Comprehensive Phosphoproteomic Analysis of Nostoc flagelliforme in Response to Dehydration Provides Insights into Plant ROS Signaling Transduction.

Terrestrial cyanobacteria, originated from aquatic cyanobacteria, exhibit a unique mechanism for drought adaptation during long-term evolution. To elucidate this diverse adaptive mechanism exhibited by terrestrial cyanobacteria from the post-translation modification aspect, we performed a global phosphoproteome analysis on the abundance of phosphoproteins in response to dehydration using Nostoc flagelliforme, a kind of terrestrial cyanobacteria having strong ecological adaptability to xeric environments. A total of 329 phosphopeptides from 271 phosphoproteins with 1168 phosphorylation sites were identified. Among these, 76 differentially expressed phosphorylated proteins (DEPPs) were identified for each dehydration treatment (30, 75, and 100% water loss), compared to control. The identified DEPPs were functionally categorized to be mainly involved in a two-component signaling pathway, photosynthesis, energy and carbohydrate metabolism, and an antioxidant system. We concluded that protein phosphorylation modifications related to the reactive oxygen species (ROS) signaling pathway might play an important role in coordinating enzyme activity involved in the antioxidant system in N. flagelliforme to adapt to dehydration stress. This study provides deep insights into the extensive modification of phosphorylation in terrestrial cyanobacteria using a phosphoproteomic approach, which may help to better understand the role of protein phosphorylation in key cellular mechanisms in terrestrial cyanobacteria in response to dehydration.

Introduction

With cyanobacteria’s evolution from aquatic to terrestrial cyanobacteria, water availability becomes the most important factor influencing its growth and evaluation. Over time, drought n class="Disease">stress is being intensified due to extreme global environmental change.[1] Terrestrial cyanobacteria exhibit a unique mechanism of drought adaptation to acclimate water loss.[2]Nostoc flagelliforme belongs to a terrestrial nitrogen-fixing cyanobacterium class, with dark brown colonies when dry, while after absorbing water, it turns light brown or dark green. This species is found in both semiarid and arid steppes and is widely distributed in western and north-western China, where it is exposed to severe environmental stress from seasonal changes in temperature and precipitation.[3] Consequently, N. flagelliforme has a high degree of ecological adaptability to xeric environments. Therefore, this species can survive in an extremely dry environment for decades and quickly regains its active physiological and metabolic state when it reabsorbs water.[4]

In the past few years, an increasing trend is observed in investigating the transcriptomic and proteomic levels in n class="Species">cyanobacterium under different stress conditions, especially under drought stress.[5−7] In this regard, hundreds of genes and proteins are being screened out that are related to stress responses. Furthermore, a large number of protein post-translation modifications (PTMs) are also being reported to be associated with different plant stress responses.[8,9] Among them, one of the most studied modifications is protein phosphorylation, which is involved in various regulatory mechanisms, for instance, transcription and translation, metabolism, homeostasis, protein degradation, and cellular signaling and communication.[10] Therefore, to have a better insight into the role of protein phosphorylation, numerous large-scale phosphoproteomic studies have been performed to elucidate its role in the growth, development, and diverse response mechanisms in various plants and cyanobacteria, such as Arabidopsis, Brachypodium distachyon L., and Synechococcus sp. Strain PCC 7002.[11−14] However, protein phosphorylation in other species is still poorly understood, particularly in terrestrial nitrogen-fixing cyanobacterium species, and specifically, the phosphoproteomic characterization of N. flagelliforme subjected to drought stress has received less attention so far.

Here, we performed a systematic study on the identities and functional roles of the Ser/n class="Chemical">Thr/Tyr phosphoproteins in N. flagelliforme. First, to explore Ser/Thr/Tyr phosphoprotein in N. flagelliforme, we did a genome-wide and site-specific phosphoproteomic analysis of N. flagelliforme by employing high-accuracy mass spectrometry in conjunction with biochemical enrichment of phosphopeptides from digested cell lysates. Furthermore, dynamic changes of phosphorylation modification of N. flagelliforme phosphoproteome were investigated in response to different dehydration conditions. This high resolution for terrestrial cyanobacterial phosphorylated proteins on Ser/Thr/Tyr residues provides further evidence in support of the emerging view that protein phosphorylation is not just limited to eukaryotes but a general and fundamental regulatory mechanism, allowing for extensive functional and evolutionary study in cyanobacteria. In addition to this, we also functionally categorized the identified phosphoproteins involved in several important biological processes (BPs) into an interactive map. This provides an exquisite interaction network of phosphoproteins in N. flagelliforme, a terrestrial cyanobacterium. Our results may help us to better look into the role of phosphorylation in key cellular mechanisms in terrestrial cyanobacteria under dehydration stress.

Results

Phosphoproteomics Establishment in N. flagelliforme

In our previous study, we used a gel-based proteomic analysis on N. n class="Species">flagelliforme in response to dehydration and rehydration.[6] To collect a large-scale data set on Ser/Thr/Tyr phosphorylation sites in N. flagelliforme, we hence performed a systematic investigation to characterize the phosphoproteome in N. flagelliforme under different water statuses (Figure A). To do this, different techniques were combined to generate a large set of data, including TiO2 affinity chromatography, iTRAQ labeling, nLC–tandem mass spectrometry (MS/MS) analysis; phosphoproteomic methods and two different search algorithms (MASCOT and pFind) were used. The identified phosphopeptides were further validated by preliminary manual inspection of MS/MS data; our approach resulted in the identification of 786 N. flagelliforme proteins detected by liquid chromatography MS/MS (LC–MS/MS) and 329 unique phosphopeptides and 1168 phosphorylation sites from 271 N. flagelliforme phosphoproteins (Figure S1), with high confidence through the combined use of protein/peptide prefractionation, which represents an informative phosphorylation data set obtained from the cyanobacteria, and Cyanobase database of N. flagelliforme (http://genome.kazusa.or.jp/cyanobase/Nostoc flagelliforme CCNUN1).

Figure 1

Workflow of the experiment to analyze the phosphoproteome, a representative MS/MS spectrum, and general description of the phosphopeptides identified. (A) Overview of the analytical workflow used in this study. Proteins were prefractionated using gel-free methods, followed by trypsin digestion and TiO2 enrichment of phosphopeptides. Phosphopeptides were separated by nano-LC–MS/MS and mass measured and fragmented using the mass spectrometer. (B) Example of an MS/MS spectrum assigned to VSSKIGVIETLLEK from phosphoglycerate kinase (PGK) (WP_100901575.1). The b and y ions including loss of ammonia and water were considered when we calculated the PTM score. (C) Distribution of singly, doubly, and triply phosphorylated peptides. The photos in panel (A) were taken by Wenyu Liang, an author listed in this manuscript.

Workflow of the experiment to analyze the phosphoproteome, a representative MS/MS spectrum, and general description of the phosphopeptides identified. (A) Overview of the analytical workflow used in tn class="Chemical">his study. Proteins were prefractionated using gel-free methods, followed by trypsin digestion and TiO2 enrichment of phosphopeptides. Phosphopeptides were separated by nano-LC–MS/MS and mass measured and fragmented using the mass spectrometer. (B) Example of an MS/MS spectrum assigned to VSSKIGVIETLLEK from phosphoglycerate kinase (PGK) (WP_100901575.1). The b and y ions including loss of ammonia and water were considered when we calculated the PTM score. (C) Distribution of singly, doubly, and triply phosphorylated peptides. The photos in panel (A) were taken by Wenyu Liang, an author listed in this manuscript.

To better analyze the reprogrammed biological pathways of N. flagelliforme in response to n class="Disease">dehydration, the accuracy of localization of these phosphorylation sites was further checked by estimating the probability-based post-translational modification (PTM) scores. There are 224 unique phosphopeptides from 271 phosphoproteins determined with localization probability >0.75 (Table S2). Details of the identified phosphopeptides, including their protein IDs, sequences, search algorithm scores, PTM scores, and localized P-values, for the samples with dehydration treatments (30, 75, and 100%) and control (0%) are provided in Tables S3–S5. A representative example for mass spectrum on a phosphopeptide sequence (VSSKIGVIETLLEK from phosphoglycerate kinase, WP_100901575.1) was presented (Figure B). Phosphorylation sites that were occupied with probability >0.75 were categorized as class I phosphorylation sites and identified (“Localization p value” = 1), whereas those with localization probability <0.75 were classified as ambiguous phosphorylation sites (Tables S3–S5). The raw data has been deposited in a publicly accessible database Peptideatlas (http://www.peptideatlas.org)[29] and can be accessed with identifier PASS00119 (http://www.peptideatlas.org/PASS/PASS00119). The phosphoproteome analysis in the current study for N. flagelliforme contained a relatively high number of proteins; in total, 344 phosphopeptides have been detected, including 329 phosphopeptides that are unique (having single phosphoprotein), while the remaining 15 phosphopeptides are mapped to multiple proteins (Figure C).

Bioinformatic Analysis on Differential Expression of Phosphopeptides

Differentially expressed phosphoproteins (DEPPs) were identified (the screening method is presented in Section 5). There are in total 76 DEPPs in N. flagelliforme exposed to all combinations between n class="Disease">dehydration treatments and control (Figure A). These DEPPs show distinct expression patterns across different dehydration treatments (Figure B). Most DEPPs exhibited gradually decreased trends when N. flagelliforme was exposed to exacerbating water loss (Figure C). There were 46, 10, 10, 13, and 21% DEPPs localized in the cytoplasm, inner membrane, extracellular matrix, periplasm, and outer membrane, respectively (Figure D).

Figure 2

Characterization of differentially expressed phosphopeptides (DEPs) in N. flagelliforme exposed to different dehydration treatments. (A) Comparison of differentially expressed phosphopeptides (DEPs) among the group samples with different dehydration treatments (30, 75, and 100% water loss) and control (0%) based on one-way analysis of variance (ANOVA) analysis. Significant change in abundance >1.2 fold, p value < 0.05. (B) Heatmap representing the results of DEPs; hierarchical clustering results are represented by a treelike thermograph in which each row represents a phosphopeptide and each column represents a set of samples. The abundance represents the logarithmic values of expression amounts of the DEPs in different samples (log2 expression). Different colors are shown in the thermogram, with red representing significantly upregulated DEPs, green representing significantly downregulated DEPs, and gray representing no quantitative information for DEP. (C) Cluster analysis of differential expression pattern for the 60 DEPs. (D) Subcellular location of DEPs.

Characterization of differentially expressed phosphopeptides (DEPs) in N. n class="Species">flagelliforme exposed to different dehydration treatments. (A) Comparison of differentially expressed phosphopeptides (DEPs) among the group samples with different dehydration treatments (30, 75, and 100% water loss) and control (0%) based on one-way analysis of variance (ANOVA) analysis. Significant change in abundance >1.2 fold, p value < 0.05. (B) Heatmap representing the results of DEPs; hierarchical clustering results are represented by a treelike thermograph in which each row represents a phosphopeptide and each column represents a set of samples. The abundance represents the logarithmic values of expression amounts of the DEPs in different samples (log2 expression). Different colors are shown in the thermogram, with red representing significantly upregulated DEPs, green representing significantly downregulated DEPs, and gray representing no quantitative information for DEP. (C) Cluster analysis of differential expression pattern for the 60 DEPs. (D) Subcellular location of DEPs.

The 76 DEPPs were further analyzed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The top 20 enriched terms from GO analysis shows that biological pathways related to protein–chromophore linkage, photosynthesis, oxidoreductase, protein modification process, cellular protein modification process, and macromolecule modification; molecular functions (MFs) related to phosphoglucosamine mutase activity and phosphotransferases; and cellular components related to light-harvesting complex, membrane part, chloroplast, protein complex, phycobilisome, vesicle, intracellular vesicle, cytoplasmic vesicle part, light-harvesting complex, cytoplasmic vesicle, chromophore, and membrane-bounded organelle are significantly enriched in the list of DEPPs (Figure A).

Figure 3

GO and KEGG pathway analysis of phosphoproteins in N. flagelliforme between 75% water loss dehydration treatment and control. (A) Functional enrichment analysis of GO in group one-way ANOVA. The abscissa indicates the GO functional classification, which is divided into three major categories, biological processes (BPs), molecular function (MFs), and cell components (CCs); ordinates denote the number of differentially expressed proteins under each functional classification; the color bar denotes the significance of enriched GO functional classification based on the P-value calculated from Fisher’s exact test; the color gradient from orange to red represents the size of the P value: the closer the red, the smaller the P value and the higher the corresponding GO functional category richness level. The label above the bar chart shows the enrichment factor (richFactor ≤1), which indicates that the number of differentially expressed proteins annotated to a GO functional class account for all of the identified proteins annotated to that GO functional class. (B) Ordinates in the graph represent the significantly enriched KEGG pathway; the abscissa represents the number of differentially expressed proteins contained in each KEGG pathway, and the bar color represents the significance of the enriched KEGG pathway, i.e., the P value is calculated based on Fisher’s exact test. The color gradient represents the size of the P value, and the color changes from orange to red. The closer the red, the smaller the P value and the higher the significant level of the corresponding KEGG pathway enrichment.

GO and KEGG pathway analysis of phosphoproteins in N. n class="Species">flagelliforme between 75% water loss dehydration treatment and control. (A) Functional enrichment analysis of GO in group one-way ANOVA. The abscissa indicates the GO functional classification, which is divided into three major categories, biological processes (BPs), molecular function (MFs), and cell components (CCs); ordinates denote the number of differentially expressed proteins under each functional classification; the color bar denotes the significance of enriched GO functional classification based on the P-value calculated from Fisher’s exact test; the color gradient from orange to red represents the size of the P value: the closer the red, the smaller the P value and the higher the corresponding GO functional category richness level. The label above the bar chart shows the enrichment factor (richFactor ≤1), which indicates that the number of differentially expressed proteins annotated to a GO functional class account for all of the identified proteins annotated to that GO functional class. (B) Ordinates in the graph represent the significantly enriched KEGG pathway; the abscissa represents the number of differentially expressed proteins contained in each KEGG pathway, and the bar color represents the significance of the enriched KEGG pathway, i.e., the P value is calculated based on Fisher’s exact test. The color gradient represents the size of the P value, and the color changes from orange to red. The closer the red, the smaller the P value and the higher the significant level of the corresponding KEGG pathway enrichment.

The top 20 terms from KEGG pathway enrichment analysis on DEPPs indicate that photosynthesis-antenna proteins, the pentose phosphate pathway, n class="Chemical">carbon fixation in photosynthetic organisms, glycolysis/gluconeogenesis, RNA degradation, methane metabolism, glutathione metabolism, oxidative phosphorylation, photosynthesis, thymidine metabolism, aminoacyl-tRNA biosynthesis, amino sugar metabolism, fructose and mannose metabolism, glyoxylate and dicarboxylate metabolism, the two-component system, and purine metabolism are significantly enriched in the list of DEPPs (Figure B). This result suggests that some phosphoproteins related to photosynthetic carbon metabolism are likely to play important roles in the biological response to dehydration in N. flagelliforme. As revealed by KEGG analysis on photosynthesis-antenna proteins, we found that allophycocyanin A, allophycocyanin B, allophycocyanin F, phycocyanin A, and phycocyanin B were downregulated, but photosynthetic electron transport subunit H was upregulated (Figure S2). Moreover, other biological pathways were also found to be significantly enriched in the DEPPs, such as the two-component system, sucrose metabolic pathway, photosystem proteins, protein metabolism, protein kinase, and ABC transport system (Figure S3 and Table S2).

Carbohydrate and energy metabolism are generally assumed to be more susceptible to n class="Disease">dehydration. To confirm whether the DEPPs related to the photosynthetic carbon metabolic pathway are involved in dehydration response, we tested the gene expression levels for GADPH, 6PGDH, G6PDH, PRK, FBA, and PGK and found that most genes showed a gradual decrease in the expression levels along with exacerbating water loss (Figure ). In particular, most genes under the 100% dehydration condition were downregulated by 10–30% compared to control, except for PRK (Figure A–F). Interestingly, we found that the metabolites related to the sucrose metabolic pathway were increased following severe dehydration stress, including fructose, sucrose, and glycogen contents (Figure S4), suggesting that sucrose synthesis increased after dehydration treatment in N. flagelliforme.

Figure 4

Reverse transcription real-time polymerase chain reaction (RT-qPCR) testing for differential phosphoproteins related to carbon metabolism in N. flagelliforme on dehydration. Each bar data represents the average of three independent replicates (± standard error (SE)). (A–F) Dehydration-induced relative expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), 6-phosphoglueonate dehydrogenase (6PGDH), glucose 6-phosphate dehydrogenase (G6PDH), phosphoribulosekinase (PRK), fructose-1,6-bisphosphatase (FBP), and phosphoglycerate kinase (PGK), respectively. The adjacent alphabetic letters to the bars reflect the significant levels based on one-way ANOVA (P < 0.05).

Reverse transcription real-time polymerase chain reaction (RT-qPCR) testing for differential phosphoproteins related to carbon metabolism in N. n class="Species">flagelliforme on dehydration. Each bar data represents the average of three independent replicates (± standard error (SE)). (A–F) Dehydration-induced relative expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), 6-phosphoglueonate dehydrogenase (6PGDH), glucose 6-phosphate dehydrogenase (G6PDH), phosphoribulosekinase (PRK), fructose-1,6-bisphosphatase (FBP), and phosphoglycerate kinase (PGK), respectively. The adjacent alphabetic letters to the bars reflect the significant levels based on one-way ANOVA (P < 0.05).

Changes in Reactive Oxygen Species (ROS) Levels and Antioxidant Enzyme Activities

As mentioned above, the oxidoreductase pathway was significantly enriched in the list of DEPPs (Figure A). Therefore, we measured the superoxide anion content, n class="Chemical">H2O2 level, and antioxidant enzyme activities in dehydrated N. flagelliforme. The levels of phosphorylation for DEPPs related to the antioxidant system were gradually decreased following dehydration treatments (Figure A). Similarly, it showed a gradual decrease in activities of peroxidase (POD), superoxide dismutase (SOD), and glutathione-S-transferase (GST) upon dehydration, although the activities of catalase (CAT) and glutathione reductase (GR) increased upon dehydration. It leads to increased contents of oxygen free radical (OFR) and H2O2 levels accordingly (Figure B–H).

Figure 5

Accumulation of the antioxidant system and activities of antioxidant enzymes in N. flagelliforme on dehydration. (A) Heatmap of differently expressed phosphorylated proteins (DEPPs) related to the antioxidant system. (B–H) Enzymatic activities and substrates related to ROS signaling pathway involved in dehydration response, including catalase (CAT) activities, peroxidase (POD), superoxide dismutase (SOD) activities, and glutathione-S-transferase (GST) activities and glutathione reductase (GR), oxygen free radical (OFR), and H2O2 contents. For panels (B–H), each bar data represents the average of three independent replicates (±SE). The alphabetic letters adjacent to the bars reflect the significant levels based on one-way ANOVA (P < 0.05).

Accumulation of the antioxidant system and activities of antioxidant enzymes in N. flagelliforme on n class="Disease">dehydration. (A) Heatmap of differently expressed phosphorylated proteins (DEPPs) related to the antioxidant system. (B–H) Enzymatic activities and substrates related to ROS signaling pathway involved in dehydration response, including catalase (CAT) activities, peroxidase (POD), superoxide dismutase (SOD) activities, and glutathione-S-transferase (GST) activities and glutathione reductase (GR), oxygen free radical (OFR), and H2O2 contents. For panels (B–H), each bar data represents the average of three independent replicates (±SE). The alphabetic letters adjacent to the bars reflect the significant levels based on one-way ANOVA (P < 0.05).

Phosphorylation Motif Analysis in N. flagelliforme

To determine whether a common sequence preference occurred for phosphopeptides at n class="Chemical">Ser/Thr/Tyr residues, we evaluated 1168 phosphorylation sites (Figure ). To identify the possible specific motifs flanking phosphorylated lysine, we extracted the over-represented motifs. Six major kinds of motifs were identified in N. flagelliforme, and the numbers of phosphopeptides for different phosphorylation motifs ranged from 11 to 35 (Figure A), among which four kinds of conserved motifs were found near to the phosphorylated serine site, including [SxE], [SxxQ], [SxxxxxY], and [SxxxxD] (Figure B). Besides, significantly enriched phosphorylation motifs near threonine sites were [TxL] and [TG] (Figure B). These findings suggest that the possible phosphorylated peptide motifs of N. flagelliforme are located downstream of serine or threonine. Interestingly, the [SP] motif was not found in N. flagelliforme. The amino acids surrounding the phosphorylation sites were shown in a heatmap. According to the heatmap, serine (S) and threonine (T) were significantly over-represented in positions −1 and −2, respectively, and other residues like glutamic acid (E), glutamine (Q), tyrosine (Y), aspartic acid (D), leucine (L), and glycine (G) were highly present in position +2, +3, +6, +5, +2, and +1, respectively. The conserved motifs found in this study not only help to predict the phosphorylation sites of unknown phosphorylated proteins but also provide a basis for the relationship between the phosphorylated protein and its kinase.

Figure 6

Phosphorylation motif analysis on all phosphorylated sites in N. flagelliforme across dehydration treatments and control. (A) Number of identified peptides containing phosphorylation sites in each motif. (B) Sequence motif analysis of phosphorylation sites. (C) Relative abundance of amino acid residues flanking the phosphorylation sites represented by an intensity map. The intensity map shows the relative abundance of six amino acids from the phosphorylation site. The colors in the intensity map represent the log10 of the ratio of frequencies (red shows enrichment, green shows depletion). Default: occurrences = 10, significance = 0.00018, background = P17036_NCBI_Nostoc_flagelliforme_18909_20171228.

Phosphorylation motif analysis on all phosphorylated sites in N. n class="Disease">flagelliforme across dehydration treatments and control. (A) Number of identified peptides containing phosphorylation sites in each motif. (B) Sequence motif analysis of phosphorylation sites. (C) Relative abundance of amino acid residues flanking the phosphorylation sites represented by an intensity map. The intensity map shows the relative abundance of six amino acids from the phosphorylation site. The colors in the intensity map represent the log10 of the ratio of frequencies (red shows enrichment, green shows depletion). Default: occurrences = 10, significance = 0.00018, background = P17036_NCBI_Nostoc_flagelliforme_18909_20171228.

Regulatory Network of Differentially Expressed Phosphoproteins

To elucidate the interactions among DEPPs and also with their potential substrates, protein–protein interaction (PPI) analysis was conducted using the STRING database (https://string-db.org/). The PPI network was built using 50 DEPPs from four functional categories: photosynthesis; n class="Chemical">carbohydrate and energy metabolism; ROS scavenging; and DNA, RNA, and protein metabolism (Figure S5). The network revealed that the kinases and phosphatases, such as histidine kinase, nucleoside-diphosphate kinase, serine/threonine protein kinase, acetate kinase, and phosphoglycerate kinase, exhibited substantial interactions with the phosphoproteins from the four functional categories. These results suggest that they could be involved in the phosphorylation or dephosphorylation of protein substrates in N. flagelliforme upon dehydration.

Collectively, dehydration stress-induced decreased levels of phosphorylation for most DEPPs are related to photosynthetic light reaction, Calvin cycle, and n class="Chemical">sucrose metabolism (Figure ). These DEPPs include ATP synthase (ATPsynth), Rubisco, phosphoglycerate kinase (PGK), fructose-bisphosphate aldolase (FBA), and glycogen synthase (GlgA). The levels of phosphorylation for DEPPs related to the reactive oxygen species (ROS) signaling pathway were also decreased, including catalase (CAT), peroxidase (POD), ascorbate peroxidase (APX), glutathione-S-transferase (GST), and glutathione reductase (GR), while the phosphorylation levels for only a few DEPPs were increased, including cytochrome b6–f complex subunit H (PetH) and histidine kinase (HistK) involved in the two-component signaling pathway.

Figure 7

Schematic presentation of phosphoproteins related to photosynthesis, carbohydrate metabolism, energy conversion, and ROS scavenging of N. flagelliforme in response to drought stress. Dehydration stress induced a number of signaling molecules (SMs) that were accumulated in the cytoplasm. These SMs are then involved in the two-component signaling system, activating some protein kinases, such as histidine kinase (HistK) and downstream transcription factors (TFs). Under extremely server dehydration stress, some defensive proteins related to ROS scavenging were impaired and degraded by phosphorylation modification and some unknown pathways. The increased and decreased phosphorylation levels of proteins were depicted in red and blue, respectively. The dotted arrow was used to represent the potential cross talk between the photosynthetic light reaction and ROS scavenging through supplying reducing power by NADPH.

Schematic presentation of phosphoproteins related to photosynthesis, carbohydrate metabolism, energy conversion, and n class="Chemical">ROS scavenging of N. flagelliforme in response to drought stress. Dehydration stress induced a number of signaling molecules (SMs) that were accumulated in the cytoplasm. These SMs are then involved in the two-component signaling system, activating some protein kinases, such as histidine kinase (HistK) and downstream transcription factors (TFs). Under extremely server dehydration stress, some defensive proteins related to ROS scavenging were impaired and degraded by phosphorylation modification and some unknown pathways. The increased and decreased phosphorylation levels of proteins were depicted in red and blue, respectively. The dotted arrow was used to represent the potential cross talk between the photosynthetic light reaction and ROS scavenging through supplying reducing power by NADPH.

Discussion

Ser/Thr/Tyr Phosphorylation Involved in the Two-Component Signaling Pathway

Two-component systems are common signaling pathways in bacteria that mediate a wide range of adaptive cellular responses.[30,31] Phosphorylation signaling proceeds via n class="Chemical">His–Asp phosphorelay cascades involving two central partners, i.e., histidine protein kinase and response regulator protein.[32] Phosphorylation modification of proteins in bacteria is usually ascribed to the two-component system for signal transduction, whereas eukaryotic organisms engage Ser/Thr kinases and phosphatases for this kind of modification.[33] In some cases, two systems are interactive with each other. Genetic and molecular studies demonstrated that some Ser/Thr/Tyr kinases or phosphatases could be linked to two-component systems in the same signal transduction pathways in some cyanobacterial strains such as Anabaena sp. PCC 7120,[34]Synechocystis sp. PCC 6803[35,36] and Synechococcus sp. Strain PCC 7002.[14,37,38] In particular, Synechocystis has two-component system eukaryotic-type Ser/Thr kinases, playing a key role in regulating cyanobacterial physiology under abiotic stress.[39] In this study, seven phosphorylated proteins (histidine kinase, Ser/Thr protein kinase, K(+)-transporting ATPase subunit B, alkaline phosphatase, nucleoside-diphosphate kinase, chemotaxis family, and hybrid sensor histidine kinase/response regulator) are involved in the two-component signaling pathway in N. flagelliforme (Table S2). Importantly, the phosphorylation level of histidine kinase was upregulated in colonies upon dehydration (Figure S3). These findings suggest that some Ser/Thr/Tyr kinases or phosphatases identified in our study could also be coupled to two-component systems in the signal transduction pathways in N. flagelliforme on dehydration. However, the function of these protein kinases and phosphatases involved in N. flagelliforme signal transduction in response to dehydration remains unclear and needs to be sorted out. Most probably, these proteins could participate in signal transduction via a cascade of Ser/Thr/Tyr phosphorylation or dephosphorylation, which is similar to that observed in eukaryotes.[32,40]

Phosphorylation Modifications Play Important Roles in the Response of the Photosynthetic Pathway to Dehydration Stress

Combining phosphorylation events with the key photosynthetic pathway could help to facilitate the integration of phosphoproteomic data with biological function, and it is well established that protein phosphorylation is involved in the regulation of the photosynthesis process in cyanobacteria,[41] which is consistent with the evidence observed in the current study that there are many DEPPs related to the photosynthetic n class="Chemical">carbon and energy metabolic pathway during dehydration response in N. flagelliforme (Table S2 and Figure ). Besides, some identified phosphoproteins involved in photosynthetic light reaction pathways are always interactive with each other, such as cpcA, cpcB, apcA, apcB, and apcF in N. flagelliforme exposed to severe dehydration stress (100% water loss) (Table S2 and Figure S5). Among these DEPPs, the presented phosphorylation of cpcA, cpcB, apcA, and apcB was previously reported in Synechococcus sp. Strain PCC 7002[14] and Synechocystis sp. PCC 6803.[42]

In higher plants, it is widely excepted that reversible phosphorylation of light-harvesting complex II (LHCII) makes a balance of the excitation pressure between the two photosystems.[43] Therefore, phosphorylation of light-harvesting complexes phycobilisome in N. n class="Species">flagelliforme are likely to be involved in the regulation of the distribution of light energy between photosystem I (PSI) and photosystem II (PSII) in response to the environmental stress, similar to that of higher plants. Although many proteins related to light-harvesting complexes phycobilisome such as cpcA, cpcB, apcA, apcB, and apcF, as well as PsbO, as a component of reaction center in PSII, photosystem II oxygen-evolving enhancer protein1 and DUF2382 domain-containing protein as photosystem reaction center subunit were phosphorylated; while only a few phosphoproteins involved in PSI and PSII show substantial changes for phosphorylation levels (Figure ), which indicates the integrity of PSI and PSII photosynthetic system could allow very quick response to rehydration for N. flagelliforme.(44) Several photosynthetic proteins are reported to be involved in various abiotic stress, including water shortage stress as reported by Zhang et al.,[45] however, the phosphoproteins, identified in our study are involved in the response to water stress, suggesting the protein degradation or activity could play important role in this response, explained by Parry and his co-workers,[46] this could be due to regulation of some upstream protein kinase as suggested by Chen et al.,[47] Therefore, in this study, we confirmed that some proteins involved in the photosynthetic pathway can be phosphorylated, and the modification of phosphorylation is important for plant adaption to abiotic stress.

Phosphorylated Proteins Are Involved in the Process of Carbohydrate Metabolism and Energy Conversion

Metabolic flux and carbon source utilization in n class="Species">cyanobacterium and eukaryotes are coordinated by phosphorylation-targeting metabolic proteins.[12,48] Previous studies have found that many protein phosphorylation modifications are involved in starch synthesis in plants,[49,50] suggesting that phosphorylation modification of proteins plays an important role in regulating carbohydrate metabolism, and this modification resulting in sucrose accumulation is beneficial to N. flagelliforme to adapt to the arid environment.[5] In this study, we confirmed that extensive phosphorylation modifications were present in photosynthetic carbon metabolism such as ribulose bisphosphate carboxylase large subunit, carbon dioxide-concentrating mechanism protein CcmM, UDP-glucose 6-dehydrogenase, glycogen synthase GlgA, and NAD(P)-dependent oxidoreductase (Table S2). Interestingly, phosphorylation levels for most proteins, such as type I glyceraldehyde-3-phosphate dehydrogenase, fructose-bisphosphate aldolase class II, ribulose bisphosphate carboxylase small subunit, 6-phosphogluconolactonase, PGK, and GAPDH, were downregulated in colonies on dehydration (Table S2 and Figure S3). Furthermore, the changes of mRNA levels of rbcS, PGK, FBP, 6PGDH, G6PDH, and GlgA were consistent with their phosphorylation levels except for PRK (Figure A–F). Since carbon metabolism is a core metabolic pathway that affects photosynthetic rates and TCA energy metabolism, many reports show that proteins involved in carbon metabolism are highly abundant and feasible to be modified by various chemicals and post-translational modifications.[51] In this study, we found the proteins, such as PGK, ATPsynth, GlgA, Rubisco, that can be phosphorylated; these proteins are differently abundant among different dehydration stresses, and these proteins are closely linked with both carbon metabolism and energy conversion metabolic pathway, suggesting the important roles of these phosphorylated proteins in these reactions under abiotic stress as reported by Wingler et al. and Li et al.[52,53] Therefore, we speculated that phosphorylated proteins played an important role in carbohydrate metabolism and energy conversion and that downregulation of some phosphorylated proteins in colonies on dehydration might help to promote energy conversion.

Phosphoproteins Involved in Stress Defense and ROS Scavenging

ROS scavenging or cell detoxification constitutes an important defense strategy in plants that involves several enzymes and proteins that reduce oxidative damage induced by n class="Chemical">water stress.[54] Many biological processes produce ROS signaling intermediates such as superoxide radicals, hydrogen peroxide, and hydroxyl radicals, including the photorespiratory pathway.[55] External environmental stimuli such as drought stress could induce cells to produce a large amount of ROS, and excessive ROS production may lead to oxidative stress and result in cell component and cell structure damage, cell function loss, and ultimately programmed cell death (PCD) or necrosis.[56,57] Therefore, balancing oxidant and antioxidant intracellular systems is critical for higher plants or cyanobacteria to adapt to diverse growth conditions through the synthesis of a large number of responsive and defensive proteins to protect cells from damage.[58] In this study, we observed that some proteins related to stress defense, osmotic protection, and ROS scavenging were phosphorylated with decreased levels of phosphorylation together with decreased activities of these proteins following severe dehydration stress in N. flagelliforme, including superoxide dismutase, peroxidase, and glutathione-S-transferase, except for catalase and glutathione reductase (Table S2 and Figures , 7). This indicates that a decrease in their phosphorylation levels together with a decrease in their activity is likely not sufficient to eliminate ROS, which causes a significant increase in the superoxide anion and H2O2 level (P < 0.01) (Figure G–H). This might reflect a stressful response mechanism through phosphorylation modification to coordinate between oxidant and antioxidant intracellular pathways in response to dehydration in N. flagelliforme. Interestingly, this coordination between phosphorylation and enzymatic activities seems also related to protein expression levels, as observed in a previous report that contents of these antioxidant enzymes were decreased along with metabolic and photosynthetic proteins when N. flagelliforme was exposed to dehydration stress;[6] one possibility is that these phosphorylated proteins may be replaced to suffer damage during the desiccation process. Therefore, these proteins might be impaired and quickly degraded as a result of hydrolyzation during the dehydration process, but more studies are needed to further explore the potential mechanism during this process (Figure ).

Possible Model of Phosphorylation Response in N. flagelliforme on Dehydration

Based on the results of present and past studies,[5,6] a proposed key responsive model to dehydration stress in N. n class="Species">flagelliforme was proposed (Figure ). When cells are subjected to dehydration, it is first supposed to perceive the external stimulus from the membrane and cell wall through a specific transporter on the plasma membrane, the signal molecules will, in turn, accumulate in the cytoplasm of the cells, some signal molecules are transported into the two-component signaling system, and the signal molecule then activates its corresponding protein kinase(s), such as histidine kinase (HistK).[59] Subsequently, activated protein kinase hence phosphorylates its downstream transcription factor(s) to facilitate their binding to specific genes and then induce the production of stress-related proteins; moderate dehydration stress (30% water loss) inhibited phosphorylation levels of proteins such as PGK, while this stress event stimulated the gene expression of PGK; however, it is under extremely severe dehydration stress (100% water loss), some stress-related proteins are impaired and consequently degraded through phosphorylated modification by specific protein kinase(s) especially in ROS scavenging pathway. This suggests that PGK1, acting a protein kinase is involved in response to dehydration stress probably due to the fact that this phosphorylation can inhibit mitochondrial pyruvate metabolism and ROS production in some reports for instance Li et al.[60] Interestingly, some stress-related proteins are impaired and consequently degraded through phosphorylated modification by specific protein kinase(s), especially in the ROS scavenging pathway. Importantly, the phosphorylation modification by kinase signaling transduction participates in various biological pathways, such as ROS scavenging, Calvin cycle, sucrose metabolism, and light reaction to regulate energy supply and osmotic balance to adapt to moderate dehydration stress in N. flagelliforme.

Conclusions

This study presents a systematic survey on phosphoproteome in terrestrial cyanobacteria, N. n class="Species">flagelliforme, in response to dehydration processes and provides a comprehensive infrastructure of phosphoproteins for understanding the mechanistic aspect of drought tolerance of terrestrial cyanobacteria related to protein phosphorylation. Here, a total of 76 out of 271 identified phosphoproteins were found with significantly differential expression for dehydration treatment compared to control. These phosphoproteins are involved in different dehydrated-induced signaling processes, such as photosynthesis, sucrose metabolism, the two-component signaling system, the ROS scavenging system, and other aspects. We concluded that protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes and an important regulatory mechanism for coordinating enzyme activity related to the antioxidant system in N. flagelliforme to adapt to dehydration stress. The results obtained here would not only extend current knowledge related to protein phosphorylation of terrestrial cyanobacteria in response to dehydration but also can serve as a sourcing hub for future studies of evolution and ecological adaptation mechanisms of terrestrial cyanobacteria in the xeric environment from the perspective of protein phosphorylation.

Experimental Section

Sample Treatment

N. flagelliforme strain was collected from the Helan Mountain in the east region of Ningxia, China. Fresh samples were collected and cultured in n class="Chemical">BG11 medium as reported earlier,[15] in which we added sodium hydroxide for the alkalinity of their natural habitats (pH 8.5). Samples were cultured in an incubator at 25 °C with continuous illumination at 40 μmol photon m–2·s–1 for 6–7 h, which was long enough for the photosynthetic activity to fully recover.[16]

Each sample was spread on a plastic net with a biomass density of approx. 5 mg·cm–2, 0.5 g dry weight spread equably forming square area ∼100 cm2. Water drops on the sample were removed with filter paper before the initial wet weight was determined. The algal mats were exposed to air at 25 °C to ensure fast n class="Chemical">water loss and were sampled at intervals of 1 h to assess water loss of 0, 30, 75, and 100%. Water loss (WL, %) = (Ww – Wt)/(Ww – Wd) × 100%, where Ww is the initial wet weight, Wt is the instantaneous weight of samples measured at certain intervals, and Wd is the dry weight.[16]

Protein Extraction

The extraction of total proteins was performed with minor modifications by following Liang et al.[6] through removing 2% Pharmalyte 3–10 from sample buffer. Protein concentrations were determined based on the Bradford assay. The protein solution was used immediately for further experiments.

Protein Digestion and iTRAQ Labeling

Protein digestion was performed according to the FASP procedure as described previously,[17] and the resulting n class="Chemical">peptide mixture was labeled using the 8-plex iTRAQ reagent according to the manufacturer’s instructions (Applied Biosystems). Briefly, ∼200 μg of proteins for each sample was incorporated into 30 μL of STD buffer, including 4% sodium dodecyl sulfate (SDS), 100 mM dithiothreitol (DTT), and 150 mM Tris–HCl (pH 8.0). The detergent, DTT, and other low-molecular-weight components were removed using UA buffer (8 M urea, 150 mM Tris–HCl pH 8.0) by repeated ultrafiltration three times (Microcon units, 30 kD). Then, 100 μL of 0.05 M iodoacetamide in UA buffer was added to block reduced cysteine residues and the samples were then incubated for 20 min in darkness. The filters were washed with 100 μL of UA buffer three times and then washed with 100 μL of DS buffer (50 mM triethylammonium bicarbonate at pH 8.5) twice. Finally, the protein suspensions were digested with 2 μg of trypsin (Promega) in 40 μL of DS buffer overnight at 37 °C, and the resulting peptides were collected as a filtrate. The peptide content was estimated by UV light spectral density at 280 nm using an extinction coefficient of 1.1 of 0.1% (g·L–1) solution that was calculated based on the frequency of tryptophan and tyrosine in vertebrate proteins. For peptide labeling, each iTRAQ reagent was dissolved in 70 μL of ethanol and added to the respective peptide mixture. The samples were labeled, multiplexed, and vacuum-dried.

Enrichment of Phosphorylated Peptides by the TiO2 Beads

The labeled peptides were then mixed, concentrated by a vacuum concentrator, and resuspended in 500 μL of loading buffer (2% n class="Chemical">glutamic acid/65% ACN/2% trifluoroacetyl (TFA)). Then, TiO2 beads were added and agitated for 40 min. Centrifugation was carried out for 1 min at 5000g, producing the first beads. The supernatant from the first centrifugation was mixed with another TiO2 bead, resulting in the second beads, which were collected as before. Both beads were combined and washed with 50 μL of washing buffer I (30% ACN/3% TFA) three times, and then, 50 μL of washing buffer II (80% ACN/0.3% TFA) for another three times to remove the remaining nonabsorbed material. Finally, phosphopeptides were eluted with 50 μL of elution buffer (40% ACN/15% NH4OH),[18] followed by lyophilization and LC–MS/MS analysis.

LC–MS/MS Analysis

The phosphopeptide solution (5 μL) mixed with 15 μL of 0.1% (v/v) trifluoroacetic acid and then 10 μL of solution mixture was injected for nano-LC–MS/MS anan class="Chemical">lysis using a Q Exactive MS (Thermo Scientific) equipped with an Easy nLC (Proxeon Biosystems, now Thermo Scientific). The peptide mixture was loaded onto a C18 reversed-phase column (15 cm long, 75 μm inner diameter, RP-C18 3 μm, packed in-house) in buffer A (0.1% formic acid) and separated with a linear gradient of buffer B (80% acetonitrile and 0.1% formic acid) at a flow rate of 250 nL·min–1 controlled by IntelliFlow technology over 240 min. The peptides were eluted with a gradient of 0–60% buffer B from 0 to 200 min, 60 to 100% buffer B from 200 to 216 min, and 100% buffer B from 216 to 240 min.

For MS analysis, n class="Chemical">peptides were analyzed in positive ion mode. MS spectra were acquired using a data-dependent top10 method dynamically choosing the most abundant precursor ions from the survey scan (300–1800 m/z) for higher-energy collisional dissociation (HCD) fragmentation. Determination of the target value is based on predictive Automatic Gain Control (pAGC). Dynamic exclusion duration was 40.0 s. Survey scans were acquired at a resolution of 70 000 at m/z 200, and the resolution for HCD spectra was set to 17 500 at m/z 200. The normalized collision energy was 27 eV, and the underfill ratio, which specifies the minimum percentage of the target value likely to be reached at maximum fill time, was defined as 0.1%. The instrument was run with peptide recognition mode enabled.

Data Processing

MS/MS spectra were searched using Mascot 2.2 (Matrix Science, London, U.K.) embedded in Proteome Discoverer 1.4 against Cyanobase (http://genome.kazusa.or.jp/cyanobase) containing 3186 protein sequences and the decoy database. For protein identification, the following options are used: peptide mass tolerance = 20 ppm, MS/MS tolerance = 0.1 Da, enzyme = trypsin, missed cleavage = 2, fixed modification = carbamidomethyl (C), iTRAQ4-/8-plex (K), iTRAQ4-/8-plex (N-term), and variable modification = oxidation (M), phosphorylation (S/T/Y). The score threshold for peptide identification was set at 1% false discovery rate (FDR), and the PhosphoRS site probability cutoff was 0.75.[19]

Bioinformatic Analysis

To obtain information on cellular function and localization of the identified phosphoproteins, which were categorized by biological process and molecular function, we used an in-house PERL script according to Gene Ontology (GO) terms extracted from the Cyanobase database (http://genome.kazusa.or.jp/cyanobase/N. flagelliforme CCNUN1).[20] The enrichment of GO categories was analyzed using Cytoscape plugin BiNGO with the default parameters. The reference GO ontology in Cytoscape ontology format was created using n class="Species">N. flagelliforme CCNUN1 GO terms. All identified phosphoproteins were also analyzed with the PSORTb program, which is a web-based tool to predict bacterial protein subcellular localization. A cutoff of 7.5 or above is used to return a final prediction; otherwise, a result of “Unknown” is returned. To investigate the motif specificities between eukaryotic and prokaryotic kinases, a reliable online searching algorithm of SCANSITE (http://scansite.mit.edu) was used to search for the identified phosphorylated sites in N. flagelliforme with the default settings.

The DEPPs, defined as differentially expressed phosphoproteins, were determined based on the changes of abundance in each protein that was phosphorylated. One-way ANOVA was used to analyze significant differences among different dehydration treatments in N. n class="Species">flagelliforme. Differentially expressed phosphoproteins (DEPPs) via one-way ANOVA analysis were analyzed by P value < 0.05 screening as significant levels. Furthermore, the FASTA protein sequences of differentially changed phosphorylated proteins were blasted against the online Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://geneontology.org/) to retrieve their KOs and subsequently mapped to pathways in KEGG.[21] The corresponding KEGG pathways were extracted.

The studied protein relative expression data was used to perform hierarchical clustering analysis. For tn class="Chemical">his purpose, Cluster3.0 (http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htmcluster/software.htm) and Java Treeview software (http://jtreeview.sourceforge.net) were used. The Euclidean distance algorithm for similarity measure and the average linkage clustering algorithm (clustering uses the centroids of the observations) for clustering were selected when performing hierarchical clustering. Heatmap is often presented as a visual aid in addition to the dendrogram.

In addition, a custom-made PERL script was written to extract the prealigned phosphopeptide sequences, and the Motif-X algorithm was used to derive significant motifs based on all identified phosphorylation sites and their surrounding ±6 residues with the parameters as previously described.[22] We also uploaded the identified n class="Chemical">phosphopeptides and phosphoproteins to the motif tool section of the PHOSIDA database (www.phosida.com) to extract the specific motifs.

The protein–protein interaction information of the studied proteins was retrieved from the IntAct molecular interaction database (http://www.ebi.ac.uk/intact/) by their gene symbols or STRING software (http://string-db.org/). The results were downloaded in the XGMML format and imported into Cytoscape software (http://www.cytoscape.org/, version 3.2.1) to visualize and further analyze functional protein–protein interaction networks. Furthermore, the degree of each protein was calculated to evaluate the importance of the protein in the PPI network.

Measurement of Physiological and Biochemical Parameters

The activities of enzymes involved in the ROS signaling pathway were determined, including n class="Chemical">superoxide dismutase (SOD) following the p-nitro-blue tetrazolium chloride (NBT) photoreduction method,[23] peroxidase (POD) according to the method described previously,[24] catalase (CAT) from the method described by Aebi,[25] and glutathione-S-transferase (GST) and glutathione reductase (GR) according to the method by Bender.[26] In addition, the contents of glucose, fructose, glycogen, oxygen free radicals (OFRs), and H2O2 were also detected according to the instructions given in the kit of Suzhou Comin Biotechnology Co. Lid (Suzhou, China). Protein concentration was determined as previously described by Bradford.[27]

qRT-PCR Analysis

The total RNA was extracted using the TIANGEN RNAprep Pure Plant Kit (Polysaccharides&Polyphenolics-rich) according to the supplier’s instructions.[6] Total RNA (1 μg) was used for cDNA synthesis using the PrimeScript RT Reagent Kit with gDNA Eran class="Chemical">ser (TakaRa) according to the manufacturer’s instructions. qRT-PCR analysis was performed using 16s rRNA expression as an internal standard to normalize the amount of cDNA. The detected genes are as follows: glyceraldehyde-3-phosphate dehydrogenase (GAPDH), 6-phosphoglueonate dehydrogenase (6PGDH), glucose6-phosphate dehydrogenase (G6PDH), phosphoribulosekinase (PRK), phosphoglycerate kinase (PGK), and fructose-1,6-bisphosphatase (FBP). The primer sequences of detected genes and PCR conditions are shown in Table S1. Transcript levels of genes were quantified by qRT-PCR using the CFX96 Touch detection system. Amplifications were performed using UltraSYBR mixture (Shanghai Sangon Biotech Co., Ltd.). All qRT-PCR expression assays were performed and analyzed at least three times in independent experiments. The relative mRNA level (normalized to the level of 16s RNA gene) of each specific transcript was determined with Bio-Rad software and calculated using the 2–ΔΔCT method.[28]

Statistical Analysis

All experiments were performed with three independent biological replicates. Data from repeated measurements were shown as the mean ± SE. A comparison of differences among the groups was carried out by using one-way anan class="Chemical">lysis of variance; P ≤ 0.05 was considered significant.