Applied and Environmental Microbiology-2021-Michels-AEM.00200-21.full

AEM Accepted Manuscript Posted Online 14 May 2021 Appl Environ Microbiol doi:10.1128/AEM.00200-21 Copyright © 2021 American Society for Microbiology All Rights Reserved Title: Amino acid analog induces stress response in marine Synechococcus Authors: Dana E Michels1, Brett Lomenick2, Tsui-Fen Chou2, Michael J Sweredoski2, Alexis Pasulka1* 93407, USA Engineering, California Institute of Technology, Pasadena, CA 91125, USA Biological Sciences Department, California Polytechnic State University, San Luis Obispo, CA Proteome Exploration Laboratory, Beckman Institute, Division of Biology and Biological 10 11 * Corresponding author: apasulka@calpoly.edu 12 13 ABSTRACT 14 Characterizing the cell-level metabolic trade-offs that phytoplankton exhibit in response to 15 changing environmental conditions is important for predicting the impact of these changes on 16 marine food web dynamics and biogeochemical cycling The time-selective proteome-labeling 17 approach, bioorthogonal noncanonical amino acid tagging (BONCAT), has potential to provide 18 insight into differential allocation of resources at the cellular level, especially when coupled with 19 proteomics However, the application of this technique in marine phytoplankton remains limited 20 We demonstrate that the marine cyanobacteria Synechococcus sp and two groups of eukaryotic 21 algae take up the modified amino acid L-homopropargylglycine (HPG), suggesting BONCAT 22 can be used to detect translationally active phytoplankton However, the impact of HPG 23 additions on growth dynamics varied between groups of phytoplankton Additionally, proteomic Downloaded from http://aem.asm.org/ on May 14, 2021 at CALIFORNIA INST OF TECHNOLOGY analysis of Synechococcus sp cells grown with HPG revealed a physiological shift in nitrogen 25 metabolism, general protein stress, and energy production, indicating a potential limitation for 26 the use of BONCAT in understanding the cell-level response of Synechococcus sp to 27 environmental change Variability in HPG sensitivity between algal groups and the impact of 28 HPG on Synechococcus sp physiology indicates that particular considerations should be taken 29 when applying this technique to other marine taxa or mixed marine microbial communities 30 31 IMPORTANCE 32 Phytoplankton form the base of the marine food web and substantially impact global energy and 33 nutrient flow Marine picocyanobacteria of the genus Synechococcus comprise a large portion of 34 phytoplankton biomass in the ocean and therefore are important model organisms The technical 35 challenges of environmental proteomics in mixed microbial communities have limited our ability 36 to detect the cell-level adaptations of phytoplankton communities to a changing environment 37 The proteome labeling technique, bioorthogonal noncanonical amino acid tagging (BONCAT), 38 has potential to address some of these challenges by simplifying proteomic analyses This study 39 explores the ability of marine phytoplankton to take up the modified amino acid, L- 40 homopropargylglycine (HPG), required for BONCAT, and investigates the proteomic response 41 of Synechococcus to HPG We demonstrate cyanobacteria can take up HPG, but also highlight 42 the physiological impact of HPG on Synechococcus, which has implications for future 43 applications of this technique in the marine environment 44 45 Downloaded from http://aem.asm.org/ on May 14, 2021 at CALIFORNIA INST OF TECHNOLOGY 24 47 INTRODUCTION Phytoplankton are critical components of marine ecosystems, accounting for half of the 48 planet’s primary productivity and influencing global nutrient cycling (1, 2) Ecological trade-offs 49 exhibited by marine phytoplankton to maximize growth (e.g., compete for nutrients) and 50 minimize loss (e.g., increase predation defenses) shape phytoplankton diversity and community 51 structure in the ocean, which in turn significantly impacts food web dynamics and 52 biogeochemical cycling (3) A cell-level approach is necessary to elucidate how these important 53 organisms allocate resources, especially in response to changing environmental conditions 54 The advent of ‘omics approaches has revolutionized our ability to describe marine 55 phytoplankton communities and their mechanisms of adaptation to changing environments at the 56 cellular level (4) Proteomic approaches allow for the detection of real-time changes in 57 phytoplankton physiology and metabolic state because they provide a deep insight into the 58 changes of a large fraction of the entire protein complement of the cell Proteomics offers an 59 advantage over transcriptomics in that not all transcripts are translated into functional proteins 60 However, due to the complexity of marine proteomes, there are challenges when applying these 61 tools to explore natural communities (5) The time-selective proteome-labeling approach, 62 bioorthogonal noncanonical amino acid tagging (BONCAT), has the potential to address some of 63 these challenges BONCAT relies on pulse-labeling organisms with noncanonical amino acids 64 (NCAAs) like the methionine (Met) surrogate L-homopropargylglycine (HPG), such that 65 NCAAs get incorporated into nascent proteins by the cell’s endogenous translational machinery 66 (6, 7) While a number of modified amino acids successfully compete with native amino acids, 67 few are able to exploit the promiscuity of the endogenous translational machinery without 68 modification to the host cell (8) Met analogs are particularly useful because the enzyme that Downloaded from http://aem.asm.org/ on May 14, 2021 at CALIFORNIA INST OF TECHNOLOGY 46 catalyzes the esterification of Met with its tRNA, methionyl-tRNA synthetase, has low 70 specificity causing misrecognition and misincorporation of such analogs in place of methionine 71 (9) Met analogs can even serve as the initiator of translation without disruption of the 72 translational machinery (10) BONCAT has been applied successfully to visualize and identify 73 translationally active cells in natural communities (11) Furthermore, the BONCAT technique 74 offers the ability to separate the labeled proteome from the bulk proteome by exploiting the 75 chemical handle of the NCAA incorporated into the newly synthesized proteins, thereby 76 simplifying proteomic analysis and reducing challenges often encountered in natural proteomics 77 (12, 13) 78 BONCAT has been applied in a variety of cultured microorganisms and ecosystems (6, 7, 79 14), but its use in marine planktonic microbial communities has been limited Initial studies 80 demonstrated uptake of NCAAs by marine heterotrophic bacteria and the ability to quantify 81 protein synthesis rates in natural populations (15, 16) Two additional studies showed uptake of 82 NCAAs by the eukaryotic phytoplankton Emiliania huxleyi (17) and the heterotrophic 83 flagellate Cafeteria burkhardae (18) However, none of these studies have moved beyond the 84 visualization of NCAA uptake via fluorescence microscopy to capture the protein-level 85 physiological response of these marine microbial populations The BONCAT approach, 86 particularly when coupled with proteomics, has potential to provide insight into the differential 87 allocation of resources at the cellular level, allowing us to explore the trade-offs marine 88 microorganisms make in response to changing environmental conditions 89 A major assumption of the BONCAT approach is that the uptake and utilization of 90 NCAAs by cells does not impact cellular physiology Most studies report that additions of 91 NCAAs have minimal impact on cells when examined by microscopy (17, 19, 20) At the protein Downloaded from http://aem.asm.org/ on May 14, 2021 at CALIFORNIA INST OF TECHNOLOGY 69 level, no changes to protein expression or degradation were observed due to NCAA additions 93 (human embryonic kidney cells; and E coli; 14); however, one study reported alterations to 94 protein abundance (Vibrio harveyi; 9) Recent work demonstrated that NCAAs cause mild 95 perturbations to the metabolome of E coli and that this impact was intensified when NCAAs 96 were added under stressful conditions (e.g., heat stress; 21) However, our understanding of the 97 limited impact of NCAAs on cells comes mostly from studies involving heterotrophic bacteria 98 and these assumptions may not be valid for autotrophic phytoplankton To address this 99 knowledge gap, we explored the use of BONCAT in Synechococcus sp., a globally important 100 marine cyanobacteria We characterized the growth of Synechococcus sp under a range of HPG 101 concentrations and optimized the fluorescence signal to detect this uptake via epifluorescence 102 microscopy In addition, we examined changes in protein expression of Synechococcus sp 103 grown with HPG under normal and nitrate-stressed conditions relative to a non-HPG control 104 Finally, we characterized the growth and quantified HPG uptake under a range of HPG 105 concentrations in two eukaryotic phytoplankton models, Ostreococcus sp and Micromonas 106 pusilla, to test whether they exhibited the same initial sensitivity to HPG additions as 107 Synechococcus sp 108 109 110 RESULTS 111 Impact of HPG concentration on phytoplankton growth dynamics 112 Phytoplankton exhibited different sensitivities to HPG concentration For both eukaryotic 113 green algal models (M pusilla and Ostreococcus sp.), growth in the presence of HPG was 114 similar to that exhibited in the negative (e.g., non-HPG) control for all HPG concentrations Downloaded from http://aem.asm.org/ on May 14, 2021 at CALIFORNIA INST OF TECHNOLOGY 92 tested (up to 100 µM; Figure 1A, B) However, for Synechococcus sp., HPG concentrations as 116 low as 25 µM disrupted normal growth dynamics (Figure 1C) Compared to the maximum 117 growth exhibited by the negative control, 25 µM HPG additions reduced Synechococcus sp 118 growth by 39%, whereas 50 µM HPG additions reduced growth by 51% 100 µM HPG 119 concentrations (the concentration used in previous studies with marine heterotrophic bacteria in 120 culture; 13) resulted in a complete crash of the Synechococcus sp culture (data not shown) 121 Synechococcus sp grew normally and reached the same maximum growth when exposed to 10 122 µM HPG and lower concentrations Synechococcus sp growth with 10 µM HPG additions was 123 characterized three additional times to confirm this result (data not shown) 124 125 Fluorescence detection of BONCAT signal 126 Epifluorescence microscopy was used to detect and visualize HPG incorporation by the 127 phytoplankton from HPG-growth experiments using the highest HPG concentration that did not 128 alter cell growth dynamics (Figure 1; 100 µM for M pusilla and Ostreococcus sp and 10 µM for 129 Synechococcus sp.) As outlined in the methods section Microscopy and Image Analysis, 130 heterotrophic bacteria present in the cultures were excluded from the data prior to interpretation 131 of HPG incorporation by the phytoplankton cells (Figures S1 and S2) While the proportion of 132 bacteria in the cultures could not be determined for Ostreococcus sp and M pusilla (because 133 bacteria were visually removed during ROI selection), the proportion of bacteria present in 134 Synechococcus sp cultures over multiple experiments ranged from 17-23% of the population 135 For all taxa, the fluorescence signal in the blue (e.g., DAPI-stained cells) and red or 136 orange (e.g., autofluorescence from phytoplankton pigments) channels were consistent between 137 the negative and positive treatments, providing visual evidence that the cells appeared healthy Downloaded from http://aem.asm.org/ on May 14, 2021 at CALIFORNIA INST OF TECHNOLOGY 115 and intact when grown in the presence of HPG (Figures 2,3,4) A bright signal in the green 139 channel (e.g., fluorescence signal of the azide-containing CR-110 fluorophore) was visually 140 apparent in cultures amended with HPG relative to negative control cultures (Figures 2,3,4) 141 Quantitative analysis based on the green fluorescence intensity revealed that the HPG-amended 142 treatments (positive) were significantly different from the negative controls for all phytoplankton 143 models across all time-points (Mann-Whitney U Test; Table S1, Figure 5) However, it is 144 important to note that while Synechococcus sp cells grown with HPG were still brighter in 145 comparison to HPG negative cells, these cells exhibited some autofluorescence in the green 146 channel due to the presence phycobiliproteins hence the greater overlap in the signal between the 147 positive and negative treatments (Figure 2, 5C) M pusilla exhibited the strongest fluorescence 148 signal as a result of HPG incorporation at 48 h (Figure 5A) In contrast, the fluorescence signal 149 as a result of HPG incorporation (e.g., CR-110) in Synechococcus sp and Ostreococcus sp 150 increased over time (Figure 5B, C) and exhibited the strongest fluorescence relative to the 151 negative control at 72 h post HPG addition 152 153 Physiological response of Synechococcus sp to HPG additions under replete and nutrient- 154 limited conditions 155 Following resuspension of cultures in the appropriate treatment media with HPG (Table 156 2), replete cultures exhibited a typical growth rate, while nitrate-limited cultures exhibited a 157 reduced growth rate (Figure 6) Control cultures (e.g., no spin conditions, no HPG) continued to 158 increase exponentially Epifluorescence microscopy revealed consistent fluorescence signals in 159 the blue (e.g., DAPI-stained cells) and orange (e.g., autofluorescence from phycobiliproteins 160 pigments) channels across treatments and time points However, green fluorescence intensity Downloaded from http://aem.asm.org/ on May 14, 2021 at CALIFORNIA INST OF TECHNOLOGY 138 (fluorescence signal of the azide-containing CR-110 fluorophore) was visually brighter for both 162 replete and nitrate-limited HPG-amended treatments compared to the control (Figure 7) This 163 visual difference in green fluorescence intensity was quantitatively and significantly different in 164 HPG-amended treatments (replete and nitrate-limited) relative to the control across both time- 165 points (Table S2, Figure S1) 166 Proteomic analysis collected 22,209 MS2 spectra, from which 5,969 peptide-to-spectrum 167 matches and 5,551 peptide groups were identified A total of 1,033 proteins were identified, 168 (representing about 36% of all predicted proteins in Synechococcus sp strain CC9311; 19) and 169 496 quantified proteins were used for analysis after filtering (see Methods section ‘Proteomics’) 170 HPG labeling was detected in 68 of the final quantified proteins Relative to the negative control, 171 222 proteins had significantly different expression (i.e., a minimum log2 fold-change of and an 172 adjusted p-value less than 0.05) in the HPG positive treatments relative to the negative control 173 (Figure 8, Table S3) Of these proteins, 122 were up-regulated and 100 were down-regulated 174 HPG labeling was detected in 25 of these significant proteins (Table S5) The proteins 175 differentially expressed in the nitrate-limited condition vs the control had a greater median log2 176 fold-change value than the proteins differentially expressed in the nitrate-replete condition vs 177 the control (Figure S2), suggesting that the added stress of nitrate limitation magnified the 178 proteomic changes caused by HPG treatment 179 Proteomic analysis revealed that HPG influenced several aspects of metabolism including 180 nitrogen metabolism, general protein stress, and energy production (Table 3) Glutamate 181 synthetase (ferredoxin-dependent glutamate synthase, Fd-GOGAT) and glutamine synthetase 182 (glnN) were significantly upregulated in HPG-treatments compared to the control Chaperonin 183 proteins (groEL1, groEL2, dnaK and htpG) and a probable cytosol aminopeptidase (pepA) were Downloaded from http://aem.asm.org/ on May 14, 2021 at CALIFORNIA INST OF TECHNOLOGY 161 also significantly upregulated in HPG treatments Many antenna proteins were upregulated in 185 HPG treatments, including phycobilisome linker polypeptides (cpcC, cpcD, cpeC, cpeD1, 186 cpeD2), phycobilisome rod-core linker polypeptides (cpcG1, cpcG, apcE), allophycocyanin 187 apoproteins (apcB1, apcD), and phycoerythrin chain peptides (cpaA2, cpaB1, cpaB2) Two other 188 antenna proteins (cpeT, cpeS) were significantly downregulated in HPG treatments relative to 189 the control Major components of photosystem I were upregulated in HPG treatments relative to 190 the control, including psaA, psaB, psaC, psaD, psaF, and psaL Some proteins of photosystem II 191 were also upregulated (psbC, psbJ, psbW and psbV) Two major proteins of ATP synthase were 192 upregulated (atpH and atpF), while one protein was downregulated (atpD) Many peptides 193 related to the phycobilisome and photosystem I were labeled with HPG including, ferredoxin, 194 psaC, psaF, cpcD, cpa2, cpaB1, and cpaB2 Additionally, one of the ATP synthase F1 195 subcomplex beta subunits (atpD) was HPG labeled The antioxidant proteins peroxiredoxin and 196 thioredoxin-dependent thiol peroxidase, prx and prxQ, respectively were also upregulated in 197 HPG treatments 198 When comparing protein expression between the two HPG positive treatments (nitrate- 199 replete vs nitrate-limited), 14 proteins were significantly different between these two groups Of 200 these proteins, were up-regulated and 10 were down-regulated (Table S5) HPG labeling was 201 detected in of these significant proteins However, for 13 of these 14 proteins, the directionality 202 of the log2 fold-changes for this pairwise comparison was the same as the directionality in the 203 pairwise comparison of HPG positive treatments relative to the control Therefore, the 204 identification of these proteins as being differentially expressed between nitrate-replete vs 205 nitrate-limited treatments largely reflects the greater magnitude of log2 fold-change values in the 206 nitrate-limited treatment rather than a meaningful biological difference in the proteome of the Downloaded from http://aem.asm.org/ on May 14, 2021 at CALIFORNIA INST OF TECHNOLOGY 184 two conditions (Table S5, Figure S2) These proteins predominantly indicate that when a cell is 208 under nutrient stress, the addition of HPG exhibits a greater interference on nitrogen metabolism 209 (upregulation of the nitrogen-responsive regulatory protein [ntcA] and glutamine synthetase [GS] 210 and downregulation of ferredoxin-nitrite reductase [Fd-nir]) and energy production (upregulation 211 of NADH dehydrogenase and downregulation of light-independent protochlorophyllide 212 reductase iron-sulfur ATP-binding protein [chlL]; TableS5) 213 214 215 DISCUSSION 216 The BONCAT technique has helped scientists begin to address key questions in 217 microbial ecology This approach provides a tool to link microbial function with phylogeny by 218 identifying translationally active cells in cultures and natural communities (14, 16) BONCAT 219 has recently been applied to study marine bacterioplankton and obtain single-cell protein 220 synthesis rates (15), but its use in marine phytoplankton communities remains limited When 221 coupled with proteomics, this technique has the potential to help elucidate the mechanisms by 222 which marine microorganisms adapt and survive in changing environmental conditions (e.g., 223 Pseudomonas aeruginosa, 13; Vibrio harveyi, 23) In this study we demonstrate that the marine 224 cyanobacteria Synechococcus sp and two groups of eukaryotic algae can take up the modified 225 amino acid, HPG Overall, our findings suggest that BONCAT can be used to detect 226 translationally active phytoplankton However, among different phytoplankton groups, we 227 observed variability in how HPG impacted normal growth dynamics (Figure 1) Furthermore, 228 despite normal growth patterns when exposed to 10 µM HPG concentrations, variations in 229 protein expression between Synechococcus sp in HPG treated cultures vs non-HPG control 10 Downloaded from http://aem.asm.org/ on May 14, 2021 at CALIFORNIA INST OF TECHNOLOGY 207 823 824 77 Mann HB, Whitney DR 1947 On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other Ann Math Statist 18:50-60 78 Smyth GK 2005 limma: Linear Models for Microarray Data, p 397-420 In Gentleman R, 825 Carey VJ, Huber W, Irizarry RA, Dudoit S (eds), Bioinformatics and Computational 826 Biology Solutions Using R and Bioconductor Statistics for Biology and Health Springer, 827 New York, NY 828 79 Vizcaino JA, Cote RG, Csordas A, Dianes JA, Fabregat A, Foster JM, Griss J, Alpi E, Birim 829 M, Contell J, O'Kelly G, Schoenegger A, Ovelleiro D, Perez-Riverol Y, Reisinger F, Rios D, 830 Wang R, Hermjakob H 2013 The Proteomics Identifications (PRIDE) database and 831 associated tools: status in 2013 Nucleic Acids Res 41(D1):D1063-9 832 833 834 FIGURE LEGENDS 835 Figure Impact of L-homopropargylglycine (HPG) concentration on phytoplankton growth 836 dynamics Phytoplankton growth measured by spectrophotometry (y axis = absorbance at 837 450nm) for Micromonas pusilla (A), Ostreococcus sp (B), Synechococcus sp (C) The x-axis 838 shows time in 24-h increments for M pusilla (A) and Ostreococcus sp (B); however, for 839 Synechococcus sp (C), the increments represent 24-h periods, but are normalized to an OD450 of 840 0.2 in order to compile data from multiple experiments into one plot Colors indicate the 841 different concentrations of HPG used and arrows indicate the time point that HPG was added to 842 the cultures In the Synechococcus sp (C) plot, the black arrow indicates HPG additions for low 843 concentration range experiment (0.2, 0.5, 1, and 5µM) and the grey arrow indicates HPG 844 additions for the mid-range concentration experiment (10, 25, and 50µM) 37 Downloaded from http://aem.asm.org/ on May 14, 2021 at CALIFORNIA INST OF TECHNOLOGY 822 846 Figure Epifluorescence microscopy images of Synechococcus sp after 24, 48, and 72 hours 847 with (+) or without (-) 10 µM L-homopropargylglycine (HPG) additions Dye or pigment names 848 shown at top of images Left: Blue channel shows DAPI-stained cells Left Center: Green 849 channel show fluorescence signal of the azide-containing CR-110 fluorophore used to detect 850 HPG Right Center: Orange channel shows the autofluorescence from phytoplankton pigments 851 Right: merged image of all three channels The exposure times were standardized within each 852 channel to enable quantitative comparison of fluorescent signals 853 854 Figure Epifluorescence microscopy images of Ostreococcus sp after 48 and 72 hours with (+) 855 or without (-) 100 µM L-homopropargylglycine (HPG) additions at time zero Dye or pigment 856 names shown at top of images Left: Blue channel shows DAPI-stained cells 857 Left Center: green channel show fluorescence signal of the azide-containing CR-110 fluorophore 858 used to detect HPG Right Center: Red channel shows the autofluorescence from phytoplankton 859 pigments Right: merged image of all three channels The exposure times were standardized 860 within each channel to enable quantitative comparison of fluorescent signals 861 862 Figure Epifluorescence microscopy images of Micromonas pusilla after 48 and 72 hours with 863 (+) or without (-) 100 µM L-homopropargylglycine (HPG) additions at time zero Dye or 864 pigment names shown at top of images Left: Blue channel shows DAPI-stained cells 865 Left Center: green channel show fluorescence signal of the azide-containing CR-110 fluorophore 866 used to detect HPG Right Center: Red channel shows the autofluorescence from phytoplankton 38 Downloaded from http://aem.asm.org/ on May 14, 2021 at CALIFORNIA INST OF TECHNOLOGY 845 pigments Right: merged image of all three channels The exposure times were standardized 868 within each channel to enable quantitative comparison of fluorescent signals 869 870 Figure Quantitative comparison of fluorescence signal of the azide-containing CR-110 871 fluorophore used to detect HPG 872 (i.e., green fluorescence intensity) in (A) Micromonas pusilla, (B) Ostreococcus sp., and 873 (C) Synechococcus sp cells across time points for positive (blue data points) and negative (pink 874 data points) L-homopropargylglycine (HPG) treatments Outliers are indicated by grey data 875 points HPG positive and negative treatments were significantly different across all time points 876 for all taxa (p-value