Membrane organization plays an important role in signaling, transport, and defense. In eukaryotes, the stability, organization, and function of membrane proteins are influenced by certain lipids and sterols, such as cholesterol. Bacteria lack cholesterol, but carotenoids and hopanoids are predicted to play a similar role in modulating membrane properties. We have previously shown that the loss of carotenoids in the plant-associated bacteria Pantoea sp. YR343 results in changes to membrane biophysical properties and leads to physiological changes, including increased sensitivity to reactive oxygen species, reduced indole-3-acetic acid secretion, reduced biofilm and pellicle formation, and reduced plant colonization. Here, using whole cell and membrane proteomics, we show that the deletion of carotenoid production in Pantoea sp. YR343 results in altered membrane protein distribution and abundance. Moreover, we observe significant differences in the protein composition of detergent-resistant membrane fractions from wildtype and mutant cells, consistent with the prediction that carotenoids play a role in organizing membrane microdomains. These data provide new insights into the function of carotenoids in bacterial membrane organization and identify cellular functions that are affected by the loss of carotenoids.
Biological membranes serve as a central scaffold for cellular machinery that regulate key physiological functions including signaling, defense, metabolism, and molecular transport1,2. Indeed, genes encoding membrane proteins account for 20–30% of the entire genetic complement of a bacterial cell3,4. These membrane proteins are important for processes such as cell motility, chemotaxis, cyclic dimeric guanosine monophosphate (c di-GMP) signaling, virulence, multidrug efflux, and outer membrane biogenesis5,6,7. Bacterial outer membrane proteins are also directly involved in bacterial acclimatization by monitoring and responding to environmental cues.
Apart from proteins and lipids, some membranes contain carotenoids. These compounds have been implicated in photoprotection, imparting coloration to plants, animals and bacteria, and used as a chromophore in photosynthesis8,9,10,11. In the photosynthetic apparatus of plants, algae, and bacteria, carotenoids are found in the light-harvesting pigment-protein complexes12. In bacterial membranes, carotenoids have been shown to reinforce membranes and modulate membrane thickness and fluidity13,14,15,16,17. These properties are essential for many key molecular processes, such as signal transduction, that involve the movement of proteins in the membrane. Therefore, changes in the structure and dynamics of a membrane due to carotenoids can further affect cellular events occurring at the membrane.
The existence of membrane microdomains has traditionally been associated with eukaryotic membranes, but recent studies have shown that some bacteria, such as Bacillus subtilis, can form functional membrane microdomains18. The formation of these microdomains in prokaryotes is thought to involve sterol analogs, such as hopanoids and carotenoids18,19. Eukaryotic membrane microdomains, sometimes called lipid rafts, are characterized by the presence of cholesterol and flotillin18,19,20. The activity of flotillin in eukaryotes is critical for the functioning of lipid raft-associated cellular processes, such as membrane trafficking and cell polarization21,22,23. Homologs of flotillin have also been identified in prokaryotes24,25,26 and these prokaryotic flotillins also appear to organize the membrane into domains, enabling protein interactions and oligomerization19,25,27. This lateral membrane organization and sub-compartmentalization is critical for efficient membrane function28.
We have shown previously that the loss of carotenoids in the plant-associated bacteria Pantoea sp. YR343 results in cells that are more susceptible to oxidative damage, but also display defects in plant root colonization, biofilm formation, and in indole-3-acetic acid secretion29. Lipid profiling of this carotenoid-deficient mutant (generated by deleting the crtB gene encoding phytoene synthase) demonstrated that the mutant strain displays an increase in phosphatidylethanolamine and unsaturated fatty acids when compared to wildtype cells17. Moreover, these differences in lipid profiles also translate to differences in membrane fluidity between the wildtype and the ΔcrtB mutant17. From these studies, it is evident that the loss of carotenoids from the membrane leads to changes in membrane properties and organization, thereby influencing cellular functions. In this paper, we examine the proteomic profile of ΔcrtB mutant cells compared to wildtype to better understand the role of carotenoids in membrane organization. To this end, we compare proteomic profiles of whole cells, membrane fractions, and the distribution of membrane proteins in detergent-resistant membrane (DRM) and detergent-sensitive membrane (DSM) fractions.
Results and discussion
Identification and quantification of protein abundances in Pantoea sp. YR343 and ΔcrtB whole cells, membrane pellet, detergent resistant membranes (DRM) and detergent sensitive membrane (DSM) samples
Because we observed changes in the membrane lipid profiles and membrane fluidity in Pantoea sp. YR343 cells lacking carotenoids17, we wanted to determine how the loss of carotenoids affected the membrane proteome. To this end, whole cell, membrane pellet (MP), DRM, and DSM samples from Pantoea sp. YR343 and ΔcrtB strains harvested during stationary phase were used for proteome characterization. Equal amounts of protein from each fraction were analyzed by proteomics, although we measured reduced protein concentrations from the mutant membrane, DRM, and DSM fractions (see Methods). Overall, an average of 2,153 and 2046 proteins were identified for wildtype and ΔcrtB whole cells, with an average proteome coverage of 44% and 42% respectively. For the membrane fractions investigated, 1,363 (28%) and 1,270 (26%) proteins were identified for wildtype and ΔcrtB MP samples, 1,311 (27%) and 1,196 (24%) proteins were identified for the wildtype and ΔcrtB DRM samples and 645 (13%) and 653 (13%) proteins were identified for wildtype and ΔcrtB DSM samples (Fig. 1a). A Venn diagram comparing the proteins identified in four different conditions is shown in Fig. 1b, illustrating the intersections between proteomes. A total of 592 and 545 proteins were found in all four wildtype and ΔcrtB mutant samples, respectively.
Principle component analysis (PCA) (Fig. 1c) of the proteome data indicated distinct groupings for proteins isolated from the whole cell, membrane pellet, and DSM fractions in each of the wildtype and ΔcrtB biological replicates (n = 3), indicating reproducibility between replicates as well as similarity between the two strains (wildtype and ΔcrtB mutant). In contrast, a scattered distribution was observed for the wildtype and ΔcrtB DRM samples (Fig. 1c). Major variance was observed in PC1 (discrete grouping of samples) with DSM samples and in PC2 (major variance) across the remaining factors. Sterols are vital components in the formation of membrane microdomains19 and proteins that localize to lipid rafts or microdomains are typically found in the detergent-resistant membrane fractions18,30. Thus, the absence of carotenoids may affect the formation, stability, or recruitment of proteins to microdomains, resulting in the observed differences between the wildtype and ΔcrtB mutant DRM fractions. Proper functioning of proteins is regulated by membrane bilayer properties such as lipid curvature, bilayer thickness, and elastic properties provided by sterols31. Molecular dynamics simulations of the carotenoid zeaxanthin with 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) bilayers have shown that carotenoids influence the physical properties of bilayers32. Unlike cholesterol which can only span one leaflet of the lipid bilayer, a C40 carotenoid can span both leaflets14. A recent study has shown that zeaxanthin can trigger the bilayer to become thinner through the process of interdigitation or compression16. It has been well established that bilayer thickness, which is modulated by phospholipid chain composition and sterol content, is a critical factor for the proper insertion and function of membrane proteins33,34. Thus, the differences in protein composition and abundance detected in the ΔcrtB mutant could be due to changes in membrane thickness as a result of the loss of carotenoids.
To test for the enrichment of membrane proteins in each fraction, we used TMHMM software, a membrane protein topology prediction method, in order to identify proteins containing at least one transmembrane helix, with a maximum number of transmembrane helices detected at 17. Approximately 24% of the proteome of Pantoea sp. YR343 consists of proteins with at least one transmembrane helix. As shown in Fig. 2, the largest enrichment of proteins with transmembrane helices for this study was found in the wildtype and ΔcrtB-DSM samples. In general, the proportion of membrane proteins to other cellular proteins is low and the limitations of solubility and separation may limit their detection and identification35. While the proportion of proteins with transmembrane helices was similar between the wildtype and mutant in the whole cell, membrane fraction, and DSM fractions, we observed that the ΔcrtB DRM fraction had fewer proteins with transmembrane helices compared to the wildtype DRM fraction (Fig. 2). This result is consistent with the idea that changes in membrane thickness or microdomain organization due to loss of carotenoids influences protein recruitment or localization, as mentioned previously. It should be noted that this analysis cannot identify membrane-associated proteins that might also be influenced by the loss of carotenoids. In addition, membrane microdomains are known to be transient structures and their identification depends on the state of cellular activity36. For this reason, it is possible that the observed proteome differences between wildtype and mutant cells are due to physiological differences between the cultures, even though care was taken to harvest both cultures during stationary phase.
Next, hierarchical clustering was used to assess the differences in abundance of all proteins identified in the four fractions for wildtype and ΔcrtB cells (Fig. 3). From this analysis, we found that each sample fraction (whole cell, membrane, DRM, or DSM) clustered together. A Student’s t-test (paired t-test) was performed and a p value cutoff of ≤ 0.05 and a fold change (FC) of ≥ 2 was used to identify proteins with relative abundances that significantly differed between the wildtype and ΔcrtB samples. In total, 240, 134, 297 and 71 proteins were differentially abundant between wildtype and ΔcrtB cells for the whole cell, MP, DRM and DSM fractions, respectively (Fig. 4). Out of these, 188 (in whole cell), 111 (in MP), 211 (in DRM) and 44 (in DSM), were significantly less abundant in the ΔcrtB mutant in comparison to the wildtype, In total, 21 proteins were found to be differentially abundant across all four fractions in both wild type and ΔcrtB cells. The observed phenotypes found in the ΔcrtB mutant may be a result of the changes in abundance or distribution of these identified proteins.
Cluster of orthologous groups (COG) analysis
To identify biological processes related to the differentially abundant proteins, functional classification of significant proteins was carried out using the COG database37. Significant proteins were grouped into 21 functional classes according to COG classification. Proteins belonging to transcription (K) and carbohydrate transport and metabolism (G) categories were abundant in whole cell samples, whereas cell wall/membrane/envelope biogenesis (M) proteins were abundant in membrane pellet, DRM, and DSM fractions (Fig. 5).
Gene ontology (GO) enrichment analysis
To gain a deeper understanding of overall changes in protein abundance and distribution between the wildtype and ΔcrtB mutant, functional in silico classification of proteins was achieved via GO analysis using the BLAST2GO tool38. All of the proteins that were differentially abundant (p value ≤ 0.05 and a FC ≥ 2) based on the proteomic analyses were organized by GO terms to determine which biological processes were affected by the loss of carotenoids (Table 1).
In the whole cell pairwise comparisons, proteins belonging to lipid biosynthesis (GO:0008610), lipid metabolism (GO: 0006629) and oligosaccharide metabolism (GO:0009311) were less abundant in the ΔcrtB mutant. Glycerophospholipids serve as the structural component of biological membranes and their alteration can affect physiology and adaptation39. Previously, we reported that the ΔcrtB mutant shows a modest increase in phosphatidylethanolamine (PE) head groups and unsaturated fatty acids when compared to wild type cells17. This observation could be the consequence of down regulation of lysophospholipase (PMI39_02976), which is important for glycerophospholipid metabolism40, in the ΔcrtB mutant. We also observed that a regulator of protease activity HflC, stomatin/ prohibitin superfamily-ybbK (2,511,379,369) appeared less abundant in the ΔcrtB mutant, although the difference did not meet the criteria to be statistically significant (p value = 0.04 but fold change = 1.4). YbbK, encoded by PMI39_01287, belongs to the reggie (flotillin) superfamily, which includes eukaryotic flotillins and the bacterial homolog FlotP which was identified in Bacillus anthracis membrane microdomains25,41,42. The apparent reduction of YbbK in the carotenoid mutant is consistent with changes to microdomain organization, which may affect cellular functions such as protein signaling and transport. It is possible that the observed reduction of indole-3-acetic acid secretion and the decreased pellicle and biofilm formation observed in the ΔcrtB mutant results from changes in membrane domain architecture.
Other proteins such as tyrosine kinase (2,511,379,927), cardiolipin synthase (2,511,380,815) and phytoene desaturase (2,511,381,490) were also less abundant in the ΔcrtB mutant whole cell fraction. Notably, four undecaprenyl phosphate proteins (Locus tags- PMI39_03112, PMI39_03113, PMI39_03114, PMI39_03115) in an operon involved in amino sugar and nucleotide sugar metabolism were less abundant in the ΔcrtB mutant43. Undecaprenyl phosphate is a 55-carbon polyisoprenoid lipid involved in bacterial cell wall biogenesis by functioning as a lipid carrier, trafficking sugar intermediates across the plasma membrane44. There is also growing evidence that polyisoprenoids increase membrane fluidity and ion permeability45,46,47,48. The downregulation of this operon may explain, at least in part, the observed decrease in membrane fluidity in the ΔcrtB mutant17.
In the membrane fractions, several proteins with functional significance at the membrane were less abundant in the ΔcrtB mutant (p ≤ 0.05 and FC ≥ 2). In particular, proteins belonging to envelope (GO:0030313), cell outer membrane (GO:0009279), membrane biogenesis, and cellular homeostasis were downregulated in the ΔcrtB mutant. Homeostasis is important for living organisms to maintain internal stability and it includes iron and metal homeostasis, membrane lipid homeostasis, and pH homeostasis. For example, TonB (PMI39_04701), an outer membrane receptor for ferrienterochelin and colicins, which are important for iron homeostasis49, was less abundant in the ΔcrtB DSM fraction.
Transcriptional analyses of the ΔcrtB mutant
The differences observed in the membrane proteomes between wildtype and the ΔcrtB mutant could be due to many factors, including differences in membrane protein insertion and stability, localization, or abundance. To better understand the basis for the observed differences, we performed transcriptional analyses to examine which gene products are transcriptionally regulated. Differentially expressed transcripts between the wildtype and ΔcrtB mutant samples were identified using KBase tools as described in the methods50. Only 5 transcripts were significantly upregulated (p value ≤ 0.05 and FC ≥ 2), whereas 879 transcripts were significantly downregulated in the ΔcrtB mutant (Supplemental Table 1). Heat maps representing the differential expression profile of wildtype and ΔcrtB mutant are shown in Fig. 6. Detailed comparisons of the protein abundances and transcriptional regulation were performed for selected functional categories.
Cell wall/membrane/envelope biogenesis
Proteins predicted to be involved in cell wall/membrane/envelope biogenesis (270 proteins total, COG category M) in Pantoea sp. YR343 were collected from the JGI IMG database. Table 2 lists all the proteins (p ≤ 0.005 and FC ≥ 1) involved in cell membrane biogenesis that were significantly differentially abundant in at least one fraction (56 total). Among these proteins, six undecaprenyl-phosphate (UDP) proteins belonging to peptidoglycan/lipopolysaccharide biosynthesis were found to be significantly less abundant in the ΔcrtB mutant (PMI39_01550, PMI39_02251, PMI39_03115, PMI39_01848, PMI39_03114, PMI39_04793). UDP gene products are involved in exopolysaccharide secretion, cationic antimicrobial peptide resistance, lipid A biogenesis, and peptidoglycan synthesis44 and most were found in the DRM fractions. Transcript data for two of the UDP proteins, undecaprenyl-phosphate 4-deoxy-4-formamido-L-arabinose transferase (PMI39_03114) and UDP-4-amino-4-deoxy-L-arabinose-oxoglutarate aminotransferase (PMI39_03115), showed downregulation at the transcript level in the ΔcrtB mutant (Table 2). Downregulation of these UDP genes may explain the observed differences in the peptidoglycan layer of the ΔcrtB mutant in comparison to the wildtype17. Another protein, UDP-galactose-lipid carrier transferase (PMI39_01848), has a transmembrane domain and was found to be less abundant in the mutant DRM fraction. The gene encoding this protein is the first gene in a large operon that shows significant similarity to operons involved in EPS biosynthesis in the related plant-associated microbes Erwinia amylovora and Pantoea stewartii51,52. It is possible that the reduction in this protein decreases EPS production in the ΔcrtB mutant, which may contribute to the defects associated with biofilm formation and plant colonization.
Outer membrane proteins (OMP) are important for transport of metabolites and toxins, membrane biogenesis, and for bacterial resistance. The folding and insertion of several OMPs are carried out by BamA along with three lipoproteins: BamB, BamC, and BamE forming the BAM machine (beta-barrel assembly)53,54. Lipoproteins are peripherally anchored membrane proteins involved in cell division, chemotaxis, signal transduction and envelope stability55,56,57. Among the 4 Bam proteins, BamA (PMI39_03681) and BamB (PMI39_03586) were found to be less abundant in the ΔcrtB mutant (Table 2). BamB was identified in both the membrane pellet and the DRM fraction. Studies have shown that BamB contains WD40 repeating units, thereby functioning as a scaffold protein in large multi-protein complexes58. It was also shown that the Bam complex increases the efficiency of folding of membrane proteins such as OmpA and EspP59. We also found that the Skp protein (PMI39_3680) was more abundant in the ΔcrtB mutant DSM fraction. Skp is a multivalent periplasmic chaperone preventing misfolding and aggregation of OMPs, such as OmpA, during transit from the inner to the outer membranes60. The changes in membrane fluidity, lipid content, and the lack of carotenoids in the ΔcrtB mutant may influence assembly of the Bam complex at the outer membrane, leading to misfolding of other OMPs, but not affect localization or function of the periplasmic Skp protein. Thus, the increased abundance of Skp in the ΔcrtB mutant may be a compensatory mechanism to maintain proper folding of proteins to protect the integrity of the cell.
Cell motility (N)
Beyond the defects in IAA secretion, biofilm formation, and root colonization previously reported29, we also observed that the ΔcrtB mutant appeared to be less motile than wildtype on swimming motility plates (Fig. 7a). To further characterize this defect, we compared motility patterns of wildtype and mutant cells by microscopy. We found that the average mean speed was 3.5 μm/s for the ΔcrtB mutant which was significantly reduced compared to 4.9 μm/s for wild type cells (Fig. 7b). Moreover, flagella staining and quantification using ImageJ indicated that the ΔcrtB mutant had significantly shorter flagella when compared to wildtype (Fig. 7c). The average flagellar length for the wildtype cells were 7.4 μm whereas the ΔcrtB mutant flagella measured 2.8 μm (Fig. 7d). To help explain the motility defect, we examined the 92 proteins predicted to be involved in cell motility, of which 18 were differentially abundant in the mutant compared to wildtype (Table 3). Among the 28 proteins that form the flagellar complex61, only 3 proteins: flagellar FliL (PMI39_02182) and two flagellar hook-associated protein 2 (PMI39_02605 and PMI39_02159) were significantly less abundant in the ΔcrtB mutant and only one of these, PMI39_02605, was significantly downregulated based on the transcriptomic data (Table 3). In Salmonella and E. coli, it was shown that FliL interacts closely with stators and the MS ring, ensuring delivery of higher torque, leading to increased motility62. In the absence of the FliL protein, single motors have been shown to rotate at lower speeds62,63. It is possible that the changes in membrane lipid composition and/or organization affect assembly or function of the flagellar motor apparatus, leading to the observed motility defects.
In addition to the proteins involved in the flagellar motor apparatus, we also found differences in protein abundance and transcriptional regulation of several methyl-accepting chemotaxis proteins (MCP) in the ΔcrtB mutant compared to wild type (Table 3). MCPs undergo reversible methylation in response to changes in the concentration of attractants or repellents in their environment64. Interestingly, two MCP proteins, encoded by PMI39_02297 and PMI39_01148, were found to be more abundant in the ΔcrtB whole cell fraction, whereas the MCP encoded by PMI39_02163 was found to be more abundant in the ΔcrtB DRM fraction. This increased abundance in the ΔcrtB mutant did not appear to be due to transcriptional upregulation (Table 3). Additional experiments are needed to distinguish whether these proteins are differentially localized, more stable, or perhaps more easily extracted from the ΔcrtB mutant.
Lipid transport and metabolism (I)
In Pantoea sp. YR343, 150 proteins are found in the lipid transport and metabolism COG category I, of which 26 proteins were found to be significantly abundant in at least one fraction (Table 4). Two choline dehydrogenases (encoded by PMI39_02890 and PMI39_00318) were significantly more abundant in all or most fractions of the ΔcrtB mutant. Surprisingly, however, these genes were transcriptionally downregulated (Table 4). Choline dehydrogenase catalyzes the first step in glycine betaine synthesis to produce the final compound betaine, an effective osmoprotectant65,66. It is possible that the lipid composition or membrane organization in the carotenoid mutant promotes choline dehydrogenase protein stability or, alternatively, promotes its efficient extraction. Other proteins such as lysophospholipase (PMI39_01261, PMI39_04916) and NAD(P) dependent dehydrogenasese (PMI39_04227, PMI39_04693, PMI39_04133) were also more abundant in the ΔcrtB mutant.
Cyclopropane-fatty-acyl-phospholipid synthase (PMI39_04767) and predicted lipid carrier protein YhbT, containing a SCP2 domain (PMI39_01503), were less abundant in the ΔcrtB DRM fractions. The physiological role of YhbT has not yet been identified, but it contains the sterol carrier protein 2 domain (SCP2), suggesting a role in lipid and sterol transport67.
Signal transduction mechanism (T)
Bacterial signal transduction networks regulate sensing and responses to environmental and intracellular parameters. In Pantoea sp. YR343, 235 proteins are predicted to be involved in signal transduction based on the COG category T (Table 5). Among these proteins, only 32 proteins were found to be differentially abundant in at least one fraction. In our data, we observed an abundance of OmpR family proteins68, including the phosphate regulon response regulator OmpR (PMI39_03347) in the ΔcrtB DRM fractions. OmpR, along with its histidine kinase partner EnvZ, are important for osmotic tolerance, virulence and motility in Acinetobacter baumanii69,70,71. In E. coli, OmpR and EnvZ regulate OmpF and OmpC proteins that are essential for responding to environmental signals.
The deletion of carotenoids in the ΔcrtB mutant leads not only to increased sensitivity to oxidative stress, but also to defects in IAA secretion, pellicle and biofilm formation, motility, and root colonization. In addition to the differences in lipid composition and membrane fluidity previously reported17, the loss of carotenoids also results in changes to the proteome of the ΔcrtB mutant compared to wildtype. We report a detailed proteome analysis comparing the wildtype and ΔcrtB mutant focusing on changes in membrane protein distribution and abundance. Consistent with the observed phenotypes in the mutant, we found that several classes of proteins belonging to membrane biogenesis, signal transduction, and cell motility were affected in the ΔcrtB mutant. The most dramatic changes to the proteome were observed in the DRM fraction, which is consistent with the idea that the DRM fraction represents membrane microdomains and the presence of cholesterol (in eukaryotes) or carotenoids and hopanoids (in prokaryotes) is vital to the organization of these domains72. In the absence of carotenoids, these microdomains may be unstable or have a change in membrane thickness which, in turn, affects protein insertion, stability, or recruitment. These data underscore the importance of bacterial membrane organization for cellular functions such as secretion, motility, and signaling.
Bacterial strains and growth conditions
Pantoea sp. YR343 and ΔcrtB cells were grown in Luria–Bertani broth (per 1 L, 10 g Bacto-tryptone, 10 g NaCl, 5 g yeast extract) medium at 28 °C with shaking to OD600 of 1 (stationary phase). The ΔcrtB mutant was constructed as described29.
Isolation of whole cell, crude membrane fraction, detergent resistant membrane and detergent sensitive membrane fractions of Pantoea sp. YR343 and ΔcrtB cells
To isolate different cell fractions, we used a modified version of the method described by Lopez18. Briefly, cells were grown in 500 mL of LB media overnight at 28 °C with vigorous shaking. Cells were collected by centrifugation (3000g for 12mins) and washed thrice in phosphate buffered saline (PBS). Cells were collected at this stage for whole cell proteomic analysis and stored at − 20 °C. Next, Buffer H (20 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES [pH 8], 20 mM NaCl, 1 mM dithiothreitol [DTT], 1 mM phenylmethylsulfonyl fluoride [PMSF]), lysozyme (1 mg/mL), PMSF (100 μM), and DNase I was added to the washed cells. Cells were disrupted using French press followed by a short centrifugation to eliminate cell debris. The membrane fraction was precipitated by ultracentrifugation (100,000×g for 1 h at 4 °C). The resulting cell pellet was resuspended in Buffer H with 10% glycerol. At this stage, a fraction of the membrane pellet was collected.
To isolate DRM and DSM fractions, the membrane pellet was incubated for 30 min at 4 °C with lysis and separation buffer (CelLytic MEM protein extraction kit from Sigma Aldrich). After incubation, the membrane pellet was mixed 1:1 with 80% sucrose and carefully overlaid with 20% sucrose. Using a swinging bucket rotor, separation was carried out at 100,000×g at 4 °C for 16 h. The DRM and DSM fractions were collected and stored at − 20 °C for proteomic analysis.
Protein extraction and digestion
Cell pellets were suspended in sodium dodecyl sulfate (SDS) lysis buffer (2% in 100 mM of NH4HCO3, 10 mM DTT). Samples were physically disrupted by bead beating (0.15 mm) at 8,000 rpm for 5 min. Crude lysates were boiled for 5 min at 90 °C. Cysteines were blocked to avoid disulfide bridge reformation by adjusting each sample to 30 mM IAA and incubating in the dark for 15 min at room temperature. Proteins were precipitated using a chloroform/methanol/water extraction. Dried protein pellets were resuspended in 2% sodium deoxycholate (SDC) (100 mM NH4HCO3) and protein amounts were estimated by performing a BCA assay (Pierce Biotechnology). In general, membrane fractions from the mutant showed a reduced protein concentration compared to wild type (MP: 3.7 mg/mL (wt) and 2.8 mg/mL (ΔcrtB); DSM: 858 µg/mL (wt) and 845 µg/mL (ΔcrtB); DRM: 165 µg/mL (wt) and 138 µg/mL (ΔcrtB)). For each sample, an aliquot of approximately 500 µg of protein was digested via two aliquots of sequencing-grade trypsin (Promega, 1:75 [w:w]) at two different sample dilutions, (overnight) followed by incubating 3 h at 37 °C. The peptide mixture was adjusted to 0.5% formaldehyde (FA) to precipitate SDC. Hydrated ethyl acetate was added to each sample at a 1:1 [v:v] ratio three times to effectively remove SDC. Samples were then placed in a SpeedVac Concentrator (Thermo Fischer Scientific) to remove ethyl acetate and further concentrate the sample. The peptide-enriched flow through was quantified using the BCA assay, desalted on RP-C18 stage tips (Pierce Biotechnology) and then stored at − 80 °C prior to LC–MS/MS analysis.
All samples were analyzed on a Q Exactive Plus mass spectrometer (Thermo Fisher Scientific) coupled with a Proxeon EASY-nLC 1200 liquid chromatography (LC) pump (Thermo Fisher Scientific) as previously described73. In brief, peptide mixtures were separated on a 75 μm inner diameter microcapillary column packed with 30 cm of Kinetex C18 resin (1.7 μm, 100 Å, Phenomenex). For each peptide mixture, a 2 μg aliquot was loaded in buffer A (0.1% formic acid, 2% acetonitrile) and eluted with a linear 150 min gradient of 2–20% of buffer B (0.1% formic acid, 80% acetonitrile), followed by an increase in buffer B to 30% for 10 min, another increase to 50% buffer for 10 min and concluding with a 10 min wash at 98% buffer A. The flow rate was kept at 200 nL/min. Mass spectra data was acquired with the Thermo Xcalibur software version 2.2, and a topN method where N could be up to 15 was employed for data-dependent acquisition73.
Peptide identification and protein inference
MS raw data files were searched against the Pantoea sp. YR343 FASTA database to which common contaminate proteins had been added. A decoy database, consisting of the reversed sequences of the target database, was appended to discern the false-discovery rate (FDR) at the spectral level. For standard database searching, the peptide fragmentation spectra (MS/MS) were analyzed by the Crux pipeline v3.074. The MS/MS were searched using the Tide algorithm75 and was configured to derive fully-tryptic peptides using default settings except for the following parameters: allowed clip nterm-methionine, a precursor mass tolerance of 10 parts per million (ppm), a static modification on cysteines (iodoacetamide; + 57.0214 Da), and dynamic modifications on methionine (oxidation; 15.9949). The results were processed by Percolator76 to estimate q values. Peptide spectrum matches (PSMs) and peptides were considered identified at a q value < 0.01. Across the entire experimental dataset, proteins were required to have at least 2 distinct peptide sequences and 2 minimum spectra per protein.
For label-free quantification, MS1-level precursor intensities were derived from MOFF77 using the following parameters: 10 ppm mass tolerance, retention time window for extracted ion chromatogram was 3 min, time window to get the apex for MS/MS precursor was 30 s. Protein intensity-based values, which were calculated by summing together quantified peptides, normalized by dividing by protein length and then LOESS and median central tendency procedures were performed on log2-transformed data using the freely available software Perseus (. Missing values were replaced by random numbers drawn from a normal distribution (width = 0.3 and downshift = 2.8).
Statistical analysis for differential abundances
For this study, we performed ANOVA with post-hoc Tukey’s test to identify differential protein abundances across the wildtype Pantoea sp. YR343 dataset comparisons or ΔcrtB mutant dataset comparisons and protein abundances were considered to have a significant change in abundance for p values < 0.05 and at least one absolute value of log2 fold-change differences > 1. To identify differential protein abundances between wildtype and ΔcrtB fractions, we performed a Student’s t-test for the pairwise comparisons. A protein was categorized as having a significant abundance difference if it passed a significance threshold requiring a p value < 0.05 and absolute value of log2 fold-change difference > 1. Hierarchical clustering (one-way; Fast Ward method) was performed to identify differential abundance patterns.
Gene ontology enrichment
Gene ontology (GO) term annotation was performed using Blast2GO38 with a blastp E-value hit filter of 1 × 10–5, an annotation cutoff value of 55 and a GO weight of 5. Using the Cytoscape79 plugin ClueGO80, observed GO biological processes were subjected to the right-sided hypergeometric enrichment test at medium network specificity selection and p-value correction was performed using the Holm–Bonferroni step-down method81. For each cluster, we required a minimum of 3 and a maximum of 8 selected GO tree levels, and each cluster was set to include a minimum of 3- 4% of genes associated with each term. The GO terms at adjusted p < 0.05 were considered significantly enriched.
RNA extraction, sequencing and analysis
Wild type and ΔcrtB cells were grown to stationary phase (OD600 = 1). RNA was extracted using RNeasy mini kit (QIAGEN, Valencia, CA) following manufacturer’s instructions and quantified using Nanodrop (Thermo Scientific). Sequencing was carried out by GENEWIZ Next Generation Sequencing Services. Transcript analysis was carried out using KBase50 (https://kbase.us/). KBase and its tools were used to generate the sample set, align and assemble reads to the genome, and identify differentially abundant genes between wild type and ΔcrtB.
To compare the swimming motility function of Pantoea sp. YR343 and ΔcrtB cells, cells were grown overnight with shaking (250 rpm) in LB medium at 28 °C. Swimming motility was examined on LB containing 0.3% w/v agar. A 5 μL aliquot of cells were inoculated in the center of the plate and incubated at 28 °C for 18 h. Live cell imaging of bacterial motility was measured using a Nikon Eclipse Ti-U inverted microscope. Cells from motility plates were inoculated in R2A media overnight at 28 °C with shaking (250 rpm). Next day, cells were reinoculated in R2A media and grown to an OD600 of 0.5. A 20 µL aliquot of cells were placed on a coverslip and 10 s videos were captured using NIS-Elements imaging software. Trajectories and velocities (pixels/frame) of Pantoea sp. YR343 and ΔcrtB cells were calculated with the “TrackMate” plugin (https://imagej.net/TrackMate).
Flagella staining was carried out using a protocol adapted from Turner et al.82. Briefly, Pantoea sp. YR343 and ΔcrtB cells from swimming plates were inoculated overnight in R2A medium at 28 °C with shaking (250 rpm). Next day, cells were diluted 1:10 in fresh R2A medium and grown to OD600 of 0.5. Motility of the culture was confirmed using a confocal microscope. Cells were collected by centrifugation (2000*g, 3 min) and washed three times in buffer (0.01 M KPO4, 0.067 M NaCl, 10−4 M (Ethylenediaminetetraacetic acid (EDTA) [pH 7.0]). Alexa Fluor 594 carboxylic acid succinimidyl ester (ThermoFisher Scientific) was added to the concentrated bacterial suspension and incubated in the dark for 1 h. Cells were then washed three times with buffer containing Brij 35 (10−4%) and 0.4% glucose. Concentrated cells were then placed on an agarose pad (1% agarose in phosphate buffered saline) and imaged using a Zeiss LSM 710 confocal microscope. Flagellar length of 30 wildtype and ΔcrtB cells were measured using ImageJ. The data are represented as the mean flagellar length in µm ± SE calculated using unpaired t-test.
All proteomics mass spectrometry data collected in this study was deposited at the ProteomeXchange Consortium via the MASSIVE repository under the project identifier MSV000085068.
Gennis, R. B. Biomembranes: Molecular Structure and Function (Springer, Berlin, 2013).
Sunshine, H. & Iruela-Arispe, M. L. Membrane lipids and cell signaling. Curr. Opin. Lipidol. 28, 408–413. https://doi.org/10.1097/mol.0000000000000443 (2017).
Schlegel, S. et al. Revolutionizing membrane protein overexpression in bacteria. Microb. Biotechnol. 3, 403–411. https://doi.org/10.1111/j.1751-7915.2009.00148.x (2010).
Wallin, E. & von Heijne, G. Genome-wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms. Protein Sci. 7, 1029–1038. https://doi.org/10.1002/pro.5560070420 (1998).
Dirienzo, J. M., Nakamura, K. & Inouye, M. The outer membrane proteins of Gram-negative bacteria: biosynthesis, assembly, and functions. Annu. Rev. Biochem. 47, 481–532 (1978).
Koebnik, R., Locher, K. P. & Van Gelder, P. Structure and function of bacterial outer membrane proteins: barrels in a nutshell. Mol. Microbiol. 37, 239–253 (2000).
Koronakis, V., Sharff, A., Koronakis, E., Luisi, B. & Hughes, C. Crystal structure of the bacterial membrane protein TolC central to multidrug efflux and protein export. Nature 405, 914 (2000).
Vershinin, A. Biological functions of carotenoids-diversity and evolution. BioFactors 10, 99–104 (1999).
Olson, J. A. & Krinsky, N. I. Introduction: the colorful, fascinating world of the carotenoids: important physiologic modulators. FASEB J. 9, 1547–1550 (1995).
Kirti, K., Amita, S., Priti, S., Mukesh Kumar, A. & Jyoti, S. Colorful world of microbes: carotenoids and their applications. Adv. Biol. https://doi.org/10.1155/2014/837891 (2014).
Boulay, C., Abasova, L., Six, C., Vass, I. & Kirilovsky, D. Occurrence and function of the orange carotenoid protein in photoprotective mechanisms in various cyanobacteria. Biochim. Biophys. Acta (BBA) Bioenerg. 1777, 1344–1354. https://doi.org/10.1016/j.bbabio.2008.07.002 (2008).
Hashimoto, H., Uragami, C. & Cogdell, R. J. Carotenoids and photosynthesis. Subcell Biochem. 79, 111–139. https://doi.org/10.1007/978-3-319-39126-7_4 (2016).
Mishra, N. N. et al. Carotenoid-related alteration of cell membrane fluidity impacts Staphylococcus aureus susceptibility to host defense peptides. Antimicrob. Agents Chemother. 55, 526–531. https://doi.org/10.1128/aac.00680-10 (2011).
Havaux, M. Carotenoids as membrane stabilizer in chloroplasts. Trends Plant Sci. 3, 147–151 (1998).
Gabrielska, J. & Gruszecki, W. I. Zeaxanthin (dihydroxy-β-carotene) but not β-carotene rigidifies lipid membranes: a 1 H-NMR study of carotenoid-egg phosphatidylcholine liposomes. Biochim. Biophys. Acta (BBA) Biomembr. 1285, 167–174 (1996).
Johnson, Q. R., Mostofian, B., Gomez, G. F., Smith, J. C. & Cheng, X. Effects of carotenoids on lipid bilayers. Phys. Chem. Chem. Phys. 20, 3795–3804 (2018).
Vijaya Kumar, S. et al. Loss of carotenoids from membranes of Pantoea sp. YR343 results in altered lipid composition and changes in membrane biophysical properties. Biochim. Biophys. Acta Biomembr. 1861, 1338–1345. https://doi.org/10.1016/j.bbamem.2019.05.009 (2019).
Bramkamp, M. & Lopez, D. Exploring the existence of lipid rafts in bacteria. Microbiol. Mol. Biol. Rev. 79, 81–100. https://doi.org/10.1128/mmbr.00036-14 (2015).
Lopez, D. & Koch, G. Exploring functional membrane microdomains in bacteria: an overview. Curr. Opin. Microbiol. 36, 76–84 (2017).
López, D. & Kolter, R. Functional microdomains in bacterial membranes. Gene Dev. 24, 1893–1902 (2010).
Bach, J. N. & Bramkamp, M. Flotillins functionally organize the bacterial membrane. Mol. Microbiol. 88, 1205–1217. https://doi.org/10.1111/mmi.12252 (2013).
Dermine, J. F. et al. Flotillin-1-enriched lipid raft domains accumulate on maturing phagosomes. J. Biol. Chem. 276, 18507–18512. https://doi.org/10.1074/jbc.M101113200 (2001).
Solomon, S. et al. The lipid raft microdomain-associated protein reggie-1/flotillin-2 is expressed in human B cells and localized at the plasma membrane and centrosome in PBMCs. Immunobiology 205, 108–119. https://doi.org/10.1078/0171-2985-00114 (2002).
Lopez, D. & Kolter, R. Functional microdomains in bacterial membranes. Gene Dev. 24, 1893–1902. https://doi.org/10.1101/gad.1945010 (2010).
Somani, V. K., Aggarwal, S., Singh, D., Prasad, T. & Bhatnagar, R. Identification of novel raft marker protein, FlotP in Bacillus anthracis. Front. Microbiol. 7, 169 (2016).
Bach, J. N. & Bramkamp, M. Dissecting the molecular properties of prokaryotic flotillins. PLoS ONE 10, e0116750 (2015).
Schneider, J., Mielich-Süss, B., Böhme, R. & Lopez, D. In vivo characterization of the scaffold activity of flotillin on the membrane kinase KinC of Bacillus subtilis. Microbiology 161, 1871–1887 (2015).
Simons, K. & Sampaio, J. L. Membrane organization and lipid rafts. Csh. Perspect. Biol. https://doi.org/10.1101/cshperspect.a004697 (2011).
Bible, A. N. et al. A carotenoid-deficient mutant in Pantoea sp. YR343, a bacteria isolated from the rhizosphere of Populus deltoides, is defective in root colonization. Front. Microbiol. 7, 491. https://doi.org/10.3389/fmicb.2016.00491 (2016).
Dubois, L., Ronquist, K. K., Ek, B., Ronquist, G. & Larsson, A. Proteomic profiling of detergent resistant membranes (lipid rafts) of prostasomes. Mol. Cell Proteomics 14, 3015–3022. https://doi.org/10.1074/mcp.M114.047530 (2015).
Andersen, O. S., Roger, E. & Koeppe, I. Bilayer thickness and membrane protein function: an energetic perspective. Annu. Rev. Biophys. Biomol. Struct. 36, 107–130. https://doi.org/10.1146/annurev.biophys.36.040306.132643 (2007).
Cerezo, J., Zúñiga, J., Bastida, A., Requena, A. & Ceron-Carrasco, J. P. Conformational changes of β-carotene and zeaxanthin immersed in a model membrane through atomistic molecular dynamics simulations. Phys. Chem. Chem. Phys. 15, 6527–6538 (2013).
Li, L. B., Vorobyov, I. & Allen, T. W. The role of membrane thickness in charged protein–lipid interactions. Biochim. Biophys. Acta (BBA) Biomembr. 1818, 135–145 (2012).
Andersen, O. S. & Koeppe, R. E. II. Bilayer thickness and membrane protein function: an energetic perspective. Annu. Rev. Biophys. Biomol. Struct. 36, 107–130. https://doi.org/10.1146/annurev.biophys.36.040306.132643 (2007).
Tan, S., Tan, H. T. & Chung, M. C. Membrane proteins and membrane proteomics. Proteomics 8, 3924–3932 (2008).
Matkó, J. & Szöllősi, J. Membrane Microdomain Signaling 15–46 (Springer, Berlin , 2005).
Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33–36 (2000).
Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676. https://doi.org/10.1093/bioinformatics/bti610 (2005).
Dalebroux, Z. D. Cues from the membrane: bacterial glycerophospholipids. J. Bacteriol. https://doi.org/10.1128/jb.00136-17 (2017).
Istivan, T. S. & Coloe, P. J. Phospholipase A in Gram-negative bacteria and its role in pathogenesis. Microbiology 152, 1263–1274. https://doi.org/10.1099/mic.0.28609-0 (2006).
Hinderhofer, M. et al. Evolution of prokaryotic SPFH proteins. BMC Evol. Biol. 9, 10. https://doi.org/10.1186/1471-2148-9-10 (2009).
Rivera-Milla, E., Stuermer, C. A. & Malaga-Trillo, E. Ancient origin of reggie (flotillin), reggie-like, and other lipid-raft proteins: convergent evolution of the SPFH domain. Cell Mol. Life Sci. 63, 343–357. https://doi.org/10.1007/s00018-005-5434-3 (2006).
Sanyal, S. & Menon, A. K. Flipping lipids: why an’ what’s the reason for?. ACS Chem. Biol. 4, 895–909. https://doi.org/10.1021/cb900163d (2009).
TouzÉ, T. & Mengin-Lecreulx, D. Undecaprenyl phosphate synthesis. EcoSal Plus https://doi.org/10.1128/ecosalplus.184.108.40.206 (2008).
Herrera, C. M., Koutsoudis, M. D., Wang, X. & von Bodman, S. B. Pantoea stewartii subsp. stewartii exhibits surface motility, which is a critical aspect of Stewart’s wilt disease development on maize. Mol. Plant Microbe Interact. 21, 1359–1370. https://doi.org/10.1094/mpmi-21-10-1359 (2008).
Hartley, M. D. & Imperiali, B. At the membrane frontier: a prospectus on the remarkable evolutionary conservation of polyprenols and polyprenyl-phosphates. Arch. Biochem. Biophys. 517, 83–97. https://doi.org/10.1016/j.abb.2011.10.018 (2012).
de Ropp, J. S. & Troy, F. A. 2H NMR investigation of the organization and dynamics of polyisoprenols in membranes. J. Biol. Chem. 260, 15669–15674 (1985).
Janas, T., Chojnacki, T., Swiezewska, E. & Janas, T. The effect of undecaprenol on bilayer lipid membranes. Acta Biochim. Pol. 41, 351–358 (1994).
Noinaj, N., Guillier, M., Barnard, T. J. & Buchanan, S. K. TonB-dependent transporters: regulation, structure, and function. Annu. Rev. Microbiol. 64, 43–60. https://doi.org/10.1146/annurev.micro.112408.134247 (2010).
Arkin, A. P. et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 36, 566 (2018).
Dolph, P. J., Majerczak, D. R. & Coplin, D. L. Characterization of a gene cluster for exopolysaccharide biosynthesis and virulence in Erwinia stewartii. J. Bacteriol. 170, 865–871 (1988).
Geider, K. Exopolysaccharides of Erwinia amylovora: structure, biosynthesis, regulation, role in pathogenicity of. Fire Blight: The Disease and Its Causative Agent, Erwinia amylovora, 117 (2000).
Han, L. et al. Structure of the BAM complex and its implications for biogenesis of outer-membrane proteins. Nat. Struct. Mol. Biol. 23, 192 (2016).
Rigel, N. W. & Silhavy, T. J. Making a beta-barrel: assembly of outer membrane proteins in Gram-negative bacteria. Curr. Opin. Microbiol. 15, 189–193. https://doi.org/10.1016/j.mib.2011.12.007 (2012).
Zückert, W. R. Secretion of bacterial lipoproteins: through the cytoplasmic membrane, the periplasm and beyond. Biochim. Biophys. Acta 1843, 1509–1516. https://doi.org/10.1016/j.bbamcr.2014.04.022 (2014).
Noinaj, N., Fairman, J. W. & Buchanan, S. K. The crystal structure of bamb suggests interactions with BamA and its role within the BAM complex. J. Mol. Biol. 407, 248–260. https://doi.org/10.1016/j.jmb.2011.01.042 (2011).
Malinverni, J. C. & Silhavy, T. J. Assembly of outer membrane β-barrel proteins: the Bam complex. EcoSal Plus. https://doi.org/10.1128/ecosalplus.4.3.8 (2011).
Gunasinghe, S. D. et al. The WD40 protein BamB mediates coupling of BAM complexes into assembly precincts in the bacterial outer membrane. Cell Rep. 23, 2782–2794. https://doi.org/10.1016/j.celrep.2018.04.093 (2018).
Hussain, S. & Bernstein, H. D. The Bam complex catalyzes efficient insertion of bacterial outer membrane proteins into membrane vesicles of variable lipid composition. J. Biol. Chem. 293, 2959–2973. https://doi.org/10.1074/jbc.RA117.000349 (2018).
Schiffrin, B. et al. Skp is a multivalent chaperone of outer membrane proteins. Nat. Struct. Mol. Biol. 23, 786–793. https://doi.org/10.1038/nsmb.3266 (2016).
Bardy, S. L., Ng, S. Y. M. & Jarrell, K. F. Prokaryotic motility structures. Microbiology 149, 295–304. https://doi.org/10.1099/mic.0.25948-0 (2003).
Partridge, J. D., Nieto, V. & Harshey, R. M. A new player at the flagellar motor: FliL controls both motor output and bias. mBio https://doi.org/10.1128/mBio.02367-14 (2015).
Zhu, S., Kumar, A., Kojima, S. & Homma, M. FliL associates with the stator to support torque generation of the sodium-driven polar flagellar motor of Vibrio. Mol. Microbiol. 98, 101–110 (2015).
Kehry, M. & Dahlquist, F. The methyl-accepting chemotaxis proteins of Escherichia coli. Identification of the multiple methylation sites on methyl-accepting chemotaxis protein I. J. Biol. Chem. 257, 10378–10386 (1982).
Wood, J. M. et al. Osmosensing and osmoregulatory compatible solute accumulation by bacteria. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 130, 437–460. https://doi.org/10.1016/S1095-6433(01)00442-1 (2001).
Tsuge, H., Nakano, Y., Onishi, H., Futamura, Y. & Ohashi, K. A novel purification and some properties of rat liver mitochondrial choline dehydrogenase. Biochim. Biophys. Acta 614, 274–284 (1980).
Schroeder, F. et al. Sterol carrier protein-2: new roles in regulating lipid rafts and signaling. Biochim. Biophys. Acta 1771, 700–718. https://doi.org/10.1016/j.bbalip.2007.04.005 (2007).
Lee, A. K., Detweiler, C. S. & Falkow, S. OmpR regulates the two-component system SsrA-SsrB in Salmonella pathogenicity island 2. J. Bacteriol. 182, 771–781 (2000).
Tipton, K. A. & Rather, P. N. An ompR-env two-component system ortholog regulates phase variation, osmotic tolerance, motility, and virulence in Acinetobacter baumannii strain AB5075. J. Bacteriol. https://doi.org/10.1128/JB.00705-16 (2017).
Foo, Y. H., Gao, Y., Zhang, H. & Kenney, L. J. Cytoplasmic sensing by the inner membrane histidine kinase EnvZ. Prog. Biophys. Mol. Biol. 118, 119–129. https://doi.org/10.1016/j.pbiomolbio.2015.04.005 (2015).
Mattison, K. & Kenney, L. J. Phosphorylation alters the interaction of the response regulator OmpR with its sensor kinase EnvZ. J. Biol. Chem. 277, 11143–11148. https://doi.org/10.1074/jbc.M111128200 (2002).
Saenz, J. P. et al. Hopanoids as functional analogues of cholesterol in bacterial membranes. Proc. Natl. Acad. Sci. U. S. A. 112, 11971–11976. https://doi.org/10.1073/pnas.1515607112 (2015).
Villalobos Solis, M. I., Giannone, R. J., Hettich, R. L. & Abraham, P. E. Exploiting the dynamic relationship between peptide separation quality and peptide coisolation in a multiple-peptide matches-per-spectrum approach offers a strategy to optimize bottom-up proteomics throughput and depth. Anal. Chem. 91, 7273–7279. https://doi.org/10.1021/acs.analchem.9b00819 (2019).
McIlwain, S. et al. Crux: rapid open source protein tandem mass spectrometry analysis. J. Proteome Res. 13, 4488–4491. https://doi.org/10.1021/pr500741y (2014).
Diament, B. J. & Noble, W. S. Faster SEQUEST searching for peptide identification from tandem mass spectra. J. Proteome Res. 10, 3871–3879. https://doi.org/10.1021/pr101196n (2011).
Kall, L., Canterbury, J. D., Weston, J., Noble, W. S. & MacCoss, M. J. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat. Methods 4, 923–925. https://doi.org/10.1038/nmeth1113 (2007).
Argentini, A. et al. moFF: a robust and automated approach to extract peptide ion intensities. Nat. Methods 13, 962–965. https://doi.org/10.1038/nmeth.4075 (2016).
Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740. https://doi.org/10.1038/nmeth.3901 (2016).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504. https://doi.org/10.1101/gr.1239303 (2003).
Bindea, G. et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091–1093. https://doi.org/10.1093/bioinformatics/btp101 (2009).
Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).
Turner, L., Ryu, W. S. & Berg, H. C. Real-time imaging of fluorescent flagellar filaments. J. Bacteriol. 182, 2793–2801 (2000).
This research was sponsored by the Genomic Science Program, U.S. Department of Energy, Office of Science, Biological and Environmental Research, as part of the Plant Microbe Interfaces Scientific Focus Area (https://pmi.ornl.gov). Oak Ridge National Laboratory is managed by UT-Battelle LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://energy.gov/downloads/doe-public-access-plan).
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Vijaya Kumar, S., Abraham, P.E., Hurst, G.B. et al. A carotenoid-deficient mutant of the plant-associated microbe Pantoea sp. YR343 displays an altered membrane proteome. Sci Rep 10, 14985 (2020). https://doi.org/10.1038/s41598-020-71672-w