Amino Acid Analog Induces Stress Response in Marine Synechococcus

ABSTRACT

Characterizing the cell-level metabolic trade-offs that phytoplankton exhibit in response to changing environmental conditions is important for predicting the impact of these changes on marine food web dynamics and biogeochemical cycling. The time-selective proteome-labeling approach, bioorthogonal noncanonical amino acid tagging (BONCAT), has potential to provide insight into differential allocation of resources at the cellular level, especially when coupled with proteomics. However, the application of this technique in marine phytoplankton remains limited. We demonstrate that the marine cyanobacteria Synechococcus sp. and two groups of eukaryotic algae take up the modified amino acid l-homopropargylglycine (HPG), suggesting that BONCAT can be used to detect translationally active phytoplankton. However, the impact of HPG addition on growth dynamics varied between groups of phytoplankton. In addition, proteomic analysis of Synechococcus cells grown with HPG revealed a physiological shift in nitrogen metabolism, general protein stress, and energy production, indicating a potential limitation for the use of BONCAT in understanding the cell-level response of Synechococcus sp. to environmental change. Variability in HPG sensitivity between algal groups and the impact of HPG on Synechococcus physiology indicates that particular considerations should be taken when applying this technique to other marine taxa or mixed marine microbial communities.

IMPORTANCE Phytoplankton form the base of the marine food web and substantially impact global energy and nutrient flow. Marine picocyanobacteria of the genus Synechococcus comprise a large portion of phytoplankton biomass in the ocean and therefore are important model organisms. The technical challenges of environmental proteomics in mixed microbial communities have limited our ability to detect the cell-level adaptations of phytoplankton communities to a changing environment. The proteome labeling technique, bioorthogonal noncanonical amino acid tagging (BONCAT), has potential to address some of these challenges by simplifying proteomic analyses. This study explores the ability of marine phytoplankton to take up the modified amino acid, l-homopropargylglycine (HPG), required for BONCAT, and investigates the proteomic response of Synechococcus to HPG. We not only demonstrate that cyanobacteria can take up HPG but also highlight the physiological impact of HPG on Synechococcus, which has implications for future applications of this technique in the marine environment.

INTRODUCTION

Phytoplankton are critical components of marine ecosystems, accounting for half of the planet’s primary productivity and influencing global nutrient cycling (1, 2). Ecological trade-offs exhibited by marine phytoplankton to maximize growth (e.g., compete for nutrients) and minimize loss (e.g., increase predation defenses) shape phytoplankton diversity and community structure in the ocean, which in turn significantly impacts food web dynamics and biogeochemical cycling (3). A cell-level approach is necessary to elucidate how these important organisms allocate resources, especially in response to changing environmental conditions.

The advent of omics approaches has revolutionized our ability to describe marine phytoplankton communities and their mechanisms of adaptation to changing environments at the cellular level (4). Proteomic approaches allow for the detection of real-time changes in phytoplankton physiology and metabolic state because they provide a deep insight into the changes of a large fraction of the entire protein complement of the cell. Proteomics offers an advantage over transcriptomics in that not all transcripts are translated into functional proteins. However, due to the complexity of marine proteomes, there are challenges when applying these tools to explore natural communities (5). The time-selective proteome-labeling approach, bioorthogonal noncanonical amino acid tagging (BONCAT), has the potential to address some of these challenges. BONCAT relies on pulse-labeling organisms with noncanonical amino acids (NCAAs) such as the methionine (Met) surrogate l-homopropargylglycine (HPG), such that NCAAs get incorporated into nascent proteins by the cell’s endogenous translational machinery (6, 7). While a number of modified amino acids successfully compete with native amino acids, few are able to exploit the promiscuity of the endogenous translational machinery without modification to the host cell (8). Met analogs are particularly useful because the enzyme that catalyzes the esterification of Met with its tRNA, methionyl-tRNA synthetase, has low specificity causing misrecognition and misincorporation of such analogs in place of methionine (9). Met analogs can even serve as the initiator of translation without disruption of the translational machinery (10). BONCAT has been applied successfully to visualize and identify translationally active cells in natural communities (11). Furthermore, the BONCAT technique offers the ability to separate the labeled proteome from the bulk proteome by exploiting the chemical handle of the NCAAs incorporated into the newly synthesized proteins, thereby simplifying proteomic analysis and reducing challenges often encountered in natural proteomics (12, 13).

BONCAT has been applied in a variety of cultured microorganisms and ecosystems (6, 7, 14), but its use in marine planktonic microbial communities has been limited. Initial studies demonstrated uptake of NCAAs by marine heterotrophic bacteria and the ability to quantify protein synthesis rates in natural populations (15, 16). Two additional studies showed uptake of NCAAs by the eukaryotic phytoplankton Emiliania huxleyi (17) and the heterotrophic flagellate Cafeteria burkhardae (18). However, none of these studies have moved beyond the visualization of NCAA uptake via fluorescence microscopy to capture the protein-level physiological response of these marine microbial populations. The BONCAT approach, particularly when coupled with proteomics, has potential to provide insight into the differential allocation of resources at the cellular level, allowing us to explore the trade-offs marine microorganisms make in response to changing environmental conditions.

A major assumption of the BONCAT approach is that the uptake and utilization of NCAAs by cells does not impact cellular physiology. Most studies report that additions of NCAAs have minimal impact on cells when examined by microscopy (17, 19, 20). At the protein level, no changes to protein expression or degradation were observed due to NCAA additions (human embryonic kidney cells [7] and Escherichia coli [14]); however, one study reported alterations to protein abundance (Vibrio harveyi [9]). Recent work demonstrated that NCAAs cause mild perturbations to the metabolome of E. coli and that this impact was intensified when NCAAs were added under stressful conditions (e.g., heat stress [21]). However, our understanding of the limited impact of NCAAs on cells comes mostly from studies involving heterotrophic bacteria, and these assumptions may not be valid for autotrophic phytoplankton. To address this knowledge gap, we explored the use of BONCAT in Synechococcus sp., a globally important marine cyanobacteria. We characterized the growth of Synechococcus sp. under a range of HPG concentrations and optimized the fluorescence signal to detect this uptake via epifluorescence microscopy. In addition, we examined changes in protein expression of Synechococcus sp. grown with HPG under normal and nitrate-stressed conditions relative to a non-HPG control. Finally, we characterized the growth and quantified HPG uptake under a range of HPG concentrations in two eukaryotic phytoplankton models, Ostreococcus sp. and Micromonas pusilla, to test whether they exhibited the same initial sensitivity to HPG additions as Synechococcus sp.

RESULTS

Impact of HPG concentration on phytoplankton growth dynamics.

Phytoplankton exhibited different sensitivities to HPG concentration. For both eukaryotic green algal models (M. pusilla and Ostreococcus sp.), growth in the presence of HPG was similar to that exhibited in the negative (e.g., non-HPG) control for all HPG concentrations tested (up to 100 μM; Fig. 1A and B). However, for Synechococcus sp., HPG concentrations as low as 25 μM disrupted normal growth dynamics (Fig. 1C). Compared to the maximum growth exhibited by the negative control, 25 μM HPG additions reduced Synechococcus growth by 39%, whereas 50 μM HPG additions reduced growth by 51%. 100 μM HPG concentrations (the concentration used in previous studies with marine heterotrophic bacteria in culture (13) resulted in a complete crash of the Synechococcus culture (data not shown). Synechococcus sp. grew normally and reached the same maximum growth when exposed to 10 μM HPG and lower concentrations. Synechococcus sp. growth with 10 μM HPG additions was characterized three additional times to confirm this result (data not shown).

FIG 1.

Impact of l-homopropargylglycine (HPG) concentration on phytoplankton growth dynamics. Phytoplankton growth was measured by spectrophotometry (y axis = absorbance at 450 nm) for Micromonas pusilla (A), Ostreococcus sp. (B), and Synechococcus sp. (C). The x axis shows time in 24-h increments for M. pusilla (A) and Ostreococcus sp. (B); however, for Synechococcus sp. (C), the increments represent 24-h periods, but are normalized to an OD₄₅₀ of 0.2 in order to compile data from multiple experiments into one plot. Colors indicate the different concentrations of HPG used, and arrows indicate the time point that HPG was added to the cultures. In the Synechococcus sp. plot (C), the black arrow indicates HPG additions for low concentration range experiment (0.2, 0.5, 1, and 5 μM), and the gray arrow indicates HPG additions for the midrange concentration experiment (10, 25, and 50 μM).

Fluorescence detection of BONCAT signal.

Epifluorescence microscopy was used to detect and visualize HPG incorporation by the phytoplankton from HPG growth experiments using the highest HPG concentration that did not alter cell growth dynamics (Fig. 1; 100 μM for M. pusilla and Ostreococcus sp. and 10 μM for Synechococcus sp.). As outlined in “Microscopy and Image Analysis” below, heterotrophic bacteria present in the cultures were excluded from the data prior to interpretation of HPG incorporation by the phytoplankton cells (see Fig. S1 and S2 in the supplemental material). While the proportion of bacteria in the cultures could not be determined for Ostreococcus sp. and M. pusilla (because bacteria were visually removed during region-of-interest [ROI] selection), the proportion of bacteria present in Synechococcus sp. cultures over multiple experiments ranged from 17 to 23% of the population.

For all taxa, the fluorescence signal in the blue (e.g., DAPI [4′,6′-diamidino-2-phenylindole]-stained cells) and red or orange (e.g., autofluorescence from phytoplankton pigments) channels were consistent between the negative and positive treatments, providing visual evidence that the cells appeared healthy and intact when grown in the presence of HPG (Fig. 2, 3, and 4). A bright signal in the green channel (e.g., fluorescence signal of the azide-containing CR-110 fluorophore) was visually apparent in cultures amended with HPG relative to negative-control cultures (Fig. 2 to 4). Quantitative analysis based on the green fluorescence intensity revealed that the HPG-amended treatments (positive) were significantly different from the negative controls for all phytoplankton models across all time points (Mann-Whitney U test [Fig. 5]; see also Table S1 in the supplemental material). However, it is important to note that while Synechococcus sp. cells grown with HPG were still brighter in comparison to HPG-negative cells, these cells exhibited some autofluorescence in the green channel due to the presence of phycobiliproteins, hence the greater overlap in the signal between the positive and negative treatments (Fig. 2 and 5C). M. pusilla exhibited the strongest fluorescence signal as a result of HPG incorporation at 48 h (Fig. 5A). In contrast, the fluorescence signal as a result of HPG incorporation (e.g., CR-110) in Synechococcus sp. and Ostreococcus sp. increased over time (Fig. 5B and C) and exhibited the strongest fluorescence relative to the negative control at 72 h after HPG addition.

FIG 2.

Epifluorescence microscopy images of Synechococcus sp. after 24, 48, and 72 h with (+) or without (−) 10 μM HPG additions. Dye or pigment names are shown at the tops of the images. (Left column) The blue channel shows DAPI-stained cells. (Left center column) The green channel shows the fluorescence signal of the azide-containing CR-110 fluorophore used to detect HPG. (Right center column) The orange channel shows the autofluorescence from phytoplankton pigments. (Right column) Merged image of all three channels. The exposure times were standardized within each channel to enable quantitative comparison of fluorescent signals.

FIG 3.

Epifluorescence microscopy images of Ostreococcus sp. after 48 and 72 h with (+) or without (−) 100 μM HPG additions. Dye or pigment names shown at top of images. (Left column) The blue channel shows DAPI-stained cells. (Left center column) The green channel shows fluorescence signal of the azide-containing CR-110 fluorophore used to detect HPG. (Right center column) The red channel shows the autofluorescence from phytoplankton pigments. (Right column) Merged image of all three channels. The exposure times were standardized within each channel to enable quantitative comparison of fluorescent signals.

FIG 4.

Epifluorescence microscopy images of Micromonas pusilla after 48 and 72 h with (+) or without (−) 100 μM HPG additions. Dye or pigment names shown at top of images. (Left column) The blue channel shows DAPI-stained cells. (Left center column) The green channel shows the fluorescence signal of the azide-containing CR-110 fluorophore used to detect HPG. (Right center column) The red channel shows the autofluorescence from phytoplankton pigments. (Right column) Merged image of all three channels. The exposure times were standardized within each channel to enable quantitative comparison of fluorescent signals.

FIG 5.

Quantitative comparison of fluorescence signal of the azide-containing CR-110 fluorophore used to detect HPG (i.e., green fluorescence intensity) in Micromonas pusilla (A), Ostreococcus sp. (B), and Synechococcus sp. (C) cells across time points for positive (blue data points) and negative (pink data points) HPG treatments. Outliers are indicated by gray data points. HPG positive and negative treatments were significantly different across all time points for all taxa (P < 0.05; Mann-Whitney U test; see Table S1 for statistics). Note that the y-axis scales are not standardized across plots.

Physiological response of Synechococcus sp. to HPG additions under nutrient-replete and nutrient-limited conditions.

After resuspension of cultures in the appropriate treatment media with HPG (Table 1), replete cultures exhibited a typical growth rate, while nitrate-limited cultures exhibited a reduced growth rate (Fig. 6). Control cultures (e.g., no centrifugation, no HPG) continued to increase exponentially. Epifluorescence microscopy revealed consistent fluorescence signals in the blue (e.g., DAPI-stained cells) and orange (e.g., autofluorescence from phycobiliproteins pigments) channels across treatments and time points. However, green fluorescence intensity (fluorescence signal of the azide-containing CR-110 fluorophore) was visually brighter for both replete and nitrate-limited HPG-amended treatments compared to the control (Fig. 7). This visual difference in green fluorescence intensity was quantitatively and significantly different in HPG-amended treatments (replete and nitrate-limited) relative to the control across both time points (see Table S2 and Fig. S1).

TABLE 1.

Experimental setup conditions to test the physiological response of Synechococcus sp. to HPG under nitrate-replete and nitrate-limited conditions

Treatment	Initial setup step	HPG amendment (μM)	Nitrate
Control	No centrifugation	None	Replete
Nitrate replete	Centrifuge, wash, and resuspend	10	Replete
Nitrate limited	Centrifuge, wash, and resuspend	10	Limited

FIG 6.

Synechococcus cell growth during the experiment testing physiological response to HPG under nitrate replete (blue) and nitrate limited (purple) conditions relative to a no HPG control (green). All experimental cultures were created from the same starting culture (pink). Data points represent triplicate cultures, and error bars represent the standard deviations from the mean. HGP was added to nitrate replete and nitrate limited treatment cultures on day 0 to begin the experiment, marked by the black arrow. Samples for proteomics were taken 72 h after HPG additions, shown by the black box.

FIG 7.

Epifluorescence microscopy images from experiment testing physiological response of Synechococcus sp. to HPG under replete and nitrate-limited conditions. Images were taken at two time points (48 and 72 h) after HPG addition for samples from nitrate-replete (+HPG), nitrate-limited (+HPG), and control (−HPG) treatments. Dye or pigment names shown at top of images. (Left column) The blue channel shows DAPI-stained cells. (Left center column) The green channel shows the fluorescence signal of the azide-containing CR-110 fluorophore used to detect HPG. (Right center column) The orange channel shows the autofluorescence from phytoplankton pigments. (Right column) Merged image of all three channels. The exposure times were standardized within each channel to enable quantitative comparison of fluorescent signals.

Proteomic analysis collected 22,209 MS² spectra, from which 5,969 peptide-to-spectrum matches and 5,551 peptide groups were identified. A total of 1,033 proteins were identified (representing about 36% of all predicted proteins in Synechococcus sp. strain CC9311 [77]), and 496 quantified proteins were used for analysis after filtering (see “Proteomics” below). HPG labeling was detected in 68 of the final quantified proteins. Relative to the negative control, 222 proteins had significantly different expression (i.e., a minimum log₂-fold change of 1 and an adjusted P value of <0.05) in the HPG-positive treatments relative to the negative control (Fig. 8; see also Table S3). Of these proteins, 122 were upregulated and 100 were downregulated. HPG labeling was detected in 25 of these significant proteins (see Table S5). The proteins differentially expressed in the nitrate-limited condition versus the control had a greater median log₂-fold change value than the proteins differentially expressed in the nitrate-replete condition versus the control (see Fig. S2), suggesting that the added stress of nitrate limitation magnified the proteomic changes caused by HPG treatment.

FIG 8.

Heat map of differential protein expression from the physiological response to HPG under nitrate-replete and nitrate-limited treatments relative to the control. Cladograms show vertical similarity among proteins (rows) and horizontal similarity among treatments (columns). The scale of the heat map indicates blue as maximum downregulation and red as maximum upregulation of protein expression, as calculated across sample groups after normalization using log₂-fold change. Only proteins that exhibited a log₂-fold change of >1 and an adjusted P value of <0.05 in both pairwise comparisons of nitrate-limited treatments relative to the control and nitrate-replete treatments relative to the control are visualized. A list of these proteins can be found in Table S4 in the supplemental material.

Proteomic analysis revealed that HPG influenced several aspects of metabolism, including nitrogen metabolism, general protein stress, and energy production (Table 2). Glutamate synthetase (ferredoxin-dependent glutamate synthase, Fd-GOGAT) and glutamine synthetase (GlnN) were significantly upregulated in HPG treatments compared to the control. Chaperonin proteins (GroEL1, GroEL2, DnaK, and HtpG) and a probable cytosol aminopeptidase (PepA) were also significantly upregulated in HPG treatments. Many antenna proteins were upregulated in HPG treatments, including phycobilisome linker polypeptides (CpcC, CpcD, CpeC, CpeD1, and CpeD2), phycobilisome rod-core linker polypeptides (CpcG1, CpcG, and ApcE), allophycocyanin apoproteins (ApcB1 and ApcD), and phycoerythrin chain peptides (CpaA2, CpaB1, and CpaB2). Two other antenna proteins (CpeT and CpeS) were significantly downregulated in HPG treatments relative to the control. Major components of photosystem I were upregulated in HPG treatments relative to the control, including PsaA, PsaB, PsaC, PsaD, PsaF, and PsaL. Some proteins of photosystem II were also upregulated (PsbC, PsbJ, PsbW, and PsbV). Two major proteins of ATP synthase were upregulated (AtpH and AtpF), while one protein was downregulated (AtpD). Many peptides related to the phycobilisome and photosystem I were labeled with HPG including, ferredoxin, PsaC, PsaF, CpcD, Cpa2, CpaB1, and CpaB2. In addition, one of the ATP synthase F1 subcomplex beta subunits (AtpD) was HPG labeled. The antioxidant proteins peroxiredoxin and thioredoxin-dependent thiol peroxidase, Prx and PrxQ, respectively, were also upregulated in HPG treatments.

TABLE 2.

Summary of major protein response of Synechococcus sp. to l-homopropargylglycine^a

Physiological response (reference[s])	Description of response	Protein(s)	+/−	Reference(s)b
General protein stress	Stressful conditions inside cell cause protein misfolding and aggregation. Increased expression of chaperone proteins is part of the core stress response in cyanobacteria.	60-kDa chaperonin 1 (GroEL, sync_2135)	+	38
		60-kDa chaperonin 2 (GroEL2, sync_2282)	+
		Chaperone protein (HtpG, sync_1398)	+
		Chaperone protein (DnaK, sync_2923)	+
Interference with nitrogen cycling	Cells are trying to boost the glutamine synthetase/glutamate synthetase (GS/GOGAT) cycle to get ammonium into carbon skeletons and distribute N throughout the cell. Enzymes are potentially inhibited by HPG, causing interference with N cycling.	Glutamine synthetase (GlnA, sync_1569) Ferredoxin-dependent glutamate synthetase (FdGOGAT, sync_0387)	+ +	35, 36, 37
Interference with energy production	Increasing energy production by increasing ATP synthase subunits.	ATP synthase proteins (AtpH, sync_2314; AtpF, sync_2315)	+	45
		ATP synthase F1 subcomplex beta subunit (AtpD*, sync_2284)	−
	Upregulating major components of photosystem I to potentially increase energy production via this reaction center.	Photosystem I proteins (PsaA, sync_0393; PsaB, sync_0394; PsaC*, sync_0133; PsaD, sync_0463; PsaF*, sync_2155; PsaL, sync_0398)	+	43
	Increasing antenna proteins of phycobilisome, especially linker proteins, to potentially increase light energy harvest by this complex. Linker proteins are especially important because they ensure proper assembly of phycobiliproteins and improve unidirectional energy flow from the outer antennae to the core.	Phycoerythrin class II gamma chain, linker polypeptide (CpcC, sync_0502)	+	41, 49
		Phycobilisome linker polypeptide* (CpcD, sync_0512)	+
		Phycobilisome rod-core linker polypeptide (CpcG1, sync_2488)	+
		Possible phycobilisome rod-core linker polypeptide (CpcG, sync_0515)	+
		Possible phycobilisome linker polypeptide (sync_0516)	+
		Phycobilisome linker polypeptide (CpeC, sync_0513)	+
		Possible phycobilisome linker polypeptide (CpeD1, sync_0511)	+
		Phycobilisome linker polypeptide (CpeD2, sync_2251)	+
		C-phycoerythrin class I beta chain (CpaB1*, sync_0495)	+
		C-phycoerythrin class II alpha chain (CpaA2*T, sync_0504)	+
		C-phycoerythrin class II beta chain (CpaB2*T, sync_0505)	+
		Allophycocyanin alpha-B subunit apoprotein (ApcD, sync_1341)	+
		Phycobilisome core-membrane linker protein (ApcE, sync_2321)	+

^aUp- or downregulation indicates a significant difference in both nitrate-replete and nitrate-limited treatments relative to the control treatment (P < 0.05). An asterisk (*) indicates an HPG-labeled protein. A superscript “T” indicates a log₂-fold change of <1 for the pairwise comparisons.

^bReferences apply to each subsection broadly.

When comparing protein expression between the two HPG-positive treatments (nitrate replete versus nitrate limited), 14 proteins were significantly different between these two groups. Of these proteins, 4 were upregulated and 10 were downregulated (see Table S5). HPG labeling was detected in 4 of these significant proteins. However, for 13 of these 14 proteins, the directionality of the log₂-fold change for this pairwise comparison was the same as the directionality in the pairwise comparison of HPG-positive treatments relative to the control. Therefore, the identification of these proteins as being differentially expressed between nitrate-replete versus nitrate-limited treatments largely reflects the greater magnitude of log₂-fold change values in the nitrate-limited treatment rather than a meaningful biological difference in the proteome of the two conditions (see Table S5 and Fig. S2). These proteins predominantly indicate that when a cell is under nutrient stress, the addition of HPG exhibits a greater interference on nitrogen metabolism (upregulation of the nitrogen-responsive regulatory protein [NtcA] and glutamine synthetase [GS] and downregulation of ferredoxin-nitrite reductase [Fd-nir]) and energy production (upregulation of NADH dehydrogenase and downregulation of light-independent protochlorophyllide reductase iron-sulfur ATP-binding protein [ChlL]; see Table S5).

DISCUSSION

The BONCAT technique has helped scientists begin to address key questions in microbial ecology. This approach provides a tool to link microbial function with phylogeny by identifying translationally active cells in cultures and natural communities (14, 16). BONCAT has recently been applied to study marine bacterioplankton and obtain single-cell protein synthesis rates (15), but its use in marine phytoplankton communities remains limited. When coupled with proteomics, this technique has the potential to help elucidate the mechanisms by which marine microorganisms adapt and survive in changing environmental conditions (e.g., Pseudomonas aeruginosa [13] and Vibrio harveyi [22]). In this study, we demonstrate that the marine cyanobacteria Synechococcus sp. and two groups of eukaryotic algae can take up the modified amino acid, HPG. Overall, our findings suggest that BONCAT can be used to detect translationally active phytoplankton. However, among different phytoplankton groups, we observed variability in how HPG impacted normal growth dynamics (Fig. 1). Furthermore, despite normal growth patterns when exposed to 10 μM HPG concentrations, variations in protein expression between Synechococcus sp. in HPG-treated cultures versus non-HPG control cultures revealed an influence of HPG on cyanobacterial cell physiology (Fig. 8 and Table 2). Therefore, the ability to use BONCAT as a tool for understanding the cell-level response to stressors may be limited in Synechococcus sp. and other cyanobacteria.

While there is evidence to suggest that some phytoplankton take up amino acids, the field lacks consensus on the prevalence, rates, and occurrences of amino acid uptake, as well as the importance of these amino acids as a nitrogen source for phytoplankton (23, 24). Visualization of fluorescently labeled cells after exposure to HPG suggest that the phytoplankton tested in this study have the ability to take up free amino acids (Fig. 2 to 5). This finding is consistent with previous work demonstrating that the coccocolithophorid Emiliania huxleyi and marine flagellate Cafeteria burkhardae can take up HPG (17, 18) and that Synechococcus sp. can take up organic compounds, including amino acids (25, 26, 78). Laboratory studies with cultured organisms have demonstrated that some phytoplankton can grow successfully on certain amino acids as a sole nitrogen source (26, 27), but conclusions from field studies with natural populations have been more variable (24, 27). However, studies on marine picocyanobacteria have shown natural population take up various amino acids (28–32, 79). While most studies assume amino acids are taken up intact, some phytoplankton possess enzymes that oxidize l-amino acids at the cell surface to obtain ammonium for cellular uptake (27, 32). These cell surface deaminases are responsible for decomposing amino acids, effectively separating the nitrogen source from the carbon backbone and allowing for uptake (33). Therefore, further work is needed to examine amino acid uptake mechanisms for different groups of phytoplankton so that we can determine whether this technique can be more broadly applied to natural phytoplankton communities.

Overall, these culture-based experiments revealed that phytoplankton exhibit varied sensitivities to HPG concentration (Fig. 1). The microeukaryotes Ostreococcus sp. and M. pusilla appeared to be less sensitive to HPG than Synechococcus sp., such that their growth (as measured via spectrophotometry) was not inhibited by HPG concentrations as high as 100 μM (Fig. 1A and B). The decrease in growth observed in Synechococcus sp. at 25 μM HPG concentrations suggests that this group experienced physiological perturbations with the addition of this modified amino acid (Fig. 1C). Very few studies have actually investigated the impact of HPG on an organism’s cellular physiology. Recent work by Steward et al. (21) revealed that HPG additions caused a shift in the metabolome of E. coli that was intensified by heat stress. However, different ecosystems and organisms are likely to have varied sensitivities to HPG additions; therefore, knowledge from one model organism (e.g., E. coli) may not be applicable to other model organisms or ecosystems. In this study, we found that Synechococcus cells exposed to 10 μM HPG exhibited protein-level changes to nitrogen metabolism, general protein stress, and energy production (Fig. 8 and Table 2), even though growth was not inhibited until the cells were exposed to 25 μM HPG. In agreement with Steward et al. (21), the changes in protein expression caused by HPG were intensified (greater log₂-fold change) when the cells were under nutrient stress (see Fig. S2). Interestingly, many of the proteins that had significantly altered expression were also labeled with HPG (Table 2). Furthermore, HPG-labeled proteins predominantly occurred within structural complexes (e.g., ATP synthase and phycobilisome). However, it is important to note that due to the strong correlation between protein abundance and sequence coverage obtained in bottom-up proteomics (34), we are more likely to see HPG incorporation in higher abundance proteins (e.g., structural and metabolic housekeeping genes). At this time, it is unclear whether the increased expression of proteins in HPG-treated cells would lead to increased products and affect metabolic pathways or whether the HPG substitutions caused structural or functional issues with those proteins. In the latter case, the increased expression could have resulted from the cells replacing polypeptides in malfunctioning HPG-labeled proteins, particularly for key structural complexes. Below, we explore the metabolic consequences of HPG addition to Synechococcus cells based on significant changes we detected at the protein level.

The upregulation of glutamine synthetase (GS) and glutamate synthetase (GOGAT) in HPG-treated cultures (Table 2) indicates potential inhibition of these enzymes by HPG and therefore key nitrogen cycling processes in the cell. In cyanobacteria, ammonium is incorporated into carbon skeletons by the GS/GOGAT cycle (35). Glutamate and glutamine produced in this cycle play important roles in the distribution of nitrogen throughout the cell to other nitrogenous compounds. Glutamate not only is the direct precursor for some amino acids and 5-aminolevulinate (the immediate precursor for phycobilin, chlorophyll and porphyrin biosynthesis) but also functions as the primary nitrogen donor for the synthesis of other nitrogen-containing metabolites (35). Specific inhibition of glutamine synthetase has been well documented in another strain of Synechococcus sp. by doping with the NCAA l-methionine-sulfoximine (MSX [36, 37]). When ammonium was provided as the sole nitrogen source, MSX acted as a specific inhibitor of GS, thereby inhibiting ammonium assimilation, essentially mimicking nitrogen starvation (37). This evidence of MSX’s functioning in other Synechococcus strains supports the idea that HPG may be inhibiting these critical enzymes of nitrogen cycling in Synechococcus sp.

A general stress response, with particular evidence for protein stress, in the presence of HPG was indicated by the upregulation of chaperonins. The upregulation of chaperonins is well documented as part of the core general stress response in cyanobacteria (38). Chaperonin proteins are thought to transiently bind polypeptides that have been forced to take nonnative conformations under stressful conditions. Through this binding, chaperonins function to temporarily hold the nonnative polypeptides and prevent aggregation in the crowded environment of the cell (38). Chaperonins with increased expression in HPG-dosed Synechococcus cells included GroEL (1 and 2), DnaK, and HtpG (eukaryotic homologs are heat shock proteins Hsp60, Hsp70, and Hsp90, respectively [35]; Table 2). DnaK also functions to solubilize aggregates of partially denatured proteins and rescue the polypeptides (38). In addition, upregulation of the cytosol aminopeptidase (PepA), which is involved in protein regulation and turnover indicates cellular stress, since this protein is upregulated in E. coli under heat stress (39). Together, the significant upregulation of this suite of proteins provides evidence that HPG additions lead to protein misfolding and stress in Synechococcus cells.

Collectively, the upregulation of proteins associated with the light harvesting capacity of Synechococcus sp. (phycobilisome antenna proteins and photosystem I) and oxidative phosphorylation indicate that cells are experiencing an interference with energy production under HPG addition (Table 2). The phycobilisome is the major light harvesting apparatus of cyanobacteria. This highly ordered supramolecular complex is made up of linker polypeptides and phycobiliproteins with unique spectral properties (phycoerythrin [PE; A_max = 560 nm], phycocyanin [PC; A_max = 620 nm] and allophycocyanin [AP; A_max = 650 nm] [31]). Linker polypeptides stabilize the phycobilisome structure and ensure optimal functioning by encouraging unidirectional flow of energy from the periphery to the core and later to the photosynthetic reaction center (40, 41). The upregulation of many proteins related to the phycobilisome, especially linker polypeptides, indicates potential instability or disfunction of the phycobilisome in HPG-treated cells. This upregulation could also indicate attempts to increase light harvesting capacity for energy production in cells treated with HPG. In addition, some of the antenna proteins related to phycoerythrin (Cpa2, CpaB1, and CpaB2) that were significantly upregulated in HPG treatments were also labeled with the NCAA. The HptG protein was also significantly upregulated in HPG-dosed treatments, providing evidence of nonnative protein folding or aggregation of linker polypeptides in HPG-treated cells. Interestingly, the stress response chaperonin, HtpG, has been shown to interact with and stabilize a 30-kDa rod linker polypeptide of the phycobilisome in a different strain of Synechococcus (42). Together, the observed changes to phycobilisome-related proteins indicates potential issues in stability or function of the major light harvesting complex in HPG-treated Synechococcus sp.

Photosystem I (PS I) in cyanobacteria is a light-driven reaction center that changes the energy of a photon into free chemical energy through oxidation of cytochrome c₆ or plastocyanin and reduction of ferredoxin or flavodoxin (43). Major components of PS I that were upregulated in HPG treatments relative to the control included PsaA and PsaB (a heterodimer of the integral reaction center), PsaC (which provides a path for the electrons out of the membrane phase and to the stromal phase, allowing ferredoxin to be reduced with high quantum efficiency), PsaD (which docks ferrodoxin or flavodoxin), PsaF (which docks plastocyanin or cytochrome c₆), and PsaL (the connecting protein for PS I trimers and state transitions). While the cause of Synechococcus sp. upregulating these critical PS I proteins in the presence of HPG remains unknown, we hypothesize this is due either to an attempt to increase the energy production by this reaction center or to cope with structural or functional issues caused by HPG substitution.

A series of electron transfer reactions harvest energy in cyanobacteria to create a transmembrane electrochemical proton gradient that is used to drive the synthesis of ATP by an F-type ATPase, allowing energy to be temporarily stored and easily accessed by many enzymes through the cell (44). ATP synthase genes examined in select cyanobacteria are arranged in two gene clusters, atp1 (codes genes atpI, atpH, atpG, atpF, atpD, atpA, and atpC) and atp2 (codes remaining genes atpB and atpE [45]). Two major proteins of cluster 1 were upregulated (AtpH and AtpF), while the following protein was downregulated and also labeled with HPG (AtpD), indicating that in the presence of HPG, Synechococcus spp. are likely experiencing interference with this important energy production complex.

The disruption to normal energy production systems described above suggests that HPG could indirectly cause oxidative stress in Synechococcus sp. Oxygen is a powerful electron acceptor yet its intermediates, which are generated through photosynthesis and electron transport, can have highly damaging effects on metabolic networks (46). Imbalances in the generation of reactive oxygen species and antioxidant responses lead to oxidative stress, which commonly occurs due to environmental stressors (e.g., UV stress and nutrient stress). The upregulation of two antioxidant proteins, peroxiredoxin (Prx) and thioredoxin-dependent thiol peroxidase (PrxQ), which are important proteins for maintaining redox homeostasis in Synechococcus sp., provide evidence of redox imbalance in HPG-treated cells (47, 48). Overexpressed PrxQs have shown protection from oxidative stress in cyanobacteria (48). Overall changes to light harvesting and energy production pathways in concert with upregulation of antioxidant proteins indicates that HPG may indirectly cause oxidative stress in Synechococcus sp.

Phytoplankton experience a consistently changing set of environmental conditions in the ocean (49). Therefore, investigating the impact of NCAAs on microbial populations in conjunction with an environmental stressor provides a more realistic evaluation of the technique (21). Populations of marine cyanobacteria are especially influenced by nutrient availability (50, 51) and, as such, nitrate limitation was a physiologically appropriate stressor to test in combination with NCAA additions. Further, there is supporting literature at the transcript level describing the response of Synechococcus sp. to nitrate stress (52, 53). These transcript level responses typically include the reduction of photopigments and photosynthetic capacity, as well as the upregulation of the nitrogen control regulator genes (53). Ideally, this investigation would have led to a better protein-level understanding of nitrate stress in Synechococcus sp. However, the inference with nitrogen cycling (as well as other core stress responses) induced by the addition of HPG limits our ability to determine whether the protein expression in nitrate-limited cells can be attributed to nitrogen stress or whether the nitrogen-stressed cells were more susceptible to the issues induced by HPG additions. Therefore, follow-up experiments without HPG could provide insight into the protein-level responses of Synechococcus sp. to environmental stressors and allow for better comparison of the protein and transcript-level responses.

Metabolic labeling of proteins with NCAAs provides a unique tool for characterizing translationally active microbial communities (14). However, if HPG directly impacts the physiology of cells, as we demonstrate here for Synechococcus sp., the use of this technique to elucidate the protein-level physiological response of microorganisms to changing environmental conditions may be limited. While Synechococcus sp. treated with 10 μM HPG exhibited the same growth dynamics as non-HPG controls (Fig. 1C), we observed significant and potentially detrimental changes in important pathways (Table 2). At this time, it is unclear whether and how these protein-level changes translated into altered metabolic rates, since growth under this concentration of HPG appeared normal. However, these effects were likely intensified at higher concentrations of HPG, such that when exposed to 25 μM HPG, Synechococcus sp. did exhibit a reduction in growth. These findings highlight the importance of coupling HPG additions with several biologically relevant rate measurements (e.g., growth and photosynthetic efficiency) to determine whether and to what extent HPG alters metabolic rates. When applied in a natural setting, these types of HPG-induced effects could alter interactions between organisms. For example, in the marine environment, there is close coupling between phytoplankton and heterotrophic bacterial growth dynamics (54, 55) because heterotrophic bacterial growth in the euphotic zone is strongly influenced by phytoplankton-derived dissolved organic matter (56–58). Marine Synechococcus sp. and heterotrophic bacteria are known to be spatially and ecologically intimate in natural settings and in culture, frequently conjoining or forming networks (59, 60). Given the tight interaction of these organisms, both physically and for essential carbon and nutrients, changes in one organism’s physiology, (e.g., Synechococcus sp.) in response to HPG could have cascading effects on the heterotrophic bacteria that exist in community with those cells.

The BONCAT technique lends itself to investigating a wide range of ecological questions. Therefore, if the goal of the study is to visualize and sort translationally active marine microorganisms (61), this technique may be appropriate. In contrast, if the goal of the application is to characterize and track the physiology and proteomic response of marine microorganisms, including cyanobacteria, to changing environmental conditions, this approach may not be appropriate. It is, however, important to recognize that the HPG concentrations used in this study were meant to maximize fluorescence intensity in order to detect HPG uptake in cyanobacteria. These higher concentrations may not always be necessary for other ecosystems and ecological questions. In addition, it would be worth exploring other commonly used NCAAs, such as l-azidohomoalanine (AHA), in these types of sensitivity experiments. AHA has been applied to an equally wide range of study systems (7, 20, 62–64) and demonstrates potentially less toxicity and disturbance to the physiology of the study system (21), although its impact has not been investigated at the proteomic level. Overall, our results highlight an imperative aspect of investigation for studies utilizing HPG to ensure the NCAA addition does not influence cell metabolism. Different taxa are likely to show various sensitivities to NCAA additions, and these considerations must be taken into account when applying this approach to study marine microbial communities.

MATERIALS AND METHODS

Culture conditions.

Experiments were conducted with three different algal cultures, including one cyanobacterium and two green algae: Synechococcus sp. (strain CC9311; CCMP3074 [65]), Micromonas pusilla (CCMP487), and Ostreococcus sp. (MBIC10636), respectively. Cultures were maintained in L1 medium minus silica (66) using salt solutions from ESAW medium rather than filtered natural seawater (67, 68). Cultures were maintained in an incubator at 18°C on a 12:12-h light-dark cycle; however, Ostreococcus sp. and M. pusilla were grown under higher light (75 μmol photons m⁻² s⁻¹) relative to Synechococcus sp. (20 to 25 μmol photons m⁻² s⁻¹). All cultures were transferred and maintained under sterile conditions but were not axenic. Heterotrophic bacteria were present, but in low densities relative to the algal strains (see “Microscopy and Image Analysis” for details).

HPG sensitivity experiments.

The three organisms were grown to exponential phase according to the conditions described above. l-homopropargylglycine (HPG; Click Chemistry Tools), a methionine analogue, was resuspended in 0.2 μM sterile-filtered water and then added to the cultures in exponential phase at a range of concentrations between 0.2 and 100 μM (Table 3). For M. pusilla and Ostreococcus sp., all HPG concentrations were tested in a single experiment. However, for Synechococcus sp., HPG concentrations were tested over the course of three experiments (high [100 μM], midrange [10, 25, and 50 μM], and low [0.2, 0.5, 1, and 5 μM]) to capture the maximum concentration of HPG that could be added without influencing cell growth dynamics. Across these experiments, we added HPG during exponential growth phase (optical density at 450 nm [OD₄₅₀] range, 0.2 to 0.3).

TABLE 3.

Exponential growth ranges and HPG concentrations tested per organism^a

Organism	OD450 ranges	HPG concentration (μM) tested
Synechococcus sp.	0.15–0.40	0.2, 0.5, 1, 5, 10, 25, 40, and 100
Micromonas pusilla	0.34–0.41	5, 10, 25, 50, and 100
Ostreococcus sp.	0.15–0.35	25, 50, and 100

^aHPG, l-homopropargylglycine.

Cultures were monitored daily by measuring the optical density (OD₄₅₀ [69, 70]). Standard curves comparing the spectrophotometric absorbance at 450 nm and cell counts (obtained using epifluorescence microscopy) showed strong correlations for all cultures (r² values: Synechococcus sp. = 0.81, M. pusilla = 0.99, and Ostreococcus sp. = 0.91). Samples for microscopy were taken 24, 48, and 72 h after the addition of HPG and fixed with 2% paraformaldehyde (PFA) at 4°C. Samples were in fixative for 24 to 48 h, centrifuged, washed in 1× phosphate-buffered saline (PBS), resuspended in 50:50 (vol/vol) ethanol-H₂O, and stored at −20°C. Line plots were generated in R (71) using the package ggplot2 (74).

Impact of HPG addition with or without nutrients.

To explore the impact of HPG on cell growth and physiology with and without nitrate, cultures of Synechococcus sp. were amended with HPG in combination with nutrient stress (via nitrate removal from culture medium). Synechococcus cultures were maintained in exponential phase in sterile L1 medium minus silica. When the culture density reached OD₄₅₀ ∼0.4 (∼1.5 × 10⁸ cells/ml), all experimental cultures were centrifuged, and the cells were washed in L1 medium without nitrogen (according to the nitrogen limitation protocol of Ludwig and Bryant [53]). Concentrated cultures were resuspended to half of the starting OD₄₅₀ in fresh L1 medium, either with nitrate (nitrate replete) or without nitrate (nitrate limited) (Table 1). These experimental cultures were amended with 10 μM HPG at the time of setup, since this concentration was determined not to affect cell growth by initial HPG sensitivity experiments (Fig. 1C). Control cultures were maintained with replete nutrients and no HPG. In order to not disrupt the exponentially growing population within the control, the control was not centrifuged. A separate centrifuge versus no-centrifuge control experiment revealed that centrifugation had a minimal impact on the protein expression of Synechococcus cultures; furthermore, this comparison revealed that centrifugation did not induce any changes to protein expression that we attribute to HPG in this study (see Table S6). All treatments were run in triplicate with a total volume of 100 ml in 250-ml Erlenmeyer flasks. All experiments were set up immediately before the onset of the light cycle, and the OD₄₅₀ was monitored daily as a proxy for cell density.

Preliminary analysis from the HPG sensitivity experiments revealed that HPG uptake by Synechococcus sp. was visible by fluorescence after 48 h and increased after 72 h. Therefore, samples for microscopy and proteomics were taken 48 and 72 h after HPG additions. The BONCAT signal showed greater intensity at 72 h compared to 48 h, indicating increased labeling at the later time point, so 72-h samples were analyzed for proteomics. For microscopy, triplicates of 1.8 ml from each culture were fixed with paraformaldehyde (PFA; 2% final concentration) for 24 h at 4°C. Samples were then centrifuged, washed in 1× PBS, resuspended in 50:50 ethanol–1× PBS, and stored at −20°C. For protein samples, 20 ml of each culture was centrifuged, resuspended in fresh L1 medium (nitrate replete or nitrate limited according to treatment), and transferred to a 1.5-ml Eppendorf tube. The sample was centrifuged again, and supernatant was removed before fast-freezing in liquid nitrogen and storage at −80°C.

BONCAT click chemistry.

The incorporation of HPG into Synechococcus sp. proteins was detected via epifluorescence microscopy. The click reaction, or copper(I)-catalyzed cycloaddition, fluorescently labels the methionine analog by incubating it with an azide-linked fluorophore and copper(II) under reducing conditions to allow copper(I) to catalyze binding of the azide to the alkyne (6). Prior to conducting the click reaction, all samples were spotted on Teflon printed slides (Electron Microscopy Sciences, PTFE printed slides) in volumes ranging from 2 to 10 μl (depending on the cell concentration) and air dried. Samples on slides were put though an ethanol dehydration series (50:50, 80:20, and then 96:4 [vol/vol] ethanol-H₂O) prior to incubation with freshly prepared click solution (4 μl of dye-premix [CuSO₄, 0.1 mM; THPTA, 0.5 mM; CR-110-azide fluorophore, 2 mM] added to 254 μl of buffer solution [sodium ascorbate, 5 mM; aminoguanidine hydrochloride, 5 mM; 1× PBS]). Slides were incubated for 30 min in the dark at room temperature in a humid chamber. Postincubation samples were washed in 1× PBS and then in H₂O. After the samples were completely dry, coverslips were mounted over wells with DAPI VectaShield mounting medium (Vector Labs).

Microscopy and image analysis.

Algal samples were analyzed with a Zeiss Axio Observer Z1 inverted epifluorescence microscope using a 100× objective. Digital images were acquired with a 6-megapixel CCD camera (Zeiss Axiocam 506 mono). The peak channel excitation and emissions wavelength/bandpass in nm were 365 and 445/50 for blue (DAPI-stained cells), 470/70 and 525/50 for green (fluorescence signal of the azide-containing CR-110 fluorophore), 550/25 and 605/70 for orange (autofluorescence from phycobiliproteins), and 440/40 and 675/50 for red (chlorophyll autofluorescence).

Images were analyzed using an in-house image analysis pipeline in MATLAB (72). In short, regions of interest (ROIs) were selected by applying a signal threshold to the images. The mean blue (e.g., DAPI-stained cells), green (e.g., fluorescence signal of the azide-containing CR-110 fluorophore), and red (or orange) (e.g., autofluorescence from chlorophyll photopigments in Micromonas pusilla and Ostreococcus sp. and autofluorescence from phycobiliprotein photopigments in Synechococcus sp.) fluorescence values were recorded for each ROI, and the data were normalized by image exposure time. For all algal strains, heterotrophic bacteria were removed during the image analysis process prior to interpretation of HPG uptake by the phytoplankton cells. For Ostreococcus sp. and M. pusilla, heterotrophic bacteria were excluded during ROI selection because their smaller size and absence of chlorophyll a made them visually distinct (see Fig. S3). For Synechococcus sp., because heterotrophic bacteria spanned the same size range and were less visually distinct, they were excluded after ROI selection by applying a cutoff based on the orange (e.g., representing phycoerythrin autofluorescence in Synechococcus sp.)-to-blue (e.g., DAPI-stained cells) signal ratio and/or the orange signal value alone (see Fig. S4). After removal of the heterotrophic bacteria from the data, box plots of the mean green (e.g., representing the fluorescence signal of the azide-containing CR-110 fluorophore) fluorescence intensity were used to visualize differences between positive and negative HPG treatments. The number of ROIs included in the final analysis varied across the algal strains based on their densities in each image: for Synechococcus sp., 500 to 1,000 ROIs were selected per image (∼5 images per treatment); for Ostreococcus sp., 100 to 500 ROIs were selected per image (∼3 images per treatment); and for M. pusilla, 100 ROIs were selected per image (∼4 images per treatment). Differences in the fluorescence intensity between positive and negative HPG samples were tested using a Mann-Whitney U test (73). Box plots were generated with custom scripts in R (71) using the package ggplot2 (74).

Proteomics.

Samples from Synechococcus sp. at the 72-h time point were selected for proteomic analysis based on the greatest BONCAT signal intensity observed via microscopy. Cell pellets were lysed on ice with 100 μl of 0.05% SDS–0.5 M TEAB lysis buffer by vortexing, syringe titration with a 23G needle (30×), and Dounce homogenization (30×). Lysates were then clarified at 16,000 × g for 5 min at 4°C, and the protein concentration was measured by using a Bradford assay. Next, 10-μg portions of lysates from each sample in 52 μl of lysis buffer were reduced with 3 mM TCEP for 1 h at 50°C, alkylated with 10 mM iodoacetamide for 15 min at room temperature, digested with 1:100 LysC for 2 h at room temperature, and digested with 1:25 trypsin overnight at 37°C.

Digests were stopped by acidifying with 3.25 μl of 100% formic acid and desalted on StageTips packed in-house with Empore C₁₈ extraction material (3M, catalog no. 2215). Desalted peptides were lyophilized and resuspended in 10 μl of 100 mM TEAB, and peptide quantification was performed with the Pierce colorimetric peptide quant assay (Thermo, catalog no. 23275). Next, 5 μg of peptides per sample was brought to a total of 10 μl in 100 mM TEAB and then labeled with 100 μg of TMT-11 (Thermo) reagents in 4 μl of anhydrous acetonitrile for 2 h at room temperature. TMT reactions were quenched with 1 μl of 5% hydroxylamine for 15 min at room temperature, combined, lyophilized, and desalted on a C₄ Macrotrap cartridge (Optimize Technologies, catalog no. 10-04818-TM) on an Agilent 1100 HPLC. SDS was then removed with HiPPR detergent removal resin (Thermo, catalog no. 88305), and the peptides were resuspended in solvent A (2% acetonitrile [ACN], 0.2% formic acid).

Liquid chromatography-mass spectrometry analysis was carried out on an EASY-nLC1000 (Thermo Fisher Scientific, San Jose, CA) coupled to an Orbitrap Fusion Tribrid mass spectrometer (Thermo Fisher Scientific). Approximately 250-ng portions of peptides were loaded onto an Aurora 25 cm × 75 μm ID, 1.6-μm C₁₈ reversed-phase column (IonOpticks, Parkville, Victoria, Australia) and separated over 136 min at a flow rate of 350 nl/min with the following gradient: 2 to 6% solvent B (7.5 min), 6 to 25% B (90 min), 25 to 40% B (30 min), 40 to 100% B (1 min), and 100% B (15 min). Solvent B consisted of 80% ACN and 0.2% formic acid. MS1 spectra were acquired in the Orbitrap at 120K resolution with a scan range from 400 to 1,500 m/z, an AGC target of 4e5, and a maximum injection rate of 50 ms in Profile mode. Features were filtered for monoisotopic peaks with a charge state of 2 to 5 and a minimum intensity of 5e3, with dynamic exclusion set to exclude features after 1 time for 60 s with a 10-ppm mass tolerance. CID fragmentation was performed with a collision energy of 35%, an activation time of 10 ms, and an activation Q of 0.25 after quadrupole isolation of features using an isolation window of 0.7 m/z, an AGC target of 1e4, and a maximum injection time of 35 ms. MS² scans were then acquired in the ion trap at rapid scan rate in Centroid mode. SPS-MS³ analysis was then performed with a precursor selection range of 400 to 1,600 m/z and a precursor ion exclusion tolerance of −50 m/z to +5 m/z. Up to 10 notches were selected using an MS² isolation window of 3 m/z for HCD fragmentation with a collision energy of 65%, which were analyzed in the Orbitrap at 50k resolution with a scan range of 100 to 500, a maximum injection time of 500 ms, and an AGC target of 50k in Centroid mode. The total cycle time was set at 3 s.

Proteomics data analysis was performed in Proteome Discoverer 2.4 (Thermo Scientific) using the Byonic search algorithm (Protein Metrics) and UniProt proteome UP000001961 Synechococcus sp. (strain CC9311), representing the full genome and expressing 2882 proteins in total. The Byonic search parameters were as follows: fully tryptic peptides with no more than two missed cleavages, precursor mass tolerance of 10 ppm and fragment mass tolerance of 0.5 Da, CID low energy fragmentation, and a maximum of two common modifications and two rare modifications. Cysteine carbamidomethylation (+57.0215) and the appropriate TMT addition to lysine and peptide N termini (+229.1629) were static modifications, whereas methionine oxidation (Dynamic – common 2) and Met → Hpg (Dynamic – rare 2) were set as variable modifications. The Byonic protein-level false discovery rate (FDR) was set to 1%. Percolator FDRs were set at 0.001 (strict) and 0.01 (relaxed). Reporter ion quantification from ms3 spectra was based on S/N ratios, used a coisolation threshold of 25%, an average reporter S/N threshold of 10, and SPS mass matches set at 0%. Normalization was performed on total peptide amount of all identified peptides. Peptide and PSM FDRs were set at 0.001 (strict) and 0.01 (relaxed), with peptide confidence at least medium, lower confidence peptides excluded, and minimum peptide length set at 6. Protein FDR validator node FDR was set at 0.001 (strict) and 0.01 (relaxed). Strict parsimony principle was set to true. Proteins with <2 quantification points in each replicate were filtered out. Statistical analysis of quantified proteins was performed using the LIMMA moderated t test (75). Significant proteins in each pairwise comparison were determined by having an adjusted P value of ≤0.05 and a log₂-fold change (log base 2 transformation of the fold change values) of at least 1. Heat maps with significant proteins were generated in R (71).

Data availability.

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository (76) with the data set identifier PXD023371.