:10.1007/s11120-015-0140-y) contains supplementary material, which is available to authorized users. & Yingchun Wang [email protected] 1

State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, No.1 West Beichen Rd, Beijing 100101, China

2

State Key Laboratory of Microbial Technology, Shandong University, Jinan 250100, China

Introduction The photosynthetic cyanobacterium Synechocystis sp. PCC 6803 (Hereafter referred to as Synechocystis) highly resembles the chloroplast of higher plants (Goksoyr 1967), and has been widely used as the model organism for photosynthesis research because of multiple unique advantages. Synechocystis is the first cyanobacterium with a completely sequenced genome and is highly transformable (Kaneko et al. 1996; Williams 1988), and thus is suitable for target mutagenesis studying the functions of unknown proteins. The organism can grow in different modes ranging from photoheterotrophy to full photoautotrophy, making it a great tool for the study of fundamental processes such as photosynthesis and carbon metabolism (Nakamura et al. 2000; Vermaas 1996). More recently, Synechocystis has been shown to have great potential as cell factories for the production of clean and renewable biofuel, including hydrogen, butanol, and ethanol (Lubner et al. 2011; Lubner et al. 2010; Qiao et al. 2012; Tian et al. 2013). Because of its importance as a model organism for photosynthesis research and as a potential cell factory for producing biofuels, the proteome of this organism has been extensively studied in the past 15 years to discover novel proteins and protein networks involved in the regulation of photosynthesis and biofuel production. The Synechocystis genome is predicted to contain 3672 putative open reading frames (ORFs). Of these, 3264 and 408 ORFs are located on the chromosome and the seven endosymbiotic plasmids, respectively (Nakao et al. 2010). Synechocystis is a gram-negative cyanobacterium and thus contains subcellular structures typically observed in other gram-negative bacteria, including outer membrane, plasma membrane, and the periplasmic space between the two distinct membrane systems (van de Meene et al. 2006). In

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addition, Synechocystis contains large amount of internal membrane structure, the thylakoid membrane that is unique to cyanobacteria harboring photosynthetic machinery. Because of the high abundance and functional significance, the Synechocystis membranes, particularly the thylakoid membrane, provide an ideal system for membrane proteomic studies aiming at technique development or discovery of novel membrane-associated functions. This review is intended to summarize the progress of proteomic studies for Synechocystis with emphasis on the increased coverage of proteome identification as the result of the continuous innovation of method development in sample preparation and instrumentation. The review also evaluates the completeness of the Synechocystis proteome identification and provides an insight into the potential factors preventing the complete identification of Synechocystis proteome by the current technology.

Overview of proteomics and Synechocystis proteomics The first proteomic study for Synechocystis, which was conducted using 2-dimensional gel electrophoresis (2-DE) coupled with N-terminal Edman sequencing, was published in 1997 (Sazuka and Ohara 1997), a year after the release of the Synechocystis genome sequence. In this pioneering study, less than 100 proteins were identified. Since then, different proteomic technologies have been developed and applied to improve the coverage of proteome identification. Two major technologies have been widely used for the large-scale identification of proteins from complex protein mixtures that may contain up to tens of thousands of proteins. The first one is peptide mass fingerprinting (PMF) coupled with two-dimensional electrophoresis (2-DEPMF) (Fig. 1). The basic idea of this method is to separate proteins in a complex sample with 2-DE (O’Farrell 1975), and each protein spot resolved by 2-D gel will be excised, trypsinized, and analyzed by PMF using matrix-assisted laser desorption and ionization-time of flight (MALDITOF) mass spectrometry (MS) for the measurement of m/z of the resulting tryptic peptides. The list of measured m/z of all tryptic peptides from a protein spot will be used to search the proteome sequence database. If a protein spot contains several tryptic peptides with m/z matching the computed m/z of in silico tryptic peptides of a protein in the database, and the number of matched peptides is equal or more than a predefined threshold, then the protein can be considered to be positively identified. Because of its easy accessibility and the advantage for direct visualization of differentially modified proteins (Magdeldin et al. 2014; Rabilloud et al. 2010), the 2-DE-PMF has been a dominant method for proteomic analysis between later 1990s and

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early 2000s. However, this technology has several major limitations that prevent the high coverage identification of Synechocystis proteome. First, the resolving power of 2-DE is limited. Though 2-DE using large gels (e.g. 24 cm) and high sensitive staining methods such as sliver- or fluorescence-staining were claimed to be able to resolve up to 1000 protein spots, many of the spots are actually the isoforms of the same protein, and the resolving power are also usually compromised by the interference of the high abundance proteins (Wang et al. 2000; Wang et al. 2009). Therefore, not many proteins can usually be identified by this method. As to Synechocystis, few single studies can identify more than 100 proteins by 2-DE-PMF, no matter the samples used are whole cell lysate or subcellular fractions (Huang et al. 2004; Huang et al. 2002; Wang et al. 2000; Wang et al. 2009). The only exception was performed by Fulda et al. (Fulda et al. 2006), in which 337 proteins were identified from the soluble fraction of Synechocystis. Nevertheless, the identification rate of the soluble proteins, as estimated to be nearly 12 %, is still very low. The scarce proteome information obtained from these studies is not sufficient for further functional studies regarding gene expression in response to different internal or external stimuli or dynamic reorganization of subcellular proteomes. Second, 2-DE is not a powerful tool to resolve membrane proteins. Integral membrane proteins contain one or more transmembrane domains (TMs) and thus are highly hydrophobic and refractory to aqueous solutions. Numerous efforts have been tried to resolve membrane proteins using 2-DE, however, most of them were without a success though a few proteins with multiple TMs can be occasionally resolved (Rabilloud et al. 1999; Tastet et al. 2003; Wilkins et al. 1998; Wilkins et al. 1996). Due to this technical limitation, the majority of proteins identified in the studies specifically designed for the isolated membranes are actually not containing TMs, though a limited number of less hydrophobic integral membrane proteins (n \ 10) can be identified in some studies (Huang et al. 2004; Huang et al. 2002; Wang et al. 2000; Wang et al. 2009). Again, the limitation prevented the further functional studies for a subset of functionally important proteins, that is, integral membrane proteins. Third, the PMF method is based on the assumption that each protein spot on the 2-D gel contains one protein. However, this is not necessarily true because many proteins could co-migrate on the 2-DE resulting in the co-existence of multiple distinct proteins in one spot. The interference of the high abundance proteins could prevent the identification of low abundance proteins in the same spot by the PMF approach. Because of the limitations, the majority of the scientists in the field of proteomics switched from 2-DE-PMF to the more powerful LC–MS based shotgun proteomics technology soon after its invention.

Photosynth Res Fig. 1 Schematic representation of the typical workflows for the analysis of Synechocystis proteome through 2-DE-PMF or LC–MS-based shotgun proteomics

The concept of shotgun proteomics, as represented by the multidimensional protein identification technology (MudPIT) developed by John Yates, for the first time, allowed the true high throughput analysis of more than one thousand proteins in a single study (Washburn et al. 2001). To date, nearly all shotgun proteomic studies, no matter whatever techniques used, are conceptually similar to the original MudPIT (Zhang et al. 2013b). Similar to the 2-DE based proteomics, sample prefractionation is also the most critical step in shotgun proteomics by reducing sample complexity in each fraction and increasing the number of total identified proteins by the combination of identified proteins in all fractions (Washburn et al. 2001). Distinct from the 2-DE, the shotgun proteomics fractionate samples at peptide but not protein level (Fig. 1), and therefore is

more compatible for membrane proteins. Furthermore, different separation method can be combined to achieve multidimensional peptide separation, for example, peptides can be separated into multiple fractions using strong cation exchange (SCX), and each SCX fraction can be further fractionated using reverse phase LC. So far, different separation techniques have been used in different proteomics studies, including SCX, reverse phase (RP), strong anion exchange (SAX), pH gradient, and isoelectric focusing specifically designed for peptide separation (Fig. 1). It is worth noting that gel-based protein separation can also be combined with the peptide separation technique to achieve more extensive separation (Beausoleil et al. 2004). Better separation, which usually means greater number of peptide fractions, is expected to result in higher coverage

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of proteome identification with the cost of longer analytic time of MS that is very expensive. Thus, the researchers usually have to find a balance between the desired coverage of proteome identification and the cost they can afford to pay. Though proven to be very powerful, the application of shotgun proteomics in the analysis of Synechocystis proteome significantly lagged behind its application in the other fields, probably due to the limited accessibility to the expensive instruments used for shotgun proteomics. The first try to apply LC–MS for the identification of Synechocystis proteome was performed by (Gan et al. 2005), in which Synechocystis was only used as a sample for comparing the efficiency of different protein or peptide prefractionation methods in improving the proteome identification rate by shotgun proteomics. In one of the methods, peptides were separated into 40 fractions by SCX and 453 proteins were identified with at least 2 peptides in each protein matched with the database sequences (Gan et al. 2005). Though the number is relatively low compared with the numbers of proteins identified using the current cutting-edge proteomics technologies, it is much greater than those usually generated by the 2-DE-PMF method. Since then, LC–MS based shotgun proteomic technique has been occasionally used for the analysis of Synechocystis proteome, but did not become the dominant tool in the field until 2010. In 2010, the Synechocystis whole cell lysate was separated into the membrane and the soluble fractions by Wegener et al., and the tryptic peptides from each fraction were further fractionated into 25 fractions using SCX. By using very long analytic RP-LC column (65 cm) that coupled with ion-trap mass spectrometer (LCQ or LTQ), a total of 1955 proteins were identified (Wegener et al. 2010). This is the first report that more than 50 % of the Synechocystis proteome has been identified in a single study, which was achieved by using many LC–MS runs and the specially designed long analytic RP column. The latter is not an easily accessible setup in a normal biological or proteomics lab. More recently, ultra-high performance liquid chromatography (UPLC) coupled with high resolution mass spectrometer such as TripleTOF 5600 (ABI SCIEX) and LTQ Orbitrap Elite (Thermo Scientific) have been available for analysis of the Synechocystis proteome (Gao et al. 2015; Gao et al. 2014a; Gao et al. 2014b; Plumb et al. 2004). These advanced instruments not only afford better peptide separation in LC, but also the higher resolution, higher sensitivity, and higher scan speed for peptide identification in MS. The application of the newer technologies significantly improved the efficiency of proteome analyses with simpler LC setup, e.g., the shorter analytic column, and less LC–MS runs (Gao et al. 2015; Gao et al. 2014b). In a study performed by Gao et al., the proteomes of the wild type (WT) Synechocystis and a

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slr0110-deletion mutant that is defective in heterotrophic growth were quantitatively compared using iTRAQ-based quantitative proteomic (Gao et al. 2014b). The whole cell lysates of the WT and the mutant were separated into the membrane and the soluble fractions. Each fraction was digested with trypsin and the resulting peptides were labeled with iTRAQ reagents before being further fractionated into 10 fractions using high pH-RP LC. The LC–MS analysis using TripleTOF 5600 quantitatively identified 2040 proteins in total from all fractions, demonstrating the power of high resolution MS in increasing the coverage of proteome identification. In the other study reported by Gao et al., proteins from the separated membrane and soluble fractions were analyzed with high resolution LTQ Orbitrap Elite MS, and a total of 2347 proteins covering 64 % of Synechocystis proteome were identified (Gao et al. 2015), representing the largest proteomic dataset ever generated for Synechocystis in a single study. Currently, identification of more than 50 % of Synechocystis proteome in a single study is a routine performance in any experienced lab using UPLC coupled with high resolution MS.

Identification of the subcellular proteomes of Synechocystis Sample prefractionation at protein or peptide level before MS analysis is critical for high coverage proteome identification. Similarly, sample prefractionation at subcellular level is also important in improving the coverage of proteomic identification by decreasing the sample complexity and reducing the interference of high abundance proteins. In addition, identification of a protein from a specific subcellular fraction provides important information for the prediction of its subcellular localization and function, which is often not sufficiently available purely from the sequence features (Huang et al. 2004; Huang et al. 2002). Subcellular fractionation is particularly important in the early-stage proteomics when analytic tools such as 2-DEPMF were not sufficiently powerful to resolve and analyze the whole proteome of an organism. Therefore, the majority of the early-stage proteomic studies for Synechocystis were designated to analyze the proteome in some specifically isolated subcellular fractions such as membrane fraction, soluble fraction, and the periplasm fraction. The membrane fraction can be further fractionated into outer membrane, plasma membrane, and the thylakoid membrane (Huang et al. 2004; Huang et al. 2002; Norling et al. 1998; Pisareva et al. 2007; Srivastava et al. 2005). Moreover, the membrane fractions can also be separated into integral and peripheral fractions according to the solubility of the membrane proteins (Gao et al. 2015; Wang et al. 2009).

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The subproteomes can not only be separated according to the subcellular spaces, but also be separated according to their functions. The first report of functional subproteomic study was performed by Kashino et al. in an effort to identify novel subunits of PSII (Kashino et al. 2002). In this study, the known PSII subunit protein CP-47 was genetically engineered to contain a polyhistidine tag at its C-terminus, and the tagged CP47 was then used to purify the PSII complex through affinity purification. The protein composition of the purified PSII complex was further analyzed by MALDI-TOF MS and N-terminal sequencing resulting in the identification of 31 distinct proteins, including PsbJ, PsbM, PsbX, PsbY, PsbZ, Psb27, and Psb28 that were identified for the first time in a purified PSII complex. Moreover, five novel proteins including Sll1638 that is highly homologous to PsbQ were also identified, demonstrating the power of proteomics approach in the identification of novel subunits of a known protein complex. For proteomic analysis of multiple known or unknown protein complexes, the protein complexes are usually separated by blue native SDS-PAGE (BN-PAGE) prior to MS identification. Using this approach, over 20 membrane protein complexes including the known PSI and PSII complexes were separated by 2D-BN-PAGE and 37 proteins were identified by PMF using MALDI-TOF MS (Herranen et al. 2004). More recently, the protein complexes in the whole cell lysate of Synechocystis were also separated by BN-PAGE, which was then cut into 60 slices and proteins in each slice were identified by LC–MS resulting in the identification of a total of 1456 proteins (Takabayashi et al. 2013). Importantly, comparison of the proteins identified in this study with those identified by Gao et al., the largest dataset generated so far, revealed that 142 proteins are unique to this study (Supplemental Table S1), suggesting that this approach can be valuable in complementing the other methods if higher coverage of proteome identification is desired. Membrane proteomics Among the three distinct membrane systems, the thylakoid membrane contains multiple layers of membranous structures whereas the other two types of membranes contain only one layer each (van de Meene et al. 2006). Therefore, the thylakoid membranes are predominant among the membranous structures in Synechocystis. Because of its importance as the major subcellular space harboring photosynthetic machineries and because of its high abundance, the attempt to specifically analyze the thylakoid membrane of Synechocystis started shortly after the first report of the proteomic analysis for this organism (Sazuka and Ohara 1997; Sazuka et al. 1999). In that study, the whole Synechocystis proteome was separated into soluble, insoluble,

thylakoid membrane, and secretory protein fractions, and the proteins in each fraction were identified with 2-DE coupled with N-terminal sequencing (Sazuka et al. 1999), only13 thylakoid membrane-specific proteins were identified. Shortly later, 50 proteins were identified from the thylakoid membranes with 2-DE-PMF (Wang et al. 2000). To further increase the number of identified proteins, the thylakoid membrane was fractionated into integral and peripheral fractions by the same group using urea extraction, and 112 proteins were identified in total from both fractions (Wang et al. 2009). It must be noted that the membrane preparations used in the three reports are actually not pure thylakoid membrane, but the total membranes enriched in thylakoid membrane because of its dominant abundance in Synechocystis. The highly purified thylakoid membrane, which was obtained using the technique that will be described in the following sections, was separated by 1-DE and 2-DE and analyzed by PMF in 2005 resulting in the identification of 77 proteins (Srivastava et al. 2005). Though the same protein identification technique was used in the three reports (Srivastava et al. 2005; Wang et al. 2000; Wang et al. 2009), only 15 proteins are overlapped across all three reports, suggesting that sample preparation and fractionation is critical for the unique identification of a subset of proteins. The method to separate plasma membrane from thylakoid membrane was first established by (Norling et al. 1998). Using aqueous polymer two-phase partitioning in combination with sucrose density centrifugation, the plasma membrane was separated from thylakoid membrane, and the purity of the isolated membranes was further confirmed with immunoblotting using probes for the marker proteins on each membrane fraction. This, for the first time, allowed the potential large scale proteomic analysis for a specific membranous structure. In 2002, the proteome of the purified plasma membrane was analyzed by (Huang et al. 2002), and a total of 63 proteins were identified including over 20 proteins that were not previously identified from the total membrane preparations (Supplemental Table S1) (Wang et al. 2000; Wang et al. 2009). Two years later, a new technique was developed by the same group to specifically purify outer membrane, from which 29 proteins were identified by 2-DE-PMF (Huang et al. 2004). These include 17 proteins that were not previously identified either in the purified plasma membrane or the purified thylakoid membrane (Huang et al. 2002; Srivastava et al. 2005) (Supplemental Table S1). Comparison of identified proteins in the three purified membrane systems revealed that many proteins can reside on at least two types of the membranes, suggesting that the three membrane systems, at least for the plasma and the thylakoid membranes, are not completely discontinuous (Huang et al. 2004; Huang et al. 2002; Zak et al. 2001).

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This is consistent with a long-known observation that the thylakoid membrane also contains respiratory electron transport chain which had been assumed to reside only on the plasma membrane (Cooley and Vermaas 2001). The separation of the different membrane systems made it possible for the in-depth analysis of the Synechocystis membrane proteome. However, a major issue in the field, which is the difficulty in the identification of integral membrane proteins, was still not satisfactorily addressed. In fact, the majority of the proteins identified in the early-stage membrane proteomic studies were predicted to contain no or only one transmembrane domains (TM) (Supplemental Table S1), and one-TM-containing proteins may not be true integral membrane proteins but signal-peptide containing proteins (Gao et al. 2015). Different methods such as extracting membranes with high concentration urea to remove high abundance peripheral membrane proteins for the enrichment of low abundance integral proteins and the use of 2D-BN-PAGE electrophoresis for the separation of membrane protein complexes can slightly increase the number of identified integral proteins (Pisareva et al. 2007; Wang et al. 2009). However, the intrinsic incompatibility of 2-DE in resolving hydrophobic proteins prevents its efficient application for the identification of membrane proteins (Rabilloud et al. 1999; Wilkins et al. 1998). Though using agarose as the supporting matrix for IEF instead of commercial immobilized pH strips was reported to effectively resolve integral proteins of PSII (Kashino et al. 2007), the ability of this technique in resolving more complex samples such as the proteins extracted from the total membranes has never been reported. The significant improvement for the analysis of the Synechocystis membrane proteome came along with the application of LC–MS-based shotgun proteomics in the field (Gan et al. 2005).The first shotgun proteomic analysis specific for Synechocystis membrane proteome was performed by Kwon et al. (2010). The major technical innovation of this study is to include an acid hydrolysis step prior to trypsin or chymotrypsin digestion of proteins. The acid hydrolysis step allows membrane proteins to be cleaved at the N-terminal or C-terminal of aspartyl groups in water-miscible organic solvents capable of solubilizing hydrophobic membrane proteins. In total 785 proteins were identified including 155 integral membrane proteins that are predicted to contain at least one TM. The number of integral membrane proteins identified by this method is much greater than those in any previous studies, demonstrating the great potential of shotgun proteomic methods in the analysis of membrane proteome. It is worth noting that this is the first study using high resolution MS, i.e., the Fourier-transform MS, for the analysis of Synechocystis proteome, though its sensitivity and scan speed are not comparable to the high resolution TripleTOF or Orbitrap MS used in the later studies.

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Though the number of identified integral membrane proteins was significantly increased compared with earlier studies conducted with 2-DE-PMF, less than 25 % of the total integral membrane proteins encoded by the Synechocystis genome, as predicted to be over 800 by the topology prediction software TMHMM version 1 (Krogh et al. 2001), was identified by this study (Kwon et al. 2010). This suggests that better methods for solubilizing membrane proteins and more powerful MS are needed to improve the identification rate of membrane proteins. The true breakthrough for the analysis of Synechocystis membrane proteome came along with the use of the newer generation high resolution MS represented by Orbitrap and TripleTOF and the filter-aided sample preparation (FASP) technique for the preparation of membrane protein samples (Manza et al. 2005; Wisniewski et al. 2009). Again, the high resolution MS and the FASP method had been widely used in the other fields for more than 5 years before its first application for the analysis of Synechocystis proteome (Gao et al. 2015; Gao et al. 2014b; Liu et al. 2012; Qiao et al. 2012). The main idea of FASP is to solubilize proteins particularly membrane proteins first with high concentration (up to 4 %) SDS, the strongest detergent expected to solubilize almost all proteins (Nelson 1971; Rabilloud 1996; Santoni et al. 2000). The SDS, which is not compatible for MS analysis, will be subsequently removed from the solubilized protein samples through buffer exchange using ultracentrifugal filters. The FASP method has been proven extremely powerful for the analysis of membrane proteome since its first introduction (Manza et al. 2005; Wisniewski et al. 2009). The combination of FASP in sample preparation and high resolution MS immediately demonstrated its power for the analysis of Synechocystis membrane proteome. In the same study conducted by Gao et al. in which 2040 proteins were identified using TripleTOF 5600, 409 proteins were predicted to contain at least one TM, representing 46 % of the total predicted integral membrane proteins encoded by the Synechocystis genome (Gao et al. 2014b). In the other study reported by the same group in which 2347 proteins were identified by LTQ Orbitrap Elite from the separated membrane and soluble fractions, 468 proteins were predicted to contain at least one TM, covering 52.4 % of the total integral membrane proteins encoded by the Synechocystis genome (Gao et al. 2015). These, for the first time, enabled the large scale qualitative and quantitative studies of the differential expression of membrane proteins at a system level for Synechocystis. For the better understanding of the impact of the technique advancement on the identification of membrane proteome, the combined list of proteins identified from total membranes and purified plasma, thylakoid, and outer membranes using 2-DE-PMF or 1-DE-PMF was compared

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with the largest membrane proteome dataset generated so far in a single study by Gao et al. (Fig. 2a) (Gao et al. 2015; Huang et al. 2004; Huang et al. 2002; Pisareva et al. 2007; Srivastava et al. 2005; Wang et al. 2000; Wang et al. 2009). The combined list of proteins identified from the earlier studies contains 237 proteins including 206 proteins that were also identified by Gao et al. from the total membranes. Only 73 proteins in the combined list from the earlier studies were predicted to contain one or more TMs including 27 proteins have two or more TMs, whereas the dataset generated by Gao et al. from the total membrane fraction contains 1999 proteins, and 438 proteins were predicted to contain at least one TM including 248 proteins

with two or more TMs (Fig. 2a). The distribution of the number of TMs in the identified proteins revealed that many highly hydrophobic proteins with more than 5 TMs were identified, whereas only 3 proteins with more than 5 TMs were identified in the combined earlier studies (Fig. 2b), confirming that the newer technique is much more powerful in identifying highly hydrophobic proteins. Furthermore, the majority of the proteins in the combined earlier studies are relatively high abundant, as indicated by their distribution on the 2D-scatter plot showing abundances for all proteins identified from the membrane fraction by Gao et al., and only a few proteins distributed in the low abundance region where proteins have smaller

Fig. 2 Comparison of the largest proteome dataset generated by a single shotgun proteomics study from the membrane fraction with the combined list of membrane proteins identified by 2-DEPMF in multiple studies. a The Venn diagram shows the numbers overlapping and uniquely identified proteins by the shotgun proteomics and the earlier studies. The first number in each parenthesis represents the number of proteins with one predicted TM, the second number represents the number of proteins with two or more predicted TMs. b The distribution of the numbers of predicted TMs in proteins identified by the shotgun proteomic study and in the combined list of the earlier studies. c The scatter plot shows the relative abundance of proteins identified by the shotgun proteomics as represented by both spectral count (x-axis) and peptide peak intensity (y-axis). The proteins that were also identified by the combined earlier studies with 0, 1, and 2 predicted TMs are displayed in red, blue, and green, respectively. Note that in the low abundance region of the plot where proteins have smaller spectral counts and lower peak intensities, fewer proteins were identified by the earlier studies

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peptide spectral counts and lower peak intensities (Fig. 2c). Thus, the newer technology is also much superior in identification of low abundance membrane proteins. Soluble proteome The soluble fraction of the Synechocystis has been traditionally isolated from the whole cell lysate by removal of the membrane fraction through centrifugal precipitation. The soluble fraction prepared in such a way actually contains proteins in cytoplasm, periplasm, thylakoid lumen, and also some peripheral membrane proteins that dissociate from the membrane during cell lysis (Gao et al. 2015). Because identification of soluble proteins is less technically challenging and because the majority of the soluble proteins can also be identified from the whole cell lysate, soluble fraction has been obviously less attractive to the scientists in the field. The first and the only proteomic study specific for the isolated soluble fraction was reported by Simon et al. in 2002, in which soluble proteins were resolved by narrower pH range (pH 4.5–5.5) 2-DE and 81 proteins were identified by PMF (Simon et al. 2002). Since then, the soluble fraction has not been analyzed independently but together with the membrane fraction to increase the coverage of proteome identification (Gao et al. 2015; Gao et al. 2014b; Wegener et al. 2010). For example, in the study reported by Gao et al. that compared the differential protein expression in WT and the slr0110-deletion mutant, the soluble proteomes and the membrane proteomes were both quantitatively identified separately, and 1508 proteins were totally identified in the soluble fraction including 1254 proteins that were also identified in the membrane fraction (Gao et al. 2014b). In the other study comparing the relative abundance of proteins in the membrane and the soluble fractions, 2131 proteins were identified in the soluble fraction including 1783 proteins that were also identified in the membrane fraction (Gao et al. 2015). Interestingly, though the vast majority of the proteins identified by Simon et al. were also identified by the two large scale shotgun proteomic studies conducted by Gao et al. using the most advanced technique and instruments, two proteins, which are Slr1135 and Sll0539, were uniquely identified by Simon et al. with 2-DE-PMF a decade earlier (Supplemental Table S1) (Simon et al. 2002), suggesting there is still room for the improvement of the current technology in analyzing complex protein samples. Other subproteomes In addition to the membrane and the soluble proteome, the most frequently analyzed subproteomes of Synechocystis, the periplasm proteome and the exoproteome have also been specifically analyzed. The periplasmic space locates between the outer membrane and the plasma membrane in

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the gram-negative cyanobacterium (van de Meene et al. 2006), and is expected to contain proteins that are involved in the substrate transport. The technique to isolate periplasmic proteins using cold osmotic shock was developed by Fulda et al. (1999), and the purified periplasmic proteins were subsequently analyzed by the same group using 2-DE-PMF (Fulda et al. 2000). A total of 57 proteins were identified that were all predicted to contain a signal peptide, suggesting that their presence in the periplasmic space is mediated through signal-peptide-dependent secretion pathways. It is notable that the most abundant protein in periplasmic space is the periplasmic iron-binding protein FutA2, whereas the most abundant proteins in the membrane and the soluble fractions are photosystem (PS) I proteins and phycobilisome (PBS) proteins, respectively. This observation suggests that a subset of proteins is highly enriched in periplasmic space for performing certain specific functions such as ion transport. The exoproteome of Synechocystis has not been analyzed by large scale proteomics until recently (Gao et al. 2014a), presumably due to its functional significance which had not been well appreciated. An exoproteome refers to all proteins in the extracellular milieu of an organism including proteins that are specifically secreted or from cell lysis. The exoproteomes of many pathogenic bacteria have been extensively studied for the identification of novel virulence determinants and for the understanding of the host-pathogen interactions (Cabrita et al. 2014; Clair et al. 2013; Siljamaki et al. 2014). In cyanobacteria, the exoproteome could also be important for their growth, survival of stress, and toxicity. The Synechocystis exoproteome was biochemically isolated by Sergeyenko and Los in (2000), and 6 most abundant proteins were identified by N-terminal sequencing (Sergeyenko and Los 2000). Recently, the Synechocystis exoproteome was extensively analyzed by Gao et al. using high resolution LC– MS and 201 proteins were identified. The two most abundant proteins in the exoproteome of the photomixotrophically cultured Synechocystis are HlyA, a cell surface protein, and FutA2, the most abundant protein in periplasmic space (Fulda et al. 2000). Nevertheless, the biological significance of their high abundance in the extracellular milieu remains to be addressed.

Quantitative proteomics for the identification of differentially expressed proteins in WT and mutant strains of Synechocystis cultured in different conditions Different environmental cues can induce differential protein expression in Synechocystis, which may allow proteomic identification of some proteins that are not

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expressed or expressed in low abundance beyond the detection limit of MS in normal growth conditions. Many environmental factors can induce significant proteome changes in Synechocystis, including light, temperature, CO2, pH, salts, metal ions, etc. Similarly, internal factors such as the deletion of a gene could also lead to significant changes of gene expression that may result in identification of a subset of proteins specifically expressed in the gene deletion mutant. Different methods have been used for the quantitative identification of Synechocystis proteins including 2-DE, 2D-difference in gel electrophoresis (DIGE), label free quantitative proteomics, and iTRAQ-based quantitative proteomics. For the 2-DE approach, proteins in different samples to be compared are separated by 2-DE, and the separated proteins are subsequently stained and scanned with a densitometer. The intensities of a particular protein in different 2D gel will be compared and analyzed with imaging processing software such as Melanie and PDQuest (Appel et al. 1997; Rosengren et al. 2003). For 2D-DIGE, proteins in different samples are differentially labeled with different fluorophores and combined in equal ratio before resolved by 2-DE. Accordingly, each protein spot on the 2D gel will emit different fluorescence that can be detected by a fluorescent densitometer. The intensities of the different fluorescence detected for a spot can be used to calculate the relative abundance of the protein in different samples (Tonge et al. 2001; Unlu et al. 1997). The 2-DE and the 2-DE-DIGE methods were dominant in the earlier studies and were usually coupled with PMF for the identification of differentially expressed proteins. The label free and the iTRAQ-based quantitative methods have been mainly used in the LC–MS-based shotgun proteomics. For label free quantitation, the peptide spectral count and/or the peak intensity are used as the major measurements of the relative abundance of a protein. The label-free technique has been proven reliable and accurate for measuring the relative abundance of proteins between different samples (Gao et al. 2015; Liu et al. 2004; Wegener et al. 2010). However, this technique requires highly reproducible LC between each runs and the accuracy of quantitation could be easily affected by the experimental conditions. For the iTRAQ-based quantitative proteomics, the tryptic peptides in each sample are usually labeled with different isobaric reagents and combined with equal ratio before LC–MS analysis. The intensities of the reporter ions from the isobaric reagents in the MS/MS spectrum, which are usually displayed as a series of peaks with different but known m/z, will be used for the calculation of the relative abundance of the proteins in different samples. Conceptually, the iTRAQ technology is similar to 2D-DIGE by combining the different samples and analyzing them simultaneously. This could significantly improve the quantitative accuracy by

avoiding the variations typically observed in studies where different samples are analyzed by LC–MS separately. Because of this advantage, the iTRAQ-based quantitative proteomics became the dominant method for the identification of differentially expressed proteins of Synechocystis in recent studies (Fuszard et al. 2013; Gao et al. 2014b; Song et al. 2014; Zhang et al. 2013a). The first differential proteomic study of Synechocystis was performed by Choi et al. for the purpose of identifying differentially expressed proteins in Synechocystis cultured in light and in dark (Choi et al. 2000). The whole cell lysates of such differentially cultured Synechocystis were separated with 2-DE, and proteins differentially displayed in the 2-D gels were identified by the combination of MALDI-TOF and N-terminal sequencing. Since the 2-DE in the study was poorly run and the proteins in each 2-D gel were not well separated, it is difficult to evaluate that the differentially displayed proteins identified by the researchers are truly due to light-induced differential gene expression or some technical artifacts. Since this primitive comparative proteomics study, more than 30 independent comparative proteomic studies have been conducted for cells cultured under different growth conditions using different techniques as summarized in Table 1. Among these treatments, high concentration of salts (Fulda et al. 2000; Fulda et al. 2006; Huang et al. 2006; Pandhal et al. 2009a; Pandhal et al. 2009b; Qiao et al. 2013a), high or low temperature (Rowland et al. 2010; Slabas et al. 2006; Suzuki et al. 2006), high or low pH (Kurian et al. 2006; Ren et al. 2014; Zhang et al. 2009), and metal ions (Chen et al. 2014b; Mehta et al. 2014) have been frequently used as the stress condition in addition to the stress conditions such as high light, CO2, or nitrogen starvation (Battchikova et al. 2010; Huang et al. 2013), and other stress conditions that are relevant to the growth and physiology of Synechocystis (Fuszard et al. 2013; Gao et al. 2009; Qiao et al. 2013b; Talamantes et al. 2014; Wegener et al. 2010). More recently, biofuel such as ethanol and butanol have been used as the stress by a group who are attempting to test the tolerance of biofuels of Synechocystis, a potential cell factory for producing such biofuels (Chen et al. 2014a; Liu et al. 2012; Qiao et al. 2012; Song et al. 2014; Tian et al. 2013). In addition to the environmental stresses, internal factors such as gene deletion could also lead to differential protein expression in the mutant compared with the WT strain. Indeed, multiple studies comparing the proteomes of different mutants with the WT strain identified many differentially expressed proteins in response to a gene deletion (Gao et al. 2014b; Li et al. 2012; Rowland et al. 2011; Slabas et al. 2006). Again, the coverage and the sensitivity of these comparative proteomic studies were mainly dictated by the proteomic technologies they used. 2-DE-PMFbased technique can usually identify less than 100 proteins

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Photosynth Res Table 1 Quantitative proteomic studies for Synechocystis Treatments

Subcellular compartments

Quantitative methods

Proteomics techniques

# Identified proteins

References

Light

WCL

2-DE

2-DE-PMF, N-terminal sequencing

17

Choi et al. (2000)

UV-B

WCL

2-DE

2-DE/MALDI-TOF/TOF

75

Gao et al. (2009)

Salt

Periplasmic PM

2-DE 2-DE

2-DE-PMF 2-DE-PMF

57 66

Fulda et al. (2000) Huang et al. (2006)

WCL

2-DE

2-DE-PMF

337

Fulda et al. (2006)

WCL

N15 labeling

2-DE-PMF

243

Pandhal et al. (2009a)

WCL

iTRAQ

Shotgun

378

Pandhal et al. (2009b)

WCL

iTRAQ

Shotgun

1686

Qiao et al. (2013a, b)

Autotrophy/ heterotrophy

WCL

2-DE

2-DE-MS

67

Kurian et al. (2006)

N starvation

WCL

iTRAQ

Shotgun

1708

Huang et al. (2013)

CO2 Limitation

WCL

iTRAQ

Shotgun

19 % of the proteome

Battchikova et al. (2010)

Metal ions

WCL

iTRAQ

Shotgun

2309

Chen et al. (2014a, b)

Phosphatidylglycerol

WCL WCL

2-DE Label free

2-DE-PMF Shotgun

\50 956

Mehta et al. (2014) Talamantes et al. (2014)

Phosphate

WCL

iTRAQ

Shotgun

120

Fuszard et al. (2013)

pH

PM

2D-DIGE

2-DE-PMF

39

Zhang et al. (2009)

WCL

iTRAQ

Shotgun

N/A

Ren et al. (2014)

Cytoplasmic and periplasmic

N/A

2-DE/MS

45

Kurian et al. (2006)

TM

iTRAQ

Shotgun

385

Rowland et al. (2010)

WCL

2-DE

2-DE-MS

N/A

Suzuki et al. (2006)

Temperature

Gene deletion mutant

Biofuel

WCL

2D-DIGE

2-DE-MS

65

Slabas et al. (2006)

WCL

2D-DIGE

2-DE-MS

65

Slabas et al. (2006)

WCL

2D-DIGE

2-DE-MS

67

Rowland et al. (2011)

PM

2D-DIGE

2-DE-MS

26

Li et al. (2012)

WCL

iTRAQ

Shotgun

1851

Zhang et al. (2013a, b)

WCL/MEM/SOL

iTRAQ

Shotgun

2040

Gao et al. (2014b)

WCL

iTRAQ

Shotgun

1537

Song et al. (2014)

WCL

iTRAQ

Shotgun

1521

Chen et al. (2014a, b)

WCL

iTRAQ

Shotgun

1452

Tian et al. (2013)

WCL

iTRAQ

Shotgun

1509

Qiao et al. (2012)

WCL

iTRAQ

Shotgun

1491

Liu et al. (2012)

Different stresses

WCL

iTRAQ

Shotgun

807

Qiao et al. (2013a, b)

33 Environmental conditions

WCL

Spectral count

Shotgun

1955

Wegener et al. (2010)

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in total and tens of differentially expressed proteins in each condition, whereas LC–MS/iTRAQ-based quantitative shotgun proteomics can usually identify more than 1000 proteins in total and up to hundreds of differentially expressed proteins in each treatment. Since the treatments and the proteomic techniques used in each individual study were different, there is no sense to describe the functional significance of the differentially expressed proteins in each study. However, the combination of these studies could lead to the identification of many proteins that may not be able to be identified otherwise. Indeed, among the totally identified 2763 proteins in these comparative studies, which are included in Supplemental Table S2, many proteins were not previously identified. The unique identification of these proteins provides important information with respect to the expression status of the corresponding coding genes at different conditions (Supplemental Table S2).

Evaluation of the completeness of the Synechocystis proteome identification To date, more than 50 independent proteomic studies aimed at identification of proteins in the whole cell lysate and subcellular compartments of WT and mutant Synechocystis cultured in different conditions. A total of 2967 proteins have been identified at least one time by the combination of all the studies (Supplemental Table S3), which is 80.8 % of the whole predicted Synechocystis proteome. Of this, the identification rate of proteins encoded by the plasmid-borne genes is 43.9 % (179 out of 408), whereas the identification rate of proteins encoded by the genes on the chromosome is 85.4 % (2787 out of 3264). The much lower identification rate of proteins encoded by plasmid-borne genes is probably due to their low expression because they are not functionally essential or their functions can be complemented by their chromosomal homologs. Functional categorization of the identified proteins revealed that the identification rates for the majority of the functional groups are above 90 %, including the groups amino acid biosynthesis and central intermediary metabolism that have been completely identified (Fig. 3). The identification rate of only 5 functional groups are less than 80 %, which as listed from low to high, are unknown, other categories, hypothetical, photosynthesis and respiration, and regulatory functions (Fig. 3). The low identification rate of hypothetical or unknown proteins is intuitively consistent with their names, as these groups of proteins are generally low abundant, poorly studied, and thus difficult to be identified. The low identification rate of photosynthesis is a bit surprising, as proteins in this group are generally high abundant. A detailed investigation

revealed that many identified proteins are small TM-containing proteins (\100 aa), including the majority of the small subunits of PSII. In addition, hydrophobicity is another reason for the low identification rate because some of the unidentified proteins in this group contain multiple TMs, such as the 14-TM containing NADH dehydrogenase subunit 4. For the other categories, we found that the majority of the unidentified proteins (*100 proteins) in this category are putative transposases involved in transposonrelated functions. The low identification rate for the transposases, as predicted by their codon usage frequency (Mrazek et al. 2001), is most likely due to their low or complete repression of expression. Consequently, the low expression of the transposases presumably warrants the stabilization of the genome by preventing high rate random transposition that could rapidly destroy a bacteria population (Doolittle et al. 1984; Mahillon and Chandler 1998). Taken together, the number of identifiable proteins in Synechocystis by the current proteomics technology is approaching the upper limit if the small proteins, highly hydrophobic proteins, and proteins that do not express at a certain condition are excluded.

Potential factors contributing to the failure of protein identification Low abundance is probably the most important factor that affects protein identification by the current proteomics technology, as exhibited by the low identification rate for the hypothetical, unknown, and transposon-related proteins described above. However, other factors may also significantly affect protein identification rate such as protein size and hydrophobicity. Therefore, it is necessary to thoroughly examine the physicochemical factors that contribute to the failure of protein identification. Of the 706 proteins that have never been identified by any proteomic studies, the total of the unidentified proteins in hypothetical, unknown, and other categories constitutes nearly 87.4 %, suggesting that the majority of the unidentified proteins are from the three groups. The effect of protein size and hydrophobicity on the identification rate can be directly visualized by the 2Dscatter plot (Fig. 4, left panel). The plot was mandatorily divided into 4 regions according to protein length and GRAVY score. In region 1, the proteins are small (\100 aa) and highly hydrophobic, the percent of unidentified protein is 65.6 %. In region 2 the proteins are small but low hydrophobic, and the percent of unidentified proteins is 52.0 %. In region 3, the proteins are relatively large ([100 aa) and highly hydrophobic (GRAVY [ 0), and 17.4 % proteins were not identified in this region. In region 4, the proteins are also relatively large but low

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Fig. 3 The distribution of the identification rates of the proteins in all 17 functional categories that were annotated by CyanoBase. The percentage of identified proteins in each group is shown to the right of the corresponding bar

Fig. 4 Analysis of the physicochemical properties of the unidentified Synechocystis proteins. Left panel, 2D representation of the distribution of protein sizes (x-axis) and GRAVY scores (y-axis) of all proteins in Synechocystis. Red indicates unidentified proteins. The plot was divided into 4 regions according to the protein size (100 aa) and GRAVY score (0) cutoffs as shown. The percentage of

unidentified proteins in each region was also shown (the numbers in parenthesis represent the number of the unidentified and the total number of proteins in each region, respectively). Right panel, 2Drepresentation of all unidentified proteins extracted from the left panel. Different functional category is represented by different color as indicated

hydrophobic, and only 12.4 % proteins were not identified in this region. The percentage of unidentified proteins decreased sequentially from region 1 to region 4, suggesting

that small and hydrophobic proteins are the most difficult to be identified, whereas large and hydrophilic proteins are the easiest to be identified. The differences in percentage of

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unidentified proteins between regions 1 and 3 and between regions 2 and 4, which are size dependent, are 48.2 and 39.6 %, respectively. In contrast, the differences of unidentified proteins between regions 1 and 2 and between regions 2 and 4, which are hydrophobicity dependent, are 13.6 and 5.0 %, respectively. This observation suggests that the size-dependent difference is much greater than the hydrophobicity-dependent difference in protein identification. Therefore, it is safe to conclude that the small proteins (\100 aa) are generally more difficult to be identified by the current proteomics technique than the larger but more hydrophobic proteins. Indeed, only 199 such small proteins have been identified so far (Supplemental Table S3), representing 43.9 % of all small proteins (n = 453) encoded by the Synechocystis genome. The identification rate is extremely low compared to the overall 80.6 % identification rate of the whole Synechocystis proteome. Two major reasons may account for the low identification rate of small proteins. First, small proteins contain fewer or even no trypsin cleavage site for the generation of tryptic peptides with sizes compatible for MS analysis. Use of different protease such as GluC in addition to trypsin may alleviate this problem. Alternatively, enriching the small proteins using size exclusion approaches and then analyzing them with different MS methods such as electron transport dissociation that is compatible for relatively larger peptides may also work (Syka et al. 2004). Second, the current shotgun proteomics usually requires that a positively identified protein has at least two peptides matching to the database sequences, which could accidently filter out many small proteins with only one peptide being identified by MS. Indeed, re-searching of the raw MS files generated by Gao et al. against the Synechocystis proteome sequence databases using 1-peptide match instead of 2-peptide match can identify nearly 2600 proteins, including some small proteins that were not identified by 2-peptide match (and unpublished data). Thus, special care needs to be taken for the identification of small proteins during the analysis of MS data. To further understand why there are so many unidentified large and low hydrophobic proteins in region 4, all the unidentified proteins were extracted and displayed in the similar scatter plot with some categories displayed as indicated (Fig. 4, right panel). As expected, many of the unidentified proteins in this region are actually either transposases or encoded by plasmids or both. The most possible reason for their escape from identification is low abundance because they are probably not functionally essential. For the unidentified protein in region 3, the majorities of them were neither transposases nor encoded by plasmids, and thus the low abundance is presumably not the most dominant reason for their failure of identification. Instead, the hydrophobicity is more likely the most

important reason as supported by the existence of many transporters in this region (Fig. 4, right panel), which are membrane spanning and highly hydrophobic. The dynamic range of protein expression level is the other factor that could affect the identification of some Synechocystis proteins. Low abundance proteins can usually be concealed by high abundance proteins for proteomic identification, and this has long been one of the major technique challenges in the field of proteomics. Like albumin in serum and ribulose-1,5-bisphosphate carboxylase/oxygenase in higher plants (Ahmed et al. 2003; Sehrawat et al. 2013), the phycobilisome proteins are the most abundant in Synechocystis and were estimated to constitute up to 20 % of the dry weight of the organism (Glazer 1988; Katoh 1988). In typical LC–MS analyses for the whole cell lysate of Synechocystis, the phycocyanin beta subunit is always the most abundant proteins estimated by the peptide spectral counts. This suggests that many of the MS scans that are expected for the analysis of different proteins have been used to repeatedly analyze peptides from the same protein. Different methods have been developed to remove the high abundance proteins to improve the identification of low abundance proteins (Bellei et al. 2011; Liu et al. 2011). However, removal of high abundance proteins by methods such as immunoprecipitation could also lead to undesired loss of proteins that specifically or nonspecifically bind to the target proteins. Genetic deletion of the genes encoding the high abundance proteins could completely remove them. Again, this approach is not trouble free because gene deletion could lead to growth defect of the organism, and also could affect the expression of many other proteins. Recently, a new promising method was developed by Fonslow et al. that were claimed to be highly efficient in identifying low abundance proteins from a complex protein mixture containing high abundance proteins (Fonslow et al. 2013). The concept of the method is to include an incomplete pre-tryptic digestion step before the complete tryptic digestion that is typically used in proteomic analysis. The pre-tryptic digestion can digest a significant fraction of the high but not the low abundance proteins, because high abundance proteins are more capable in competing for trypsin, as reasoned by the researchers, and hence can be preferentially digested. The digested proteins can then be removed using a centrifugal filters with defined molecular weight cutoff, and the remaining samples can be completely digested by newly added trypsin using the normal digestion methods. This method was shown to be highly efficient in identifying low abundance proteins in yeast and human whole cell lysate. Thus, it will be interesting to investigate whether the method can work equally efficient in identification of low abundance Synechocystis proteins through getting rid of the interference of the high abundance phycobilisome proteins.

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In addition to the factors described above, protein modification may also prevent the identification of these proteins, particularly for small proteins with limited number of tryptic peptides. Synechocystis, and other cyanobacteria as well, is known to have many different types of protein modifications, including phosphorylation, acetylation, glycosylation, and oxidation (Chardonnet et al. 2014; Gao et al. 2014a; Guo et al. 2014; Mikkat et al. 2014). Investigations aimed at large scale identification of specific types of protein modifications in Synechocystis and the other cyanobacteria have started recently (Yang et al. 2013; Yang et al. 2014). However, detailed description of these works is beyond the scope of this review. Future perspectives Higher coverage of proteome identification always warrants more comprehensive functional information, which is critical for the understanding of the mechanism underlying the phenotypic responses. Moreover, the high quality raw mass spectral data used for the high coverage proteome identification can also be used to correct the annotation of the existing proteome sequence databases, as demonstrated by the finding of new translation initiation sites for some cyanobacterial proteins in recent proteogenomic studies (Ishino et al. 2007; Yang et al. 2014). Though over 80 % of the total Synechocystis proteome can be potentially identified by the combination of different studies with different treatment and different techniques, less than 65 % of the total proteome can be identified in any single study so far, resulting in unidentification of potential key protein players in some specific processes of interest. Thus, more innovative efforts in method development are desired to reach the limit of the Synechocystis proteome identification, yet the way to which is probably not too long. In the last decade, the major impetus that drove the increase of the identification rate for Synechocystis proteome is the fast development of proteomics technologies, more specifically, the development of LC–MS-based shotgun proteomics and the application of high resolution MS. The most advanced MS used for the analysis of Synechocystis proteome so far are LTQ Orbitrap Elite and TripleTOF 5600, which can typically identify over 2000 but less than 2400 proteins in a study with acceptable number of peptide fractions (Gao et al. 2015; Gao et al. 2014b). The most advanced and commercially available technologies of MS, which have been implemented in Orbitrap fusion and TripleTOF 6600 (Hebert et al. 2014; Senko et al. 2013), are expected to further increase the proteome identification rate if they are available for the analysis of Synechocystis proteome. In the other end, sample prefractionation as represented by the purification of proteins in different subcellular compartments, can also

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help to increase the identification rate. Unfortunately, the techniques of sample prefractionation have not been well combined with the most advanced proteomics techniques so far, probably due to the limited accessibility to the expensive instruments. This is evident that the purified plasma, outer, and thylakoid membranes as well as the purified periplasmic proteins have not been analyzed by LC–MS-based shotgun proteomics using high resolution MS. Analyzing these purified fractions with the cuttingedge proteomic technologies will undoubtedly identify many proteins that have not been previously identified, because the prefractionation techniques have already demonstrated their power in identifying unique proteins, even with the outdated MS technology (Supplemental Table S3). Using these sample prefractionation methods and the most advanced proteomics technologies, the high coverage and high resolution Synechocystis proteome atlas can be generated in the similar way as the one for human proteome (Kim et al. 2014; Wilhelm et al. 2014). The atlas will include expression, localization, and functional information for all Synechocystis proteins, and will provide the most valuable resource for the research community using Synechocystis as the model organism. Acknowledgments This work was supported by the grants 2012CB910900 and 2011CB915402 from the Ministry of Science and Technology of China and the grant 31300677 (to L.G.) from the National Science Foundation of China.

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Toward the complete proteome of Synechocystis sp. PCC 6803.

The proteome of the photosynthetic model organism Synechocystis sp. PCC 6803 has been extensively analyzed in the last 15 years for the purpose of ide...
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